GUPNet++: Geometry Uncertainty Propagation Network for Monocular 3D Object Detection.

Geometry plays a significant role in monocular 3D object detection. It can be used to estimate object depth by using the perspective projection between object's physical size and 2D projection in the image plane, which can introduce mathematical priors into deep models. However, this projection process also introduces error amplification, where the error of the estimated height is amplified and reflected into the projected depth. It leads to unreliable depth inferences and also impairs training stability. To tackle this problem, we propose a novel Geometry Uncertainty Propagation Network (GUPNet++) by modeling geometry projection in a probabilistic manner. This ensures depth predictions are well-bounded and associated with a reasonable uncertainty. The significance of introducing such geometric uncertainty is twofold: (1). It models the uncertainty propagation relationship of the geometry projection during training, improving the stability and efficiency of the end-to-end model learning. (2). It can be derived to a highly reliable confidence to indicate the quality of the 3D detection result, enabling more reliable detection inference. Experiments show that the proposed approach not only obtains (state-of-the-art) SOTA performance in image-based monocular 3D detection but also demonstrates superiority in efficacy with a simplified framework.

Co-stabilization effects of gluten/carrageenan to the over-heated myofibrillar protein: Inhibit the undesirable gel weakening and protein over-aggregations.

High-temperature (120 °C) sterilization is an indispensable process for manufacturing ready-to-eat surimi products, yet risking the denaturation of their myofibrillar proteins (MP), thus significantly reducing the gelling properties. To resolve this problem, herein, a synergistic co-strengthening strategy was designed. The negatively charged polysaccharide carrageenan (CG) was introduced into MP simultaneously with wheat gluten, followed by 120 °C thermal treatment for 30 min. A substantial enhancement in mechanical strength, up to four times greater (from 9.86 to 42.38 g·cm), was observed for MP gels, which even surpassed that subjected to conventional gelation processes at 90 °C (36.53 g·cm). Gels that were concurrently added with gluten and CG exhibited porous networks, uniform water distribution, and improved water holding capacity. Accordingly, over-aggregation behaviors of MP were restricted, as evidenced by their reduced particle sizes and polymer dispersity index. Other heat-induced protein deteriorations at 120 °C, i.e., changes of secondary structures and disulfide bonding conformations, were also alleviated. By varying the CG types, it was shown that the κ-CG/gluten-added MP achieved highest gel strength, while the ι-CG/gluten combination may better stabilize the moisture in gel networks. This study introduces a co-reinforcement paradigm and scientific insights to the quality improvement of ready-to-eat meat products.

Integrating network pharmacology and experimental verification to study the mechanism of Polygonum hydropiper total flavonoids against stress-induced gastric mucosal damage.

Context: Polygonum hydropiper L. (P. hydropiper) has outstanding clinical efficacy in treating both acute and chronic gastroenteritis. However, the definite mechanism remains unclear. Objective: This study aimed to explore the potential mechanisms of the total flavonoid of P. hydropiper (FPH) in stress-induced gastric mucosal damage (SGMD) rats through a combination of network pharmacology, molecular docking, and animal experiments. Methods: Network pharmacology and molecular docking were utilized to predict the potential mechanisms of FPH against SGMD. In experimental studies, SGMD rat models were established using water-immersion restraint stress (WIRS). FPH was administered at doses of 140, 70, and 35 mg/kg, with ranitidine serving as a positive control, through gavage once daily for 6 consecutive days after model establishment. Stomach and serum specimens were analyzed using HE staining, Western blotting, qPCR, and ELISA to investigate the protective mechanism of FPH in SGMD. Results: The network pharmacology analysis identified 16 active ingredients and 183 common targets, with potential pathways including PI3K/Akt, MAPK and Keap1/Nrf2. In vivo experiments demonstrated that FPH intervention alleviated SGMD pathological changes, reduced elevated serum IL-6 and TNF-α levels, and enhanced SOD and GSH activity in rats. Additionally, FPH increased the protein expression of p62, Nrf2, HO-1, PI3K, and p-Akt, along with mRNA levels of Nrf2, p62, and HO-1. Conclusions: FPH exerts a gastric mucosal protective effect by upregulating antioxidant gene expression through the PI3K/Akt and Keap1/Nrf2 pathways. This study provides an experimental basis for the potential clinical treatment of SGMD with the traditional Chinese medicine P. hydropiper.

