Modeling the dynamic impacts of maritime network blockage on global supply chains.

Recent phenomena such as pandemics, geopolitical tensions, and climate change-induced extreme weather events have caused transportation network interruptions, revealing vulnerabilities in the global supply chain. A salient example is the March 2021 Suez Canal blockage, which delayed 432 vessels carrying cargo valued at $92.7 billion, triggering widespread supply chain disruptions. Our ability to model the spatiotemporal ramifications of such incidents remains limited. To fill this gap, we develop an agent-based complex network model integrated with frequently updated maritime data. The Suez Canal blockage is taken as a case study. The results indicate that the effects of such blockages go beyond the directly affected countries and sectors. The Suez Canal blockage led to global losses of about $136.9 ($127.5-$147.3) billion, with India suffering 75% of these losses. Global losses show a nonlinear relationship with the duration of blockage and exhibit intricate trends post blockage. Our proposed model can be applied to diverse blockage scenarios, potentially acting as an early-alert system for the ensuing supply chain impacts. Furthermore, high-resolution daily data post blockage offer valuable insights that can help nations and industries enhance their resilience against similar future events.

The impact of China's carbon emissions trading policy on corporate investment expenditures: Evidence from carbon-intensive industries.

The carbon emissions trading (CET) policy internalises the cost of carbon emission reductions borne by companies, which will affect the companies' investment and management decisions. From a micro perspective, this paper analyzes the impact on company investment expenditure and its transmission mechanism by implementing the CET policy. Based on panel data of China's A-share listed companies from eight carbon-intensive industries spanning 2010 to 2020, the time-varying difference-in-difference model and its extended model are used to evaluate the impact of the policy in the pilot areas. The results show that: first, based on the cost effect and legality theories, CET policy can reduce the investment expenditure of the companies by 71.95%. Second, CET policy reduces corporate investment expenditures by increasing corporate debt financing costs. When debt financing costs increase by 120.25%, the investment expenditures will reduce by 2.56% indirectly while the intermediary effect of equity financing costs is not significant. Finally, with the implementation of CET policy, the inhibitory effect on corporate investment expenditures has gradually increased. CET policy has a more significant inhibitory effect on investment expenditures of nonstate-owned companies and small-scale companies. The results have passed the robustness test and provide evidence for the policy-maker to balance microeconomic entity development and carbon reduction, and for companies to make optimization investment and financing decisions in response to policy shocks effectively.

Prognostic prediction and comparison of three staging programs for patients with advanced (T2-T4) esophageal squamous carcinoma after radical resection.

Purpose: Lymph node-based staging protocols are frequently employed to evaluate the prognosis of esophageal cancer, yet their accuracy remains contentious. The present study was conducted to assess the prognostic significance of three lymph node staging systems, namely N stage, lymph node rate (LNR), and log odds of positive lymph nodes (LODDS), in patients diagnosed with advanced (T2-T4) esophageal squamous cell carcinoma (ESCC). Methods: This cohort comprised 319 eligible patients, with an additional 409 individuals retrieved from the Surveillance, Epidemiology, and End Results (SEER) database, forming the validation cohort. Differences in overall survival (OS) of patients between groups were assessed using the log-rank test. Prognostic independent risk variables were identified, and lymph nodes (LN) prognostic models were built using multivariate Cox regression analysis. Besides, the predictive accuracy of each model was evaluated utilizing the (-2) log-likelihood ratio (-2LLR), the likelihood ratio χ2 score (LRχ2), the Akaike information criterion (AIC), and Harrell's concordance index (C-index). To further evaluate the potential superiority of the model, a nomogram was constructed for comparison with the conventional Tumor Node Metastasis (TNM) staging approach. Results: Independent prognostic factors for advanced ESCC include the N stage, LNR, and LODDS. Herein, LODDS presented higher values for C-index and LRχ2, and lower values for AIC and -2LLR in OS compared to the others. Consequently, a nomogram was constructed based on LODDS. Calibration curves exhibited strong agreement, and assessment through C-index, receiver operating characteristic (ROC) curves, and clinical decision curve analysis (DCA) demonstrated promising clinical applicability. Conclusion: LODDS emerges as a promising future prognostic indicator. After surgery, the proposed model holds the potential to provide valuable treatment recommendations for patients with advanced ESCC.

