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Artykuły w czasopismach na temat „Automated depression estimation”

Autor: Grafiati

Data publikacji: 16 listopada 2024

Data aktualizacji: 31 lipca 2025

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Mohamed, Islam Ismail, Mohamed Tarek El-Wakad, Khaled Abbas Shafie, Mohamed A. Aboamer, and Nader A. Rahman Mohamed. "Major depressive disorder: early detection using deep learning and pupil diameter." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 2 (2024): 916. http://dx.doi.org/10.11591/ijeecs.v35.i2.pp916-932.

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Major depressive disorder stands as a highly prevalent mental disorder on a global scale. Detecting depression at its early stages holds paramount importance for effective treatment. However, due to the coexistence of depression with other conditions and the subjective nature of diagnosis, early identification poses a significant challenge. In recent times, machine learning techniques have emerged as valuable tools for the development of automated depression estimation systems, aiding in the diagnostic process. In this particular study, a deep learning approach utilizing pupil diameter was emp

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Islam, Ismail Mohamed Mohamed Tarek El-Wakad Khaled Abbas Shafie Mohamed A. Aboamer Nader A. Rahman Mohamed. "Major depressive disorder: early detection using deep learning and pupil diameter." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 2 (2024): 916–32. https://doi.org/10.11591/ijeecs.v35.i2.pp916-932.

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Major depressive disorder stands as a highly prevalent mental disorder on a global scale. Detecting depression at its early stages holds paramount importance for effective treatment. However, due to the coexistence of depression with other conditions and the subjective nature of diagnosis, early identification poses a significant challenge. In recent times, machine learning techniques have emerged as valuable tools for the development of automated depression estimation systems, aiding in the diagnostic process. In this particular study, a deep learning approach utilizing pupil diameter was emp

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Bensassi, I., J. Lopez-Castroman, R. Calati, and P. Courtet. "Hippocampal Volume Recovery After Depression: Evidence from an Elderly Sample." European Psychiatry 41, S1 (2017): S170. http://dx.doi.org/10.1016/j.eurpsy.2017.01.2061.

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ObjectivesStructural neuroimaging studies have revealed a consistent pattern of volumetric reductions in both the hippocampus and the anterior cingulate cortex (ACC) of individuals with a major depressive episode (MDE). This study investigated hippocampal and ACC volume differences in the elderly comparing currently depressed individuals and individuals with a past lifetime history of MDE versus healthy controls.MethodsWe studied non-demented individuals from a cohort of community-dwelling people aged 65 and over (ESPRIT study). T1-weighted magnetic resonance images were used to acquire anatom

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KALPANA, V., S. T. HAMDE, and L. M. WAGHMARE. "NON-INVASIVE ESTIMATION OF DIABETES RELATED FEATURES FROM ECG USING GRAPHICAL PROGRAMAMING LANGUAGE AND MATLAB." Journal of Mechanics in Medicine and Biology 12, no. 04 (2012): 1240016. http://dx.doi.org/10.1142/s0219519412400167.

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Electrocardiography deals with the electrical activity of the heart. The condition of cardiac health is given by the electrocardiogram (ECG). ECG analysis is one of the most important aspects of research in the field of biomedical sciences and healthcare. The precision in the identification of various parameters in the ECG is of great importance for the reliability of an automated ECG analyzing system and diagnosis of cardiac diseases. Many algorithms have been developed in the last few years, each with their own advantages and limitations. In this work, we have developed an algorithm for 12-l

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Zhang, Xin, Binayak Ojha, Hermann Bichlmaier, Ingo Hartmann, and Heinz Kohler. "Extensive Gaseous Emissions Reduction of Firewood-Fueled Low Power Fireplaces by a Gas Sensor Based Advanced Combustion Airflow Control System and Catalytic Post-Oxidation." Sensors 23, no. 10 (2023): 4679. http://dx.doi.org/10.3390/s23104679.

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In view of the tremendous emissions of toxic gases and particulate matter (PM) by low-power firewood-fueled fireplaces, there is an urgent need for effective measures to lower emissions to keep this renewable and economical source for private home heating available in the future. For this purpose, an advanced combustion air control system was developed and tested on a commercial fireplace (HKD7, Bunner GmbH, Eggenfelden, Germany), complemented with a commercial oxidation catalyst (EmTechEngineering GmbH, Leipzig, Germany) placed in the post-combustion zone. Combustion air stream control of the

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Copăcean, Loredana, Luminiţa Cojocariu, M. Simon, I. Zisu, and C. Popescu. "GEOMATIC TECHNIQUES APPLIED FOR REMOTE DETERMINATION OF THE HAY QUANTITY IN AGROSILVOPASTORAL SYSTEMS." Present Environment and Sustainable Development 14, no. 2 (2020): 89–101. http://dx.doi.org/10.15551/pesd2020142006.

