Yongmiao Hong, Oliver Linton, Jiajing Sun, and Meiting Zhu’s contribution to the Discussion of “the Discussion Meeting on Probabilistic and statistical aspects of machine learning”

This paper proposes a neural network-based approach for automating offline change-point de-tection. The authors show that CUSUM and generalized CUSUM are a special case of their neuralnetwork class. They emphasize misclassification error rates and their theoretical contribution is toestablish some elegant results for these under i.i.d. unit variance Gaussian data with a possiblechange in mean

Dynamic Changes in the Global MicroRNAome and Transcriptome Identify Key Nodes Associated With Ovarian Development in Chickens

The analysis of gene expression patterns during ovarian follicle development will advance our understanding of avian reproductive physiology and make it possible to improve laying performance. To gain insight into the molecular regulation of ovarian development, a systematic profiling of miRNAs and mRNAs at four key stages was conducted, using ovarian tissues from hens at 60 days of age (A), 100 days (B), 140 days-not yet laying (C), and 140 days-laying (D). Comparisons of consecutive stages yielded 73 differentially expressed miRNAs (DEMs) (14 for B vs. A, 8 for C vs. B, and 51 for D vs. C) and 2596 differentially expressed genes (DEGs) (51 for B vs. A, 20 for C vs. B, and 2579 for D vs. C). In addition, 174 DEMs (22 for C vs. A, 74 for D vs. A, and 78 for D vs. B) and 3205 DEGs (118 for C vs. A, 2284 for D vs. A, and 2882 for D vs. B) were identified between nonconsecutive stages. Some DEGs are involved in the Wnt and TGF-beta signaling pathways, which are known to affect ovarian development and ovulation. An integrative analysis of the miRNA and mRNA profiles identified 3166 putative miRNA-mRNA regulatory pairs containing 84 DEMs and 1047 DEGs. Functional annotation of the networks provides strong evidence that the miRNA regulatory networks may play vital roles in ovarian development and ovulation. Ten DEMs and 10 genes were validated by real-time quantitative PCR. The candidate miRNA-mRNA pairs gga-miR-200a-3p-SFRP4, gga-miR-101-3p-BMP5, gga-miR-32-5p-FZD4, and gga-miR-458b-5p-CTNNB1 potentially associated with ovarian development

Association between NF-κB Activation in Peripheral Blood Mononuclear Cells and Late Skin and Subcutaneous Fibrosis following Radiotherapy

Background. This study aimed at evaluating the association between the speed of nuclear factor-kappa B (NF-κB) activation in peripheral blood mononuclear cells (PBMCs) and late skin and subcutaneous fibrosis in patients with head and neck squamous cell carcinoma (HNSCC) after radiotherapy. Methods. The speed of NF-κB activation was represented by the nuclear p65 expression ratio before and after irradiation. The optimal time point to measure the ratio was determined by Western blot in the PBMCs from healthy outpatients ranging from 0 to 12 hours after ex vivo irradiation. We recruited patients with HNSCC who had received ratiotherapy and who were under regular follow-up care. We assessed the association between the risk of developing ≥grade 2 late fibrosis and the nuclear p65 expression ratio in the PBMCs after ex vivo irradiation in these patients. Results. The maximum nuclear p65 ratio was observed at 1 hour after ex vivo irradiation in the PBMCs from the healthy outpatients. The speed of NF-κB activation was then represented by the nuclear p65 ratio in the PBMCs before and 1 hour after ex vivo irradiation. A total of 200 patients with HNSCC were recruited, 32.50% (n=65) of which presented with ≥grade 2 late fibrosis. There was a significant association between the speed of NF-κB activation in the PBMCs and an increased risk of developing ≥grade 2 late fibrosis in these patients (P=0.004). Subgroup analysis suggested that this finding was independent of the known clinical characteristics. Conclusions. The speed of NF-κB activation might be a potential predictor of late toxicity in cancer patients after radiotherapy. Prospective studies are needed for validation

