Function and Mechanism of Antiviral Wasp Venom Peptide Protopolybia-MP III and Its Derivatives against HSV-1.

Viruses are one of the leading causes of human disease, and many highly pathogenic viruses still have no specific treatment drugs. Therefore, producing new antiviral drugs is an urgent matter. In our study, we first found that the natural wasp venom peptide Protopolybia-MP III had a significant inhibitory effect on herpes simplex virus type 1 (HSV-1) replication in vitro by using quantitative real-time PCR (qPCR), Western blotting, and plaque-forming assays. Immunofluorescence analysis showed Protopolybia-MP III could enter cells, and it inhibited multiple stages of the HSV-1 life cycle, including the attachment, entry/fusion, and post-entry stages. Furthermore, ultracentrifugation and electron microscopy detected that Protopolybia-MP III significantly suppressed HSV-1 virion infectivity at different temperatures by destroying the integrity of the HSV-1 virion. Finally, by comparing the antiviral activity of Protopolybia-MP III and its mutants, a series of peptides with better anti-HSV-1 activity were identified. Overall, this work found the function and mechanism of the antiviral wasp venom peptide Protopolybia-MP III and its derivatives against HSV-1 and laid the foundation for the research and development of wasp venom-derived antiviral candidate peptide drugs.

Cationicity Enhancement on the Hydrophilic Face of Ctriporin Significantly Reduces Its Hemolytic Activity and Improves the Antimicrobial Activity against Antibiotic-Resistant ESKAPE Pathogens.

The ESKAPE pathogen-associated antimicrobial resistance is a global public health issue, and novel therapeutic strategies are urgently needed. The short cationic antimicrobial peptide (AMP) family represents an important subfamily of scorpion-derived AMPs, but high hemolysis and poor antimicrobial activity hinder their therapeutic application. Here, we recomposed the hydrophilic face of Ctriporin through lysine substitution. We observed non-linear correlations between the physiochemical properties of the peptides and their activities, and significant deviations regarding the changes of antimicrobial activities against different bacterial species, as well as hemolytic activity. Most importantly, we obtained two Ctriporin analogs, CM5 and CM6, these two have significantly reduced hemolytic activity and more potent antimicrobial activities against all tested antibiotic-resistant ESKAPE pathogens. Fluorescence experiments indicated they may perform the bactericidal function through a membrane-lytic action model. Our work sheds light on the potential of CM5 and CM6 in developing novel antimicrobials and gives clues for optimizing peptides from the short cationic AMP family.

Improving Classification Performance in Dendritic Neuron Models through Practical Initialization Strategies.

A dendritic neuron model (DNM) is a deep neural network model with a unique dendritic tree structure and activation function. Effective initialization of its model parameters is crucial for its learning performance. This work proposes a novel initialization method specifically designed to improve the performance of DNM in classifying high-dimensional data, notable for its simplicity, speed, and straightforward implementation. Extensive experiments on benchmark datasets show that the proposed method outperforms traditional and recent initialization methods, particularly in datasets consisting of high-dimensional data. In addition, valuable insights into the behavior of DNM during training and the impact of initialization on its learning performance are provided. This research contributes to the understanding of the initialization problem in deep learning and provides insights into the development of more effective initialization methods for other types of neural network models. The proposed initialization method can serve as a reference for future research on initialization techniques in deep learning.

Yellow leaf green tea modulates the AMPK/ACC/SREBP1c signaling pathway and gut microbiota in high-fat diet-induced mice to alleviate obesity.

BACKGROUND: Yellow leaf green tea (YLGT) is a new variety of Camellia sinensis (L.) O. Ktze, which has yellow leaves and the unique qualities of 'three green through three yellow'. The present study aimed to investigate the anti-obesity effect of YLGT in mice fed a high-fat diet (HFD) and to explore the potential mechanisms by regulating the AMPK/ACC/SREBP1c signaling pathways and gut microbiota. RESULTS: The results showed that YLGT aqueous extract reduced body weight, hepatic inflammation, fat accumulation and hyperlipidemia in HFD-induced C57BL/6J mice, and also accelerated energy metabolism, reduced fat synthesis and suppressed obesity by activating the AMPK/CPT-1α signaling pathway and inhibiting the FAS/ACC/SREBP-1c signaling pathway. Fecal microbiota transplantation experiment further confirmed that the alteration of gut microbiota (e.g. increasing unclassified_Muribaculaceae and decreasing Colidextribacter) might be an important cause of YLGT water extract inhibiting obesity. CONCLUSION: In conclusion, YLGT has a broad application prospect in the treatment of obesity and the development of anti-obesity function beverages. © 2024 Society of Chemical Industry.

