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 inter-specific variations in the temperature dependence of pollen germination and tube length growth and to explore how these variations differ for pollen from elevational gradients. METHODS: We focused on three conifer species, Pinus contorta, Picea engelmannii, and Pinus ponderosa, with pollen collected from 350 to 2200m elevation in Washington State, USA. We conducted pollen viability tests at temperatures from 5 to 40°C in 5°C intervals. After testing for four days, we took images of these samples under a microscope to monitor pollen germination percentage (GP) and tube length (TL). We applied the Gamma function to describe the temperature dependence of GP and TL and estimated key parameters, including the optimal temperature for GP (Topt_GP) and TL (Topt_TL). KEY RESULTS: Results showed that pollen from three species and different elevations within a species have different GP, TL, Topt_GP, and Topt_TL. The population with a higher Topt_GP would also have a higher Topt_TL, while Topt_TL was generally higher than Topt_GP, i.e., a positive but not one-to-one relationship. However, only Pinus contorta showed that populations from higher elevations have lower Topt_GP and Topt_TL and vice versa. The variability in GP increased at extreme temperatures, whereas the variability in TL was greatest near Topt_TL. CONCLUSIONS: Our study demonstrates the temperature dependences of three conifers across a wide range of temperatures. Pollen germination and tube growth are highly sensitive to temperature conditions and vary among species and elevations, affecting their reproduction success during warming. Our findings can provide valuable insights to advance our understanding of how conifer pollen responds to rising temperatures.

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. METHODS: In this paper, we propose FedSurv, an asynchronous federated learning (FL) framework designed to predict survival time using clinical information and positron emission tomography (PET)-based features. This study used two datasets: a public radiogenic dataset of non-small cell lung cancer (NSCLC) from the Cancer Imaging Archive (RNSCLC), and an in-house dataset from the Chonnam National University Hwasun Hospital (CNUHH) in South Korea, consisting of clinical risk factors and F-18 fluorodeoxyglucose (FDG) PET images in NSCLC patients. Initially, each dataset was divided into multiple clients according to histological attributes, and each client was trained using the proposed DL model to predict individual survival time. The FL framework collected weights and parameters from the clients, which were then incorporated into the global model. Finally, the global model aggregated all weights and parameters and redistributed the updated model weights to each client. We evaluated different frameworks including single-client-based approach, centralized learning and FL. RESULTS: We evaluated our method on two independent datasets. First, on the RNSCLC dataset, the mean absolute error (MAE) was 490.80±22.95 d and the C-Index was 0.69±0.01. Second, on the CNUHH dataset, the MAE was 494.25±40.16 d and the C-Index was 0.71±0.01. The FL approach achieved centralized method performance in PET-based survival time prediction and outperformed single-client-based approaches. CONCLUSIONS: Our results demonstrated the feasibility and effectiveness of employing FL for individual survival prediction in NSCLC patients, using clinical information and PET-based features.

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. Here, we applied a germination paper system to study the adventitious RSA and root phenology of Populus trichocarpa stem cuttings using time-series image-based phenotyping augmented by FSPM. We found a significant correlation between timing of root initiation and thermal time at cutting collection (P value = 0.0061, R2 = 0.875), but little correlation with RSA. We also present a use of RhizoVision [1] for automatically extracting FSPM parameters from time series images and evaluating FSPM simulations. A high accuracy of the parameterization was achieved in predicting 2D growth with a sensitivity rate of 83.5%. This accuracy was lost when predicting 3D growth with sensitivity rates of 38.5% to 48.7%, while overall accuracy varied with phenotyping methods. Despite this loss in accuracy, the new method is amenable to high throughput FSPM parameterization and bridges the gap between advances in time-series phenotyping and FSPMs.

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. FABP4 was expressed in 91 of the 246 CRC tissues (37.0%). FABP4 expression was significantly correlated with older age, right-sided colon cancer, perineural invasion, higher pT stage, lymph node metastasis, and higher pTNM stage. However, there was no significant correlation between FABP4 expression and sex, tumor size, tumor differentiation, vascular or lymphatic invasion, or distant metastasis. Nuclear FABP4 expression was not significantly correlated with cytoplasmic FABP4 expression (P = 0.412). FABP4 expression was significantly correlated with nuclear pNF-κB expression (P = 0.001), and was significantly higher in CRC with a low immunoscore than in CRC with a high immunoscore (P < 0.001). There were significant correlations between FABP4 expression and worse overall and recurrence-free survival rates (P < 0.001 and P = 0.007, respectively). FABP4 expression was significantly correlated with aggressive tumor behaviors and pathological characteristics. In addition, patients with CRC with FABP4 expression had worse survival rates.

