Overexpression of soybean GmDHN9 gene enhances drought resistance of transgenic Arabidopsis.

Soybean is one of the important oil crops and a major source of protein and lipids. Drought can cause severe soybean yields. Dehydrin protein (DHN) is a subfamily of LEA proteins that play an important role in plant responses to abiotic stresses. In this study, the soybean GmDHN9 gene was cloned and induced under a variety of abiotic stresses. Results showed that the GmDHN9 gene response was more pronounced under drought induction. Subcellular localization results indicated that the protein was localized in the cytoplasm. The role of transgenic Arabidopsis plants in drought stress response was further studied. Under drought stress, the germination rate, root length, chlorophyll, proline, relative water content, and antioxidant enzyme content of transgenic Arabidopsis thaliana transgenic genes were higher than those of wild-type plants, and transgenic plants contained less O2-, H2O2 and MDA contents. In short, the GmDHN9 gene can regulate the homeostasis of ROS and enhance the drought resistance of plants.

Exploring Point-BEV Fusion for 3D Point Cloud Object Tracking with Transformer.

With the prevalent use of LiDAR sensors in autonomous driving, 3D point cloud object tracking has received increasing attention. In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in consecutive frames. Motivated by the success of transformers, we propose Point Tracking TRansformer (PTTR), which efficiently predicts high-quality 3D tracking results in a coarse-to-fine manner with the help of transformer operations. PTTR consists of three novel designs. 1) Instead of random sampling, we design Relation-Aware Sampling to preserve relevant points to the given template during subsampling. 2) We propose a Point Relation Transformer for effective feature aggregation and feature matching between the template and search region. 3) Based on the coarse tracking results, we employ a novel Prediction Refinement Module to obtain the final refined prediction through local feature pooling. In addition, motivated by the favorable properties of the Bird's-Eye View (BEV) of point clouds in capturing object motion, we further design a more advanced framework named PTTR++, which incorporates both the point-wise view and BEV representation to exploit their complementary effect in generating high-quality tracking results. PTTR++ substantially boosts the tracking performance on top of PTTR with low computational overhead. Extensive experiments over multiple datasets show that our proposed approaches achieve superior 3D tracking accuracy and efficiency. Code will be available at https://github.com/Jasonkks/PTTR.

The levels and related factors of posttraumatic growth among nurses: A systematic review and meta-analysis.

WHAT IS KNOWN ON THE SUBJECT?: Nurses often encounter traumatic events in their clinical practice. Trauma can have both negative effects, such as post-traumatic stress disorder and positive effects, such as growth and positive emotions. Posttraumatic growth is beneficial for nurses, as it contributes to positive mental health outcomes and increases life satisfaction. WHAT THE PAPER ADDS TO EXISTING KNOWLEDGE?: Our study confirms that nurses commonly experience posttraumatic growth after being exposed to traumatic events, and the level of growth is moderate. We have identified individual, work-related, and social and organizational factors that may influence the occurrence of posttraumatic growth among nurses. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: Our findings provide valuable insights for interventions aimed at promoting posttraumatic growth among nurses. It is crucial to consider individual factors, work-related factors, and social and organizational factors when designing these interventions. By addressing these factors, healthcare professionals can support nurses in coping with trauma and facilitate their posttraumatic growth. ABSTRACT: INTRODUCTION: While the negative effects of trauma on nurses have been well-documented, it is equally important to focus on ways to promote posttraumatic growth (PTG) among nurses. AIM: This study aims to explore the levels and related factors of PTG among nurses. METHOD: A comprehensive search was conducted across nine databases up to 26 December 2022. The pooled mean score for PTG was calculated and presented with a 95% confidence interval. Subgroup and meta-regression analyses were performed to identify potential moderators of PTG among nurses. RESULTS: A total of 55 studies, involving 35,621 nurses from 11 countries and regions, were included in this review. The pooled scores of the PTG indicated that nurses experienced a moderate level of PTG (55.69, 95% CI: 50.67-60.72). DISCUSSION: Nurses commonly experience PTG following exposure to traumatic events, with a moderate level of PTG. Our systematic review highlights the potential determinants of PTG among nurses, including individual, work-related, and social and organizational factors. IMPLICATIONS FOR PRACTICE: Targeting these factors in interventions can provide nurses with the necessary resources and support to effectively cope with stress and trauma, and potentially facilitate PTG.

