Miracle-dz/UniFAR-FTUs huggingface dataset:
Mapping Research Trends and Hotspots in the Link between Alzheimer’s Disease and Gut Microbes over the Past Decade: A Bibliometric Analysis

Alzheimer’s disease (AD) is a globally prevalent neurodegenerative disorder, the underlying causes and mechanisms of which remain elusive. The emerging interest in the potential connection between gut microbes and AD prompted our study to investigate this field through bibliometric analysis. To examine research trends over the past decade, we collected relevant data using search terms associated with gut microbiota and AD from the Web of Science Core Collection. Our analysis involved various tools, including R (version 4.2.2), VOSviewer (version 1.6.18), CiteSpace software (version 6.2.R1), and an online bibliometric platform. Our findings identified a total of 1170 articles published between 2012 and 2022, indicating a consistent growth of research interest in this area. Notably, China significantly contributed with 40.7% (374) of the publications, signifying its prominent role in this field. Among the journals, the Journal of Alzheimer’s Disease published the highest number of articles (57; 4.9%). In terms of author influence, Wang Y, with an H-index of 13, emerged as the most influential author. Additionally, Shanghai Jiaotong University was the most productive institution, accounting for 66 articles (5.6%). Through keyword analysis, we grouped high-frequency keywords into six clusters: gut microbiota, AD, neuroinflammation, gut-brain axis, oxidative stress, and chain fatty acids. Chain fatty acids, oxidative stress, and the gut-brain axis emerged as dominant research topics in the past five years. Recent studies have specifically focused on “nlrp3 inflammasome” and “clearance” (2020–2022), indicating shifting research interests within this field. This bibliometric analysis aims to provide insights into the evolving landscape of research on the gut microbiota and AD. Our results identify key research trends and themes while highlighting potential research gaps. The findings offer valuable perspectives for future investigations, advancing our understanding of AD and exploring potential therapeutic strategies.
EFFECT OF SYNBIOTIC ON INTESTINAL HISTOMORPHOMETRY AND GROWTH RATE IN QUAILS, EXPERIMENTALLY INFECTED WITH FIELD STRAIN OF SALMONELLA GALLINARUM

This study was planned to investigate the effect of synbiotic on intestinalhistomorphometry and the growth rate of quails experimentally infected with a field strain ofSalmonella gallinarum. Day old Japanese quails (120), with an average body weight of 6.09±1.1 g,were randomly assigned into 4 groups A, B, C, and D. Quails of groups A and B were given synbioticon a daily and weekly basis respectively, along with challenge while group C was negative control andgroup D was the positive control group. A total of 60 organ samples of the intestine were collectedfrom apparently healthy and freshly dead quails respectively to isolate the field strain of Salmonellagallinarum. Bacterial isolation and molecular identification were performed in accordance withlaboratory diagnostic culture techniques and PCR. Results from the current study indicate thatnonantibiotic feed additives such as synbiotics boosted the gut histomorphometric parametersincluding the villus height, villus width, and crypt depth under the challenge of Salmonella gallinarum.There was a significant increase in all these parameters due to synbiotic feeding except for the negativecontrol group which showed the lowest values. On day 21, DD achieved a maximum villus height of636.88 ± 65.93b µm in the duodenal mucosa, whereas, maximum villus width of 182.01 ± 15.40c µmin duodenal mucosa was achieved by group AA. Maximum villus height of 276.89 ± 21.16b µm andcrypt depth of 26.66 ± 1.15a µm in jejunum mucosa were recorded in group AA. Statistical results byusing the technique of one-way ANOVAs indicated that there was a significant increase inmorphmetric parameters of duodenal and jejunum mucosa in the groups fed synbiotic as compared tothe negative control. Hence results illustrate that there is an overall increase in histological parametersof duodenal and jejunum mucosa in the groups fed synbiotic as compared with the negative controlgroup. This study supports the beneficial effects of synbiotic on intestinal health and the growth rate ofJapanese quails by improving FCR.
Genetics of strabismus

