Distinct Clusters Research Articles

PROFILES OF OLDER INCARCERATED PERSONS WHO ATTEMPT SUICIDE FOLLOWING RELEASE

Abstract Release from incarceration is a strong risk factor for suicide. Yet little is known regarding factors associated with suicide attempt among older adults who reentered the community following incarceration. Leveraging a national cohort of U.S. veterans age ≥50 released from incarceration between FY2007 and FY2018 (N=30,584), we conducted a 1:3 matched case-control study and compared demographic and comorbidity factors between those who attempted suicide (cases, N=643) and those with no suicide attempt (controls, N=1,929) up to 13 years post-release. Comorbidity profiles were determined using latent class analysis. As compared with controls, cases had higher proportions of chronic illnesses (e.g., chronic lung disease [35.2% vs. 19.6%], diabetes [32.2% vs. 19.4%], Hepatitis C [24.9% vs. 9.6%]); depression (75% vs. 28.7%); substance use disorders (SUDS) (83.0% vs. 39.4%); and other serious mental illnesses (SMI) (e.g., schizophrenia [34.2% vs. 13.2%], psychotic illness [39.5% vs. 12.5%]). We found four distinct latent clusters among cases (entropy statistic = 0.80): (1) “Minimal Comorbidity” (18%); (2) “PTSD/SUD” (35%); (3) “High Psychiatric/SMI” (32%); and (4) “High Medical/Psychiatric Comorbidity” (15%), and four clusters among controls (entropy statistic = 0.84): (1) “Minimal Comorbidity” (50%); (2) “SMI” (8%); (3) “Tobacco dependence/SUD” (33%); and (4) “High Medical/Psychiatric Comorbidity” (9%). Compared with controls, fewer cases were likely to have the “Minimal Comorbidity” profile (18% vs. 50%), whereas more were likely to have profiles with SMI (32% vs. 8%). Establishing comorbidity profiles of older persons reentering the community after incarceration may help determine who is more likely to die by suicide, thus informing prevention efforts.

Advanced hemodynamic and cluster analysis for identifying novel RV function subphenotypes in patients with pulmonary hypertension

BackgroundQuantifying right ventricular (RV) function is important to describe the pathophysiology of in pulmonary hypertension (PH). Current phenotyping strategies in PH rely on few invasive hemodynamic parameters to quantify RV dysfunction severity. The aim of this study was to identify novel RV phenotypes using unsupervised clustering methods on advanced hemodynamic features of RV function. MethodsParticipants were identified from the University of Arizona Pulmonary Hypertension Registry (n=190). RV-pulmonary artery coupling (Ees/Ea), RV systolic (Ees) and diastolic function (Eed) was quantified from stored RV pressure waveforms. Consensus clustering analysis with bootstrapping was used to identify the optimal clustering method. Pearson correlation analysis was used to reduce collinearity between variables. RV cluster subphenotypes were characterized using clinical data and compared to pulmonary vascular resistance (PVR) quintiles. ResultsFive distinct RV clusters (C1-C5) with distinct RV subphenotypes were identified using k-medoids with a Pearson distance matrix. Clusters 1 and 2 both have low diastolic stiffness (Eed) and afterload (Ea) but RV-PA coupling (Ees/Ea) is decreased in C2. Intermediate cluster (C3) has a similar Ees/Ea as C2 but with higher PA pressure and afterload. Clusters C4 and C5 have increased Eed and Ea but C5 has a significant decrease in Ees/Ea. Cardiac output was high in C3 distinct from the other clusters. In the PVR quintiles, contractility increased and stroke volume decreased as a function of increased afterload. World Symposium PH classifications were distributed across clusters and PVR quintiles. ConclusionsRV-centric phenotyping offers an opportunity for a more precise-medicine based management approach.