Application of deep learning in the diagnosis and evaluation of ulcerative colitis disease severity.

Background: Achieving endoscopic and histological remission is a critical treatment objective in ulcerative colitis (UC). Nevertheless, interobserver variability can significantly impact overall assessment performance. Objectives: We aimed to develop a deep learning algorithm for the real-time and objective evaluation of endoscopic disease activity and prediction of histological remission in UC. Design: This is a retrospective diagnostic study. Methods: Two convolutional neural network (CNN) models were constructed and trained using 12,257 endoscopic images and biopsy results sourced from 1124 UC patients who underwent colonoscopy at a single center from January 2018 to December 2022. Mayo Endoscopy Subscore (MES) and UC Endoscopic Index of Severity Score (UCEIS) assessments were conducted by two experienced and independent reviewers. Model performance was evaluated in terms of accuracy, sensitivity, and positive predictive value. The output of the CNN models was also compared with the corresponding histological results to assess histological remission prediction performance. Results: The MES-CNN model achieved 97.04% accuracy in diagnosing endoscopic remission of UC, while the MES-CNN and UCEIS-CNN models achieved 90.15% and 85.29% accuracy, respectively, in evaluating endoscopic severity of UC. For predicting histological remission, the CNN models achieved accuracy and kappa values of 91.28% and 0.826, respectively, attaining higher accuracy than human endoscopists (87.69%). Conclusion: The proposed artificial intelligence model, based on MES and UCEIS evaluations from expert gastroenterologists, offered precise assessment of inflammation in UC endoscopic images and reliably predicted histological remission.

Application of deep learning in the diagnosis and evaluation of ulcerative colitis disease severity Why was this study done? This study aimed to develop a real-time and objective diagnostic tool to reduce subjectivity when evaluating ulcerative colitis (UC) endoscopic disease activity and to predict histological remission without mucosal biopsy. What did the researchers do? We developed and validated a deep learning algorithm that uses UC endoscopic images to predict the Mayo Endoscopic Score (MES), US Endoscopic Index of Severity Score (UCEIS), and histological remission. What did the researchers find? The constructed MES- and UCEIS-based models both achieved high accuracy and performance in predicting histological remission, outperforming human endoscopists. What do the findings mean? The efficiency and performance of the deep learning algorithm rivaled that of expert assessments, which may assist endoscopists in making more objective evaluations of UC severity and in predicting histological remission.

Characterization of the Hemolytic Activity of Mastoparan Family Peptides from Wasp Venoms.

Biologically active peptides have attracted increasing attention in research on the development of new drugs. Mastoparans, a group of wasp venom linear cationic α-helical peptides, have a variety of biological effects, including mast cell degranulation, activation of protein G, and antimicrobial and anticancer activities. However, the potential hemolytic activity of cationic α-helical peptides greatly limits the clinical applications of mastoparans. Here, we systematically and comprehensively studied the hemolytic activity of mastoparans based on our wasp venom mastoparan family peptide library. The results showed that among 55 mastoparans, 18 had strong hemolytic activity (EC50 ≤ 100 µM), 14 had modest hemolytic activity (100 µM < EC50 ≤ 400 µM) and 23 had little hemolytic activity (EC50 > 400 µM), suggesting functional variation in the molecular diversity of mastoparan family peptides from wasp venom. Based on these data, structure-function relationships were further explored, and, hydrophobicity, but not net charge and amphiphilicity, was found to play a critical role in the hemolytic activity of mastoparans. Combining the reported antimicrobial activity with the present hemolytic activity data, we found that four mastoparan peptides, Parapolybia-MP, Mastoparan-like peptide 12b, Dominulin A and Dominulin B, have promise for applications because of their high antimicrobial activity (MIC ≤ 10 µM) and low hemolytic activity (EC50 ≥ 400 µM). Our research not only identified new leads for the antimicrobial application of mastoparans but also provided a large chemical space to support the molecular design and optimization of mastoparan family peptides with low hemolytic activity regardless of net charge or amphiphilicity.

