Protein profile analysis of Jilin white goose testicles at different stages of the laying cycle by DIA strategy.

BACKGROUND: Jilin white goose is an excellent local breed in China, with a high annual egg production and laying eggs mainly from February to July each year. The testis, as the only organ that can produce sperm, can affect the sexual maturity and fecundity of male animals. Its growth and development are affected and regulated by a variety of factors. Proteomics is generally applied to identify and quantify proteins in cells and tissues in order to understand the physiological or pathological changes that occur in tissues or cells under specific conditions. Currently, the female poultry reproductive system has been extensively studied, while few related studies focusing on the regulatory mechanism of the reproductive system of male poultry have been conducted. RESULTS: A total of 1753 differentially expressed proteins (DEPs) were generated in which there were 594, 391 and 768 different proteins showing differential expression in three stages, Initial of Laying Cycle (ILC), Peak of Laying Cycle (PLC) and End of Laying Cycle (ELC). Furthermore, bioinformatics was used to analyze the DEPs. Gene ontology (GO) enrichment, Clusters of Orthologous Groups (COG), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction (PPI) network analysis were adopted. All DEPs were found to be implicated in multiple biological processes and pathways associated with testicular development, such as renin secretion, Lysosomes, SNARE interactions in vesicle trafficking, the p53 signaling pathway and pathways related to metabolism. Additionally, the reliability of transcriptome results was verified by real-time quantitative PCR by selecting the transcript abundance of 6 selected DEPs at the three stages of the laying cycle. CONCLUSIONS: The funding in this study will provide critical insight into the complex molecular mechanisms and breeding practices underlying the developmental characteristics of testicles in Jilin white goose.

Gut microbiota-derived LCA mediates the protective effect of PEDV infection in piglets.

BACKGROUND: The gut microbiota is a critical factor in the regulation of host health, but the relationship between the differential resistance of hosts to pathogens and the interaction of gut microbes is not yet clear. Herein, we investigated the potential correlation between the gut microbiota of piglets and their disease resistance using single-cell transcriptomics, 16S amplicon sequencing, metagenomics, and untargeted metabolomics. RESULTS: Porcine epidemic diarrhea virus (PEDV) infection leads to significant changes in the gut microbiota of piglets. Notably, Landrace pigs lose their resistance quickly after being infected with PEDV, but transplanting the fecal microbiota of Min pigs to Landrace pigs alleviated the infection status. Macrogenomic and animal protection models identified Lactobacillus reuteri and Lactobacillus amylovorus in the gut microbiota as playing an anti-infective role. Moreover, metabolomic screening of the secondary bile acids' deoxycholic acid (DCA) and lithocholic acid (LCA) correlated significantly with Lactobacillus reuteri and Lactobacillus amylovorus, but only LCA exerted a protective function in the animal model. In addition, LCA supplementation altered the distribution of intestinal T-cell populations and resulted in significantly enriched CD8+ CTLs, and in vivo and in vitro experiments showed that LCA increased SLA-I expression in porcine intestinal epithelial cells via FXR receptors, thereby recruiting CD8+ CTLs to exert antiviral effects. CONCLUSIONS: Overall, our findings indicate that the diversity of gut microbiota influences the development of the disease, and manipulating Lactobacillus reuteri and Lactobacillus amylovorus, as well as LCA, represents a promising strategy to improve PEDV infection in piglets. Video Abstract.

Deterministic learning-based neural identification and knowledge fusion.

Recent deterministic learning methods have achieved locally-accurate identification of unknown system dynamics. However, the locally-accurate identification means that the neural networks can only capture the local dynamics knowledge along the system trajectory. In order to capture a broader knowledge region, this article investigates the knowledge fusion problem of deterministic learning, that is, the integration of different knowledge regions along different individual trajectories. Specifically, two kinds of knowledge fusion schemes are systematically introduced: an online fusion scheme and an offline fusion scheme. The online scheme can be viewed as an extension of distributed cooperative learning control to cooperative neural identification for sampled-data systems. By designing an auxiliary information transmission strategy to enable the neural network to receive information learned from other tasks while learning its own task, it is proven that the weights of all localized RBF networks exponentially converge to their common true/ideal values. The offline scheme can be regarded as a knowledge distillation strategy, in which the fused network is obtained by offline training through the knowledge learned from all individual system trajectories via deterministic learning. A novel weight fusion algorithm with low computational complexity is proposed based on the least squares solution under subspace constraints. Simulation studies show that the proposed fusion schemes can successfully integrate the knowledge regions of different individual trajectories while maintaining the learning performance, thereby greatly expanding the knowledge region learned from deterministic learning.