Research Progress on Food-Derived Bioactive Peptides: An Overview of the 3rd International Symposium on Bioactive Peptides.

Interest in food-derived bioactive peptides is on the rise. In 2023, the 3rd International Symposium on Bioactive Peptides (ISBP) was held in Niagara Falls, Canada, to provide a platform for knowledge exchange, networking, and collaboration among researchers in this field. This article aims to provide a high-level overview of the key progress and emerging trends in bioactive peptides based on the 3rd ISBP. This review highlights the production of bioactive peptides from sustainable sources through the integration of artificial intelligence and wet-lab research, the emerging roles of bioactive peptides in cognitive function, and the ability of peptides to act as taste modifiers. The emerging research trend in bioactive peptides focuses on utilizing novel processing technologies, understanding peptide-receptor interactions, applying omics in mechanistic studies, conducting clinical trials, and facilitating product development and commercialization.

Epidemiology of respiratory pathogens in patients with acute respiratory infections during the COVID-19 pandemic and after easing of COVID-19 restrictions.

We aimed to investigate the epidemiological characteristics of non-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) respiratory pathogens among patients with acute respiratory infections (ARIs) in Shijiazhuang, China, during the coronavirus disease 2019 (COVID-19) pandemic (January 2021--December 2022) and after the relaxation of COVID-19 restrictive measures (January 2022--December 2023). This retrospective study enrolled 6,633 ARIs patients who visited the Hebei General Hospital between 2021- and 2023. Nasopharyngeal swabs samples were collected for multiplex PCR detection of 13 common respiratory pathogens. Respiratory pathogens were detected in 31.58% of individuals diagnosed with ARIs, whileereas a co-infection with multiple pathogens was observed in 8.5% of the ARI patients. In the years 2021 and 2022, 326 (27.63%) and 283 (24.38%) respiratory pathogens were found to be positive, respectively, during the COVID-19 pandemic. However, in 2023, subsequent to the easing of COVID-19 restrictions, the positivity rate significantly rose to 34.62%, with 4,292 cases identified. The majority of positive cases over the last three3 years were concentrated in patients under 14 years old. The predominant pathogens identified were human rhinovirus (HRVs) (15.08%) in 2021, mycoplasma pneumonia (MP) (6.46%) in 2022, and influenza A virus (FluA) (11.35%) in 2023. Seasonal prevalence patterns of most pathogens were affected, except for parainfluenza virus (PIV). There was a simultaneous increase in the positive cases and positivity rates of FluA and adenovirus (ADV) Iin 2023, compared to 2021 and 2022. Additionally, the infection rates of respiratory syncytial virus (RSV), MP, and coronavirus (CoV) in 2023 either exceeded or were comparable to those in 2021 and 2022. Conversely, the positivity rates of PIV, RVs, metapneumovirus (MPV), and influenza B virus (FluB) were lower in 2023 compared to 2021 or 2022. IMPORTANCE: The implementation of strict non-pharmaceutical interventions (NPIs) during the coronavirus disease 2019 (COVID-19) pandemic may lead to changes in the epidemiological features of respiratory pathogens, as well as the occurrence of immune debt, potentially causing a resurgence in respiratory pathogen activity following the easing of strict NPIs measures. There are limited reports on the epidemiological characteristics of respiratory pathogens among patients of all ages with acute respiratory infections (ARIs) during the COVID-19 pandemic and after the easing of COVID-19 restrictions. Our study investigated the epidemiology of 13 respiratory pathogens in Shijiazhuang, China, from January 2021 to December 2023. Thisese data isare crucial for the ongoing surveillance of epidemiological shifts in respiratory pathogens during and post the -COVID-19 pandemic, and serves as a scientific foundation for the prevention and management of ARIs.

An antifouling electrochemical sensor based on a U-shaped four-in-one peptide and poly(3,4-ethylenedioxythiophene) for vancomycin detection in fresh goat milk.

