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Das, Rabindra N., Frank D. Egitto, Barry Bonitz, Erich Kopp, Mark D. Poliks, and Voya R. Markovich. "Package-Interposer-Package (PIP): A Breakthrough Package-on-Package (PoP) Technology for 3D-Integration." International Symposium on Microelectronics 2012, no. 1 (2012): 000561–67. http://dx.doi.org/10.4071/isom-2012-wa15.
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Package on Package (PoP) stacking has become an attractive method for 3D integration to meet the demands of higher functionality in ever smaller packages, especially when coupled with the use of stacked die. To accomplish this, new packaging designs need to be able to integrate more dies with greater function, higher I/O counts, smaller pitches, and greater heat densities, while being pushed into smaller and smaller footprints. A new 3D “Package Interposer Package” (PIP) solution is suitable for combining multiple memory, ASICs, stacked die, stacked packaged die, etc., into a single package. This approach also favors system integration with high density power delivery by appropriate interposer design and thermal management. Traditional Package on Package (PoP) approaches use direct solder connections between the substrates and are limited to use of single (or minimum) die on the bottom substrate, to reduce warpage and improve stability. For PIP, the stability imparted by the interposer reduces warpage, allowing assemblers of the PIP to select the top and bottom components (substrates, die, stacked die, modules) from various suppliers. This mitigates the problem of variation in warpage trends from room temperature to reflow temperature for different substrates/modules when combined with other packages. PIP facilitates more space-efficient designs, and can accommodate any stacked die height without compromising warpage and stability. PIP can accommodate modules with stacked die on organic, ceramic, or silicon board substrates, where each can be detached and replaced without affecting the rest of the package. Thus, PIP will be economical for high-end electronics, since a damaged, non-factional part of the package can be selectively removed and replaced. A variety of interposer structures were used to fabricate Package Interposer Package (PIP) modules. Electrical connections were formed during reflow using a tin-lead eutectic solder paste. Interconnection among substrates (packages) in the stack was achieved using interposers. Plated through holes in the interposers, formed by laser or mechanical drilling and having diameters ranging from 50 μm to 250 μm, were filled with an electrically conductive adhesive and cured. The adhesive-filled and cured interposers were reflowed with circuitized substrates to produce a PIP structure. In summary, the present work describes an integrated approach to develop 3D PIP constructions on various stacked die or stacked packaged die configurations.
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Pettersen, Claire, Larry F. Bliven, Mark S. Kulie, et al. "The Precipitation Imaging Package: Phase Partitioning Capabilities." Remote Sensing 13, no. 11 (2021): 2183. http://dx.doi.org/10.3390/rs13112183.
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Surface precipitation phase is a fundamental meteorological property with immense importance. Accurate classification of phase from satellite remotely sensed observations is difficult. This study demonstrates the ability of the Precipitation Imaging Package (PIP), a ground-based, in situ precipitation imager, to distinguish precipitation phase. The PIP precipitation phase identification capabilities are compared to observer records from the National Weather Service (NWS) office in Marquette, Michigan, as well as co-located observations from profiling and scanning radars, disdrometer data, and surface meteorological measurements. Examined are 13 events with at least one precipitation phase transition. The PIP-determined onsets and endings of the respective precipitation phase periods agree to within 15 min of NWS observer records for the vast majority of the events. Additionally, the PIP and NWS liquid water equivalent accumulations for 12 of the 13 events were within 10%. Co-located observations from scanning and profiling radars, as well as reanalysis-derived synoptic and thermodynamic conditions, support the accuracy of the precipitation phases identified by the PIP. PIP observations for the phase transition events are compared to output from a parameterization based on wet bulb and near-surface lapse rates to produce a probability of solid precipitation. The PIP phase identification and the parameterization output are consistent. This work highlights the ability of the PIP to properly characterize hydrometeor phase and provide dependable precipitation accumulations under complicated mixed-phase and rain and snow (or vice versa) transition events.
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Winarsih, Murni. "PENINGKATAN KEMAMPUAN GURU DALAM PEMBELAJARAN TUNARUNGU MELALUI PELATIHAN CONTEXTUAL LEARNING DI SEKOLAH INKLUSIF." Perspektif Ilmu Pendidikan 28, no. 2 (2014): 114. http://dx.doi.org/10.21009/pip.282.5.
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Abstract: This study aimed at producing a set of contextual-learning based training package for teachers of inclusiveschool to teach hearing impaired students. The absence of the instructional packages for hearing impairedstudents and the lack of the teachers’ competence at inclusive school were underlied the need for this research. Borgand Gall’s Research and Development methods were employed in this study and Instructional Development Model(MPI) was applied to develop the package. At the end, the research produced a set of contextual-learning-basedtraining package for teachers to teach hearing impaired students, it included the design of training, user’s guidefor instructure and participant, 4 modules with soft-copy (CD) of training materials that had been tested for theeffectivity, efficiency, and feasibility. Besides, the research provided a number some of recommendation.
 Keywords: a training package, inclusive school, teaching the hearing impairment, contextual learning
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Das, Rabindra N., Frank D. Egitto, Steven G. Rosser, Erich Kopp, Barry Bonitz, and Raj Rai. "3D Integration of System-in-Package (SiP) Using Organic Interposer: Toward SiP-Interposer-SiP for High-End Electronics." International Symposium on Microelectronics 2013, no. 1 (2013): 000531–37. http://dx.doi.org/10.4071/isom-2013-wa43.
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The demand for high-performance, lightweight, portable computing power is driving the industry toward 3D integration to meet the demands of higher functionality in ever smaller packages. To accomplish this, new packaging needs to be able to integrate multiple substrates, multiple dies with greater function, higher I/O counts, smaller pitches, and greater heat densities, while being pushed into smaller and smaller footprints. The approaches explored in this paper include eliminating active chip packages by directly attaching the chip to the System-in-Package (SiP) with flip chip technology. Additionally, the area devoted to passive components can be greatly reduced by embedding many of the capacitors and resistors. In some instances, the connector systems that were consuming large amounts of space in the traditional Printed Wiring Board (PWB) assembly can be reduced with a small pitch connector system. This PWB assembly can then be transformed into a much smaller SiP with the full surface area on both sides of the package effectively utilized by active and passive components. The miniaturized SiP with its reduced package size and embedded passives provides a high wireability package with excellent communication from top to bottom. In the present study, we also report a novel 3D “Package-Interposer-Package” (PIP) solution for combining multiple SiP substrates into a single package. A variety of interposer structures were used to fabricate SiP-Interposer-SiP modules. Electrical connections were formed during reflow using a tin-lead eutectic solder paste. Interconnection among substrates (packages) in the stack was achieved using interposers. Plated through holes in the interposers, formed by laser or mechanical drilling and having diameters ranging from 50 μm to 250 μm, were filled with an electrically conductive adhesive and cured. The adhesive-filled and cured interposers were reflowed with circuitized substrates to produce a PIP structure. In summary, the present work describes an integrated approach to develop 3D PIP solutions incorporating various SiP configurations.
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Das, Rabindra, Frank D. Egitto, Steven G. Rosser, Erich Kopp, and Barry Bonitz. "3D Integration of System-in-Package (SiP): Toward SiP-Interposer-SiP for High-End Electronics." Additional Conferences (Device Packaging, HiTEC, HiTEN, and CICMT) 2013, DPC (2013): 000618–34. http://dx.doi.org/10.4071/2013dpc-tp12.
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The demand for high-performance, lightweight, portable computing power is driving the industry toward 3D integration to meet the demands of higher functionality in ever smaller packages. To accomplish this, new packaging needs to be able to integrate multiple substrates, multiple dies with greater function, higher I/O counts, smaller pitches, and greater heat densities, while being pushed into smaller and smaller footprints. The approaches explored in this paper include eliminating active chip packages by directly attaching the chip to the System-in-Package (SiP) with flip chip technology. Additionally, the area devoted to passive components can be greatly reduced by embedding many of the capacitors and resistors. In some instances, the connector systems that were consuming large amounts of space in the traditional Printed Wiring Board (PWB) assembly can be reduced with a small pitch connector system. This PWB assembly can then be transformed into a much smaller SiP with the full surface area on both sides of the package effectively utilized by active and passive components. The miniaturized SiP with its reduced package size and demand for passives requires a high wireability package with embedded passives and excellent communication from top to bottom. In the present study, we also report novel 3D “Package Interposer Package” (PIP) solution for combining multiple SiP substrates into a single package. A variety of interposer structures were used to fabricate SiP-Interposer-SiP modules. Electrical connections were formed during reflow using a tin-lead eutectic solder paste. Interconnection among substrates (packages) in the stack was achieved using interposers. Plated through holes in the interposers, formed by laser or mechanical drilling and having diameters ranging from 50 m to 250 m, were filled with an electrically conductive adhesive and cured. The adhesive-filled and cured interposers were reflowed with circuitized substrates to produce a PIP structure. In summary, the present work describes an integrated approach to develop 3D PIP solutions on various SiP configurations.