A kinetic-assisted growth curve prediction method for Chlamydomonas reinhardtii incorporating transfer learning.

Traditional predictions of microalgal growth states rely on empirical or easily implementable kinetic models, leading to significant biases and elevated cost. This study proposes a kinetic-assisted machine learning method for predicting the growth curve of microalgal biomass under small sample conditions. Firstly, a microalgae growth kinetic model is constructed based on the logistic model. A two-stage kinetic fitting strategy is specified to account for the light-dark ratio. The Box-Behnken method is employed for experimental design. Then, using Two-stage TrAdaboost.R2 algorithm, the kinetic model is utilized as the source domain, and the experimental design data serves as the target domain for training machine learning models. The results indicate that the proposed method outperforms a single machine learning model in terms of prediction and has the potential to rapidly estimate microalgal growth trends under different conditions and accurately predict harvested biomass, potentially reducing the need for laborious, expensive, and time-consuming laboratory trials.

SAMCL: Subgraph-Aligned Multiview Contrastive Learning for Graph Anomaly Detection.

Graph anomaly detection (GAD) has gained increasing attention in various attribute graph applications, i.e., social communication and financial fraud transaction networks. Recently, graph contrastive learning (GCL)-based methods have been widely adopted as the mainstream for GAD with remarkable success. However, existing GCL strategies in GAD mainly focus on node-node and node-subgraph contrast and fail to explore subgraph-subgraph level comparison. Furthermore, the different sizes or component node indices of the sampled subgraph pairs may cause the "nonaligned" issue, making it difficult to accurately measure the similarity of subgraph pairs. In this article, we propose a novel subgraph-aligned multiview contrastive approach for graph anomaly detection, named SAMCL, which fills the subgraph-subgraph contrastive-level blank for GAD tasks. Specifically, we first generate the multiview augmented subgraphs by capturing different neighbors of target nodes forming contrasting subgraph pairs. Then, to fulfill the nonaligned subgraph pair contrast, we propose a subgraph-aligned strategy that estimates similarities with the Earth mover's distance (EMD) of both considering the node embedding distributions and typology awareness. With the newly established similarity measure for subgraphs, we conduct the interview subgraph-aligned contrastive learning module to better detect changes for nodes with different local subgraphs. Moreover, we conduct intraview node-subgraph contrastive learning to supplement richer information on abnormalities. Finally, we also employ the node reconstruction task for the masked subgraph to measure the local change of the target node. Finally, the anomaly score for each node is jointly calculated by these three modules. Extensive experiments conducted on benchmark datasets verify the effectiveness of our approach compared to existing state-of-the-art (SOTA) methods with significant performance gains (up to 6.36% improvement on ACM). Our code can be verified at https://github.com/hujingtao/SAMCL.

Lactobacillus rhamnosus GG ATCC53103 and Lactobacillus plantarum JL01 improved nitrogen metabolism in weaned piglets by regulating the intestinal flora structure and portal vein metabolites.

