Network Mapping Research Articles

An innovative patient clustering method using data envelopment Analysis–Discriminant analysis and artificial neural networks: A case study in healthcare systems

A major lesson healthcare managers learned from the COVID-19 outbreak is the need for more effective patient classification and medical resource allocation for future pandemics. In their view, hospitalization mortality could be greatly reduced if more effective systems for patient classification were in place before the outbreak to evaluate and assign treatment facilities. This study presents a scalable patient clustering approach using a Self-Organizing Map (SOM) of the Artificial Neural Network (ANN) to cluster patients for appropriate treatment allocation. The patients’ membership is forecasted using Data Envelopment Analysis–Discriminant Analysis (DEA-DA). The objectives of this research are to develop a flexible framework that healthcare systems can adopt to cluster patients based on specific testing criteria from medical records and to assign them to suitable medical centers with appropriate treatment resources. This method aims to enhance healthcare system efficiency by ensuring patients with severe illnesses receive care at well-equipped centers, while those with milder symptoms are directed to other suitable facilities. The approach is scalable and adaptable to any type of widespread illness and aims to increase recovery rates and decrease mortality rates, as confirmed by the case study results.

A Comprehensive Network Map of Interleukin-26 Signaling Pathway.

Interleukin-26 (IL-26) is a cytokine that belongs to the IL-20 subfamily and is primarily expressed in T helper 1 cells and Th17 memory CD4+ cells. Its receptor complex, consisting of IL-20R1 and IL-10R2, activates a signaling pathway involving several proteins such as Janus kinase 1 and tyrosine-protein kinase, signal transducer and activator of transcription (STAT) 1, and STAT3. This leads to the initiation of downstream signaling cascades that play a crucial role in various biological processes, including inflammation, immune response regulation, atopic dermatitis, macrophage differentiation, osteoclastogenesis, antibacterial host defense, anti-apoptosis, and tumor growth. In this study, we curated literature data pertaining to IL-26 signaling. The curated map includes a total of seven activation/inhibition events, 16 catalysis events, 33 gene regulation events, 25 protein expression types, two transport events, and three molecular associations.

Prosopagnosie et syndrome de Capgras : deux syndromes en miroir ?

First described in the 19th century in patients with brain lesions, prosopagnosia was named in 1947 by Joachim Bodamer (1910–1985), a German neuropsychiatrist. The term refers to the inability to recognise a familiar face, a rarely isolated symptom. Prosopagnosia is thought to result from brain lesions, predominantly at the ventral right temporo-occipital junction. There are more common cases of developmental prosopagnosia, where no lesion has been identified. Capgras syndrome (CS), described by Joseph Capgras (1873–1950), a psychiatrist, consists of the persistent belief that a close relative, identified, has been replaced by a look-alike. SC, considered a delusional disorder of identification or familiarity, has been described in chronic psychoses, mainly schizophrenia. It is also reported in the course of neurodegenerative diseases (mainly Alzheimer's disease, Lewy body dementia, Parkinson's dementia). Lesion-based forms have recently been described. Prosopagnosia and SC were initially thought to result from mirror-image lesions involving a single factor, one affecting an overt ventral circuit for face recognition (in the case of prosopagnosia), the other a covert dorsal circuit involved in emotional and vegetative responses (in the case of CS). Lesion network mapping has been used in both lesion-related conditions and suggests a more complex figure. Lesions causing prosopagnosia were correlated with one of the main regions associated with face recognition, the right fusiform face area, and anticorrelated with left frontal regions possibly involved in visual attentional control. In lesional forms of CS or other delusional misidentification syndromes, there was a correlation with left retrosplenial cortex, an area associated with familiarity processing, and a correlation with right frontal regions associated with belief evaluation. This finding supports the intervention of two factors in CS, one generating the impaired familiarity associated with perception and the other the genesis and persistence of a delusional idea. The debate on the mechanisms of CS is not settled, especially as it is limited to its lesional forms, but studies using lesion network mapping already mark a more general paradigm shift, with attention shifting from lesions (clinico-anatomical method) to circuit dysfunction.

Multi-Omics Reveals the Role of Arachidonic Acid Metabolism in the Gut-Follicle Axis for the Antral Follicular Development of Holstein Cows.