Binding sites and design strategies for small molecule GLP-1R agonists.

Glucagon-like peptide-1 receptor (GLP-1R) is a pivotal receptor involved in blood glucose regulation and influencing feeding behavior. It has received significant attention in the treatment of obesity and diabetes due to its potent incretin effect. Peptide GLP-1 receptor agonists (GLP-1RAs) have achieved tremendous success in the market, driving the vigorous development of small molecule GLP-1RAs. Currently, several small molecules have entered the clinical research stage. Additionally, recent discoveries of GLP-1R positive allosteric modulators (PAMs) are also unveiling new regulatory patterns and treatment methods. This article reviews the structure and functional mechanisms of GLP-1R, recent reports on small molecule GLP-1RAs and PAMs, as well as the optimization process. Furthermore, it combines computer simulations to analyze structure-activity relationships (SAR) studies, providing a foundation for exploring new strategies for designing small molecule GLP-1RAs.

Glycomol: A pervasive tool for structure predication of natural saponin products basing on MS data.

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Research Progress on Mechanical Strength of Rice Stalks.

As one of the most important food crops in the world, rice yield is directly related to national food security. Lodging is one of the most important factors restricting rice production, and the cultivation of rice varieties with lodging resistance is of great significance in rice breeding. The lodging resistance of rice is directly related to the mechanical strength of the stalks. In this paper, we reviewed the cell wall structure, its components, and its genetic regulatory mechanism, which improved the regulatory network of rice stalk mechanical strength. Meanwhile, we analyzed the new progress in genetic breeding and put forward some scientific problems that need to be solved in this field in order to provide theoretical support for the improvement and application of rice breeding.

Baseline hepatobiliary MRI for predicting chemotherapeutic response and prognosis in initially unresectable colorectal cancer liver metastases.

PURPOSE: To evaluate the performance of hepatobiliary MRI parameters as predictors of clinical response to chemotherapy in patients with initially unresectable colorectal cancer liver metastases (CRLM). METHODS: Eighty-five patients with initially unresectable CRLM were retrospectively enrolled from two hospitals and scanned using gadobenate dimeglumine-enhanced MRI before treatment. Therapy response was evaluated based on the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Conventional parameters (i.e., signal intensity [SI]) and radiomics features of portal venous phase (PVP) and hepatobiliary phase (HBP) images were analyzed between the responders and non-responders. Next, the combined model was constructed, and the area under the receiver operating characteristic (ROC) curve (AUC) was calculated. The relationship between the combined model and progression-free survival (PFS) was analyzed using Cox regression. RESULTS: Of the 85 patients from two hospitals, 42 were in the response group, and 43 were in the non-response group. Upon conducting five-fold cross-validation, the normalized relative enhancement (NRE) of CRLM during the PVP yielded an AUC of 0.625. Additionally, a radiomics feature derived from the tumor area in the HBP achieved an AUC of 0.698, while a separate feature extracted from the peritumoral region in the HBP recorded an AUC of 0.709. The model that integrated these three features outperformed the individual features, achieving an AUC of 0.818. Furthermore, the combined model exhibited a significant correlation with PFS (P < 0.001). CONCLUSION: The combined model, based on baseline hepatobiliary MRI, aids in predicting chemotherapeutic response and PFS in patients with initially unresectable CRLM.

ScribSD+: Scribble-supervised medical image segmentation based on simultaneous multi-scale knowledge distillation and class-wise contrastive regularization.

Despite that deep learning has achieved state-of-the-art performance for automatic medical image segmentation, it often requires a large amount of pixel-level manual annotations for training. Obtaining these high-quality annotations is time-consuming and requires specialized knowledge, which hinders the widespread application that relies on such annotations to train a model with good segmentation performance. Using scribble annotations can substantially reduce the annotation cost, but often leads to poor segmentation performance due to insufficient supervision. In this work, we propose a novel framework named as ScribSD+ that is based on multi-scale knowledge distillation and class-wise contrastive regularization for learning from scribble annotations. For a student network supervised by scribbles and the teacher based on Exponential Moving Average (EMA), we first introduce multi-scale prediction-level Knowledge Distillation (KD) that leverages soft predictions of the teacher network to supervise the student at multiple scales, and then propose class-wise contrastive regularization which encourages feature similarity within the same class and dissimilarity across different classes, thereby effectively improving the segmentation performance of the student network. Experimental results on the ACDC dataset for heart structure segmentation and a fetal MRI dataset for placenta and fetal brain segmentation demonstrate that our method significantly improves the student's performance and outperforms five state-of-the-art scribble-supervised learning methods. Consequently, the method has a potential for reducing the annotation cost in developing deep learning models for clinical diagnosis.