At present, most studies have shown that probiotics have a positive regulatory effect on the nutritional metabolism of the body, but the mechanism is still unclear. Here, 48 piglets were divided into four groups. The control group was not fed probiotics, the Lac group was fed L. Rhamnosus GG ATCC53103, the Rha group was fed L. Plantarum JL01, and the mix group was fed two types of probiotics. Nitrogen metabolism and mRNA levels of mTOR and S6K in skeletal muscle were observed in each group. Then, metagenome and non-targeted metabonomics were used to observe the changes of intestinal microorganisms and plasma metabolites in portal channels after probiotics feeding. Finally, we combined the results of omics analysis to reveal the mechanism of probiotics on nitrogen metabolism in weaned piglets. The results showed that L. Rhmnosus GG ATCC53103 and L. Plantarum JL01 increased nitrogen apparent digestibility, nitrogen deposition rate, and nitrogen utilization rate of weaned piglets (P < 0.05); the relative expression of mTOR and SK6 mRNA in skeletal muscle increased significantly (P < 0.05). When L. rhamnosus GG ATCC53103 and L. plantarum JL01 were combined, we found that Clostridium and Prevotella significantly increased in the jejunum (P < 0.05). The relative abundance of Lactobacillus, Ruminococcus, Streptococcus, and Prevotella in the ileum increased significantly (P < 0.05). Compared with the control group, L-Tryptophan, 3-Phosphonyloxypyruvate, cis-Aconitate, and Carbamoyl phosphate were significantly increased in the mixed group portal vein. The result of the combinatorial analysis showed that the significantly increased microorganisms could encode the enzyme genes for the synthesis of L-Tryptophan, 3-Phosphonooxypyruvate, cis-Aconitate, and Carbamoyl phosphate. In summary, our results demonstrated that L. Rhamnosus GG ATCC53103 and L. Plantarum JL01 could stimulate the expression of skeletal muscle protein synthesis genes of weaned piglets by modulating the structure of the gut microbiota and its metabolites, thereby improving nitrogen metabolism in weaned piglets.

The effect of combined dietary supplementation of herbal additives on carcass traits, meat quality, immunity and cecal microbiota composition in Hungarian white geese.

The present study was performed to investigate the effects of dietary supplementation with herbal additives on meat quality, slaughter performance and the cecal microbial community in Hungarian white geese. A total of 60 newborn geese were assigned equally into the control group (CON) and the herbal complex supplemented group (HS). The dietary supplementations consisted of Compound Herbal Additive A (CHAA) including Pulsatilla, Gentian and Rhizoma coptidis, and Compound Herbal Additive B (CHAB) containing Codonopsis pilosula, Atractylodes, Poria cocos and Licorice. The geese in the HS group received a basal diet supplemented with 0.2% CHAA from day 0 to day 42 at the postnatal stage. Then from day 43 to day 70, the geese in HS group were provide a basal diet with 0.15% CHAB. The geese in the CON group were only provided with the basal diet. The results showed that the slaughter rate (SR), half chamber rates (HCR), eviscerated rate (ER) and breast muscle rate (BMR) in the HS group tended to increase slightly compared with the CON group (ns). In addition, the shear force, filtration rate and pH value of breast muscle and thigh muscle in the HS group were slightly enhanced compared to the CON group (ns). Significant increased levels in carbohydrate content, fat content and energy (P < 0.01) and significant decreased levels in cholesterol content (P < 0.01) were observed in the muscle of the HS group. The total amino acid (Glu, Lys, Thr and Asp) content in the muscle increased in HS group than in the CON group (P < 0.01). Dietary herb supplementations significantly increased the levels of IgG in serum (P < 0.05) on day 43 and higher levels of IgM, IgA and IgG (P < 0.01) were also observed in the HS group on day 70. Furthermore, 16S rRNA sequencing results indicated that herbal additives increased the growth of beneficial bacteria and inhibited the proliferation of harmful bacteria in the geese caecum. Altogether, these results offer crucial insights into the potential benefits of incorporating CHAA and CHAB into the diets of Hungarian white goose. The findings indicate that such supplementations could significantly improve meat quality, regulate the immune system and shape the intestinal microbiota composition.

Transcriptome Analysis Reveals Genes Associated with Flooding Tolerance in Mulberry Plants.

Mulberry (Morus alba), a widely distributed economic plant, can withstand long-term flooding stress. However, the regulatory gene network underlying this tolerance is unknown. In the present study, mulberry plants were subjected to submergence stress. Subsequently, mulberry leaves were collected to perform quantitative reverse-transcription PCR (qRT-PCR) and transcriptome analysis. Genes encoding ascorbate peroxidase and glutathione S-transferase were significantly upregulated after submergence stress, indicating that they could protect the mulberry plant from flood damage by mediating ROS homeostasis. Genes that regulate starch and sucrose metabolism; genes encoding pyruvate kinase, alcohol dehydrogenase, and pyruvate decarboxylase (enzymes involved in glycolysis and ethanol fermentation); and genes encoding malate dehydrogenase and ATPase (enzymes involved in the TCA cycle) were also obviously upregulated. Hence, these genes likely played a key role in mitigating energy shortage during flooding stress. In addition, genes associated with ethylene, cytokinin, abscisic acid, and MAPK signaling; genes involved in phenylpropanoid biosynthesis; and transcription factor genes also showed upregulation under flooding stress in mulberry plants. These results provide further insights into the adaptation mechanisms and genetics of submergence tolerance in mulberry plants and could aid in the molecular breeding of these plants.

