Strategies for enhancing yield and quality of forage-grain dual-purpose ratoon rice: role of first-season density and nitrogen management.

The "forage-grain dual-purpose" model helps ease land-use competition and supports high-yield, high-quality rice production. However, integrated strategies to simultaneously improve silage rice and ratoon rice yield and quality across both seasons require further systematic study. A two-year field study (2022-2023) was conducted using two widely cultivated indica hybrid rice cultivars, F You 498 (FY498) and Chuankangyou Simiao (CKYSM), in Southwest China.

Identifying Semantic Component for Robust Molecular Property Prediction.

Although graph neural networks have achieved great success in the task of molecular property prediction in recent years, their generalization ability under out-of-distribution (OOD) settings is still under-explored. Most of the existing methods rely on learning discriminative representations for prediction, often assuming that the underlying semantic components are correctly identified. However, this assumption does not always hold, leading to potential misidentifications that affect model robustness.

Inferring cell-type-specific gene regulatory network from cellular transcriptomics data with GeneLink.

Deciphering cell-type-specific gene regulatory networks (ctGRNs) is crucial for elucidating fundamental biological processes, such as tissue development and cancer progression. However, accurately inferring ctGRNs from high-dimensional transcriptomic data poses a significant challenge, primarily due to issues like data sparsity, cell heterogeneity, and over-smoothing (i.e.

Molecular mechanism of Golden Pomfret myofibrillar protein digestion inhibition by malondialdehyde-mediated oxidation based on peptidomics analysis.

This study investigated the effects of malondialdehyde (MDA) induced oxidation on the digestion of the Golden Pomfret myofibrillar protein. Results showed that protein digestibility decreased significantly with increased MDA concentration ( < 0.05).

Resveratrol attenuates hepatic inflammation and oxidative stress in collagen-induced arthritis (CIA) mice via the Nrf2/Keap1 pathway.

Background: Rheumatoid arthritis can affect extra-articular organs such as the liver, and the problem of drug-induced liver injury caused by traditional antirheumatic drugs for improving the condition cannot be ignored. This study aims to investigate the therapeutic effects of resveratrol (Res) on hepatic inflammation and oxidative stress in mice with collagen-induced arthritis (CIA) and to elucidate the relationship between the regulatory mechanisms of the nuclear factor erythroid 2-related factor 2 (Nrf2)/Kelch-like ECH-associated protein 1 (Keap1) signaling pathway.

Methods: In this study, we used chicken type II collagen in combination with complete Freund's adjuvant to induce arthritis in a mouse model, and Res was administered by tube feeding to detect the serum biochemical liver function and inflammation levels, oxidative stress, and apoptosis in the livers of mice.

Causal Representation Learning from Multi-modal Biomedical Observations.

Prevalent in biomedical applications (e.g., human phenotype research), multi-modal datasets can provide valuable insights into the underlying physiological mechanisms.

Melatonin refines ovarian mitochondrial dysfunction in PCOS by regulating the circadian rhythm gene Clock.

Mitochondrial dysfunction is present in the ovaries of patients with polycystic ovary syndrome (PCOS). Melatonin (MT) has shown promise in treating PCOS by improving mitochondrial dysfunction, though the underlying mechanisms remain unclear. In this study, we first assessed the levels of proteins associated with mitochondrial autophagy and dynamics in ovary granulosa cells (GCs) of PCOS patients and in the ovaries of DHEA-induced PCOS mice.

Evaluating rice lipid content, yield, and quality in response to nitrogen application rate and planting density.

To investigate the effects of nitrogen (N) application rate and planting density (D) on the contents of lipid and free fatty acid, fatty acid composition, yield and quality of rice grain, a field experiment was conducted using Koshihikari (japonica) as experimental material from 2021 to 2022 with three N levels (90, 150 and 210 kg ha, denoted as N1, N2 and N3, respectively) and three transplanting densities (19.0 × 10, 26.7 × 10 and 40.

Positioning exercises in improving the quality of magnetic-controlled capsule endoscopy.

