Miracle-dz/UniFAR-FTUs huggingface dataset:
Safety assessment of equine allogeneic tenogenic primed mesenchymal stem cells in horses with naturally occurring tendon and ligament injuries

Background Mesenchymal stem cells provide a valuable treatment option in orthopedic injuries in horses. Objectives The aim of this study was to evaluate the hematological, biochemical, immunological and immunomodulatory parameters following intralesional treatment with tenogenic primed equine allogeneic peripheral blood-derived mesenchymal stem cells (tpMSCs) in client-owned horses with naturally occurring superficial digital flexor tendon (SDFT) and suspensory ligament (SL) injuries. Methods The immunogenicity and immunomodulatory capacities of tpMSCs were assessed in a modified mixed lymphocyte reaction, including peripheral blood mononuclear cells (PBMCs) of 14 horses with SDFT and SL injuries after treatment with tpMSCs. In a second study, 18 horses with SDFT and SL injuries received either an intralesional injection with tpMSCs (n = 9) or no treatment (n = 9). Results The tpMSCs did not provoke a cellular immune response (p < 0.001) and were able to immunomodulate stimulated T lymphocytes (p < 0.001) in vitro. Therapeutic use of tpMSCs did not result in relevant hematologic or biochemical abnormalities. Main limitations Both studies had a small sample size. No statistical analyses were performed in the second study. Fibrinogen was only analyzed in a single horse prior to treatment. Conclusion Co-incubation of tpMSCs and PBMCs of horses that have been previously exposed to tpMSCs did not elicit a cellular immune response and tpMSCs were able to immunomodulate stimulated T lymphocytes. Intralesional treatment with tpMSCs did not provoke abnormal changes in hematological and biochemical parameters.
A New High Density 3D Stackable Via RRAM for Computing-in-Memory SOC Applications

A novel 3D stackable Via resistive random access memory (RRAM) latch and Via RRAM logic gates implemented in 16-nm FinFET logic process for computing-in-memory (CIM) applications are proposed. Via RRAM latch array can provide more than 60 Mb/mm2 of latch storage density to achieve stable full-swing high-speed output. By simplifying the readout circuit, latch array allows flexible configuration in advanced SOC. Three-Dimensional Via RRAM logic gates compute the stored data internally. The features show a promising way to reduce the von Neumann bottleneck.
Changes in Macronutrients during Dieting Lead to Weight Cycling and Metabolic Complications in Mouse Model

Weight cycling is a major challenge in obesity management. Caloric restriction is known to promote this phenomenon, but the impact of macronutrient changes during dieting remains unclear. This study aimed to determine the role of macronutrient changes in weight maintenance without caloric restriction by alternating between two hypercaloric diets: a high-carbohydrate, high-fat Western diet (WD) and a low-carbohydrate, high-fat diet (LCHDF). Obesity was induced in 8-week-old C57BL/6 male mice by 10 weeks of WD feeding. Then, the mice were subjected to 12 weeks of LCHFD interspersed with WD (I-WD), 3 periods of 2-week LCHFD followed by 2 periods of 3-week WD, or 12 weeks of continuous WD (C-WD). C-WD and I-WD mice were compared to standard diet (SD) mice. In the I-WD group, each LCHFD period decreased weight gain, but mice regained weight after WD resumption. I-WD mice exhibited obesity, dyslipidemia, and glucose intolerance, similarly to the C-WD mice. I-WD mice also developed nonalcoholic steatohepatitis, associated with an increase in type-III collagen gene expression and a decrease in FGF21 protein levels, in comparison with SD. I-WD mice developed weight cycling despite maintaining a high caloric consumption, suggesting that changes in macronutrients during dieting are also a trigger of weight regain.
Penerapan Model Problem Based Learning untuk Meningkatkan Kemampuan Pemecahan Masalah Matematis Siswa di Kelas VIII – A SMP Negeri 1 Stabat