New Results on Rapid Dynamical Pattern Recognition via Deterministic Learning From Sampling Sequences.

Rapid dynamical pattern recognition based on the deterministic learning method (DLM-based RDPR) aims to rapidly recognize the most similar dynamical pattern pair from perspectives of differences in inherent system dynamics. The basic mechanism is to use available recognition errors to reflect the differences in the dynamics of dynamical pattern pairs and then to make a decision based on a minimal recognition error (MRE) principle. This article focuses on providing a rigorous theoretical analysis of the MRE principle in DLM-based RDPR under the sampled-data framework. Specifically, we seek a unified methodology from the similarity definition to the measure implementation and then to derive general sufficient conditions and necessary conditions for the MRE principle. The main idea is to: 1) from the average signal energy aspect, define a time-dependent dynamics-based similarity in dynamical pattern pairs and reestablish the measure of recognition errors generated from the DLM-based RDPR; 2) introduce the energy-based Lyapunov method to establish the interrelation between the dynamical distance and the recognition error; and 3) derive sufficient conditions and necessary conditions from two directions of the interrelation. The proposed conditions distinguish themselves from virtually all of the existing DLM-based RDPR works with only sufficient conditions in the sense that it is shown in a rigorous analysis that under what conditions, the pattern pair recognized based on the MRE principle is indeed the most similar one. Therefore, the proposed work makes the DLM-based RDPR possess good interpretability and provides strong theoretical guidance in engineering applications.

Sensitivity-based dynamic performance assessment for model predictive control with Gaussian noise.

Economic model predictive control and tracking model predictive control are two popular advanced process control strategies used in various of fields. Nevertheless, for a given process, which controller should be chosen to achieve better performance is uncertain when noise exists. To this end, a sensitivity-based performance assessment approach is proposed to pre-evaluate the dynamic economic and tracking performance of them and guide the controller selection in this work. First, their controller gains around the optimal steady state are evaluated using the sensitivities of corresponding constrained dynamic programming problems. Second, the controller gains are substituted into the control loop to derive the propagation of process and measurement noise. Subsequently, the Taylor expansion is introduced to simplify the calculation of variance and mean of each variable. Finally, the tracking and economic performance surfaces are plotted and the performance indices are precisely calculated through integrating the objective functions and the probability density functions. Moreover, boundary moving (i.e., back off) and target moving can be pre-configured to guarantee the stability of controlled processes based on the proposed approach. Extensive simulations under different cases prove that the proposed approach can provide useful guidance on performance assessment and controller design.

Lactiplantibacillus plantarum Regulated Intestinal Microbial Community and Cytokines to Inhibit Salmonella typhimurium Infection.

Lactic acid bacteria (LAB) are recognized as food-grade safe microorganisms and have many beneficial effects. LAB could maintain the host intestinal homeostasis and regulate intestinal microbial community to exert antibacterial effects. In this study, Lactiplantibacillus plantarum (L. plantarum, Lp01) strain isolated from pig intestine was orally administered to C57BL/6 mice, and mice were then infected with Salmonella typhimurium (ATCC14028). The protective effects of L. plantarum were evaluated by monitoring body weight loss, survival rates, bacterial loads in tissue, colon histopathology analysis, and cytokine secretion. 16S rRNA gene sequencing was also utilized to detect the dynamics of the blind gut microbial community in mice. We found that L. plantarum could significantly reduce the body weight loss and improve the survival rates. The survival rate in the L. P-Sty group was up to 67.5%, which was much higher than that in the STY group (25%). Counting of bacterial loads displayed that the colony-forming unit (CFU) of S. typhimurium in the spleen (p < 0.05) and the liver (p < 0.05) from L. P-Sty group both decreased, compared with STY group. Intestinal histopathology showed that it alleviated the intestinal injury caused by Salmonella, inhibited the secretion of pro-inflammatory cytokines, and promoted anti-inflammatory cytokines (p < 0. 01). In addition, L. plantarum also significantly ameliorated the intestinal gut microbiome disturbance caused by Salmonella. It displayed an obvious increase of beneficial bacteria including Lactobacillus and Bacteroidetes and reduction of pathogenic bacteria like Proteobacteria. In conclusion, L. plantarum could regulate microbial community to inhibit Salmonella typhimurium infection.