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The paper presents a descriptive model, applicable in agricultural theory and practice, for determining the quantity of alfalfa hay obtained from a land surface, using remote investigations, by geospatial methods and means. The working algorithm was tested in a rural area located in the northern part of Romania, in the Humor Depression, and the data acquisition was made with DJI Phantom 4 Pro - Unmanned Aerial Vehicle equipment. For the automated calculation of the amount of alfalfa hay harvested from a certain surface and stored as haystacks, the following steps were carried out: processing t

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An, Yi, Zhen Qu, Ning Xu, and Zhaxi Nima. "Automatic depression estimation using facial appearance." Journal of Image and Graphics 25, no. 11 (2020): 2415–27. http://dx.doi.org/10.11834/jig.200322.

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Sun, Hao, Jiaqing Liu, Shurong Chai, et al. "Multi-Modal Adaptive Fusion Transformer Network for the Estimation of Depression Level." Sensors 21, no. 14 (2021): 4764. http://dx.doi.org/10.3390/s21144764.

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Depression is a severe psychological condition that affects millions of people worldwide. As depression has received more attention in recent years, it has become imperative to develop automatic methods for detecting depression. Although numerous machine learning methods have been proposed for estimating the levels of depression via audio, visual, and audiovisual emotion sensing, several challenges still exist. For example, it is difficult to extract long-term temporal context information from long sequences of audio and visual data, and it is also difficult to select and fuse useful multi-mod

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Qin, Jinghui, Changsong Liu, Tianchi Tang, et al. "Mental-Perceiver: Audio-Textual Multi-Modal Learning for Estimating Mental Disorders." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 23 (2025): 25029–37. https://doi.org/10.1609/aaai.v39i23.34687.

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Mental disorders, such as anxiety and depression, have become a global concern that affects people of all ages. Early detection and treatment are crucial to mitigate the negative effects these disorders can have on daily life. Although AI-based detection methods show promise, progress is hindered by the lack of publicly available large-scale datasets. To address this, we introduce the Multi-Modal Psychological assessment corpus (MMPsy), a large-scale dataset containing audio recordings and transcripts from Mandarin-speaking adolescents undergoing automated anxiety/depression assessment intervi

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Kashid, Onkar, Rashmi Bhumbare, Eshwar Dange, Ajit Waghmare, and Raj Nikam. "Depression Monitoring System via Social Media Data using Machine Learning frameworkk." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 3431–37. http://dx.doi.org/10.22214/ijraset.2023.51811.

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Abstract: Stress and Depression is one of the most widely recognized and handicapping mental issue that relevantly affects society. Automatic health monitoring systems could be crucial and important to improve depression and stress detection system using social networking. Sentiment Analysis alludes to the utilization of natural language processing and content mining approaches planning to recognize feeling or opinion. Full of feeling Computing is the examination and advancement of frameworks and gadgets that can perceive, decipher, process, and mimic human effects. Sentiment Analysis and deep

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Dong, Mingran, and Xinrui Wang. "Enhancing Depression Detection through Multimodal Emotion Recognition and Wearable Technologies." Highlights in Science, Engineering and Technology 119 (December 11, 2024): 632–40. https://doi.org/10.54097/ezrccb22.

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Traditional methods for diagnosing and treating depression often lack the precision to accurately identify the diverse symptoms and parameters associated with the disorder. With advancements in artificial intelligence and key technologies, multimodal emotion recognition has emerged as a promising approach to enhance depression detection. This paper explores the integration of physiological data such as EEG (electroencephalogram), ECG (electrocardiogram), and GSR (galvanic skin response), along with data from wearable devices, to develop a highquality feature extraction method for emotion analy

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Kaur, Chamandeep, Preeti Singh, and Sukhtej Sahni. "Electroencephalography-Based Source Localization for Depression Using Standardized Low Resolution Brain Electromagnetic Tomography – Variational Mode Decomposition Technique." European Neurology 81, no. 1-2 (2019): 63–75. http://dx.doi.org/10.1159/000500414.