In vitro embryonic technology is crucial for improving farm animal reproduction but is hampered by the poor quality of oocytes and insufficient development potential. This study investigated the relationships among changes in the gut microbiota and metabolism, serum features, and the follicular fluid metabolome atlas. Correlation network maps were constructed to reveal how the metabolites affect follicular development by regulating gene expression in granulosa cells. The superovulation synchronization results showed that the number of follicle diameters from 4 to 8 mm, qualified oocyte number, cleavage, and blastocyst rates were improved in the dairy heifers (DH) compared with the non-lactating multiparous dairy cows (NDC) groups. The gut microbiota was decreased in Rikenellaceae_RC9_gut_group, Alistipes, and Bifidobacterium, but increased in Firmicutes, Cyanobacteria, Fibrobacterota, Desulfobacterota, and Verrucomicrobiota in the NDC group, which was highly associated with phospholipid-related metabolites of gut microbiota and serum. Metabolomic profiling of the gut microbiota, serum, and follicular fluid further demonstrated that the co-metabolites were phosphocholine and linoleic acid. Moreover, the expression of genes related to arachidonic acid metabolism in granulosa cells was significantly correlated with phosphocholine and linoleic acid. The results in granulosa cells showed that the levels of PLCB1 and COX2, participating in arachidonic acid metabolism, were increased in the DH group, which improved the concentrations of PGD2 and PGF2α in the follicular fluid. Finally, the expression levels of apoptosis-related proteins, cytokines, and steroidogenesis-related genes in granulosa cells and the concentrations of steroid hormones in follicular fluid were determinants of follicular development. According to our results, gut microbiota-related phosphocholine and linoleic acid participate in arachidonic acid metabolism in granulosa cells through the gut-follicle axis, which regulates follicular development. These findings hold promise for enhancing follicular development and optimizing oocyte quality in subfertile dairy cows.

Trends in Emotional Labor Research: Bibliometric and Science Mapping Analysis

Emotional labor is about employees in the service sector involving their emotions while performing the service job as required. Emotions are also used for commercial purposes. The study analyzes data on emotional labor through bibliometric and science mapping methods. For the analyses, data between 1982 and 2024 are scanned in the Web of Science (WoS) database, and data for 2,925 documents are accessed. After loading the data, the VOSviewer program is utilized to analyze it for bibliometric and science mapping purposes. Subsequently, the investigation unveiles the foremost authors, countries, journals, institutions, and keywords in emotional labor research. This is followed by analyzing their co-authorship, co-citation, bibliographic coupling, and keyword co-occurrence models. As a result, in emotional labor research, it is found that Alicia A. Grandey is the most productive and most cited author, the USA is the most productive country, Frontiers in Psychology is the most productive journal, and the most frequently encountered keywords are burnout, surface acting, job satisfaction, deep acting, and emotional exhaustion, respectively. The analysis findings reveal a collaborative nexus among authors, countries, and institutions, offering visual network maps and comprehensive insights into emotional labor's present status and trends within the scientific literature.

Agri-inputs Sourcing Networks of Rice Farmers in Andhra Pradesh and Kerala, India

Agricultural inputs are the underlying elements that are vital for agricultural production. Timely availability and quality of the inputs also have a significant impact on production. Thereby, the study aims to investigate the structure and dynamics of agricultural input sourcing networks among rice farmers in two districts each of Andhra Pradesh (AP) and Kerala. A total of 120 farmers were randomly selected and surveyed. Social network analysis was deployed for the analysis and to delineate the network maps. The results revealed that commercial banks were the major source of financial resources among all four districts. Cooperative banks with an eigen-centrality value of 0.96 were on par with the commercial banks in providing credit to the farmers in Palakkad region of Kerala. In seeds, private dealers were major players in Kurnool, while neighbouring farmers were the major source in East Godavari of AP. In Kerala, Krishi Bhavan was the major source of seeds for farmers in both districts. For other inputs such as fertilisers and plant protection chemicals, farmers in Kurnool sourced majorly from private dealers while East Godavari farmers sourced primarily from both private dealers and cooperatives. In Kerala, farmers sourced other inputs primarily from cooperatives in Thrissur and in Palakkad, farmers sourced them from a farmer producer organisation.