Strabismus, or misalignment of the eyes, is the most common ocular disorder in the pediatric population, affecting approximately 2%–4% of children. Strabismus leads to the disruption of binocular vision, amblyopia, social and occupational discrimination, and decreased quality of life. Although it has been recognized since ancient times that strabismus runs in families, its inheritance patterns are complex, and its precise genetic mechanisms have not yet been defined. Family, population, and twin studies all support a role of genetics in the development of strabismus. There are multiple forms of strabismus, and it is not known if they have shared genetic mechanisms or are distinct genetic disorders, which complicates studies of strabismus. Studies assuming that strabismus is a Mendelian disorder have found areas of linkage and candidate genes in particular families, but no definitive causal genes. Genome-wide association studies searching for common variation that contributes to strabismus risk have identified two risk loci and three copy number variants in white populations. Causative genes have been identified in congenital cranial dysinnervation disorders, syndromes in which eye movement is limited or paralyzed. The causative genes lead to either improper differentiation of cranial motor neurons or abnormal axon guidance. This article reviews the evidence for a genetic contribution to strabismus and the recent advances that have been made in the genetics of comitant strabismus, the most common form of strabismus.
Immune landscape in rejection of renal transplantation revealed by high-throughput single-cell RNA sequencing

Background: The role of the cellular level in kidney transplant rejection is unclear, and single-cell RNA sequencing (scRNA-seq) can reveal the single-cell landscape behind rejection of human kidney allografts at the single-cell level. Methods: High-quality transcriptomes were generated from scRNA-seq data from five human kidney transplantation biopsy cores. Cluster analysis was performed on the scRNA-seq data by known cell marker genes in order to identify different cell types. In addition, pathways, pseudotime developmental trajectories and transcriptional regulatory networks involved in different cell subpopulations were explored. Next, we systematically analyzed the scoring of gene sets regarding single-cell expression profiles based on biological processes associated with oxidative stress. Results: We obtained 81,139 single cells by scRNA-seq from kidney transplant tissue biopsies of three antibody-mediated rejection (ABMR) patients and two acute kidney injury (AKI) patients with non-rejection causes and identified 11 cell types, including immune cells, renal cells and several stromal cells. Immune cells such as macrophages showed inflammatory activation and antigen presentation and complement signaling, especially in rejection where some subpopulations of cells specifically expressed in rejection showed specific pro-inflammatory responses. In addition, patients with rejection are characterized by an increased number of fibroblasts, and further analysis of subpopulations of fibroblasts revealed their involvement in inflammatory and fibrosis-related pathways leading to increased renal rejection and fibrosis. Notably, the gene set score for response to oxidative stress was higher in patients with rejection. Conclusion: Insight into histological differences in kidney transplant patients with or without rejection was gained by assessing differences in cellular levels at single-cell resolution. In conclusion, we applied scRNA-seq to rejection after renal transplantation to deconstruct its heterogeneity and identify new targets for personalized therapeutic approaches.
Zeno-free adaptive event-triggered control design

This paper presents a new general framework for adaptive event-triggered control strategy to extend average inter-event interval, while maintaining the performance of the system. The proposed event-triggering mechanism is acquired from input to state stability conditions, which is defined in terms of system states as well as an adaptation parameter. Under the Lipschitz assumption, a positive lower bound on sampling durations is also established that is essential to restrain the Zeno behavior. Applying the proposed method to linear time-invariant systems, leads to sufficient conditions to guarantee asymptotic stability in the form of matrix inequalities. Moreover, it is shown that there exist more degrees of freedom to improve the performance criterion from theoretical aspects. Finally, in order to show capability of the proposed method and its better performance compared with some recent works, numerical simulations are presented.
An Efficient Edge Preserving Interpolation Method for Underwater Acoustic Image Resolution Enhancement

Underwater acoustic images are acquired using sonar instrument that uses sound propagation to navigate and map the sea ﬂoor. The sonar devices are eﬀectively used to create images of large area of the seabed. However, the visual perception of the object in the acoustic image depends on refraction, which is a function of changes in the speed of sound in successive layers of water. And refraction depends mainly on temperature, slightly on salinity and hydrostatic pressure. The quality and resolution of sonar imaging of the bottom depends on many other factors such as pitch, yaw and heave of the side scan sonar, the presence of volume scatterers in the water body, the distance of the sonar from the bottom and orientation of the object. Generally, the objects in an acoustic image would be of small size compared to their normal size as the distance between the sonar and object is larger. To detect and recognize the objects in the images, the resolution should be enhanced. In this paper, we propose an eﬃcient edge preserving interpolation method for underwater acoustic image resolution enhancement which preserves the edge sharpness. The method handles the diagonal pixels in the ﬁrst pass, in turn ﬁlls the horizontal and vertical pixels in the second pass. The results obtained are compared with the state-of-the-art interpolation techniques and the performance measures such as Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measurement (SSIM) shows an improved result.
Identification of Key Modules and Hub Genes Involved in Regulating the Color of Chicken Breast Meat Using WGCNA

