High-throughput plant phenotyping-the use of imaging and remote sensing to record growth dynamics-is becoming more widely used. The first step in this process is typically segmentation, which requires a well-labeled training dataset enable accurate segmentation overlapping plants. However, preparing such data both time labor intensive. To solve problem, we propose image processing pipeline using self-supervised sequential convolutional neural network method for in-field phenotyping systems....

Electroencephalogram (EEG) provides noninvasive measures of brain activity and is found to be valuable for the diagnosis some chronic disorders. Specifically, pre‐treatment EEG signals in alpha theta frequency bands have demonstrated association with antidepressant response, which well‐known a low response rate. We aim design an integrated pipeline that improves rate patients major depressive disorder by developing treatment policy guided resting state recordings other effects modifiers....

Floral nectar is a rich secretion produced by the nectary gland and offered as reward to attract pollinators leading improved seed set. Nectars are composed of complex mixture sugars, amino acids, proteins, vitamins, lipids, organic inorganic acids. This composition influenced several factors, including floral morphology, mechanism secretion, time flowering, visitation pollinators. The objective this study was determine contributions flowering time, plant phylogeny, pollinator selection on...

High-throughput phenotyping enables the efficient collection of plant trait data at scale. One example involves using imaging systems over key phases a crop growing season. Although resulting images provide rich for statistical analyses phenotypes, image processing extraction is required as prerequisite. Current methods are mainly based on supervised learning with human labeled or semisupervised mixture and unsupervised data. Unfortunately, preparing sufficiently large training both time...

Major depressive disorder (MDD) presents challenges in diagnosis and treatment due to its complex heterogeneous nature. Emerging evidence indicates that reward processing abnormalities may serve as a behavioral marker for MDD. To measure processing, patients perform computer-based tasks involve making choices or responding stimulants are associated with different outcomes. Reinforcement learning (RL) models fitted extract parameters various aspects of characterize how make decisions tasks....

Major depressive disorder (MDD), a leading cause of years life lived with disability, presents challenges in diagnosis and treatment due to its complex heterogeneous nature. Emerging evidence indicates that reward processing abnormalities may serve as behavioral marker for MDD. To measure processing, patients perform computer-based tasks involve making choices or responding stimulants are associated different outcomes, such gains losses the laboratory. Reinforcement learning (RL) models...

ABSTRACT Mental disorders present challenges in diagnosis and treatment due to their complex heterogeneous nature. Electroencephalogram (EEG) has shown promise as a source of potential biomarkers for these disorders. However, existing methods analyzing EEG signals have limitations addressing heterogeneity capturing brain activity patterns between regions. This paper proposes novel random effects state-space model (RESSM) large-scale multi-channel resting-state signals, accounting the...

Major depressive disorder (MDD) is one of the leading causes disability-adjusted life years. Emerging evidence indicates presence reward processing abnormalities in MDD. An important scientific question whether are due to reduced sensitivity received rewards or learning ability. Motivated by probabilistic task (PRT) experiment EMBARC study, we propose a semiparametric inverse reinforcement (RL) approach characterize reward-based decision-making MDD patients. The model assumes that subject's...

ABSTRACT High-throughput phenotyping is a modern technology to measure plant traits efficiently and in large scale by imaging systems over the whole growth season. Those images provide rich data for statistical analysis of phenotypes. We propose pipeline extract analyze field systems. The proposed include following main steps: segmentation from images, automatic calculation segmented functional curve fitting extracted traits. To deal with challenging problem we novel approach on image pixel...

Electroencephalogram (EEG) provides noninvasive measures of brain activity and is found to be valuable for diagnosis some chronic disorders. Specifically, pre-treatment EEG signals in alpha theta frequency bands have demonstrated association with anti-depressant response, which well-known low response rate. We aim design an integrated pipeline that improves the rate major depressive disorder patients by developing individualized treatment policy guided resting state recordings other effects...

Mental disorders present challenges in diagnosis and treatment due to their complex heterogeneous nature. Electroencephalogram (EEG) has shown promise as a potential biomarker for these disorders. However, existing methods analyzing EEG signals have limitations addressing heterogeneity capturing brain activity patterns between regions. This paper proposes novel random effects state-space model (RESSM) large-scale multi-channel resting-state signals, accounting the of connectivities groups...

High-dimensional functional data has become increasingly prevalent in modern applications such as high-frequency financial and neuroimaging analysis. We investigate a class of high-dimensional linear regression models, where each predictor is random element an infinite dimensional function space, the number predictors p can potentially be much greater than sample size n. Assuming that unknown coefficient functions belongs to some reproducing kernel Hilbert space (RKHS), we regularized...