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Pettersen, Claire, Larry F. Bliven, Annakaisa von Lerber, et al. "The Precipitation Imaging Package: Assessment of Microphysical and Bulk Characteristics of Snow." Atmosphere 11, no. 8 (2020): 785. http://dx.doi.org/10.3390/atmos11080785.
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Remote-sensing observations are needed to estimate the regional and global impacts of snow. However, to retrieve accurate estimates of snow mass and rate, these observations require augmentation through additional information and assumptions about hydrometeor properties. The Precipitation Imaging Package (PIP) provides information about precipitation characteristics and can be utilized to improve estimates of snowfall rate and accumulation. Here, the goal is to demonstrate the quality and utility of two higher-order PIP-derived products: liquid water equivalent snow rate and an approximation of volume-weighted density called equivalent density. Accuracy of the PIP snow rate and equivalent density is obtained through intercomparison with established retrieval methods and through evaluation with colocated ground-based observations. The results confirm the ability of the PIP-derived products to quantify properties of snow rate and equivalent density, and demonstrate that the PIP produces physically realistic snow characteristics. When compared to the National Weather Service (NWS) snow field measurements of six-hourly accumulation, the PIP-derived accumulations were biased only +2.48% higher. Additionally, this work illustrates fundamentally different microphysical and bulk features of low and high snow-to-liquid ratio events, through assessment of observed particle size distributions, retrieved mass coefficients, and bulk properties. Importantly, this research establishes the role that PIP observations and higher-order products can serve for constraining microphysical assumptions in ground-based and spaceborne remotely sensed snowfall retrievals.
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Sukawati, Rina. "PENYUSUNAN PAKET PELATIHAN PENGEMBANGAN KECERDASAN INTRAPERSONAL UNTUK MAHASISWA." Perspektif Ilmu Pendidikan 16, no. VIII (2007): 22–31. http://dx.doi.org/10.21009/pip.162.3.
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The purpose of this research was to develop training package for intrapersonal intelligence development for the students of Educational Technology Department, School of Education, State University of Jakarta. The package was develop by employing Instructional Development Model. The research resulted in training package for intrapersonal intelligence development for the university students which is expected to be applicable or adjustable for trainings and the other universities for the similar purposes. A set of recommendation is given in using this package to be effective and efficient.
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Adeniran, S. A. Adedeji I. O. &. Ajayi O. "Assessment, Development and Validation of Programmed-Instructional-Package for Teaching Basic-Technology among upper Basic School Students in Gusau Metropolis, Zamfara State." Zamfara International Journal of Education 5, no. 1 (2025): 50–57. https://doi.org/10.5281/zenodo.15296274.
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This study employed a developmental research type design. The development involved the 5 stages of a model; these are Analysis, Design, Development, Implementation and Evaluation (ADDIE) model. Purposive sampling technique was used to select five Basic Technology teachers from two Upper Basic Schools in Gusau with a minimum of eight years of teaching experience and computer literacy and 45 Upper Basic 9 Basic Technology students. The data collected was analysed using mean and standard deviation. The findings of this study revealed that the Educational Technology experts agreed that the package was is user-friendly and suitable for learning as a result the average mean responses of 3.64 by them. Also, the findings of this study further revealed that almost all the teachers of Basic Technology were willing to teach through the use of the Programmed Instructional Package (PIP) because of its effectiveness with the average mean of responses of 3.82 which indicated that Basic Technology teachers agreed that the PIP was very effective for teaching and learning Basic Technology. This study concluded that the use of the package by the students is more preferable to teachers This study recommended among others that the students should explore the opportunities offered by PIP for revision as well as for individualized learning; teachers should develop and utilize PIP for teaching Basic Technology concepts so as to increase teachers’ knowledge on new innovations in ICT-Based instructional strategies
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Takefuji, Yoshiyasu. "Python Programming in PyPI for Translational Medicine." International Journal of Translational Medicine 1, no. 3 (2021): 323–31. http://dx.doi.org/10.3390/ijtm1030019.
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This is the world’s first tutorial article on Python Packaging for beginners and practitioners for translational medicine or medicine in general. This tutorial will allow researchers to demonstrate and showcase their tools on PyPI packages around the world. Nowadays, for translational medicine, researchers need to deal with big data. This paper describes how to build an executable Python Package Index (PyPI) code and package. PyPI is a repository of software for the Python programming language with 5,019,737 files and 544,359 users (programmers) as of 19 October 2021. First, programmers must understand how to scrape a dataset over the Internet; second, they must read the dataset file in csv format; third, build a program to compute the target values; fourth, convert the Python program to the PyPI package.; and fifth, upload the PyPI package. This paper depicts a covidlag executable package as an example for calculating the accurate case fatality rate (CFR) and the lag time from infection to death. You can install the covidlag by pip terminal command and test it. This paper also introduces deathdaily and scorecovid packages on PyPI Stats, which can inform how many users have downloaded the specified PyPI package. The usefulness and applicability of a developed tool can be verified by PyPI Stats with the number of downloaded users.
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Arini, Ira. "MENINGKATKAN HASIL BELAJAR IPS KELAS VIII SMP DENGAN PAKET PEMBELAJARAN BERBASIS MASALAH." Perspektif Ilmu Pendidikan 30, no. 1 (2016): 11. http://dx.doi.org/10.21009/pip.301.2.
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MENINGKATKAN HASIL BELAJAR IPS KELAS VIII SMP DENGAN PAKET PEMBELAJARAN BERBASIS MASALAHIra Arinie-mail: ira.arini@gmail.comTeknologi Pendidikan, Program Pascasarjana Universitas Negeri JakartaJalan Rawamangun Muka Jakarta TimurAbstrak: Penelitian ini bertujuan untuk menghasilkan paket pembelajaran IPS berbasis masalah dalam proses pembelajaran di kelas VIII SMP dilengkapi dengan buku siswa, buku guru, media pembelajaran dan kumpulan evaluasi agar tujuan pembelajaran dapat tercapai secara optimal. Dilaksanakan di SMP wilayah Kecamatan Rangkasbitung, Kabupaten Lebak, Banten pada bulan September 2014 sampai dengan Mei 2015 dengan metode research and development model pengembangan Derek Rowntree. Hasil penelitian adalah produk bernama Paket Pembelajaran Aplikasi Konsep IPS yang terdiri dari: buku siswa, buku guru, media pembelajaran dan kumpulan evaluasi. Hasil evaluasi formatif: pakar, face to face tryout, dan field test menyatakan bahwa produk tersebut telah dianalisis secara keseluruhan memiliki kategori “sangat baik”, sehingga menjadi produk akhir yang layak digunakan sebagai paket pembelajaran IPS berbasis masalah yang menarik, menyenangkan dan tidak membosankan. 
 Kata-kata Kunci: pengembangan, paket pembelajaran IPS, pembelajaran berbasis masalah.
 IMPROVING STUDENTS’ LEARNING ACHIEVEMENT IN SOCIAL SCIENCE WITH PROBLEM-BASED INSTRUCTIONAL PACKAGEAbstract: The purpose of this research is to produce problem-based instructional package for Social Science Subject in Grade VIII, Junior Secondary School. The package consisted of students book, teacher’s manual, instructional media, and evaluation materials. The research was conducted in Junior Secondary School in September 2014 through May 2015 in Rangkasbitung Sub-district, Lebak District, Banten applying research and developmentmodel introduced by Derek Rowntree. The research produced Instructional Package for Social Science Application consisting of students book, teacher’s manual, instructional media, and evaluation materials. Formative evaluation indicated by expert review, face to face try out, and field test was ‘very good’. The final product is feasible to be used as the problem-based instructional package for Social Science which is interesting, joyful and motivating.Keywords: Keywords: development, Social Science instructional package, problem based learning
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Helms, Charles Nelson, Stephen Joseph Munchak, Ali Tokay, and Claire Pettersen. "A comparative evaluation of snowflake particle shape estimation techniques used by the Precipitation Imaging Package (PIP), Multi-Angle Snowflake Camera (MASC), and Two-Dimensional Video Disdrometer (2DVD)." Atmospheric Measurement Techniques 15, no. 22 (2022): 6545–61. http://dx.doi.org/10.5194/amt-15-6545-2022.
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Abstract. Measurements of snowflake particle shape are important for studying snow microphysics. While a number of instruments exist that are capable of measuring particle shape, this study focuses on the measurement techniques of three digital video disdrometers: the Precipitation Imaging Package (PIP), the Multi-Angle Snowflake Camera (MASC), and the Two-Dimensional Video Disdrometer (2DVD). To gain a better understanding of the relative strengths and weaknesses of these instruments and to provide a foundation upon which comparisons can be made between studies using data from different instruments, we perform a comparative analysis of the shape measurement algorithms employed by each of the three instruments by applying the algorithms to snowflake images captured by PIP during the ICE-POP 2018 field campaign. Our analysis primarily focuses on the measurement of the aspect ratio of either the particle itself, in the case of PIP and MASC, or of the particle bounding box, in the case of PIP and 2DVD. Both PIP and MASC use shape-fitting algorithms to measure aspect ratio. While our analysis of the MASC aspect ratio suggests that the measurements are reliable, our findings indicate that both the ellipse and rectangle aspect ratios produced by PIP underperformed considerably due to the shortcomings of the PIP shape-fitting techniques. We also demonstrate that reliable measurements of aspect ratio can be retrieved from PIP by reprocessing the raw PIP images using either the MASC ellipse-fitting algorithm or a tensor-based ellipse-fitting algorithm. Because of differences in instrument design, 2DVD produces measurements of particle horizontal and vertical extent rather than length and width. Furthermore, the 2DVD measurements of particle horizontal extent can be contaminated by horizontal particle motion. Our findings indicate that, although the correction technique used to remove the horizontal motion contamination performs remarkably well with snowflakes despite being designed for use with raindrops, the 2DVD measurements of particle horizontal extent are less reliable than those measured by PIP.