Schizophrenia plausible protective effect of microRNA-137 is potentially related to estrogen and prolactin in female patients.

Background: Schizophrenia (SCZ) is a serious chronic mental disorder. Our previous case-control genetic association study has shown that microRNA-137 (miR-137) may only protect females against SCZ. Since estrogen, an important female sex hormone, exerts neuroprotective effects, the relationship between estrogen and miR-137 in the pathophysiology of SCZ was further studied in this study. Methods: Genotyping of single-nucleotide polymorphism rs1625579 of miR-137 gene in 1,004 SCZ patients and 896 healthy controls was conducted using the iMLDR assay. The effect of estradiol (E2) on the miR-137 expression was evaluated on the human mammary adenocarcinoma cell line (MCF-7) and the mouse hippocampal neuron cell line (HT22). The relationships between serum E2, prolactin (PRL), and peripheral blood miR-137 were investigated in 41 SCZ patients and 43 healthy controls. The miR-137 and other reference miRNAs were detected by real-time fluorescent quantitative reverse transcription-PCR. Results: Based on the well-known SNP rs1625579, the distributions of protective genotypes and alleles of the miR-137 gene were not different between patients and healthy controls but were marginally significantly lower in female patients. E2 upregulated the expression of miR-137 to 2.83 and 1.81 times in MCF-7 and HT22 cells, respectively. Both serum E2 and blood miR-137 were significantly decreased or downregulated in SCZ patients, but they lacked expected positive correlations with each other in both patients and controls. When stratified by sex, blood miR-137 was negatively correlated with serum E2 in female patients. On the other hand, serum PRL was significantly increased in SCZ patients, and the female patients had the highest serum PRL level and a negative correlation between serum PRL and blood miR-137. Conclusion: The plausible SCZ-protective effect of miR-137 may be female specific, of which the underlying mechanism may be that E2 upregulates the expression of miR-137. This protective mechanism may also be abrogated by elevated PRL in female patients. These preliminary findings suggest a new genetic/environmental interaction mechanism for E2/miR-137 to protect normal females against SCZ and a novel E2/PRL/miR-137-related pathophysiology of female SCZ, implying some new antipsychotic ways for female patients in future.

Genetic contribution of synapse-associated protein 97 to cerebellar functional connectivity changes in first-episode schizophrenia.

Our previous study data suggested that the synapse-associated protein 97 (SAP97) rs3915512 polymorphism is significantly related to clinical performance in schizophrenia. The cerebellum exhibits abundant expression of SAP97, which is involved with negative symptoms, cognition and emotion in schizophrenia. As functional dysconnectivity with the cortical-subcortical-cerebellar circuitry has been widely shown in patients with schizophrenia, cortical-subcortical-cerebellar dysconnectivity can therefore be considered a possible intermediate phenotype that connects risk genes with schizophrenia. In this study, resting-state functional magnetic resonance imaging (fMRI) was applied to evaluate whether the SAP97 rs3915512 polymorphism changes cortical/subcortical-cerebellar resting-state functional connectivity (RSFC) in 104 Han Chinese subjects (52 first-episode schizophrenia (FES) patients and 52 matched healthy controls (HCs)). To examine RSFC between cortical/subcortical regions and the cerebellum, a ROI (region of interest)-wise functional connectivity analysis was conducted. The association between abnormal cortical/subcortical-cerebellar connectivity and clinical manifestation was further assessed in FES patients with different genotypes. The interactive effect of disease and genotype on RSFC was found between the frontal gyrus (rectus) and cerebellum. A positive correlation was suggested between RSFC in the cerebellum and the hostility scores in FES patients with the A allele, and no correlation was found in FES patients with the TT genotype. The current findings identified that SAP97 may be involved in the process of mental symptoms in FES patients via cerebellar connectivity depending on the rs3915512 polymorphism genotype.

Interpretability Diversity for Decision-Tree-Initialized Dendritic Neuron Model Ensemble.

