An integrative process-based model of fruit growth as a function of carbon and water fluxes modulated by endogenous abscisic acid in blueberry fruit.

Fruit growth is driven by the interaction of environmental cues and phytohormonal signals. Biophysical models have captured the general trend of fruit growth but often overlook the regulatory role of phytohormones. This study integrates a biophysical framework with the quantitative response of endogenous abscisic acid (ABA) in fruit.

Big team science reveals promises and limitations of machine learning efforts to model physiological markers of affective experience.

Researchers are increasingly using machine learning to study physiological markers of emotion. We evaluated the promises and limitations of this approach via a big team science competition. Twelve teams competed to predict self-reported affective experiences using a multi-modal set of peripheral nervous system measures.

Arsenic and zinc cycling in Cambodian rice fields of the Tonle Sap floodplain.

Cambodians heavily rely on rice as an essential staple food crop and source of income. The Tonle Sap floodplain is one of Cambodia's largest rice-producing regions. Natural flooding and irrigation on paddies within the floodplain likely influence the cycling of important trace elements such as zinc (Zn), an essential and often limiting micronutrient, and arsenic (As), a toxin for both humans and plants.

Salicaceae endophyte inoculation alters stomatal patterning and improves the intrinsic water-use efficiency of Populus trichocarpa after a water deficit.

Microorganisms may enhance plant resilience to water stress by influencing the host physiology and anatomy at the leaf level. Bacterial and yeast endophytes, isolated from wild poplar and willow, can improve the intrinsic water-use efficiency (iWUE) of cultivated poplar (Populus) under water deficits by lowering stomatal conductance (gsw). However, the relevance of stomatal anatomy underlying this reduction remains unclear.

Multi-horizon event detection for in-hospital clinical deterioration using dual-channel graph attention network.

Objective: In hospitals globally, the occurrence of clinical deterioration within the hospital setting poses a significant healthcare burden. Rapid clinical intervention becomes a crucial task in such cases. In this research, we propose an end-to-end deep learning architecture that interpolates high-dimensional sequential data for the early detection of clinical deterioration events.

Temperature dependence of pollen germination and tube growth in conifers relates to their distribution along an elevational gradient in Washington State, USA.

Background And Aims: Pollen germination and tube growth are essential processes for successful fertilization. They are among the most temperature-vulnerable stages and subsequently affect seed production and determine population persistence and species distribution under climate change. Our study aims to investigate intra- and interspecific variations in the temperature dependence of pollen germination and tube length growth and to explore how these variations differ for pollen from elevational gradients.

A comparison between centralized and asynchronous federated learning approaches for survival outcome prediction using clinical and PET data from non-small cell lung cancer patients.

Background And Objective: Survival analysis plays an essential role in the medical field for optimal treatment decision-making. Recently, survival analysis based on the deep learning (DL) approach has been proposed and is demonstrating promising results. However, developing an ideal prediction model requires integrating large datasets across multiple institutions, which poses challenges concerning medical data privacy.

Bridging Time-series Image Phenotyping and Functional-Structural Plant Modeling to Predict Adventitious Root System Architecture.

Root system architecture (RSA) is an important measure of how plants navigate and interact with the soil environment. However, current methods in studying RSA must make tradeoffs between precision of data and proximity to natural conditions, with root growth in germination papers providing accessibility and high data resolution. Functional-structural plant models (FSPMs) can overcome this tradeoff, though parameterization and evaluation of FSPMs are traditionally based in manual measurements and visual comparison.

Multi-scale context UNet-like network with redesigned skip connections for medical image segmentation.

Background And Objective: Medical image segmentation has garnered significant research attention in the neural network community as a fundamental requirement for developing intelligent medical assistant systems. A series of UNet-like networks with an encoder-decoder architecture have achieved remarkable success in medical image segmentation. Among these networks, UNet2+ (UNet++) and UNet3+ (UNet+++) have introduced redesigned skip connections, dense skip connections, and full-scale skip connections, respectively, surpassing the performance of the original UNet.

Clinicopathological significance and prognostic implication of nuclear fatty acid-binding protein 4 expression in colorectal cancer.

This study aimed to evaluate the clinicopathological significance and prognostic role of fatty acid-binding protein 4 (FABP4) expression in colorectal cancer (CRC). Nuclear expression of FABP4 was investigated by immunohistochemistry for FABP4 on 246 human CRC tissues. The correlations between FABP4 expression, and clinicopathological characteristics and survival, was evaluated in patients with CRC.