Mapping and analyzing the scientific literature on drug abuse

This scientometric study provides a comprehensive analysis of drug abuse research within the top 10 countries indexed in the Web of Science from 2010 to August 2023. It highlights the significant contributions of authors like Kendler KS and institutions such as the University of California System, emphasizing the pivotal role they play in advancing our understanding of drug abuse. The United States, China, and Iran emerge as leading contributors, with funding predominantly provided by institutions like the United States Department of Health and Human Services, NIH USA, and NIDA. The study identifies four distinct research clusters focusing on alcohol, cocaine, opioids, and cannabis, offering nuanced insights into specific areas of emphasis within drug abuse research. These findings underscore the need for collaborative, multi-faceted approaches involving researchers, institutions, and countries to effectively address the complex challenges posed by drug abuse. Building upon these insights, future research and policy initiatives can work towards more effective strategies for prevention, intervention, and support, ultimately contributing to improvements in public health and well-being.

Global lineage evolution pattern of sars-cov-2 in Africa, America, Europe, and Asia: A comparative analysis of variant clusters and their relevance across continents.

The objective of this study is to provide a comparative analysis of variant clusters and their relevance across Africa, America, Europe, and Asia, in order to understand the evolutionary patterns of the virus across different regions and to inform the development of targeted interventions and genomic surveillance eforts. The study analyzed the global lineage evolution pattern of 74, 075 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from 32 countries across four continents, focusing on variant clusters and their relevance across regions. Variants were weighted according to their hierarchical level. The correlation between variants was visualized through Dimensionality reduction analysis and Pairwise Pearson's correlation. We presented a reconstructed phylogenetic tree based on correlation analysis and variant weights. The analysis revealed that each continent had distinct variant clusters and different evolutionary patterns. The Americas had two clustered variants before lineage divergence and a downstream confluence lineage, Europe had bifurcation into two global lineages with an early occurrence of certain cluster while Asia had a downstream confluence of two large lineages diverging by two distinct clusters. Based on the cluster patterns of shared variants of the SARS-CoV-2 virus, Africa demonstrated a relatively clear distinction among three distinct regions. The study provides insights into the evolutionary patterns of SARS-CoV-2 and highlights the importance of international collaboration in tracking and responding to emerging variants. The study found that the global pandemic was driven by Omicron variants that evolved with significant differences between countries and regions, and with different patterns across continents.

Variability, character association and genetic divergence studies in cowpea [Vigna unguiculata (I.)Walp] genotypes

The present experiment comprised of 46 cowpea genotypes to estimate genetic variability, interrelation among 13 quantitative characters and genetic divergence during 2020. High heritability coupled with highKharifgenetic advance as of mean was observed for the characters , seed yield per plant, number of per centviz.clusters per plant, number of pods per plant, plant height, days to 50% flowering and days to maturity. High phenotypic and genotypic coefficient of variation, heritability and high genetic advance as of mean per cent(GAM) were observed in seed yield per plant, number of clusters per plant, number of pods per plant and plant height. Seed yield per plant was found positively and significantly correlated with pods per plant, seeds per pod, pods per cluster, pod length and 100 seed weight. Path analysis revealed that number of seeds per pod, number of pods per plant and 100 seed weight would be selected as important towards seed yield charactersas they showed a high positive direct effect on seed yield. D analysis revealed that all the genotypes were 2grouped into seven distinct clusters. Cluster V had the maximum intra-cluster distance. The crossing among the genotypes of clusters V and VII, II and V, III and VI would result in novel recombinants as they showed high inter-cluster distance. Increased contribution to the divergence was found for the characters like plant height, seed yield per plant, number of clusters per plant and days to 50% flowering. Cluster V was found to be best performing for agronomic characters, followed by Cluster VI and Cluster III. Cluster II was best performing for nutritional value and earliness

Preoperative and Postoperative Segmental and Overall Range of Motion in Patients Undergoing Lumbar Spinal Fusion Using HA-Infused PEEK and HA-Treated Titanium Alloy Interbody Cages.

