Miracle-dz/UniFAR-FTUs huggingface dataset:
Anti‐skin aging effect of sea buckthorn proanthocyanidins in D‐galactose‐induced aging mice

Abstract Oxidative stress in skin cells caused by changes in the external environment is one of the principal causes of skin aging. Sea buckthorn proanthocyanidins (SBPs) have good free radical scavenging ability. We established a senescence model by injecting 500 mg/kg D‐galactose into the dorsal necks of mice, and then different doses of SBP (25, 50, and 100 mg/kg) were gavaged to explore the effects of SBP on the skin tissues of senescent mice and elucidate the related mechanism of action. The results reveal that SBP can alleviate the skin aging phenomenon caused by D‐galactose‐induced aging. It can also enhance the total antioxidant capacity in the body, thereby strengthening the body's antioxidant defense capability. In addition, SBP can effectively improve skin aging by regulating the TGF‐β1/Smads pathway and MMPs/TIMP system, increasing the relative content of Col I and tropoelastin, further maintaining the stability of collagen fiber and elastic fiber structure. These results will provide the development and production of the antioxidant function of cosmetics and health products, providing a new train of thought.
Pre-adjusted Three-Dimensional Plate Employing Printing versus Conventional Plate in the Management of Mandibular Fractures - A Comparative Study

Abstract Introduction: The aim of this study was to compare the efficacy of pre-adjusted three-dimensional (3D) plating system employing 3D printing with conventional 3D plating in the management of mandibular fractures. Materials and Methods: A randomised, clinical trial was conducted where the study sample (n = 20) was divided into two groups. In Group 1, 3D plate and in Group 2, pre-bent 3D plate was fixed to the fracture site. The parameters assessed were number of bends required for adaptation, duration of fixation, pain, occlusal stability, reduction in lingual splaying and post-operative complications. Results: Statistically significant difference was seen for the number of bends required (P = 0.000, P < 0.01) and duration of fracture fixation (P = 0.001, P < 0.01). There was statistically significant difference between the values of pain during the adaptation of 3D plate (P = 0.033, P < 0.05). Discussion: The application of pre-adjusted 3D plate is superior to conventional 3D plating in terms of reducing number of bends, duration of fixation and pain during adaptation.
and the interior of Effect of high-pressure processing on reduction of Listeria monocytogenes in packaged Queso Fresco 1

The effect of high-hydrostatic-pressure processing (HPP) on the survival of a 5-strain rifampicin-resistant cocktail of Listeria monocytogenes in Queso Fresco (QF) was evaluated as a postpackaging intervention. Queso Fresco was made using pasteurized, homogenized milk, and was starter-free and not pressed. In phase 1, QF slices (12.7 × 7.6 × 1 cm), weighing from 52 to 66 g, were surface inoculated with L. monocytogenes (ca. 5.0 log 10 cfu/g) and individually double vacuum packaged. The slices were then warmed to either 20 or 40°C and HPP treated at 200, 400, and 600 MPa for hold times of 5, 10, 15, or 20 min. Treatment at 600 MPa was most effective in reducing L. monocytogenes to below the detection level of 0.91 log 10 cfu/g at all hold times and temperatures. High-hydrostatic-pressure processing at 40°C, 400 MPa, and hold time ≥15 min was effective but resulted in wheying-off and textural changes. In phase 2, L. monocytogenes was inoculated either on the slices (ca. 5.0 log 10 cfu/g; ON) or in the curds (ca. 7.0 log 10 cfu/g; IN) before the cheese block was formed and sliced. The slices were treated at 20°C and 600 MPa at hold times of 3, 10, and 20 min, and then stored at 4 and 10°C for 60 d. For both treatments, L. monocytogenes became less resistant to pressure as hold time increased, with greater percentages of injured cells at 3 and 10 min than at 20 min, at which the lethality of the process increased. For the IN treatment, with hold times of 3 and 10 min, growth of L. monocytogenes increased the first week of storage, but was delayed for 1 wk, with a hold time of 20 min. Longer lag times in growth of L. monocytogenes during storage at 4°C were observed for the ON treatment at hold times of 10 and 20 min, indicating that the IN treatment may have provided a more protective environment with less injury to the cells than the ON treatment. Similarly, HPP treat-ment for 10 min followed by storage at 4°C was the best method for suppressing the growth of the endogenous microflora with bacterial counts remaining below the level of detection for 2 out of the 3 QF samples for up to 84 d. Lag times in growth were not observed during storage of QF at 10°C. Although HPP reduced L. monocytogenes immediately after processing, a second preservation technique is necessary to control growth of L. monocytogenes during cold storage. However, the results also showed that HPP would be effective for slowing the growth of microorganisms that can shorten the shelf life of QF.
Memantine protects the cultured rat hippocampal neurons treated by NMDA and amyloid β1–42