Simple Summary To explore the gene networks and key genes involved in the regulation in meat color of chicken breast muscle, weighted gene co-expression network analysis (WGCNA) was performed based on transcriptome data from the pectoralis major muscle of two yellow feather chicken breeds. The results suggest that genes involved in the regulation of mitochondrial activity and lipid oxidation may play a crucial role in the formation of meat color. The present study advanced our understanding of the molecular mechanisms contributing to meat color and identified potential molecular markers for breeding chicken breast meat color. Abstract Meat color is one of the most important economic traits in chickens. However, the gene network and regulatory mechanisms contributing to meat color traits in chickens remain largely unknown. In the present study, we performed weighted gene co-expression network analysis (WGCNA) based on RNA-Seq datasets of 16 pectoralis major muscle samples from two yellow-feather chicken breeds to identify the modules and hub genes related to meat color in chickens. A total of 18,821 genes were used to construct the weighted gene co-expression network, and 29 co-expression gene modules were identified. Among these modules, five modules including blue, brown, steel blue, paleturquoise and orange modules were found to be significantly correlated with meat color traits. Furthermore, several genes within the association module involved in the regulation of mitochondrial activity (e.g., ATP5L, UQCR10 and COX7C) and lipid oxidation (e.g., CAV3, RBP4A and APOH) were identified as hub genes that may play a crucial role in the regulation of meat color. These results provide valuable information to improve our understanding of gene expression and regulation in relation to meat color traits and contribute to future molecular breeding for improving meat color in chickens.
Analysis of Smart Manufacturing Technologies for Industry Using AI Methods

Smart manufacturing technologies have gained significant attention in the industrial sector due to their potential to revolutionize traditional manufacturing processes. Among these technologies, artificial intelligence (AI) methods have emerged as powerful tools for enhancing efficiency, productivity, and decision-making in manufacturing operations. This paper presents an analysis of smart manufacturing technologies for industry using AI methods. The analysis focuses on the application of AI techniques such as machine learning, deep learning, and data analytics in various aspects of smart manufacturing, including predictive maintenance, process optimization, quality control, and supply chain management. The paper provides an overview of the key AI methods employed in smart manufacturing and discusses their benefits and challenges. It also highlights case studies and real-world implementations of AI-based smart manufacturing systems. The findings of this analysis demonstrate the significant contributions of AI methods in enabling intelligent and autonomous manufacturing systems. The paper concludes with insights into the future directions and potential impact of AI-driven smart manufacturing technologies in industry, emphasizing the importance of continued research and development in this field to unlock the full potential of smart manufacturing in the industry. Smart manufacturing technologies have revolutionized the industrial sector by enhancing productivity, efficiency, and flexibility. Artificial intelligence (AI) methods, such as machine learning and data analytics, play a crucial role in enabling smart manufacturing systems to optimize processes and make informed decisions. This research paper aims to analyse the application of AI methods in smart manufacturing technologies. The study explores various AI-based approaches used in different stages of smart manufacturing, including data acquisition, data analysis, process optimization, and predictive maintenance. The research provides insights into the benefits, challenges, and potential future developments of AI in smart manufacturing, offering valuable guidance for industries aiming to implement these technologies.
Evaluation of tick repellency activity and toxicity of commiphora swynnertonii (burtt) exudates

This thesis reports the repellency activity and toxicity of Commiphora swynnertonii exudates. The exudates were extracted using hexane and chloroform solvents. The crude extracts were tested for repellency activity at concentration of 110, 100, 90, 80, 70, 60 and 50 mg/ml against Rhipicephalus appendiculatus larvae. The crude extracts were further evaluated for the acute and subacute toxicity using albino mice and rats, respectively. The results showed that repellency activity appeared to be concentration and time dependent. In acute toxicity, oral administration of C. swynnertonii hexane and chloroform extract at a doses of 500, 1000, and 2000 mg/kg body weight induced no clinical signs of toxicity in the mice during 14 days of experimental period. However, C. swynnertonii chloroform extract showed some toxic properties in some of blood parameters and in vital organs (liver and kidney) of treated mice. In subacute toxicity, C. swynnertonii hexane extract showed significant change in some of hematological and biochemical parameters. Even though rats treated with C. swynnertonii chloroform extract did not develop any observable sign of toxicity, some toxic properties was observed in some of blood parameters and in vital organs (liver and kidney). It is concluded that, C. swynnertonii hexane and chloroform extracts considered as good repellency agents against Rhipicephalus appendiculatus larvae. For the toxicity evaluation, C. swynnertonii hexane extracts was nontoxic while C. swynnertonii chloroform extract had toxic properties deduced from change in blood parameters and internal organs of treated animals.
A Radiomics-Based Classifier for the Progression of Oropharyngeal Cancer Treated with Definitive Radiotherapy