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. In this paper, we have proposed a novel and intelligent approach for the classification of human facial emotions. The proposed approach comprises customized ResNet18 by employing transfer learning with the integration of triplet loss function (TLF), followed by SVM classification model. Using deep features from a customized ResNet18 trained with triplet loss, the proposed pipeline consists of a face detector used to locate and refine the face bounding box and a classifier to identify the facial expression class of discovered faces. RetinaFace is used to extract the identified face areas from the source image, and a ResNet18 model is trained on cropped face images with triplet loss to retrieve those features. An SVM classifier is used to categorize the facial expression based on the acquired deep characteristics. In this paper, we have proposed a method that can achieve better performance than state-of-the-art (SoTA) methods on JAFFE and MMI datasets. The technique is based on the triplet loss function to generate deep input image features. The proposed method performed well on the JAFFE and MMI datasets with an accuracy of 98.44% and 99.02%, respectively, on seven emotions; meanwhile, the performance of the method needs to be fine-tuned for the FER2013 and AFFECTNET datasets.

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. Measurements used to describe the acquisition of different resources should then account for differential root responses within a single system. To demonstrate this, we grew Panicum virgatum in split-root systems that vertically partitioned high water availability from nutrient availability so that root systems must absorb the resources separately to fully meet plant demands. We evaluated root elongation, surface area, and branching, and we characterized traits using an order-based classification scheme. Plants allocated approximately 3/4th of primary root length towards water acquisition, whereas lateral branches were progressively allocated towards nutrients. However, root elongation rates, specific root length, and mass fraction were similar. Our results support the existence of differential root functioning within perennial grasses. Similar responses have been recorded in many plant functional types suggesting a fundamental relationship. Root responses to resource availability can be incorporated into root growth models via maximum root length and branching interval parameters.

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. With an increase in the PEO content, the resulting PEO/nEM composites are desensitized to relatively high electrical spark discharges. However, the maximum explosion-induced pressure decreases significantly, and the combustion flame fails to propagate when the PEO content exceeds 15 wt.%. Therefore, the optimal PEO content in a nEM matrix must be accurately determined to achieve a compromise between sensitivity and reactivity. To demonstrate their potential application as composite solid propellants (CSPs), 3D-printed disks composed of PEO/nEM composites were assembled using additive manufacturing. They were cross-stacked with conventional potassium nitrate (KNO3)/sucrose (C12H22O11)-based disk-shaped CSPs in a combustion chamber of small rocket motors. Propulsion tests indicated that the specific impulse of KNSU/PEO/nEM (nEMs: 3.4 wt.%)-based CSPs was at a maximum value, which is approximately three times higher than that of KNSU CSPs without nEMs. This suggests that the addition of an optimized amount of polymer to nEMs is beneficial for various CSPs with compromised sensitivity and reactivity and excellent 3D formability, which can significantly enhance the propulsion of small projectiles.

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. In this study, 33 articles on the diagnosis of schizophrenia, depression, anxiety, bipolar disorder, post-traumatic stress disorder (PTSD), anorexia nervosa, and attention deficit hyperactivity disorder (ADHD) were retrieved from various search databases using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) review methodology. These publications were chosen based on their use of machine learning and deep learning technologies, individually assessed, and their recommended methodologies were then classified into the various disorders included in this study. In addition, the difficulties encountered by the researchers are discussed, and a list of some public datasets is provided.

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

Background/Aims: Helicobacter pylori (H. pylori) 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 H. pylori infection. Methods: We prospectively enrolled asymptomatic adults who were diagnosed with erythematous/exudative gastritis during screening esophagogastroduodenoscopy. A rapid urease test was performed in all participants to diagnose H. pylori infection. The severity of erythematous/exudative gastritis was determined based on the Sydney classification system. Two investigators independently evaluated the endoscopic findings. The primary endpoint was H. pylori infection rate according to the severity of erythematous/exudative gastritis (mild vs. moderate-to-severe). Results: A total of 177 patients with erythematous/exudative gastritis were included. The rate of H. pylori infection was 86.4% in all patients. Of 177 included patients, 78 were at mild degree, 48 were at moderate degree, and 51 were at severe degree. The inter-observer variation was 4.6% and kappa value was 0.593. H. pylori infection rate was similar between patients with mild erythematous/exudative gastritis and those with moderate-to-severe erythematous/exudative gastritis (91.0% vs. 82.8%, p=0.115). Even after adjusting potential confounding variables, the severity of erythematous/exudative gastritis was not associated with H. pylori infection rate. Conclusions: H. pylori infection is commonly observed in patients with erythematous/exudative gastritis. However, the severity of erythematous/exudative gastritis is not associated with H. pylori infection rate.