Prognostic value of KLFs family genes in renal clear cell carcinoma

Numerous studies have shown that the Krüppel-like factors (KLFs) family of transcription factors regulate various eukaryotic physiological processes including the proliferation, differentiation, senescence, death, and carcinogenesis of animal cells. In addition, they are involved in the regulation of key biological processes such as cell cycle, DNA repair, and immune response. Current studies focus on investigating the role of KLFs in normal physiological conditions and the incidence and development of diseases. However002C the significance of KLFs family genes in clear cell renal cell carcinoma (ccRCC) remains partly understood; therefore, an in-depth investigation of their role and clinical value in this cancer is desired. The study aimed to investigate the role of KLF family genes in the incidence, development, and prognosis of ccRCC, and to identify the related potential biomarkers and therapeutic targets. The expression of KLFs in the RNA sequencing data of 613 ccRCCs from the TCGA database was analyzed using R software, and UALCAN and GEPIA assessed the expression of KLF genes in ccRCC. Real-time fluorescence quantitative PCR analysis was performed using 10 pairs of paired ccRCC sample tissues and renal cancer cell lines from the First Affiliated Hospital of Nanchang University. Overall survival (OS), progression-free interval (PFI), and disease-specific survival (DSS) of Kidney Clear Cell Carcinoma (KIRC) samples at differential expressions of KLFs in the TCGA database were analyzed using the R software, followed by generating a nomogram prediction model. GSCALite assessed the interactions of KLF genes with miRNAs and generated network maps. Protein interaction network maps of 50 neighboring genes associated with KLF mutations were analyzed using STRING with GO and KEGG functional enrichment analyses. The cBioPortal determined the probability of KLF gene mutations and their impact on OS and disease-free survival (DFS) in patients with ccRCC. Immune cell infiltration of KLFs was analyzed using TIMER. Finally, GSCALite was used to analyze the drug sensitivity and associated pathways of action of KLFs. Correlation validation using cellular experiments. KLF3/5/9/15 were significantly downregulated in ccRCC tissues, whereas KLF16/17 were upregulated compared with the adjacent tissues. Patients with high mRNA levels of KLF16/17 showed significantly lower OS, PFI, and DSS, whereas KLF3/5/9 showed a reverse trend. In patients with ccRCC, a significant correlation was observed between KLF mutations and OS and DSS. Furthermore, the correlation of KLF3/5/9 with immune cell infiltration was stronger than that of KLF15/16, while KLF17 was significantly associated with the Epithelial-Mesenchymal Transition (EMT) pathway. Overexpression of KLF5 inhibits the proliferative and migratory capacity of renal cancer cells (786-O and OS-RC-2), as well as their sensitivity to relevant small molecule drugs. Our research revealed the expression levels and biological significance of KLF genes in ccRCC, particularly highlighting the potential of KLF5 as a promising biomarker and therapeutic target for effective prognosis and diagnosis of ccRCC.

Https://wjarr.com/ArchiveIssue-2024-Vol23-Issue2

The objective of this study was to elucidate the mechanism by which Citri Reticulatae Pericarpium (CRP) treated Alzheimer’s disease (AD) using network pharmacology and molecular docking. Initially, a screening process identified 98 target compounds and 628 related component targets within CRP. Additionally, 2483 AD targets were retrieved from disease databases. Subsequently, an overlapping targets map was constructed, integrating CRP and AD targets, followed by the creation of a protein-protein interaction network map to identify 66 targets closely associated with the treatment of AD using CRP. These targets were identified through topological attribute analysis. To gain further insights, GO function and KEGG pathway enrichment analyses were conducted on the 66 identified targets. The results revealed enrichment in various biological pathways, including the relaxin signaling pathway, calcium signaling pathway, HIF-1 signaling pathway, and IL-17 signaling pathway. Finally, molecular docking verification was performed on the targets and active components of CRP. Active components, such as flavanone, tangeretin, flavonol, carvacrol, and perillaldehyde, were found to form hydrogen bonds with targets, with binding energies below 0 kg/mol. This study utilized network pharmacology and molecular docking methods to systematically elucidate the mechanism by which CRP treats AD. The findings provided a theoretical foundation for future research and clinical investigations

Identifying authorial roles in research: A Kano model-based bibliometric analysis for the Journal of Medicine (Baltimore) 2023.

