Globally, public acceptance of waste-to-energy (WtE) incinerators is a crucial factor in implementing national waste-to-energy policies. This study adds to the literature on anti-incinerator sentiment by drawing upon an extended psychological-emotional model that integrates place-, trust-, and fairness-based pathways. A total of 338 residents in the Asuwei area in North Beijing completed a survey on a proposal to construct a WtE incinerator in the vicinity. Hierarchical regression analyses indicate that place attachment positively enhances anti-incinerator sentiment through direct effects, as well as through moderation and mediation effects between risk perception and opposing willingness. Further, institutional trust negatively moderates the impact of perceived risk on anti-incinerator sentiment, in addition to directly reducing perceived risk. Trust also influences anti-incinerator sentiment via risk perception, attesting to the effectiveness of a casual model of trust. Likewise, fairness perception acts as another determinant of opposing sentiment, similar to trust. We further reveal that procedural fairness plays a more significant role in predicting anti-incinerator sentiment than does distributional fairness. These findings demonstrate the importance of using a range of instrumental and more affective strategies to promote social acceptance of renewable energy infrastructure.

Nuclear magnetic resonance (NMR) is a technique used for many years by chemists for elucidation of molecular structure. Technological progress over the years has enabled this technique, making it easier to use. Thus, the LF-NMR (low-field NMR) was introduced as side technique, characterized by low man-agement costs and shorter analysis time than its main counterpart. The application of 1H LF-NMR for the quantification of ethyl alcohol in different alcoholic matrices is herein described, comparing results ob-tained with this technique with those determined by a reference gascromatographic method.

Sentiment analysis plays a crucial role in understanding customer feedback, guiding product development, and informing business decisions. This paper introduces term sentiment entropy (TSE), a novel weighting method that leverages the distribution of sentiment labels associated with words to enhance text classification accuracy. TSE complements traditional TF-IDF techniques by capturing the nuances of sentiment variation across different contexts. Experiments across diverse public datasets demonstrate TSE's potential to improve sentiment analysis performance, especially in capturing subtle sentiment shifts and adapting to specific domains. While computational cost and label quality pose challenges, TSE offers a promising avenue for refining sentiment analysis and opening new research frontiers.

In this very personal hunt for the meaning of the bacterial cell cycle, the snark, I briefly revisit and update some of the mechanisms we and many others have proposed to regulate the bacterial cell cycle. These mechanisms, which include the dynamics of calcium, membranes, hyperstructures and networks, are based on physical and physico-chemical concepts such as ion condensation, phase transition, crowding, liquid crystal immiscibility, collective vibrational modes, reptation, and water availability. I draw on ideas from subjects such as the ‘prebiotic ecology’ and phenotypic diversity to help with the hunt. Given the fundamental nature of the snark, I would expect that its capture would make sense of other parts of biology. The route therefore followed by the hunt has involved trying to answer questions like “why do cells replicate their DNA?”, “why is DNA replication semi-conservative?”, “why is DNA a double helix?”, “why do cells divide?”, “is cell division a spandrel?” and “how are catabolism and anabolism balanced?”. Here, I propose some relatively unexplored, experimental approaches to testing snark-related hypotheses and, finally, I propose some possibly original ideas about DNA packing, about phase separations, and about computing with populations of virtual bacteria.

In this study, the microbial strain was isolated from the humus soil to ferment Sophora flavescens and Eleutherococcus sessiliflorus extracts. The isolated microbial was identified as Bacillus genus by 16S rRNA sequence analysis. The fermented plant extracts exhibited the antifungal effects against four types of the plant pathogens such as P. carotorum, B. cinerea, C. fructicola Sau-3, and C. gloeosporioides according to incubation time. Especially, the fermented plant extracts showed the most active activities for Colletotrichum genus to inhibit mycelium growth. Metabolite changes in fermented S. flavescens and E. sessiliflorus extracts were confirmed through LC-Q-TOF/MS. Flavonoid and peptide derivatives were improved in fermented S. flavescens and E. sessiliflorus extracts compared to their unfermented one, respectively. This study suggested that the isolated Bacillus microbial fermentation could be a valuable tool in improving the bioactivity of S. flavescens and E. sessiliflorus extracts to have potential with more environmentally-friendly antifungal agents.

Astragalus plants belong to the legume family and have obvious rhizosphere effect. The northwestern area of Yunnan China is the main distribution region of Astragalus plants with high application value, but low recognition and resource reserves. The aim of this research is to explore the species diversity of cultivable rhizosphere fungi derived from A. acaulis, A. forrestii, and A. ernestii growing in special high-cold environment of northwest Yunnan, and discover anti-infective components from these fungi. A total of 93 fungal strains belonging to 38 species in 18 genera were isolated and identified. Antibacterial and antimalarial screening yielded 10 target strains. Among them, the ethyl acetate crude extract of the fungus Aspergillus calidoustus AA12 from A. acaulis fermentation substance showed a broad antibacterial spectrum and the best antimalarial activity. Further chemical investigation led to the first discovery of seven compounds from the species of A. calidoustus, including a sesterterpine 6-epi-ophiobolin G (1), two sesquiterpenes penicisochroman A (2), pergillin (3) and 7-methyl-2-(1-methylethylethlidene)-furo[3,2-H]isoquinoline-3-one (4), and three polyketides trypacidin (5), 1,2-seco-trypacidin (6) and questin (7). Compound 1 exhibited moderate to strong antibacterial activity against six Gram-positive pathogens with the minimum inhibitory concentration (MIC) ranging from 25 to 6.25 μg/mL, and prominent inhibitory effect on the biofilm of Streptococcus agalactiae at MIC value of 3.125 μg/mL. This compound also displayed potent antimalarial activity against Plasmodium falciparum strains 3D7 and chloroquine-resistant Dd2 at the half maximal inhibitory concentration (IC50) values of 3.319 and 4.340 µmol/L at 72 h, respectively. This study contributed to our understanding of the cultivable rhizosphere fungi Astragalus-derived in special high-cold environment and further increased the library of fungi available for natural anti-infectious products screening.