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Gondhalekar, Yash, Rafael S. de Souza, and Ana L. Chies-Santos. "galmask: A Python Package for Unsupervised Galaxy Masking." Research Notes of the AAS 6, no. 6 (2022): 128. http://dx.doi.org/10.3847/2515-5172/ac780b.
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Abstract Galaxy morphological classification is a fundamental aspect of galaxy formation and evolution studies. Various machine learning tools have been developed for automated pipeline analysis of large-scale surveys, enabling a fast search for objects of interest. However, crowded regions in the image may pose a challenge as they can lead to bias in the learning algorithm. In this Research Note, we present galmask, an open-source package for unsupervised galaxy masking to isolate the central object of interest in the image. galmask is written in Python and can be installed from PyPI via the pip command.
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Maahn, Maximilian, Dmitri Moisseev, Isabelle Steinke, Nina Maherndl, and Matthew D. Shupe. "Introducing the Video In Situ Snowfall Sensor (VISSS)." Atmospheric Measurement Techniques 17, no. 2 (2024): 899–919. http://dx.doi.org/10.5194/amt-17-899-2024.
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Abstract. The open-source Video In Situ Snowfall Sensor (VISSS) is introduced as a novel instrument for the characterization of particle shape and size in snowfall. The VISSS consists of two cameras with LED backlights and telecentric lenses that allow accurate sizing and combine a large observation volume with relatively high pixel resolution and a design that limits wind disturbance. VISSS data products include various particle properties such as maximum extent, cross-sectional area, perimeter, complexity, and sedimentation velocity. Initial analysis shows that the VISSS provides robust statistics based on up to 10 000 unique particle observations per minute. Comparison of the VISSS with the collocated PIP (Precipitation Imaging Package) and Parsivel instruments at Hyytiälä, Finland, shows excellent agreement with the Parsivel but reveals some differences for the PIP that are likely related to PIP data processing and limitations of the PIP with respect to observing smaller particles. The open-source nature of the VISSS hardware plans, data acquisition software, and data processing libraries invites the community to contribute to the development of the instrument, which has many potential applications in atmospheric science and beyond.
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Mallet, Vincent, Carlos Oliver, Jonathan Broadbent, William L. Hamilton, and Jérôme Waldispühl. "RNAglib: a python package for RNA 2.5 D graphs." Bioinformatics 38, no. 5 (2021): 1458–59. http://dx.doi.org/10.1093/bioinformatics/btab844.
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Abstract Summary RNA 3D architectures are stabilized by sophisticated networks of (non-canonical) base pair interactions, which can be conveniently encoded as multi-relational graphs and efficiently exploited by graph theoretical approaches and recent progresses in machine learning techniques. RNAglib is a library that eases the use of this representation, by providing clean data, methods to load it in machine learning pipelines and graph-based deep learning models suited for this representation. RNAglib also offers other utilities to model RNA with 2.5 D graphs, such as drawing tools, comparison functions or baseline performances on RNA applications. Availability and implementation The method is distributed as a pip package, RNAglib. Data are available in a repository and can be accessed on rnaglib's web page. The source code, data and documentation are available at https://rnaglib.cs.mcgill.ca. Supplementary information Supplementary data are available at Bioinformatics online.
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Al-Otaibi, Yasser K., Noura Al-Nowaiser, and Arshur Rahman. "Reducing hospital-acquired pressure injuries." BMJ Open Quality 8, no. 1 (2019): e000464. http://dx.doi.org/10.1136/bmjoq-2018-000464.
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Hospital-acquired pressure injury is a common preventable condition. Our hospital is a 144-bed governmental hospital in the Kingdom of Saudi Arabia that was found to have a 7.5% prevalence of hospital-acquired pressure injury in 2016. The aim of the improvement project was to reduce the prevalence of pressure injuries in our hospital from 7.5% to below 4% by the end of 2017. Our strategy for improvement was based on the Institute for Healthcare Improvement Model for Improvement. The change strategy was based on implementing an evidence-based risk assessment tool and a bundled evidence-based pressure injury prevention (PIP) intervention termed PIP bundle. After implementing the change package, we observed a reduction in the prevalence of pressure injuries by 84% (RR 0.16;95% CI 0.07 to 0.3; p value <0.0001) over a period of 12 weeks, in addition to an improvement in the compliance of pressure injury risk assessment and PIP interventions. The use of an evidenced-based bundled approach to prevent hospital-acquired pressure injuries has resulted in a significant reduction in the rate of pressure injuries. Improvement results were sustainable. In addition, our outcome measure exhibited minimal variability.
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Hulsen, Tim. "BioVenn – an R and Python package for the comparison and visualization of biological lists using area-proportional Venn diagrams." Data Science 4, no. 1 (2021): 51–61. http://dx.doi.org/10.3233/ds-210032.
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One of the most popular methods to visualize the overlap and differences between data sets is the Venn diagram. Venn diagrams are especially useful when they are ‘area-proportional’ i.e. the sizes of the circles and the overlaps correspond to the sizes of the data sets. In 2007, the BioVenn web interface was launched, which is being used by many researchers. However, this web implementation requires users to copy and paste (or upload) lists of IDs into the web browser, which is not always convenient and makes it difficult for researchers to create Venn diagrams ‘in batch’, or to automatically update the diagram when the source data changes. This is only possible by using software such as R or Python. This paper describes the BioVenn R and Python packages, which are very easy-to-use packages that can generate accurate area-proportional Venn diagrams of two or three circles directly from lists of (biological) IDs. The only required input is two or three lists of IDs. Optional parameters include the main title, the subtitle, the printing of absolute numbers or percentages within the diagram, colors and fonts. The function can show the diagram on the screen, or it can export the diagram in one of the supported file formats. The function also returns all thirteen lists. The BioVenn R package and Python package were created for biological IDs, but they can be used for other IDs as well. Finally, BioVenn can map Affymetrix and EntrezGene to Ensembl IDs. The BioVenn R package is available in the CRAN repository, and can be installed by running ‘install.packages(“BioVenn”)’. The BioVenn Python package is available in the PyPI repository, and can be installed by running ‘pip install BioVenn’. The BioVenn web interface remains available at https://www.biovenn.nl.
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Jo, Jongkwon, Seungha Jung, Joongyang Park, Youngsoon Kim, and Mingon Kang. "Hi-LASSO: High-performance python and apache spark packages for feature selection with high-dimensional data." PLOS ONE 17, no. 12 (2022): e0278570. http://dx.doi.org/10.1371/journal.pone.0278570.
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High-dimensional LASSO (Hi-LASSO) is a powerful feature selection tool for high-dimensional data. Our previous study showed that Hi-LASSO outperformed the other state-of-the-art LASSO methods. However, the substantial cost of bootstrapping and the lack of experiments for a parametric statistical test for feature selection have impeded to apply Hi-LASSO for practical applications. In this paper, the Python package and its Spark library are efficiently designed in a parallel manner for practice with real-world problems, as well as providing the capability of the parametric statistical tests for feature selection on high-dimensional data. We demonstrate Hi-LASSO’s outperformance with various intensive experiments in a practical manner. Hi-LASSO will be efficiently and easily performed by using the packages for feature selection. Hi-LASSO packages are publicly available at https://github.com/datax-lab/Hi-LASSO under the MIT license. The packages can be easily installed by Python PIP, and additional documentation is available at https://pypi.org/project/hi-lasso and https://pypi.org/project/Hi-LASSO-spark.
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Geda, Robel, Steven M. Crawford, Lucas Hunt, Matthew Bershady, Erik Tollerud, and Solohery Randriamampandry. "PetroFit: A Python Package for Computing Petrosian Radii and Fitting Galaxy Light Profiles." Astronomical Journal 163, no. 5 (2022): 202. http://dx.doi.org/10.3847/1538-3881/ac5908.
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Abstract PetroFit is an open-source Python package based on Astropy and Photutils that can calculate Petrosian profiles and fit galaxy images. It offers end-to-end tools for making accurate photometric measurements, estimating morphological properties, and fitting 2D models to galaxy images. Petrosian metric radii can be used for model parameter estimation and aperture photometry to provide accurate total fluxes. Correction tools are provided for improving Petrosian radii estimates affected by galaxy morphology. PetroFit also provides tools for sampling Astropy-based models (including custom profiles and multicomponent models) onto image grids and enables point-spread function convolution to account for the effects of seeing. These capabilities provide a robust means of modeling and fitting galaxy light profiles. We have made the PetroFit package publicly available on GitHub ( PetroFit/petrofit ) and PyPi (pip install petrofit).