Observer-based dynamical pattern recognition via deterministic learning.

In this paper, based on the sampled-data observer and the deterministic learning theory, a rapid dynamical pattern recognition approach is proposed for univariate time series composed of the output signals of the dynamical systems. Specifically, locally-accurate identification of inherent dynamics of univariate time series is first achieved by using the sampled-data observer and the radial basis function (RBF) networks. The dynamical estimators embedded with the learned knowledge are then designed by resorting to the sampled-data observer. It is proved that generated estimator residuals can reflect the difference between the system dynamics of the training and test univariate time series. Finally, a recognition decision-making scheme is proposed based on the residual norms of the dynamical estimators. Through rigorous analysis, recognition conditions are given to guarantee the accurate recognition of the dynamical pattern of the test univariate time series. The significance of this paper lies in that the difficult problems of dynamical modeling and rapid recognition for univariate time series are solved by incorporating the sampled-data observer design and the deterministic learning theory. The effectiveness of the proposed approach is confirmed by a numerical example and compressor stall warning experiments.

The Interaction Between SMAD1 and YAP1 Is Correlated with Increased Resistance of Gastric Cancer Cells to Cisplatin.

Drug resistance is a major obstacle leading to treating failure and poor outcome in gastric cancer (GC). This study explores the interaction between SMAD family member 1 (SMAD1) and Yes1-associated transcriptional regulator (YAP1) and their roles in cisplatin (DDP) resistance in GC. Transcriptome analysis predicted that SMAD1 is highly expressed in DDP-resistant cells. Elevated SMAD1 expression was detected in GC tissue and cells, especially in DDP-resistant cells (MKN-45/DDP and AGS/DDP). SMAD1 downregulation in cells decreased 50% inhibition value of DDP, reduced proliferation, migration, and invasion, and promoted cell cycle arrest and apoptosis. A protein-protein interaction network suggested a possible SMAD1 and YAP1 interaction in GC. The SMAD1 and YAP1 interaction was validated by chromatin immunoprecipitation (ChIP), co-immunoprecipitation (Co-IP), and luciferase assays. SMAD1 bound to YAP1 and activated its transcription. SMAD1 formed complexes with YAP1 in nucleus, and YAP1 upregulation enhanced SMAD1 activity as well. Upregulation of YAP1 restored the malignant behaviors of GC cells suppressed by SMAD1 silencing. In vivo, SMAD1 silencing suppressed growth and DDP resistance of xenograft tumors in nude mice, and this suppression was blocked by YAP1 overexpression again. In conclusion, this study demonstrates that SMAD1 can interact with YAP1 to enhance the DDP resistance of GC cells.

Efficiency and Mechanism of Surface Reinforcement for Recycled Coarse Aggregates via Magnesium Phosphate Cement.

Recycled aggregate concrete (RAC) exhibits inferior mechanical and durability properties owing to the deterioration of the recycled coarse aggregate (RCA) surface quality. To improve the surface properties of RCA, the reinforcement efficiency of RAC, and the maneuverability of the surface treatment method, this study used magnesium phosphate cement (MPC), a clinker-free low-carbon cement with excellent bonding properties, to precoat RCA under three-day pre-conditioning. Moreover, variable amounts of fly ash (FA) or granulated blast furnace slag (GBFS) were utilized to partly substitute MPC to enhance the compressive strength and chloride ion penetration resistance. Subsequently, FA-MPC and GBFS-MPC hybrid slurries with the best comprehensive performance were selected to coat the RCA for optimal reinforcement. The crushing value and water absorption of RCA, as well as the mechanical strengths and durability of RAC, were investigated, and microstructures around interfaces were studied via BSE-EDS and microhardness analysis to reveal the strengthening mechanism. The results indicated that the comprehensive property of strengthening paste was enhanced significantly through substituting MPC with 10% FA or GBFS. Surface coating resulted in a maximum reduction of 8.15% in the crushing value, while the water absorption barely changed. In addition, modified RAC outperformed untreated RAC regarding compressive strength, splitting tensile strength, and chloride ion penetration resistance with maximum optimization efficiencies of 31.58%, 49.75%, and 43.11%, respectively. It was also evidenced that the improved MPC paste properties enhanced the performance of modified RAC. Microanalysis revealed that MPC pastes exhibited an excellent bond with RCA or new mortar, and the newly formed interfacial transition zone between MPC and the fresh mortar exhibited a dense microstructure and outstanding micro-mechanical properties supported with an increase in the average microhardness value of 30.2-33.4%. Therefore, MPC pastes incorporating an appropriate mineral admixture have enormous potential to be utilized as effective RCA surface treatment materials and improve the operability of RCA application in practice.