Retrospective observational radiographic analysis. Determine how single level lumbar interbody fusion (LIF) alters segmental range of motion (ROM) at adjacent levels and decreases overall ROM. This study included 54 patients who underwent single-level anterior (ALIF, 39%), thoraco-LIF (TLIF, 26%), posterior LIF (PLIF, 22%), or lateral LIF (LLIF, 13%) (L2-3/L3-4/L4-5/L5-S1: 4%/13%/35%/48%). Segmental ROM from L1-2 to L5-S1 and the overall lumbar ROM (L1-S1) were assessed from preoperative and postoperative flexion-extension radiographs. K-means cluster analysis was used to identify ROM subgroups. The overall L1-S1 ROM decreased 14% (25.5 ± 20.4° to 22.0 ± 17.2°, P = .104) postoperatively. ROM at the fusion level decreased 77% (4.8 ± 5.0° to 1.1 ± 1.1°, P < .001). Caudal adjacent segment ROM decreased 12% (5.2 ± 5.7° to 4.6 ± 4.4°, P = .345) and cranially ROM increased 34% (4.3 ± 5.0° to 5.7 ± 5.7°, P = .05). K-cluster analysis identified 3 distinct clusters (P < .05). Cluster 1 lost more ROM and had less improvement in patient-reported outcomes measures (PROMs) than average. Cluster 2 had less ROM loss than average with worse PROMs improvement. Cluster 3 did not have changes in ROM and better improvement in PROMs than average. Successful fusion was verified in 96% of all instrumented segments with >6months follow-up (ROM <4°). Following single-level L IF, patients should expect a loss of 3.3°, or 14% of overall lumbar motion with increases in ROM of the cranial segment. However, specific clusters of patients exist that experience different relative changes in ROM and PROMs.

Compound Classification and Consideration of Correlation with Chemical Descriptors from Articles on Antioxidant Capacity Using Natural Language Processing.

In recent times, there has been a substantial increase in the number of articles focusing on antioxidants. However, the development of a comprehensive estimator for antioxidant capacity remains elusive due to the challenge of integrating information from these articles. Furthermore, the complexity of the antioxidant mechanism, which involves a multitude of factors, makes it difficult to establish a simple equation or correlation. Hence, there is a pressing need for a model that can effectively interpret the collective knowledge from these articles, especially from a chemistry perspective. In this research, we employed natural language processing techniques, specifically Word2Vec, to analyze articles related to antioxidant capacity. We extracted representation vectors of compound names from these documents and organized them into 10 distinct clusters. In our investigation of two of these clusters, we unveiled that the majority of the compounds in question were flavonoids and flavonoid glycosides. To establish a link between the descriptors and clusters, we utilized kernel density estimation and generated scatter plots to visualize their similarity. These visualizations clearly indicated a strong relationship between the descriptors and clusters, affirming that a tangible connection exists between word vectors and compound descriptors through a document analysis conducted with natural language processing techniques. This study represents a pioneering approach that utilizes document analysis to shed light on the field of antioxidant capacity research, marking a significant advancement in this domain.

Cluster Analysis of Personality Types Using Respondents’ Big Five Personality Traits

This study utilized a mixed model approach, incorporating k-means clustering analysis for data examination, discriminant analysis for classification, and multilayer perceptron neural network analysis for prediction. After removing inadequate samples and outliers, the total number of observations was 19,692 for this study, which was collected through an interactive online personality test (i.e., Big Five Personality Traits) in 2012. The empirical results based on the k-means clustering analysis identified four different personality clusters using the total score of Big Five Personality Traits (Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness to Experience). The empirical results obtained from the k -means clustering analysis revealed the presence of four distinct personal clusters, determined by the total scores of the Big Five Personality Traits. The accuracy of the clustering analysis was further tested using discriminant analysis, which indicated significant difference among the cluster means and correctly classified 95.5% of the original grouped cases. For predictive modeling, a multilayer perceptron neural network framework was used. The network had a 5-6-4 structure and was employed to determine the personality classification of participants. Notably, the model achieved 99.4% accuracy in correctly classifying the training grouped cases and 99.2% accuracy for the testing grouped cases. The results of this study offer valuable insights into understanding the personalities of participants, with implications for various domains such as psychology, social sciences, cultural studies, and economics.