Water hyacinth derived hierarchical porous biochar absorbent: Ideal peroxydisulfate activator for efficient phenol degradation via an electron-transfer pathway.

In this paper, a facile hydrothermal pretreatment and molten salt activation route was presented for preparing a self-doped porous biochar (HMBC) from a nitrogenous biomass precursor of water hyacinth. With an ultrahigh specific surface area (2240 m2 g-1), well-developed hierarchical porous structure, created internal structural defects and doped surface functionalities, HMBC exhibited an excellent adsorption performance and catalytic activity for phenol removal via peroxydisulfate (PDS) activation. Specifically, the porous structure promoted the adsorption of PDS on HMBC, forming a highly active HMBC/PDS* complex and thereby increasing the oxidation potential of the system. Meanwhile, the carbon defective structure, graphitic N and CO groups enhanced the electron transfer process, favoring the HMBC/PDS system to catalyze phenol oxidation via an electron transfer dominated pathway. Thus, the system degraded phenol effectively with an ultralow activation energy of 4.9 kJ mol-1 and a remarkable oxidant utilization efficiency of 8.2 mol mol-oxidant-1 h-1 g-1. More importantly, the system exhibited excellent resistance to water quality and good adaptability for decontaminating different organic pollutants with satisfactory mineralization efficiency. This study offers valuable insights into the rational designing of a low-cost biochar catalyst for efficient PDS activation towards organic wastewater remediation.

Protocol for preparation of LiCl-based ultra-hygroscopic curdlan heat exchanger for dehumidification.

Desiccant-coated heat exchangers provide a practical solution for the efficient removal of moisture from the air. Here, we present a protocol to synthesize an ultra-hygroscopic polymer to develop a LiCl loaded in curdlan hydrogel (LiCl@Cur)-coated heat exchanger for deep dehumidification. We describe steps for preparing the curdlan gel solution, hydrogel, LiCl solution, and LiCl@Cur. We then detail procedures for preparing curdlan-coated and LiCl@Cur-coated heat exchangers. The coated heat exchanger described in this protocol has a maximum dehumidification capacity of 12 g/kg. For complete details on the use and execution of this protocol, please refer to Pan et al. (2023).1.

Prevalence of sarcopenic obesity in patients with diabetes and adverse outcomes: A systematic review and meta-analysis.

BACKGROUND & AIMS: Sarcopenic obesity (SO), which refers to the coexistence of sarcopenia and obesity. The aim of this systematic review and meta-analysis was first to assess the prevalence of SO in patients with diabetes, and second, to evaluate possible adverse outcomes. METHODS: This study was conducted in adherence to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines and the data were collated by means of meta-analysis and narrative synthesis. We comprehensively and systematically searched PubMed, Embase, Web of Science, CBM, CNKI, Wanfang Database from the establishment of the database to December 2022, and collected related studies on SO in patients with diabetes. Using Stata 16.0 software to pool the estimates for the prevalence of SO in patients with diabetes, and a descriptive systematic review of possible adverse outcomes was performed. RESULTS: The prevalence of 20 included studies were pooled by a random-effects model, which showed that the prevalence of SO in patients with diabetes was 27%. Subgroup analyses showed that the prevalence of SO was higher among studies using BIA (24%) to assess muscle mass, and those focused on literature of moderate-quality (29%), being published from 2013 to 2016 (29%), female patients with diabetes (17%), North Americans (30%). SO in patients with diabetes can lead to adverse outcomes including decreased glomerular filtration rate, massive proteinuria, cognitive decline, and insulin resistance. CONCLUSION: The systematic review and meta-analysis revealed a prevalence of 27% for SO in patients with diabetes, and it is associated with potential serious adverse outcomes. Therefore, we should attach importance to the screening of SO in patients with diabetes and early detection of susceptible groups, then selecting appropriate interventions to reduce the occurrence of it and various adverse outcomes in this demographic.