The landscape of research roles within academic journals often remains uncharted territory, with authorial contributions frequently reduced to linear hierarchies (e.g., professor and assistant professor). The Kano model, traditionally used in customer satisfaction research, offers a nuanced framework for identifying the multifaceted roles of authors in scholarly publications. This study utilizes the Kano model to dissect and categorize the roles of authors in the medicine field. To conform to the hypothesis, China is the research leader while the US is the research collaborator, as reflected in the publications of the journal of Medicine (Baltimore) in the year 2023. We conducted a comprehensive bibliometric analysis of all research articles published in the journal of Medicine (Baltimore) in 2023. The Kano model was applied to classify authors into 5 categories reflective of their research roles: followers, leaders, partners, contributors, and collaborators. Data on author publications and co-authorship networks with multi-author rates (MARs) were analyzed to assign Kano categories based on the authorship positions of first and corresponding authors. Descriptive statistics and network analysis tools were used to interpret the data, including radar plots, geographical maps, and Kano diagrams. The analysis covered 1976 articles, uncovering a complex network of author roles that extends beyond the conventional binary distinction of lead and supporting authors (i.e., leading, and following researchers). A research leader in China and a collaborator in the US were conformed to support the hypothesis, based on their publications (1148 vs 51) and MARs (12.20% vs 19.61%). The Kano classification was visually adapted to classify authors (or entities) into 5 categories. The combined choropleth and geographical network maps were illustrated to identify author roles in research briefly. The Kano model serves as an effective tool for uncovering the diverse contributions of authors in medical research. By moving beyond the lead and follower dichotomy, this study highlights the intricate ecosystem of authorial roles, emphasizing the importance of each in advancing knowledge within the field of medicine. Future application of the Kano model could foster a more collaborative and inclusive recognition of contributions across various disciplines.

Policy design and implementation: a quantitative evaluation and empirical study of China's rural sports policy based on the PMC index model.

The formulation and implementation of a rural sports policy is an important means of promoting rural sports, improving the physical wellbeing of farmers, and enhancing the cohesion of rural communities. However, introducing such a policy faces problems in the process of specific policy practices, such as poor effective implementation, a lagging implementation effect, and goal cognitive bias. How to look at the current rural sports policy implementation blockage problem and the governance of the blockage, in order to improve the level of rural sports public service, is the focus of this paper's research. On this basis, this paper selects 56 policy texts, issued from 2002 to 2023, that are highly relevant to rural sports and have high timeliness and authority from the sports policies issued in China. Also, ROST CM6 software is used to count high-frequency words; this study then draws keyword social network mapping for the visual analysis of policy preferences and selects 20 rural sports policy texts as typical samples. Finally, a policy modeling research consistency (PMC) index model is used to evaluate the texts comprehensively and quantitatively. The results show that the overall design of China's rural sports policies is relatively reasonable. However, the consistency and effectiveness of their implementation need to be improved. Twenty representative policy texts have an average PMC index score of 5.96, with a concave index of 3.04 (which is good overall), with the highest mean value for rural sports policies at the national level. This is followed by the second highest value at the municipal and county levels, and the smallest at the provincial level. Therefore, in the future formulation and implementation of rural sports policies, a multi-dimensional rural sports policy system should be constructed. This would help to strengthen the consistency and effectiveness of the implementation of the policy system and promote the high-quality development of rural sports.

Pharmacogenomic discovery of genetically targeted cancer therapies optimized against clinical outcomes

Despite the clinical success of dozens of genetically targeted cancer therapies, the vast majority of patients with tumors caused by loss-of-function (LoF) mutations do not have access to these treatments. This is primarily due to the challenge of developing a drug that treats a disease caused by the absence of a protein target. The success of PARP inhibitors has solidified synthetic lethality (SL) as a means to overcome this obstacle. Recent mapping of SL networks using pooled CRISPR-Cas9 screens is a promising approach for expanding this concept to treating cancers driven by additional LoF drivers. In practice, however, translating signals from cell lines, where these screens are typically conducted, to patient outcomes remains a challenge. We developed a pharmacogenomic (PGx) approach called “Clinically Optimized Driver Associated-PGx” (CODA-PGX) that accurately predicts genetically targeted therapies with clinical-stage efficacy in specific LoF driver contexts. Using approved targeted therapies and cancer drugs with available real-world evidence and molecular data from hundreds of patients, we discovered and optimized the key screening principles predictive of efficacy and overall patient survival. In addition to establishing basic technical conventions, such as drug concentration and screening kinetics, we found that replicating the driver perturbation in the right context, as well as selecting patients where those drivers are genuine founder mutations, were key to accurate translation. We used CODA-PGX to screen a diverse collection of clinical stage drugs and report dozens of novel LoF genetically targeted opportunities; many validated in xenografts and by real-world evidence. Notable examples include treating STAG2-mutant tumors with Carboplatin, SMARCB1-mutant tumors with Oxaliplatin, and TP53BP1-mutant tumors with Etoposide or Bleomycin.