This review explores the nuanced decision-making surrounding postoperative radiotherapy for diffuse low-grade glioma (DLGG) patients, emphasizing the need for personalized treatment to balance the benefits of tumor control with the risks of long-term side effects. Radiotherapy is a cornerstone of DLGG treatment, offering significant improvements in progression-free survival, but its potential for cognitive decline and vascular lesions necessitates careful consideration of individual patient factors. The review delves into the complexities of radiotherapy timing, revealing that while early radiotherapy enhances progression-free survival, it does not significantly impact overall survival and may lead to earlier onset of cognitive decline. Conversely, delayed radiotherapy may mitigate cognitive decline but may result in shorter progression-free survival and the onset of neurological symptoms. The review advocates for a patient-centered approach, emphasizing the importance of weighing individual risks and benefits, and highlights the potential of artificial intelligence in predicting cognitive decline and the need to revisit previous research findings in light of the 2021 WHO (World Health Organization) classification update, underscoring the evolving nature of DLGG treatment and the ongoing quest for optimal therapeutic strategies.

In volleyball, the jump serve is a fundamental and crucial skill that is one of the most commonly used serving techniques. This study aimed to examine the kinematic differences between straight line (SL) and diagonal line (DL) in volleyball jump serves by focusing on torso and attack arm joint motions. Three-dimensional coordinate data were captured using a motion capture system (200 Hz) in 14 right-handed male professional volleyball players who performed SL and DL jump serves. This result suggests that the pelvis and torso backward rotation angles during the arm-clocking phase were smaller when performing DL serves. During ball contact, the servers exhibited greater forward rotation angles of the pelvis and torso to control the upper body facing the diagonal direction of the target zone. In addition, lowering the forward tilt angle of the trunk helped the servers maintain body balance in the air and increased their wrist flexion angle, preventing the ball from flying out of bounds when performing DL. These findings highlight the importance of controlling pelvic and torso rotations in the transverse plane when adjusting for different serving lines. These insights can guide volleyball athletes and coaches in refining their serving techniques.

Bartonella species, intracellular parasitic zoonotic pathogens, has drawn increasing attention for their diverse host range and associated human diseases. This study investigates Bartonella infection in small mammals in Yunnan Province, China, focusing on tissue tropism. 333 small mammals were sampled from 13 species, 3 orders, 4 families, and 4 genera in Heqing and Gongshan Counties. Conventional PCR and real-time quantitative PCR (qPCR) were utilized for detection and quantification, followed by bioinformatic analysis of obtained DNA sequences. Results show a 31.5% detection rate, varying across species. Notably, Apodemus chevrieri, Eothenomys eleusis, Niviventer fulvescens, Rattus tanezumi, Episoriculus leucops, Anourosorex squamipes, and Ochotona Thibetana exhibited infection rates of 44.4%, 27.7%, 100.0%, 6.3%, 60.0%, 23.5%, and 22.2%, respectively. Genetic analysis identified 30, 10, and 5 strains based on ssrA, rpoB, and gltA genes, with nucleotide identities ranging from 92.1% to 100.0%. Bartonella strains were assigned to B. grahamii, B. rochalimae, B. sendai, B. koshimizu, B. phoceensis, B. taylorii, and a new species identified in the Episoriculus leucops (GS136). Tissue distribution analysis revealed varied copy numbers across different tissues, with the highest load in spleen. These findings underscore Bartonella's diverse species and host range in Yunnan Province, highlighting its broad tissue tropism in small mammals.

In the rapidly evolving field of sentiment analysis, the introduction of Transformer-based architectures, particularly the BERT model, has markedly improved accuracy levels, setting new standards in the analysis of textual sentiment. These advancements have been instrumental in enhancing the model's ability to grasp the nuances and complexities inherent in human language, thereby providing deeper insights into the sentiment expressed in various texts. However, the impressive performance of such deep learning models comes at the cost of increased computational demands and a lack of transparency in their decision-making processes. These challenges have reignited interest in rule-based sentiment analysis methods, which utilize sentiment lexicons for a more straightforward and computationally economical approach to determining text sentiment. Despite being overshadowed by the rise of machine learning models in recent years, these lexicon-based methods possess distinct advantages, including ease of interpretation and lower resource requirements, making them particularly appealing for certain applications. This paper seeks to re-evaluate the relevance and effectiveness of two prominent lexicon-based sentiment analysis methods, SO-CAL and SentiStrength, which have been specifically adapted for the language, in light of the advancements represented by Transformer-based models like SentBERT. Through a comprehensive comparative analysis, we examine the performance of these methodologies in contrast to SentBERT across an extensive collection of 16 text corpora, spanning a variety of genres and contexts. Our findings reveal a nuanced landscape of performance, where SentBERT's advanced capabilities typically afford it a significant advantage in accurately capturing sentiment. Nevertheless, in a surprising turn, the SO-CAL method exhibits exceptional performance on a substantial portion of the datasets, underscoring the continuing value and potential of lexicon-based approaches in sentiment analysis. This study not only highlights the strengths and weaknesses of both deep learning and lexicon-based methods but also opens the door for future hybrid approaches that could leverage the best of both worlds to achieve even greater accuracy and efficiency in sentiment analysis tasks.

The enormous potential of renewable bioresources is expected to play a key role in the development of the EU’s sustainable circular economy. In this context, inexhaustible, biodegradable, non-toxic and carbon-neutral forest-origin resources are very attractive for the development of novel sustainable products. The main structural component of wood is cellulose, which, in turn, is the feedstock of nanocellulose, one of the most explored nanomaterials. Different applications of nanocellulose have been proposed, including packaging, functional coatings, insulating materials, nanocomposite fields among others. However, the intrinsic flammability of nanocellulose restricts its use in some industries where fire risk is a concern. This paper overviews the most recent studies of the fire resistance of nanocellulose-based materials, focusing on thin films, coatings and aerogels. Along with effectiveness, increased attention to sustainable approaches is considered in developing novel fire-resistant coatings. The great potential of bio-based fire-resistant materials, combined with conventional non-halogenated fire retardants FRs, has been established. The formulation methods, types of fire retardants and their action modes, and methods used for analysing fire retardancy are discussed in the frame of this overview.

Two secondary waste materials, municipal incinerator bottom ash (MIBA) and sewage sludge ash (SSA), were mixed with clay for ceramic manufacturing in this study. Specimens with 5 different MIBA replacement amount of 0%, 5%,10%, 15%, and 20%(wt) and 3 different SSA replacement amount of 0%, 10%, and 20%(wt) were prepared and then a series of tests and analysis were conducted to investigate how the two materials affect the quality of the final product and to what extent. It concludes that a mix with up to 20% of SSA and 5% of MIBA could result in quality tiles complying with specifications for interior or exterior flooring applications at certain kiln temperatures.