Nonspecific adsorption of biomolecules (notably, proteins) and bacteria from unsterilized food may occur on sensor surfaces, which is still a challenge for food safety sensing. To achieve sensitive detection of unsterilized raw-food materials, in this study, a U-shaped four-in-one peptide with the sequence Ac-FLKLLKKLL-DOPA3-PPPPEEKDQDKEKaa that exhibited anchoring, antifouling, antibacterial, and recognition properties was designed. The peptide-modified sensor surface effectively prevented bacterial adhesion and proliferation while resisting biomolecule adsorption (signal inhibition rate as low as 0.51 % in single-protein solutions). A highly conductive polymer layer of poly(3,4-ethylenedioxythiophene) was introduced to improve the electrochemical performance before U-shaped four-in-one peptide anchoring. The proposed sensor could accurately detect vancomycin, with a wide linear range and limit of detection of 0.05-10 µg mL-1 and 2.06 ng mL-1 (S/N = 3), respectively. Satisfactory recovery rates (101.3-105.3 %) were achieved using diluted fresh goat milk.

Cotton Pectate Lyase GhPEL48_Dt Promotes Fiber Initiation Mediated by Histone Acetylation.

GhPEL48_Dt, a Pectate lyase (PEL, EC4.2.2.2), is a crucial enzyme involved in cell-wall modification and pectin degradation. Studies have shown that the GhPEL48_Dt also plays a significant role in cotton-fiber development; however, the specific function and regulatory mechanism of GhPEL48_Dt in cotton-fiber development are still not fully understood. Here, we found that the histone deacetylase inhibitor-Trichostatin A significantly reduces the transcript levels of GhPEL48_Dt and its enzyme activity. Further, silencing of GhPEL48_Dt significantly inhibits the initiation and elongation of cotton fibers by promoting pectin degradation, and the heterologous expression of GhPEL48_Dt promotes the development of trichomes and root hairs in Arabidopsis, which suggests that GhPEL48_Dt plays a positive and conserved role in single cell i.e., fiber, root hair, and leaf trichome development. Collectively, this paper provides a comprehensive analysis of the fundamental characteristics and functions of GhPEL48_Dt in fiber development, including the regulatory role of histone acetylation on GhPEL48_Dt, which contributes to the understanding of pectin degradation pathways and establishes a theoretical foundation for elucidating its regulatory mechanism.

Metabolomics and gut microbiota analysis reveal the differential efficacy of areca nut and charred areca nut in treating constipation.

Background: Areca nut (AN) is a traditional Chinese herbal medicine used for centuries to treat gastrointestinal (GI) disorders. Charred AN (CAN) is a processed product of AN with similar therapeutic effects. This study aimed to investigate the therapeutic mechanisms of AN and CAN for constipation via metabolomics and gut microbiota analysis. Methods: In this study, the rats were randomly divided into 5 groups (n = 6): control, constipation model, positive drug, AN treatment, and CAN treatment groups. Constipation was induced by intragastric administration of loperamide hydrochloride, followed by 14-day treatment with mosapride, AN, or CAN. The efficacy difference between AN and CAN was assessed by evaluating the weight gain, fecal water content, GI transit rate, colonic histopathology, serum levels of GI hormones, gut microbiota, and fecal metabolites. Results: The results demonstrated that both AN and CAN could alleviate loperamide-induced constipation. Furthermore, they significantly elevated the serum levels of motilin, vasoactive intestinal peptide, substance P, and acetylcholine. 16S rRNA analysis revealed that AN regulated the relative abundance of Bacillus, UCG-005, norank_f_Muribaculaceae, Candidatus_Saccharimonas, and Ruminococcus, whereas CAN modulate the relative abundance of Lactobacillus, Bacillus, norank_f_Muribaculaceae, Ruminococcus, unclassified_f_Oscillospiraceae, and unclassified_f_Prevotellaceae. Moreover, the metabolic profile of AN- and CAN-treated rats was also different, where AN treatment involved pathways of citrate cycle (TCA) and tyrosine, alanine, aspartate, and glutamate metabolisms. Whereas CAN treatment involved pathways of steroid and primary bile acid biosynthesis, as well as pyrimidine and purine metabolisms. Spearman correlation analysis indicated a close relationship between gut microbiota and fecal metabolites. Conclusion: In summary, this study revealed that AN may protect GI mucosa, enhance GI motility, and alleviate constipation symptoms by regulating the relative abundance of specific gut microbiota (Bacillus, UCG-005, norank_f_Muribaculaceae, Candidatus_Saccharimonas, Ruminococcus) as well as citrate cycle or tyrosine, alanine, aspartate, and glutamate metabolic pathways. Furthermore, CAN was observed to promote gastric emptying and intestinal propulsion, thereby alleviating constipation, by modulating the relative abundance of specific gut microbiota (Lactobacillus, Bacillus, norank_f_Muribaculaceae, Ruminococcus, unclassified_f_Oscillospiraceae, unclassified_f_Prevotellaceae) as well as steroid and primary bile acid biosynthesis, as well as pyrimidine and purine metabolic pathways.

