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
Food Science & Technology
Xinxin Zhang, Shangke Li, Yang Shan, Pao Li, Liwen Jiang, Xia Liu, Wei Fan
Summary: This study uses a near-infrared diffuse reflectance spectroscopy system to accurately determine the soluble solids content of citrus without causing damage. The results show that the NIRDRS light can penetrate the thick peel to some extent, and the selection of specific characteristic variables can improve the accuracy of the quantitative analysis models with fewer variables.
JOURNAL OF FOOD PROCESSING AND PRESERVATION
(2022)
Article
Psychiatry
Weiliang Yang, Huiming Niu, Yiqiong Jin, Jie Cui, Meijuan Li, Yuying Qiu, Duihong Lu, Gang Li, Jie Li
Summary: This study found that patients with schizophrenia showed impaired dynamic functional connectivity patterns of the thalamus, and these changes were correlated with clinical features. The results suggest the important role of the thalamus in the pathophysiology of schizophrenia.
JOURNAL OF PSYCHIATRIC RESEARCH
(2023)
Article
Chemistry, Analytical
Katarzyna Wlodarska, Pawel Piasecki, Ana Lobo-Prieto, Katarzyna Pawlak-Lemanska, Tomasz Gorecki, Ewa Sikorska
Summary: This study evaluated and compared the potential of different optical spectroscopic techniques for quality assessment of apple juices. Calibration models based on NIR spectra of fruit showed high predictive ability for certain quality parameters, while models based on juice spectra performed best for others. The results provide insights for the development of fast quality control methods for juices.
MICROCHEMICAL JOURNAL
(2021)
Article
Chemistry, Analytical
Etil Guzelmeric, Durmus Ozdemir, Nisa Beril Sen, Cansel Celik, Erdem Yesilada
Summary: The plant source of propolis determines its chemical composition, which can be identified and quantified using HPTLC and HPLC techniques. This study compared the amounts of marker components in propolis samples using HPTLC images and validated HPLC methods, and successfully demonstrated the feasibility of quantifying propolis using HPTLC images.
JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS
(2023)
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
Spectroscopy
Feng Chen, Wanjie Lu, Yanwu Chu, Deng Zhang, Cong Guo, Zhifang Zhao, Qingdong Zeng, Jiaming Li, Lianbo Guo
Summary: Fiber-optic laser-induced breakdown spectroscopy (FO-LIBS) is suitable for remote analysis and complex environments, but limited by fiber loss and attenuation; this study used linear and nonlinear models to quantify trace metal elements in pig iron, with the nonlinear SVM model showing the best performance.
SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY
(2021)
Article
Mathematical & Computational Biology
Yeonhee Park, Zhihua Su, Dongjun Chung
Summary: Partial least squares (PLS) regression is a superior alternative to ordinary least squares regression with demonstrated prediction performance. However, treating categorical variables as continuous in PLS regression may result in biased estimates and invalid inferences. This study proposes an envelope-based partial PLS estimator that considers the conditional distributions of the response and continuous predictors on categorical predictors, achieving more efficient estimation and better predictions. The method is applied to identify cytokine-based biomarkers for COVID-19 patients, revealing associations with clinical information.
STATISTICS IN MEDICINE
(2022)
Article
Chemistry, Applied
Lorenzo Palombi, Maria Tufariello, Miriana Durante, Anna Fiore, Antonietta Baiano, Francesco Grieco
Summary: This study examined the impact of changes in craft beer formulation on its volatolomic, acidic, and olfactory profiles. The results showed significant differences in olfactory attributes and the concentration of certain compounds among different samples. The study also revealed the first investigation of the three-factors interaction on the sensory-volatolomic profile of craft beers through a comprehensive multivariate approach.
Article
Chemistry, Applied
Ulisses F. Oliveira, Annanda M. Costa, Jussara Roque, Wilson Cardoso, Sergio Y. Motoike, Marcio H. P. Barbosa, Reinaldo F. Teofilo
Summary: A method for early quantification of unripe macaw fruits oil content using near-infrared spectroscopy and partial least squares was proposed, which could accurately predict the oil content within 30 days after fruit harvest, thus reducing the costs of quality control and storage for macaw palm.
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
Neurosciences
Stefan Czoschke, Cora Fischer, Tara Bahador, Christoph Bledowski, Jochen Kaiser
Summary: The study showed concurrent representations of pitch and location of a single object in multiple brain regions, supporting feature integration in working memory.
