Non-alcoholic fatty liver disease (NAFLD) has emerged as a significant liver ailment attributed to factors like obesity and diabetes. While ongoing research explores treatments for NAFLD, further investigation is imperative to address this escalating health concern. NAFLD manifests as hepatic steatosis, precipitating insulin resistance and metabolic syndrome. This study aims to validate the regenerative potential of chimeric fibroblast growth factor 21 (FGF21)-Hepatocyte Growth Factor Receptor (HGFR) in NAFLD-afflicted liver cells. AML12, a murine hepatocyte cell line, was utilized to gauge the regenerative effects of chimeric FGF21/HGFR expression. Polysaccharide accumulation was affirmed through periodic acid-Schiff (PAS) staining, while LDL uptake was microscopically observed with labeled LDL. mRNA analysis via RT-PCR revealed reduced expression of key NAFLD markers, including acetyl-CoA carboxylase 1 (ACC1) and sterol regulatory element-binding protein (SREBP) cleavage-activating protein (SCAP), alongside elevated levels of hepatocyte growth factor (HGF), hepatocyte nuclear factor 4 alpha (HNF4A), and albumin (ALB). These findings affirm the hepatoregenerative properties of chimeric FGF21/HGFR within AML12 cells, opening novel avenues for NAFLD therapeutic exploration.

Leukemia, contributing to 3% of all cancer cases in the United States, presents a significant challenge in treatment due to its diverse subtypes, each dis-playing unique genetic and molecular abnormalities. The aggressive growth of ab-normal blood cells compromises immune function and poses challenges in developing effective therapeutic approaches. Unlike solid tumors, surgical resection is not an option, limiting treatment strategies to chemotherapy, immunotherapy, and radiation therapy, which may be nonspecific and lead to organ damage and toxicity. Anthracyclines, widely used in hematological tumors, face limitations due to severe cardiotoxicity, causing progressive cardiomyopathy and, in severe cases, congestive heart failure. Heart failure incidence is significantly higher in cancer patients treated with anthracyclines over 5, 10, and 15 years. Early detection of left ventricular dysfunction and cardiotoxicity is crucial to mitigate progression to congestive heart failure (CHF) in this population. Recent research by Bayrón-Marrero et al. reveals that lower levels of plasma soluble TREM-Like Transcript-1 (s-TLT-1), a platelet-specific receptor, correlate with left ven-tricular dysfunction in cardiovascular disease patients. Those with s-TLT-1 levels ex-ceeding 544 pg/mL exhibit a significant association with congestive heart failure. These findings suggest the potential use of s-TLT-1 as a diagnostic screening tool for early detection and treatment of anthracycline-induced CHF in leukemia patients.

Systemic sclerosis (SSc) is a multisystem connective tissue disease characterized by pathological processes involving autoimmunity, vasculopathy, and resultant extensive skin and organ fibrosis. Recent studies have demonstrated activation and aberrant wound healing responses in the epithelial layer of the skin in this disease, implicating the epithelial keratinocytes as a source of pro-fibrotic and inflammatory mediators. In this paper we investigated the role of Immunoglobulin G (IgG) autoantibodies directed against epithelial cells, as potential initiators and propagators of pathological keratocyte activation and the ensuing SSc fibrotic cascade. A keratinocyte cell-based ELISA is used to evaluation binding of SSc IgG. SSc skin biopsies were stained by immunofluorescence for the presence of IgG in the keratinocyte layer. Moreover, IgG purified from SSc sera is evaluated for the potential to activate keratinocytes in tissue culture and to induce signaling in TLR2 & 3 reporter cell lines. We demonstrate enhanced binding of SSc IgG to keratinocytes, and activation of these cells leading to release of IL-1α, representing a potential initiating pathway in this disease.

Polycystic ovary syndrome (PCOS) usually comes along with metabolic disturbances attributed to androgen excess and obesity, but the contribution of each has not been defined, and the occurrence of metabolic disturbances is frequently not investigated. We evaluated 99 PCOS and 41 non-PCOS women. Clinical biomarkers of glucose-, liver-, and endothelial-related metabolic alterations were measured; participants were categorized into four groups according with obesity (OB) and hyperandrogenemia (HA) statuses: Healthy (no-HA, lean), HA (HA, lean), OB (no-HA, OB), and HAOB (HA, OB). Metabolic disturbances were highly frequent in PCOS women (70%). BMI correlated with all biomarkers, while free testosterone (FT) only with glucose- and hepatic-related indicators. Despite insulin sensitivity and liver enzymes were associated with FT, women with obesity exhibited lower M [coeff = 8.56 – 0.080(FT) –3.71(Ob); p <0.001)] and higher aspartate aminotransferase (coeff = 26.27 + 0.532(FT) + 8.08(Ob); p = 0.015)] than lean at the same level of FT. Women with obesity exhibited greater risk of metabolic disorders than lean, independently of hyperandrogenemia. Clinicians are compelled to search for metabolic alterations in PCOS women; obesity must be handled in all cases, but hyperandrogenemia needs to be managed also in those with glucose- or liver-related disturbances.