Sentiment analysis has become an important tool for understanding public opinion across languages and domains. Recently, there has been an increase in the number of studies on sentiment analysis in low-resource languages such as Kazakh. This is important to ensure that modern text analysis technologies are accessible to all users, regardless of their language background. The aim of the study is to create a sentiment analysis model for analyzing texts in Kazakh. As part of this work, we aim to use fine-tuning techniques on our own dataset for already existing models, thus improving their accuracy and efficiency for analyzing Kazakh language texts. This paper presents a manually collected dataset "KazIntTelCom" from the city information service 2GIS, consisting of user reviews, manually annotated by the authors taking into account the polarity of sentiment (i.e. negative positive or neutral). This dataset was used to fine-tune two pre-trained multi-lingual Transformer-based sentiment analysis models taken from the HuggingFace platform. The distillBERT and XLM-RoBERTa models were used for tuning. Also, the models were tested on the dataset "KazSAnDRA". The results show that accurate tuning even on a relatively small dataset gives a significant increase in performance, which is confirmed by an increase in the accuracy index by 20%-30%. In addition, false misses and false detections are analyzed, which allows us to identify directions for further improvement of the models. The contribution of this work, in addition to the dataset, is the analysis of model errors, which will help future developers to make more accurate settings of hyperparameters of training for sentiment analysis in Kazakh. These results are important for natural language processing and their adaptation to low-resource languages, promoting more inclusive and equitable access to modern analytical tools. Thus, this study demonstrates the effectiveness of the Transformers architecture for sentiment analysis in Kazakh and opens new opportunities for further model improvement.

According to the IPCC guidelines, CO₂ emissions from biomass should be excluded from the entire amount of CO₂ emissions when calculating CO₂ emissions and should be separately reported due to the “carbon neutrality.” Sewage sludge is one of the representative biomass fuels. And it is mixed with fossil fuels in terms of greenhouse gas reduction or is used as fuel to replace fossil fuels by itself. According to the results of this study, biomass contents of both the sewage sludge and the sewage sludge incineration exhaust gases did not amount to 100%. At present, in many countries(South Korea, Japan, and Germany), when calculating greenhouse gas emissions from sewage sludge incinerator, all CO₂ emissions from sewage sludge are judged to be biomass and only those greenhouse gas emissions that correspond to Non-CO₂ gases are calculated as greenhouse gas emissions. However, since, according our results, the content of sewage sludge is not 100% biomass, if CO₂ emissions are excluded according to the existing greenhouse gas emission calculation method, the amount of emissions may be underestimated. Therefore, to accurately calculate greenhouse gas emissions from sewage sludge incinerator, CO₂ emissions should be calculated in consideration of the fossil carbon contents of sewage sludge.

A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article, and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions. Python, a versatile programming language, holds vast potential for Sentiment Analysis (SA). Leveraging the Requests and TextBlob libraries, we have crafted a user-friendly code that enables Industrial Engineers (IE) and managers, particularly those who are new to Python, to extract and analyze sentiment efficiently. We aim to provide IE/managers in service companies requiring SA capabilities with a simple yet effective Python solution. While machine learning resources like PyTorch/TensorFlow are commonly utilized in SA, offering pre-built algorithms and tools for training, and implementing machine learning models, we sought to exploit Python's versatility by integrating additional web-scraping libraries. Thus, by using a lexicon-based approach, we intend to deliver an informative and practical article. The code in this article is twofold; firstly, it can be easily adapted by IE/managers possessing basic Python skills; secondly, we aim to inspire junior IE/managers to develop their own customized coding solutions tailored to specific organizational needs.

Cross-lingual sentiment analysis is crucial for understanding and interpreting emotions expressed in text across diverse linguistic contexts. However, cross-lingual sentiment analysis faces challenges such as differences in emotional lexicons, data imbalance, and the need for multi-language sentiment normalization. In this study, we propose a novel approach to address these challenges by leveraging low-resource language training techniques to enhance cross-lingual sentiment analysis. Our method aims to bridge the gap in emotional lexicons by adapting sentiment analysis models to diverse linguistic contexts. Additionally, we tackle issues of data imbalance through innovative data augmentation strategies tailored to each language, and we introduce a multi-language sentiment normalization technique to ensure consistent sentiment interpretation across different languages. Our method achieved state-of-the-art results in twelve languages across four domains, demonstrating superior performance in both text-level and sentence-level sentiment analysis tasks. By conducting comprehensive evaluations across diverse linguistic contexts and domains, we showcase the versatility and effectiveness of our approach in achieving top-tier results in cross-lingual sentiment analysis.

Elemental sulfur, commonly known for its wide range of biological activity, has a long history of being used to protect all garden and vegetable crops from a range of pests and diseases, including powdery mildew, ascochyta blight, clubroot, plant mites, oidium, anthracnose, and scab. In the present study, a quick and environmentally friendly approach has been developed for the synthesis of sulfur nanoparticles with antibacterial activity. Sulfur nanoparticles (SNPs) were prepared by modifying the surface of elemental sulfur using various polyelectrolyte-surfactant mixtures (PSM) including NaCMC-SDBS and PHMG-CTAB. The SNPs were characterized by UV–visible spectrophotometry, X-ray diffraction (XRD), and thermogravimetric analysis (TG/DSC), scanning electron microscopy (SEM), with the SNPs showing an almost spherical shape with an average size in the range of 150-200 nm. The antibacterial activity of the sulfur nanoparticles was tested using against gram-positive S. aureus and E. faecium and gram-negative E. coli and P. aeruginosa bacteria. From this, it could be seen that SNPs exhibited significant antimicrobial activity against gram-positive bacteria, i.e., S. aureus and E. faecium. The in vitro cytotoxicity of the SNPs-1 and SNPs-2 studied to normal (MeT-5A) and tumorous (MCF-7) human cell lines was assessed in the concentration range from 500 μg/ml to 0.12 mg/ml, from which it was determined as being non-cytotoxic. The received products can be considered for potential application in agriculture and medicine.

