Article
Biochemical Research Methods
Ruoyu Tang, Xinyu He, Ruiqi Wang
Summary: The study presents a general computational method for constructing maps between different cell fates and parametric conditions by systematic perturbations. The method does not require accurate parameter measurements or bifurcations. The maps obtained can help in understanding how systematic perturbations drive cell fate decisions and transitions, providing valuable information for predicting and controlling cell states.
Article
Automation & Control Systems
Zhonghao Xie, Xi'an Feng, Xiaojing Chen
Summary: This paper proposes a robust method for PLS based on the idea of least trimmed squares (LTS), which effectively deals with high-dimensional regressors. By formulating the LTS problem as a concave maximization problem, the complexity of solving LTS is simplified. The results from simulation and real data sets demonstrate the effectiveness and robustness of the proposed approach.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2022)
Article
Psychology, Multidisciplinary
Christian Habeck, Yunglin Gazes, Yaakov Stern
Summary: This study investigated cognitive reserve and activation patterns during a verbal Sternberg fMRI task in participants of different age groups. The findings showed a maintenance-related activation pattern that increased with memory load and demonstrated higher inter-subject similarity in older participants with better task accuracy and neuropsychological function.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Multidisciplinary Sciences
Emilio Gomez-Gonzalez, Alejandro Barriga-Rivera, Beatriz Fernandez-Munoz, Jose Manuel Navas-Garcia, Isabel Fernandez-Lizaranzu, Francisco Javier Munoz-Gonzalez, Ruben Parrilla-Giraldez, Desiree Requena-Lancharro, Pedro Gil-Gamboa, Cristina Rosell-Valle, Carmen Gomez-Gonzalez, Maria Jose Mayorga-Buiza, Maria Martin-Lopez, Olga Munoz, Juan Carlos Gomez-Martin, Maria Isabel Relimpio-Lopez, Jesus Aceituno-Castro, Manuel A. Perales-Esteve, Antonio Puppo-Moreno, Francisco Jose Garcia-Cozar, Lucia Olvera-Collantes, Raquel Gomez-Diaz, Silvia de los Santos-Trigo, Monserrat Huguet-Carrasco, Manuel Rey, Emilia Gomez, Rosario Sanchez-Pernaute, Javier Padillo-Ruiz, Javier Marquez-Rivas
Summary: This study demonstrates the feasibility of using hyperspectral image analysis in the visible and near-infrared range for primary screening of SARS-CoV-2. By applying spectral feature descriptors, partial least square-discriminant analysis, and artificial intelligence, information can be extracted from fluid samples and analyzed quantitatively and descriptively. The proposed technology is reagent-free, fast, scalable, and could significantly reduce the number of molecular tests required for COVID-19 mass screening, even in resource-limited settings.
SCIENTIFIC REPORTS
(2022)
Article
Multidisciplinary Sciences
Freeh N. Alenezi
Summary: The study introduces a method for variable selection in high dimensional data modeling, using majority scoring with backward elimination in PLS to improve prediction accuracy. The method performs well in predicting corn and diesel contents, while also examining the impact of data properties on prediction behavior.
SCIENTIFIC REPORTS
(2021)
Article
Chemistry, Analytical
Marina Antonio, Renato L. Carneiro, Ruben M. Maggio
Summary: This study evaluated the feasibility of using middle- and near-infrared spectroscopy, as well as Raman spectroscopy, coupled with multivariate calibration to quantify MLXForm I in commercial raw material. The results showed that NIR-PLS had the best predictive capacity.
MICROCHEMICAL JOURNAL
(2022)
Article
Environmental Sciences
Onuwa Okwuashi, Christopher E. Ndehedehe, Dupe Nihinlola Olayinka
Summary: This research explores the novel application of Tensor Partial Least Squares (TPLS) for hyperspectral image classification. The results show that TPLS performed better than unfolded PLS, but fell short of traditional classifiers.
GEOCARTO INTERNATIONAL
(2022)
Article
Automation & Control Systems
Zhonghao Xie, Xi'an Feng, Limin Li, Xiaojing Chen
Summary: In this study, a new robust PLS method called partial least median of squares (PLMS) regression is proposed by incorporating the idea of least median of squares. Unlike most existing robust methods, the PLMS problem is solved using modern optimization techniques rather than heuristic processes or reweighting strategies. Comparisons are made with a classical PLS method and two efficient robust PLS methods, demonstrating the effectiveness and robustness of the proposed approach through simulations and real-world data sets.