This study introduces an original comprehensive system centered on identifying specific terms that indicate a user's position, particularly the discrete values representing latitude and longitude. This system not only detects these terms but also retrieves the corresponding numerical data for accurate and efficient determination of locations. The importance of this study can be applied various fields, notably aiding offline operations of military personnel, who often lack internet access. In such scenarios, precise awareness of location is vital for strategic manoeuvres, rescue operations, and navigating unfamiliar landscapes. The system allows these personnel by allowing them to extract exact location coordinates from spoken terms, thereby enhancing their awareness even in challenging surroundings. Apart from its military utility, the project holds broader significance. Teams responding to emergencies, personnel involved in disaster management, and exploratory missions can all gain from this technology during disruptions in communication infrastructure. Furthermore, travelers, adventurers, and outdoor enthusiasts can utilize this system to accurately determine their positions in remote areas without relying on online maps. We used offline speech recognition techniques to precisely transcribe spoken terms, achieving an accuracy of over 91.3% and a word error rate of 4.2%. For sound recognition, the OpenAI Whisper model was used, and a conversion process from SpeechRecognition to AudioSegmentation was implemented, followed by transforming the audio into .wav format, we have also developed the interface of the app to use it efficiently using Streamlit. This was done to ensure seamless compatibility with the Whisper model and uninterrupted audio input. By training the system to identify specific linguistic linked to location, it achieves robust detection and extraction of relevant terms. This approach eliminates the necessity for constant internet connectivity, rendering it exceptionally useful in remote, offline, and resource-limited situations.

A facile iridium/graphene-catalyzed methodology providing an efficient synthetic route for C-N bond formation is reported. This catalyst can directly promote the formation of C-N bonds, without pre-activation steps, and without solvents, alkalis and other additives. This protocol provides a direct N -alkylation of amines using a variety of primary and secondary alcohols with good selectivity and excellent yields. Charmingly, the use of diols resulted in intermolecular cyclization of amines, and such products are privileged structures in biologically active compounds. Two examples illustrate the advantages of this catalyst in organic synthesis: the tandem catalysis to synthesize hydroxyine, and the intermolecular cyclyzation to synthesize cyclizine. Water is the only by-product, which makes this catalytic process sustainable and environmentally friendly.

The etiology of autism spectrum disorder (ASD) has not yet been completely elucidated. Through time, multiple attempts have been made to uncover the causes of ASD. Different theories have been proposed, such as that it is caused by “Gods Wrath,” alternations in the gut–brain axis with an emphasis on gut dysbiosis, post vaccine complications, and genetic or even autoimmune causes. In this review we present data covering over 170 000 participants at age ranging from 2 to 14 years focusing on human cell changes rather than using animal models found in previous studies. The male/female ratio was 4:1. Data collection occurred in many countries covering ethnically diversified subjects. Moreover, we aim to show how the progress in genetic techniques influence the explanation found in medical White Papers based on the human genetic samples we have observed from our studies

The etiology of autism spectrum disorder (ASD) has not yet been completely elucidated. Through time, multiple attempts have been made to uncover the causes of ASD. Different theories have been proposed, such as that it is caused by “Gods Wrath,” alterations in the gut–brain axis with an emphasis on gut dysbiosis, post vaccine complications, and genetic or even autoimmune causes. In this review we present data covering over 170 000 participants at age ranging from 2 to 14 years focusing on human cell changes rather than using animal models found in previous studies. The male/female ratio was 4:1. Data collection occurred in many countries covering ethnically diversified subjects. Moreover, we aimed to show how the progress in genetic techniques influences the explanation of medical White Papers in ASD area.

The Internet of Medical Things(IoMT) has powerful cloud computing ability and efficient data collection ability, which can improve the accuracy and convenience of medical work. As most communications are over open networks, data should encrypted before uploading to ensure the confidentiality of sensitive user data. Searching for encrypted data has become a challenging problem. Public key Encryption with Keyword Search (PEKS) supports the search of encrypted data and provides data privacy protection. PEKS has become a trendy technology, but PEKS still has the following problems: 1. The cloud server, as a semi-honest but curious third party, is likely to return some wrong search results to save computing and bandwidth resources. 2. The single keyword search inevitably produces irrelevant results, wasting computing and bandwidth resources. 3. Most PEKS schemes use bilinear pairing, so the computational efficiency is relatively low. 4. PEKS schemes based on Public Key Infrastructure (PKI) or identity-based cryptography inevitably face problems with certificate management or key escrow. 5. Most PEKS schemes face serious security threats such as Keyword Guessing Attacks (KGA) and File Injection Attacks (FIA). To solve the above problems, we propose a new certificateless verifiable bilinear pair-free conjunctive keyword search encryption scheme(CLVPFCKS) for IoMT using multi-signature.