A large core size and bending resistance are very important properties of mid-infrared energy transfer fibers, but large core sizes usually lead to a deterioration of bending properties. A negative-curvature nested node-free anti-resonant hollow-core fiber (AR-HCF) based on quartz is proposed. It was made by adding a nested layer to a previous AR-HCF design to provide an additional anti-resonance region while keeping the gap between adjacent tubes strictly correlated to the core diameter to produce a node-free structure. These features improve the fiber’s bending resistance while achieving a larger core diameter. The simulation results show that the radial air-glass anti-resonant layer is increased by the introduction of the nested anti-resonant tube, and the weak interference overlap between the fiber core and the cladding mode is reduced, so the fiber core’s limiting loss and the sensitivity to bending are effectively reduced. When the capillary wall thickness t of the fiber is 0.71 μm, the core diameter D is 70 μm, the ratio of the inner diameter of the cladding capillary to the core diameter d/D is 0.62, and the diameter of the nested tube is d0=29 μm, the fiber has a lower limiting loss at the wavelength of 2.79 μm, and the limiting loss is 3.28×10-4 dB/m. At the same time, the optimized structure also has good bending resistance. When the bending radius is 30 mm, the bending loss is only 4.72×10-2 dB/m. To our knowledge, this is the first research to achieve an anti-bending low-loss micro-structure hollow fiber with a bending radius of less than 30 mm in the 2.79 μm band. An anti-bending low-loss anti-resonant hollow-core fiber with this structure constitutes a reliable choice for the light guiding system of a 2.79 μm band erbium laser therapy instrument.

In this paper we describe a method which combines sentiment analysis with machine learning techniques and/or multivariate statistical analysis. By applying this methodology it is possible to classify a collection of texts into two or more groups or clusters. On the basis of a number of previously defined clusters, the novelty of the outlined approach is the use of the sentiment analysis results as input to the machine learning model or multivariate statistical analysis. Once the classifier has been obtained, we can assign a given text into one of the pre-established clusters. The groups or clusters can represent different time periods, classes of texts transcribed from different conversations, etc. The method is illustrated through an example taken from one of the two studies in which we have applied this methodology. In one of the studies, the method was used to classify press news of a volcanic eruption, while in the other study it was used to classify the conversations recorded between a chatbot with different kinds of speakers (humans or chatbots). This last study was the seminal work in which we introduced this methodology.

Telomeres, located at the ends of each chromosome play a vital role in determining cellular fate and aging. Accelerated shortening of telomeres may also result in accelerated aging and occurrence of age-related degenerative diseases. Anti-aging treatments may help in increasing the telomere length along with improving quality of life, reducing dependency for daily life activities, increasing life expectancy and reducing the risks associated with aging. In this study, we evaluated the effect of anti-aging integrative therapy in 36 individuals. The treatment included a combination of HBOT, ozone therapy, colon hydrotherapy, etc. Telomere length was used as a biomarker for objectively quantifying the outcome of the treatment. The effect of treatment was also assessed on various aging symptoms. At the end of the treatment, it was found that that 97.22% individuals showed an increase in telomere length with an average increase of 0.43 Kbp and 94.44% individuals showed reduced biological age with an average decrease of 3.8 years. These improvements were statistically significant. Improvements were also recorded in the majority of the symptoms. No major adverse events were observed. These results indicate that antiaging integrative therapy has the potential to reverse aging symptoms, cellular aging and improve quality of life.

In order to tackle the issue of severe rocking phenomenon under seismic conditions in structures equipped with steel spring isolation bearings, this paper investigates a novel type of anti-rocking bearing. Firstly, the structural configuration and working principle of the novel anti-rocking bearing are introduced, and a design method for bearing parameters is proposed. Secondly, a finite element analysis model is established using SAP2000 software to conduct nonlinear dynamic time history analysis under seismic loading. The analysis results show that the structural arrangement of the novel anti-rocking bearing reduces both the vertical displacement difference and the rocking angle of the isolation layer. The bearing exhibits a certain level of anti-rocking effect, but it may cause significant tensile forces in some bearings, necessitating the installation of anti-tensile reinforcement. The effectiveness of the anti-rocking effect improves as the stiffness of the steel tension rod in the bearing increases; however, it also leads to higher maximum tension in individual bearings as well as the total tension in all bearings. For structures equipped with the novel anti-rocking bearing, the acceleration amplifies under most cases, with amplification coefficients ranging from 0.82 to 1.55. The performance of the novel anti-rocking bearing varies significantly for different seismic waves. The stiffness of the steel tension rod should be determined considering the reference pressure of the bearing and the maximum designed tension of the bearing. Through the finite element simulation of the bearing, the mechanical properties of the bearing are basically the same as the theoretical analysis results. For the cylinder and the joint bearing, certain structural measures need to be taken to ensure its strength and stiffness.

The digital era has significantly amplified the volume of online reviews, presenting both opportunities and challenges in harnessing insights from consumer feedback. Aspect-Based Sentiment Analysis (ABSA) has emerged as a crucial tool for distilling sentiments from these reviews, providing valuable data for enhancing product and service quality. This study introduces an advanced hybrid model, AdvSentiNet, which integrates adversarial training into the state-of-the-art framework to elevate the precision of sentiment detection at the aspect level. By employing an adversarial network, where a generative model competes against a classifier by crafting highly realistic synthetic samples, we aim to bolster the model's resilience against varied data samples. This innovative approach, unexplored in its entirety within the realm of ABSA, demonstrated remarkable performance improvements on benchmark datasets. For instance, accuracy on the SemEval 2015 dataset escalated from 81.7% to 82.5%, and for the SemEval 2016 dataset, it surged from 84.4% to 87.3%.

Recent advancements in machine learning have ushered in innovative techniques for augmenting datasets, particularly through contrastive learning in the computer vision domain. This study pioneers the application of contrastive learning for sentiment analysis, introducing a novel approach termed EmoConLearn. By fine-tuning contrastive learning embeddings, EmoConLearn significantly surpasses BERT-based embeddings in sentiment analysis accuracy, as evidenced by our evaluations on the DynaSent dataset. This research further delves into the efficacy of EmoConLearn across various domain-specific datasets, highlighting its versatility. Additionally, we investigate upsampling strategies to mitigate class imbalance, further enhancing EmoConLearn's performance in sentiment analysis benchmarks.