An Integrative Process-Based Model for Biomass and Yield Estimation of Hardneck Garlic (Allium sativum).

We introduce an integrative process-based crop model for garlic (Allium sativum). Building on our previous model that simulated key phenological, morphological, and physiological features of a garlic plant, the new garlic model provides comprehensive and integrative estimations of biomass accumulation and yield formation under diverse environmental conditions. This model also showcases an application of Cropbox to develop a comprehensive crop model. Cropbox is a crop modeling framework featuring declarative modeling language and a unified simulation interface for building and improving crop models. Using Cropbox, we first evaluated the model performance against three datasets with an emphasis on biomass and yield measured under different environmental conditions and growing seasons. We then applied the model to simulate optimal planting dates under future climate conditions for assessing climate adaptation strategies between two contrasting locations in South Korea: the current growing region (Gosan, Jeju) and an unfavorable cold winter region (Chuncheon, Gangwon). The model simulated the growth and development of a southern-type cultivar (Namdo, ND) reasonably well. Under Representative Concentration Pathway (RCP) scenarios, an overall delay in optimal planting date from a week to a month, and a slight increase in potential yield were expected in Gosan. Expansion of growing region to northern area including Chuncheon was expected due to mild winter temperatures in the future and may allow ND cultivar production in more regions. The predicted optimal planting date in the new region was similar to the current growing region that favors early fall planting. Our new integrative garlic model provides mechanistic, process-based crop responses to environmental cues and can be useful for assessing climate impacts and identifying crop specific climate adaptation strategies for the future.

Efficacy and safety of tretinoin 0.05% cream to prevent hyperpigmentation during narrowband UV-B phototherapy in patients with facial vitiligo: a randomized clinical trial.

BACKGROUND: Narrowband UV-B (NBUVB) phototherapy is the mainstay of vitiligo treatment, but hyperpigmentation is one of the limitations. Meanwhile, topical tretinoin is effective against pigmentary disorders. OBJECTIVE: To determine whether tretinoin 0.05% cream would prevent hyperpigmentation when patients with facial vitiligo underwent phototherapy. METHODS: A randomized, controlled, split-face trial was conducted. Adult patients with stable, non-segmental facial vitiligo were enrolled. The left/right sides of the face were randomly allocated to receive either topical tretinoin 0.05% cream or moisturizer twice daily. The entire face was subjected to NBUVB phototherapy twice weekly for 12 weeks. The degree of hyperpigmentation was assessed as the delta L* (brightness) value of the darkest spot in each side of the face at baseline and every 4 weeks. The degree of repigmentation was assessed. RESULTS: Twenty-five patients were enrolled; 21 completed the study. The delta L* value was significantly different between the two groups: -0.5% in the tretinoin group and -8.7% in the control group at 12 weeks (p = .002). Marked repigmentation was achieved in 15 patients of both groups. CONCLUSIONS: Tretinoin 0.05% cream prevented hyperpigmentation during NBUVB phototherapy in patients with facial vitiligo, and did not compromise the overall treatment response. TRIAL REGISTRATION: ClinicalTrials.gov NCT03933774.

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. Therefore, in this paper, we proposed a self-relation attention and temporal awareness (SRA-TA) module to tackle this problem with vocal bursts, which could capture the dependency in a long-term period and focus on the salient parts of the audio signal as well. Our proposed method contains three main stages. Firstly, the latent features are extracted using a self-supervised learning model from the raw audio signal and its Mel-spectrogram. After the SRA-TA module is utilized to capture the valuable information from latent features, all features are concatenated and fed into ten individual fully-connected layers to predict the scores of 10 emotions. Our proposed method achieves a mean concordance correlation coefficient (CCC) of 0.7295 on the test set, which achieves the first ranking of the high-dimensional emotion task in the 2022 ACII Affective Vocal Burst Workshop & Challenge.