This study aims to determine whether applying the Problem Based Learning model can improve the mathematical problem solving ability of students of class VIII-A SMP Negeri 1 Stabat on SPLDV. This research is a class action research conducted in two cycles. Each cycle consists of 2 meetings. The subjects in this study were students of class VIII-A SMP Negeri 1 Stabat totalling 30 students. The object of this research is the improvement of students' mathematical problem solving ability through the application of the Problem Based Learning learning model on SPLDV material at SMP Negeri 1 Stabat in the 2023/2024 school year. Based on the results of data analysis, the average initial test score was 47.11 with the number of students who reached mastery as many as 4 students (13.33%) and students who had not reached mastery as many as 26 students (86.67%). Then learning was carried out with the Problem Based Learning learning model in cycle I and there was an increase in the problem solving ability test with an average value of 63.44 with 19 students or as many as (63.33%). Then students who have not reached completeness are 11 students or as many as (36.67%). Then in cycle II there was an increase with the average value of the student problem solving ability test being 84.11, with a total completeness of 26 students or as many as (86.67%) and the number of students who had not reached completeness was 4 students or as many as (13.33%). The level of student completeness of the problem solving ability test given in cycle II reached the good category, therefore it can be said that learning has been achieved. Thus it can be said that the class has reached more than 85% of students who have been completed to solve problems. So it can be concluded that using the Problem Based Learning model can improve students' mathematical problem solving skills.
Optimum Green Synthesis, Characterization, and Antibacterial Activity of Silver Nanoparticles Prepared from an Extract of Aloe fleurentinorum

The synthesis of metal nanoparticles through the use of plant extract is a process that is not only simple but also inexpensive, quick, and favorable to the environment. As a result, it is utilized in a wide variety of fields. When synthesizing silver nanoparticles (AgNPs), several different kinds of plant extracts were utilized. The manufacture of silver nanoparticles was carried out in this study using an environmentally friendly technique. The aqueous extract of the Aloe fleurentinorum plant was utilized as a stabilizing and reducing agent. To determine the optimal conditions for the synthesis of silver nanoparticles, it was necessary to investigate the impact of several parameters on the process. These parameters included the reactant volume ratio, pH values, temperature, and reaction time. To get crystallite and stable silver nanoparticles, an aqueous solution of AgNO3 (0.01M) was added to an aqueous extract of Aloe fleurentinorum plant at a temperature of 60 degrees Celsius and a pH of 8. The mixture was then stirred with a magnetic stirrer for ninety minutes (90 minutes). Using a variety of methods (UV-vis spectrophotometer, FTIR, XRD, SEM, EDX, and XPS), several approaches were utilized to investigate and describe the green-produced AgNPs. Through the use of the SEM method, it was demonstrated that the morphology of AgNPs is tetrahedral. It was determined using X-ray diffraction that the size of crystalline AgNPs was 26.7 nm. AgNPs that have been optimally synthesized have antibacterial properties that are both significant and effective against various bacterial species that have been tested at varying doses.
Reinforced Self-Supervised Training for Few-Shot Learning

Few-shot learning is an open problem to learning a new concept with little supervision from limited labeled data. As an alternative knowledge for few-shot learning, self-supervised learning can extract supervisory signals directly from unlabeled data. However, existing self-supervised few-shot methods which directly take the summation of two tasks, have two fundamental bottlenecks: 1) representation bias: how to extract efficacious supervisory signals in self-supervision and eliminate the disturbance of undesirable shortcuts with limited examples and 2) objective conflict: how to adaptively trade-off the self-supervision and supervision to achieve the optimal model performance. To address the above problems, in this paper, we propose a novel approach named ReInforced SElf-supervised training (RISE) for few-shot learning. RISE leverages agent-relative supervision to eliminate the undesirable shortcut learning of self-supervised training. Meanwhile, it dynamically explores the balance between supervisory signals from self-supervised tasks and inherent supervision from few-shot tasks to avoid the trade-off dilemma. Therefore, the new pattern for training self-supervision can be resilient to few-shot learning and enhance the performance for few-shot identification. Extensive experiments on several public benchmark datasets verify the effectiveness of our approach.
Population assessment and feeding ecology of brown hyenas (hyaena brunnea) in Mountain Zebra National Park, Eastern Cape, South Africa

