Journal articles: 'NIPALS'

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Ochoa-Muñoz, Andrés F., and Javier E. Contreras-Reyes. "Multiple Factor Analysis Based on NIPALS Algorithm to Solve Missing Data Problems." Algorithms 16, no. 10 (2023): 457. http://dx.doi.org/10.3390/a16100457.

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Missing or unavailable data (NA) in multivariate data analysis is often treated with imputation methods and, in some cases, records containing NA are eliminated, leading to the loss of information. This paper addresses the problem of NA in multiple factor analysis (MFA) without resorting to eliminating records or using imputation techniques. For this purpose, the nonlinear iterative partial least squares (NIPALS) algorithm is proposed based on the principle of available data. NIPALS presents a good alternative when data imputation is not feasible. Our proposed method is called MFA-NIPALS and, based on simulation scenarios, we recommend its use until 15% of NAs of total observations. A case of groups of quantitative variables is studied and the proposed NIPALS algorithm is compared with the regularized iterative MFA algorithm for several percentages of NA.

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de Souza, Valter Cesar, Sergio Augusto Rodrigues, and Luís Roberto Almeida Gabriel Filho. "Comparison of principal component analysis algorithms for imputation in agrometeorological data in high dimension and reduced sample size." PLOS ONE 19, no. 12 (2024): e0315574. https://doi.org/10.1371/journal.pone.0315574.

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Meteorological data acquired with precision, quality, and reliability are crucial in various agronomy fields, especially in studies related to reference evapotranspiration (ETo). ETo plays a fundamental role in the hydrological cycle, irrigation system planning and management, water demand modeling, water stress monitoring, water balance estimation, as well as in hydrological and environmental studies. However, temporal records often encounter issues such as missing measurements. The aim of this study was to evaluate the performance of alternative multivariate procedures for principal component analysis (PCA), using the Nonlinear Iterative Partial Least Squares (NIPALS) and Expectation-Maximization (EM) algorithms, for imputing missing data in time series of meteorological variables. This was carried out on high-dimensional and reduced-sample databases, covering different percentages of missing data. The databases, collected between 2011 and 2021, originated from 45 automatic weather stations in the São Paulo region, Brazil. They were used to create a daily time series of ETo. Five scenarios of missing data (10%, 20%, 30%, 40%, 50%) were simulated, in which datasets were randomly withdrawn from the ETo base. Subsequently, imputation was performed using the NIPALS-PCA, EM-PCA, and simple mean imputation (IM) procedures. This cycle was repeated 100 times, and average performance indicators were calculated. Statistical performance evaluation utilized the following indicators: correlation coefficient (r), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), Normalized Root Mean Square Error (nRMSE), Willmott Index (d), and performance index (c). In the scenario with 10% missing data, NIPALS-PCA achieved the lowest MAPE (15.4%), followed by EM-PCA (17.0%), while IM recorded a MAPE of 24.7%. In the scenario with 50% missing data, there was a performance reversal, with EM-PCA showing the lowest MAPE (19.1%), followed by NIPALS-PCA (19.9%). The NIPALS-PCA and EM-PCA approaches demonstrated good results in imputation (10% ≤ nRMSE < 20%), with NIPALS-PCA excelling in the 10%, 20%, and 30% scenarios, and EM-PCA in the 40% and 50% scenarios. Based on statistical evaluation, the NIPALS-PCA, EM-PCA, and IM imputation models proved suitable for estimating missing ETo data, with PCA imputation models in the NIPALS and EM algorithms showing the most promise. Future research should explore the effectiveness of various imputation methods in diverse climatic and geographical contexts, as well as develop new techniques considering the temporal and spatial structure of meteorological data, to advance understanding and climate prediction.

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Aurélien, Njamen Kengdo Arsène, and Kwatcho Kengdo Steve. "Gestion Des Donnees Manquantes Dans Les Bases De Donnees En Sciences Sociales : Algorithme Nipals Ou Imputation Multiple?" European Scientific Journal, ESJ 12, no. 35 (2016): 390. http://dx.doi.org/10.19044/esj.2016.v12n35p390.

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The main objective of this paper is to assess the robustness of imputation methods to fill up the series of secondary data in social sciences. The methodology used, especially that of mean imputation, multiple imputation and NIPALS algorithm, is based on a simulation using observed data. Results show a close similarity between the observed data and the data obtained by multiple imputation, mean imputation and NIPALS algorithm. The results also suggest that multiple imputation provides values substantially similar to observed data.

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Miyashita, Yoshikatsu, Toshiaki Itozawa, Hiroyuki Katsumi, and Shin-Ichi Sasaki. "Comments on the NIPALS algorithm." Journal of Chemometrics 4, no. 1 (1990): 97–100. http://dx.doi.org/10.1002/cem.1180040111.

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Patel, Nikesh, Kavitha Sivanathan, and Prashant Mhaskar. "Polymethyl Methacrylate Quality Modeling with Missing Data Using Subspace Based Model Identification." Processes 9, no. 10 (2021): 1691. http://dx.doi.org/10.3390/pr9101691.

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This paper addresses the problem of quality modeling in polymethyl methacrylate (PMMA) production. The key challenge is handling the large amounts of missing quality measurements in each batch due to the time and cost sensitive nature of the measurements. To this end, a missing data subspace algorithm that adapts nonlinear iterative partial least squares (NIPALS) algorithms from both partial least squares (PLS) and principal component analysis (PCA) is utilized to build a data driven dynamic model. The use of NIPALS algorithms allows for the correlation structure of the input–output data to minimize the impact of the large amounts of missing quality measurements. These techniques are utilized in a simulated case study to successfully model the PMMA process in particular, and demonstrate the efficacy of the algorithm to handle the quality prediction problem in general.

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Hedi, Hedi. "PENAKSIRAN PARAMETER PLS DENGAN METODE ALGORITMA NIPALS MENGGUNAKAN BAHASA PEMROGRAMAN STATISTICA." Sigma-Mu 9, no. 2 (2017): 9–15. http://dx.doi.org/10.35313/sigmamu.v9i2.971.

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Prediksi nilai variabel respon didasarkan pada beberapa variabel prediktor dinamakan analisis regresi. Dalam praktiknya, permasalahan yang sering muncul adalah variabel-variabel prediktor yang saling berkorelasi (multikolinieritas). Untuk mengatasi masalah tersebut diterapkan metode Partial Least Squares Regression (PLS). Penaksiran parameter pada PLS diperoleh dengan menggunakan algoritma NIPALS (Non Linier Iterative Least Square) yang langkah-lanngkah perhitungannya ditentukan dengan bahasa pemrograman STATISTICA

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Sarkodie, Samuel Asumadu, and Philip Kofi Adom. "Determinants of energy consumption in Kenya: A NIPALS approach." Energy 159 (September 2018): 696–705. http://dx.doi.org/10.1016/j.energy.2018.06.195.

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Ergon, R. "Re-interpretation of NIPALS results solves PLSR inconsistency problem." Journal of Chemometrics 23, no. 2 (2009): 72–75. http://dx.doi.org/10.1002/cem.1180.