JOURNAL OF NEUROSCIENCE
(2021)
Article
Spectroscopy
Yong Hao, Yuanhang Lu, Xiyan Li
Summary: In this study, a stability monitor model was established using the multivariate statistical process control (MSPC) method, and a mixed modeling approach combining robust regression (Rob-Reg) and partial least squares regression (PLSR) was employed to eliminate the variability influence of sample and instrument states. The results showed that MSPC effectively monitored the consistency of the same batch samples measured at different times or different batches, and the Rob-Reg method outperformed the PLSR method in predicting the different batches of samples.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2022)
Article
Computer Science, Software Engineering
Eric Verner, Helen Petropoulos, Bradley Baker, Henry Jeremy Bockholt, Jill Fries, Anastasia Bohsali, Rajikha Raja, Duc Hoai Trinh, Vince Calhoun
Summary: BrainForge is a cloud-based neuroimaging analysis platform that allows users to archive and process data, as well as share results with colleagues. It addresses various challenges faced by researchers in neuroimaging data analysis, including software, reproducibility, computational resources, and data sharing.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2023)
Article
Clinical Neurology
Patrick A. Stokes, Preetish Rath, Thomas Possidente, Mingjian He, Shaun Purcell, Dara S. Manoach, Robert Stickgold, Michael J. Prerau
Summary: Transient oscillatory events in the sleep electroencephalogram, particularly sleep spindles, play important roles in memory consolidation, aging, and psychiatric and neurodegenerative disorders. This paper introduces a novel approach to electroencephalographic phenotyping, characterizing a generalized class of transient time-frequency events using continuous dynamics. The results demonstrate the stereotyped temporal evolution of transient events during sleep and the potential clinical applications in identifying differences in patients with schizophrenia.
Article
Neurosciences
Lei Wu, Vince Calhoun
Summary: The study of human brain connectivity provides insights into brain function and its relationship to behavior and cognition. Integrating structural connectivity and functional connectivity into a single framework is challenging. In this study, a new method called joint connectivity matrix independent component analysis (cmICA) is introduced, which allows for the integration of these two types of connectivity measurements using functional magnetic resonance imaging (MRI) and diffusion-weighted MRI data.
HUMAN BRAIN MAPPING
(2023)
Article
Neurosciences
Noah Lewis, Robyn Miller, Harshvardhan Gazula, Vince Calhoun
Summary: Deep learning is effective for classifying biological sex based on fMRI, but research on the most relevant brain features for this classification is lacking. Model interpretability is important for understanding deep learning models, but little work has been done on the relationship between temporal dimension of fMRI signals and sex classification. In this study, a methodology is provided to address underspecification and instability in feature explanation models, and sex differences in functional brain networks are explored using intrinsic connectivity networks.
Article
Neurosciences
Marlena Duda, Armin Iraji, Judith M. Ford, Kelvin O. Lim, Daniel H. Mathalon, Bryon A. Mueller, Steven G. Potkin, Adrian Preda, Theo G. M. Van Erp, Vince D. Calhoun
Summary: By using spatially constrained independent component analysis (scICA), this study found that rsfMRI scans of just 2-5 minutes can provide good clinical utility without significant loss of individual functional network connectivity (FNC) information from longer scan lengths.
HUMAN BRAIN MAPPING
(2023)
Article
Engineering, Biomedical
Anton Orlichenko, Gang Qu, Gemeng Zhang, Binish Patel, Tony W. W. Wilson, Julia M. M. Stephen, Vince D. D. Calhoun, Yu-Ping Wang
Summary: In this study, we developed an interpretable multivariate classification/regression algorithm called LatSim, which is suitable for small sample size and high feature dimension datasets. Results showed that LatSim achieved higher predictive accuracy compared to other methods, and identified functional brain networks associated with brain age, sex, and intelligence prediction. This research provides new insights for algorithm design and neuroscience research.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2023)
Correction
Biochemistry & Molecular Biology
Sean R. McWhinney, Katharina Brosch, Vince D. Calhoun, Benedicto Crespo-Facorro, Nicolas A. Crossley, Udo Dannlowski, Erin Dickie, Lorielle M. F. Dietze, Gary Donohoe, Stefan Du Plessis, Stefan Ehrlich, Robin Emsley, Petra Furstova, David C. Glahn, Alfonso Gonzalez-Valderrama, Dominik Grotegerd, Laurena Holleran, Tilo T. J. Kircher, Pavel Knytl, Marian Kolenic, Rebekka Lencer, Igor Nenadic, Nils Opel, Julia-Katharina Pfarr, Amanda L. Rodrigue, Kelly Rootes-Murdy, Alex J. Ross, Kang Sim, Antonin Skoch, Filip Spaniel, Frederike Stein, Patrik Svancer, Diana Tordesillas-Gutierrez, Juan Undurraga, Javier Vaquez-Bourgon, Aristotle Voineskos, Esther Walton, Thomas W. Weickert, Cynthia Shannon Weickert, Paul M. Thompson, Theo G. M. van Erp, Jessica A. Turner, Tomas Hajek
MOLECULAR PSYCHIATRY
(2023)
Review
Biochemistry & Molecular Biology
Esther Walton, Vilte Baltramonaityte, Vince Calhoun, Bastiaan T. Heijmans, Paul M. Thompson, Charlotte A. M. Cecil
Summary: Epigenetic mechanisms, such as DNA methylation (DNAm), have been studied as potential biomarkers and mechanisms underlying brain-based disorders. However, there is little understanding of the relationship between DNAm and individual differences in the brain, especially during development. This systematic review examines the field of Neuroimaging Epigenetics and finds inconsistent findings regarding DNAm-brain associations and a lack of replication or meta-analysis. The authors propose three recommendations to advance the field, including a focus on development, large prospective studies, and interdisciplinary collaboration.