A crucial role is played by personal biomedical data when it comes to maintaining proficient access to health records by patients as well as health professionals. However, it is difficult to get a unified view pertaining to health data that have been scattered across various health center/hospital sections. To be specific, health records are distributed across many places and cannot be found integrated easily. In recent years, blockchain is regarded as a promising explanation that helps to achieve individual biomedical information sharing in a secured way along with privacy preservation, because of its benefit of immutability. This research work put forwards a blockchain-based managing scheme that helps to establish interpretation improvements pertaining to electronic biomedical systems. In this scheme, two blockchain were employed to construct the base of it, where the second blockchain algorithm is used to generate a secure sequence for the hash key that generated in first blockchain algorithm. The adaptively feature enable the algorithm to use multiple data types and combine between various biomedical images and text records as well. All the data, including keywords, digital records as well as the identity of patients are private key encrypted along with keyword searching capability so as to maintain data privacy preservation, access control and protected search. The obtained results which show the low latency (less than 750 ms) at 400 requests / second indicate the ability to use it within several health care units such as hospitals and clinics.

The topic concerns the so far unknown mechanism of bubble effect in a large mass of moistured cellulose heated with mineral oil. The so far well-known phenomenon of bubble effect occurs in the Hot Spot area, i.e., in the place where the hot metal of the windings is in contact with the winding paper. As a result of presented in the article model tests, it was shown that cyclical heating of the transformer windings causes significant drying of the winding paper what reduces the possibility of bubble effect in the Hot Spot area, while there is a possibility of bubble effect appearing in large volume of pressboard heated with mineral oil. It has been shown that the pressure in the air cushion over oil surface, after the bubble effect starts in the pressboard, depends on the ratio of the mass of cellulose to the mass of oil: the greater the ratio, the greater the pressure in the air cushion. The ar-ticle presents the dependence of bubble effect initiation temperature in pressboard heated with oil on pressboard moisture.

Multidrug-resistant tuberculosis emerged as a serious challenge to tuberculosis management and control. In the Eastern Cape, the Beijing variants are prevalent and a driving force of multidrug-resistant tuberculosis; hence, we investigated the distribution of gene mutations in Beijing strains compared to non-Beijing strains. Multidrug-resistant tuberculosis and heteroresistant isolates were identified in 412 sputum cultures by drug susceptibility testing. The isolates were analyzed for mutations in three genes associated with resistance to antituberculosis first-line drugs: katG and inhA promoters for isoniazid and rpoB for rifampicin. All isolates were genotyped by spoligotyping. There were more males than females and a more economically active age group in the study. The most prevalent mutations in rpoB resistance were in S531L, katG in S315Tb, and inhA in c-15tb. Heteroresistance was found in 18 isolates. Beijing variants were predominant. Most of the heteroresistant isolates were INH, with heteroresistance occurring more in the inhA gene mutation region c-15tb. Beijing and LAM variants were found more frequently in INH heteroresistant isolates. Mutations in katG S315Tb and rpoB S531L were higher in Beijing variants. The Beijing family is a major contributor to the epidemiological picture and accounts for most of the multidrug-resistant tuberculosis in the study area.

The growth in online child exploitation material is a significant challenge for European Law Enforcement Agencies (LEAs). One of the most important sources of such online information corresponds to audio material that needs to be analyzed to find evidence in a timely and practical manner. That is why LEAs require a next-generation AI-powered platform to process audio data from online sources. We propose the use of speech recognition and keyword spotting to transcribe audiovisual data and to detect the presence of keywords related to child abuse. The considered models are based on two of the most accurate neural-based architectures to date: Wav2vec2.0 and Whisper. The systems are tested under an extensive set of scenarios in different languages. Additionally, keeping in mind that obtaining data from LEAs is very sensitive, we explore the use of federated learning to have more robust systems for the addressed application, while maintaining the privacy of the data to LEAs. The considered models achieved a word error rate between 11% and 25%, depending on the language. In addition, the systems are able to recognize a set of spotted words with true positives rates between 82% and 98%, depending on the language. Finally, federated learning strategies show that they can maintain and even improve the performance of the systems when compared to centralized trained models. The proposed systems sit the basis for an AI-powered platform for automatic analysis of audio in the context of forensic applications within child abuse. The use of federated learning is also promising for the addressed scenario, where data privacy is an important issue to be managed.