SENTIMENT ANALYSIS OF OMNIBUS LAW USING SUPPORT VECTOR MACHINE (SVM) WITH LINEAR KERNEL

The Omnibus Law is a recently enacted legislation that has been implemented within the regulatory framework of Indonesia. This legal framework, often denoted as the Universal Sweeping Law, consolidates multiple legal norms into a singular regulation. The Omnibus law encompasses a total of 11 distinct clusters, one of which pertains specifically to labor regulations. Nevertheless, the Omnibus law has elicited diverse reactions among the Indonesian populace, particularly on the Twitter platform. The researchers employed scraping techniques to extract tweets from Twitter users. A total of 3067 data points were collected during the period from March 20, 2022 to May 20, 2022. The data were subsequently categorized into positive, negative, and neutral sentiments. They were then assigned weights and classified using the Suport Vector Machine (SVM) method. The objective was to identify the public's sentiments towards the Omnibus law and evaluate the accuracy of the Support Vector Machine (SVM) method. The accuracy of the SVM algorithm with a linear kernel is found to be 97.05% based on its classification performance. There is a greater level of public concern and attention directed towards positive responses in relation to the Omnibus law, as opposed to negative responses. The positive responses encompassed the provision of favorable legislation to assist young entrepreneurs, whereas the negative responses pertained to concerns regarding persistently low wages for workers, despite the implementation of the Omnibus Law.

Unraveling the landscape of large language models: a systematic review and future perspectives

PurposeThe rapid rise of large language models (LLMs) has propelled them to the forefront of applications in natural language processing (NLP). This paper aims to present a comprehensive examination of the research landscape in LLMs, providing an overview of the prevailing themes and topics within this dynamic domain.Design/methodology/approachDrawing from an extensive corpus of 198 records published between 1996 to 2023 from the relevant academic database encompassing journal articles, books, book chapters, conference papers and selected working papers, this study delves deep into the multifaceted world of LLM research. In this study, the authors employed the BERTopic algorithm, a recent advancement in topic modeling, to conduct a comprehensive analysis of the data after it had been meticulously cleaned and preprocessed. BERTopic leverages the power of transformer-based language models like bidirectional encoder representations from transformers (BERT) to generate more meaningful and coherent topics. This approach facilitates the identification of hidden patterns within the data, enabling authors to uncover valuable insights that might otherwise have remained obscure. The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.FindingsThe analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.Practical implicationsThis classification offers practical guidance for researchers, developers, educators, and policymakers to focus efforts and resources. The study underscores the importance of addressing challenges in LLMs, including potential biases, transparency, data privacy, and responsible deployment. Policymakers can utilize this information to shape regulations, while developers can tailor technology development based on the diverse applications identified. The findings also emphasize the need for interdisciplinary collaboration and highlight ethical considerations, providing a roadmap for navigating the complex landscape of LLM research and applications.Originality/valueThis study stands out as the first to examine the evolution of LLMs across such a long time frame and across such diversified disciplines. It provides a unique perspective on the key areas of LLM research, highlighting the breadth and depth of LLM’s evolution.

The tertiary lymphoid structure-related signature identified PTGDS in regulating PD-L1 and promoting the proliferation and migration of glioblastoma