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Kulie, Mark S., Claire Pettersen, Aronne J. Merrelli, et al. "Snowfall in the Northern Great Lakes: Lessons Learned from a Multisensor Observatory." Bulletin of the American Meteorological Society 102, no. 7 (2021): E1317—E1339. http://dx.doi.org/10.1175/bams-d-19-0128.1.
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AbstractA multisensor snowfall observational suite has been deployed at the Marquette, Michigan, National Weather Service Weather Forecast Office (KMQT) since 2014. Micro Rain Radar (MRR; profiling radar), Precipitation Imaging Package (PIP; snow particle imager), and ancillary ground-based meteorological observations illustrate the unique capabilities of these combined instruments to document radar and concomitant microphysical properties associated with northern Great Lakes snowfall regimes. Lake-effect, lake-orographic, and transition event case studies are presented that illustrate the variety of snowfall events that occur at KMQT. Case studies and multiyear analyses reveal the ubiquity of snowfall produced by shallow events. These shallow snowfall features and their distinctive microphysical fingerprints are often difficult to discern with conventional remote sensing instruments, thus highlighting the scientific and potential operational value of MRR and PIP observations. The importance of near-surface lake-orographic snowfall enhancement processes in extreme snowfall events and regime-dependent snow particle microphysical variability controlled by regime and environmental factors are also highlighted.
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Leszczuk, Mikołaj, Marek Kobosko, Jakub Nawała, Filip Korus, and Michał Grega. "“In the Wild” Video Content as a Special Case of User Generated Content and a System for Its Recognition." Sensors 23, no. 4 (2023): 1769. http://dx.doi.org/10.3390/s23041769.
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In the five years between 2017 and 2022, IP video traffic tripled, according to Cisco. User-Generated Content (UGC) is mainly responsible for user-generated IP video traffic. The development of widely accessible knowledge and affordable equipment makes it possible to produce UGCs of quality that is practically indistinguishable from professional content, although at the beginning of UGC creation, this content was frequently characterized by amateur acquisition conditions and unprofessional processing. In this research, we focus only on UGC content, whose quality is obviously different from that of professional content. For the purpose of this paper, we refer to “in the wild” as a closely related idea to the general idea of UGC, which is its particular case. Studies on UGC recognition are scarce. According to research in the literature, there are currently no real operational algorithms that distinguish UGC content from other content. In this study, we demonstrate that the XGBoost machine learning algorithm (Extreme Gradient Boosting) can be used to develop a novel objective “in the wild” video content recognition model. The final model is trained and tested using video sequence databases with professional content and “in the wild” content. We have achieved a 0.916 accuracy value for our model. Due to the comparatively high accuracy of the model operation, a free version of its implementation is made accessible to the research community. It is provided via an easy-to-use Python package installable with Pip Installs Packages (pip).
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Bredesen-Aa, Bjørn André, and Marc Rehmsmeier. "Gnocis: An integrated system for interactive and reproducible analysis and modelling of cis-regulatory elements in Python 3." PLOS ONE 17, no. 9 (2022): e0274338. http://dx.doi.org/10.1371/journal.pone.0274338.
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Gene expression is regulated through cis-regulatory elements (CREs), among which are promoters, enhancers, Polycomb/Trithorax Response Elements (PREs), silencers and insulators. Computational prediction of CREs can be achieved using a variety of statistical and machine learning methods combined with different feature space formulations. Although Python packages for DNA sequence feature sets and for machine learning are available, no existing package facilitates the combination of DNA sequence feature sets with machine learning methods for the genome-wide prediction of candidate CREs. We here present Gnocis, a Python package that streamlines the analysis and the modelling of CRE sequences by providing extensible APIs and implementing the glue required for combining feature sets and models for genome-wide prediction. Gnocis implements a variety of base feature sets, including motif pair occurrence frequencies and the k-spectrum mismatch kernel. It integrates with Scikit-learn and TensorFlow for state-of-the-art machine learning. Gnocis additionally implements a broad suite of tools for the handling and preparation of sequence, region and curve data, which can be useful for general DNA bioinformatics in Python. We also present Deep-MOCCA, a neural network architecture inspired by SVM-MOCCA that achieves moderate to high generalization without prior motif knowledge. To demonstrate the use of Gnocis, we applied multiple machine learning methods to the modelling of D. melanogaster PREs, including a Convolutional Neural Network (CNN), making this the first study to model PREs with CNNs. The models are readily adapted to new CRE modelling problems and to other organisms. In order to produce a high-performance, compiled package for Python 3, we implemented Gnocis in Cython. Gnocis can be installed using the PyPI package manager by running ‘pip install gnocis’. The source code is available on GitHub, at https://github.com/bjornbredesen/gnocis.
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Ponzoni, Luca, Daniel A. Peñaherrera, Zoltán N. Oltvai, and Ivet Bahar. "Rhapsody: predicting the pathogenicity of human missense variants." Bioinformatics 36, no. 10 (2020): 3084–92. http://dx.doi.org/10.1093/bioinformatics/btaa127.
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Abstract Motivation The biological effects of human missense variants have been studied experimentally for decades but predicting their effects in clinical molecular diagnostics remains challenging. Available computational tools are usually based on the analysis of sequence conservation and structural properties of the mutant protein. We recently introduced a new machine learning method that demonstrated for the first time the significance of protein dynamics in determining the pathogenicity of missense variants. Results Here, we present a new interface (Rhapsody) that enables fully automated assessment of pathogenicity, incorporating both sequence coevolution data and structure- and dynamics-based features. Benchmarked against a dataset of about 20 000 annotated variants, the methodology is shown to outperform well-established and/or advanced prediction tools. We illustrate the utility of Rhapsody by in silico saturation mutagenesis studies of human H-Ras, phosphatase and tensin homolog and thiopurine S-methyltransferase. Availability and implementation The new tool is available both as an online webserver at http://rhapsody.csb.pitt.edu and as an open-source Python package (GitHub repository: https://github.com/prody/rhapsody; PyPI package installation: pip install prody-rhapsody). Links to additional resources, tutorials and package documentation are provided in the 'Python package' section of the website. Supplementary information Supplementary data are available at Bioinformatics online.
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Schirle, Claire E., Steven J. Cooper, Mareile Astrid Wolff, et al. "Estimation of Snowfall Properties at a Mountainous Site in Norway Using Combined Radar and In Situ Microphysical Observations." Journal of Applied Meteorology and Climatology 58, no. 6 (2019): 1337–52. http://dx.doi.org/10.1175/jamc-d-18-0281.1.
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AbstractThe ability of in situ snowflake microphysical observations to constrain estimates of surface snowfall accumulations derived from coincident, ground-based radar observations is explored. As part of the High-Latitude Measurement of Snowfall (HiLaMS) field campaign, a Micro Rain Radar (MRR), Precipitation Imaging Package (PIP), and Multi-Angle Snow Camera (MASC) were deployed to the Haukeliseter Test Site run by the Norwegian Meteorological Institute during winter 2016/17. This measurement site lies near an elevation of 1000 m in the mountains of southern Norway and houses a double-fence automated reference (DFAR) snow gauge and a comprehensive set of meteorological observations. MASC and PIP observations provided estimates of particle size distribution (PSD), fall speed, and habit. These properties were used as input for a snowfall retrieval algorithm using coincident MRR reflectivity measurements. Retrieved surface snowfall accumulations were evaluated against DFAR observations to quantify retrieval performance as a function of meteorological conditions for the Haukeliseter site. These analyses found differences of less than 10% between DFAR- and MRR-retrieved estimates over the field season when using either PIP or MASC observations for low wind “upslope” events. Larger biases of at least 50% were found for high wind “pulsed” events likely because of sampling limitations in the in situ observations used to constrain the retrieval. However, assumptions of MRR Doppler velocity for mean particle fall speed and a temperature-based PSD parameterization reduced this difference to +16% for the pulsed events. Although promising, these results ultimately depend upon selection of a snowflake particle model that is well matched to scene environmental conditions.
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Pettersen, Claire, Mark S. Kulie, Larry F. Bliven, et al. "A Composite Analysis of Snowfall Modes from Four Winter Seasons in Marquette, Michigan." Journal of Applied Meteorology and Climatology 59, no. 1 (2020): 103–24. http://dx.doi.org/10.1175/jamc-d-19-0099.1.