Coupling intention and actions of vehicle-pedestrian interaction: A virtual reality experiment study

The interactions between vehicles and pedestrians are complex due to their interdependence and coupling. Understanding these interactions is crucial for the development of autonomous vehicles, as it enables accurate prediction of pedestrian crossing intentions, more reasonable decision-making, and human-like motion planning at unsignalized intersections. Previous studies have devoted considerable effort to analyzing vehicle and pedestrian behavior and developing models to forecast pedestrian crossing intentions. However, these studies have two limitations. First, they mainly focus on investigating variables that explain pedestrian crossing behavior rather than predicting pedestrian crossing intentions. Moreover, some factors such as age, sensation seeking and social value orientation, used to establish decision-making models in these studies are not easily accessible in real-world scenarios. In this paper, we explored the critical factors influencing the decision-making processes of human drivers and pedestrians respectively by using virtual reality technology. To do this, we considered available kinematic variables and analyzed the internal relationship between motion parameters and pedestrian behavior. The analysis results indicate that longitudinal distance and vehicle acceleration are the most influential factors in pedestrian decision-making, while pedestrian speed and longitudinal distance also play a crucial role in determining whether the vehicle yields or not. Furthermore, a mathematical relationship between a pedestrian's intention and kinematic variables is established for the first time, which can help dynamically assess when pedestrians desire to cross. Finally, the results obtained in driver-yielding behavior analysis provide valuable insights for autonomous vehicle decision-making and motion planning

The association between serum ferritin and blood pressure in adult women: a large cross-sectional study

Background Studies on the relationships between ferritin and blood pressure remain limited, especially in adult women. The aim of the present study was to investigate the associations between serum ferritin and blood pressure among adult women. Methods Using the National Health and Nutrition Examination Survey, a cross-sectional study, including 5521 adult women, was performed. Weighted multivariate regressions, subgroup analyses, threshold effect analyses, and sensitivity analysis were used. Results The authors found that serum ferritin was independently and positively correlated to diastolic blood pressure (DBP), and this positive correlation kept present among women who are 26–30 years old, non-pregnant women, Mexican American women, and women of other races in the subgroup analyses. Additionally, no significant association was found between serum ferritin and systolic blood pressure (SBP), except in women aged 26–30, Mexican American women, and women of other races. In pregnant women, the association between serum ferritin and SBP was an inverted U-shaped curve with an inflection point at 39.5 ng/mL. Conclusions The authors demonstrated that serum ferritin was positively correlated to DBP in adult women, which may provide a novel reference for clinical management

Biomass Pyrolysis Technology by Catalytic Fast Pyrolysis, Catalytic Co-Pyrolysis and Microwave-Assisted Pyrolysis: A Review

With the aggravation of the energy crisis and environmental problems, biomass resource, as a renewable carbon resource, has received great attention. Catalytic fast pyrolysis (CFP) is a promising technology, which can convert solid biomass into high value liquid fuel, bio-char and syngas. Catalyst plays a vital role in the rapid pyrolysis, which can increase the yield and selectivity of aromatics and other products in bio-oil. In this paper, the traditional zeolite catalysts and metal modified zeolite catalysts used in CFP are summarized. The influence of the catalysts on the yield and selectivity of the product obtained from pyrolysis was discussed. The deactivation and regeneration of the catalyst were discussed. Catalytic co-pyrolysis (CCP) and microwave-assisted pyrolysis (MAP) are new technologies developed in traditional pyrolysis technology. CCP improves the problem of hydrogen deficiency in the biomass pyrolysis process and raises the yield and character of pyrolysis products, through the co-feeding of biomass and hydrogen-rich substances. The pyrolysis reactions of biomass and polymers (plastics and waste tires) in CCP were reviewed to obtain the influence of co-pyrolysis on composition and selectivity of pyrolysis products. The catalytic mechanism of the catalyst in CCP and the reaction path of the product are described, which is very important to improve the understanding of co-pyrolysis technology. In addition, the effects of biomass pretreatment, microwave adsorbent, catalyst and other reaction conditions on the pyrolysis products of MAP were reviewed, and the application of MAP in the preparation of high value-added biofuels, activated carbon and syngas was introduced