Background: Good gastric preparation is indispensable for Magnetic-controlled Capsule Endoscopy (MCE) examination, but there is no consensus yet. We aim to explore the clinical application value of positioning exercises in improving the quality of MCE examination.

Methods: Clinical data of 326 patients who underwent MCE examination from January 2020 to December 2023 were collected.

Optimizing rice yield, quality and nutrient use efficiency through combined application of nitrogen and potassium.

Reasonable nitrogen (N) and potassium (K) application rates can effectively improve fertilizer use efficiency, rice yield and quality. A two-year field experiment was conducted with combined application of three N rates (135, 180, and 225 kg ha, denoted as N1-N3) and four K rates (0, 90, 135, and 180 kg ha, denoted as K0-K3) using super indica hybrid rice cultivar Yixiangyou (YXY) 2115 to explore the effects of co-application of N and K on rice growth and development. The results indicated that the combined application of N and K had significantly interactive effects on dry matter (DM) accumulation, nutrients absorption, N harvest index (NHI), K harvest index (KHI), spikelets per panicle and most rice quality indexes.

Responses of Yield and Photosynthetic Characteristics of Rice to Climate Resources under Different Crop Rotation Patterns and Planting Methods.

Climate is the most important environmental factor influencing yield during rice growth and development. To investigate the relationships between climate and yield under different crop rotation patterns and planting methods, three typical rotation patterns (vegetable-rice (V), rape-rice (R), and wheat-rice (W)) and two mechanical planting methods (mechanical transplanting (T1) and mechanical direct seeding (T2)) were established. The results showed that compared to the V rotation pattern, the average daily temperature (ADT) during the sowing to heading stage increased under both R and W rotation patterns, which significantly shortened the growth period.

Effects of water stress on starch synthesis and accumulation of two rice cultivars at different growth stages.

Rice is a water intensive crop and soil water conditions affect rice yield and quality. However, there is limited research on the starch synthesis and accumulation of rice under different soil water conditions at different growth stages. Thus, a pot experiment was conducted to explore the effects of IR72 (indica) and Nanjing (NJ) 9108 (japonica) rice cultivars under flood-irrigated treatment (CK, 0 kPa), light water stress treatment (L, -20 ± 5 kPa), moderate water stress treatment (M, -40 ± 5 kPa) and severe water stress treatment (S, -60 ± 5 kPa) on the starch synthesis and accumulation and rice yield at booting stage (T1), flowering stage (T2) and filling stage (T3), respectively.

Giant electrically tunable magnon transport anisotropy in a van der Waals antiferromagnetic insulator.

Anisotropy is a manifestation of lowered symmetry in material systems that have profound fundamental and technological implications. For van der Waals magnets, the two-dimensional (2D) nature greatly enhances the effect of in-plane anisotropy. However, electrical manipulation of such anisotropy as well as demonstration of possible applications remains elusive.

Invariant Pattern Recognition with Log-Polar Transform and Dual-Tree Complex Wavelet-Fourier Features.

In this paper, we propose a novel method for 2D pattern recognition by extracting features with the log-polar transform, the dual-tree complex wavelet transform (DTCWT), and the 2D fast Fourier transform (FFT2). Our new method is invariant to translation, rotation, and scaling of the input 2D pattern images in a multiresolution way, which is very important for invariant pattern recognition. We know that very low-resolution sub-bands lose important features in the pattern images, and very high-resolution sub-bands contain significant amounts of noise.

LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data.

Motivation: From a systematic perspective, it is crucial to infer and analyze gene regulatory network (GRN) from high-throughput single-cell RNA sequencing data. However, most existing GRN inference methods mainly focus on the network topology, only few of them consider how to explicitly describe the updated logic rules of regulation in GRNs to obtain their dynamics. Moreover, some inference methods also fail to deal with the over-fitting problem caused by the noise in time series data.

Response of yield and quality of giant embryo rice to nitrogen application and analysis of lipid-lowering effect.