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Background: Electroencephalography (EEG) may be used as an objective diagnosis tool for diagnosing various disorders. Recently, source localization from EEG is being used in the analysis of real-time brain monitoring applications. However, inverse problem reduces the accuracy in EEG signal processing systems. Objectives: This paper presents a new method of EEG source localization using variational mode decomposition (VMD) and standardized the low resolution brain electromagnetic tomography (sLORETA) inverse model. The focus is to compare the effectiveness of the proposed approach for EEG signa

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Ghosh, Priyanka, Siddharth Talwar, and Arpan Banerjee. "Unsupervised Characterization of Prediction Error Markers in Unisensory and Multisensory Streams Reveal the Spatiotemporal Hierarchy of Cortical Information Processing." eneuro 11, no. 5 (2024): ENEURO.0251–23.2024. http://dx.doi.org/10.1523/eneuro.0251-23.2024.

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Elicited upon violation of regularity in stimulus presentation, mismatch negativity (MMN) reflects the brain's ability to perform automatic comparisons between consecutive stimuli and provides an electrophysiological index of sensory error detection whereas P300 is associated with cognitive processes such as updating of the working memory. To date, there has been extensive research on the roles of MMN and P300 individually, because of their potential to be used as clinical markers of consciousness and attention, respectively. Here, we intend to explore with an unsupervised and rigorous source

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Ruiz, Francisco J., Miguel A. Segura-Vargas, Paula Odriozola-González, and Juan C. Suárez-Falcón. "Psychometric properties of the Automatic Thoughts Questionnaire-8 in two Spanish nonclinical samples." PeerJ 8 (September 16, 2020): e9747. http://dx.doi.org/10.7717/peerj.9747.

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Background The ATQ is a widely used instrument consisting of 30 items that assess the frequency of negative automatic thoughts. However, the extensive length of the ATQ could compromise its measurement efficiency in survey research. Consequently, an 8-item shortened version of the ATQ has been developed. This study aims to analyze the validity of the ATQ-8 in two Spanish samples. Method The ATQ-8 was administered to a total sample of 1,148 participants (302 undergraduates and 846 general online population). To analyze convergent construct validity, the questionnaire package also included the D

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Brennan, Michael J., Christopher C. Hennon, and Richard D. Knabb. "The Operational Use of QuikSCAT Ocean Surface Vector Winds at the National Hurricane Center." Weather and Forecasting 24, no. 3 (2009): 621–45. http://dx.doi.org/10.1175/2008waf2222188.1.

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Abstract The utility and shortcomings of near-real-time ocean surface vector wind retrievals from the NASA Quick Scatterometer (QuikSCAT) in operational forecast and analysis activities at the National Hurricane Center (NHC) are described. The use of QuikSCAT data in tropical cyclone (TC) analysis and forecasting for center location/identification, intensity (maximum sustained wind) estimation, and analysis of outer wind radii is presented, along with shortcomings of the data due to the effects of rain contamination and wind direction uncertainties. Automated QuikSCAT solutions in TCs often fa

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Wang, Jitao, Zhenke Wu, Sung Won Choi, et al. "The Dosing of Mobile-Based Just-in-Time Adaptive Self-Management Prompts for Caregivers: Preliminary Findings From a Pilot Microrandomized Study." JMIR Formative Research 7 (September 14, 2023): e43099. http://dx.doi.org/10.2196/43099.

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Background Caregivers of people with chronic illnesses often face negative stress-related health outcomes and are unavailable for traditional face-to-face interventions due to the intensity and constraints of their caregiver role. Just-in-time adaptive interventions (JITAIs) have emerged as a design framework that is particularly suited for interventional mobile health studies that deliver in-the-moment prompts that aim to promote healthy behavioral and psychological changes while minimizing user burden and expense. While JITAIs have the potential to improve caregivers’ health-related quality

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Riemann, Dieter, Raphael J. Dressle, Fee Benz, Laura Palagini, and Bernd Feige. "The Psychoneurobiology of Insomnia: Hyperarousal and REM Sleep Instability." Clinical and Translational Neuroscience 7, no. 4 (2023): 30. http://dx.doi.org/10.3390/ctn7040030.