The development of many small (<400 km2), enclosed game reserves in the Eastern Cape Province of South Africa over the past 25 years has contributed greatly to the conservation of large carnivores. However, the brown hyena (Hyaena brunnea) is one of the least studied large carnivores in South Africa. Nevertheless, the reintroduction of this species (n=3 in 2008) into Mountain Zebra National Park (MZNP) provided the perfect opportunity to broaden our understanding of the role that this carnivore plays in an enclosed system. Camera trap data was collected for just over a year from April 2014 to April 2015 and brown hyena density estimates were calculated using spatially explicit capture-recapture analysis. Left-side images of brown hyenas were used in the analysis and 12 individuals were positively identified. The best model to estimate brown hyena density included a road covariate and estimated brown hyena density to be 6-10 individuals/100 km2 (an absolute abundance of between 12 and 21 individuals), which is higher than densities calculated for brown hyenas in other arid, open systems. In, addition, brown hyena scat samples were collected over a five year period from April 2011 to June 2015 and standard techniques for scat analysis were used to identify prey items. Cheetah (Acinonyx jubatus) and lion (Panthera leo) kill site data were used to investigate the impacts of these species on the diet of brown hyenas. Before the release of lions brown hyenas predominantly scavenged on medium-sized mammals, which was what the cheetahs mainly killed. However, after the release of the lions, brown hyenas predominantly scavenged on large mammals, which was what the lions primarily killed. The results from my study indicate that brown hyenas are most likely reaching high densities in enclosed systems, due to increased scavenging opportunities provided by other large predators. The rapid increase of brown hyena densities from small founder populations in enclosed reserves could result in inbreeding. Therefore, in order to successfully conserve brown hyenas and other large carnivores in South Africa, continual post-release monitoring and possible implementation of meta-population management schemes is required.
Formulation of Creative Tourism Products at The Area of Pendawa Beach Natural Tourism Village Kutuh Badung Bali

-This research was conducted in the Pandawa beach tourism area of Kutuh village, sub-district of South Kuta, Badung. The purpose of this reseach is to formulate tourism products that are in accordance with the potential that exists on Pendawa beach that can be categorized as creative tourism products and determine the management model of coastal tourism areas. Data collection was carried out using observation, interviews, focus grop discussion methods. The analytical tool used to analyze data is a qualitative analysis technique in the form of descriptive analysis and synthesis analysis. Based on the results of the analysis shows that creative tourism products that can be formulated based on the potential that exists on the Pandawa coast and based on the criteria of creative tourism products that are viewed from three dimensions, namely: Educational tours, Paragliding, Coral Reef Transplantation, Padma Bhuana tower, Maritime Museum, Cultivation Tourism and Seaweed Culinary. Pandawa coastal area management model, can be seen from two aspects, namely: 1) Functional management that is applied to pandawa beach tourism object is to use three (3) functions, namely: a) Strategy function, management of pandawa beach attractions setting goals and make operational plans. b) The function of internal components, management of Pandawa beach tourism organizing, staffing, directing, and operational supervision, c) Functions of external constituents, management of Pandawa beach tourism cooperating with third parties namely private and regional government. 2) From the systemic model, the management of the Pandawa beach tourism area that follows the management process comprehensively, the steps are as follows: a) Identify the tourism facilities offered on the Pandawa coast, b) The effective management structure, c) Determine the people who right, d) Making policies and rules, e) Doing coordination vertically and horizontally, and f) Making accountability reports.
The Neural Mechanism Underlying Differentiated In-Group Versus Out-Group Face Recognition and Memory, Identification, Empathy and Pro-social Behavior: Evidence from fMRI and ERP Studies.