To construct a strong classifier ensemble, base classifiers should be accurate and diverse. However, there is no uniform standard for the definition and measurement of diversity. This work proposes a learners' interpretability diversity (LID) to measure the diversity of interpretable machine learners. It then proposes a LID-based classifier ensemble. Such an ensemble concept is novel because: 1) interpretability is used as an important basis for diversity measurement and 2) before its training, the difference between two interpretable base learners can be measured. To verify the proposed method's effectiveness, we choose a decision-tree-initialized dendritic neuron model (DDNM) as a base learner for ensemble design. We apply it to seven benchmark datasets. The results show that the DDNM ensemble combined with LID obtains superior performance in terms of accuracy and computational efficiency compared to some popular classifier ensembles. A random-forest-initialized dendritic neuron model (RDNM) combined with LID is an outstanding representative of the DDNM ensemble.

Semantic matching based legal information retrieval system for COVID-19 pandemic.

Recently, the pandemic caused by COVID-19 is severe in the entire world. The prevention and control of crimes associated with COVID-19 are critical for controlling the pandemic. Therefore, to provide efficient and convenient intelligent legal knowledge services during the pandemic, we develop an intelligent system for legal information retrieval on the WeChat platform in this paper. The data source we used for training our system is "The typical cases of national procuratorial authorities handling crimes against the prevention and control of the new coronary pneumonia pandemic following the law", which is published online by the Supreme People's Procuratorate of the People's Republic of China. We base our system on convolutional neural network and use the semantic matching mechanism to capture inter-sentence relationship information and make a prediction. Moreover, we introduce an auxiliary learning process to help the network better distinguish the relation between two sentences. Finally, the system uses the trained model to identify the information entered by a user and responds to the user with a reference case similar to the query case and gives the reference legal gist applicable to the query case.

Characterization of the Molecular Diversity and Degranulation Activity of Mastoparan Family Peptides from Wasp Venoms.

Wasp stings have become an increasingly serious public health problem because of their high incidence and mortality rates in various countries and regions. Mastoparan family peptides are the most abundant natural peptides in hornet venoms and solitary wasp venom. However, there is a lack of systematic and comprehensive studies on mastoparan family peptides from wasp venoms. In our study, for the first time, we evaluated the molecular diversity of 55 wasp mastoparan family peptides from wasp venoms and divided them into four major subfamilies. Then, we established a wasp peptide library containing all 55 known mastoparan family peptides by chemical synthesis and C-terminal amidation modification, and we systematically evaluated their degranulation activities in two mast cell lines, namely the RBL-2H3 and P815 cell lines. The results showed that among the 55 mastoparans, 35 mastoparans could significantly induce mast cell degranulation, 7 mastoparans had modest mast cell degranulation activity, and 13 mastoparans had little mast cell degranulation activity, suggesting functional variation in mastoparan family peptides from wasp venoms. Structure-function relationship studies found that the composition of amino acids in the hydrophobic face and amidation in the C-terminal region are critical for the degranulation activity of mastoparan family peptides from wasp venoms. Our research will lay a theoretical foundation for studying the mechanism underlying the degranulation activity of wasp mastoparans and provide new evidence to support the molecular design and molecular optimization of natural mastoparan peptides from wasp venoms in the future.

Electroacupuncture reverses endothelial cell death and promotes angiogenesis through the VEGF/Notch signaling pathway after focal cerebral ischemia-reperfusion injury.

BACKGROUND: Angiogenesis is an important mechanism of recovery from ischemic stroke. Recent studies have found that there is a close relationship between the VEGF/Notch pathway and angiogenesis. It is unknown whether EA can exert a brain protection effect and promote angiogenesis by acting on the VEGF/Notch signaling pathway after focal cerebral ischemia-reperfusion injury (CIRI). METHODS: The Middle Cerebral Artery occlusion/Reperfusion (MCAo/R) model was established, in which rats were subjected to occlusion with ischemic intervention for 30 min, followed by reperfusion for 8 h, 1 day, 3 days, and 7 days. The first EA treatment was performed 90 min after the animal model was successfully established, and then EA treatments were performed once a day for 7 days. The 2,3,5-triphenyltetrazolium chloride staining and neurological deficit examination were performed to assess the level of CIRI and neuroprotection by EA. Expression levels of VEGFA, Notch1, and Hes1 proteins were measured via western blotting, while the morphological changes of ECs and microvasculature in the cortex were determined using an ultrastructural observation method. RESULTS: EA treatment of PC6, GV26, and SP6 can significantly improve the neurological function of MCAO/R rats, reduce the volume of cerebral infarction, and modulate the ultrastructure of ECs and microvessels in pathological states. Western blotting revealed that EA increased VEGFA protein expression at 8 h and 3 days after CIRI, as well as Notch1 protein expression at 1 and 7 days. Subsequently, EA activated the VEGF/Notch pathway, increasing the expression of the downstream target protein Hes1, reversing EC death, and promoting angiogenesis. CONCLUSION: Our findings showed that EA plays a role in promoting angiogenesis following focal CIRI, and we hypothesized that this was due to the regulation of ECs by the EA-activated VEGF/Notch signaling pathway.