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AbstractPresented are four winter seasons of data from an enhanced precipitation instrument suite based at the National Weather Service (NWS) Office in Marquette (MQT), Michigan (250–500 cm of annual snow accumulation). In 2014 the site was augmented with a Micro Rain Radar (MRR) and a Precipitation Imaging Package (PIP). MRR observations are utilized to partition large-scale synoptically driven (deep) and surface-forced (shallow) snow events. Coincident PIP and NWS MQT meteorological surface observations illustrate different characteristics with respect to snow event category. Shallow snow events are often extremely shallow, with MRR-indicated precipitation heights of less than 1500 m above ground level. Large vertical reflectivity gradients indicate efficient particle growth, and increased boundary layer turbulence inferred from observations of spectral width implies increased aggregation in shallow snow events. Shallow snow events occur 2 times as often as deep events; however, both categories contribute approximately equally to estimated annual accumulation. PIP measurements reveal distinct regime-dependent snow microphysical differences, with shallow snow events having broader particle size distributions and comparatively fewer small particles and deep snow events having narrower particle size distributions and comparatively more small particles. In addition, coincident surface meteorological measurements indicate that most shallow snow events are associated with surface winds originating from the northwest (over Lake Superior), cold temperatures, and relatively high surface pressures, which are characteristics that are consistent with cold-air outbreaks. Deep snow events have meteorologically distinct conditions that are accordant with midlatitude cyclones and frontal structures, with mostly southwest surface winds, warmer temperatures approaching freezing, and lower surface pressures.
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Bloice, Marcus D., Peter M. Roth, and Andreas Holzinger. "Biomedical image augmentation using Augmentor." Bioinformatics 35, no. 21 (2019): 4522–24. http://dx.doi.org/10.1093/bioinformatics/btz259.
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Abstract Motivation Image augmentation is a frequently used technique in computer vision and has been seeing increased interest since the popularity of deep learning. Its usefulness is becoming more and more recognized due to deep neural networks requiring larger amounts of data to train, and because in certain fields, such as biomedical imaging, large amounts of labelled data are difficult to come by or expensive to produce. In biomedical imaging, features specific to this domain need to be addressed. Results Here we present the Augmentor software package for image augmentation. It provides a stochastic, pipeline-based approach to image augmentation with a number of features that are relevant to biomedical imaging, such as z-stack augmentation and randomized elastic distortions. The software has been designed to be highly extensible meaning an operation that might be specific to a highly specialized task can easily be added to the library, even at runtime. Although it has been designed as a general software library, it has features that are particularly relevant to biomedical imaging and the techniques required for this domain. Availability and implementation Augmentor is a Python package made available under the terms of the MIT licence. Source code can be found on GitHub under https://github.com/mdbloice/Augmentor and installation is via the pip package manager (A Julia version of the package, developed in parallel by Christof Stocker, is also available under https://github.com/Evizero/Augmentor.jl).
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Rakovsky, Stanislav Aleksandrovich. "Detecting Malicious Activity in Open-Source Projects Using Machine Learning Methods." Proceedings of the Institute for System Programming of the RAS 36, no. 3 (2024): 161–66. http://dx.doi.org/10.15514/ispras-2024-36(3)-11.
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The Python Package Index (PyPI) serves as the primary repository for projects for the Python programming language, and the package manager pip uses it by default. PyPI is a free and open-source platform: anyone can register a user on PyPI and publish their project, as well as examine the source code if necessary. The platform does not vet projects published by users, allowing for the possibility to report a malicious project via e-mail. Nonetheless, every less than month analysts repeatedly discover new malicious packages on PyPI. Organizations working in the field of open repository security vigilantly monitor emerging projects. Unfortunately, this is not enough: some malicious projects are detected and removed only several months after publication. This paper proposes an automatic feature selection algorithm based on bigrams and code properties, and trains an ET classifier capable of reliably identifying certain types of malicious logic in code. Malicious code repositories MalRegistry and DataDog were used as the training sample. After training, the model was tested on the three latest releases of all existing projects on PyPI, and it succeeded in detecting 28 previously undiscovered malicious projects, the oldest of which had been around for almost one and a half years. The approach used in this work also allows for real-time scanning of published projects, which can be utilized for prompt detection of malicious activity. In this work, the additional focus lays on methos that do not require an expert for feature selection and control, thereby reducing the burden on human resources.
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Agustin, Yoga Handoko, Yosep Septiana, and Arbi Yuan Aspahany. "Search Optimization of PIP Scholarship Recipients In Web-Based Student Data Application Using The Levenshtein Distance Algorithm." sinkron 8, no. 4 (2023): 2069–81. http://dx.doi.org/10.33395/sinkron.v8i4.12898.
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Realizing that education is very important, the government supports every citizen to get education. One of the government programs is the Smart Indonesia Program. PlP is a scholarship designed to help school-age children from poor/vulnerable families to continue to receive education services, both through formal elementary to high school/vocational schools and non-formal pathways from package a to package c and special education. SDN II Babakanloa has not been touched by technology for processing student data. So that the student section has difficulties in recording and updating student data. Student names have unique identities and errors often occur in typing the keywords to be searched. This results in an information that is desired or sought can not be found. Therefore we need a web-based data application that can provide keyword corrections in searching for student names. This study aims to create a web-based student data application by optimizing corrections to typing keywords searched by implementing the Levenshtein Distance Algorithm and also making it easier to process and search student data. The development method used is the Rational Unified Process (RUP) with the stages of Inception, Elaboration, Construction, and Transition. Designed using the CodeIgniter Framework with the PHP and JavaScript programming languages. The application of the Levenshtein Distance Algorithm can optimize the search for student data and reduce the occurrence of search errors by School Operators. The application of the Levenshtein Distance Algorithm produces a very good accuracy rate of 94% of the results of student data correction. accordance with the expectations of the School Operator. So it shows that the application of the Levenshtein Distance Algorithm is appropriate to use in optimizing the search.
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Tareen, Ammar, and Justin B. Kinney. "Logomaker: beautiful sequence logos in Python." Bioinformatics 36, no. 7 (2019): 2272–74. http://dx.doi.org/10.1093/bioinformatics/btz921.
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Abstract Summary Sequence logos are visually compelling ways of illustrating the biological properties of DNA, RNA and protein sequences, yet it is currently difficult to generate and customize such logos within the Python programming environment. Here we introduce Logomaker, a Python API for creating publication-quality sequence logos. Logomaker can produce both standard and highly customized logos from either a matrix-like array of numbers or a multiple-sequence alignment. Logos are rendered as native matplotlib objects that are easy to stylize and incorporate into multi-panel figures. Availability and implementation Logomaker can be installed using the pip package manager and is compatible with both Python 2.7 and Python 3.6. Documentation is provided at http://logomaker.readthedocs.io; source code is available at http://github.com/jbkinney/logomaker.
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Kachwaha, Shivani, Phuong H. Nguyen, Lan Mai Tran, et al. "Specificity Matters: Unpacking Impact Pathways of Individual Interventions within Bundled Packages Helps Interpret the Limited Impacts of a Maternal Nutrition Intervention in India." Journal of Nutrition 152, no. 2 (2021): 612–29. http://dx.doi.org/10.1093/jn/nxab390.
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ABSTRACT Background To address gaps in coverage and quality of nutrition services, Alive & Thrive (A&T) strengthened the delivery of maternal nutrition interventions through government antenatal care (ANC) services in Uttar Pradesh, India. The impact evaluation of the A&T interventions compared intensive ANC (I-ANC) with standard ANC (S-ANC) areas and found modest impacts on micronutrient supplementation, dietary diversity, and weight-gain monitoring. Objectives This study examined intervention-specific program impact pathways (PIPs) and identified reasons for limited impacts of the A&T maternal nutrition intervention package. Methods We used mixed methods: frontline worker (FLW) surveys (n = ∼500), counseling observations (n = 407), and qualitative in-depth interviews with FLWs, supervisors, and block-level staff (n = 59). We assessed 7 PIP domains: training and materials, knowledge, supportive supervision, supply chains, data use, service delivery, and counseling. Results Exposure to training improved in both I-ANC and S-ANC areas with more job aids used in I-ANC compared with S-ANC (90% compared with 70%), but gaps remained for training content and refresher trainings. FLWs’ knowledge improvement was higher in I-ANC than S-ANC (22–36 percentage points), but knowledge of micronutrient supplement benefits and recommended foods was insufficient (&lt;50%). Most FLWs received supervision (&gt;90%), but supportive supervision was limited by staff vacancies and competing work priorities. Supplies of iron–folic acid and calcium supplements were low in both areas (30–50% stock-outs). Use of monitoring data during review meetings was higher in I-ANC than S-ANC (52% compared with 36%) but was constrained by time, understanding, and data quality. Service provision improved in both I-ANC and S-ANC areas, but counseling on supplement benefits and weight-gain monitoring was low (30–40%). Conclusions Systems-strengthening efforts improved maternal nutrition interventions in ANC, but gaps remained. Taking an intervention-specific perspective to the PIP analysis in this package of services was critical to understand how common and specific barriers influenced overall program impact.
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Das, Rabindra N., Frank D. Egitto, John M. Lauffer, Tim Antesberger, and Voya R. Markovich. "Z-axis Interconnections for Next Generation Packaging." International Symposium on Microelectronics 2011, no. 1 (2011): 000469–75. http://dx.doi.org/10.4071/isom-2011-wa1-paper4.