Study on Pharmacological Activities and Mechanisms of the Essential Oil of the Flowers of Hemerocallis citrina Baroni (EOFHCB) in the Treatment of Anxiety Disorders by GC-MS, Network Pharmacology, and Molecular Docking.

BACKGROUND: Hemerocallis citrina Baroni (Huanghuacai), a plant of the genus Hemerocallis in the family Asphodelaceae, is widely planted in China. Based on our survey results, the chemical compounds in the essential oil of the flowers of Hemerocallis citrina Baroni (EOFHCB) and relevant pharmacological activities have never been studied systematically. OBJECTIVE: To preliminarily decipher the pharmacological activities and mechanisms of EOFHCB in the treatment of anxiety disorders by GC-MS, Network Pharmacology, and Molecular docking. METHODS: EOFHCB compositions were identified using GC-MS, and their targets were predicted using Swiss Target Prediction databases. The targets of anxiety disorders were obtained by GeneCards, DisGeNET, and OMIM databases. The STRING database was used to construct the protein-protein interaction networks, and the DAVID database was used to carry out GO enrichment and KEGG pathway enrichment analysis. The EOFHCB-components-targetspathways- anxiety disorders network was constructed by Cytoscape software (Version 3.10.0). Finally, the result was verified by molecular docking. RESULTS: 28 chemical components were identified by GC-MS, including 3-furanmethanol (28.43%), 2-methyl-1-butanol (27.13%), nerolidol (10.62%), and so on, which correspond to 241 potential targets. Several 2440 biological processes, 187 cellular compositions, and 311 molecular functions were enriched by GO enrichment analysis and 174 pathways by KEGG enrichment analysis. The key targets are PTGS 2, SRC, DRD 2, ESR 1, MAOB, and SLC6A4. The most important pathway is the neuroactive ligand-receptor interaction. CONCLUSION: EOFHCB exerts its therapeutic effects on anxiety disorders through multicomponents, multi-targets, and multi-pathways, which provided new ideas and methods for the in-depth research of aromatic Chinese medicine in the treatment of anxiety disorders.

[Bibliometrics Analysis of Studies on Hypertrophic Cardiomyopathy From 2018 to 2022].

Objective To analyze the research progress and hot topics in hypertrophic cardiomyopathy from 2018 to 2022.Methods The publications in the field of hypertrophic cardiomyopathy from January 1,2018 to December 31,2022 were retrieved from Web of Science core collection database and included for a bibliometric analysis.Results A total of 6355 publications were included,with an average citation frequency of 7 times.The year 2021 witnessed the most publications (1406).The analysis with VOSviewer showed that the research on sudden death related to hypertrophic cardiomyopathy,especially the predictive value of late gadolinium-enhanced cardiac MRI in sudden death,was a hot topic.In addition,gene detection and the new drug mavacamten became hot research topics.The United States was the country with the largest number of publications and the highest citation frequency in this field.Chinese scholars produced the second largest number of publications,which,however,included few high-quality research results.Conclusions Risk stratification and prevention of sudden death is still an important and hot research content in the field of hypertrophic cardiomyopathy.Chinese scholars should carry out multi-center cooperation in the future to improve the research results.

Two Cases of False Elevation of MCHC.

BACKGROUND: Mean corpuscular hemoglobin concentration (MCHC) is one of the parameters detected by blood cell analyzers, often used together with mean corpuscular volume (MCV) and mean corpuscular hemoglobin content (MCH) as diagnostic indicators for anemia classification. It has important clinical value in early detection of the cause of anemia and the underlying etiology of anemia. Therefore, the accuracy of MCHC results is of great significance for the diagnosis and treatment of diseases. METHODS: We reported two cases of false elevation of MCHC. Considering the possibility of cold agglutination and lipid blood interference detection, we used 37℃ water bath and plasma exchange to correct for interference on the sample. RESULTS: After correcting the interference, MCHC returned to normal, consistent with the patient's disease status. Therefore, the two cases of abnormal elevation of MCHC are considered to be pseudo elevation caused by interference. CONCLUSIONS: For specimens with abnormally elevated MCHC levels, experimenters should first analyze possible interfering factors and choose effective methods to correct different interferences, providing accurate testing reports for doctors and patients.