Genetic variations in DOCK4 contribute to schizophrenia susceptibility in a Chinese cohort: A genetic neuroimaging study.

BACKGROUND: Emerging evidence suggests that the DOCK4 gene increases susceptibility to schizophrenia. However, no study has hitherto repeated this association in Chinese, and further investigated the relationship between DOCK4 and clinical symptoms in schizophrenic patients using clinical scales and functional magnetic resonance imaging (fMRI). METHODS: In this study, we genotyped three single nucleotide polymorphisms (SNPs) (rs2074127, rs2217262, and rs2074130) within the DOCK4 gene using a case-control design (including 1289 healthy controls and 1351 patients with schizophrenia). 55 first-episode schizophrenia (FES) patients and 59 healthy participants were divided by the genotypes of rs2074130 into CC and CT+TT groups. We further investigated the association with clinical symptoms and neural characteristics (brain activation/connectivity and nodal network metrics). RESULTS: Our results showed significant associations between all selected SNPs and schizophrenia (all P < 0.05). In patients, letter fluency and motor speed scores of T allele carriers were significantly higher than the CC group (all P < 0.05). Interestingly, greater brain activity, functional connectivity, and betweenness centrality (BC) in language processing and motor coordination were also observed in the corresponding brain zones in patients with the T allele based on a two-way ANCOVA model. Moreover, a potential positive correlation was found between brain activity/connectivity of these brain regions and verbal fluency and motor speed. CONCLUSION: Our findings suggest that the DOCK4 gene may contribute to the onset of schizophrenia and lead to language processing and motor coordination dysfunction in this patient population from China.

Giant seminal vesicle cyst with hemorrhage in Zinner syndrome: A case report.

RATIONALE: Zinner syndrome (ZS) is a rare congenital malformation of the urogenital tract that is associated with seminal vesicle cysts, ejaculatory duct obstruction, and ipsilateral renal agenesis. This unique condition was first reported by Zinner (1914). ZS is caused by malformation of the distal mesonephric duct during embryogenesis. To our knowledge, no giant seminal vesicle cysts with hemorrhage in ZS have been reported in the current study. PATIENT CONCERNS: A 63-year-old man presented with chronic hypogastralgia with no history of lower urinary tract symptoms, hematuria, or trauma. Physical examination revealed no localized uplift or percussive pain in either kidney. No tenderness in the ureter stroke region, no localized eminence in the suprapubic region of the bladder, and no tenderness in the bladder region was observed. Digital rectal examination revealed a cystic mass with a smooth surface in the anterior wall of the rectum with no tenderness or unclear boundaries. No blood staining was observed in the finger sheaths. DIAGNOSES: Computed tomography scan revealed that the right kidney was absent, with a mass similar to a cord above the right seminal vesicle cyst. Contrast-enhanced pelvic magnetic resonance imaging (MRI) confirmed a short T1 and T2 signal shadow similar to a cord above the right seminal vesicle cyst. The boundary was clear, with the upper part leading to the "renal region" and the lower part connecting to the right seminal vesicle cyst. Contrast-enhanced MRI showed local parenchymal cysts with cyst wall enhancement but no intrathecal enhancement. This suggested a hemorrhagic cyst. A diagnosis of Zinner syndrome was established. INTERVENTIONS: The patient was diagnosed with a giant seminal vesicle cyst with hemorrhage in ZS. The patient had no obvious symptoms; therefore, regular follow-ups were performed. OUTCOMES: MRI of the patient 1 month later showed that the hematoma in the seminal vesicle cyst was not absorbed. LESSONS: Giant seminal vesicle cysts with hemorrhage in ZS are rare. To patients without symptom, regular follow-up can be adopted.