BackgroundTertiary lymphoid structure (TLS) is a unique organ that carries out tumor cell elimination at tumor sites. It is continuously stimulated by inflammatory tumor signals and has been found to augment immunotherapy response. However, the detailed mechanisms behind it still need to be defined. MethodsTo explore and grasp the whole picture of TLS from a pan-cancer view, we collected nine TLS-related genes from previous studies. We performed a comprehensive analysis of 9637 samples across 33 tumor types accessed from The Cancer Genome Atlas (TCGA) database. EdU, Transwell, and flow cytometry were performed on the feature gene PTGDS in U251 cells. The regulatory role of PTGDS on PD-L1 expression and macrophage polarization was verified. ResultsAlteration analysis showed that mutations of TLS-related genes were widespread and relatively high. Clustering analysis based on the expression of these nine genes obtained two distinct clusters, with high EIF1AY and PTGDS in cluster 2 and better overall survival in cluster 1. To distinguish the two clusters, we utilized six machine learning algorithms and filtrated EIF1AY, PTGDS, SKAP1, and RBP5 as the characteristic genes, among which the former two genes were proved to be hazardous. PTGDS was found to regulate PD-L1 expression and also promoted the proliferation and migration of U251 cells. The knockdown of PTGDS could reduce the migration of macrophages and inhibit the polarization of macrophages into M2-phenotype. In addition, we established a TLS score to demonstrate patients’ TLS activity. The low TLS-score group overlapped with cluster 1 and displayed a better prognosis. Besides, the low TLS-score group was related to better immunotherapy responses. The HE staining of histopathological sections confirmed that the low TLS-score group exhibited higher infiltration of immune cells. ConclusionThis study reveals broad molecular, tumorigenic, and immunogenic signatures for further functional and therapeutic studies of tertiary lymphoid structure. The TLS score we established effectively predicted immunotherapy response and patients’ survival. Its future application and combination await more research.

Multilocus phylogeography of Italian Moorish geckos adds insights into the evolutionary history of European populations

AbstractGeckos of clade III of the Tarentola mauritanica species complex are widespread throughout southern Europe and northern Africa. We investigated the genetic variability of the Italian populations by performing a widespread sampling throughout the mainland and the two main islands of Sicily and Sardinia. We analysed 199 newly generated sequences of the mitochondrial 16S rRNA gene and 269 nuclear genotypes inferred from nine microsatellite loci from 307 individuals. We found 13 new mitochondrial haplotypes in Italy, whereas previous findings reported a single haplotype widespread throughout the country and in the rest of Europe, which currently make Italy the centre of genetic diversity of this taxon. There was no evidence of mitochondrial DNA structuring with geographic correlation. At the population genetic level, our multilocus approach based on nuclear markers returned a shallow genetic structure. Nonetheless, we disclosed the presence of at least four distinct genetic clusters (namely the Adriatic, two Tyrrhenian and the Calabrian clusters). Our findings do not support the two hypotheses proposed to explain the low level of mitochondrial polymorphism in this taxon, namely the genetic hitch‐hiking due to selective sweep and the historical human‐mediated colonization hypotheses. Based on the fossil record, the presence in Italy of this taxon since the Pleistocene Epoch is plausible. Given the discordance in genetic structure between mitochondrial and nuclear DNA, the exact role of the Italian Peninsula in shaping the observed patterns of genetic diversity during the Pleistocenic climatic oscillations needs further investigation.

Identification and validation of prognostic and tumor microenvironment characteristics of necroptosis index and BIRC3 in clear cell renal cell carcinoma.