Sustainability: Is It a Strategic Management Research Fashion?

This article aims to identify the relationship between sustainability and strategic management to determine whether sustainability can be considered a strategic management research fashion. This involves a bibliometric analysis of recent academic literature from 2021 to 2023 to identify the latest academic research, key trends, collaboration and keyword networks within this relationship. The analysis was conducted using two datasets from the Scopus database. These datasets focus on English-language journal articles on business, management and accounting. The first covers academic research on strategic management, while the second expands to sustainability and sustainable development. The results show that strategic management research focusing on sustainability has recently grown faster (24.70%) and with higher funding frequency (22.4%) than the whole strategic management research field (14.30% and 17.5%, respectively). Furthermore, the geographical analysis of co-authorship identified articles from 88 countries, suggesting a broad interest in this relationship. Notably, the strategic management network mapping revealed a unique, sustainable development, corporate social responsibility, and sustainability cluster. Moreover, extended mapping revealed four clusters: strategic and innovation-driven sustainability, operational corporate sustainability, crisis management and environmental economics, and sustainable supply chain and resource management. The thematic analysis further highlights well-developed sustainability and strategic management research topics like digitalisation, circular economy, sustainable supply chain management, sustainable development goals, industry 4.0, COVID-19, environmental sustainability, etc. that are contributing to the progress of sustainability and strategic management research. The results thus confirm the rapid growth and widespread coverage of research on sustainability and strategic management, highlighting sustainability as a strategic management research fashion.

Lightweight transmission line defect identification method based on OFN network and distillation method

AbstractEdge devices are increasingly utilized for defect detection in power line inspection, necessitating algorithms that optimize model size and accuracy. This study introduces a lightweight detection method using the Optimized Feature Network with MobileOne‐FPN‐NASHead (OFN network) OFN network and distillation technique. The OFN incorporates a lightweight backbone, neural network search, re‐parametrization, and a feature pyramid to create a compact yet effective detection network. To enhance feature learning, a heterogeneous distillation approach is applied, leveraging a modified YOLOv8 as a teacher network. This modification includes an explicit visual centre for improved global and local information extraction, crucial for dense target detection in power line inspections. Additionally, the Minimize the points distance IoU (MPDloU) loss function is used to improve localization accuracy over the the Complete‐Intersection Over Union (CIoU) loss. Experimental results show a 1.1% mean Average Precision (mAP) increase for the enhanced YOLOv8 and a 70.2% mAP for the OFN network with 18.95 GFLOPs and 343 FPS, achieving a commendable balance between model efficiency and detection performance. The research underscores the viability of the OFN for edge computing in power line defect detection, highlighting the potential of innovative algorithmic structures in this application.

Efficient O-type mapping and routing of large-scale neural networks to torus-based ONoCs

Efficient O-type mapping and routing of large-scale neural networks to torus-based ONoCs

Application of artificial intelligence in rheumatic disease: a bibliometric analysis