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Yang, Zhanshan, and Xilan Liu. "Roundoff Error Analysis of an Algorithm Based on Householder Bidiagonalization for Total Least Squares Problems." Mathematics 9, no. 20 (2021): 2550. http://dx.doi.org/10.3390/math9202550.

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For large-scale problems, how to establish an algorithm with high accuracy and stability is particularly important. In this paper, the Householder bidiagonalization total least squares (HBITLS) algorithm and nonlinear iterative partial least squares for total least squares (NIPALS-TLS) algorithm were established, by which the same approximate TLS solutions was obtained. In addition, the propagation of the roundoff error for the process of the HBITLS algorithm was analyzed, and the mixed forward-backward stability of these two algorithms was proved. Furthermore, an upper bound of roundoff error was derived, which presents a more detailed and clearer approximation of the computed solution.

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Vandeginste, B. G. M., C. Sielhorst, and M. Gerritsen. "The NIPALS algorithm for the calculation of the principal components of a matrix." TrAC Trends in Analytical Chemistry 7, no. 8 (1988): 286–87. http://dx.doi.org/10.1016/0165-9936(88)80007-4.

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Aluja, Tomás, and Víctor Manuel González. "GNM-NIPALS: Estimación general no métrica y no lineal por mínimos cuadrados parciales iterativos." Revista de Matemática: Teoría y Aplicaciones 21, no. 1 (2014): 85. http://dx.doi.org/10.15517/rmta.v21i1.14140.

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Stott, Alexander E., Bruno Scalzo Dees, Ilia Kisil, and Danilo P. Mandic. "A class of multidimensional NIPALS algorithms for quaternion and tensor partial least squares regression." Signal Processing 160 (July 2019): 316–27. http://dx.doi.org/10.1016/j.sigpro.2019.03.002.

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Gonzalez Rojas, Víctor Manuel, Gabriel Conde Arango, and Andrés Felipe Ochoa Muñoz. "Análisis de Componentes Principales en presencia de datos faltantes: el principio de datos disponibles." Scientia et Technica 26, no. 2 (2021): 219–28. http://dx.doi.org/10.22517/23447214.20591.

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En este trabajo proponemos utilizar el principio de datos disponibles derivado del algoritmo NIPALS (Nonlinear estimation by Iterative Partial Least Square) para trabajar el Análisis de Componentes Principales (ACP) en presencia de datos faltantes. Esta propuesta es importante puesto que no realiza imputación de datos, ni se descartan individuos de la base datos, el método propuesto trabaja con los elementos pares disponibles para conformar las matrices de cuasicorrelación en y en la descomposición espectral de estas matrices permite a través de las relaciones de transición realizar un ACP convencional. Del estudio de simulación realizado se encontró que a medida que aumenta el porcentaje de datos faltantes disminuye la inercia explicada en el primer plano factorial. Se desarrolló el algoritmo de solución bajo el entorno de programación R y se anexa el código para uso libre.

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Wang, Peiwen, Isabel Mendes, and Ramona Franić. "Investigating the Relationship between Aquaculture Investments, Training, and Environmental Factors in Guangdong: An Alternative Perspective." Fishes 8, no. 5 (2023): 237. http://dx.doi.org/10.3390/fishes8050237.

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This study investigates the interplay between investment, training, and environmental factors in the aquaculture industry in the Guangdong region of China. Using NIPALS regression to address multicollinearity, we identify the factors that significantly impact losses of aquaculture products due to environmental factors. Our findings highlight the importance of targeted training and education for fisherfolks and extension staff to enhance environmental management practices and reduce losses. We also emphasize the need to consider regional variability and challenges in developing universal models. Based on our results, we propose using innovative technology, fostering public–private partnerships, and adapting to regional variability to address environmental challenges. Finally, we suggest establishing a comprehensive monitoring and evaluation system to assess the effectiveness of interventions and promote evidence-based decision-making for sustainable development in the region’s aquaculture sector.

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Ismail, I. Fasfous, S. Al-Degs Yahya, and M. Mallah Asmaa. "ASSESSMENT OF DIFFERENT PARTIAL LEAST SQUARES VARIANTS FOR DETERMINATION OF BINARY-DRUG SYSTEM EXHIBITING INTENSE SPECTRAL OVERLAP." INDIAN DRUGS 53, no. 11 (2016): 26–37. http://dx.doi.org/10.53879/id.53.11.10683.

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Amoxicillin (AMO) and clavulanic acid (CLA) are popular activate pharmaceutical ingredients that are widely used due to their efficient medical activity. However, this binary system suffers from intense spectral overlap (93.6%). Inspite of the intense spectral overlap and serious nonlinearity in the current system, both drugs were accurately quantified by multivariate calibration. The performance of different partial least squares PLS variants (NIPALS, SIMPLS, Kernel and Bidiagonalization) for accurate quantification of AMOMO-CLA in commercial formulation was outlined. Partial response and partial residual plots confirmed a serious nonlinearity in the binary system. Compared to other algorithms, PLS-Kernel exhibited a better performance for drugs quantification and seven latent variables were necessary for accurate quantification: 94.0(9.6%) and 95.6(5.2%) for AMOMO and CLA, respectively. The intense spectral overlap, nonlinearity, and non-modelled excipients are effectively handled by PLS-Kernel calibration.

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Patel, Nikesh, Brandon Corbett, Johan Trygg, Chris McCready, and Prashant Mhaskar. "Subspace Based Model Identification for an Industrial Bioreactor: Handling Infrequent Sampling Using Missing Data Algorithms." Processes 8, no. 12 (2020): 1686. http://dx.doi.org/10.3390/pr8121686.

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This manuscript addresses the problem of modeling an industrial (Sartorius) bioreactor using process data. In the context of the Sartorius Bioreactor, it is important to appropriately address the problem of dealing with a large number of variables, which are not always measured or are measured at different sampling rates, without taking recourse to simpler interpolation- or imputation-based approaches. To this end, a dynamic model for the Sartorius Bioreactor is developed via appropriately adapting a recently presented subspace model identification technique, which in turn uses nonlinear iterative partial least squares (NIPALS) algorithms to gracefully handle the missing data. The other key contribution is evaluating the ability of the identification approach to provide insight into the process by computing interpretable variables such as metabolite rates. The results demonstrate the ability of the proposed approach to model data from the Sartorius Bioreactor.

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Siti, Mariana Che Mat Nor, Milleana Shaharudin Shazlyn, Ismail Shuhaida, Hila Zainuddin Nurul, and Leong Tan Mou. "A comparative study of different imputation methods for daily rainfall data in east-coast Peninsular Malaysia." Bulletin of Electrical Engineering and Informatics 9, no. 2 (2020): 635–43. https://doi.org/10.11591/eei.v9i2.2090.

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Rainfall data are the most significant values in hydrology and climatology modelling. However, the datasets are prone to missing values due to various issues. This study aspires to impute the rainfall missing values by using various imputation method such as Replace by Mean, Nearest Neighbor, Random Forest, Non-linear Interactive Partial Least-Square (NIPALS) and Markov Chain Monte Carlo (MCMC). Daily rainfall datasets from 48 rainfall stations across east-coast Peninsular Malaysia were used in this study. The dataset were then fed into Multiple Linear Regression (MLR) model. The performance of abovementioned methods were evaluated using Root Mean Square Method (RMSE), Mean Absolute Error (MAE) and Nash-Sutcliffe Efficiency Coefficient (CE). The experimental results showed that RF coupled with MLR (RF-MLR) approach was attained as more fitting for satisfying the missing data in east-coast Peninsular Malaysia.