Multi-Label Classification in Patient-Doctor Dialogues With the RoBERTa-WWM-ext + CNN (Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach With Whole Word Masking Extended Combining a Convolutional Neural Network) Model: Named Entity Study

BackgroundWith the prevalence of online consultation, many patient-doctor dialogues have accumulated, which, in an authentic language environment, are of significant value to the research and development of intelligent question answering and automated triage in recent natural language processing studies. ObjectiveThe purpose of this study was to design a front-end task module for the network inquiry of intelligent medical services. Through the study of automatic labeling of real doctor-patient dialogue text on the internet, a method of identifying the negative and positive entities of dialogues with higher accuracy has been explored. MethodsThe data set used for this study was from the Spring Rain Doctor internet online consultation, which was downloaded from the official data set of Alibaba Tianchi Lab. We proposed a composite abutting joint model, which was able to automatically classify the types of clinical finding entities into the following 4 attributes: positive, negative, other, and empty. We adapted a downstream architecture in Chinese Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach (RoBERTa) with whole word masking (WWM) extended (RoBERTa-WWM-ext) combining a text convolutional neural network (CNN). We used RoBERTa-WWM-ext to express sentence semantics as a text vector and then extracted the local features of the sentence through the CNN, which was our new fusion model. To verify its knowledge learning ability, we chose Enhanced Representation through Knowledge Integration (ERNIE), original Bidirectional Encoder Representations from Transformers (BERT), and Chinese BERT with WWM to perform the same task, and then compared the results. Precision, recall, and macro-F1 were used to evaluate the performance of the methods. ResultsWe found that the ERNIE model, which was trained with a large Chinese corpus, had a total score (macro-F1) of 65.78290014, while BERT and BERT-WWM had scores of 53.18247117 and 69.2795315, respectively. Our composite abutting joint model (RoBERTa-WWM-ext + CNN) had a macro-F1 value of 70.55936311, showing that our model outperformed the other models in the task. ConclusionsThe accuracy of the original model can be greatly improved by giving priority to WWM and replacing the word-based mask with unit to classify and label medical entities. Better results can be obtained by effectively optimizing the downstream tasks of the model and the integration of multiple models later on. The study findings contribute to the translation of online consultation information into machine-readable information

Biomass Pyrolysis Technology by Catalytic Fast Pyrolysis, Catalytic Co-Pyrolysis and Microwave-Assisted Pyrolysis: A Review

With the aggravation of the energy crisis and environmental problems, biomass resource, as a renewable carbon resource, has received great attention. Catalytic fast pyrolysis (CFP) is a promising technology, which can convert solid biomass into high value liquid fuel, bio-char and syngas. Catalyst plays a vital role in the rapid pyrolysis, which can increase the yield and selectivity of aromatics and other products in bio-oil. In this paper, the traditional zeolite catalysts and metal modified zeolite catalysts used in CFP are summarized. The influence of the catalysts on the yield and selectivity of the product obtained from pyrolysis was discussed. The deactivation and regeneration of the catalyst were discussed. Catalytic co-pyrolysis (CCP) and microwave-assisted pyrolysis (MAP) are new technologies developed in traditional pyrolysis technology. CCP improves the problem of hydrogen deficiency in the biomass pyrolysis process and raises the yield and character of pyrolysis products, through the co-feeding of biomass and hydrogen-rich substances. The pyrolysis reactions of biomass and polymers (plastics and waste tires) in CCP were reviewed to obtain the influence of co-pyrolysis on composition and selectivity of pyrolysis products. The catalytic mechanism of the catalyst in CCP and the reaction path of the product are described, which is very important to improve the understanding of co-pyrolysis technology. In addition, the effects of biomass pretreatment, microwave adsorbent, catalyst and other reaction conditions on the pyrolysis products of MAP were reviewed, and the application of MAP in the preparation of high value-added biofuels, activated carbon and syngas was introduced