Atomic-scale strain engineering of atomically resolved Pt clusters transcending natural enzymes.

Strain engineering plays an important role in tuning electronic structure and improving catalytic capability of biocatalyst, but it is still challenging to modify the atomic-scale strain for specific enzyme-like reactions. Here, we systematically design Pt single atom (Pt1), several Pt atoms (Ptn) and atomically-resolved Pt clusters (Ptc) on PdAu biocatalysts to investigate the correlation between atomic strain and enzyme-like catalytic activity by experimental technology and in-depth Density Functional Theory calculations. It is found that Ptc on PdAu (Ptc-PA) with reasonable atomic strain upshifts the d-band center and exposes high potential surface, indicating the sufficient active sites to achieve superior biocatalytic performances. Besides, the Pd shell and Au core serve as storage layers providing abundant energetic charge carriers. The Ptc-PA exhibits a prominent peroxidase (POD)-like activity with the catalytic efficiency (Kcat/Km) of 1.50 × 109 mM-1 min-1, about four orders of magnitude higher than natural horseradish peroxidase (HRP), while catalase (CAT)-like and superoxide dismutase (SOD)-like activities of Ptc-PA are also comparable to those of natural enzymes. Biological experiments demonstrate that the detection limit of the Ptc-PA-based catalytic detection system exceeds that of visual inspection by 132-fold in clinical cancer diagnosis. Besides, Ptc-PA can reduce multi-organ acute inflammatory damage and mitigate oxidative stress disorder.

Pursuing Extreme Descriptive Power and Generality in Chemical Bond Theories: A Method to Decipher "Interatomic Genomes" from Interatomic Electron Structures.

The description and analysis of chemical bonds have been difficult following the popularization of electronic structure calculations. Although many attempts have been made from the perspective of electronic structure, the sheer volume of information in the electronic structure has left contemporary chemical bond analysis methods grappling with an inescapable "Trilemma" where the model briefness, generality, and descriptiveness (descriptive power) cannot be obtained simultaneously. To push the generality and descriptiveness to their extremes, herein a general machine learning-based framework is introduced to compact chemical bonds into a detailed residue-by-residue "genome" with matched encoding/decoding tools. The framework fuses the quantum mechanical aspects, auto feature extraction, nanostructures and/or simulations, and generative models. The encoded genomes are information-dense and decodable, where 100% generality is guaranteed. The descriptiveness of genomes appears to be broader than most known models. As a proof of concept, the realization presented in this work compacts the complete information regarding two critical chemical bonds in thiolate-protected gold nanoclusters, the S-Au and Au-Au bonds, from a Bosonic-Fermionic character perspective into 8-valued genomes. The machine learning component is trained based on 26,528 density functional theory simulated electron localization function images. With an exploration of the space span for the genome, bond polarization, hybridization, intrusion of other atoms, alignments, crystal orientation, atomic motions, and more details are observed. Furthermore, it has emerged from extensive generation tests that molecules and solids can be integrated in such a concise manner than is typically achieved with purely geometric representations. To showcase the intraclass complexity of S-Au and Au-Au bonds visually, a roadmap is plotted by summarizing and correlating the similarities of 8-value-genomes. Furthermore, genomes can be associated with realistic indices easily with a simple multilayer perception architecture as a simple calculating tool. Besides, there are 3 sets of applications, including a set of chemisorption, a set of molecular dynamical analysis, and a set of ultrafast processes, showcasing the interpretability potentials of interatomic genomes in the geometric structures, kinetic properties, and vibration characteristics of molecular systems. As the framework rose to the challenge of nanoclusters from a complicated mesoscopic family of material, the displayed generality and comprehensiveness indicate that the model may "understand" chemical bonds in a machine's way.