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Ochoa Muñoz, Andrés Felipe, Víctor Manuel Gonzalez Rojas, and Campo Elías Pardo Turriago. "Missing data in multiple correspondence analysis under the available data principle of the NIPALS algorithm." DYNA 86, no. 211 (2019): 249–57. http://dx.doi.org/10.15446/dyna.v86n211.80261.

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Multiple correspondence analysis (MCA) in the presence of missing data is usually performed by removing the records that have missing or not available (NA) data; sometimes, an entire row or column of a data matrix is removed, which is not ideal because relevant information on an individual or variable of the study is lost. In some cases, it is assumed that the missing data are a category of the qualitative variable, resulting in a greater variance dispersion in the new axes. Possible solutions to this problem can be the imputation of the missing data or using an algorithm suited to the presence of this type of data. This work is focused on performing the MCA method in the presence of missing data, without using imputation techniques, by using the available data principle of the nonlinear estimation by iterative partial least squares (NIPALS) algorithm [25].

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صالح, رباب عبد الرضا. "مقارنة بين طرائق المربعات الصغرى الجزئية و المركبات الرئيسية باستعمال المحاكاة". Journal of Economics and Administrative Sciences 22, № 87 (2016): 50. http://dx.doi.org/10.33095/jeas.v22i87.725.

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المستخلص تعد طريقة المركبات الرئيسة والمربعات الصغرى الجزئية من الطرائق المهمة في تحليل الانحدار حيث ان الاثنان تستعملان لتحويل مجموعه من المتغيرات ذات الارتباط العالي الى مجموعة من المتغيرات المستقلة الجديدة تعرف بالمركبات وتكون هذه المركبات خطية متعامدة مستقلة بعضها عن البعض الاخر باستعمال تحويلات خطية ويستعمل الاثنان ايضا في تخفيض الابعاد .  تم في هذا البحث استعمال طريقة المربعات الصغرى الجزئية باستعمال خوارزمية التكرار غير الخطي للمربعات الصغرى الجزئية Non-linear Iterative partial least squares NIPALS(PLS1) وطريقة انحدار المركبات الرئيسية بخوارزمية تجزئة القيم المفردة ((SVD) Singular value decomposition ). اذ تم اجراء المقارنة للطريقتين المذكورتين آنفا من خلال تجارب المحاكاة عندما يتوزع الخطأ توزيعا طبيعيا لحجوم عينات وابعاد متغيرات مختلفة ، واتضح من خلال المقارنة ان طريقة المربعات الصغرى الجزئية افضل من طريقة المركبات الرئيسية في حالة كون عدد المشاهدات اكبر من عدد المتغيرات وكذلك في حالة كون عدد المتغيرات اكبر من عدد المشاهدات. .

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Castillo-Ibarra, Emilio, Marco A. Alsina, Cesar A. Astudillo, and Ignacio Fuenzalida-Henríquez. "PFA-Nipals: An Unsupervised Principal Feature Selection Based on Nonlinear Estimation by Iterative Partial Least Squares." Mathematics 11, no. 19 (2023): 4154. http://dx.doi.org/10.3390/math11194154.

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Unsupervised feature selection (UFS) has received great interest in various areas of research that require dimensionality reduction, including machine learning, data mining, and statistical analysis. However, UFS algorithms are known to perform poorly on datasets with missing data, exhibiting a significant computational load and learning bias. In this work, we propose a novel and robust UFS method, designated PFA-Nipals, that works with missing data without the need for deletion or imputation. This is achieved by considering an iterative nonlinear estimation of principal components by partial least squares, while the relevant features are selected through minibatch K-means clustering. The proposed method is successfully applied to select the relevant features of a robust health dataset with missing data, outperforming other UFS methods in terms of computational load and learning bias. Furthermore, the proposed method is capable of finding a consistent set of relevant features without biasing the explained variability, even under increasing missing data. Finally, it is expected that the proposed method could be used in several areas, such as machine learning and big data with applications in different areas of the medical and engineering sciences.

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Sinaga, Vetranella T. R. A., Diah Safitri, and Rita Rahmawati. "PERBANDINGAN REGRESI KOMPONEN UTAMA DENGAN REGRESI KUADRAT TERKECIL PARSIAL PADA INDEKS PEMBANGUNAN MANUSIA PROVINSI JAWA TIMUR." Jurnal Gaussian 8, no. 4 (2019): 496–505. http://dx.doi.org/10.14710/j.gauss.v8i4.26749.

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The multiple regression classic assumptions are used to give linear unbiased and minimum variance estimator. In Human Development Index (HDI) and influencing factors in East Java, there are two variables with VIF more than 10 so the assumption of non-multicollinearity is not fulfilled. Principal component regression (PCR) and partial least squares regression (PLS-R) can solve this problem. By doing principal component analysis, there are two linear combinations to take as the new independent variables which are free from collinearity. In the PLS-R, NIPALS algorithm is used to calculate the components and other structures and to estimate the parameter. While in PCR all independent variables are significant, the percentage of households with drinking water is feasibles is not significant in the model. PLS-R’s is 95,85% is greater than PCR’s = 93,42%. PCR’s PRESS = 81,78 is greater than PLS-R’s PRESS = 61,0595.Keywords: Human Development Index (HDI), Multicollinearity, Principal Component Regression, Partial Least Squares Regression, , PRESS

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TANG, KAI-LIN, WEI-JIA YAO, TONG-HUA LI, YI-XUE LI, and ZHI-WEI CAO. "CANCER CLASSIFICATION FROM THE GENE EXPRESSION PROFILES BY DISCRIMINANT KERNEL-PLS." Journal of Bioinformatics and Computational Biology 08, supp01 (2010): 147–60. http://dx.doi.org/10.1142/s0219720010005130.

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Cancer diagnosis depending on microarray technology has drawn more and more attention in the past few years. Accurate and fast diagnosis results make gene expression profiling produced from microarray widely used by a large range of researchers. Much research work highlights the importance of gene selection and gains good results. However, the minimum sets of genes derived from different methods are seldom overlapping and often inconsistent even for the same set of data, partially because of the complexity of cancer disease. In this paper, cancer classification was attempted in an alternative way of the whole gene expression profile for all samples instead of partial gene sets. Here, the three common sets of data were tested by NIPALS-KPLS method for acute leukemia, prostate cancer and lung cancer respectively. Compared to other conventional methods, the results showed wide improvement in classification accuracy. This paper indicates that sample profile of gene expression may be explored as a better indicator for cancer classification, which deserves further investigation.

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Park, Yeonju, Isao Noda, and Young Mee Jung. "Smooth Factor Analysis (SFA) to Effectively Remove High Levels of Noise from Spectral Data Sets." Applied Spectroscopy 72, no. 5 (2017): 765–75. http://dx.doi.org/10.1177/0003702817752126.