Antimicrobial peptides represent a promising alternative to traditional drugs in relation to cost, toxicity and, primarily, the growing problem of drug resistance. Here we report on the activity against HSV-1 and HSV-2 of a previously described wide-spectrum synthetic decapeptide, Killer Peptide (KP). As determined by plaque reduction assays, treatment with KP at 100 μg/mL resulted in a reduction in the viral yield titer of 3.5 Logs for HSV-1 and 4.1 Logs for HSV-2. Further evaluation of KP antiviral activity focused on the early stages of virus replicative cycle, including the determination of the residual infectivity of viral suspensions treated with KP. A direct effect of the peptide on viral particles, impairing virus absorption and penetration, was shown. The toxicity profile proved to be extremely good with a selectivity index of 29.6 for HSV-1 and 156 for HSV-2. KP was also active against an acyclovir (ACV)-resistant HSV-1 strain, while HSV-1 subcultures in the presence of sub-inhibitory doses of KP did not lead to the emergence of resistant strains. Finally, the antiviral action of KP proved to be synergistic with that of ACV. Overall, these results demonstrate that KP could represent an interesting addition/alternative to acyclovir for antiviral treatment.

Encephalitis is a condition with a variety of etiologies, clinical presentations and degrees of severity. The causes of these disorders include both neuroinfections and autoimmune diseases in which host antibodies are pathologically directed against self antigens. In autoimmune encephalitis, autoantibodies are expressed in the central nervous system. The incidence of this disease is approximately 4% of all reported cases of encephalitis. Autoimmune encephalitis can be induced by antibodies against neuronal surface antigens such as N-methyl-D-aspartate-activated glutamate receptors (NMDAR), ⍺-amino-3-hydroxy-5-methyl-4-isoxazole propionate receptors (AMPAR) or gangliosides GQ1b, DPPX, CASPR2, LGI1, as well as by antibodies against neuronal intracellular antigens. The paper presents a number of both mental and neurological symptoms of autoimmune encephalitis. Moreover, the coexistence of psychoses, neoplastic diseases and the methods of diagnosing autoimmune encephalitis are discussed. Attention was also drawn to the fact that early diagnosis, as well as early initiation of targeted treatment, increases the chance of a successful course of the therapeutic process.

The field of domain adaptation, particularly in cross-domain sentiment classification, leverages labeled data from a source domain alongside unlabeled or sparsely labeled data from a target domain. The objective is to enhance performance in the target domain by mitigating the discrepancies in data distributions. Traditional methods in this area have focused on identifying and differentiating between pivotal sentiment words (shared across domains) and domain-specific sentiment words. In our work, we introduce a novel framework called Sentiment Domain Adapter (SDA), which incorporates a Category Attention Network (CAN) alongside a Convolutional Neural Network (CNN). This approach treats pivotal and domain-specific words as part of a collective category of attributes, which SDA learns to discern automatically, thereby enhancing domain adaptation. Additionally, SDA seeks to provide interpretative insights by learning these category attributes. Our model's optimization targets three main goals: (1) supervised classification accuracy, (2) minimizing the disparity in category feature distribution, and (3) maintaining domain invariance. Evaluations on three benchmark datasets for sentiment analysis affirm that SDA surpasses several established baselines.

Toll-like receptor 4 (TLR4) is a member of the TLR family. Members of the TLR family play an important role in innate immune responses and are induced by recognition of pathogen-associated molecular patterns. They are also involved in cell proliferation and apoptosis in cancer. We investigated the role of TLR4 in apoptotic cell death in gynecological cancer cells; gynecological cancer is associated with infertility and spontaneous abortion. To examine the effect of TLR4 activation on apoptotic signaling in cancer cells, cultured primary cancer cells were treated with the TLR4 agonist lipopolysaccharide (LPS). The morphology of cancer cells was compared with normal myometrial cells. Enhanced growth rate and loss of contact inhibition with cellular overlap was observed in the cancer cells. The molecular mechanism analysis revealed differential expression of tumor suppressor genes in LPS-treated cancer cells. The expression of apoptosis-related caspase-3 was increased significantly in cancer cells with TLR4 activation after exposure to LPS. Taken together, these results suggest the pro-apoptotic activity of TLR4 as a potential therapeutic target for the treatment of gynecological cancers.

Sepsis is a serious disease that can lead to mortality. The etiology of the disease is connected to how the body as a host reacts toward microbes such as bacteria, viruses, and fungi invasion. Current research shows that during sepsis, the reaction toward microbe attacks takes two main phases. The first is known as a cytokine storm, while the second is a vicious cycle of pro-inflammatory responses followed by a strict anti-inflammatory response led by CD4+ Tregs. Various immunomodulatory therapies have been proposed to break the cycle of pro- and anti-inflammatory reactions to sepsis. However, clinical trials did not show promising results, indicating further research into the mechanism that Treg uses to inhibit proinflammatory reactions. We used next-generation sequencing (NGS) analysis of bulk RNA-seq and single-cell RNA-seq data to examine the exact molecular pathways used by Treg to inhibit the immune response in sepsis. Our approach identified ACOD1 (Aconitate Decarboxylase 1) as a primary mediator of Treg suppression of immune cells. We identified and validated CD36 as a downstream target of ACOD1. CD36 is a known metabolite regulator in Tregs. Taken together, our results indicate that targeting ACOD1 could prove valuable in regulating Treg function during sepsis.

Metallo-β-lactamases (MBLs) are members of the structurally conserved but functionally diverse MBL-fold superfamily of metallohydrolases. MBLs are a major concern for global health care as they efficiently inactivate β-lactam antibiotics, including the “last-resort” carbapenems, and no clinically suitable inhibitors are currently available. Increasingly, promiscuous β-lactamase activity is also observed in other members of the superfamily, including from viruses, which represents an underexplored reservoir for future pathways to antibiotic resistance. Here, two such MBL-fold enzymes from Bacillus phages, the cyclic mononucleotide-degrading proteins ApycGoe3 and ApycGrass, are shown to efficiently degrade β-lactam substrates in vitro. In particular, ApycGrass displays a distinct preference for carbapenem substrates with a catalytic efficiency that is within one order of magnitude of that of the clinically relevant MBL NDM-1. Mutagenesis experiments also demonstrate that the loss of a metal-bridging aspartate residue reduces nuclease activity up to 35-fold, but improves carbapenemase activity. In addition, we propose that the oligomeric state significantly influences β-lactamase activity by modifying access to the active site pocket. Together, these observations hint at a possible new avenue of resistance via the spread of phage-borne MBL-fold enzymes with β-lactamase activity.