The utilization of artificial intelligence (AI) in rheumatic diseases has enhanced the diagnostic accuracy of rheumatic diseases, enabled the prediction of patient outcomes, expanded treatment options, and facilitated the provision of individualized medical solutions. The research in this field has been progressively growing in recent years. Consequently, there is a need for bibliometric analysis to elucidate the current state of advancement and predominant research foci in AI applications within rheumatic diseases. Additionally, it is crucial to identify key contributors and their interrelations in this field. This study aimed to conduct a bibliometric analysis to investigate the current research hotspots and collaborative networks in the application of AI in rheumatic disease in recent years. A comprehensive search was conducted in Web of Science for articles on artificial intelligence in rheumatic diseases, published in SSCI and SCI-EXPANDED until January 1, 2024. Utilizing software tools like VOSviewers and CiteSpace, we analyzed various parameters including publication year, journal, country, institution, and authorship. This analysis extended to examining cited authors, generating reference and citation network graphs, and creating co-citation network and keyword maps. Additionally, research hotspots and trends in this domain were evaluated. As of January 1, 2024, a total of 3508 articles have been published on the application of artificial intelligence (AI) in rheumatic disease, exhibiting a steady rise in both the annual publication frequency and rate. “Scientific Reports” emerged as the leading journal in terms of relevant publications. The United States stood out as the predominant country in terms of the volume of published papers, with the University of California, San Francisco (UCSF) being the most prolific and frequently cited institution. Among authors, Young Ho Lee and Valentina Pedoia were noted for their significant contributions, with Pedoia achieving the highest average citation count per publication. Machine learning emerged as a prominent and central keyword. The trend indicates a growing interest in AI research within rheumatologic diseases, with its role expected to become increasingly pivotal in the field. This study presents a comprehensive summary of research trends and developments in the application of artificial intelligence (AI) in rheumatic diseases. It offers insights into potential collaborations and prospects for future research, clarifying the research frontiers and emerging directions in recent years. The findings of this study serve as a valuable reference for scholars studying rheumatology and immunology.

Volatile organic compounds (VOCs) in urban public transportation: Seasonal influences and variations

The present work aims to analyze the information related to the organic pollutants present inside the public transport units of León Guanajuato, Mexico, along winter, spring and fall seasons. Through the application of different selection criteria, a database was generated that allowed the analysis of the different selected pollutants and determine their behavior throughout the spring, autumn and winter seasons. These analyzes were carried out using different statistical tools like ANOVA, PCA, and correlation network maps. At least 23 compounds were selected as common pollutants along all the seasons, including aromatics, aliphatic, halogenated, polycyclic, and compounds with heteroatoms species. Results showed that the concentrations of Volatile Organic Compounds s such as hexane (1.5–3.0 µg/m³), dichlorobenzene (2.0–4.5 µg m−3), and toluene (5.0–12.0 µg m−3) varied significantly across the seasons, with higher levels observed in winter. The study highlights the influence of external environmental factors on indoor air quality within public transport units proven through statistical analysis. The results allowed to mention that the concentrations of Volatile Organic Compounds can vary significantly in different season along the year, as well as that the main pollutants vary according to the season, of which there are compounds that are only present during these periods.

Network analysis and experimental verification of Salvia miltiorrhiza Bunge-Reynoutria japonica Houtt. drug pair in the treatment of non-alcoholic fatty liver disease

ContextThere are currently no approved specific clinical drugs for non-alcoholic fatty liver disease (NAFLD). Salvia miltiorrhiza Bunge-Reynoutria japonica Houtt. drug pair (SRDP) has been widely used in the treatment of chronic liver diseases. However, the mechanism of SRDP treating NAFLD remains unclear.ObjectiveBased on network analysis and in vitro experimental verification, we investigated the effect of SRDP on lipid deposition and explored its possible mechanism for the treatment of NAFLD.MethodsThe TCMSP platform was used to screen the active metabolites of SRDP and corresponding targets. The GeneCards and OMIM databases were used to screen the NAFLD targets. The drug-disease intersecting targets were extracted to obtain the potential targets. Then the protein–protein interaction (PPI) and drug-active metabolites-target-disease network map was constructed. The DAVID database was performed to GO and KEGG pathway enrichment analysis for the intersecting targets. The core active metabolite and signaling pathway were verified by in vitro experiments.ResultsNetwork analysis predicted 59 active metabolites and 89 targets of SRDP for the treatment of NAFLD. 112 signaling pathways were enriched for KEGG pathways, including PI3K-AKT signaling pathway,etc. It was confirmed that luteolin, the core active metabolite of SRDP, effectively reduced fat accumulation and intracellular triglyceride content in HepG2 fatty liver cell model. Luteolin could inhibit mTOR pathway by inhibiting PI3K-AKT signaling pathway phosphorylation, thereby activating autophagy to alleviate NAFLD.Discussion and conclusionThe results of this study validate and predict the possible role of various active metabolites of SRDP in the treatment of NAFLD through multiple targets and signaling pathways. The core active metabolite of SRDP, luteolin can alleviate NAFLD by acting on the PI3K-AKT-mTOR signaling pathway to induce autophagy.