Robust Human Face Emotion Classification Using Triplet-Loss-Based Deep CNN Features and SVM.

Human facial emotion detection is one of the challenging tasks in computer vision. Owing to high inter-class variance, it is hard for machine learning models to predict facial emotions accurately. Moreover, a person with several facial emotions increases the diversity and complexity of classification problems.

Perennial grass root system specializes for multiple resource acquisitions with differential elongation and branching patterns.

Roots optimize the acquisition of limited soil resources, but relationships between root forms and functions have often been assumed rather than demonstrated. Furthermore, how root systems co-specialize for multiple resource acquisitions is unclear. Theory suggests that trade-offs exist for the acquisition of different resource types, such as water and certain nutrients.

Additive Manufacturing and Combustion Characteristics of Polyethylene Oxide/Aluminum/Copper Oxide-Based Energetic Nanocomposites for Enhancing the Propulsion of Small Projectiles.

The application of nanoscale energetic materials (nEMs) composed of metal and oxidizer nanoparticles (NPs) in thermal engineering systems is limited by their relatively high sensitivity and complex three-dimensional (3D) formability. Polymers can be added to nEMs to lower the sensitivity and improve the formability of 3D structures. In this study, the effect of the addition of polyethylene oxide (PEO; polymer) on the combustion characteristics of aluminum (Al; fuel)/copper oxide (CuO; oxidizer)-based nEMs is investigated.

A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis.

Combating mental illnesses such as depression and anxiety has become a global concern. As a result of the necessity for finding effective ways to battle these problems, machine learning approaches have been included in healthcare systems for the diagnosis and probable prediction of the treatment outcomes of mental health conditions. With the growing interest in machine and deep learning methods, analysis of existing work to guide future research directions is necessary.

Self-Relation Attention and Temporal Awareness for Emotion Recognition via Vocal Burst.

Speech emotion recognition (SER) is one of the most exciting topics many researchers have recently been involved in. Although much research has been conducted recently on this topic, emotion recognition via non-verbal speech (known as the vocal burst) is still sparse. The vocal burst is concise and has meaningless content, which is harder to deal with than verbal speech.

[Prediction of Infection by Endoscopic Severity of Erythematous/exudative Gastritis in Asymptomatic Adults].

Background/aims: () infection highly correlates with erythematous/exudative gastritis, which is one of the endoscopic findings of the Sydney classification system. The present study aimed to evaluate the association between endoscopic severity of erythematous/exudative gastritis and infection.

Methods: We prospectively enrolled asymptomatic adults who were diagnosed with erythematous/exudative gastritis during screening esophagogastroduodenoscopy.

Effects of Multiple Filters on Liver Tumor Segmentation From CT Images.

Segmentation of liver tumors from Computerized Tomography (CT) images remains a challenge due to the natural variation in tumor shape and structure as well as the noise in CT images. A key assumption is that the performance of liver tumor segmentation depends on the characteristics of multiple features extracted from multiple filters. In this paper, we design an enhanced approach based on a two-class (liver, tumor) convolutional neural network that discriminates tumor as well as liver from CT images.

EEG-Based Emotion Recognition by Convolutional Neural Network with Multi-Scale Kernels.

Besides facial or gesture-based emotion recognition, Electroencephalogram (EEG) data have been drawing attention thanks to their capability in countering the effect of deceptive external expressions of humans, like faces or speeches. Emotion recognition based on EEG signals heavily relies on the features and their delineation, which requires the selection of feature categories converted from the raw signals and types of expressions that could display the intrinsic properties of an individual signal or a group of them. Moreover, the correlation or interaction among channels and frequency bands also contain crucial information for emotional state prediction, and it is commonly disregarded in conventional approaches.

Esophagus Segmentation in CT Images via Spatial Attention Network and STAPLE Algorithm.

One essential step in radiotherapy treatment planning is the organ at risk of segmentation in Computed Tomography (CT). Many recent studies have focused on several organs such as the lung, heart, esophagus, trachea, liver, aorta, kidney, and prostate. However, among the above organs, the esophagus is one of the most difficult organs to segment because of its small size, ambiguous boundary, and very low contrast in CT images.

Age estimation based on 3D post-mortem computed tomography images of mandible and femur using convolutional neural networks.

Age assessment has attracted increasing attention in the field of forensics. However, most existing works are laborious and requires domain-specific knowledge. Modern computing power makes it is possible to leverage massive amounts of data to produce more reliable results.