Multimodal fusion diagnosis of depression and anxiety based on CNN-LSTM model.

BACKGROUND: In recent years, more and more people suffer from depression and anxiety. These symptoms are hard to be spotted and can be very dangerous. Currently, the Self-Reported Anxiety Scale (SAS) and Self-Reported Depression Scale (SDS) are commonly used for initial screening for depression and anxiety disorders. However, the information contained in these two scales is limited, while the symptoms of subjects are various and complex, which results in the inconsistency between the questionnaire evaluation results and the clinician's diagnosis results. To fully mine the scale data, we propose a method to extract the features from the facial expression and movements, which are generated from the video recorded simultaneously when subjects fill in the scale. Then we collect the facial expression, movements and scale information to establish a multimodal framework for improving the accuracy and robustness of the diagnosis of depression and anxiety. METHODS: We collect the scale results of the subjects and the videos when filling in the scales. Given the two scales, SAS and SDS, we construct a model with two branches, where each branch processes the multimodal data of SAS and SDS, respectively. In the branch, we first build a convolutional neural network (CNN) to extracts the facial expression features in each frame of images. Secondly, we establish a long short-term memory (LSTM) network to further embedding the facial expression feature and build the connections between various frames, so that the movement feature in the video can be generated. Thirdly, we transform the scale scores into one-hot format, and feed them into the corresponding branch of the network to further mining the information of the multimodal data. Finally, we fuse the embeddings of these two branches to generate inference results of depression and anxiety. RESULTS AND CONCLUSIONS: Based on the score results of SAS and SDS, our multimodal model further mines the video information, and can reach the accuracy of 0.946 in diagnosing depression and anxiety. This study demonstrates the feasibility of using our CNN-LSTM-based multimodal model for initial screening and diagnosis of depression and anxiety disorders with high diagnostic performance.

Stochastic growth tree networks with an identical fractal dimension: Construction and mean hitting time for random walks.

There is little attention paid to stochastic tree networks in comparison with the corresponding deterministic analogs in the current study of fractal trees. In this paper, we propose a principled framework for producing a family of stochastic growth tree networks T possessing fractal characteristic, where t represents the time step and parameter m is the number of vertices newly created for each existing vertex at generation. To this end, we introduce two types of generative ways, i.e., Edge-Operation and Edge-Vertex-Operation. More interestingly, the resulting stochastic trees turn out to have an identical fractal dimension d = ln ⁡ 2 ( m + 1 ) / ln ⁡ 2 regardless of the introduction of randomness in the growth process. At the same time, we also study many other structural parameters including diameter and degree distribution. In both extreme cases, our tree networks are deterministic and follow multiple-point degree distribution and power-law degree distribution, respectively. Additionally, we consider random walks on stochastic growth tree networks T and derive an expectation estimation for mean hitting time ⟨ H ⟩ in an effective combinatorial manner instead of commonly used spectral methods. The result shows that on average, the scaling of mean hitting time ⟨ H ⟩ obeys ⟨ H ⟩ = | T |, where | T | represents vertex number and exponent λ is equivalent to 1 + ln ⁡ 2 / ln ⁡ 2 ( m + 1 ). In the meantime, we conduct extensive experimental simulations and observe that empirical analysis is in strong agreement with theoretical results.

Schixator, a new FXa inhibitor from Schistosoma japonicum with antithrombotic effect and low bleeding risk.

Schistosoma japonicum is a parasitic worm that lives in the mesenteric vein of its host and feeds on blood, suggesting that it might be a natural resource of novel anticoagulants. Here, by comprehensive analyses of the genomic sequences of Schistosoma japonicum, a new Kunitz-type gene precursor was identified. The Kunitz-type gene precursor codes for an 18-residue signal peptide and a 60-residue mature peptide. The Kunitz peptide was functionally expressed, and it had apparent inhibitory activity towards the intrinsic coagulation pathway but no effect on the extrinsic coagulation pathway even at the high concentration of 3 µM. Enzyme and inhibitor experiments further showed that the Kunitz domain peptide was a potent and selective FXa inhibitor, so it was named Schixator (Schistosoma FXa inhibitor). Schixator inhibits coagulation factor FXa with a Ki of 2.66 nM, but had weak inhibitory activity towards chymotrypsin, FXIa, plasma kallikrein, and plasmin, and no inhibitory activity towards trypsin, elastase, FIIa or FXIIa. In vivo, the intravenous administration of Schixator into mice dramatically decreased the number of thrombi in the carotid artery in an FeCl3-induced thrombus formation model without producing bleeding complications. To the best of our knowledge, Schixator is the first potent and selective FXa inhibitor from parasitic worms with antithrombotic effects and a low bleeding risk that provides a new clue for lead drug discovery against thrombosis-associated human diseases.