Alzheimer’s disease (AD) is a devastating neurodegenerative condition with no effective treatments. Recent research highlights the role of NMDA receptors in AD development, as excessive activation of these receptors triggers excitotoxicity. Memantine, an NMDA receptor antagonist, shows promise in curbing excitotoxicity. What sets our study apart is our novel exploration of memantine’s potential to protect hippocampal neurons from neurotoxicity induced by NMDA and amyloid β1–42, a hallmark of AD. To achieve this, we conducted a series of experiments using rat hippocampal cell cultures. We employed Hoechst and propidium iodide double staining to assess neuronal viability. Analyzing the viability of neurons in normal conditions compared to their status after 24 h of exposure to the respective agents revealed compelling results. The incubation of hippocampal neurons with NMDA or amyloid β1–42 led to a more than twofold increase in the number of apoptotic and necrotic neurons. However, when memantine was co-administered with NMDA or amyloid β1–42, we witnessed a notable augmentation in the number of viable cells. This unique approach not only suggests that memantine may act as a neuroprotective agent but also emphasizes the relevance of hippocampal neuron cultures as valuable models for investigating excitotoxicity and potential AD treatments.
Virtual Shadow Puppet Play Generation Based on Alphapose

: As a precious art in China, shadow play has been spreading for more than 2000 years. But with the development of society, shadow play is no longer popular and attractive to the younger generation. Therefore, digital shadow play plays a very important role in the protection and dissemination of shadow play. In spite of this, there are still some deficiencies in this way of spreading shadow play culture today. It is usually used by placing the shadow puppet model in a designated position in a pre-built place and making adjustments. Of course, this method takes a lot of time, and compared with traditional shadow play, this kind of shadow play is not flexible and vivid. This paper proposes to use motion capture technology to make digital shadow play. The method adopted in this paper is based on the open source code of AlphaPose, Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time. First of all, the action of the characters is captured by the live action and high-definition camera, and finally the shadow puppet animation is generated according to the captured action.
An Indoor Fire Detection Method Based on Multi-Sensor Fusion and a Lightweight Convolutional Neural Network

Indoor fires pose significant threats in terms of casualties and economic losses globally. Thus, it is vital to accurately detect indoor fires at an early stage. To improve the accuracy of indoor fire detection for the resource-constrained embedded platform, an indoor fire detection method based on multi-sensor fusion and a lightweight convolutional neural network (CNN) is proposed. Firstly, the Savitzky–Golay (SG) filter is used to clean the three types of heterogeneous sensor data, then the cleaned sensor data are transformed by means of the Gramian Angular Field (GAF) method into matrices, which are finally integrated into a three-dimensional matrix. This preprocessing stage will preserve temporal dependency and enlarge the characteristics of time-series data. Therefore, we could reduce the number of blocks, channels and layers in the network, leading to a lightweight CNN for indoor fire detection. Furthermore, we use the Fire Dynamic Simulator (FDS) to simulate data for the training stage, enhancing the robustness of the network. The fire detection performance of the proposed method is verified through an experiment. It was found that the proposed method achieved an impressive accuracy of 99.1%, while the number of CNN parameters and the amount of computation is still small, which is more suitable for the resource-constrained embedded platform of an indoor fire detection system.
In-Line Detection of Clinical Mastitis by Identifying Clots in Milk Using Images and a Neural Network Approach