JOURNAL OF CHEMOMETRICS
(2022)
Article
Neurosciences
Ying Zhou, Clayton E. Curtis, Kartik K. Sreenivasan, Daryl Fougnie
Summary: This study investigates the relationship between working memory and attention using fMRI and machine learning. The results demonstrate that selecting items in working memory and shifting attention utilize similar neural mechanisms. These shared mechanisms control the relative gains of neural populations and encode behaviorally relevant information.
JOURNAL OF NEUROSCIENCE
(2022)
Article
Behavioral Sciences
Shanna Kousaie, Jen-Kai Chen, Shari R. Baum, Natalie A. Phillips, Debra Titone, Denise Klein
Summary: This study examined the impact of language learning timing on bilinguals' phonological and non-verbal working memory performance and neural correlates. Despite similar behavioral performance, different groups showed differences in neural recruitment patterns during task performance, indicating a specific effect of language learning timing on executive function related to language.
Article
Computer Science, Artificial Intelligence
Zhonghao Xie, Xi'an Feng, Xiaojing Chen
Summary: Partial least squares (PLS) is effective for high-dimensional regression problems. It was developed based on empirical risk minimization and assumes that the test and training data are drawn from the same distribution. Subsampling via an influence function is a promising technique to address the violation of this assumption.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Agriculture, Dairy & Animal Science
G. Rovere, G. de los Campos, A. L. Lock, L. Worden, A. Vazquez, K. Lee, R. J. Tempelman
Summary: Through analyzing a large number of milk samples, the study found that Bayesian regression methods outperformed partial least squares in predicting milk fatty acids, and identified spectral regions associated with fatty acids as well as the impact of carbon number and unsaturation level on the strength of associations.
JOURNAL OF DAIRY SCIENCE
(2021)
Article
Mathematics
Laura Vicente-Gonzalez, Jose Luis Vicente-Villardon
Summary: This paper proposes a generalization of Partial Least Squares Regression (PLS-R) for a matrix of several binary responses and a set of numerical predictors, referred to as Partial Least Squares Binary Logistic Regression (PLS-BLR). The paper also describes the use of Biplot and Triplot graphical representations for visualizing PLS-BLR models and provides an application to real data. The conclusion is that the proposed method and its visualization using Triplots are powerful tools for interpreting the relations between predictors and responses.
Article
Neurosciences
Chang Yan, Thomas B. Christophel, Carsten Allefeld, John-Dylan Haynes
Summary: Studies using fMRI and multi-voxel pattern analyses investigated the retention of verbal working memory contents in Chinese native speakers memorizing Chinese characters. Results suggest that language-related brain regions, particularly Broca's area and left premotor cortex, play a key role in maintaining verbal working memory content, while the early visual cortex is unlikely to be involved in memory retention.
Article
Multidisciplinary Sciences
Qinqing Xiong, Wenju Wang, Mingya Wang, Chunhui Zhang, Xuechun Zhang, Chun Chen, Mingshi Wang
Summary: This study proposes a hybrid neural network model SOM-NARX based on the correlation of predictors for ozone prediction. The model filters predictors using MIC, transforms them into feature sequences using SOM, and makes predictions using NARX networks. The results show that the correlation of predictors, classification numbers of SOM, neuron numbers, and delay steps can affect prediction accuracy. Model comparison shows that the SOM-NARX model outperforms other models in terms of RMSE, MAE, and MAEP.