Effects of Environmental Factors on the Diversity of Grasshopper Communities along Altitude Gradients in Xizang, China.

To determine the grasshopper species composition, altitudinal distribution patterns, and their main drivers, we conducted a study in Xizang using 33 sample plots ranging from 600 to 4100 m. Grasshoppers were collected from August to October during 2020-2022 using sweep nets. A total of 1159 grasshoppers from six families, 28 genera, and 44 species were identified, with Omocestus cuonaensis and Aserratus eminifrontus as the dominant species, comprising 30.03% and 10.26% of total grasshoppers, respectively. The results showed that species richness and the Margalef richness index of grasshopper communities decreased significantly (p < 0.05) with increasing altitude, peaking at 1100-1600 m and lowest values at 2600-3100 m. Similarly, the Shannon-Wiener index and Simpson dominance index also decreased significantly (p < 0.05) with an increase in altitude, showing the highest and lowest values at 600-1100 m and 3100-3600 m, respectively. The Jaccard similarity coefficients among grasshopper communities varied from 0 to 0.40 across altitudinal gradients, indicating different degrees of dissimilarity. The results of Pearson correlation analyses showed that the Shannon-Wiener index, species richness, Margalef richness index, and Simpson dominance index of grasshopper communities were significantly negatively correlated with the temperature factors and soil pH, but they were significantly positively correlated with the moisture factors. Hierarchical partitioning identified annual mean temperature-daily difference, precipitation in the coldest season, and driest month precipitation as the primary factors explaining variance in grasshopper community diversity in Xizang. These findings provided greater insights into the mechanisms underlying insect community structure, distribution patterns, and diversity in Xizang ecosystems, including implications for the effects of global warming on insect communities.

Gut dysbiosis of Rana zhenhaiensis tadpoles after lead (Pb) exposure based on integrated analysis of microbiota and gut transcriptome.

Lead (Pb) is a ubiquitously detected heavy metal pollutant in aquatic ecosystems. Previous studies focused mainly on the response of gut microbiota to Pb stress, with less emphasis on gene expression in intestine, thereby limiting the information about impacts of Pb on intestinal homeostasis in amphibians. Here, microbial community and transcriptional response of intestines in Rana zhenhaiensis tadpoles to Pb exposure were evaluated. Our results showed that 10 µg/L Pb significantly decreased bacterial diversity compared to the controls by the Simpson index. Additionally, 1000 µg/L Pb exposure resulted in a significant reduction in the abundance of Fusobacteriota phylum and Cetobacterium genus but a significant expansion in Hafnia-Obesumbacterium genus. Moreover, transcriptome analysis revealed that about 90 % of the DEGs (8458 out of 9450 DEGs) were down-regulated in 1000 µg/L Pb group, mainly including genes annotated with biological functions in fatty acid degradation, and oxidative phosphorylation, while up-regulated DEGs involved in metabolism of xenobiotics by cytochrome P450. The expression of Gsto1, Gsta5, Gstt4, and Nadph showed strong correlation with the abundance of genera Serratia, Lactococcus, and Hafnia-Obesumbacterium. The findings of this study provide important insights into understanding the influence of Pb on intestinal homeostasis in amphibians.

A robust ensemble deep learning framework for accurate diagnoses of tuberculosis from chest radiographs.