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. First, the contrast and intensity values in CT images are adjusted and high frequencies are removed using Hounsfield units (HU) filtering and standardization. Then, the liver tumor is segmented from entire images with multiple filter U-net (MFU-net). Finally, a quantitative analysis is carried out to evaluate the segmentation results using three different methods: boundary-distance-based metrics, size-based metrics, and overlap-based metrics. The proposed method is validated on CT images from the 3Dircadb and LiTS dataset. The results demonstrate that the multiple filters are useful for extracting local and global feature simultaneously, minimizing the boundary distance errors, and our approach demonstrates better performance in heterogeneous tumor regions of 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. Therefore, in our method, the correlation between 32 channels and frequency bands were put into use to enhance the emotion prediction performance. The extracted features chosen from the time domain were arranged into feature-homogeneous matrices, with their positions following the corresponding electrodes placed on the scalp. Based on this 3D representation of EEG signals, the model must have the ability to learn the local and global patterns that describe the short and long-range relations of EEG channels, along with the embedded features. To deal with this problem, we proposed the 2D CNN with different kernel-size of convolutional layers assembled into a convolution block, combining features that were distributed in small and large regions. Ten-fold cross validation was conducted on the DEAP dataset to prove the effectiveness of our approach. We achieved the average accuracies of 98.27% and 98.36% for arousal and valence binary classification, respectively.

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. To address these challenges, we propose a fully automated framework for the esophagus segmentation from CT images. The proposed method is based on the processing of slice images from the original three-dimensional (3D) image so that our method does not require large computational resources. We employ the spatial attention mechanism with the atrous spatial pyramid pooling module to locate the esophagus effectively, which enhances the segmentation performance. To optimize our model, we use group normalization because the computation is independent of batch sizes, and its performance is stable. We also used the simultaneous truth and performance level estimation (STAPLE) algorithm to reach robust results for segmentation. Firstly, our model was trained by k-fold cross-validation. And then, the candidate labels generated by each fold were combined by using the STAPLE algorithm. And as a result, Dice and Hausdorff Distance scores have an improvement when applying this algorithm to our segmentation results. Our method was evaluated on SegTHOR and StructSeg 2019 datasets, and the experiment shows that our method outperforms the state-of-the-art methods in esophagus segmentation. Our approach shows a promising result in esophagus segmentation, which is still challenging in medical analyses.

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. Therefore, it is logical to use automated age estimation approaches to handle large datasets. In this study, a fully automated age prediction approach was proposed by assessing 3D mandible and femur scans using deep learning. A total of 814 post-mortem computed tomography scans from 619 men and 195 women, within the age range of 20-70, were collected from the National Forensic Service in South Korea. Multiple preprocessing steps were applied for each scan to normalize the image and perform intensity correction to create 3D voxels that represent these parts accurately. The accuracy of the proposed method was evaluated by 10-fold cross-validation. The initial cross-validation results illustrated the potential of the proposed method as it achieved a mean absolute error of 5.15 years with a concordance correlation coefficient of 0.80. The proposed approach is likely to be faster and potentially more reliable, which could be used for age assessment in the future.

Multi-Level Seg-Unet Model with Global and Patch-Based X-ray Images for Knee Bone Tumor Detection.

Tumor classification and segmentation problems have attracted interest in recent years. In contrast to the abundance of studies examining brain, lung, and liver cancers, there has been a lack of studies using deep learning to classify and segment knee bone tumors. In this study, our objective is to assist physicians in radiographic interpretation to detect and classify knee bone regions in terms of whether they are normal, begin-tumor, or malignant-tumor regions. We proposed the Seg-Unet model with global and patched-based approaches to deal with challenges involving the small size, appearance variety, and uncommon nature of bone lesions. Our model contains classification, tumor segmentation, and high-risk region segmentation branches to learn mutual benefits among the global context on the whole image and the local texture at every pixel. The patch-based model improves our performance in malignant-tumor detection. We built the knee bone tumor dataset supported by the physicians of Chonnam National University Hospital (CNUH). Experiments on the dataset demonstrate that our method achieves better performance than other methods with an accuracy of 99.05% for the classification and an average Mean IoU of 84.84% for segmentation. Our results showed a significant contribution to help the physicians in knee bone tumor detection.