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Smooth factor analysis (SFA) is introduced as an effective method of removing heavy noise from spectral data sets. A modified form of the nonlinear iterative partial least squares (NIPALS) algorithm involving the smoothing of factors at each step is used in SFA. Compared with the conventional smoothing techniques for individual spectra, SFA is much more effective in the treatment of very noisy spectra (∼40% noise level). Smooth factor analysis invokes a large number of smooth factors to retain pertinent spectral information for high fidelity without distortion. This approach can be used as an effective general pretreatment procedure for multivariate spectral data analysis, such as principal component analysis (PCA) and partial least squares (PLS). This SFA method was also applied to the real experimental data, and its results successfully demonstrated the powerful potential for effective noise removal. Furthermore, this treatment is found to be very helpful to assist effective interpretation of two-dimensional correlation spectroscopy (2D-COS) spectra with very high noise level, which was not possible before.

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Hariyanto, Yuanita Amalia, Aditya Lapu Kalua, Bukroanah Amir Makkau, and Imam Jayanto. "Training on antibacterial liquid soap making from nipah leaf extract to support SDGs-3 good health and well being." Community Empowerment 9, no. 12 (2024): 1785–93. https://doi.org/10.31603/ce.12570.

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Wori Village, located in the buffer zone of the Likupang Tourism Special Economic Zone (KEK), requires development of both its natural and human resources. Natural resource optimization can be achieved through diversification, specifically by producing liquid soap from the nipah plant. Human resource development necessitates increased public awareness of skin health and personal hygiene. This Community Service aimed to educate the Wori Village community on personal hygiene and provide a practical solution for reducing skin diseases through the use of antibacterial liquid soap. The methodology comprised socialization, training, technology application, mentoring, evaluation, and plans for program sustainability. The resulting NIPALS soap met quality standards, exhibiting a pH of 7. Evaluation demonstrated a 96.7% increase in participants' knowledge of personal hygiene and soap-making (pre-test vs. post-test). Furthermore, 95.5% of participants expressed satisfaction with the soap (4.5% considered it average). Positive user experiences included a 97% absence of itching, 76.7% reporting a tightening effect, and 70% noting moisturization. These results, supported by positive hedonic test results, confirm the successful production of liquid soap by the participants.

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Munawar, Agus A. "Rapid Classification Of Agricultural Products Based On Their Electro-Optic Properties Using Near Infrared Reflectance And Chemometrics." Rona Teknik Pertanian 8, no. 1 (2015): 10–18. http://dx.doi.org/10.17969/rtp.v8i1.2683.

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Abstract. Near infrared technology have been widely applied in many fields, including agriculture especially in sorting and grading process. The advantage of this technology: simple sample preparation, rapid, effective and non-destructive. The main objective of this study is to evaluate the feasibility of NIR technology in classifying several agricultural products based on their electro-optic properties. NIR diffuse reflectance spectra of apples, bananas, mangoes, garlics, tomatoes, green grapes, red grapes and oranges were acquired in wavelength range of 1000-2500 nm with gradual increment of 2 nm. Chemometrics methods were applied in combination with NIR spectra data. Classification was performed by applying principal component analysis (PCA) followed by non-iterative partial least square (NIPALS) cross validation. The results showed that NIR and chemometrics was able to differentiate and classify these agricultural products with two latent variables (2 PCs) and total explained variance of 97% (88% PC1 and 9% PC2). Furthermore, it also showed that multiplicative scatter correction (MSC) was found to be effective spectra correction or enhancement method and increased classification accuracy and robustness. It may conclude that NIR technology combined with chemometrics was feasible to apply as a rapid and non-destructive method for sorting and grading agricultural products. Rapid Classification Of Agricultural Products Based On Their Electro-Optic Properties Using Near Infrared Reflectance And ChemometricsAbstract. Aplikasi teknologi near infra red (NIR) telah digunakan dalam banyak bidang, termasuk untuk bidang pertanian terutama pada proses sortasi dan grading. Keunggulan metode ini antara lain : rapid, efektif, simultan dan tanpa merusak objek yang dikaji. Tujuan utama dari studi ini adalah untuk mengkaji potensi NIR dalam mengklasifikasi beberapa produk pertanian berdasarkan karakteristik sifat elektro-optik dari produk tersebut. Spektrum NIR pada panjang gelombang 1000 – 2500 nm dengan increment 2 nm diakuisisi untuk produk pertanian : apel, pisang, manga, bawang putih, tomat, anggur hijau, anggur merah dan jeruk. Metode chemo metrics digunakan dalam studi ini untuk dikombinasikan dengan spektrum NIR. Klasifikasi produk pertanian dilakukan dengan menerapkan metode principal component analysis (PCA) yang disertai dengan metode non-iterative partial least square (NIPALS) cross validation. Hasil studi menunjukkan bahwa kombinasi NIR dan chemo metrics mampu membedakan dan mengklasifikasi produk pertanian tersebut dengan menggunakan dua latent variable pada PCA (2 PCs) dengan total explained variance 97% (88% PC1 dan 9% PC2). Selain itu, dari studi ini juga didapatkan bahwa perbaikan data spectrum dengan metode multiplicative scatter correction (MSC) sebelum klasifikasi mampu meningkatkan akurasi hasil klasifikasi. Secara umum, dapat disimpulkan bahwa teknologi NIR dan chemo metrics dapat dijadikan sebagai metode yang efektif untuk sortasi dan atau grading produk pertanian.

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Sasongko, S. B., K. A. Ibrahim, and A. Ahmad. "Fault Analysis Of Process System Using Multi Block Principal Component Analysis." REAKTOR 7, no. 02 (2017): 61. http://dx.doi.org/10.14710/reaktor.7.02.61-65.

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This research looks into the issues of the quality improvement based on process control instead of product control using multivariate statistical process contro. A deterministic model of a proton exchange membrane fuel cell (PEM-FC) power plant was used as a case study to represent a multi variable or mukti equipment system. A three-step approach is proposed which can be classified into fault detection, fault isolation, and faulr diagnosis. The fault detection and the isolation utilize the multivariate analysis and yhe contro chart method , which uses the series multi-block principal component analysis of extended of PCA method. The series block principal component abalysis is solved using the non linear iteration partial least squares (NIPALS) algorithm. The SB-PCA can advangeouly incorporate the control chart, namely, T2 Hotelling control chart. In the fault diagnosis chart, the normalized variable method was successfully applied in this study with promising results. As a conclution, the result of this study demonstrated the potentials of multivariate statistical process control in solving fault detection and diagnosis problem for multi variable and multi equipment system.Keywords : statistical process control, principal component, fault analysis

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van Wolde, Ellen. "The Niphal as middle voice and its consequence for meaning." Journal for the Study of the Old Testament 43, no. 3 (2019): 453–78. http://dx.doi.org/10.1177/0309089217743160.