Introduction: Tuberculosis (TB) stands as a paramount global health concern, contributing significantly to worldwide mortality rates. Effective containment of TB requires deployment of cost-efficient screening method with limited resources. To enhance the precision of resource allocation in the global fight against TB, this research proposed chest X-ray radiography (CXR) based machine learning screening algorithms with optimization, benchmarking and tuning for the best TB subclassification tasks for clinical application. Methods: This investigation delves into the development and evaluation of a robust ensemble deep learning framework, comprising 43 distinct models, tailored for the identification of active TB cases and the categorization of their clinical subtypes. The proposed framework is essentially an ensemble model with multiple feature extractors and one of three fusion strategies-voting, attention-based, or concatenation methods-in the fusion stage before a final classification. The comprised de-identified dataset contains records of 915 active TB patients alongside 1,276 healthy controls with subtype-specific information. Thus, the realizations of our framework are capable for diagnosis with subclass identification. The subclass tags include: secondary tuberculosis/tuberculous pleurisy; non-cavity/cavity; secondary tuberculosis only/secondary tuberculosis and tuberculous pleurisy; tuberculous pleurisy only/secondary tuberculosis and tuberculous pleurisy. Results: Based on the dataset and model selection and tuning, ensemble models show their capability with self-correction capability of subclass identification with rendering robust clinical predictions. The best double-CNN-extractor model with concatenation/attention fusion strategies may potentially be the successful model for subclass tasks in real application. With visualization techniques, in-depth analysis of the ensemble model's performance across different fusion strategies are verified. Discussion: The findings underscore the potential of such ensemble approaches in augmenting TB diagnostics with subclassification. Even with limited dataset, the self-correction within the ensemble models still guarantees the accuracies to some level for potential clinical decision-making processes in TB management. Ultimately, this study shows a direction for better TB screening in the future TB response strategy.

Nonmetal Doping Modulates Fe Single-Atom Catalysts for Enhancement in Peroxidase Mimicking via Symmetry Disruption, Distortion, and Charge Transfer.

Developing biomimetic catalysts with excellent peroxidase (POD)-like activity has been a long-standing goal for researchers. Doping nonmetallic atoms with different electronegativity to boost the POD-like activity of Fe-N-C single-atom catalysts (SACs) has been successfully realized. However, the introduction of heteroatoms to regulate the coordination environment of the central Fe atom and thus influence the activation of the H2O2 molecule in the POD-like reaction has not been extensively explored. Herein, the effect of different doping sites and numbers of heteroatoms (P, S, B, and N) on the adsorption and activation of H2O2 molecules of Fe-N sites is thoroughly investigated by density functional theory (DFT) calculations. In general, alternation in the catalytic efficiency directly depends on the transfer of electrons and the geometrical shifts near the Fe-N site. First, the symmetry disruption of the Fe-N4 site by P, S, and B doping is beneficial to the activation of H2O2 due to a significant reduction in the adsorption energies. In some cases, without Fe-N4 site disruption, the configurations fail to modulate the adsorption behavior of H2O2. Second, Fe-N-P/S configurations exhibit a stronger affinity for H2O2 molecules due to the significant out-of-plane distortions induced by larger atomic radii of P and S. Moreover, the synergistic effects of Fe and doping atoms P, S, and B with weaker electronegativity than that of N atoms promote electron donation to generated oxygen-containing intermediates, thus facilitating subsequent electron transfer with other substrates. This work demonstrates the critical role of tuning the coordinating environment of Fe-N active centers by heteroatom doping and provides theoretical guidance for controlling the types by breaking the symmetry of SACs to achieve optimal POD-like catalytic activity and selectivity.

Fear generalization modulated by shock intensity and protein synthesis inhibitor.