Simple Summary The study focused on improving the detection of clinical bovine mastitis, the inflammation of the udder in cows as a response to intramammary infection, which can be identified by the presence of clots in the milk. Currently, automated milking systems do not detect this important disease very accurately. To address this, we developed a clots detection program using a neural network. This neural network was trained to recognize clots in milk samples from dairy cows by using a large number of pictures of milk filter socks, some with and some without clots. These pictures were divided into different sets for training, validating, and testing the program, respectively. The settings of the neural network were optimized using a genetic algorithm. The program’s interpretations were explained using a method called integrated gradients. The program was found to be 100% accurate in identifying clots in the test pictures. This suggests that the method could be very useful for automatically checking for clinical mastitis on dairy farms, although further field validation through integration into the existing systems is needed. Abstract Automated milking systems (AMSs) already incorporate a variety of milk monitoring and sensing equipment, but the sensitivity, specificity, and positive predictive value of clinical mastitis (CM) detection remain low. A typical symptom of CM is the presence of clots in the milk during fore-stripping. The objective of this study was the development and evaluation of a deep learning model with image recognition capabilities, specifically a convolutional neural network (NN), capable of detecting such clots on pictures of the milk filter socks of the milking system, after the phase in which the first streams of milk have been discarded. In total, 696 pictures were taken with clots and 586 pictures without. These were randomly divided into 60/20/20 training, validation, and testing datasets, respectively, for the training and validation of the NN. A convolutional NN with residual connections was trained, and the hyperparameters were optimized based on the validation dataset using a genetic algorithm. The integrated gradients were calculated to explain the interpretation of the NN. The accuracy of the NN on the testing dataset was 100%. The integrated gradients showed that the NN identified the clots. Further field validation through integration into AMS is necessary, but the proposed deep learning method is very promising for the inline detection of CM on AMS farms.
Multi-dimension metabolic prognostic model for gastric cancer

Background Metabolic reprogramming is involved in different stages of tumorigenesis. There are six widely recognized tumor-associated metabolic pathways, including cholesterol catabolism process, fatty acid metabolism, glutamine metabolic process, glycolysis, one carbon metabolic process, and pentose phosphate process. This study aimed to classify gastric cancer patients into different metabolic bio-similar clusters. Method We analyzed six tumor-associated metabolic pathways and calculated the metabolic pathway score through RNA-seq data using single sample gene set enrichment analysis. The consensus clustering analysis was performed to classify patients into different bio-similar clusters by multi-dimensional scaling. Kaplan–Meier curves were presented between different metabolic bio-similar groups for OS analysis. Results A training set of 370 patients from the Cancer Genome Atlas database with primary gastric cancer was chosen. Patients were classified into four metabolic bio-similar clusters, which were identified as metabolic non-specificity, metabolic-active, cholesterol-silence, and metabolic-silence clusters. Survival analysis showed that patients in metabolic-active cluster and metabolic-silence cluster have significantly poor prognosis than other patients (p=0.031). Patients in metabolic-active cluster and metabolic-silence cluster had significantly higher intra-tumor heterogeneity than other patients (p=0.032). Further analysis was performed in metabolic-active cluster and cholesterol-silence cluster. Three cell-cycle-related pathways, including G2M checkpoints, E2F targets, and MYC targets, were significantly upregulated in metabolic-active cluster than in cholesterol-silence cluster. A validation set of 192 gastric cancer patients from the Gene Expression Omnibus data portal verified that metabolic bio-similar cluster can predict prognosis in gastric cancer. Conclusion Our study established a multi-dimension metabolic prognostic model in gastric cancer, which may be feasible for predicting clinical outcome.
Identity Work and Intercultural Awareness Through Cultural Excavation: Teacher Candidates Studying Abroad