In this paper we propose a novel self-supervised approach of keywords and keyphrases retrieval and extraction by an end-to-end deep learning approach, which is trained by contextually self-labelled corpus. Our proposed approach is novel to use contextual and semantic features to extract the keywords and has outperformed the state of the art. Through the experiment the proposed approach has been proved to be better in both semantic meaning and quality than the existing popular algorithms of keyword extraction. In addition, we propose to use contextual features from bidirectional transformers to automatically label short-sentence corpus with keywords and keyphrases to build the ground truth. This process avoids the human time to label the keywords and do not need any prior knowledge. To the best of our knowledge, our published dataset in this paper is a fine domain-independent corpus of short sentences with labelled keywords and keyphrases in the NLP community.

Background. Keyword co-occurrence analysis is a crucial tool for comprehending research trends, identifying relevant studies, and gaining insight into the connections between various concepts and topics. Objective. This study focuses on analyzing the co-occurrence of keywords using FP-growth algorithm and direct search methods. Materials and methods. The methodology involved extracting bibliometric data of Energies journal for 2023-2024 from MDPI publisher platform, keyword lemmatization and keyword co-occurrence estimation. Clustering and visualization were performed using Multidendrograms and Scimago Graphica software. Results. The results showed that the FP-growth algorithm can achieve a close match with the direct search results, which facilitates data preparation for clustering. In addition, finding the co-occurrence of three or more keywords significantly reduced the number of possible combinations, which allowed the identification of specific research topics. Conclusions. This study highlights the usefulness of the FP-growth algorithm in keyword analysis and provides insights into ways to refine search queries to abstract databases for the purpose of designing and writing literature and systematic reviews.

s: The lack of historical employment data for college graduates, the need to solve the system cold-start problem and the consideration of reciprocity of job recommendation in job recommendation, lead to low recommendation satisfaction and immature application of the existing job recommendation methods. The article presents a new approach to job recommendation using college graduates as the object of study. In the screening stage, a semantic keyword iterative algorithm is applied to compute the similarity between the resume and recruitment texts. This algorithm enhances the intersectionality of keywords in the calculation process, maximizing the utilization of resume information to enhance the accuracy of text similarity calculations. The ranking phase utilizes in-school data to build a social network between college graduates and graduated students, and solves the system's cold-start problem by using the social network to recommend jobs for college graduates where graduated students are employed. Building upon the amalgamation of the semantic keyword iterative algorithm and the social network job recommendation method outlined above, we introduce a dual-dimensional matching approach involving specialty and salary. This enhancement is designed to elevate the reciprocity of job recommendations. The analysis of the results indicates that the average satisfaction rate (AR) and normalized discounted cumulative gain (NDCG) values for the newly proposed job recommendation method surpass those of other methods, demonstrating its superior effectiveness. The method caters to the preferences of graduate job seekers, aligns with job recruitment requirements, and offers extensive job search assistance to a broad spectrum of graduates.

The objective of this article: analysis of research trends on the topic "Materials Science for Energy Engineering" for the period 2012-2021. Materials and methods: bibliometric analysis of data from the Scopus abstract database.Results. It is shown that this topic is developing faster than the general energy topics.The key feature of the research trends is a significant increase in work on the development of materials for renewable energy and a decrease in the number of publications related to nuclear power.The primary goal of many studies is to increase the efficiency of renewable energy production through the use of new materials.The importance of the topic is evidenced by the emergence of new journals that quickly entered the first quartile of the abstract databases Scopus and Web of Science, for example: ACS Applied Energy Materials.There has been a significant increase of interest in perovskite solar cells, layered semiconductors, triboelectric nanogenerators, cobalt compounds, sulfur compounds, and oxygen and hydrogen evolution reaction during Electrolysis.Publications on electric batteries are well represented in all time intervals. The focus is on electrode materials and battery efficiency.