Article
Neurosciences
Sonja M. C. de Zwarte, Rachel M. Brouwer, Ingrid Agartz, Martin Alda, Silvia Alonso-Lana, Carrie E. Bearden, Alessandro Bertolino, Aurora Bonvino, Elvira Bramon, Elizabeth E. L. Buimer, Wiepke Cahn, Erick J. Canales-Rodriguez, Dara M. Cannon, Tyrone D. Cannon, Xavier Caseras, Josefina Castro-Fornieles, Qiang Chen, Yoonho Chung, Elena de la Serna, Caterina del Mar Bonnin, Caroline Demro, Annabella Di Giorgio, Gaelle E. Doucet, Mehmet Cagdas Eker, Susanne Erk, Mar Fatjo-Vilas, Scott C. Fears, Sonya F. Foley, Sophia Frangou, Janice M. Fullerton, David C. Glahn, Vina M. Goghari, Jose M. Goikolea, Aaron L. Goldman, Ali Saffet Gonul, Oliver Gruber, Tomas Hajek, Emma L. Hawkins, Andreas Heinz, Ceren Hidiroglu Ongun, Manon H. J. Hillegers, Josselin Houenou, Hilleke E. Hulshoff Pol, Christina M. Hultman, Martin Ingvar, Viktoria Johansson, Erik G. Jonsson, Fergus Kane, Matthew J. Kempton, Marinka M. G. Koenis, Miloslav Kopecek, Bernd Kraemer, Stephen M. Lawrie, Rhoshel K. Lenroot, Machteld Marcelis, Venkata S. Mattay, Colm McDonald, Andreas Meyer-Lindenberg, Stijn Michielse, Philip B. Mitchell, Dolores Moreno, Robin M. Murray, Benson Mwangi, Leila Nabulsi, Jason Newport, Cheryl A. Olman, Jim van Os, Bronwyn J. Overs, Aysegul Ozerdem, Giulio Pergola, Marco M. Picchioni, Camille Piguet, Edith Pomarol-Clotet, Joaquim Radua, Ian S. Ramsay, Anja Richter, Gloria Roberts, Raymond Salvador, Aybala Saricicek Aydogan, Salvador Sarro, Peter R. Schofield, Esma M. Simsek, Fatma Simsek, Jair C. Soares, Scott R. Sponheim, Gisela Sugranyes, Timothea Toulopoulou, Giulia Tronchin, Eduard Vieta, Henrik Walter, Daniel R. Weinberger, Heather C. Whalley, Mon-Ju Wu, Nefize Yalin, Ole A. Andreassen, Christopher R. K. Ching, Sophia I. Thomopoulos, Theo G. M. van Erp, Neda Jahanshad, Paul M. Thompson, Rene S. Kahn, Neeltje E. M. van Haren
Summary: First-degree relatives of patients diagnosed with schizophrenia show widespread thinner cortex, while relatives of patients diagnosed with bipolar disorder show widespread larger cortical surface area. Both groups have lower IQ scores compared to controls, with schizophrenia relatives showing more pronounced brain abnormalities and bipolar disorder relatives showing weaker effects after adjusting for IQ or educational attainment.
HUMAN BRAIN MAPPING
(2022)
Article
Neurosciences
Ania M. Fiksinski, Carrie E. Bearden, Anne S. Bassett, Rene S. Kahn, Janneke R. Zinkstok, Stephen R. Hooper, Wanda Tempelaar, Donna McDonald-McGinn, Ann Swillen, Beverly Emanue, Bernice Morrow, Raquel Gur, Eva Chow, Marianne van den Bree, Joris Vermeesch, Stephen Warren, Michael Owen, Therese van Amelsvoor, Stephan Eliez, Doron Gothelf, Arango Celso, Wendy Kates, Tony Simon, Kieran Murphy, Gabriela Repetto, Damian Heine Sune, Stefano Vicar, Joseph Cubells, Marco Armando, Nicole Philip, Linda Campbell, Sixto Garcia-Minaur, Maude Schneider, Vandana Shashi, Jacob Vorstman, Elemi J. Breetvelt
Summary: Pathogenic genetic variants can impact cognitive development, and understanding variant-specific cognitive trajectories is important for identifying patients at risk for comorbid conditions. A study on individuals with 22q11.2 deletion syndrome (22q11DS) demonstrated that using variant-specific IQ-Z-scores resulted in a 30% decrease of required sample size compared to standard IQ-based approaches to detect the association between IQ decline and schizophrenia risk. This approach may facilitate a more clinically informative interpretation of IQ data by identifying individuals deviating from their expected cognitive trajectories.
NEUROPSYCHOPHARMACOLOGY
(2022)
Review
Neurosciences
Samuel J. Millard, Carrie E. Bearden, Katherine H. Karlsgodt, Melissa J. Sharpe
Summary: Schizophrenia is a severe psychiatric disorder affecting 21 million people worldwide, characterized by symptoms such as psychosis and cognitive deficits. The learning paradox in schizophrenia, involving difficulties in learning from rewarding events and 'overlearning' about irrelevant information, remains elusive in terms of a cohesive framework. Recent research on dopamine's role in reinforcement learning presents new possibilities for understanding how dopamine signaling contributes to the symptomatology of schizophrenia.