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The central thesis of this article is that improved knowledge of the Niphal can offer us arguments in favour of (or against) certain meanings of verbs in biblical texts. Proceeding from general linguistic studies of middle voice to Biblical Hebrew linguistics, the differences between the active, reflexive, middle and passive voices are clarified. Subsequently, it is shown that the Niphal expresses neither the reflexive nor passive voice, but predominantly marks the middle voice. The Niphal describes an event in which the subject is concerned with itself, though not reflexively as a differentiated object, but as an undifferentiated middle, while reference to an external Agent is absent. These insights are applied to various texts and verbs in the Hebrew Bible, namely, 3 Niphals of the verb גמל‎ (‘wean’), 7 Niphals (feminine singular) of the verb טמא‎ (‘defile’), and 16 usages of אסף‎ Niphal in contexts of dying.

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Hayder Osman Hussein, Hayder Osman Hussein, and Rabab Abdul-Ridha Saleh Rabab Abdul-Ridha Saleh. "Comparison Between the Partial Least Squares Method and Principal Components Using Genetic Algorithm with an Application." Journal of Economics and Administrative Sciences 31, no. 145 (2025): 143–62. https://doi.org/10.33095/s8543c81.

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The problem of multicollinearity among independent variables in a regression model was addressed in this study using Partial Least Squares (PLS) and Principal Component Analysis (PCA). The influence exerted by multicollinearity can deteriorate the results of regression modeling; that is, it makes the traditional technique OLS less reliable. The research is centered on applying advanced algorithms for Partial Least Squares (SIMPL and O-PLS) as well as for PCA (NIPALS and SVD) on real-life data scenarios, as well as integrating genetic algorithm (GAs) with these algorithms to optimize predictive performance. The relative efficiency of these methods is evaluated primarily through the amplitude of the Mean Square Error (MSE) used as a criterion for comparison. The results show the effectiveness of PLS-OPLS above PCA in terms of the lowest before and after embedding genetic algorithms into the MSE. All this underpins the effectiveness of PLS in minimizing multicollinearity thereby allowing for the formulation and prediction of very highly predictive models. More and more indication of subtle class was revealed with regard to fine tuning advanced GA technique in favor of enhancing regression modeling for complex data analysis. The ongoing research will help in opening numerous possibilities to reinforce regression methodology, especially when one factor in an array of applications representing a relatively significant level of prediction accuracy.

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Mat Hussin, Nur Farah Amieera, Shazlyn Milleana Shaharudin, Nurul Ainina Filza Sulaiman, et al. "Rainfall Prediction Using Statistical Downscaling Based on Support Vector Machine in Selangor." International Journal on Advanced Science, Engineering and Information Technology 15, no. 2 (2025): 387–94. https://doi.org/10.18517/ijaseit.15.2.12420.

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Global climate change gains notoriety in literature discussions for potentially triggering extreme change intensity and regularity, like floods and droughts. In this study, the amount of daily rainfall in Selangor was predicted using a downscaling model based on the machine learning technique of the Support Vector Machine (SVM) approach. The collected atmospheric data (predictor) and daily rainfall data (predictand) between 2008 and 2018 used incorporate five imputation methods: mean imputation, K-nearest neighbor, Nonlinear Iterative Partial Least Squares (NIPALS) algorithm, Markov Chain Monte Carlo (MCMC) multiple imputation algorithm, and Expectation Maximization (EM) algorithm. The predictor selection was obtained using Principal Component Analysis (PCA). Primarily, gamma, cost, and epsilon were determined using K-fold cross-validation. Once the parameter value was identified, varying kernel types (linear, RBF, polynomial, and sigmoid) allowed the SVM performance as a regression model to be measured. The SVM model was developed by first handling missing data using imputation methods. The model generating the lowest RMSE value performs best because the difference of the estimated and observed value is minor. PCA efficiently reduced data dimension while retaining key variabilities. The SVM model with a Radial Basis Function (RBF) kernel outperformed others in predicting daily rainfall by displaying the lowest RMSE during calibration (13.95071) and validation (12.60423). Based on the study, the SVM model performance is limited when applied to this dataset. For future studies, exploring advanced imputation techniques and broadening the methodology to other tropical climates for broader applicability are recommended.

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Gonzalez Rojas, Victor Manuel. "Interbattery Factor Analysis via PLS: the missing data case." Revista Colombiana de Estadística 39, no. 2 (2016): 247. http://dx.doi.org/10.15446/rce.v39n2.52724.

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<p>In this article we develop the Inter-battery Factor Analysis (IBA) byusing PLS (Partial Least Squares) methods. As the PLS methods are algorithms that iterate until convergence, an adequate intervention in some of their stages provides a solution to problems such as missing data. Specifically, we take the iterative stage of the PLS regression and implement the “available data” principle from the NIPALS (Non-linear estimation by Iterative Partial Least Squares) algorithm to allow the algorithmic development of the IBA with missing data. We provide the basic elements to correctly analyse and interpret the results. This new algorithm for IBA, developedunder the R programming environment, fundamentally executes iterative convergent sequences of orthogonal projections of vectors coupled with the available data, and works adequately in bases with or without missing data. To present the basic concepts of the IBA and to cross-reference the results derived from the algorithmic application, we use the complete Linnerud database for the classical analysis; then we contaminate this database with a random sample that represents approximately 7% of the non-available (NA) data for the analysis with missing data. We ascertain that the results obtained from the algorithm running with complete data are exactly the same as those obtained from the classic method for IBA, and that the results withmissing data are similar. However, this might not always be the case, as it depends on how much the ‘original’ factorial covariance structure is affected by the absence of information. As such, the interpretation is only valid in relation to the available data.</p>

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Mat Hussin, Nur Farah Amieera, Shazlyn Milleana Shaharudin, Nurul Ainina Filza Sulaiman, et al. "Rainfall Prediction Using Statistical Downscaling Based on Support Vector Machine in Selangor." International Journal on Advanced Science, Engineering and Information Technology 15, no. 1 (2025): 345–52. https://doi.org/10.18517/ijaseit.15.1.12410.

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Global climate change gains notoriety in literature discussions for potentially triggering extreme change intensity and regularity, like floods and droughts. In this study, the amount of daily rainfall in Selangor was predicted using a downscaling model based on the machine learning technique of the Support Vector Machine (SVM) approach. The collected atmospheric data (predictor) and daily rainfall data (predictand) between 2008 and 2018 used incorporate five imputation methods: mean imputation, K-nearest neighbor, Nonlinear Iterative Partial Least Squares (NIPALS) algorithm, Markov Chain Monte Carlo (MCMC) multiple imputation algorithm, and Expectation Maximization (EM) algorithm. The predictor selection was obtained using Principal Component Analysis (PCA). Primarily, gamma, cost, and epsilon were determined using K-fold cross-validation. Once the parameter value was identified, varying kernel types (linear, RBF, polynomial, and sigmoid) allowed the SVM performance as a regression model to be measured. The SVM model was developed by first handling missing data using imputation methods. The model generating the lowest RMSE value performs best because the difference of the estimated and observed value is minor. PCA efficiently reduced data dimension while retaining key variabilities. The SVM model with a Radial Basis Function (RBF) kernel outperformed others in predicting daily rainfall by displaying the lowest RMSE during calibration (13.95071) and validation (12.60423). The most fitting parameter set for the SVM model is C set to 4.00, y set to 1.935, and e set to 0.2. Based on the study, the SVM model performance is limited when applied to this dataset. For future studies, exploring advanced imputation techniques and broadening the methodology to other tropical climates for broader applicability are recommended.