Fragile X syndrome (FXS) is the most frequent monogenic form of autism spectrum disorder (ASD). Autistic FXS is caused by loss of the fmr1 gene product, the fragile X mental retardation protein (FMRP), triggering physiological and behavioral abnormalities. It is correlated with clock components for behavioral circadian rhythm. Mutation of this gene causes the disturbances in sleep patterns and circadian behavior commonly observed in patients with autistic FXS, accompanied by frequent dysregulation of melatonin synthesis and melatonin-dependent signaling. These changes impair vigilance, learning and memory, and are also linked to autistic behavior including the abnormal anxiety response. However, although several possible causes, symptoms, and clinical features of ASD have been investigated, the correlation between an altered circadian rhythm and autistic FXS has not been extensively studied. Recent works have highlighted the impact of melatonin on the nervous, immune, and metabolic systems. Even though utilization of melatonin for sleep disorder in ASD has been considered in clinical research, further studies should be aimed at its neuroprotective role in ASD during developmental period. In this review, we focus on the regulatory circuits involved in melatonin dysregulation and circadian system disruption in those with autistic FXS. Additionally, we discuss the neuroprotective effect of melatonin intervention. This may improve neuroplasticity and physical capability. We also review the underlying molecular mechanisms, and suggest that melatonin may be a useful novel treatment for autistic FXS, countering the adverse effects of circadian variation.

The flux of opinions in newsletters and social media platforms significantly influences market perceptions regarding corporate entities and their financial instruments. This paper introduces an advanced model, the Sentiment Dynamics Analyzer (SDA), which leverages a refined hierarchical architecture of Transformers to effectively discern the sentiment embedded within financial texts, ranging from news headlines to microblogs. We have enhanced a RoBERTa model with specialized sentiment lexicons and an augmented layer of Transformer models tailored for nuanced sentence-level sentiment detection, aiming to assign a sentiment score from -1 to +1. Our evaluations demonstrate that SDA not only surpasses the previous best methods but also exhibits superior performance over robust baseline models. This success underscores the utility of integrating tailored contextual analyses with sector-specific sentiment insights in improving predictive accuracy.

The exploration and understanding of sentiments and emotions through textual analysis are foundational to advancements in natural language processing (NLP). While sentiment analysis, which involves discerning the polarity of text, has been extensively explored, the nuanced detection of complex emotions within textual content is still an emerging area of study. In this paper, we introduce EmoLeverage, a model leveraging Transformer architecture and enhanced with a novel Fusion Adapter module. This model aims to deepen emotion detection capabilities by drawing insights from fundamental sentiment analysis tasks, focusing exclusively on textual data. EmoLeverage sets a new standard in the field by demonstrating that deep, nuanced understanding of emotions is achievable through text alone, challenging and surpassing existing methodologies in emotion identification on key datasets. This is a significant step forward, considering the complexity of human emotions and the subtleties involved in their expression through language. By concentrating on textual input, EmoLeverage offers a promising avenue for applications where audio or visual data is not available, highlighting the versatility and potential of text-based emotional analysis. Our approach illustrates the untapped potential of leveraging sophisticated NLP techniques to enhance the detection and comprehension of a wide array of emotions from text. The success of EmoLeverage in outperforming conventional models in emotion recognition tasks underscores the model's effectiveness in parsing and understanding the intricate expressions of human emotions through linguistic cues alone. This achievement paves the way for more accurate, efficient, and nuanced emotion detection tools, potentially transforming various sectors by providing deeper insights into human emotional states through text.

The rise of the Internet has enabled people to express their opinions on various subjects through social media, blogs, and website comments. As a result, there has been a significant increase in research on sentiment analysis. However, most of the research efforts have focused on analyzing sentiment in English-language data, neglecting the wealth of information available in other languages. In this paper, we provide a comprehensive review of the current state-of-the-art in multilingual sentiment analysis. The survey investigates techniques for data preprocessing, representation learning, and feature extraction in multilingual sentiment analysis. It explores cross-lingual transfer learning, domain adaptation, and data augmentation methods that enhance the performance of sentiment analysis models, particularly in low-resource languages and domains. It provides insights into the state-of-the-art approaches, challenges, and opportunities in this evolving field and encourages further advancements in multilingual sentiment analysis research.

Herbal Coffee is getting more popular due to its unique flavor and higher concentration of bioactive compounds, mainly polyphenols. Rosemary is a unique herb with potent antioxidant properties. A green technique called ultrasound aids in releasing bioactive substances from the plant cell wall. In this context, ultrasound-assisted herbal coffee prepared and their anti-diabetic, anti-Alzheimer, and antioxidant activities were measured while the LC/MS was used to determine the bioactive components from the herbal coffee infusion. A total of 95 secondary metabolites were putatively reported, including alkaloids, phenolic acids, and flavonoids, which we identified in the herbal coffee infusion. The total phenolic content (61.5 ± 5.1 mg GAE/g), total flavonoid content (19.3 ± 0.9 mg QE/g), and total condensed tannin (7.6 ± 0.7 mg CE/g) of herbal coffee infusion were observed to be highest of the two remaining samples. The herbal coffee extracts were used to determine all samples' antioxidant, anti-diabetic, and anti-Alzheimer activities. Using LC-MS/MS, we semi-quantified 19 phenolic metabolites. The results show numerous bioactive components in herbal coffee infusion with more potent antioxidant, antidiabetic, and anti-Alzheimer potential. Moreover, more studies must be conducted to confirm how the metabolomic profile of herbal coffee affects its flavor profile.