Hierarchical Active Learning With Qualitative Feedback on Regions.

Learning classification models in practice usually requires numerous labeled data for training. However, instance-based annotation can be inefficient for humans to perform. In this article, we propose and study a new type of human supervision that is fast to perform and useful for model learning. Instead of labeling individual instances, humans provide supervision to data regions, which are subspaces of the input data space, representing subpopulations of data. Since labeling now is performed on a region level, 0/1 labeling becomes imprecise. Thus, we design the region label to be a qualitative assessment of the class proportion, which coarsely preserves the labeling precision but is also easy for humans to do. To identify informative regions for labeling and learning, we further devise a hierarchical active learning process that recursively constructs a region hierarchy. This process is semisupervised in the sense that it is driven by both active learning strategies and human expertise, where humans can provide discriminative features. To evaluate our framework, we conducted extensive experiments on nine datasets as well as a real user study on a survival analysis of colorectal cancer patients. The results have clearly demonstrated the superiority of our region-based active learning framework against many instance-based active learning methods.

Side-Chain Molecular Engineering of Triazole-Based Donor-Acceptor Polymeric Photocatalysts with Strong Electron Push-Pull Interactions.

A new series of donor-acceptor (D-A)-type semiconductive polymers were generated by the integration of electron-deficient alkyl chain anchored triazole (TA) moieties and electron-rich pyrene units into the polymer skeleton. The polymer series demonstrated satisfactory light-harvesting ability and suitable band gaps. In the series, polymer P-TAME benefits from a minimized exciton binding energy, strongest D-A interaction, and favorable hydrophilicity, affording an outstanding photocatalytic H2 evolution rate of ca. 100 µmol h-1 (10 mg polymer, AQY420 nm =8.9 %) and H2 O2 production rate of ca. 190 µmol h-1 (20 mg polymer) under visible-light irradiation, which is superior to most currently reported polymers. All polymers in the series can mediate water oxidation reactions to evolve O2 . Thus, these TA-based polymers open up a new avenue toward tailor-made efficient photocatalysts with broad photocatalytic activities.

USP20 is a predictor of poor prognosis in colorectal cancer and associated with lymph node metastasis, immune infiltration and chemotherapy resistance.

Background: Colorectal cancer (CRC) is a highly prevalent malignancy with a poor prognosis. USP20 can support progression of variety of tumors. USP20 was shown to promote breast tumor metastasis, and proliferation of oral squamous carcinoma cells. However, the role of USP20 in CRC remains unclear. Methods: We used bioinformatics to analyze the expression and prognosis of USP20 in pan-cancer and explore the relationship between USP20 expression and immune infiltration, immune checkpoints, and chemotherapy resistance in CRC. The differential expression and prognostic role of USP20 in CRC was validated by qRT-PCR and immunohistochemistry. Cox univariate and multivariate analyses were performed to assess risk factors for poor prognosis of CRC, and new prognostic prediction models were constructed and evaluated by decision curve analysis (ROC) and receiver operating characteristic (DCA). USP20 was overexpressed in CRC cell lines to explore the effect of USP20 on the functionalities of CRC cells. Enrichment analyses were used to explore the possible mechanism of USP20 in CRC. Results: The expression of USP20 was lower in CRC tissues than adjacent normal tissues. Compared with low USP20 expression patients, CRC patients with high USP20 expression level had shorter OS. Correlation analysis showed that USP20 expression was associated with lymph node metastasis. Cox regression analysis revealed USP20 as an independent risk factor for poor prognosis in CRC patients. ROC and DCA analyses showed that the performance of the newly constructed prediction model was better than the traditional TNM model. Immune infiltration analysis shown that USP20 expression is closely associated with T cell infiltration in CRC. A co-expression analysis showed that USP20 expression was positively correlated with several immune checkpoint genes including ADORA2A, CD160, CD27 and TNFRSF25 genes and positively associated with multiple multi-drug resistance genes such as MRP1, MRP3, and MRP5 genes. USP20 expression positively correlated with the sensitivity of cells to multiple anticancer drugs. Overexpression of USP20 enhanced the migration and invasive ability of CRC cells. Enrichment pathway analyses showed the USP20 may play a role via the Notch pathway, Hedgehog pathway and beta-catenin pathway. Conclusion: USP20 is downregulated in CRC and associated with prognosis in CRC. USP20 enhances CRC cells metastasis and is associated with immune infiltration, immune checkpoints, and chemotherapy resistance.