The Multifunction of TRIM26: From Immune Regulation to Oncology.

Ubiquitination, a crucial post-translational modification, plays a role in nearly all physiological processes. Its functional execution depends on a series of catalytic reactions involving numerous proteases. TRIM26, a protein belonging to the TRIM family, exhibits E3 ubiquitin ligase activity because of its RING structural domain, and is present in diverse cell lineages. Over the last few decades, TRIM26 has been documented to engage in numerous physiological and pathological processes as a controller, demonstrating a diverse array of biological roles. Despite the growing research interest in TRIM26, there has been limited attention given to examining the protein's structure and function in existing reviews. This review begins with a concise overview of the composition and positioning of TRIM26 and then proceeds to examine its roles in immune response, viral invasion, and inflammatory processes. Simultaneously, we demonstrate the contribution of TRIM26 to the progression of various diseases, encompassing numerous malignancies and neurologic conditions. Finally, we have investigated the potential areas for future research on TRIM26.

Multigenerational Adaptation Can Enhance the Pathogen Resistance of Plants via Changes in Rhizosphere Microbial Community Assembly.

Plants withstand pathogen attacks by recruiting beneficial bacteria to the rhizosphere and passing their legacy on to the next generation. However, the underlying mechanisms involved in this process remain unclear. In our study, we combined microbiomic and transcriptomic analyses to reveal how the rhizosphere microbiome assembled through multiple generations and defense-related genes expressed in Arabidopsis thaliana under pathogen attack stress. Our results showed that continuous exposure to the pathogen Pseudomonas syringae pv tomato DC3000 led to improved growth and increased disease resistance in a third generation of rps2 mutant Arabidopsis thaliana. It could be attributed to the enrichment of specific rhizosphere bacteria, such as Bacillus and Bacteroides. Pathways associated with plant immunity and growth in A. thaliana, such as MAPK signaling pathways, phytohormone signal transduction, ABC transporter proteins, and flavonoid biosynthesis, were activated under the influence of rhizosphere bacterial communities. Our findings provide a scientific basis for explaining the relationship between beneficial microbes and defense-related gene expression. Understanding microbial communities and the mechanisms involved in plant responses to disease can contribute to better plant management and reduction of pesticide use.

A 3D ray traced biological neural network learning model.

Training large neural networks on big datasets requires significant computational resources and time. Transfer learning reduces training time by pre-training a base model on one dataset and transferring the knowledge to a new model for another dataset. However, current choices of transfer learning algorithms are limited because the transferred models always have to adhere to the dimensions of the base model and can not easily modify the neural architecture to solve other datasets. On the other hand, biological neural networks (BNNs) are adept at rearranging themselves to tackle completely different problems using transfer learning. Taking advantage of BNNs, we design a dynamic neural network that is transferable to any other network architecture and can accommodate many datasets. Our approach uses raytracing to connect neurons in a three-dimensional space, allowing the network to grow into any shape or size. In the Alcala dataset, our transfer learning algorithm trains the fastest across changing environments and input sizes. In addition, we show that our algorithm also outperformance the state of the art in EEG dataset. In the future, this network may be considered for implementation on real biological neural networks to decrease power consumption.

Decoding the Role of Secondary Motor Cortex Neuronal Ensembles during Cocaine Self-Administration: Insights from Longitudinal in vivo Calcium Imaging via Miniscopes.