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Munawar, Agus Arip, Kiman Siregar, and Agussabti Agussabti. "Near Infrared Technology As a Robust and Environmental Friendly Approach To Biofuel Analysis: Rapid Biodiesel Classification and Quality Prediction." Rona Teknik Pertanian 10, no. 2 (2017): 1–10. http://dx.doi.org/10.17969/rtp.v10i2.10005.

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Abstract. The use of ethanol and biodiesel, which are alternative fuels or biofuels, has increased in the last few years. Modern ofﬁcial standards list 25 parameters that must be determined to certify biodiesel quality. In order to determine biofuel quality, several methods were already widely used in which most of them were based on solvent extraction followed by other laboratory procedures. Yet, these methods are expensive, laborious and complicated processing for samples. Near infrared reflectance spectroscopy (NIRS) can be considered as a fast, pollution-free and non-destructive method in determining biofuel quality parameters. The objective of this study is to apply near infrared technology in classifying biodiesel based on KOH (0.3, 0.5 and 0.7) and to predict related biodiesel quality properties (water content, linolenic fatty acid, oleic acid, and stearic acid) based on its infrared reflectance. Biodiesel infrared spectrum was acquired in wavelength range from 1000 to 2500 nm for different mentioned three KOH content. Principal component analysis (PCA) with non-iterative partial least square (NIPALS) was applied to analyze biodiesel spectral data. The result showed that two principal components (PC1=97% ad PC2 = 2%) based on infrared reflectance data were successfully able to recognize and classify biodiesel based on their used KOH. Moreover, the wavelength range of 1000 – 1140 were to be believed related to linolenic fatty acid whilst 1450 nm and 1930 nm were associated with water content. Stearic acid can be predicted in wavelength range of 1330 – 1380 nm and wavelength range of 1725 – 1790 nm were related to oleic acid of biodiesel. This may conclude that infrared technology was feasible to use as a rapid, effective and non-invasive method in biofuel classification and evaluation.

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Long, Xiao, and Ru Zhao. "Nipple Retractor to Correct Inverted Nipples." Breast Care 6, no. 6 (2011): 463–65. http://dx.doi.org/10.1159/000335221.

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Centuori, Susanna, Tea Burmaz, Luca Ronfani, et al. "Nipple Care, Sore Nipples, and Breastfeeding: A Randomized Trial." Journal of Human Lactation 15, no. 2 (1999): 125–30. http://dx.doi.org/10.1177/089033449901500210.

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Sałek, Agnieszka, Katarzyna Karłowicz - Bodalska, and Dominik Marciniak. "Polish pharmacy professionals' beliefs about homeopathic medicines - a survey." Farmacja Polska 79, no. 3 (2023): 147–56. http://dx.doi.org/10.32383/farmpol/171465.

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Wstęp: Homeopatia jest alternatywną metodą leczenia, opartą na zasadzie podobieństwa, która została opracowana przez Samuela Hahnemanna w XVIII wieku. Metoda ta polega na podawaniu pacjentowi substancji, które mogą wywoływać objawy podobne do zgłaszanych przez niego dolegliwości, w bardzo dużych rozcieńczeniach. Mimo że dowody naukowe przemawiają na niekorzyść tej metody, produkty homeopatyczne są określane przez europejskie prawo mianem leków i przechodzą specjalna, uproszczoną procedurę dopuszczenia do obrotu. Na terenie Polski pacjent może zakupić produkty lecznicze homeopatyczne wyłącznie w aptece. W związku z powyższym uzyskana w niej rekomendacja może mieć wpływ na to, czy pacjent zdecyduje się na stosowanie leku homeopatycznego, czy też nie. Materiały i metody: Badanie miało na celu analizę stosunku polskich farmaceutów i techników farmaceutycznych do leków homeopatycznych. Przeprowadzono anonimową ankietę wśród fachowych pracowników aptek, w której oceniali oni swoją wiedzę, przekonania i rekomendacje dotyczące leków homeopatycznych. W okresie od stycznia do kwietnia 2023 roku ankietę wypełniło 298 respondentów – 279 farmaceutów oraz 19 techników. Zebrane odpowiedzi poddano analizie statystycznej w programie STATISTICA v. 13.3. Istotność statystyczną sprawdzono stosując test zgodności χ2 Pearsona, przyjmując za poziom istotności wartość p ≤ 0,05. Do oceny globalnych zależności między kluczowymi analizowanymi zmiennymi bez względu na ich skalę wykorzystano analizę składowych głównych PCA. Zbudowany model PCA estymowano wykorzystując algorytm iteracyjny NIPALS. Wyniki: Spośród 298 respondentów biorących udział w badaniu, 285 pracowało w aptekach ogólnodostępnych. Ponad dwie trzecie respondentów miało osobiste doświadczenia związane ze stosowaniem leków homeopatycznych, a około jedna trzecia z nich polecała takie leki swoim pacjentom. Skłonność do polecania leków homeopatycznych korelowała z występowaniem w przeszłości u badanego pozytywnych skutków stosowania leków homeopatycznych. Leki homeopatyczne najczęściej polecały kobiety i osoby po 50. roku życia. Ponad połowa fachowych pracowników aptek uznała leki homeopatyczne za nieskuteczne i wykazujące wyłącznie efekt placebo. W pytaniu o to, w jakich schorzeniach polecają leki homeopatyczne, farmaceuci i technicy najczęściej wskazywali objawy przeziębienia i grypy, bolesne ząbkowanie oraz siniaki i krwiaki. Wnioski: Wyniki badania sugerują, że większość fachowych pracowników aptek nie poleca swoim pacjentom leków homeopatycznych. Problem oceny preparatów nieposiadających udowodnionego działania terapeutycznego za skuteczne dotyka głównie kobiet po 50. roku życia oraz respondentów z małych miast. Poniższe badanie warto w przyszłości pogłębić, skupiając się na tych grupach respondentów. Zdobyta w ten sposób wiedza pozwoli ocenić, w jakim stopniu ta grupa pracowników polega na dowodach naukowych i czy jest świadoma podejmowania decyzji jedynie w oparciu o własne przekonania.

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Hyakusoku, Hiko, and Takafumi Chin. "Usefulness of the Nipple-Suspension Piercing Device After Correction of Inverted Nipples." Aesthetic Plastic Surgery 30, no. 4 (2006): 396–98. http://dx.doi.org/10.1007/s00266-005-0018-z.

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ADACHI, Yoshitaka, Sotaro TAKA, Sho YAMAGUCHI, and Satoru SAITO. "Comparison of Pinching Hardness Characteristics between Nursing Mother Nipples and Baby Bottle Nipple." Japanese Journal for Medical Virtual Reality 17, no. 1 (2020): 1–14. http://dx.doi.org/10.7876/jmvr.17.1.

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Popkin, Joel. "Data Watch: The U.S. National Income and Product Accounts." Journal of Economic Perspectives 14, no. 2 (2000): 215–24. http://dx.doi.org/10.1257/jep.14.2.215.