Stereoselective Assembly of Hydrogen-Bonded Anionic Cages Dictated by Organophosphate-Based Chiral Nodes.

Inspired by the signal transduction function of organophosphates in biological systems, bioactive organophosphates were utilized for the first time as chiral nodes to dictate the stereoselective assembly of hydrogen-bonded anionic cages. Phosphonomycin (antibiotics), tenofovir (antivirals), adenosine monophosphate (natural product, AMP) and clindamycin phosphate (antibiotics) were assembled with an achiral bis-monourea ligand, thereby leading to the stereoselective formation of quadruple or triple helicates. The extent of the stereoselectivity could be enhanced by either lowering the temperature or adding stronger-binding cations as templates. With the chiral anionic cages as the host, some enantioselectivity was achieved when binding chiral quaternary ammonium cations.

Efficient Architecture Search for Continual Learning.

Continual learning with neural networks, which aims to learn a sequence of tasks, is an important learning framework in artificial intelligence (AI). However, it often confronts three challenges: 1) overcome the catastrophic forgetting problem; 2) adapt the current network to new tasks; and 3) control its model complexity. To reach these goals, we propose a novel approach named continual learning with efficient architecture search (CLEAS). CLEAS works closely with neural architecture search (NAS), which leverages reinforcement learning techniques to search for the best neural architecture that fits a new task. In particular, we design a neuron-level NAS controller that decides which old neurons from previous tasks should be reused (knowledge transfer) and which new neurons should be added (to learn new knowledge). Such a fine-grained controller allows finding a very concise architecture that can fit each new task well. Meanwhile, since we do not alter the weights of the reused neurons, we perfectly memorize the knowledge learned from the previous tasks. We evaluate CLEAS on numerous sequential classification tasks, and the results demonstrate that CLEAS outperforms other state-of-the-art alternative methods, achieving higher classification accuracy while using simpler neural architectures.

Meta-DETR: Image-Level Few-Shot Detection With Inter-Class Correlation Exploitation.

Few-shot object detection has been extensively investigated by incorporating meta-learning into region-based detection frameworks. Despite its success, the said paradigm is still constrained by several factors, such as (i) low-quality region proposals for novel classes and (ii) negligence of the inter-class correlation among different classes. Such limitations hinder the generalization of base-class knowledge for the detection of novel-class objects. In this work, we design Meta-DETR, which (i) is the first image-level few-shot detector, and (ii) introduces a novel inter-class correlational meta-learning strategy to capture and leverage the correlation among different classes for robust and accurate few-shot object detection. Meta-DETR works entirely at image level without any region proposals, which circumvents the constraint of inaccurate proposals in prevalent few-shot detection frameworks. In addition, the introduced correlational meta-learning enables Meta-DETR to simultaneously attend to multiple support classes within a single feedforward, which allows to capture the inter-class correlation among different classes, thus significantly reducing the misclassification over similar classes and enhancing knowledge generalization to novel classes. Experiments over multiple few-shot object detection benchmarks show that the proposed Meta-DETR outperforms state-of-the-art methods by large margins. The implementation codes are publicly available at https://github.com/ZhangGongjie/Meta-DETR.

The Antiviral Activity of Caprylic Monoglyceride against Porcine Reproductive and Respiratory Syndrome Virus In Vitro and In Vivo.