Lactobacillus plantarum synergistically regulates M1 macrophage polarization in resistance against Salmonella enterica serovar Typhimurium infection.

Macrophage polarization affects the progression of pathogenic bacterial infections. Lactobacillus is widely used to interact with macrophages and to exert specific immunomodulatory activities. In this study, we investigated the regulation of macrophage polarization against Salmonella enterica serotype Typhimurium (STM) by Lactobacillus plantarum JL01 (LP), to explore prevention and treatment strategies for salmonellosis. We assessed the in vitro differential polarization of RAW 264.7 macrophages and mouse bone marrow macrophages (BMMs) by LP against STM, by measuring protein and cytokine levels, and bactericidal activity. In addition, we assessed the protective effects of LP against STM by evaluating weight loss, survival, the burden of STM in tissues, the polarization of macrophages in the spleen and mesenteric lymph nodes (MLNs), intestinal histopathology, and cytokine production. LP slightly affected the polarization of RAW 264.7, a slight M1-skewing. LP promoted the RAW 264.7 bactericidal activity against STM. In BMMs, M1 polarization induced by LP was significantly lower than the M1-positive phenotype. The combination of LP with M1 synergistically improved M1 polarization and bactericidal activity against STM compared to the individual effects. LP promoted the activation of the NF-κB signaling pathway. Supplementation with the NF-κB inhibitor decreased M1 polarization induced by LP. We observed the protective effect of LP against STM in C57BL/6 mice, through a decrease in weight loss, mortality, STM burden in the liver, and promotion of macrophage M1 and M2 polarization in the spleen and MLNs; though M1 was higher, it did not cause inflammatory damage. In summary, LP can synergistically promote M1 polarization in combination with the M1 phenotype through the NF-κB signaling pathway and increases resistance against S. Typhimurium infection. These findings will lay the foundation for the prevention and treatment of S. Typhimurium infections in the future.

Identification of pyroptosis-related subtypes, development of a prognostic model, and characterization of tumour microenvironment infiltration in gastric cancer.

As a new programmed death mode, pyroptosis plays an indispensable role in gastric cancer (GC) and has strong immunotherapy potential, but the specific pathogenic mechanism and antitumor function remain unclear. We comprehensively analysed the overall changes of pyroptosis-related genes (PRGs) at the genomic and epigenetic levels in 886 GC patients. We identified two molecular subtypes by consensus unsupervised clustering analysis. Then, we calculated the risk score and constructed the risk model for predicting prognostic and selected nine PRGs related genes (IL18RAP, CTLA4, SLC2A3, IL1A, KRT7,PEG10, IGFBP2, GPA33, and DES) through LASSO and COX regression analyses in the training cohorts and were verified in the test cohorts. Consequently, a highly accurate nomogram for improving the clinical applicability of the risk score was constructed. Besides, we found that multi-layer PRGs alterations were correlated with patient clinicopathological features, prognosis, immune infiltration and TME characteristics. The low risk group mainly characterized by increased microsatellite hyperinstability, tumour mutational burden and immune infiltration. The group had lower stromal cell content, higher immune cell content and lower tumour purity. Moreover, risk score was positively correlated with T regulatory cells, M1 and M2 macrophages. In addition, the risk score was significantly associated with the cancer stem cell index and chemotherapeutic drug sensitivity. This study revealed the genomic, transcriptional and TME multiomics features of PRGs and deeply explored the potential role of pyroptosis in the TME, clinicopathological features and prognosis in GC. This study provides a new immune strategy and prediction model for clinical treatment and prognosis evaluation.