NEUROPSYCHOPHARMACOLOGY
(2022)
Review
Clinical Neurology
Eun-Jin Cheon, Carrie E. Bearden, Daqiang Sun, Christopher R. K. Ching, Ole A. Andreassen, Lianne Schmaal, Dick J. Veltman, Sophia Thomopoulos, Peter Kochunov, Neda Jahanshad, Paul M. Thompson, Jessica A. Turner, Theo G. M. van Erp
Summary: This review compares the main brain abnormalities in schizophrenia, bipolar disorder, major depressive disorder, and 22q11.2 Deletion Syndrome. The findings suggest that the effect sizes of brain abnormalities are generally in the same direction and scale in severity with the disorders. Furthermore, the effect sizes for 22q11.2 Deletion Syndrome appear to be larger than for the other psychiatric disorders, which is consistent with the idea of larger effects on the brain of rare genetic variants.
PSYCHIATRY AND CLINICAL NEUROSCIENCES
(2022)
Article
Psychiatry
Michelle A. Worthington, Jean Addington, Carrie E. Bearden, Kristin S. Cadenhead, Barbara A. Cornblatt, Matcheri Keshavan, Daniel H. Mathalon, Thomas H. McGlashan, Diana O. Perkins, William S. Stone, Ming T. Tsuang, Elaine F. Walker, Scott W. Woods, Tyrone D. Cannon
Summary: The study focuses on individuals in the clinical high-risk period before first episode of psychosis (CHR-P) who do not transition to psychosis, and aims to develop a predictive model for remission outcomes. Using a data-driven machine-learning approach, the researchers identified clinical and demographic predictors of symptomatic remission in CHR-P individuals. The study found that individuals who eventually experienced remission had lower baseline prodromal symptoms. This study highlights the importance of understanding factors contributing to resilience and recovery in CHR-P individuals.
SCHIZOPHRENIA BULLETIN
(2022)
Article
Cardiac & Cardiovascular Systems
Nadine A. Kasparian, Anjali Sadhwani, Renee Sananes, Elizabeth Blumenfeld, Jennifer L. Butcher, Adam R. Cassidy, Stephany M. Cox, Joslyn Kenowitz, Thomas A. Miller, Jacqueline H. Sanz, Kelly R. Wolfe, Dawn Ilardi
Summary: COVID-19 has had a significant impact on the provision of neurodevelopmental care. The Cardiac Neurodevelopmental Outcome Collaborative established a Task Force to assess the telehealth practices of cardiac neurodevelopmental programs during COVID-19. Results showed that telehealth has enabled the continuation of at least some cardiac neurodevelopmental services, although barriers related to language, ability, and technology contribute to disparities in access.
CARDIOLOGY IN THE YOUNG
(2023)
Review
Biochemistry & Molecular Biology
Ania M. Fiksinski, Gil D. Hoftman, Jacob A. S. Vorstman, Carrie E. Bearden
Summary: This review discusses the association between rare pathogenic genetic variants and neurodevelopmental outcomes, particularly Autism Spectrum Disorders (ASD), Schizophrenia Spectrum Disorders (SSD), and Intellectual Disability (ID). It addresses three main questions: how to integrate the genetic risk factor with the current psychiatric diagnostic classification system, what can be learned from individuals with common genetic basis, and the factors contributing to variable penetrance and pleiotropy of neuropsychiatric phenotypes. The review focuses on the 22q11.2 deletion syndrome (22q11DS) and explores its specific genotype-phenotype associations, the opportunity to study behaviorally defined classifications, and the mechanisms underlying variable penetrance and pleiotropy. Animal and in vitro studies are discussed in relation to human studies, and research priorities for the field are proposed.
MOLECULAR PSYCHIATRY
(2023)
Article
Biochemistry & Molecular Biology
Meghan A. Collins, Jie Lisa Ji, Yoonho Chung, Cole A. Lympus, Yvette Afriyie-Agyemang, Jean M. Addington, Bradley G. Goodyear, Carrie E. Bearden, Kristin S. Cadenhead, Heline Mirzakhanian, Ming T. Tsuang, Barbara A. Cornblatt, Ricardo E. Carrion, Matcheri Keshavan, Wiliam S. Stone, Daniel H. Mathalon, Diana O. Perkins, Elaine F. Walker, Scott W. Woods, Albert R. Powers, Alan Anticevic, Tyrone D. Cannon
Summary: Progressive grey matter loss has been observed among individuals who convert to psychosis, and this study found that accelerated cortical thinning precedes psychosis onset and can differentiate converters from non-converters. These findings highlight the importance of identifying neurobiological mechanisms prior to conversion for early intervention.