Porcine reproductive and respiratory syndrome (PRRS) is a disease with a major economic impact in the global pig industry, and this study aims to identify potential anti-PRRSV drugs. We examined the cytotoxicity of four medium-chain fatty acids (MCFAs) (caprylic, caprylic monoglyceride, decanoic monoglyceride, and monolaurin) and their inhibition rate against PRRSV. Then the MCFAs with the best anti-PRRSV effect in in vitro assays were selected for subsequent in vivo experiments. Potential anti-PRRSV drugs were evaluated by viral load assay, pathological assay, and cytokine level determination. The results showed that caprylic monoglyceride (CMG) was the least toxic to cells of the four MCFAs, while it had the highest PRRSV inhibition rate. Then the animals were divided into a low-CMG group, a medium-CMG group, and a high-CMG group to conduct the in vivo evaluation. The results indicated that piglets treated with higher concentrations of caprylic monoglyceride were associated with lower mortality and lower viral load after PRRSV infection (p < 0.05). The pulmonary pathology of the piglets also improved after CMG treatment. The levels of pro-inflammatory cytokines (IL-6, IL-8, IL-1ß, IFN-γ, TNF-α) were significantly downregulated, and the levels of anti-inflammatory cytokine (IL-10) were significantly upregulated in the CMG-treated piglets compared to the positive control group (p < 0.05). Taken together, the present study revealed for the first time that caprylic monoglyceride has strong antiviral activity against PRRSV in vitro and in vivo, suggesting that caprylic monoglyceride could potentially be used as a drug to treat PRRS infection.

Heteroatom-Doped Hierarchically Porous Biochar for Supercapacitor Application and Phenol Pollutant Remediation.

Biochars are considered as promising materials in energy storage and environmental remediation because of their unique physicochemical properties and low cost. However, the fabrication of multifunctional biochar materials with a well-developed hierarchical porous structure as well as self-doped functionalities via a facile strategy remains a challenge. Herein, we demonstrate a heteroatom-doped porous biochar, prepared by a hydrothermal pretreatment followed by a molten salt activation route. With the creation of a high specific surface area (1501.9 m2/g), a hierarchical porous structure, and the incorporation of oxygen-/nitrogen-functional groups, the as-prepared biochar (BC-24) exhibits great potential for supercapacitor application and organic pollutant elimination. The assembled biochar electrode delivers a specific capacitance of 378 F/g at 0.2 A/g with a good rate capability of 198 F/g at 10 A/g, and excellent cycling stability with 94.5% capacitance retention after 10,000 recycles. Moreover, BC-24 also exhibits superior catalytic activity for phenol degradation through peroxydisulfate (PDS) activation. The phenol (0.2 mM) can be effectively absorbed and then completely degraded within only 25 min over a wide pH range with low catalyst and PDS dosages. More importantly, TOC analysis indicates 81.7% of the phenol is mineralized within 60 min, confirming the effectiveness of the BC-24/PDS system. Quenching experiments and EPR measurements reveal that SO4·- and ·OH as well as 1O2 are involved in the phenol degradation, while the non-radical pathway plays the dominant role. This study provides valuable insights into the preparation of cost-effective carbon materials for supercapacitor application and organic contaminant remediation.

Less is More: A Shortcut for Anionocages Design Based on (RPO3 2- )-Monourea Coordination.

Anionocages have been developed as a unique family of hydrogen bonded cages. However, strategies for constructing anionocages are mainly limited to that based on (PO4 3- )-bisurea coordination, neither the ligands nor the anions lack the simplicity and diversity of the maturely developed analogues based on metal coordination (i.e. metallocage). We report herein a more simple strategy for anionocages design based on (RPO3 2- )-monourea coordination, utilizing monourea rather than bisurea as the hydrogen binding donor, and RPO3 2- rather than PO4 3- as the acceptor. Two fluorescent, quadruple helicate anionocages were constructed by a bis-monourea ligand, and dianions PhOPO3 2- (H1 ) or HOPO3 2- (H1A ), respectively, which were capable of encapsulating a series of cation guests. As revealed by molecular modeling, H1 features remarkable guest-adaptive cavity breathing without change of the quadruple helicate topology, which allowed the encapsulation of different sized guests in an "induced fit" manner.