Recent findings in our lab demonstrated that the risk of cocaine relapse is closely linked to the hyperexcitability of cortical pyramidal neurons in the secondary motor cortex (M2), noticeable 45 days after cocaine intravenous self-administration (IVSA). The present study was designed to explore the underlying mechanisms of neuronal alterations in M2. Our hypothesis was that M2 neurons were affected directly by cocaine taking behaviors. This hypothesis was tested by monitoring individual neuronal activity in M2 using MiniScopes for in vivo Ca 2+ imaging in C57BL/6J mice when they had access to cocaine IVSA as a reinforcement (RNF) contingent to active lever press (ALP) but not to inactive lever press (ILP). With support of our established pipeline to processing Ca 2+ imaging data, the current study was designed to monitor M2 neuronal ensembles at the single-neuron level in real time with high temporal resolution and high throughput in each IVSA session and longitudinally among multiple IVSA sessions. Specifically, five consecutive 1-hr daily IVSA sessions were used to model the initial cocaine taking behaviors. Besides detailed analyses of IVSA events (ALP, ILP, and RNF), the data from Ca 2+ imaging recordings in M2 were analyzed by (1) comparing neuronal activation within a daily IVSA session (i.e., the first vs. the last 15 min) and between different daily sessions (i.e., the first vs. the last IVSA day), (2) associating Ca 2+ transients with individual IVSA events, and (3) correlating Ca 2+ transients with the cumulative effects of IVSA events. Our data demonstrated that M2 neurons are exquisitely sensitive to and significantly affected by concurrent operant behaviors and the history of drug exposure, which in turn sculpt the upcoming operant behaviors and the response to drugs. As critical nodes of the reward loop, M2 neurons appear to be the governing center orchestrating the establishment of addiction-like behaviors.

Synthesis and hygrothermal aging of polycarbonates containing a bisphenol fluorene moiety.

This study aims to synthesize a specific type of polycarbonate with high refractive index, low birefringence, and resistance to hygrothermal aging by copolymerizing 2,2'-bis(2-hydroxyethoxy)-1,1'-binaphthyl (BNE) with 9,9-bis[4-(2-hydroxyethoxy)phenyl]fluorene (BPEF). Comparative analysis revealed that the copolymer synthesized from BNE and BPEF demonstrated superior hydrolytic stability relative to the bisphenol A-based polycarbonate. This augmented stability can be attributed to the monomers' higher pKa values, rendering acidic substances less capable of dissociating and thereby mitigating ester hydrolysis under hygrothermal conditions. Furthermore, the investigation probed into the phenomenon of physical aging in copolymerized polycarbonate when exposed to hygrothermal environments. It was discerned that the enthalpy loss, observable under both dry and hygrothermal conditions, could be linearly correlated with the difference between the aging temperature and the glass transition temperature (Tg), signifying a close correlation between the magnitude of physical aging and Tg. A lower Tg in the copolymerized polycarbonate led to more pronounced physical aging within the same timeframe, resulting in an augmentation of tensile strength and modulus, while higher Tg effectively mitigated the physical aging phenomenon.

The Effects of Different Durations of Night-Time Supplementary Lighting on the Growth, Yield, Quality and Economic Returns of Tomato.

To achieve higher economic returns, we employ inexpensive valley electricity for night-time supplementary lighting (NSL) of tomato plants, investigating the effects of various durations of NSL on the growth, yield, and quality of tomato. Tomato plants were treated with supplementary light for a period of 0 h, 3 h, 4 h, and 5 h during the autumn-winter season. The findings revealed superior growth and yield of tomato plants exposed to 3 h, 4 h, and 5 h of NSL compared to their untreated counterparts. Notably, providing lighting for 3 h demonstrated greater yields per plant and per trough than 5 h exposure. To investigate if a reduced duration of NSL would display similar effects on the growth and yield of tomato plants, tomato plants received supplementary light for 0 h, 1 h, 2 h, and 3 h at night during the early spring season. Compared to the control group, the stem diameter, chlorophyll content, photosynthesis rate, and yield of tomatoes significantly increased upon supplementation with lighting. Furthermore, the input-output ratios of 1 h, 2 h, and 3 h NSL were calculated as 1:10.11, 1:4.38, and 1:3.92, respectively. Nonetheless, there was no detectable difference in yield between the 1 h, 2 h, and 3 h NSL groups. These findings imply that supplemental LED lighting at night affects tomato growth in the form of light signals. Night-time supplemental lighting duration of 1 h is beneficial to plant growth and yield, and its input-output ratio is the lowest, which is an appropriate NSL mode for tomato cultivation.