Background and Objectives: Mechanical and biological complications can lead to system fracture or screw loss on dental implants. Narrow and regular platforms have been used without a consensus about the effect of distance the abutment from the prosthetic platform margin. The aim of this study is to evaluate different insertion torques in the deformation of tri-channel platform connections through two- and three-dimensional measurements with micro-CT. Materials and Methods: 164 implants were divided into groups (platform diameter and type): 3.5, 3.75, and 4.3 mm NP (Narrow Platform), and 4.3 mm RP (Regular Platform). Each implant-platform group was then divided into four subgroups (n = 10) with different torques: T45 (45 Ncm), T80 (80 Ncm), T120 (120 Ncm) and T150 (150 Ncm). The implant-abutment-screw assemblies were scanned and the images obtained were analyzed. Results: A significant difference was observed for the linear and volume measures between the different platforms (p <0.01) and the different implant insertion torques (p <0.01). Qualitative analysis suggested higher deformation resistance for the 3.75 NP compared to the 3.5 NP, and RP was more resistant compared to the NP. Conclusions: The 0.25-mm increment in the implant platform did not increase the resistance to the applied insertion torques; the 4.3-mm implant was significantly stronger compared to the 3.5-mm implant and the proposed micro-CT analysis was considered valid for both 2D and 3D analyses of micro gaps, qualitatively and quantitatively.

Crataegus Oxyacantha is used in the treatment of cardiovascular diseases. In related to your biosafety, only in vitro and in vivo genotoxicity of the fruit and the leaf is described, however, the teratogenic potential is unknown. The aim this study was evaluating the transplacental genotoxicity effect of aqueous and hydroalcoholic extract of leaves C. oxyacantha in a rat model and the quantification of malondialdehyde (MDA) in liver. Three different doses of the aqueous and hydroalcoholic extracts of the C. oxyacantha leaf were administered orally (500, 1000 and 2000 mg/kg) to Wistar rats during 5 days through the pregnancy term (16-21 days), sampling in rats were every 24 h during the last 6 days of gestation and only one sample was taken in neonates at birth. A sample of the mother's and neonate's liver was taken for the determination of MDA. The results show that, at the hepatic level, the evaluated doses of extracts C. oxyacantha in pregnant rats and their pups did not show cytotoxicity. However, the aqueous and hydroalcoholic extract generated cytotoxic and genotoxic damage in the short term. On the other hand, only the aqueous extract showed a teratogenic effect. Based on these results, the aqueous and hydroalcoholic extracts of the C. oxyacantha leaf should not be administered during pregnancy.

Studies have shown that STK11 mutation plays a critical role in affecting the lung adenocarcinoma (LUAD) tumor immune environment. By training an Inception-Resnet-v2 deep convolutional neural network model, we were able to classify STK11-mutated and wild type LUAD tumor histopathology images with a promising accuracy (per slide AUROC=0.795). Dimensional reduction of the activation maps before the output layer of the test set images revealed that fewer immune cells were accumulated around cancer cells in STK11-mutation cases. Our study demonstrated that deep convolutional network model can automatically identify STK11 mutations based on histopathology slides and confirmed that the immune cell density was the main feature used by the model to distinguish STK11-mutated cases.

The main objective of this paper is to identify the major research areas of ChatGPT through term and keyword co-occurrence network mapping techniques. For conducting the present study, total of 577 publications were retrieved from the Lens database for the network visualization. The findings of the study showed that “chatgpt” occurrence in maximum number of times followed by its related terms such as artificial intelligence, large language model, gpt, study etc. This study will be helpful to library and information science as well as computer or information technology professionals.

Thanks to rapid development of artificial intelligence technology in recent years, the current artificial intelligence technology is contributing to many part of society. Education, environment, medical care, military, tourism, economy, politics, etc. are having a very large impact on society as a whole. For example, in the field of education, there is an artificial intelligence tutoring system that automatically assigns tutors based on student's level. In the field of economics, there are quantitative investment methods that automatically analyze large amounts of data to find investment laws to create investment models or predict changes in financial markets. As such, artificial intelligence technology is being used in various fields. So, it is very important to know exactly what factors have an important influence on each field of artificial intelligence technology and how the relationship between each field is connected. Therefore, it is necessary to analyze artificial intelligence technology in each field. In this paper, we analyze patent documents related to artificial intelligence technology. We propose a method for keyword analysis within factors using artificial intelligence patent data sets for artificial intelligence technology analysis. This is a model that relies on feature engineering based on deep learning model named KeyBERT, and using vector space model. A case study of collecting and analyzing artificial intelligence patent data was conducted to show how the proposed model can be applied to real-world problems.