ABSTRACT Reflecting upon self-identity is important for teachers and teacher candidates, many of whom have different backgrounds from their multicultural, multilingual students. In this study I analyze data from cultural excavation activities completed by ten teacher candidates during study abroad in Peru, in which they critically reflected upon their own identities in relation to the cultural environments of their study abroad experience. Findings from activities and the Intercultural Development Inventory showed increased intercultural sensitivity among most participants, while focus group responses pointed to the cultural excavation activities as scaffolding this movement. Participant responses also demonstrated increased contextual awareness regarding their and others’ identities, a crucial notion for combatting essentialist conceptions of culture.
Designing an information system to automate service management at the enterprise

In this paper we consider the development of an automated information system for a coffee shop that meets the current standards and needs of this business. Emphasis is placed on both hardware infrastructure and content structure. This AIS system provides all the necessary functionality for a coffee shop to successfully run its business, including convenient menu search, online ordering and payment. In addition, the application helps to reduce the workload of the staff, as many tasks that were previously performed by employees can now be automated and performed by the system being developed.
A multitemporal snow-covered remote sensing image matching method considering global and contextual features

ABSTRACT Multitemporal snow remote sensing image matching is an important data processing step for snow monitoring and environmental change analysis. The extensive snow coverage in snow remote sensing images weakens local feature saliency, resulting in significant feature differences between two images and making it difficult to obtain consistency in local features. This poses a great challenge to image matching tasks. To address this issue, we propose a multitemporal snow remote sensing image matching method that considers global and contextual features. This method can extract consistent features between two images and perform matching tasks even in cases of extensive snow coverage. Specifically, our method enhances the ability to aggregate global information by extracting global positional information and contextual features of the images at different scales and convolutional fields, obtaining robust matching descriptors with nonlocal information. We design corresponding loss functions, incorporating average precision loss before extracting contextual features, and combining it with description loss and keypoints detection loss for training. Extensive experiments demonstrate that our method achieves good results in the task of multitemporal snow remote sensing image matching, which improves the match precision and Recall by 11.5% and 7.5%, respectively, compared with the next best results in the experiments.
Significance of the Archaean Bimodal Volcanics from the Ramagiri-Schist Belt in the Formation of Eastern Dharwar Craton

The central arm of the trident shaped Ramagiri schist belt includes a central block which consists predominantly of bimodal (mafic-felsic) volcanics, apart from minor metasedimentary units. The geological set up of the various rock types suggests that the central block could be a tectonic melange. The metatholeiites and the felsic volcanics of the central block show similar LREE - enriched and fractionated REE patterns; the metatholeiites however. show a much larger variation in abundances. It has been suggested that the protoliths to the Ramagiri central mafic suite of bimodal volcanics were formed by different extents of melting of the metasomatically enriched mantle sources and were emplaced in an island arc setting. We suggest here that partial melting of similar metatholeiites, constituting an arc crust, at - 8 kbar pressures and at -1000 °C temperatures, produced magmas to the associated felsic volcanics. The geochemical characteristics of the mafic and felsic volcanic rocks in the Ramagiri belt, as well as the adjoining Sandur belt, have many similarities to those of the modern day island arc volcanic suites. The idea that the Phanerozoic style magmatic and accretionary tectonic processes operated during the Late Archaean suggested from the studies of granitic rocks in the Eastern Dharwar craton is corroborated by the metavolcanics of the Ramagiri schist belt.
Resting state correlates of picture description informativeness in left vs. right hemisphere chronic stroke