In the context of perceiving individuals within and outside of social groups, there are distinct cognitive processes and mechanisms in the brain. Extensive research in recent years has delved into the neural mechanisms that underlie differences in how we perceive individuals from different social groups. To gain a deeper understanding of these neural mechanisms, we present a comprehensive review from the perspectives of facial recognition and memory, intergroup identification, empathy, and pro-social behavior. Specifically, we focus on studies that utilize functional magnetic resonance imaging (fMRI) and event-related potential (ERP) techniques to explore the relationship between brain regions and behavior. Findings from fMRI studies reveal that the brain regions associated with intergroup differentiation in perception and behavior do not operate independently but instead exhibit dynamic interactions. Similarly, ERP studies indicate that the amplitude of neural responses shows various combinations in relation to perception and behavior.
Organic Amendments: Enhancing Plant Tolerance to Salinity and Metal Stress for Improved Agricultural Productivity

Salinity and metal stress are significant abiotic factors that negatively influence plant growth and development. These factors lead to diminished agricultural yields on a global scale. Organic amendments have emerged as a potential solution for mitigating the adverse effects of salinity and metal stress on plants. When plants experience these stresses, they produce reactive oxygen species, which can impair protein synthesis and damage cellular membranes. Organic amendments, including biochar, vermicompost, green manure, and farmyard manure, have been shown to facilitate soil nitrogen uptake, an essential component for protein synthesis, and enhance various plant processes such as metabolism, protein accumulation, and antioxidant activities. Researchers have observed that the application of organic amendments improves plant stress tolerance, plant growth, and yield. They achieve this by altering the plant’s ionic balance, enhancing the photosynthetic machinery, boosting antioxidant systems, and reducing oxidative damage. The potential of organic amendments to deal effectively with high salinity and metal concentrations in the soil is gaining increased attention and is becoming an increasingly popular practice in the field of agriculture. This review aims to provide insights into methods for treating soils contaminated with salinity and heavy metals by manipulating their bioavailability through the use of various soil amendments.
Predicting the Need for Cardiovascular Surgery: A Comparative Study of Machine Learning Models

This research examines the efficacy of ensemble Machine Learning (ML) models, mainly focusing on Deep Neural Networks (DNNs), in predicting the need for cardiovascular surgery, a critical aspect of clinical decision-making. It addresses key challenges such as class imbalance, which is pivotal in healthcare settings. The research involved a comprehensive comparison and evaluation of the performance of previously published ML methods against a new Deep Learning (DL) model. This comparison utilized a dataset encompassing 50,000 patient records from a large hospital between 2015-2022. The study proposes enhancing the efficacy of these models through feature selection and hyperparameter optimization, employing techniques like grid search. A novel aspect of this research was the comparison of a newly developed DNN model with existing ensemble models based on similar cardiovascular datasets. The results indicated the DNN model's superior predictive accuracy, demonstrating an Area Under the Curve (AUC) of 74%, alongside notable precision (68%) and recall (72%) for the minority class, which indicates patients requiring surgery. The model further achieved a 70% F1-Score and a balanced accuracy rate of 72%, significantly outperforming the existing ensemble models in every key performance metric. The study underscores the transformative potential of DNNs in predictive modeling for cardiovascular care and highlights the importance of integrating advanced ML techniques into clinical workflows. Future research should delve into the practical application and integration of these models.
Intrauterine Growth Restriction Affects Colonic Barrier Function via Regulating the Nrf2/Keap1 and TLR4-NF-κB/ERK Pathways and Altering Colonic Microbiome and Metabolome Homeostasis in Growing–Finishing Pigs