RATIONALE: Maladaptive fear responses, including sensitized threat reactions and overgeneralization, contribute to anxiety disorders such as generalized anxiety disorder and post-traumatic stress disorder. Although stress intensity influences the generation and extent of these maladaptive fears, the underlying mechanisms remain unclear. OBJECTIVES: The present study examined whether varying footshock stress intensity and inhibition of protein synthesis have differential effect on fear sensitization and generalization in mice. METHODS: Mice were subjected to a classic fear conditioning protocol involving five different levels of footshock intensities. Prior to fear acquisition, the protein synthesis inhibitor cycloheximide (CHX) was administered intraperitoneally. Fear sensitization to white noise and fear generalization to tones with frequencies differing from the conditioned tone were assessed at either 2 or 4 days after fear acquisition. RESULTS: The results showed that, although varying shock intensities (except the lowest) led to a similar pattern of increased freezing during auditory cues in fear acquisition, the extent of both fear sensitization and generalization increased with the intensity of the footshock in the following days. As shock intensities increased, there was a proportional rise in sensitized fear to white noise and generalized freezing to tones with frequencies progressively closer to the conditioned stimulus. Mildest shocks did not induce discriminative conditioned fear memory, whereas the most intense shocks led to pronounced fear generalization. Administration of CHX before fear acquisition did not affect sensitized fear but reduced generalization of freezing to tones dissimilar from the conditioned stimulus in the group exposed to the most intense shock. CONCLUSIONS: Our results suggest that maladaptive fear responses elicited by varying stress intensities exhibit distinct characteristics. The effect of CHX to prevent overgeneralization without affecting discriminative fear memory points to potential therapeutic approaches for fear-related disorders, suggesting the possibility of mitigating overgeneralization while preserving necessary fear discrimination.

Effect of cooking conditions on chickpea flour functionality and its protein physicochemical properties.

Chickpea is an important food legume that usually undergoes various processing treatments to enhance nutritional value and functional properties. This study aimed to investigate the effects of different cooking conditions on physicochemical, structural, and functional properties of chickpea, especially its protein macromolecules. Kabuli chickpea seeds were processed by water cooking at different temperatures (63, 79, 88, and 96°C), followed by evaluating flour solubility, water-holding capacity (WHC), pasting property, as well as the total protein profile and fractionated protein distributions. Cooking treatments significantly decreased flour solubility (from 39.45 to 25.21 g/100 g flour) and pasting viscosity (peak and final viscosities, from 1081 to 300.5 cP and 1323 to 532 cP, respectively), while increasing WHC (from 0.862 to 1.144 g H2O/g flour) of chickpea flour (p < 0.05). These behaviors were enhanced by increasing cooking temperature. Meanwhile, cooking induced a significant change of chickpea proteins, modifying the albumin- and globulin-like fractions of chickpea protein to display glutelin-like behavior. The current study provides potential approaches for manipulating chickpea flour functionalities (e.g., solubility, viscosity, and WHC) to address the process and product challenges and favor product innovation.

AlphaFold 2-based stacking model for protein solubility prediction and its transferability on seed storage proteins.

Accurate protein solubility prediction is crucial in screening suitable candidates for food application. Existing models often rely only on sequences, overlooking important structural details. In this study, a regression model for protein solubility was developed using both the sequences and predicted structures of 2983 E. coli proteins. The sequence and structural level properties of the proteins were bioinformatically extracted and subjected to multilayer perceptron (MLP). Moreover, residue level features and contact maps were utilized to construct a graph convolutional network (GCN). The out-of-fold predictions of the two models were combined and fed into multiple meta-regressors to create a stacking model. The stacking model with support vector regressor (SVR) achieved R2 of 0.502 and 0.468 on test and external validation datasets, respectively, displaying higher performance compared to existing regression models. Based on the improved performance compared to its based models, the stacking model effectively captured the strength of its base models as well as the significance of the different features used. Furthermore, the model's transferability was indirectly validated on a dataset of seed storage proteins using Osborne definition as well as on a case study using molecular dynamic simulation, showing potential for application beyond microbial proteins to food and agriculture-related ones.

Validation of the Capacity for the Psychotherapy Process Scale for Use in Adolescent Patients.

Although pre-treatment assessments of the capacity for the psychotherapy process can aid in identifying patients experiencing great difficulties in therapy and in tailoring therapies for individual patients, limited information exists for adolescents. To address this gap, this study followed the World Health Organization's age standards for adolescents (younger adolescents aged 10-14 years; older adolescents aged 15-19 years), examined the psychometric properties of the Capacity for Psychotherapy Process Scale (CFPPS; mainly used for adult patients) in these two groups of adolescents, and compared their capacities for the psychotherapy process. The participants were 434 younger adolescent (mean age = 13.00 ± 1.08 years; 70.0% female) and 883 older adolescent outpatients (mean age = 16.68 ± 1.29 years; 62.3% female) at the department of psychiatry of the hospital in Guangzhou, China. The results of exploratory and confirmatory factor analyses validated the 5-factor model (motivation, belief, self-revelation, persistence, and insight) in both groups. The scale also demonstrated good internal consistency. Furthermore, the CFPPS exhibited small or no associations with pre-treatment sleep problems, depression symptoms, or anxiety symptoms but was a significant predictor of working alliance and psychological benefit in therapy. The capacity for the psychotherapy process among younger adolescents was lower than that among older adolescents. The CFPPS appears to be a reliable and validated instrument for measuring the capacity for the psychotherapy process among adolescent outpatients in China. Therapists should provide therapy tailored to the Chinese adolescents' capacity. Future studies are needed to examine the predictive utility of the CFPPS for the whole sessions of the psychotherapy.