Introduction Despite a growing emphasis on discourse processing in clinical neuroscience, relatively little is known about the neurobiology of discourse production impairments. Individuals with a history of left or right hemisphere stroke can exhibit difficulty with communicating meaningful discourse content, which implies both cerebral hemispheres play a role in this skill. However, the extent to which successful production of discourse content relies on network connections within domain-specific vs. domain-general networks in either hemisphere is unknown. Methods In this study, 45 individuals with a history of either left or right hemisphere stroke completed resting state fMRI and the Cookie Theft picture description task. Results Participants did not differ in the total number of content units or the percentage of interpretative content units they produced. Stroke survivors with left hemisphere damage produced significantly fewer content units per second than individuals with right hemisphere stroke. Intrinsic connectivity of the left language network was significantly weaker in the left compared to the right hemisphere stroke group for specific connections. Greater efficiency of communication of picture scene content was associated with stronger left but weaker right frontotemporal connectivity of the language network in patients with a history of left hemisphere (but not right hemisphere) stroke. No significant relationships were found between picture description measures and connectivity of the dorsal attention, default mode, or salience networks or with connections between language and other network regions. Discussion These findings add to prior behavioral studies of picture description skills in stroke survivors and provide insight into the role of the language network vs. other intrinsic networks during discourse production.
Knowledge About Alzheimer’s Disease in Saudi Arabia

Background Alzheimer’s disease (AD) is the most common neurodegenerative dementia which constitutes a major healthcare problem globally. This study aimed to assess the pattern and determinants of knowledge about AD in Saudi Arabia. Methodology In this cross-sectional study, information was collected from 286 participants using a validated questionnaire on seven subdomains of knowledge about AD. SPSS (IBM Corp., Armonk, NY, USA) was used for data analysis. Different tests of significance such as the chi-square test and principal component factor analyses were employed. The level of significance was set at 0.05. Results The 25 questions on knowledge, attitudes, and practice about AD had a high Cronbach’s alpha (0.911). All scores on the seven subdomains of knowledge, attitudes, and practice about AD were significantly correlated with each other (p < 0.05). The knowledge of the population about AD was very low, with the majority of the correct responses for questions on the different subdomains of knowledge about AD ranging from 10% to 67% (76% of the answers were below 50% correct answers). Females and smokers had higher scores on most subdomains compared to males and non-smokers (p < 0.05). Conclusions Knowledge of the population about AD is very low. As the subdomains of knowledge on AD are correlated, healthcare professionals should implement health education programs on AD to increase the knowledge of the population regarding the course, symptoms, risk factors, treatment and management, and caregiving and reveal their association with the impact of AD on the health of affected individuals.
Neural Graph Refinement for Robust Recognition of Nuclei Communities in Histopathological Landscape

Accurate classification of nuclei communities is an important step towards timely treating the cancer spread. Graph theory provides an elegant way to represent and analyze nuclei communities within the histopathological landscape in order to perform tissue phenotyping and tumor profiling tasks. Many researchers have worked on recognizing nuclei regions within the histology images in order to grade cancerous progression. However, due to the high structural similarities between nuclei communities, defining a model that can accurately differentiate between nuclei pathological patterns still needs to be solved. To surmount this challenge, we present a novel approach, dubbed neural graph refinement, that enhances the capabilities of existing models to perform nuclei recognition tasks by employing graph representational learning and broadcasting processes. Based on the physical interaction of the nuclei, we first construct a fully connected graph in which nodes represent nuclei and adjacent nodes are connected to each other via an undirected edge. For each edge and node pair, appearance and geometric features are computed and are then utilized for generating the neural graph embeddings. These embeddings are used for diffusing contextual information to the neighboring nodes, all along a path traversing the whole graph to infer global information over an entire nuclei network and predict pathologically meaningful communities. Through rigorous evaluation of the proposed scheme across four public datasets, we showcase that learning such communities through neural graph refinement produces better results that outperform state-of-the-art methods.