Ice accumulation poses considerable challenges in transportation, notably in the domain of general aviation. The present study combines the strengths and limitations of conventional aircraft de-icing techniques with the emerging trend toward all-electric aircraft. The study aims to utilize laser-induced graphene (LIG) technology to create a multifunctional surface, seamlessly integrating hydrophobic properties with efficient electrical heating to mitigate surface icing effectively. We investigated the utilization of a 10.6 μm CO2 laser for direct writing on polyimide (PI), a widely-used insulating encapsulation material. From the thermomechanical perspective, our initial analysis using COMSOL Multiphysics software revealed that when the laser power P exceeds 5 W, the PI substrate experience ablative damage. The experimental results show that when P ≤ 5W, an increase in power has a positive impact on the quality, surface porosity, roughness reduction, line-spacing reduction, and water contact-angle enhancement of the graphene. Conversely, when P > 5 W, higher power negatively affects both the substrate and the graphene structure by inducing excessive ablation. However, it influences the graphene line height positively and is consistent with overall experimental-simulation congruence. Furthermore, the incorporation of high-quality graphene resulted in a surface that exhibited higher contact angles (CA > 120°), lower energy consumption, and higher heating efficiency compared to the use of traditional electrically heated materials for anti-icing applications. The potential applications of this one-step fabrication method extend across various industries, particularly aviation, marine engineering, and other ice-prone domains. Moreover, the method has extensive prospects for addressing pivotal challenges associated with ice formation and serves an innovative and efficient anti-icing technology.

This paper presents the Advanced Syntactic-Graph Convolutional Model (ASGCM), a pioneering approach in Aspect-Based Sentiment Analysis (ABSA) that integrates syntactic dependency features within a graph convolution framework. ASGCM stands out for its novel use of dependency edge encoding and tag-based graph convolutions, providing a fine-grained analysis of sentiments associated with specific aspects in text. This model meticulously captures the intricacies of syntactic structures, thereby offering enhanced precision in sentiment analysis. Notably, ASGCM incorporates a dual-layer graph convolution system: one layer processes syntactic dependencies (edges), while the other interprets semantic roles (tags), ensuring a comprehensive understanding of both structural and contextual elements in text. We rigorously tested ASGCM on multiple datasets, including both English and Chinese languages, and our findings reveal a significant improvement in sentiment classification accuracy compared to existing models. The versatility of ASGCM makes it a robust tool for diverse linguistic environments, setting a new standard for ABSA methodologies.

Feedback trading theory is one of the most primitive theories about financial market. But for a long time，researches and modelings on this topic are rarely seen. The model in this paper shows the effects of sentiment shocks on asset prices in a market characterized by feedback trading in the long run. We find that，generally，feedback trading will lead to cognitive bias effect and trading inducement effect. Cognitive bias effect increases with the feedback trading parameter ( FTP) . In our model，the abnormal volatility of asset prices is captured by cognitive bias effect，sentiment shock effect and trading inducement effect．

Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fuelled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19's informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naive Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

The feedback collection and analysis has remained an important subject matter since long. The traditional techniques for student feedback analysis are based on questionnaire-based data collection and analysis. However, the student expresses their feedback opinions on online social media sites, which need to be analyzed. This study aims at the development of fuzzy-based sentiment analysis system for analyzing student feedback and satisfaction by assigning proper sentiment score to opinion words and polarity shifters present in the input reviews. Our technique computes the sentiment score of student feedback reviews and then applies fuzzy-logic module to analyze and quantify student’s satisfaction at the fine-grained level. The experimental results reveal that the proposed work has outperformed the baseline studies as well as state-of-the-art machine learning classifiers.

A Smart Home is characterized by the presence of a huge number of small, low power devices, along with more classical devices. According to the Internet of Things (IoT) paradigm, all of them are expected to be always connected to the Internet in order to provide enhanced services. In this scenario, an attacker can undermine both the network security and the user’s security/privacy. Traditional security measures are not sufficient, because they are too difficult to setup and are either too weak to effectively protect the user or too limiting for the new services effectiveness. The paper suggests to dynamically adapt the security level of the smart home network according to the user perceived risk level what we have called network sentiment analysis. The security level is not fixed, established by a central system (usually by the Internet Service Provider) but can be changed with the users cooperation. The security of the smart home network is improved by a distributed firewalling and Intrusion Detection Systems both to the smart home side as to the Internet Service Provider side. These two parts must cooperate and integrate their actions for reacting dynamically to new and ongoing threats. Moreover, the level of network sentiment detected can be propagate to nearby home networks (e.g. the smart home networks of the apartments inside a building) to increase/decrease their level of security, thus creating a true in-line Intrusion Prevention System (IPS). The paper also presents a test bed for Smart Home to detect and counteract to different attacks against the IoT devices,,Wi-Fi and Ethernet connections .

Sentiment analysis is a technique used to understand the publics’ opinion towards an event, product, or organization. For example, positive or negative opinion or attitude towards electric vehicle (EV) brands. This provides companies with valuable insight about the public's opinion of their products and brands. In the field of natural language processing (NLP), transformer models have shown great performances over the traditional machine learning algorithms. However, these models have not been explored extensively in the EV domain. EV companies are becoming signif-icant competitors in the automotive industry and are projected to cover up to 30% of the United States light vehicle market by 2030 [1]. In this study, we present a comparative study of large language models (LLMs) including bidirectional encoder representations from transformers (BERT), robustly optimized BERT approach and generalized autoregressive pretraining for language un-derstanding using Lucid motors and Tesla motors YouTube datasets. Results evidenced LLMs like BERT and her variants are off-the-shelf algorithms for sentiment analysis, specifically, when fi-ne-tuned. Furthermore, our findings presents the need for domain adaptation whilst utilizing LLMs. Finally, the experimental results showed that RoBERTa achieved consistent performance across the EV datasets with a F1 score of at least 92%.

In the digital age, social media platforms have become a critical channel for brands to engage with their audience and manage their reputation. This paper explores the application of sentiment analysis in social media to aid brand reputation management. Sentiment analysis, a subset of natural language processing (NLP), involves determining the emotional tone behind online discussions and opinions. By leveraging machine learning algorithms and NLP techniques, brands can analyze vast amounts of social media data to gauge public sentiment, identify emerging trends, and detect potential issues before they escalate. This study reviews current methodologies in sentiment analysis, including supervised and unsupervised learning approaches, and discusses their effectiveness in extracting actionable insights from social media content. Additionally, it examines case studies where sentiment analysis has been successfully implemented to enhance brand reputation, address customer concerns, and tailor marketing strategies. The findings highlight the importance of continuous monitoring and adaptation to dynamic social media landscapes for maintaining a positive brand image. This paper provides a comprehensive overview of sentiment analysis tools and techniques, offering practical recommendations for brands seeking to leverage social media sentiment data for proactive reputation management.