The residues 4 to 6 at the N-terminus in particular modulate fibril propagation of ß-microglobulin.

The ΔN6 truncation is the main posttranslational modification of ß-microglobulin (ßM) found in dialysis-related amyloid. Investigation of the interaction of wild-type (WT) ßM with N-terminally truncated variants is therefore of medical relevance. However, it is unclear which residues among the six residues at the N-terminus are crucial to the interactions and the modulation of amyloid fibril propagation of ßM. We herein analyzed homo- and heterotypic seeding of amyloid fibrils of WT human ßM and its N-terminally-truncated variants ΔN1 to ΔN6, lacking up to six residues at the N-terminus. At acidic pH 2.5, we produced amyloid fibrils from recombinant, WT ßM and its six truncated variants, and found that ΔN6 ßM fibrils exhibit a significantly lower conformational stability than WT ßM fibrils. Importantly, under more physiological conditions (pH 6.2), we assembled amyloid fibrils only from recombinant, ΔN4, ΔN5, and ΔN6 ßM but not from WT ßM and its three truncated variants ΔN1 to ΔN3. Notably, the removal of the six, five or four residues at the N-terminus leads to enhanced fibril formation, and homo- and heterotypic seeding of ΔN6 fibrils strongly promotes amyloid fibril formation of WT ßM and its six truncated variants, including at more physiological pH 6.2. Collectively, these results demonstrated that the residues 4 to 6 at the N-terminus particularly modulate amyloid fibril propagation of ßM and the interactions of WT ßM with N-terminally truncated variants, potentially indicating the direct relevance to the involvement of the protein's aggregation in dialysis-related amyloidosis.

Random growth scale-free networked models with an identical degree distribution and a tunable assortativity index.

In this work, we propose two kinds of graphic operations by using triangle configuration, based on which we establish a family of random growth networked models G(t;p) where notations t and p represent time step and probability parameter, respectively. By studying some fundamental structural parameters both analytically and numerically, we show that (1) all the realizations G(t;p) follow the same power-law degree distribution with exponent γ=2+ln⁡3/ln⁡2 regardless of probability p and thus have scale-free feature; (2) each model G(t;p) has a relatively high clustering coefficient; and (3) while network G(t;1) has a small average path length, it is not a unique model possessing small-world property mainly because its diameter D(t;1) does not reach the theoretical lower bound. Next, we make use of assortativity index R to quantify the tendency of forming connection between vertices and observe that (1) model G(t;0) exhibits disassortative mixing because the corresponding index R(t;0) is non-positive, and (2) model G(t;1) is in the opposite direction. As a result, we demonstrate that random model G(t;p) has a tunable quantity R(t;p) controlled by probability p. In addition, we exactly determine the total number of spanning trees of deterministic models G(t;1) and G(t;0) and also calculate the entropy of spanning trees.

Decision-Tree-Initialized Dendritic Neuron Model for Fast and Accurate Data Classification.

This work proposes a decision tree (DT)-based method for initializing a dendritic neuron model (DNM). Neural networks become larger and larger, thus consuming more and more computing resources. This calls for a strong need to prune neurons that do not contribute much to their network's output. Pruning those with low contribution may lead to a loss of accuracy of DNM. Our proposed method is novel because 1) it can reduce the number of dendrites in DNM while improving training efficiency without affecting accuracy and 2) it can select proper initialization weight and threshold of neurons. The Adam algorithm is used to train DNM after its initialization with our proposed DT-based method. To verify its effectiveness, we apply it to seven benchmark datasets. The results show that decision-tree-initialized DNM is significantly better than the original DNM, k-nearest neighbor, support vector machine, back-propagation neural network, and DT classification methods. It exhibits the lowest model complexity and highest training speed without losing any accuracy. The interactions among attributes can also be observed in its dendritic neurons.