MOLECULAR PSYCHIATRY
(2023)
Article
Chemistry, Analytical
Hanlu Yang, Trung Vu, Qunfang Long, Vince Calhoun, Tuelay Adali
Summary: This study proposes a framework for subgroup identification of psychiatric patients using functional connectivity profiles obtained from fMRI data. The pipeline incorporates a data-driven method and constraint-based independent component analysis to identify meaningful subgroups with similar activation patterns in certain brain areas. The identified subgroups show significant group differences in multiple meaningful brain areas.
Article
Engineering, Biomedical
Lan Yang, Chen Qiao, Huiyu Zhou, Vince D. Calhoun, Julia M. Stephen, Tony W. Wilson, Yuping Wang
Summary: This study proposes an explainable multimodal deep dictionary learning method to uncover the commonality and specificity of different modalities in brain developmental differences. The results show that the proposed model can achieve better reconstruction and identify age-related differences in reoccurring patterns.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2023)
Article
Neurosciences
Paul A. Taylor, Richard C. Reynolds, Vince Calhoun, Javier Gonzalez-Castillo, Daniel A. Handwerker, Peter A. Bandettini, Amanda F. Mejia, Gang Chen
Summary: Neuroimaging studies often display only a small fraction of the collected data, which hides important information and leads to issues of selection bias and irreproducibility. Instead, it is suggested to highlight as many results as possible through visualization to improve scientific communication and understanding.
Article
Clinical Neurology
Bryan S. Baxter, Dimitrios Mylonas, Kristi S. Kwok, Christine E. Talbot, Rudra Patel, Lin Zhu, Mark Vangel, Robert Stickgold, Dara S. Manoach
Summary: This study evaluated the effects of closed-loop auditory stimulation on slow oscillations and sleep spindles and their relation to motor procedural memory consolidation. The results showed that despite its strong effects on sleep physiology, closed-loop auditory stimulation failed to enhance motor procedural memory. These findings provide insights for further improving closed-loop auditory stimulation as an intervention to manipulate sleep oscillatory dynamics and improve memory, including better sound calibration and real-time predictive algorithms to target slow oscillations more precisely.
Article
Neuroimaging
Mohammad S. E. Sendi, Elaheh Zendehrouh, Charles A. Ellis, Zening Fu, Jiayu Chen, Robyn L. Miller, Elizabeth C. Mormino, David H. Salat, Vince D. Calhoun
Summary: This study investigated the association between static and dynamic functional network connectivity (FNC) and Alzheimer's disease (AD) genetic risk using a data-driven approach. The results showed that AD genetic risk is related to a weakening of connectivity within the visual sensory network (VSN) and spending more time in a state with reduced VSN connectivity.
NEUROIMAGE-CLINICAL
(2023)
Article
Psychiatry
Lavinia Carmen Uscatescu, Martin Kronbichler, Sarah Said-Yurekli, Lisa Kronbichler, Vince Calhoun, Silvia Corbera, Morris Bell, Kevin Pelphrey, Godfrey Pearlson, Michal Assaf
Summary: Intrinsic neural timescales (INT) determine the duration of information storage in different brain areas. Previous studies have shown a hierarchy of INT from posterior to anterior regions in typically developed individuals (TD), as well as individuals with autism spectrum disorder (ASD) and schizophrenia (SZ), with shorter INT observed in both patient groups. This study aimed to replicate these group differences by comparing INT in TD, ASD, and SZ. The results partially replicated previous findings, showing reduced INT in the left lateral occipital gyrus and the right post-central gyrus in SZ compared to TD. Direct comparison between the patient groups also showed significantly reduced INT in SZ compared to ASD in these two areas. However, the previously reported correlations between INT and symptom severity were not replicated in this study. These findings help identify the brain areas that may play a crucial role in sensory peculiarities observed in ASD and SZ.
Article
Medicine, Research & Experimental
Allen J. Chang, Rebecca Roth, Eleni Bougioukli, Theodor Ruber, Simon S. Keller, Daniel L. Drane, Robert E. Gross, James Welsh, Anees Abrol, Vince Calhoun, Ioannis Karakis, Erik Kaestner, Bernd Weber, Carrie McDonald, Ezequiel Gleichgerrcht, Leonardo Bonilha
Summary: Chang et al. classified individuals with Temporal Lobe Epilepsy (TLE), Alzheimer's disease, and healthy controls using a convolutional neural network algorithm applied to magnetic resonance imaging (MRI) scans. They were able to distinguish people with TLE, including those without easily identifiable TLE-associated MRI features.
COMMUNICATIONS MEDICINE
(2023)