Identification of topical research issues using bibliometric data is complicated by the fact that the citation of publications from recent years has not yet been formed. In this paper, it is proposed to use the average citation of the journal over two years rather than the article citation to estimate to estimate the weight of the keyword occurring in the sample under consideration. In order to identify the terms that characterize relevant research topics, it is proposed to represent the term co-occurrence network in coordinates of the average occurrence of the term per year and the average normalized citation of the term to visualize the graph. Furthermore, this methodology proposes the use of preprocessing of keywords using a lemmatization dictionary. 3,696 bibliometric records for 2022–2024 from the ScienceDirect platform on the topic of industry digitalization were used for the analysis. The VOSviewer and Scimago Graphica programs were used sequentially. The former was used to display the overall landscape of the study, while the latter was used to analyze in more detail the individual slices of bibliometric data obtained with VOSviewer. A ‘convex hull’ was used to facilitate the perception of cluster boundaries. After analysing the data and highlighting the terms, it is proposed to provide context by quoting strings from publications and defining of lesser-known terms. The industry digitalization is not only a technical and technological issue but also an economic one, as evidenced by terms such as ‘digital economy’ and ‘Industry 5.0’.

The development of one technology can be portrayed by common methods like Gartner’s hype cycle or S-curve, however, there is no method to characterize the feature distributions of multiple technologies within a specific domain. This study proposes a big data-based method in terms of four proposed features, namely versatility, significance, commerciality, and disruptiveness, to characterize the technologies within a given domain. The features of technologies are quantitively portrayed using the representative keywords and volumes of returned search results from Google and Google Scholar in two-dimensional analytical spaces of technique and application. We demonstrate the applicability of this method using 452 technologies in the domain of intelligent robotics. The results of our assessment indicate that the versatility values are normally distributed, while the values of significance, commerciality, and disruptiveness follow power-law distributions, in which few technologies possess higher feature values. We also show that significant technologies are more likely to be commercialized or causing potential disruption, as such technologies have higher scores in these features. Further, we validly prove the robustness of our approach via comparing historical trends with literature and characterizing technologies in reduced analytical spaces. Our method can be widely applied in analyzing feature distributions of technologies in different domains, and it can potentially be exploited in decisions like investment, trade, and science policy.

Cancer is a complex disease, considered a major public health problem worldwide. Among the types of cancer, melanoma, and leukemias present high mortality rates in Brazil and worldwide. Currently, conventional cytotoxic treatments cause severe side effects by the non-selectivity between normal cells and cancer cells. Therefore, molecules of natural origin with more efficient anticancer properties and that present fewer adverse effects are of extreme importance for cancer therapy and improvement in patients’ quality of life. This study isolated, identified, and characterized the chemical structure of a new anthraquinone present in the extract of the roots of Senna velutina. In addition, we sought to evaluate the anticancer potential of this molecule against melanoma and leukemic cell lines and identify the pathways of cell death involved. To this end, a novel anthraquinone was isolated from the barks of the roots of S. velutina, analyzed by HPLC-DAD, and its molecular structure was determined by NMR. Subsequently, their cytotoxic activity was evaluated by the MTT method against non-cancerous, melanoma, and leukemic cells. The migration of melanoma cells was evaluated by the scratch assay. By flow cytometry technique, the apoptosis process, caspase-3 activation, analysis of mitochondrial membrane potential, and measurement of ROS were evaluated. In addition, the pharmacological cell death inhibitors NEC-1, RIP-1, BAPTA, Z-VAD, and Z-DEVD were used to confirm the related cell death mechanisms. With the results, it was possible to elucidate the novel compound characterized as 2'-OH-Torosaol I. In normal cells, the compound showed no cytotoxicity in PBMC but reduced the cell viability of all melanoma and leukemic cell lines evaluated. 2'-OH-Torosaol I inhibited chemotaxis of B16F10-Nex2, SK-Mel-19, SK-Mel28 and SK-Mel-103. The cytotoxicity of the compound was induced by apoptosis via the intrinsic pathway with reduced mitochondrial membrane potential, increased levels of reactive oxygen species, and activation of caspase-3. In addition, the inhibitors demonstrated the involvement of necroptosis and CA+ in the death process and confirmed caspase-dependent apoptosis death as one of the main programmed-cell death pathways induced by 2'-OH-Torosaol I. Taken together, the data characterize the novel anthraquinone 2'-OH-Torosaol I, demonstrating its anticancer activity and potential application in cancer therapy.

(1) Background: The COVID-19 pandemic significantly affected worldwide, with varying responses implemented to control its spread. This study aimed to compare the epidemic data compiled by the World Health (2) Methods: Organization to understand the impact of the measures adopted by each country on the mortality rate. The increase or decrease in the number of confirmed cases was understood in logarithmic terms, for which logarithmic growth rates “K” were used. The mortality rate was calculated as the percentage of deaths from the confirmed cases, which was also used for logarithmic comparison. (3) Results: Countries that effectively detected and isolated patients had a mortality rate 10 times lower than those that did not. Although strict lockdowns were once effective, they could not be implemented on an ongoing basis. In fact, after their cancelation, large outbreaks occurred because of medical breakdowns. The virus variants mutated with increased infectivity, which impeded the measures that were once effective, including vaccinations. Although the designs of mRNA vaccines were renewed, they could not keep up with the virus mutation rate. The only effective defence was steadily identifying and isolating patients. (4) Conclusions: These findings have crucial implications for the complete containment of the pandemic and future pandemic preparedness.