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In this paper, the use of electrically conducting adhesives (ECA) to form z-axis interconnections for next generation packaging is discussed. In particular, current efforts related to Z-axis interconnections for device level fabrication, integration, and electrical performance are highlighted. A few optimized ECAs were used for hole fill applications to fabricate Z-axis interconnections in laminates. Conductive joints were formed during composite lamination using the ECA. Around 5,000 to 200,000 through holes in the joining cores, formed by laser or mechanical drilling, and having diameters ranging from 50 μm to 750 μm, were filled with an optimized conducting adhesive. The adhesive-filled joining cores/layers were laminated with circuitized subcomposites to produce a composite structure. As a case study, a variety of z-axis interconnect constructions for a flip-chip plastic ball grid array package, rigid-flex, rigid-rigid, package-interposer-package (PIP), RF structures, and PWBs were fabricated and evaluated at both the subcomposite and composite levels to understand structural and electrical integrity. Electrically, S-parameter measurements showed very low loss at multi-gigahertz frequencies. The losses were low enough to support typical SERDES up to 15 Gbps over 750 mm. The present process allows fabrication of z-interconnect conductive joints having diameters in the range of 55 to 500 μm. The processes and materials used to achieve smaller feature dimensions, satisfy stringent registration requirements, and achieve robust electrical interconnections are discussed.
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Hsu, Jui-Teng, and Preshanth Jagannathan. "Zeeman Spectral Line Fitting." Research Notes of the AAS 8, no. 12 (2024): 307. https://doi.org/10.3847/2515-5172/ad9c6c.
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Abstract The Zeeman effect plays a pivotal role as an observational technique for directly assessing the magnitude of magnetic fields within star-forming regions. Over time, Zeeman effect analysis has primarily relied upon software tools such as the Astronomical Image Processing System and MIRIAD. These tools necessitate manual involvement in the analysis and line fitting processes. This study delves into two potential automated methodologies for Zeeman analysis: one grounded in a least squares framework and the other embracing the maximum-likelihood approach. Our investigation reveals that the utilization of Markov Chain Monte Carlo, anchored in the maximum-likelihood framework, results in improved spectral line fitting outcomes with more favorable residuals. Concluding our efforts, we have encapsulated the automated analysis procedure within a software package, called Zeeman_modelling, installable through pip, accompanied by a straightforward command-line interface.
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Vogt, Frédéric P. A., Loris Foresti, Daniel Regenass, et al. "ampycloud: an open-source algorithm to determine cloud base heights and sky coverage fractions from ceilometer data." Atmospheric Measurement Techniques 17, no. 16 (2024): 4891–914. http://dx.doi.org/10.5194/amt-17-4891-2024.
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Abstract. Ceilometers are used routinely at aerodromes worldwide to derive the height and sky coverage fraction of cloud layers. This information, possibly combined with direct observations by human observers, contributes to the production of meteorological aerodrome reports (METARs). Here, we present ampycloud, a new algorithm, and its associated Python package for automatic processing of ceilometer data with the aim of determining the sky coverage fraction and base height of cloud layers above aerodromes. The ampycloud algorithm was developed at the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss) as part of the AMAROC (AutoMETAR/AutoReport rOund the Clock) program to help in the fully automatic production of METARs at Swiss civil aerodromes. ampycloud is designed to work with no direct human supervision. The algorithm consists of three distinct, sequential steps that rely on agglomerative clustering methods and Gaussian mixture models to identify distinct cloud layers from individual cloud base hits reported by ceilometers. The robustness of the ampycloud algorithm stems from the first processing step, which is simple and reliable. It constrains the two subsequent processing steps that are more sensitive but also better suited to handling complex cloud distributions. The software implementation of the ampycloud algorithm takes the form of an eponymous, pip-installable Python package developed on GitHub and made publicly accessible.
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Stuart, Robyn M., Jamie A. Cohen, Cliff C. Kerr, et al. "HPVsim: An agent-based model of HPV transmission and cervical disease." PLOS Computational Biology 20, no. 7 (2024): e1012181. http://dx.doi.org/10.1371/journal.pcbi.1012181.
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In 2020, the WHO launched its first global strategy to accelerate the elimination of cervical cancer, outlining an ambitious set of targets for countries to achieve over the next decade. At the same time, new tools, technologies, and strategies are in the pipeline that may improve screening performance, expand the reach of prophylactic vaccines, and prevent the acquisition, persistence and progression of oncogenic HPV. Detailed mechanistic modelling can help identify the combinations of current and future strategies to combat cervical cancer. Open-source modelling tools are needed to shift the capacity for such evaluations in-country. Here, we introduce the Human papillomavirus simulator (HPVsim), a new open-source software package for creating flexible agent-based models parameterised with country-specific vital dynamics, structured sexual networks, and co-transmitting HPV genotypes. HPVsim includes a novel methodology for modelling cervical disease progression, designed to be readily adaptable to new forms of screening. The software itself is implemented in Python, has built-in tools for simulating commonly-used interventions, includes a comprehensive set of tests and documentation, and runs quickly (seconds to minutes) on a laptop. Performance is greatly enhanced by HPVsim’s multi-scale modelling functionality. HPVsim is open source under the MIT License and available via both the Python Package Index (via pip install) and GitHub (hpvsim.org).
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Gopinathan Nair, Amogh, Nick Rabas, Sara Lejon, et al. "Unorthodox PCNA Binding by Chromatin Assembly Factor 1." International Journal of Molecular Sciences 23, no. 19 (2022): 11099. http://dx.doi.org/10.3390/ijms231911099.
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The eukaryotic DNA replication fork is a hub of enzymes that continuously act to synthesize DNA, propagate DNA methylation and other epigenetic marks, perform quality control, repair nascent DNA, and package this DNA into chromatin. Many of the enzymes involved in these spatiotemporally correlated processes perform their functions by binding to proliferating cell nuclear antigen (PCNA). A long-standing question has been how the plethora of PCNA-binding enzymes exert their activities without interfering with each other. As a first step towards deciphering this complex regulation, we studied how Chromatin Assembly Factor 1 (CAF-1) binds to PCNA. We demonstrate that CAF-1 binds to PCNA in a heretofore uncharacterized manner that depends upon a cation-pi (π) interaction. An arginine residue, conserved among CAF-1 homologs but absent from other PCNA-binding proteins, inserts into the hydrophobic pocket normally occupied by proteins that contain canonical PCNA interaction peptides (PIPs). Mutation of this arginine disrupts the ability of CAF-1 to bind PCNA and to assemble chromatin. The PIP of the CAF-1 p150 subunit resides at the extreme C-terminus of an apparent long α-helix (119 amino acids) that has been reported to bind DNA. The length of that helix and the presence of a PIP at the C-terminus are evolutionarily conserved among numerous species, ranging from yeast to humans. This arrangement of a very long DNA-binding coiled-coil that terminates in PIPs may serve to coordinate DNA and PCNA binding by CAF-1.
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Wortmann, Ulrich G., Tina Tsan, Mahrukh Niazi, et al. "The Earth Science Box Modeling Toolkit (ESBMTK 0.14.0.11): a Python library for research and teaching." Geoscientific Model Development 18, no. 4 (2025): 1155–67. https://doi.org/10.5194/gmd-18-1155-2025.
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Abstract. The Earth Science Box Modeling Toolkit (ESBMTK) is a Python library that streamlines the creation and analysis of box models in earth sciences. With its modular, object-oriented design, ESBMTK simplifies the study of systems such as the long-term carbon cycle or the impact of atmospheric CO2 variations on ocean chemistry. By standardizing and clarifying how models are defined, the library enhances code readability and serves as a self-documenting tool, making it approachable for undergraduate students and efficient for researchers. ESBMTK automatically translates user-defined models into equations which are solved using established numerical libraries. It also includes built-in functionality for common tasks such as ocean–atmosphere gas exchange, marine carbonate chemistry, isotope effects, and perturbation scenarios. The library's core interface is stable, supported by comprehensive documentation, and available as open-source software through the pip and conda package management systems.
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Wieser, Penny, Maurizio Petrelli, Jordan Lubbers, et al. "Thermobar: An open-source Python3 tool for thermobarometry and hygrometry." Volcanica 5, no. 2 (2022): 349–84. http://dx.doi.org/10.30909/vol.05.02.349384.
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We present Thermobar, a new open-source Python3 package for calculating pressures, temperatures, and melt compositions from mineral and mineral-melt equilibrium. Thermobar allows users to perform calculations with >100 popular parametrizations involving liquid, olivine-liquid, olivine-spinel, pyroxene only, pyroxene-liquid, two pyroxene, feldspar-liquid, two feldspar, amphibole only, amphibole-liquid, and garnet equilibria. Thermobar is the first open-source tool which can match up all possible pairs of phases from a given region, and apply various equilibrium tests to identify pairs from which to calculate pressures and temperatures (e.g. pyroxene-liquid, two pyroxene, feldspar-liquid, two feldspar, amphibole-liquid). Thermobar also contains functions allowing users to propagate analytical errors using Monte-Carlo methods, convert pressures to depths using different crustal density profiles, plot mineral classification and mineral-melt equilibrium diagrams, calculate liquid viscosities, and convert between oxygen fugacity values, buffer positions and Fe speciation in a silicate melt. Thermobar can be downloaded using pip and extensive documentation is available at https://thermobar.readthedocs.io/.