Simple Summary Oropharyngeal cancer is the most common type of head-and-neck squamous cell carcinoma. Although patients with HPV-associated cancers have a better prognosis than patients with HPV-negative cancers, there is a lack of robust biomarkers that describe the relative risk for disease progression. To investigate this problem, we extracted quantitative descriptors from medical images (known as radiomics features) that could not otherwise be assessed by the naked eye. We built a machine learning model based on clinical and radiomics features to predict whether a patient will exhibit disease progression at 2 years post-treatment. These findings are important for identifying patients treated with definitive radiotherapy with low and high risk of disease progression and formulating patient-specific treatment strategies. Abstract In this study, we investigated whether radiomics features from pre-treatment positron emission tomography (PET) images could be used to predict disease progression in patients with HPV-positive oropharyngeal cancer treated with definitive proton or x-ray radiotherapy. Machine learning models were built using a dataset from Mayo Clinic, Rochester, Minnesota (n = 72) and tested on a dataset from Mayo Clinic, Phoenix, Arizona (n = 22). A total of 71 clinical and radiomics features were considered. The Mann–Whitney U test was used to identify the top 2 clinical and top 20 radiomics features that were significantly different between progression and progression-free patients. Two dimensionality reduction methods were used to define two feature sets (manually filtered or machine-driven). A forward feature selection scheme was conducted on each feature set to build models of increased complexity (number of input features from 1 to 6) and evaluate model robustness and overfitting. The machine-driven features had superior performance and were less prone to overfitting compared to the manually filtered features. The four-variable Gaussian Naïve Bayes model using the ‘Radiation Type’ clinical feature and three machine-driven features achieved a training accuracy of 79% and testing accuracy of 77%. These results demonstrate that radiomics features can provide risk stratification beyond HPV-status to formulate individualized treatment and follow-up strategies.
Millimeter-Wave Transmitter with LTCC Antenna and Silicon Lens

— Millimeter-wave (mm-wave) transmitters are often fabricated using advanced technology and require a sophisticated manufacturing facility. Access to such technologies is often very limited and difficult to gain particularly at the initial stage of research. Therefore, to increase the accessibility of mm-wave transmitters, this study proposes a design that can be assembled in a standard microwave laboratory from commercially available or externally ordered components. The transmitter demonstrated in this paper operates above 100 GHz and is based on a low-temperature co-fired ceramic board in which the antenna array, microstrip lines, and power-supply lines are fabricated in a single process. Different technologies are used to assemble the module, e.g., wire-bonding, soldering, and wax adhesion. Advantages and disadvantages of the proposed design are given based on experimental evaluation of the prototype. Although the performance of the developed transmitter is not as good as that of the similar modules available in the recent literature, the results confirm the feasibility of a mm-wave transmitter that is assembled without employing advanced technologies and superior machinery.
Effect of web-implemented exercise interventions on depression and anxiety in patients with neurological disorders: a systematic review and meta-analysis

Introduction Web-implemented exercise intervention is the latest and innovative method to improve people's mental health. Currently, many studies have proven that web-implemented interventions are effective to improve depression and anxiety in adults. However, the influence of different web-implemented exercise interventions on depression and anxiety in patients with neurological disorders is still unclear. Objective The study aims to systematically summarize the type and content of web-implemented exercise interventions and quantify the effect of different web-implemented exercise interventions on depression and anxiety in patients with neurological disorders. Methods Four literature databases (PubMed, Web of Science, China National Knowledge Infrastructure, and WanFang data) were searched. The literature search considered studies published in English or Chinese before October 13, 2022. Randomized controlled trials (RCTs) that participants accepted web-implemented interventions were included. Two authors independently extracted data and assessed the risk of bias for included studies. Standardized mean differences (SMD) with 95% CI were used to integrate the effect size. Results 16 RCTs (a total of 963 participants) were included. The results showed that web-implemented exercise intervention had a significant effect on depression (SMD = −0.80; 95% CI, −1.09 to −0.52; I2 = 75%; P < 0.00001) and anxiety (SMD = −0.80; 95% CI, −1.23 to −0.36; I2 = 75%; P = 0.0003) in patients with a neurological disorder. The subgroup analysis showed that the effectiveness of the web-implemented exercise intervention was influenced by several factors, such as web-implemented exercise intervention type, component, and intervention duration. Conclusion Web-implemented exercise intervention has a relieving effect on depression and anxiety symptoms in patients with neurological disorders. Additionally, the intervention type, intervention duration, and component can influence the effect size. Systematic review registration https://www.crd. york.ac. uk/PROSPERO/#recordDetails, identifier: CRD42023409538.