In Ovo Injection of CHIR-99021 Promotes Feather Follicle Development via Modulating the Wnt Signaling Pathway and Transcriptome in Goose Embryos (Anser cygnoides).

Feather performs important physiological functions in birds, and it is also one of the economic productions in goose farming. Understanding and modulating feather follicle development during embryogenesis are essential for bird biology and the poultry industry. CHIR-99021 is a potent Wnt/ß-catenin signaling pathway activator associated with feather follicle development. In this study, goose embryos (Anser cygnoides) received an in ovo injection of CHIR-9902, which was conducted at the beginning of feather follicle development (E9). The results showed that feather growth and feather follicle development were promoted. The Wnt signaling pathway was activated by the inhibition of GSK-3ß. Transcriptomic analyses showed that the transcription changes were related to translation, metabolism, energy transport, and stress in dorsal tissue of embryos that received CHIR-99021, which might be to adapt and coordinate the promoting effects of CHIR-99021 on feather follicle development. This study suggests that in ovo injection of CHIR-99021 is a potential strategy to improve feather follicle development and feather-related traits for goose farming and provides profiling of the Wnt signaling pathway and transcriptome in dorsal tissue of goose embryos for further understanding of feather follicle development.

MOOD 2020: A Public Benchmark for Out-of-Distribution Detection and Localization on Medical Images.

Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deployment of machine learning algorithms in medicine. When the algorithms encounter cases that deviate from the distribution of the training data, they often produce incorrect and over-confident predictions. OoD detection algorithms aim to catch erroneous predictions in advance by analysing the data distribution and detecting potential instances of failure. Moreover, flagging OoD cases may support human readers in identifying incidental findings. Due to the increased interest in OoD algorithms, benchmarks for different domains have recently been established. In the medical imaging domain, for which reliable predictions are often essential, an open benchmark has been missing. We introduce the Medical-Out-Of-Distribution-Analysis-Challenge (MOOD) as an open, fair, and unbiased benchmark for OoD methods in the medical imaging domain. The analysis of the submitted algorithms shows that performance has a strong positive correlation with the perceived difficulty, and that all algorithms show a high variance for different anomalies, making it yet hard to recommend them for clinical practice. We also see a strong correlation between challenge ranking and performance on a simple toy test set, indicating that this might be a valuable addition as a proxy dataset during anomaly detection algorithm development.

Boron-Locked Starazine - A Soluble and Fluorescent Analogue of Starphene.

A starlike heterocyclic molecule containing an electron-deficient nonaaza-core structure and three peripheral isoquinolines locked by three tetracoordinate borons, namely isoquinoline-nona-starazine (QNSA), is synthesized by using readily available reactants through a rather straightforward approach. This new heteroatom-rich QNSA possesses a quasi-planar π-backbone structure, and bears phenyl substituents on borons which protrude on both sides of the π-backbones endowing it with good solubility in common organic solvents. Contrasting to its starphene analogue, QNSA shows intense fluorescence with a quantum yield (PLQY) of up to 62 % in dilute solution.

Simultaneous biodegradation of dimethyl sulfide and 1-propanethiol by Pseudomonas putida S-1 and Alcaligenes sp. SY1: "Lag" cause, reduction, and kinetics exploration.

Simultaneous biodegradation of malodorous 1-propanethiol (PT) and dimethyl sulfide (DMS) by Pseudomonas putida S-1 and Alcaligenes sp. SY1 was investigated and the interactions implicated were explored. Results showed that PT was completely degraded in 33 h, while a lag of 10 h was observed for DMS degradation alone, and the lag was even extended to 81 h in the binary mixture. Mechanism analysis found that the lag was mainly attributed to the exposure of DMS degrader (Alcaligenes sp. SY1), rather than PT metabolites and PT degrader. The exposure time and PT concentration also influenced the lag duration much. Citric acid could effectively reduce the lag. Pseudo-first-order model was proved suitable for the description of PT degradation, revealing that PT degradation could be enhanced in presence of DMS with a concentration of < 50 mg L-1. A modified Gompertz model, incorporated the lag phase, was developed for the description of DMS degradation in the mixture, revealing that DMS degradation depended on the initial PT concentration, and when the lag was not considered, PT with low-concentration could promote DMS biodegradation, while a higher concentration (> 20 mg L-1) cast negative effect.