This research analyzed the impact of quality of service as perceived by Hapkido students on their exercise continuation and recommendation intentions. It also identified the measures to reduce the rate of student dropout, strengthen competitiveness, and create more efficient marketing strategies for consumer patterns that are rapidly diversifying Hapkido. A questionnaire survey method was conducted with 300 middle and high school students aged 14–19 years having Hapkido training of three months to two years in Incheon and Bucheon during March–April 2019. Frequency, factor, reliability, correlation, and standard multiple regression analyses were conducted on the surveyed data. The conclusions are as follows. First, considering the impact of service quality on exercise continuation intention, service quality positively affects reliability, personification, and perceptual openness; in terms of possibility, it positively affects typicality, personification, and perceptual openness; and in terms of reinforcement, it positively affects reliability and perceptual openness. Second, examining the impact of service quality on recommendation intention positively affects reliability, personification, and perceptual openness. Third, exercise continuation intention positively affects recommendation intention.

This study explores the integration of genome sequencing and polymerase chain reaction (PCR) based methods for tracking the diversity of Covid-19 variants in wastewater. The research focuses on monitoring various Omicron subvariants during a period of significant viral evolution. Genome sequencing, particularly using Oxford Nanopore Technology (ONT), provides a detailed view of emerging variants, surpassing the limitations of PCR-based detection kits that rely on known sequences. This study highlights the effectiveness of combining these methodologies to enhance early detection and inform public health strategies, especially in regions with limited clinical sequencing capabilities.

Research investigating the neural substrates underpinning parental behaviour has recently gained momentum. Particularly, the hypothalamic medial preoptic area (MPOA) has been identified as a crucial region for parenting. The current study conducted a scientometric analysis of publications from 01 January 1972 to 19 January 2021 using CiteSpace software to determine trends in the scientific literature exploring the relationship between MPOA and parental behaviour. In total, 677 scientific papers were analysed, producing a network of 1509 nodes and 5498 links. Four major clusters were identified: "C-Fos Expression'', "Lactating Rat'', "Medial Preoptic Area Interaction'' and "Parental Behavior''. Their content suggests an initial trend in which the properties of the MPOA in response to parental behavior were studied, followed by a growing attention towards the presence of a brain network, including the reward circuits, regulating such behavior. Furthermore, while attention was initially directed uniquely to maternal behavior, it has recently been extended to the understanding of paternal behaviors as well. Finally, although the majority of the studies were conducted on rodents, recent publications broaden the implications of previous documents to human parental behavior, giving insight into the mechanisms underlying postpartum depression. Potential directions in future works were also discussed.

In vitro glucose uptake in yeast cell facilitated by Abelmoschus esculentus L. (okra seed) for management of type 2 diabetes was studied. Plant material was collected, identified, processed, and stored for further use. 80% methanol was employed for extraction and sonicated to release antidiabetic-bioactive component in solution and was filtered, concentrated, freeze-dried, and fractionated using standard techniques. Glucose uptake at an initial concentration of 5mM/L and 10mM/L by the crude extract was consistent to that of the known standard drug while at 25mM/L glucose concentration was equivalent with the crude extract. Also, at 0.625 mg/mL the linear equations, and R2 demonstrations that the crude extract was higher in dose predictability than the standard drug as presented by the equation; y = 35.754x - 57.822, and R² = 0.9502 (95%). The extract-fractions were employed to evaluate the ability of yeast cell line culture to take up glucose from the system through DPPH, FRAP, lipid peroxidation and anti-diabetes effect of extract-fraction assays. Extract-fractions were found to poses antioxidant activity high enough to inhibit stress-related diseases. The extract fractions were active as drug candidates both at low and high concentrations and were better compared with the standard drug and standard antioxidant was comparable. The high bioactive extract fractions require encapsulation with a nanoparticle as a drug candidate for type 2 diabetes patients. And an animal trial of the drug candidate may be necessary to monitor the in vivo performances of the extract fractions and subsequent human volunteered trial.