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Chronic insomnia (insomnia disorder—ID) afflicts up to 10% of the adult population, increases with age and affects more women than men. ID is associated with significant daytime impairments and an increased risk for developing major somatic and mental disorders, especially depression and anxiety disorders. Almost all insomnia models assume persistent hyperarousal on cognitive, emotional, cortical and physiological levels as a central pathophysiological component. The marked discrepancy between only minor objective alterations in polysomnographic parameters of sleep continuity and the profound

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Chen, Ming, Jane S. Hankins, Min Zhang, Justin Gatwood, James E. Bailey, and Kenneth I. Ataga. "Prevalence and Time Trends of Oral Anticoagulant Utilization in Adults with Sickle Cell Disease." Blood 142, Supplement 1 (2023): 5294. http://dx.doi.org/10.1182/blood-2023-188030.

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Background: Sickle cell disease (SCD) is an inherited red blood cell disorder, caused by a genetic mutation in the β-hemoglobin chain. SCD is associated with chronic activation of coagulation and an increased risk of venous thromboembolism (VTE). Although the American Society of Hematology guidelines recommend indefinite anticoagulation for unprovoked or recurrent provoked VTE, no guidance is given regarding choice of anticoagulant due to a paucity of data. Existing evidence on the use of anticoagulants in patients with SCD has been mostly focused on oral anticoagulants, was limited by small s

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Uddin, Md Azher, Joolekha Bibi Joolee, and Kyung-Ah Sohn. "Deep Multi-Modal Network Based Automated Depression Severity Estimation." IEEE Transactions on Affective Computing, 2022, 1. http://dx.doi.org/10.1109/taffc.2022.3179478.

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Agarwal, Navneet, Gaël Dias, and Sonia Dollfus. "Multi-view graph-based interview representation to improve depression level estimation." Brain Informatics 11, no. 1 (2024). http://dx.doi.org/10.1186/s40708-024-00227-w.

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AbstractDepression is a serious mental illness that affects millions worldwide and consequently has attracted considerable research interest in recent years. Within the field of automated depression estimation, most researchers focus on neural network architectures while ignoring other research directions. Within this paper, we explore an alternate approach and study the impact of input representations on the learning ability of the models. In particular, we work with graph-based representations to highlight different aspects of input transcripts, both at the interview and corpus levels. We us

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Giffard, Julia, Renjie Li, Eddy Roccati, et al. "Rapid repetitive syllable sounds associate with episodic memory, executive function, and working memory in cognitively healthy and subjectively impaired older adults." GeroScience, June 24, 2025. https://doi.org/10.1007/s11357-025-01739-x.

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Abstract Non-invasive screening tools for Alzheimer’s disease (AD) risk are needed. Decline in episodic memory and subjective cognitive impairment (SCI) are both associated with elevated AD risk. We investigated associations between three cognitive domains (episodic memory, executive function, and working memory) and motor speech performance in older adults with healthy cognition (HC) or SCI. In total, 1014 community-dwelling participants (cross-sectional sample: 843 HC, mean 66.9 years, 72.8% female; 171 SCI, mean 66.3 years, 70.8% female) remotely completed cognitive tests (Paired Associates

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Milintsevich, Kirill, Kairit Sirts, and Gaël Dias. "Towards automatic text-based estimation of depression through symptom prediction." Brain Informatics 10, no. 1 (2023). http://dx.doi.org/10.1186/s40708-023-00185-9.

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AbstractMajor Depressive Disorder (MDD) is one of the most common and comorbid mental disorders that impacts a person’s day-to-day activity. In addition, MDD affects one’s linguistic footprint, which is reflected by subtle changes in speech production. This allows us to use natural language processing (NLP) techniques to build a neural classifier to detect depression from speech transcripts. Typically, current NLP systems discriminate only between the depressed and non-depressed states. This approach, however, disregards the complexity of the clinical picture of depression, as different people

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Pérez, Anxo, Javier Parapar, and Álvaro Barreiro. "Automatic depression score estimation with word embedding models." Artificial Intelligence in Medicine, August 2022, 102380. http://dx.doi.org/10.1016/j.artmed.2022.102380.

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Chamanzar, Alireza, Jonathan Elmer, Lori Shutter, Jed Hartings, and Pulkit Grover. "Noninvasive and reliable automated detection of spreading depolarization in severe traumatic brain injury using scalp EEG." Communications Medicine 3, no. 1 (2023). http://dx.doi.org/10.1038/s43856-023-00344-3.