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Karamjeet, Singh Gulati, Sihra Anupreet, Veena Khandelwal Dr., and Dogadov Sergej. "An Alternative Fashion to Automate the Appropriateness of ALT-Text using Microsoft Computer Vision API." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 4 (2022): 57–63. https://doi.org/10.35940/ijrte.D7332.1111422.
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<strong>Abstract.</strong> Designing and releasing of software’s in production that contains images takes a lot of time due to the need of finding ALT-text attributes for the images embedded in the applications. This paper automates the task of writing ALT-text attributes in HTML, especially if image integration is large with the use of python PIP package and Microsoft Computer Vision API. This will save huge time and efforts of the developers by automating the task of captioning images manually up to a great extent. The challenge that confronts us is the quality of annotations generated by the machine with respect to the human generated annotations. To study the appropriateness of the captions delivered by APIs, a blend of human and machine assessment was used. We have noticed a high similarity in human and machine generated annotations as we obtained individual and cumulative BLEU score metric . Another metric is confidence score with a percentage mean of 0.5 .Also, we have calculated the time taken per caption which is 1.6 seconds per image which took 6.01 minutes to caption 200 images..
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Bajiya, Nisha, Shubham Choudhury, Anjali Dhall, and Gajendra P. S. Raghava. "AntiBP3: A Method for Predicting Antibacterial Peptides against Gram-Positive/Negative/Variable Bacteria." Antibiotics 13, no. 2 (2024): 168. http://dx.doi.org/10.3390/antibiotics13020168.
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Most of the existing methods developed for predicting antibacterial peptides (ABPs) are mostly designed to target either gram-positive or gram-negative bacteria. In this study, we describe a method that allows us to predict ABPs against gram-positive, gram-negative, and gram-variable bacteria. Firstly, we developed an alignment-based approach using BLAST to identify ABPs and achieved poor sensitivity. Secondly, we employed a motif-based approach to predict ABPs and obtained high precision with low sensitivity. To address the issue of poor sensitivity, we developed alignment-free methods for predicting ABPs using machine/deep learning techniques. In the case of alignment-free methods, we utilized a wide range of peptide features that include different types of composition, binary profiles of terminal residues, and fastText word embedding. In this study, a five-fold cross-validation technique has been used to build machine/deep learning models on training datasets. These models were evaluated on an independent dataset with no common peptide between training and independent datasets. Our machine learning-based model developed using the amino acid binary profile of terminal residues achieved maximum AUC 0.93, 0.98, and 0.94 for gram-positive, gram-negative, and gram-variable bacteria, respectively, on an independent dataset. Our method performs better than existing methods when compared with existing approaches on an independent dataset. A user-friendly web server, standalone package and pip package have been developed to facilitate peptide-based therapeutics.
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Isachenko, Olha V. "Estimates of the accuracy of identification of a nonlinear dynamic system using step test signals." Informatics. Culture. Technology 1, no. 1 (2024): 245–49. http://dx.doi.org/10.15276/ict.01.2024.37.
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Machine learning (ML) has become widespread in everyday life. On its basis, programs with artificial intelligence work, on the basis of which many virtual assistants have evolved. ML plays an important role in various spheres of activity of many enterprises. ML helps to automate many processes, simplifying the functioning of the company. Named Entity Recognition (NER) models allow to automatically select and search for information according to certain criteria in mobile data extracted, for example, by the logistic method. Support for NER by Python models makes it possible to flexibly program specific requests that are generated in the forensic examination process. Open source creates a unique opportunity to continuously improve the model by training it on datasets. A powerful NER package is the spaCy framework, which helps to simplify data and extract detailed information from input data, train a model, perform model tuning, and more. spaCy is compatible with 64-bit CPython 3.7+ and runs on Unix/Linux, macOS/OS X and Windows. The latest spaCy releases are available over pip and conda.
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Le, Minda, and V. Chandrasekar. "Ground Validation of Surface Snowfall Algorithm in GPM Dual-Frequency Precipitation Radar." Journal of Atmospheric and Oceanic Technology 36, no. 4 (2019): 607–19. http://dx.doi.org/10.1175/jtech-d-18-0098.1.
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AbstractExtensive evaluations have been performed on the dual-frequency classification module in the Global Precipitation Mission (GPM) Dual-Frequency Precipitation Radar (DPR) level-2 algorithm. Both rain type classification and melting-layer detection continue to show promising results in the validations. Surface snowfall identification is a feature newly added in the classification module to the recently released version to provide a surface snowfall flag for each qualified vertical profile. This algorithm is developed upon vertical features of Ku- and Ka-band reflectivity and dual-frequency ratio from DPR. In this paper, we validate this surface snowfall identification algorithm with ground radars including NEXRAD, NASA Polarimetric Radar (NPOL), and CSU–CHILL radar during concurrent precipitation events and GPM validation campaign Olympic Mountain Experiment (OLYMPEX). Other ground truth such as Precipitation Imaging Package (PIP) and ground report is also included in the validation. Based on 16 validation cases in the years 2014–18, the average match ratio between surface snowfall flag from space radar and ground radar is around 87.8%. Promising agreements are achieved with different validation sources. Algorithm limitation and potential improvement are discussed.
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Gulati, Karamjeet Singh, Anupreet Sihra, Dr Veena Khandelwal, and Sergej Dogadov. "An Alternative Fashion to Automate the Appropriateness of ALT-Text using Microsoft Computer Vision API." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 4 (2022): 57–63. http://dx.doi.org/10.35940/ijrte.d7332.1111422.
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Designing and releasing of software’s in production that contains images takes a lot of time due to the need of finding ALT-text attributes for the images embedded in the applications. This paper automates the task of writing ALT-text attributes in HTML, especially if image integration is large with the use of python PIP package and Microsoft Computer Vision API. This will save huge time and efforts of the developers by automating the task of captioning images manually up to a great extent. The challenge that confronts us is the quality of annotations generated by the machine with respect to the human generated annotations. To study the appropriateness of the captions delivered by APIs, a blend of human and machine assessment was used. We have noticed a high similarity in human and machine generated annotations as we obtained individual and cumulative BLEU score metric . Another metric is confidence score with a percentage mean of 0.5 .Also, we have calculated the time taken per caption which is 1.6 seconds per image which took 6.01 minutes to caption 200 images.
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Van Zandt, Judah, and Erik A. Petigura. "ethraid: A Simple Method for Characterizing Long-period Companions Using Doppler, Astrometric, and Imaging Constraints." Astronomical Journal 167, no. 6 (2024): 250. http://dx.doi.org/10.3847/1538-3881/ad390b.
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Abstract We present ethraid, an open-source Python package designed to measure the mass (m c ) and separation (a) of a bound companion from measurements covering a fraction of the orbital period. ethraid constrains m c and a by jointly modeling radial velocity, astrometric, and/or direct imaging data in a Bayesian framework. Partial orbit data sets, especially those with highly limited phase coverage, are represented well by a few method-specific summary statistics. By modeling these statistics rather than the original data, ethraid optimizes computational efficiency with minimal reduction in accuracy. ethraid uses importance sampling to efficiently explore the often broad posteriors that arise from partial orbits. The core computations of ethraid are implemented in Cython for speed. We validate ethraid's performance by using it to constrain the masses and separations of the planetary companions to HD 117207 and TOI-1694. We designed ethraid to be both fast and simple, as well as to give broad, “quick look” constraints on companion parameters using minimal data. ethraid is pip installable and available on Zenodo and GitHub.
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Roy, Aryan, Jim Pivarski, and Chad Wells Freer. "An array-oriented Python interface for FastJet." Journal of Physics: Conference Series 2438, no. 1 (2023): 012011. http://dx.doi.org/10.1088/1742-6596/2438/1/012011.
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Abstract Analysis on HEP data is an iterative process in which the results of one step often inform the next. In an exploratory analysis, it is common to perform one computation on a collection of events, then view the results (often with histograms) to decide what to try next. Awkward Array is a Scikit-HEP Python package that enables data analysis with array-at-a-time operations to implement cuts as slices, combinatorics as composable functions, etc. However, most C++ HEP libraries, such as FastJet, have an imperative, one-particle-at-a-time interface, which would be inefficient in Python and goes against the grain of the array-at-a-time logic of scientific Python. Therefore, we developed fastjet, a pip-installable Python package that provides FastJet C++ binaries, the classic (particle-at-a-time) Python interface, and the new array-oriented interface for use with Awkward Array. The new interface streamlines interoperability with scientific Python software beyond HEP, such as machine learning. In one case, adopting this library along with other array-oriented tools accelerated HEP analysis code by a factor of 20. It was designed to be easily integrated with libraries in the Scikit-HEP ecosystem, including Uproot (file I/O), hist (histogramming), Vector (Lorentz vectors), and Coffea (high-level glue). We discuss the design of the fastjet Python library, integrating the classic interface with the array oriented interface and with the Vector library for Lorentz vector operations. The new interface was developed as open source.