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GDP is arguably the most widely used economic statistic. It is part of the National Income and Product Accounts (NIPAs). The concepts, methods and source data for the NIPAs are described in this article. It contains citations to more detailed information obtainable from the publications and web sites of the U.S. Bureau of Economic Analysis, the agency responsible for the NIPAs. A brief history describes the evolution of the accounts. It explains the growing congruence between the U.S. accounts and the UN's System of National Accounts (SNA). The integration of financial and satellite accounts into the NIPAs is summarized.

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Manialawy, Yousef, Saifur R. Khan, Alpana Bhattacharjee, and Michael B. Wheeler. "The magnesium transporter NIPAL1 is a pancreatic islet–expressed protein that conditionally impacts insulin secretion." Journal of Biological Chemistry 295, no. 29 (2020): 9879–92. http://dx.doi.org/10.1074/jbc.ra120.013277.

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Type 2 diabetes is a chronic metabolic disease characterized by pancreatic β-cell dysfunction and peripheral insulin resistance. Among individuals with type 2 diabetes, ∼30% exhibit hypomagnesemia. Hypomagnesemia has been linked to insulin resistance through reduced tyrosine kinase activity of the insulin receptor; however, its impact on pancreatic β-cell function is unknown. In this study, through analysis of several single-cell RNA-sequencing data sets in tandem with quantitative PCR validation in both murine and human islets, we identified NIPAL1 (NIPA-like domain containing 1), encoding a magnesium influx transporter, as an islet-enriched gene. A series of immunofluorescence experiments confirmed NIPAL1's magnesium-dependent expression and that it specifically localizes to the Golgi in Min6-K8 cells, a pancreatic β-cell–like cell line (mouse insulinoma 6 clone K8). Under varying magnesium concentrations, NIPAL1 knockdown decreased both basal insulin secretion and total insulin content; in contrast, its overexpression increased total insulin content. Although the expression, distribution, and magnesium responsiveness of NIPAL1 in α-TC6 glucagonoma cells (a pancreatic α-cell line) were similar to the observations in Min6-K8 cells, no effect was observed on glucagon secretion in α-TC6 cells under the conditions studied. Overall, these results suggest that NIPAL1 expression is regulated by extracellular magnesium and that down-regulation of this transporter decreases glucose-stimulated insulin secretion and intracellular insulin content, particularly under conditions of hypomagnesemia.

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SARKODIE, KWAME, Christopher Bruce Konu, Michael Edem Kweku Donkor, William Apau Marfo, Gertrude Osei Tutu, and Prosper A. Akaba. "Design and development of a low-cost non-intrusive passive acoustic sensor." Journal of the Ghana Institution of Engineering (JGhIE) 23, no. 3 (2023): 52–63. http://dx.doi.org/10.56049/jghie.v23i3.135.

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In this paper, we report on the development and performance study of a low-cost non-intrusive passive acoustic sensor (NIPAS), suitable for gas flow rate measurement and monitoring in pipelines and other similar applications. Detailed information on the design considerations, development, construction, and assembly of the sensor and its unit are also provided in this paper. The complete NIPAS unit is made up of two sensor heads fixed at the test section of a designed flow loop to receive the acoustic emission signals generated from the varying gas flow rate and processed by a control unit for data collection and analysis. The electronic unit was enclosed in a Perspex casing for easy interfacing and protection. Observations on the performance of the developed NIPAS are presented as a voltage-time signal and analyzed in both time and frequency domains. Calibration of the NIPAS was done using suitable statistical features, which are the mean, standard deviation, and coefficient of variation of the signals. Results obtained indicate that the developed NIPAS unit shows proof of concept and it is suitable for measuring and monitoring gas flow rates in pipes.

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Park, Jun Ho, Syeo Young Wee, Hyun Gyo Jeong, and Chang Yong Choi. "A Novel Lactiferous Duct Preserving Method for Inverted Nipples: An Inlay Wrap-Around Flaps Supporting the Nipple Column." Archives of Aesthetic Plastic Surgery 23, no. 1 (2017): 45–48. http://dx.doi.org/10.14730/aaps.2017.23.1.45.

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Coca, Kelly Pereira, Lisa H. Amir, Maria dos Remédios da Silva Alves, Márcia Barbieri, Karla Oliveira Marcacine, and Ana Cristina Freitas Abrão. "Measurement tools and intensity of nipple pain among women with or without damaged nipples: A quantitative systematic review." Journal of Advanced Nursing 75, no. 6 (2019): 1162–72. http://dx.doi.org/10.1111/jan.13908.

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Hediger, Lacie, and Kristen Koenig. "Are nipple shells and Hoffman's exercises effective antenatal treatments for improving breastfeeding success in women with inverted nipples?" Evidence-Based Practice 23, no. 9 (2020): 34–35. http://dx.doi.org/10.1097/ebp.0000000000000684.

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Matsuki, Hitomi, and Shoji Oura. "Feasible Nipple Preservation Techniques for Breast Cancer with Slight Nipple Retraction." Case Reports in Oncology 17, no. 1 (2024): 1014–18. http://dx.doi.org/10.1159/000540892.

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Introduction: Nipple retraction has long been regarded as an absolute contraindication factor for nipple preservation in breast cancer surgery. Case Presentation: A 62-year-old woman was referred to our hospital for the treatment of breast cancer with slight nipple retraction. Imaging findings showed neither direct cancer infiltration to the nipple-areolar complex nor lymphadenopathy. Due to the patient’s strong preference, we attempted to preserve the nipple-areolar complex as follows. First, we made a quasi-complete skin flap except for the nipple-areolar complex area in a thick flap manner. Second, we fully skeletonized the sub-nipple mammary gland. Next, we pinched the skeletonized sub-nipple mammary gland with the scissors and pushed the scissors toward the nipple base as close as possible with the blades kept open. After that, we resected the nipple base using the scissors on pulling the mammary gland toward the opposite direction of the nipple. Frozen section and postoperative pathological study showed clear surgical margins and no lymph node metastasis. The patient developed temporary superficial dermal necrosis of the nipple but healed without leaving any sequelae in the nipple-areolar complex. The patient has been well for 20 months without any recurrences. Conclusion: Our nipple-preserving techniques enable safe nipple preservation even for breast cancer patients with slight nipple retraction.

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Chung, Jae-Ho, Da-Som Kim, Hyun-Dong Yeo, Seung-Pil Jung, Seung-Ha Park, and Eul-Sik Yoon. "Secondary nipple reconstruction using two surgical techniques." Archives of Plastic Surgery 48, no. 6 (2021): 590–98. http://dx.doi.org/10.5999/aps.2021.00304.

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Background Although the initial projection after primary nipple reconstruction is excellent, nipple projection gradually flattens in most cases due to multiple causes. Although various methods have been reported to rebuild the nipple after nipple flattening, the most effective method of secondary nipple reconstruction remains unknown. The aim of this study was to review our institution’s experiences with secondary nipple reconstruction.Methods We conducted a retrospective review from March 2012 to January 2019. We performed secondary nipple reconstruction if the primary reconstructed nipple height differed by more than 6 mm from the normal nipple height. We chose the method of nipple revision according to the degree of tissue scarring and the remaining nipple projection.Results We performed secondary nipple reconstruction on a total of 27 nipples, using pursestring sutures for 19 nipples and star flaps in eight nipples. The median follow-up period was 8 months (range, 6–19 months) after the final nipple reconstruction. Among the 19 nipples reconstructed using purse-string sutures, 10 (53%) demonstrated acceptable projection of more than 5 mm. Among the eight nipples reconstructed using star flaps, six (75%) showed acceptable projection of more than 5 mm. Most of the patients (73%) were satisfied (scores of 4 or 5) with the nipple reconstruction overall.Conclusions Few studies have presented favorable outcomes of secondary nipple reconstruction. When the star flap and purse-string suture methods were used depending on the remaining nipple height and scarring, appropriate projection could be achieved.