Necroptosis is a form of programmed cell death; it has an important role in tumorigenesis and metastasis. However, details of the regulation and function of necroptosis in clear cell renal cell carcinoma (ccRCC) remain unclear. It is necessary to explore the significance of necroptosis in ccRCC. Necroptosis-related clusters were discerned through the application of Consensus Clustering. Based on the TCGA and GEO databases, we identified prognostic necroptosis-related genes (NRGs) with univariate COX regression analysis. The necroptosis-related model was constructed through the utilization of LASSO regression analysis, and the immune properties, tumor mutation burden, and immunotherapy characteristics of the model were assessed using multiple algorithms and datasets. Furthermore, we conducted comprehensive GO, KEGG, and GSVA analyses to probe into the functional aspects of biological pathways. To explore the expression and of hub gene (BIRC3) in different ccRCC cell types and cell lines, single-cell sequencing data was analysed and we performed Quantitative Real-time PCR to detect the expression of BIRC3 in ccRCC cell lines. Function of BIRC3 in ccRCC was assessed through Cell Counting Kit-8 (CCK8) assay (for proliferation), transwell and wound healing assays (for migration and invasion). Distinct necroptosis-related clusters exhibiting varying prognostic implications, and enrichment pathways were identified in ccRCC. A robust necroptosis-related model formulated based on the expression of six prognostic NRGs, presented substantial predictive capabilities of overall survival and was shown to be related with patients' immune profiles, tumor mutation burden, and response to immunotherapy. Notably, the hub gene BIRC3 was markedly upregulated in both ccRCC tissues and cell lines, and showed significant correlations with immunosuppressive cells, immune checkpoints, and oncogenic pathways. Downregulation of BIRC3 demonstrated a negative regulatory effect on ccRCC cell proliferation migration and invasion. The necroptosis-related model assumed a pivotal role in determining the prognosis, tumor mutation burden, immunotherapy response, and immune cell infiltration characteristics among ccRCC patients. BIRC3 exhibited significant correlations with the immunosuppressive microenvironment, which highlighted its potential for informing the design of innovative immunotherapies for ccRCC patients.

Artificial Intelligence in Digital Marketing: Insights from a Comprehensive Review

Artificial intelligence (AI) has rapidly emerged as a transformative force in multiple sectors, with digital marketing being a prominent beneficiary. As AI technologies continue to advance, their potential to reshape the digital marketing landscape becomes ever more apparent, leading to profound implications for businesses and their digital outreach strategies. This research seeks to answer the pivotal question: “How could AI applications be leveraged to optimize digital marketing strategies”? Drawing from a systematic literature review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, this study has identified 211 pertinent articles. Through a comprehensive bibliometric analysis, the findings were categorized into distinct clusters, namely: AI/ML (Machine Learning) Algorithms, Social Media, Consumer Behavior, E-Commerce, Digital Advertising, Budget Optimization, and Competitive Strategies. Each cluster offers insights into how AI applications can be harnessed to augment digital marketing efforts. The conclusion synthesizes key findings and suggests avenues for future exploration in this dynamic intersection of AI and digital marketing. This research contributes to the field by providing a comprehensive bibliometric analysis of AI in digital marketing, identifying key trends, challenges, and future directions. Our systematic approach offers valuable insights for businesses and researchers alike, enhancing the understanding of AI’s evolving role in digital marketing strategies.

Lithofacies Identification from Wire-Line Logs Using an Unsupervised Data Clustering Algorithm

Stratigraphic identification from wire-line logs and core samples is a common method for lithology classification. This traditional approach is considered superior, despite its significant financial cost. Artificial neural networks and machine learning offer alternative, cost-effective means for automated data interpretation, allowing geoscientists to extract insights from data. At the same time, supervised and semi-supervised learning techniques are commonly employed, requiring a sufficient amount of labeled data to be generated through manual interpretation. Typically, there are abundant unlabeled geophysical data while labeled data are scarcer. Supervised and semi-supervised techniques partially address the cost issue. An underutilized class of machine-learning-based methods, unsupervised data clustering, can perform consonant classification by grouping similar data without requiring known results, presenting an even more cost-effective solution. In this study, we examine a state-of-the-art unsupervised data clustering algorithm called piecemeal clustering to identify lithofacies from wire-line logs, effectively addressing these challenges. The piecemeal clustering algorithm groups similar wire-log signatures into clusters, determines the number of clusters present in the data, and assigns each signature to one of the clusters, each of which represents a lithofacies. To evaluate the performance, we tested the algorithm on publicly released data from ten wells drilled in the Hugoton and Panoma fields of southwest Kansas and northwest Oklahoma, respectively. The data consist of two major groups: marine and non-marine facies. The study herein is centered around addressing two fundamental research questions regarding the accuracy and practicality of the piecemeal clustering algorithm. The algorithm successfully identified nine distinct clusters in our dataset, aligning with the cluster count observed in previously published works employing the same data. Regarding mapping accuracy, the results were notable, with success rates of 81.90% and 45.20% with and without considering adjacent facies, respectively. Further detailed analysis of the results was conducted for individual types of facies and independently for each well. These findings suggest the algorithm’s precision in characterizing the geological formations. To assess its performance, a comprehensive comparative analysis was conducted, encompassing other data clustering algorithms, as well as supervised and semi-supervised machine learning techniques. Notably, the piecemeal clustering algorithm outperformed alternative data clustering methods. Furthermore, despite its unsupervised nature, the algorithm demonstrated competitiveness by yielding results comparable to, or even surpassing, those obtained through supervised and semi-supervised techniques.