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Thongkittidilok, C., Y. Li, D. Wildt, and N. Songsasen. "137 DYNAMIC SHIFT IN CYTOPLASMIC LIPIDS IN CAT OOCYTES DURING OVARIAN FOLLICLE DEVELOPMENT." Reproduction, Fertility and Development 28, no. 2 (2016): 198. http://dx.doi.org/10.1071/rdv28n2ab137.
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Cytoplasmic lipids play key roles during oocyte and embryo development. However, there is little information on the changes in lipid types during intraovarian follicular and oocyte growth. Here, we used matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry to understand lipid composition in the cat oocyte during differing stages of folliculogenesis. Follicles at different developmental stages were mechanically isolated from cat ovaries within 4 h after routine ovariohysterectomy. Oocytes with granulosa cells were recovered from secondary (preantral stage, n = 387), early antral (<0.5 mm diameter, n = 177), small antral (0.5–1 mm, n = 144), and antral (>1 mm, n = 120) follicles, then subjected to lipid extraction and MALDI-TOF analysis (n = 3 replicates). Resulting mass spectra data were evaluated using MALDIquant in R package to eliminate baseline (background) and to identify peak values. The latter maximal values for each follicle stage were selected and subjected to principal component analysis to identify similarities and differences in mass spectra profile among oocytes from varying developmental stages. Peaks were compared to those calculated molecular formulae available in the Lipid MAP database. Error estimates were calculated using Excel (Microsoft Corp., Redmond, WA), and lipid species identification performed using Lipid MS Predict. Twenty-nine lipid species from six glycerophospholipids groups (glycerophosphates, PA; glycerophosphoserines, PS; glycerophosphoinositols, PI; glycerophosphoglycerols, PG; glycerophosphoinositol monophosphates, PIP; and glycerophosphoethanolamines, PE) were identified, 15 being found in more than 1 developmental stage. Two species in particular, [PI(29:4)+Na]+ and [PIP(23:0)+Na]+, were the most abundant lipids and were identified in oocytes from all developmental stages. There were dynamic shifts in lipid species expressed at different follicle stages. Oocytes from secondary and antral follicles contained more lipid types (15 and 22, respectively) than early (10) and small antral (4) counterparts. Four (PA, PS, PI, and PIP) of the 6 glycerophospholipids were found only in oocytes from secondary and antral follicles. Oocytes from small antral follicles also lacked PA, PS, and PG, whereas PG was not found in early antral stage oocytes. In summary, we showed for the first time in the cat that, similar to goats and rats, there are temporal changes in lipid types within the oocyte during folliculogenesis. We suspect that these changing dynamics, including shifts in presence or absence of lipid species with follicle stage, may be playing key roles in oocyte growth and viability. Our findings also serve as in vivo benchmarks for parallel studies focused on enhancing an in vitro culture system for early-stage ovarian follicles to preserve fertility of genetically valuable domestic and wild felids.
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Neumann, Jacob, Yen Ting Lin, Abhishek Mallela, et al. "Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit." Bioinformatics 38, no. 6 (2022): 1770–72. http://dx.doi.org/10.1093/bioinformatics/btac004.
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Abstract Summary Bayesian inference in biological modeling commonly relies on Markov chain Monte Carlo (MCMC) sampling of a multidimensional and non-Gaussian posterior distribution that is not analytically tractable. Here, we present the implementation of a practical MCMC method in the open-source software package PyBioNetFit (PyBNF), which is designed to support parameterization of mathematical models for biological systems. The new MCMC method, am, incorporates an adaptive move proposal distribution. For warm starts, sampling can be initiated at a specified location in parameter space and with a multivariate Gaussian proposal distribution defined initially by a specified covariance matrix. Multiple chains can be generated in parallel using a computer cluster. We demonstrate that am can be used to successfully solve real-world Bayesian inference problems, including forecasting of new Coronavirus Disease 2019 case detection with Bayesian quantification of forecast uncertainty. Availability and implementation PyBNF version 1.1.9, the first stable release with am, is available at PyPI and can be installed using the pip package-management system on platforms that have a working installation of Python 3. PyBNF relies on libRoadRunner and BioNetGen for simulations (e.g. numerical integration of ordinary differential equations defined in SBML or BNGL files) and Dask.Distributed for task scheduling on Linux computer clusters. The Python source code can be freely downloaded/cloned from GitHub and used and modified under terms of the BSD-3 license (https://github.com/lanl/pybnf). Online documentation covering installation/usage is available (https://pybnf.readthedocs.io/en/latest/). A tutorial video is available on YouTube (https://www.youtube.com/watch?v=2aRqpqFOiS4&t=63s). Supplementary information Supplementary data are available at Bioinformatics online.
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Kaldemeyer, Cord. "ZNES Clustering." March 10, 2020. https://doi.org/10.5281/zenodo.3726585.
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This package contains clustering functionalities for the Center for Sustainable Energy Systems (ZNES) based on the <em>tsam</em> package. Notes: The package can be installed using the pip installer e.g. using <em>pip install -e znes_clustering</em>
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Kaldemeyer, Cord. "(A)pplication (s)tarter for (o)emof solph (m)odels." March 10, 2020. https://doi.org/10.5281/zenodo.3726565.
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Tokay, Ali, Annakaisa von Lerber, Claire Pettersen, Mark S. Kulie, Dmitri N. Moisseev, and David B. Wolff. "Retrieval of Snow Water Equivalent by the Precipitation Imaging Package (PIP) in the Northern Great Lakes." Journal of Atmospheric and Oceanic Technology, July 20, 2021. http://dx.doi.org/10.1175/jtech-d-20-0216.1.
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AbstractPerformance of the Precipitation Imaging Package (PIP) for estimating the snow water equivalent (SWE) is evaluated through a comparative study with the collocated National Oceanic and Atmospheric Administration National Weather Service snow stake field measurements. The PIP together with a vertically pointing radar, a weighing bucket gauge, and a laser-optical disdrometer was deployed at the NWS Marquette, Michigan office building for a long-term field study supported by the National Aeronautics and Space Administration’s Global Precipitation Measurement mission Ground Validation program. The site was also equipped with a weather station. During the 2017-18 winter, the PIP functioned nearly uninterrupted at frigid temperatures accumulating 2345.8 mm of geometric snow depth over a total of 499 hours. This long record consists of 30 events, and the PIP-retrieved and snow stake field measured SWE differed less than 15% in every event. Two of the major events with the longest duration and the highest accumulation are examined in detail. The particle mass with a given diameter was much lower during a shallow, colder, uniform lake-effect event than in the deep, less cold, and variable synoptic event. This study demonstrated that the PIP is a robust instrument for operational use, and is reliable for deriving the bulk properties of falling snow.
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Chen, Yangkang, Yunfeng Chen, Sergey Fomel, Alexandros Savvaidis, Omar M. Saad, and Yapo Abolé Serge Innocent Oboué. "Pyekfmm: A Python Package for 3D Fast-Marching-Based Travel-Time Calculation and its Applications in Seismology." Seismological Research Letters, May 4, 2023. http://dx.doi.org/10.1785/0220230042.
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Abstract We present a multifunctional open-source package—pyekfmm for eikonal-based travel-time calculation in 2D and 3D heterogeneous anisotropic media based on the well-documented fast marching method. Different from existing travel-time calculation packages, the pyekfmm package offers a seamless compilation of the backbone C programs in the Python environment through a state-of-the-art pip installation. As a result, the users can use the pyekfmm package for different scientific purposes with the convenience enabled by Python interfaces and with the efficiency offered by C programs. The pyekfmm package offers the option of travel-time calculation to second-order accuracy. More importantly, the pyekfmm provides the option for travel-time calculation in anisotropic media, which enables its exclusive applications in special cases in which strong anisotropy exists. We introduce the mathematical principles and the structure of the package in detail. To demonstrate its potential, we apply it to a wide spectrum of applications, including travel-time calculation in different situations with increasing complexities, ray tracing, earthquake source location, relocation, and surface-wave tomography.
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Moškon, Miha. "CosinorPy: a python package for cosinor-based rhythmometry." BMC Bioinformatics 21, no. 1 (2020). http://dx.doi.org/10.1186/s12859-020-03830-w.
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Abstract Background Even though several computational methods for rhythmicity detection and analysis of biological data have been proposed in recent years, classical trigonometric regression based on cosinor still has several advantages over these methods and is still widely used. Different software packages for cosinor-based rhythmometry exist, but lack certain functionalities and require data in different, non-unified input formats. Results We present CosinorPy, a Python implementation of cosinor-based methods for rhythmicity detection and analysis. CosinorPy merges and extends the functionalities of existing cosinor packages. It supports the analysis of rhythmic data using single- or multi-component cosinor models, automatic selection of the best model, population-mean cosinor regression, and differential rhythmicity assessment. Moreover, it implements functions that can be used in a design of experiments, a synthetic data generator, and import and export of data in different formats. Conclusion CosinorPy is an easy-to-use Python package for straightforward detection and analysis of rhythmicity requiring minimal statistical knowledge, and produces publication-ready figures. Its code, examples, and documentation are available to download from https://github.com/mmoskon/CosinorPy. CosinorPy can be installed manually or by using pip, the package manager for Python packages. The implementation reported in this paper corresponds to the software release v1.1.