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Lim, Soyeoun, Gyeongmin Park, Hye-jeong Choi, Woon Jung Kwon, Byeong Seong Kang, and Minseo Bang. "Use of preoperative mammography, ultrasonography, and MRI to predict nipple areolar complex involvement in breast cancer." British Journal of Radiology 92, no. 1102 (2019): 20190074. http://dx.doi.org/10.1259/bjr.20190074.

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Objective: To identify the predictive factors of cancer invading into the nipple. Methods: Patients with breast cancer undergoing mastectomy between May 2009 and March 2019 were reviewed retrospectively. Of these, those with breast cancer within 2 cm of the nipple areolar complex on ultrasonography were included in this study. Clinicopathological data of the primary tumor and imaging findings from mammography, ultrasonography, and MRI were compared between cases with and without nipple involvement by cancer. Results: In total, 156 of the 821 patients identified were included in the analysis. Of them, 29 had nipple involvement by cancer. Univariate analysis revealed that the following imaging results were significantly associated with nipple involvement: perineural invasion, lymphovascular invasion, lymph node metastasis; relation type between the tumor and the nipple on ultrasonography; periareolar skin thickening on mammography; and short tumor-nipple distance, continuous enhancement between the nipple and tumor, skin enhancement, and nipple enhancement on MRI. However, on multivariate logistic regression analysis, only invasion type of tumor on ultrasonography and nipple enhancement and short tumor-nipple distance on MRI were significantly correlated with nipple involvement by cancer. Conclusion: Imaging findings on preoperative mammography, ultrasonography and MRI are effective predictors for nipple involvement by cancer. Advances in knowledge: Preoperative mammography, ultrasonography, and MRI help predict nipple involvement by breast cancer.

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Merrouni, Meryem Alami, Majda Bendahhou Idrissi, Mohamed Karam Saoud, et al. "Nipple adenoma: case report." Obstetrics & Gynecology International Journal 14, no. 2 (2023): 59–60. http://dx.doi.org/10.15406/ogij.2023.14.00694.

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Nipple adenoma is a very uncommon, benign neoplasm that involves the nipple. A palpable mass of the nipple associated with nipple discharge and erosion or ulceration is the common clinical presentation.1 This rare benign tumor can be mistaken for breast carcinoma, Paget's disease, or galactophoric ectasia.2 This case report describes a 35-year-old woman with a painful nipple lesion and serous discharge. Clinical examination revealed a hypertrophied left nipple with a well-defined, firm, bluish nodule. Biopsy confirmed the diagnosis of nipple adenoma. The patient underwent surgical excision and reconstruction of the nipple. Histological examination revealed simple florid ductal hyperplasia and no malignancy. Erosive nipple adenomatosis is a rare condition that should be considered in the presence of nipple erosion or tumor. Diagnosis is anatomo-pathological, and treatment is usually surgical. The prognosis is excellent.

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Charfeddine, Cherine, Nadia Laroussi, Rahma Mkaouar, et al. "Expanding the clinical phenotype associated with NIPAL4 mutation: Study of a Tunisian consanguineous family with erythrokeratodermia variabilis—Like Autosomal Recessive Congenital Ichthyosis." PLOS ONE 16, no. 10 (2021): e0258777. http://dx.doi.org/10.1371/journal.pone.0258777.

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Erythrokeratodermia variabilis (EKV) is a rare disorder of cornification usually associated with dominant mutations in the GJB3 and GJB4 genes encoding connexins (Cx)31 and 30.3. Genetic heterogeneity of EKV has already been suggested. We investigated at the clinical and genetic level a consanguineous Tunisian family with 2 sisters presenting an autosomal recessive form of EKV to better characterize this disease. Mutational analysis initially screened the connexin genes and Whole-exome sequencing (WES) was performed to identify the molecular aetiology of the particular EKV phenotype in the proband. Migratory shaped erythematous areas are the initial presenting sign followed by relatively stable hyperkeratotic plaques are the two predominates characteristics in both patients. However, remarkable variability of morphological and dominating features of the disease were observed between patients. In particular, the younger sister (proband) exhibited ichthyosiform-like appearance suggesting Autosomal Recessive Congenital Ichthyosis (ARCI) condition. No causative mutations were detected in the GJB3 and GJB4 genes. WES results revealed a novel missense homozygous mutation in NIPAL4 gene (c.835C>G, p.Pro279Ala) in both patients. This variant is predicted to be likely pathogenic. In addition, in silico analysis of the mutated 3D domain structure predicted that this variant would result in NIPA4 protein destabilization and Mg2+ transport perturbation, pointing out the potential role of NIPAL4 gene in the development and maintenance of the barrier function of the epidermis. Taken togheter, these results expand the clinical phenotype associated with NIPAL4 mutation and reinforce our hypothesis of NIPAL4 as the main candidate gene for the EKV-like ARCI phenotype.

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Sanchez, Daisy, Erica Bloomquist, Heather Wright, and Yoav Barnavon. "Immediate Nipple Reconstruction With Areolar Flaps After Nipple Excision." Annals of Plastic Surgery 91, no. 2 (2023): 211–14. http://dx.doi.org/10.1097/sap.0000000000003570.

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Abstract Nipple-sparing mastectomies (NSMs) have become a common surgical approach in the management of invasive breast cancers and ductal carcinoma in situ, and as a risk-reducing approach in genetically predisposed patients. The current standard of care in the management of positive nipple margin after NSM is total excision of the nipple-areola complex. In this article, we aimed to present a case series describing a novel approach to positive nipple margins with nipple-only excision and immediate nipple reconstruction using areolar flaps in patients who underwent NSM for noninvasive tumors. We conducted a retrospective review of patients who underwent NSM and were found to have positive nipple margins and underwent subsequent nipple excision with immediate areolar flap reconstruction. We identified 6 patients who underwent NSM and were found to have nipple margins—5 for ductal carcinoma in situ and 1 for invasive ductal carcinoma. These patients underwent nipple excision with immediate reconstruction using “sickle” flaps. We concluded that if nipple excision and immediate reconstruction with areolar sickle flaps can be performed, it results in good aesthetic outcomes without compromising oncologic results.

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Dennis, Cindy-Lee, Nancy Schottle, Ellen Hodnett, and Karen McQueen. "An All-Purpose Nipple Ointment Versus Lanolin in Treating Painful Damaged Nipples in Breastfeeding Women: A Randomized Controlled Trial." Breastfeeding Medicine 7, no. 6 (2012): 473–79. http://dx.doi.org/10.1089/bfm.2011.0121.

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Journal articles on the topic 'NIPALS'

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