Screening for Genes Related to Meat Production Traits in Duroc × Bama Xiang Crossbred Pigs by Whole Transcriptome Sequencing.

The meat production traits of pigs are influenced by the expression regulation of multiple gene types, including mRNAs, miRNAs, and lncRNAs. To study the differences in meat production traits at the transcriptional level among individuals with different growth rates, the longissimus dorsi samples from eight Duroc × Bama Xiang F2 crossbred pigs with a fast growth rate (high gTroup) or a slow growth rate (low group) were selected to perform whole transcriptome sequencing and ceRNA regulatory network construction. This study first analyzed the differences in physiological and biochemical indicators, muscle histological characteristics, and muscle fiber types. A total of 248 mRNAs, 25 miRNAs, and 432 lncRNAs were identified as differentially expressed by whole transcriptome sequencing. Key genes that may influence meat production traits include MTMR14, PPP1R3A, PYGM, PGAM2, MYH1, and MYH7. The ceRNA regulatory network map showed that ENSSSCG00000042061-ssc-mir-208b-MYH7, ENSSSCG00000042223-ssc-mir-146a-MTMR14, ENSSSCG00000045539-ssc-mir-9-3-MYH1, and ENSSSCG00000047852-ssc-mir-103-1-PPP1R3A may be the key factors affecting meat production traits through their regulatory relationships. This study provides valuable insights into the molecular mechanisms underlying porcine muscle development and can aid in improving meat production traits.

Well-being in middle-aged and older adults who volunteer: a qualitative network analysis

Objective Social participation is essential for the health of older adults. Formal volunteering can reduce loneliness and is associated with numerous positive health outcomes. Older adults have limited resources to meet their social and well-being needs. We explore factors contributing to the well-being of middle-aged and older adults who engage in formal volunteering using a qualitative social network approach. Methods Between 2022 and 2023, qualitative semi-structured interviews were conducted with 28 members of social clubs in Germany, aged 45–80 (mean age 62 years). Thirteen participants were retired. Eight interviewees were female and twenty were male. Participants created unstructured egocentric network maps of individuals or organisations important to their well-being. Participants reflected on their social networks using these maps, which allowed for deeper insights into changing network dynamics. The data were analysed using thematic analysis. Results We developed six themes: intrapersonal, interpersonal and social network characteristics affecting well-being, self-fulfilment, feeling indispensable and unfulfilled expectations. Formal social participation, particularly through volunteering, contributed significantly to the well-being. Conclusion Formal volunteering can aid successful role substitution and compensation, especially after retirement. This may prevent loneliness and increase well-being in older age.

High-performance deep spiking neural networks with 0.3 spikes per neuron

Communication by rare, binary spikes is a key factor for the energy efficiency of biological brains. However, it is harder to train biologically-inspired spiking neural networks than artificial neural networks. This is puzzling given that theoretical results provide exact mapping algorithms from artificial to spiking neural networks with time-to-first-spike coding. In this paper we analyze in theory and simulation the learning dynamics of time-to-first-spike-networks and identify a specific instance of the vanishing-or-exploding gradient problem. While two choices of spiking neural network mappings solve this problem at initialization, only the one with a constant slope of the neuron membrane potential at threshold guarantees the equivalence of the training trajectory between spiking and artificial neural networks with rectified linear units. For specific image classification architectures comprising feed-forward dense or convolutional layers, we demonstrate that deep spiking neural network models can be effectively trained from scratch on MNIST and Fashion-MNIST datasets, or fine-tuned on large-scale datasets, such as CIFAR10, CIFAR100 and PLACES365, to achieve the exact same performance as that of artificial neural networks, surpassing previous spiking neural networks. Our approach accomplishes high-performance classification with less than 0.3 spikes per neuron, lending itself for an energy-efficient implementation. We also show that fine-tuning spiking neural networks with our robust gradient descent algorithm enables their optimization for hardware implementations with low latency and resilience to noise and quantization.