Intrauterine growth restriction (IUGR) pigs are characterized by long-term growth failure, metabolic disorders, and intestinal microbiota imbalance. The characteristics of the negative effects of IUGR at different growth stages of pigs are still unclear. Therefore, this study explored through multi-omics analyses whether the IUGR damages the intestinal barrier function and alters the colonization and metabolic profiles of the colonic microbiota in growing-finishing pigs. Seventy-two piglets (36 IUGR and 36 NBW) were allocated for this trial to analyze physiological and plasma biochemical parameters, as well as oxidative damage and inflammatory response in the colon. Moreover, the colonic microbiota communities and metabolome were examined using 16s rRNA sequencing and metabolomics technologies to reveal the intestinal characteristics of IUGR pigs at different growth stages (25, 50, and 100 kg). IUGR altered the concentrations of plasma glucose, total protein, triglycerides, and cholesterol. Colonic tight junction proteins were markedly inhibited by IUGR. IUGR decreased plasma T-AOC, SOD, and GSH levels and colonic SOD-1, SOD-2, and GPX-4 expressions by restraining the Nrf2/Keap1 signaling pathway. Moreover, IUGR increased colonic IL-1β and TNF-α levels while reducing IL-10, possibly through activating the TLR4-NF-κB/ERK pathway. Notably, IUGR pigs had lower colonic Streptococcus abundance and Firmicutes-to-Bacteroidetes ratio at the 25 kg BW stage while having higher Firmicutes abundance at the 100 kg BW stage; moreover, IUGR pigs had lower SCFA concentrations. Metabolomics analysis showed that IUGR increased colonic lipids and lipid-like molecules, organic acids and derivatives, and organoheterocyclic compounds concentrations and enriched three differential metabolic pathways, including linoleic acid, sphingolipid, and purine metabolisms throughout the trial. Collectively, IUGR altered the nutrient metabolism, redox status, and colonic microbiota community and metabolite profiles of pigs and continued to disrupt colonic barrier function by reducing antioxidant capacity via the Nrf2/Keap1 pathway and activating inflammation via the TLR4-NF-κB/ERK pathway during the growing-finishing stage. Moreover, colonic Firmicutes and Streptococcus could be potential regulatory targets for modulating the metabolism and health of IUGR pigs.
Complete Removable Denture Retained by a Symphyseal Single Implant: A Case Report

Complete denture is a real challenge for any practitioner. The aim of implantology is to optimize prosthetic balance and ensure that prostheses are perfectly integrated from a bio-functional viewpoint, despite an unfavorable anatomophysiological context. Here, we present a case managed in our department concerning a mandibular complete removable denture retained by a medial symphyseal implant. A 61-year-old fully edentulous mandibular patient with a Kennedy Applegate class I edentulous maxilla in good general health consulted for the renewal of his mandibular complete denture, which was deemed unstable and non-retentive. The exo-oral examination was normal. The mandibular crest was heavily resorbed, especially posteriorly, and covered with slightly inflammatory fibromucosa. The patient refused bone augmentation surgery. We performed a piezographic mandibular prosthesis retained by a single standard implant at the level of the mandibular symphysis. The prosthesis was stable and retentive. Masticatory comfort and efficiency were satisfactory and the psychological integration of the prosthesis was improved. Several studies have shown that a single symphyseal implant is a therapeutic alternative that completes the therapeutic range in specific cases. It should be limited to the mandibular arch in elderly patients with reduced bone volume. Given the lack of randomized controlled trials, routine use of this new approach is not recommended, and further studies are required.
Social Media Analysis Training for Digital Talent Development in Indonesia

The development of digital technology has allowed people to share opinions on social media, send emails, make purchases online, to make phone calls every day. As a result, the amount of data continues to grow rapidly day by day. Most of the data available today is public and accessible to anyone, such as social media data, blogs, news, discussion forums, public government data, and others. With the immense value of this abundant source of social media data, there is an opportunity to extract knowledge or insights from this unstructured social media data, especially to understand opinions, current trends, or influential actors on information spread on the internet. As part of Telkom Surabaya's IT Community Service team that supports student development in SMA/SMK/MA, we propose solutions to the main problems faced today, namely in the field of data analysis. The solutions we offer are also in line with the government's program to increase Digital Talent in Indonesia. In the midst of increasing demand for Digital Talent, there is still a gap between the need for digital talent and job opportunities in Indonesia. The program we will create is Social Media Analysis Training, which will provide an understanding of the benefits of open social media data in general, how to take insights from social media data, and solve problems in various fields.