Giant embryo rice is known as a highly nutritious functional rice because it is rich in gamma-aminobutyric acid (GABA), which has various regulatory functions in the human body. To study the response of giant embryo rice yield and quality to nitrogen (N) application, and to verify the effect of giant embryo brown rice on alleviating hyperlipidemia in rats. In this study, field experiments were conducted in 2020 and 2021 using the giant embryo rice varietiers J20 (japonica) and Koshihikari (japonica) rice as experimental materials and five N levels, 0 (N), 90 (N), 135 (N), 180 (N) and 225 (N) kg ha.

The third international hackathon for applying insights into large-scale genomic composition to use cases in a wide range of organisms.

In October 2021, 59 scientists from 14 countries and 13 U.S. states collaborated virtually in the Third Annual Baylor College of Medicine & DNANexus Structural Variation hackathon.

Inferring causal gene regulatory network GreyNet: From dynamic grey association to causation.

Gene regulatory network (GRN) provides abundant information on gene interactions, which contributes to demonstrating pathology, predicting clinical outcomes, and identifying drug targets. Existing high-throughput experiments provide rich time-series gene expression data to reconstruct the GRN to further gain insights into the mechanism of organisms responding to external stimuli. Numerous machine-learning methods have been proposed to infer gene regulatory networks.

Graph attention network for link prediction of gene regulations from single-cell RNA-sequencing data.

Motivation: Single-cell RNA sequencing (scRNA-seq) data provides unprecedented opportunities to reconstruct gene regulatory networks (GRNs) at fine-grained resolution. Numerous unsupervised or self-supervised models have been proposed to infer GRN from bulk RNA-seq data, but few of them are appropriate for scRNA-seq data under the circumstance of low signal-to-noise ratio and dropout. Fortunately, the surging of TF-DNA binding data (e.

Unintentional Action Localization via Counterfactual Examples.

How do humans localize unintentional action like " A boy falls down while playing skateboard "? Cognitive science shows that an 18-month-old baby understands the intention by observing the actions and comparing the feedback. Motivated by this evidence, we propose a causal inference approach that constructs a video pool containing intentional knowledge, conducts the counterfactual intervention to observe intentional action, and compares the unintentional action with intentional action to achieve localization. Specifically, we first build a video pool, where each video contains the same action content as an original unintentional action video.

Probabilistic Temporal Modeling for Unintentional Action Localization.

Humans have the inherent advantage of understanding action intention, while it is an enormous challenge to train the machine to localize unintentional action in videos due to the lack of reliable annotations for stable training. The annotations of unintentional action are unreliable since different annotators are affected by their subjective appraisals and intrinsic ambiguity, which brings heavy difficulties for the training. To address this issue, we propose a probabilistic framework for unintentional action localization by modeling the uncertainty of annotations.

Remaining Useful Life prediction of rolling bearings based on risk assessment and degradation state coefficient.

Prediction of Remaining Useful Life (RUL) of bearings is very important for the condition-based maintenance of the rotating machinery. In order to predict the RUL more accurately, an approach of RUL prediction based on risk assessment and degradation state coefficient is proposed. The Mahalanobis Distance (MD) is calculated depending on the characteristics of vibration signals in the time domain and time-frequency domain.

Electrically switchable van der Waals magnon valves.

Van der Waals magnets have emerged as a fertile ground for the exploration of highly tunable spin physics and spin-related technology. Two-dimensional (2D) magnons in van der Waals magnets are collective excitation of spins under strong confinement. Although considerable progress has been made in understanding 2D magnons, a crucial magnon device called the van der Waals magnon valve, in which the magnon signal can be completely and repeatedly turned on and off electrically, has yet to be realized.

Person Re-Identification via Attention Pyramid.

In this paper, we propose an attention pyramid method for person re-identification. Unlike conventional attention-based methods which only learn a global attention map, our attention pyramid exploits the attention regions in a multi-scale manner because human attention varies with different scales. Our attention pyramid imitates the process of human visual perception which tends to notice the foreground person over the cluttered background, and further focus on the specific color of the shirt with close observation.