Infrared Thermal Imaging Analysis in Screening for Toddler’s Fracture: A Proof-of-Concept Study

This study explored and developed high-resolution infrared thermal (HRIT) imaging for screening toddler’s fractures. A toddler’s fracture is a common tibial fracture in children younger than six years old. The study included 39 participants admitted to an emergency department with a suspected toddler’s fracture. X-ray confirmed eight participants with a toddler’s fracture (20.5%). Infrared images of participants were recorded on their index visit, focusing on region-of-interests on the injured and the contralateral (uninjured) legs. The uninjured leg acted as a thermal reference. Six statistical measures obtained from the images were analyzed. These were maximum, mean, standard deviation, median, interquartile range, and skewness. The Shapiro–Wilk test indicated that the measures were from a normal distribution. A two-sample t-test indicated that the majority of the six measures had significantly different means (p &lt; 0.05) when comparing the participants with and without a fracture. Similarly, the first principal component (PC1), obtained through principal component analysis of the six measures, was significantly different (p &lt; 0.05) comparing participants with and without a fracture. Visualization of the statistical measures and their PC1 demonstrated distinct clustering. This study demonstrated that HRIT imaging is valuable for screening for toddler’s fractures, but a larger follow-on study will be required to confirm the findings.

Impact of Season on Intestinal Bacterial Communities and Pathogenic Diversity in Two Captive Duck Species.

Vertebrates and their gut bacteria interact in complex and mutually beneficial ways. The intestinal microbial composition is influenced by several external influences. In addition to food, the abiotic elements of the environment, such as temperature, humidity, and seasonal fluctuation are also important determinants. Fecal samples were collected from two captive duck species, Baikal teal (Sibirionetta formosa) and common teal (Anas crecca) across four seasons (summer, autumn, winter, and spring). These ducks were consistently fed the same diet throughout the entire experiment. High throughput sequencing (Illumina Mi-seq) was employed to analyze the V4-V5 region of the 16sRNA gene. The dominant phyla in all seasons were Proteobacteria and Firmicutes. Interestingly, the alpha diversity was higher in winter for both species. The NMDS, PCoA, and ANOSIM analysis showed the distinct clustering of bacterial composition between different seasons, while no significant differences were discovered between duck species within the same season. In addition, LefSe analysis demonstrated specific biomarkers in different seasons, with the highest number revealed in winter. The co-occurrence network analysis also showed that during winter, the network illustrated a more intricate structure with the greatest number of nodes and edges. However, this study identified ten potentially pathogenic bacterial species, which showed significantly enhanced diversity and abundance throughout the summer. Overall, our results revealed that season mainly regulated the intestinal bacterial community composition and pathogenic bacteria of captive ducks under the instant diet. This study provides an important new understanding of the seasonal variations in captive wild ducks' intestinal bacterial community structure. The information available here may be essential data for preventing and controlling infections caused by pathogenic bacteria in captive waterbirds.