Functional group induced transformations in stacking and electron structure in Mo2CTx/NiS heterostructures.

The two-dimensional transition metal carbide/nitride family (MXenes) has garnered significant attention due to their highly customizable surface functional groups. Leveraging modern material science techniques, the customizability of MXenes can be enhanced further through the construction of associated heterostructures. As indicated by recent research, the Mo2CTx/NiS heterostructure has emerged as a promising candidate exhibiting superior physical and chemical application potential. The geometrical structure of Mo2CTx/NiS heterostructure is modeled and six possible configurations are validated by Density Functional Theory simulations. The variation in functional groups leads to structural changes in Mo2CTx/NiS interfaces, primarily attributed to the competition between van der Waals and covalent interactions. The presence of different functional groups results in significant band fluctuations near the Fermi level for Ni and Mo atoms, influencing the role of atoms and electron's ability to escape near the interface. This, in turn, modulates the strength of covalent interactions at the MXenes/NiS interface and alters the ease of dissociation of the MXenes/NiS complex. Notably, the Mo2CO2/NiS(P63/mmc) heterostructure exhibits polymorphism, signifying that two atomic arrangements can stabilize the structure. The transition process between these polymorphs is also simulated, further indicating the modulation of the electronic level of properties by a sliding operation.

Genome-wide identification and expression analysis of the WRKY gene family in Rhododendron henanense subsp. lingbaoense.

Background: This work explored the characteristics of the WRKY transcription factor family in Rhododendron henanense subsp. lingbaoense (Rhl) and the expression patterns of these genes under abiotic stress by conducting bioinformatics and expression analyses. Methods: RhlWRKY genes were identified from a gene library of Rhl. Various aspects of these genes were analyzed, including genetic structures, conserved sequences, physicochemical properties, cis-acting elements, and chromosomal location. RNA-seq was employed to analyze gene expression in five different tissues of Rhl: roots, stems, leaves, flowers, and hypocotyls. Additionally, qRT-PCR was used to detect changes in the expression of five RhlWRKY genes under abiotic stress. Result: A total of 65 RhlWRKY genes were identified and categorized into three subfamilies based on their structural characteristics: Groups I, II, and III. Group II was further divided into five subtribes, with shared similar genetic structures and conserved motifs among members of the same subtribe. The physicochemical properties of these proteins varied, but the proteins are generally predicted to be hydrophilic. Most proteins are predicted to be in the cell nucleus, and distributed across 12 chromosomes. A total of 84 cis-acting elements were discovered, with many related to responses to biotic stress. Among the identified RhlWRKY genes, there were eight tandem duplicates and 97 segmental duplicates. The majority of duplicate gene pairs exhibited Ka/Ks values <1, indicating purification under environmental pressure. GO annotation analysis indicated that WRKY genes regulate biological processes and participate in a variety of molecular functions. Transcriptome data revealed varying expression levels of 66.15% of WRKY family genes in all five tissue types (roots, stems, leaves, flowers, and hypocotyls). Five RhlWRKY genes were selected for further characterization and there were changes in expression levels for these genes in response to various stresses. Conclusion: The analysis identified 65 RhlWRKY genes, among which the expression of WRKY_42 and WRKY_17 were mainly modulated by the drought and MeJA, and WRKY_19 was regulated by the low-temperature and high-salinity conditions. This insight into the potential functions of certain genes contributes to understanding the growth regulatory capabilities of Rhl.