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Abstract Background Spreading depolarizations (SDs) are a biomarker and a potentially treatable mechanism of worsening brain injury after traumatic brain injury (TBI). Noninvasive detection of SDs could transform critical care for brain injury patients but has remained elusive. Current methods to detect SDs are based on invasive intracranial recordings with limited spatial coverage. In this study, we establish the feasibility of automated SD detection through noninvasive scalp electroencephalography (EEG) for patients with severe TBI. Methods Building on our recent WAVEFRONT algorithm, we desi

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Cao, Cui, and Lang He. "LOGLformer: Integrating local and global characteristics for depression scale estimation from facial expressions." Review of Scientific Instruments 96, no. 3 (2025). https://doi.org/10.1063/5.0231737.

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According to a publication by the World Health Organization, depression is projected to emerge as the leading mental health issue. In the domain of affective computing, deep learning techniques are frequently employed to represent facial dynamics using both local and global perspectives for the purpose of automatic depression detection (ADD). Yet, current models overlook the crucial interplay between local and global dynamics in discerning the significant features essential for ADD. Addressing this oversight, a novel hybrid computational architecture, named LOGLFormer, has been introduced. Thi

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Jin, Nani, Renjia Ye, and Peng Li. "Diagnosis of depression based on facial multimodal data." Frontiers in Psychiatry 16 (January 28, 2025). https://doi.org/10.3389/fpsyt.2025.1508772.

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IntroductionDepression is a serious mental health disease. Traditional scale-based depression diagnosis methods often have problems of strong subjectivity and high misdiagnosis rate, so it is particularly important to develop automatic diagnostic tools based on objective indicators.MethodsThis study proposes a deep learning method that fuses multimodal data to automatically diagnose depression using facial video and audio data. We use spatiotemporal attention module to enhance the extraction of visual features and combine the Graph Convolutional Network (GCN) and the Long and Short Term Memory

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Brennan, Michael J., Christopher C. Hennon, and Richard D. Knabb. "The Operational Use of QuikSCAT Ocean Surface Vector Winds at the National Hurricane Center." June 1, 2009. https://doi.org/10.1175/2008waf2222188.1.

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The utility and shortcomings of near-real-time ocean surface vector wind retrievals from the NASA Quick Scatterometer (QuikSCAT) in operational forecast and analysis activities at the National Hurricane Center (NHC) are described. The use of QuikSCAT data in tropical cyclone (TC) analysis and forecasting for center location/identification, intensity (maximum sustained wind) estimation, and analysis of outer wind radii is presented, along with shortcomings of the data due to the effects of rain contamination and wind direction uncertainties. Automated QuikSCAT solutions in TCs often fail to sho

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Rostami, Reza, Reza Kazemi, Zahra Nasiri, Somayeh Ataei, Abed L. Hadipour, and Nematollah Jaafari. "Cold Cognition as Predictor of Treatment Response to rTMS; A Retrospective Study on Patients With Unipolar and Bipolar Depression." Frontiers in Human Neuroscience 16 (July 25, 2022). http://dx.doi.org/10.3389/fnhum.2022.888472.

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BackgroundCognitive impairments are prevalent in patients with unipolar and bipolar depressive disorder (UDD and BDD, respectively). Considering the fact assessing cognitive functions is increasingly feasible for clinicians and researchers, targeting these problems in treatment and using them at baseline as predictors of response to treatment can be very informative.MethodIn a naturalistic, retrospective study, data from 120 patients (Mean age: 33.58) with UDD (n = 56) and BDD (n = 64) were analyzed. Patients received 20 sessions of bilateral rTMS (10 Hz over LDLPFC and 1 HZ over RDLPFC) and w

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Mertens, Stien, Lennart Verbraeken, Heike Sprenger, et al. "Monitoring of drought stress and transpiration rate using proximal thermal and hyperspectral imaging in an indoor automated plant phenotyping platform." Plant Methods 19, no. 1 (2023). http://dx.doi.org/10.1186/s13007-023-01102-1.

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Abstract Background Thermography is a popular tool to assess plant water-use behavior, as plant temperature is influenced by transpiration rate, and is commonly used in field experiments to detect plant water deficit. Its application in indoor automated phenotyping platforms is still limited and mainly focuses on differences in plant temperature between genotypes or treatments, instead of estimating stomatal conductance or transpiration rate. In this study, the transferability of commonly used thermography analysis protocols from the field to greenhouse phenotyping platforms was evaluated. In

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