Development and testing of an endometrial status assessment test based on RNAampliSeq technology

BACKGROUND: Approximately two thirds of all implantation failures are due to impaired endometrial receptivity. The lack of implantation may be due to an implantation window failure found in approximately 25-30% of women with a history of failure to perform assisted reproductive technology protocols. The receptivity phase in such patients can be shifted in terms of timing, have a short duration, or not form. Currently, there are several commercial tests for determining endometrial receptivity based on transcriptomic data. However, these tests differ very significantly from each other in the set of genes studied, and the exact mechanism of endometrial receptivity is still not fully understood.&#x0D; AIM: The aim of this study was to create and check our own test based on RNA AmpliSeq technology to assess the receptivity status of the endometrium and the implantation window.&#x0D; MATERIALS AND METHODS: We previously created an RNAampliSeq panel containing 421 gens. With its use, the differential expression of these genes was analyzed in 38 endometrial samples taken in the proliferative and receptive phases.&#x0D; RESULTS: The studied samples form clearly distinguishable clusters with the receptive and proliferative endometria. 271 genes from our panel are differentially expressed in different phases of the menstrual cycle.&#x0D; CONCLUSIONS: We have created and tested a model that allows clearly distinguishing between the proliferative endometrium and the receptive one and identifying patients with disorders of the menstrual cycle.

Forecasting coherence resonance in a stochastic Fitzhugh–Nagumo neuron model using reservoir computing

We delve into the intriguing realm of reservoir computing to predict the intricate dynamics of a stochastic FitzHugh–Nagumo neuron model subjected to external noise. Through innovative reservoir design and training, we unveil the remarkable capacity of a reservoir computer to forecast the behavior of this stochastic system across a wide spectrum of noise intensity variations. Notably, our reservoir computer astutely replicates the intricate phenomenon of coherence resonance in the stochastic FitzHugh–Nagumo neuron, signifying the superior modeling capabilities of this approach. A detailed examination of the microscopic dynamics within the reservoir’s hidden layer reveals the emergence of distinct neuronal clusters, each displaying unique behaviors. Certain neurons within the reservoir are adept at faithfully reproducing the dynamical traits of the neuron, particularly the spike generation mechanism. In contrast, the remaining neurons within the reservoir seem to emulate stochastic influences with remarkable precision, accurately capturing the moments of spike generation in the neuron under the sway of noise. This innovative reservoir design proves to be highly effective across a diverse range of noise control parameters, faithfully replicating the essential characteristics of the original stochastic FitzHugh–Nagumo neuron. These findings illuminate the potential of reservoir computing to model and predict the dynamics of complex stochastic systems, showcasing its adaptability and versatility in understanding and simulating natural phenomena.

Growth, Substrate, and Metabolite Changes of Probiotic Bifidobacterium animalis subsp. lactis in Soy (Tofu) Whey

Soy (tofu) whey is one of the two side-streams from tofu processing, which has been shown to be suitable for microbial growth. In this work, probiotic Bifidobacterium animalis subsp. lactis Bl-04 and B94 were used to ferment soy whey with different supplements to explore the possibility of harnessing Bifidobacterium fermentation to upcycle soy whey. Soy whey was supplemented with different supplements (control, CN; glucose, G; glucose + cysteine, GC; glucose + cysteine + yeast extract, GCY) and inoculated with either B. lactis Bl-04 or B94. Growth, substrate utilization, and metabolic products were monitored before and after fermentation. Bl-04 managed to grow in all four media, while B94 needed cysteine to grow. The contents of sucrose, cysteine, methionine, and succinic acid decreased in the fermented samples. Acetic and lactic acids were produced in fermented soy whey ranging from 0.49–2.66 g/L and 0.58–2.88 g/L, respectively, with vitamin B12 at 2.06–4.56 μg/L. Increases in isoflavone aglycones (0.19–25.05 mg/L) and iron (0.03–0.12 mg/L) were observed. The PCA analysis of volatiles showed a distinct clustering due to short-chain fatty acids (acetic, butyric, and isovaleric acid), 2,3-butanedione (diacetyl), H2S, and 3-methylthiophene. Overall, the selection of suitable bifidobacterial strains and supplements for soy whey fermentation can open avenues to upcycle soy whey.