The emergence of Artificial Intelligence (AI) driven Keyword Spotting (KWS) technologies has revolutionized human to machine interaction. Yet, the challenge of end-to-end energy efficiency, memory footprint and system complexity of current Neural Network (NN) powered AI-KWS pipelines has remained ever present. This paper evaluates KWS utilizing a learning automata powered machine learning algorithm called the Tsetlin Machine (TM). Through significant reduction in parameter requirements and choosing logic over arithmetic based processing, the TM offers new opportunities for low-power KWS while maintaining high learning efficacy. In this paper we explore a TM based keyword spotting (KWS) pipeline to demonstrate low complexity with faster rate of convergence compared to NNs. Further, we investigate the scalability with increasing keywords and explore the potential for enabling low-power on-chip KWS.

Insulator defect detection is of great significance to compromise the stability of the power transmission line. The state-of-the-art network of object detection, YOLOv5, has been widely used on insulator and defect detection. However, YOLOv5 network has some limitations like poor detection rate and high computational loads in detecting small insulator defects. To solve these problems, we proposed a light-weight network for insulator and defect detection. In this network, we introduced Ghost module into YOLOv5 backbone and neck to reduce the parameters and model size to enhance the performance in unmanned aerial vehicles (UAVs). Besides, we added small object detection anchors and layers for small defect detection. In addition, we optimized the backbone of YOLOv5 by applying convolutional block attention module (CBAM) to focus on critical information for insulator and defect detection and suppress uncritical information. The experiment result shows the mean average precision (mAP) 0.5 and the mAP0.5:0.95 of our model can reach 99.4% and 91.7%, the parameters and model weight are reduced to 3807372 and 8.79M, which can easily deploy to embedded devices like UAVs. And the speed of detection can reach 10.9ms/image, which can meet the real-time detection requirement.

Keywords: Agrobacterium 1; Xenorhabdus 2; NR-AMP 3 T-DNA keyword 4; TI-plasmid 5; Intact/Cured/T-DNA Deleted 6 Sensitive/Resistant 7; EMA_PF2 8; HPLC 9

This paper presents an innovative testing framework, testFAILS, designed for the rigorous evaluation of AI Linguistic Systems, with a particular emphasis on various iterations of ChatGPT. Leveraging orthogonal array coverage, this framework provides a robust mechanism for assessing AI systems, addressing the critical question, "How should we evaluate AI?" While the Turing test has traditionally been the benchmark for AI evaluation, we argue that current publicly available chatbots, despite their rapid advancements, have yet to meet this standard. However, the pace of progress suggests that achieving Turing test-level performance may be imminent. In the interim, the need for effective AI evaluation and testing methodologies remains paramount. Our research, which is ongoing, has already validated several versions of ChatGPT, and we are currently conducting comprehensive testing on the latest models, including ChatGPT-4, Bard and Bing Bot, and the LLaMA model. The testFAILS framework is designed to be adaptable, ready to evaluate new bot versions as they are released. Additionally, we have tested available chatbot APIs and developed our own application, AIDoctor, utilizing the ChatGPT-4 model and Microsoft Azure AI technologies

(1) Background: Adverse childhood experiences (ACEs) and positive childhood experiences (PCEs) have potential to impact long-term health outcomes. Understanding these interrelationships can inform the development of interventions to promote societal health (2) Methods: D/PMH was defined as a diagnosis of depression and/or more than 14 days of poor mental health in the previous month. Those reporting poor physical health for more than 14 days in the previous month or categorising their physical health as ‘fair’ or ‘poor’ were deemed to have PG/PPH. Seven categorical questions based on subscales in the Child and Youth Resilience Measure-28 were used to define PCEs.(3) Results: Increased ACEs exposure use significantly associated with increased odds of D/PHM (p<0.001), increased odds of PG/PHH (p=0.010) and decreased odds of AR-SES (p<0.001). Increased exposure to PCEs was significantly associated with decreased odds of D/PHM (p<0.001), increased odds of PG/PHH (p<0.001) and decreased odds of AR-SES (p< 0.001). For all outcomes, exposure to PCEs reduced the strength of the association between ACEs and outcomes. (4) Conclusions: A positive dose response between the number of ACEs and adult D/PMH, PG/PPH and lower AR-SES exists within the Australian population. PCEs weaken this association, demonstrating their mitigating potential. PCEs promoted adult outcomes, irrespective of ACEs.

This study employs a bibliometric analysis of the emerging trends in the area of Money Laundering for Ter-rorism Financing to provide direction for future research in the area. The authors used VOSviewer and an-alysed 2577 published documents generated from the SCOPUS search using the PRISMA methodology. The findings reveal a growing research interest in this area, with a focus on understanding the complex inter-play between money laundering and terrorism financing. Emerging trends such as the use of cryptocurren-cies, trade-based money laundering, and the role of non-profit organizations in facilitating these activities are highlighted. The research contributes to the broader understanding of money laundering for terrorist financing by providing insights into the evolving research landscape and identifying areas for future re-search.