Key Genes Associated with Pyroptosis in Gout and Construction of a miRNA-mRNA Regulatory Network

This study aimed to analyze key hub genes related to pyroptosis in gout and construct a miRNA-mRNA regulatory network using bioinformatic tools to elucidate the pathogenesis of gout and offer novel ideas to develop targeted therapeutic strategies for gout. Methods: The GSE160170 dataset was downloaded from the GEO database. The expression data extracted from the dataset were used to screen for differentially expressed genes (DEGs), which intersected with pyroptosis-related genes. These DEGs were analyzed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and a protein–protein interaction (PPI) network was constructed to identify pyroptosis-related hub DEGs. The relationship between upstream miRNAs and the hub genes was analyzed, miRNA-mRNA networks belonging to gout disease were constructed and samples from patients with gout were used for experimental verification. The CTDbase tool was used to analyze the identified hub genes and construct a molecular docking model. Results: A total of 943 DEGs (380 upregulated and 563 downregulated) were identified by analyzing the data of patients with early-stage gout and healthy control individuals in the GSE160170 dataset. DEGs and pyroptosis-related genes were intersected to obtain 17 pyroptosis-related DEGs associated with gout; of which, 12 were upregulated, and five were downregulated. The results of GO and KEGG analyses revealed that the DEGs were enriched in inflammatory and immune signaling pathways. Additionally, the DEGs were found to regulate inflammatory responses and were associated with apoptosis. TNF, IL-1β, NLRP3, CXCL8, PTGS2, NFE2L2, CASP8, and CD274 were identified as key hub genes in the PPI network, and a miRNA-mRNA network was constructed, which had 16 edges. Experimental validation revealed that PTGS2 and NFE2L2 were significantly upregulated, and CASP8 and CD274 were significantly downregulated in gout. In addition, miR-128-3p, miR-16-5p, miR-155-5p, and miR-20a-5p (associated with the miRNA-mRNA regulatory network) were significantly downregulated in gout. Five potential therapeutic drugs with stable PTGS2 binding were selected to develop a molecular docking model. Conclusion: A miRNA-mRNA potential regulatory network was constructed based on pyroptosis-related DEGs associated with gout. miR-16-5p, miR-128-3p, miR-20a-5p, and miR-155-5p can potentially influence pyroptosis and the occurrence and development of gout by affecting the expression of the PTGS2, CASP8, NFE2L2, and CD274 genes. Screening of celecoxib and resveratrol and other targeted drugs with stable binding. The findings of this study offer valuable insights into the regulatory mechanisms of gout and may help to identify Biomarkers and develop targeted therapeutic strategies for gout

Nr5a2 ensures inner cell mass formation in mouse blastocyst

Summary: Recent studies have elucidated Nr5a2’s role in activating zygotic genes during early mouse embryonic development. Subsequent research, however, reveals that Nr5a2 is not critical for zygotic genome activation but is vital for the gene program between the 4- and 8-cell stages. A significant gap exists in experimental evidence regarding its function during the first lineage differentiation’s pivotal period. In this study, we observed that approximately 20% of embryos developed to the blastocyst stage following Nr5a2 ablation. However, these blastocysts lacked inner cell mass (ICM), highlighting Nr5a2’s importance in first lineage differentiation. Mechanistically, using RNA sequencing and CUT&Tag, we found that Nr5a2 transcriptionally regulates ICM-specific genes, such as Oct4, to establish the pluripotent network. Interference with or overexpression of Nr5a2 in single blastomeres of 2-cell embryos can alter the fate of daughter cells. Our results indicate that Nr5a2 works as a doorkeeper to ensure ICM formation in mouse blastocyst