MOLECULAR PSYCHIATRY
(2023)
Article
Psychiatry
Esra Sefik, Michelle Boamah, Jean Addington, Carrie E. Bearden, Kristin S. Cadenhead, Barbara A. Cornblatt, Matcheri S. Keshavan, Daniel H. Mathalon, Diana O. Perkins, William S. Stone, Ming T. Tsuang, Scott W. Woods, Tyrone D. Cannon, Elaine F. Walker
Summary: This study found clinically relevant deviations in cerebellar cortex and white matter structures among CHR individuals, highlighting the importance of considering the complex interplay between sex and age when studying the neuromaturational substrates of psychosis risk.
SCHIZOPHRENIA BULLETIN
(2023)
Editorial Material
Psychology, Developmental
Carrie E. Bearden
JOURNAL OF THE AMERICAN ACADEMY OF CHILD AND ADOLESCENT PSYCHIATRY
(2023)
Article
Psychology, Clinical
Jasmine Modasi, Vahe Khachadourian, Kathleen O'Hora, Leila Kushan, George M. Slavich, Grant S. Shields, Eva Velthorst, Carrie E. Bearden
Summary: This study investigated the relationship between lifetime stressors and symptomatic outcomes in patients with 22q11Del and 22q11Dup. The results showed that lifetime chronic and acute stressors were associated with positive symptoms in 22q11Del, but not with negative or general symptoms. In contrast, lifetime stressors were not associated with psychotic symptoms in 22q11Dup.
PSYCHOLOGICAL MEDICINE
(2023)
Editorial Material
Clinical Neurology
Adam R. Cassidy, Jacqueline H. Sanz
CHILD NEUROPSYCHOLOGY
(2023)
Article
Neurosciences
Benson S. Ku, Meghan Collins, Deidre M. Anglin, Anthony M. Diomino, Jean Addington, Carrie E. Bearden, Kristin S. Cadenhead, Tyrone D. Cannon, Barbara A. Cornblatt, Benjamin G. Druss, Matcheri Keshavan, Daniel H. Mathalon, Diana O. Perkins, William S. Stone, Ming T. Tsuang, Scott W. Woods, Elaine F. Walker
Summary: The study found an inverse relationship between ethnoracial minority density and risk of psychotic spectrum disorders. It also revealed associations between area-level ethnoracial minority density during childhood, cortical thickness, and social engagement. Lower levels of ethnoracial minority density during childhood were associated with reduced cortical thickness in the right fusiform gyrus and right insula, especially among youth with lower social engagement.
NEUROPSYCHOPHARMACOLOGY
(2023)
Review
Psychology, Clinical
Simon Kapler, Laura Adery, Gil D. Hoftman, Carolyn M. Amir, Vardui Grigoryan, Ziva D. Cooper, Carrie E. Bearden
Summary: Cannabis use is associated with increased incidence of psychotic disorders and exacerbation of symptoms in both healthy individuals and those with psychosis spectrum disorders. The impact on conversion to psychotic disorder is unclear. Harm reduction approaches such as reducing frequency of use and enhancing patient-provider communication are recommended. Further research is needed.
PSYCHOLOGICAL MEDICINE
(2023)
Article
Psychiatry
Benson S. Ku, Jean Addington, Carrie E. Bearden, Kristin S. Cadenhead, Tyrone D. Cannon, Michael T. Compton, Barbara A. Cornblatt, Benjamin G. Druss, Sinan Guloksuz, Daniel H. Mathalon, Diana O. Perkins, Ming T. Tsuang, Elaine F. Walker, Scott W. Woods, Ricardo E. Carrion
Summary: Although studies have shown that social fragmentation is a risk factor for schizophrenia and other psychotic disorders, its impact on social functioning is still unknown. This study found that social fragmentation during childhood predicts maladaptation to school and poorer social functioning during adulthood.
SCHIZOPHRENIA BULLETIN
(2023)