Unveiling the Nature and Strength of Selenium-Centered Chalcogen Bonds in Binary Complexes of SeO2 with Oxygen-/Sulfur-Containing Lewis Bases: Insights from Theoretical Calculations.

Among various non-covalent interactions, selenium-centered chalcogen bonds (SeChBs) have garnered considerable attention in recent years as a result of their important contributions to crystal engineering, organocatalysis, molecular recognition, materials science, and biological systems. Herein, we systematically investigated π-hole-type Se∙∙∙O/S ChBs in the binary complexes of SeO2 with a series of O-/S-containing Lewis bases by means of high-level ab initio computations. The results demonstrate that there exists an attractive interaction between the Se atom of SeO2 and the O/S atom of Lewis bases. The interaction energies computed at the MP2/aug-cc-pVTZ level range from -4.68 kcal/mol to -10.83 kcal/mol for the Se∙∙∙O chalcogen-bonded complexes and vary between -3.53 kcal/mol and -13.77 kcal/mol for the Se∙∙∙S chalcogen-bonded complexes. The Se∙∙∙O/S ChBs exhibit a relatively short binding distance in comparison to the sum of the van der Waals radii of two chalcogen atoms. The Se∙∙∙O/S ChBs in all of the studied complexes show significant strength and a closed-shell nature, with a partially covalent character in most cases. Furthermore, the strength of these Se∙∙∙O/S ChBs generally surpasses that of the C/O-H∙∙∙O hydrogen bonds within the same complex. It should be noted that additional C/O-H∙∙∙O interactions have a large effect on the geometric structures and strength of Se∙∙∙O/S ChBs. Two subunits are connected together mainly via the orbital interaction between the lone pair of O/S atoms in the Lewis bases and the BD*(OSe) anti-bonding orbital of SeO2, except for the SeO2∙∙∙HCSOH complex. The electrostatic component emerges as the largest attractive contributor for stabilizing the examined complexes, with significant contributions from induction and dispersion components as well.

Enhanced reduction of Cd uptake by wheat plants using iron and manganese oxides combined with citrate in Cd-contaminated weakly alkaline arable soils.

Iron (Fe) and manganese (Mn) oxides can be used to remediate Cd-polluted soils due to their excellent performance in heavy metal adsorption. However, their remediation capability is rather limited, and a higher content of available Mn and Fe in soils can reduce Cd accumulation in wheat plants due to the competitive absorption effect. In this study, goethite and cryptomelane were first respectively used to immobilize Cd in Cd-polluted weakly alkaline soils, and sodium citrate was then added to increase the content of available Mn and Fe content for further reduction of wheat Cd absorption. In the first season, the content of soil-available Cd and Cd in wheat plants significantly decreased when cryptomelane, goethite and their mixture were used as the remediation agents. Cryptomelane showed a better remediation effect, which could be attributed to its higher adsorption performance. The grain Cd content could be decreased from 0.35 mg kg-1 to 0.25 mg kg-1 when the content of cryptomelane was controlled at 0.5%. In the second season, when sodium citrate at 20 mmol kg-1 was further added to the soils with 0.5% cryptomelane treatment in the first season, the content of soil available Cd was increased by 14.8%, and the available Mn content was increased by 19.5%, leading to a lower Cd content in wheat grains (0.16 mg kg-1) probably due to the competitive absorption. This work provides a new strategy for the remediation of slightly Cd-polluted arable soils with safe and high-quality production of wheat.

Advanced Design, Synthesis, and Evaluation of Highly Selective Wee1 Inhibitors: Enhancing Pharmacokinetics and Antitumor Efficacy.

Wee1 is a kinase that regulates cell cycle arrest in response to DNA damage. Wee1 inhibition is a potential strategy to suppress the growth of tumors with defective p53 or DNA repair pathways. However, the development of Wee1 inhibitors faces some challenges. AZD1775, the first-in-class Wee1 inhibitor, has poor kinase selectivity and dose-limiting toxicity. Here, we report the discovery of 12h, a highly selective and potent Wee1 inhibitor with a favorable pharmacokinetic profile. 12h showed strong antiproliferative effects against Lovo cells, a colorectal cancer cell line, both in vitro and in vivo. Moreover, 12h showed a clean kinase profile and effectively induced cell apoptosis. Our results suggest that 12h is a promising drug candidate for further development as a novel anticancer agent.