Highly Dispersion Cu2O QDs Decorated Bi2WO6 S-Scheme Heterojunction for Enhanced Photocatalytic Water Oxidation.

Developing suitable photocatalysts for the oxygen evolution reaction (OER) is still a challenging issue for efficient water splitting due to the high requirements to create a significant impact on water splitting reaction kinetics. Herein, n-type Bi2WO6 with flower-like hierarchical structure and p-type Cu2O quantum dots (QDs) are coupled together to construct an efficient S-scheme heterojunction, which could enhance the migration efficiency of photogenerated charge carriers. The electrochemical properties are investigated to explore the transportation features and donor density of charge carriers in the S-scheme heterojunction system. Meanwhile, the as-prepared S-scheme heterojunction presents improved photocatalytic activity towards water oxidation in comparison with the sole Bi2WO6 and Cu2O QDs systems under simulated solar light irradiation. Moreover, the initial O2 evolution rate of the Cu2O QDs/Bi2WO6 heterojunction system is 2.3 and 9.7 fold that of sole Bi2WO6 and Cu2O QDs systems, respectively.

The levels and related factors of compassion fatigue and compassion satisfaction among family caregivers: A systematic review and meta-analysis of observational studies.

Family caregivers play an increasingly important role in providing care services, especially for the aging population. Currently, evidence on compassion fatigue among family caregivers remains limited. Our study aims to assess the levels and related factors of compassion fatigue and compassion satisfaction in family caregivers. We searched nine electronic databases for published observational studies up to 7 October 2021. In our studies, the pooled mean scores of compassion fatigue and compassion satisfaction were 42.35 (95% CI: 28.45-56.24) and 36.41 (95% CI: 33.63-39.19), respectively. We considered background, role-related, and social psychological variables to influence the risk of compassion fatigue and compassion satisfaction. Current data suggest that overall compassion fatigue and compassion satisfaction levels were moderate, thus highlighting the potential risk of compassion fatigue for family caregivers. In the future, these findings can be used to develop programs that will mitigate compassion fatigue and improve compassion satisfaction in family caregivers.

MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge.

Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the tasks of detecting, segmenting, and classifying nuclei can free up the pathologists' time for higher value tasks and reduce errors due to fatigue and subjectivity. To encourage the computer vision research community to develop and test algorithms for these tasks, we prepared a large and diverse dataset of nucleus boundary annotations and class labels. The dataset has over 46,000 nuclei from 37 hospitals, 71 patients, four organs, and four nucleus types. We also organized a challenge around this dataset as a satellite event at the International Symposium on Biomedical Imaging (ISBI) in April 2020. The challenge saw a wide participation from across the world, and the top methods were able to match inter-human concordance for the challenge metric. In this paper, we summarize the dataset and the key findings of the challenge, including the commonalities and differences between the methods developed by various participants. We have released the MoNuSAC2020 dataset to the public.

Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019.

This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series, which helped sorting out a profusion of AutoML solutions for Deep Learning (DL) that had been introduced in a variety of settings, but lacked fair comparisons. All input data modalities (time series, images, videos, text, tabular) were formatted as tensors and all tasks were multi-label classification problems. Code submissions were executed on hidden tasks, with limited time and computational resources, pushing solutions that get results quickly. In this setting, DL methods dominated, though popular Neural Architecture Search (NAS) was impractical. Solutions relied on fine-tuned pre-trained networks, with architectures matching data modality. Post-challenge tests did not reveal improvements beyond the imposed time limit. While no component is particularly original or novel, a high level modular organization emerged featuring a "meta-learner", "data ingestor", "model selector", "model/learner", and "evaluator". This modularity enabled ablation studies, which revealed the importance of (off-platform) meta-learning, ensembling, and efficient data management. Experiments on heterogeneous module combinations further confirm the (local) optimality of the winning solutions. Our challenge legacy includes an ever-lasting benchmark (http://autodl.chalearn.org), the open-sourced code of the winners, and a free "AutoDL self-service."