The exponential growth of multimodal content across social media platforms, comprising text, images, audio, and video, has catalyzed substantial interest in artificial intelligence, particularly in multi-modal sentiment analysis (MSA). This study presents a comprehensive survey of 30 research papers published between 2020 and 2024 by eminent publishers such as Elsevier, ACM, IEEE, Springer, and others indexed in Google Scholar. Our analysis primarily focuses on exploring multimodal fusion techniques and features, with specific emphasis on the integration of text and image data. Additionally, the article offers an overview of the evolution, definition, and historical context of MSA. It delves into the current challenges and potential advantages of MSA, investigating recent datasets and sophisticated models. Furthermore, the study provides insights into prospective research directions. Notably, this review offers valuable recommendations for advancing research and developing more robust MSA models, thus serving as a valuable resource for both academic and industry researchers engaged in this burgeoning field.

Glucagon-like peptide-1 (GLP-1) agonists are gaining attention as a potential treatment for obesity. While studies have shown that these medications greatly impact dietary habits, their relationship to consumer food waste is unexplored. We analyzed data from 505 U.S. consumers who self-report GLP-1 medication use and found that 25% agree that they waste more food since beginning the medication with 61% disagreeing with this statement. Respondents are less likely to agree with this statement if they have been on the medication a longer time and more likely to agree if they report experiencing nausea since beginning the medication. Several dietary changes are also significantly associated with self-reported changes in food waste since starting the medication. With previous literature finding GLP-1’s significant impact on individual food consumption and dietary choices, our findings suggest that the effects of this novel class of anti-obesity medications may cast a significant impact on food waste patterns. With the potential for widespread adoption of these medications, and given that approximately one-third of food in the United States is wasted by consumers, understanding the interaction of these two consumer trends is critical for projecting their joint impact on the food system.

Acute lung injury (ALI) is characterized by inflammation of the lung tissue and oxidative injury caused by excessive accumulation of reactive oxygen species. Studies have suggested that anti-inflammatory or antioxidant agents could be used for the treatment of ALI with a good outcome. Therefore, our study aimed to test whether the mycelium extract of Sanghuangporus sanghuang (SS-1), believed to exhibit antioxidant and anti-inflammatory properties, could be used against the excessive inflammatory response associated with LPS-induced ALI in mice and to investigate its possible mechanism of action. The experimental results showed that the administration of SS-1 could inhibit LPS-induced inflammation. SS-1 could reduce the number of inflammatory cells, inhibit MPO activity, regulate the TLR4/PI3K/Akt/mTOR pathway and the signal transduction of NF-κB and MAPK pathways in the lung tissue, and inhibit HNGB1 activity in BALF. In addition, SS-1 could affect the synthesis of antioxidant enzymes HO-1 and Trx-1 in the lung tissue and regulate signal transduction in the KAP1/Nrf2/Keap1 pathway. Histological results showed that administration of SS-1 prior to induction could inhibit the large-scale LPS-induced neutrophil infiltration of the lung tissue. Therefore, based on all experimental results, we propose that SS-1 exhibits a protective effect against LPS-induced (ALI) in mice. The mycelium of S. sanghuang can potentially be used for the treatment or prevention of inflammation-related diseases

We provide representations for the generalized Drazin inverse of an anti-triangular matrix of the form $\left( \begin{array}{cc} a&b\\ 1&0 \end{array} \right)$ in a Banach algebra $\mathcal{A}$, under the condition that $ab=ba$. Specifically, we present the representation of Drazin inverse for these types of anti-triangular matrices in Banach algebras.

Currently, in some domestic and foreign mines, the backfill drilling pipeline experience a rupture phenomenon even when the wear degree is low. This results in a delay in production due to the filling becoming 'sick'. This paper presents, for the first time, the damage mechanism from a mechanical perspective and re-derives the anti-extrusion strength model of the backfill drilling pipeline. We investigate the influence of law on the anti-extrusion strength of pipelines from the perspective of stratum and cement ring. We then verify the theoretical and simulation results through engineering examples. The results demonstrate that the Mises stress criterion is a suitable modification principle for the anti-extrusion strength model of the backfill drilling pipeline. The anti-extrusion strength of the pipeline is related to the elastic modulus and Poisson's ratio of the stratum, and the thickness of the cement ring. It is negatively affected by the depth of the stratum. For hard strata, a cement ring with a smaller elastic modulus is suitable, while for soft stratum, a cement ring with a larger elastic modulus is recommended. When the missing angle of the cement ring is less than 60°, the stress concentration factor increases up to 2.2. The stress unloading capacity of the cement ring ranges from 32.7% to 37.8%, and optimal performance of the cement ring is achieved when it has high strength and low rigidity. The backfill filling pipeline of a copper mine abroad was destroyed due to external extrusion force exceeding its anti-extrusion strength value. The modified pipeline anti-extrusion strength model is 18.2% higher than the casing API strength value. This finding can inform the design of the backfill filling pipeline for China's kilometer-deep wells in the future.

Numerous goods and services are now offered through online platforms due to the recent growth of online transactions like e-commerce. Users have trouble locating the product that best suits them from the numerous products available in online shopping. Many studies in deep learning-based recommender systems (RSs) have focused on the intricate relationships between the attributes of users and items. Deep learning techniques have used consumer or item-related traits to improve the quality of personalized recommender systems in many areas, such as tourism, news, and e-commerce. Various companies, primarily e-commerce, utilize sentiment analysis to enhance product quality and effectively navigate today's business environment. Customer feedback regarding a product is gathered through sentiment analysis, which uses contextual data to split it into separate polarities. The explosive rise of the e-commerce industry has resulted in a large body of literature on e-commerce from different perspectives. Researchers have made an effort to categorize the recommended future possibilities for e-commerce study as the field has grown. There are several challenges in e-commerce, such as fake reviews, frequency of user reviews, advertisement click fraud, and code-mixing. In this review, we introduce an overview of the preliminary design for e-commerce. Second, the concept of deep learning, e-commerce, and sentiment analysis are discussed. Third, we represent different versions of the commercial dataset. Finally, we explain various difficulties facing RS and future research directions.

At the end of 2019, while the world was being hit by the COVID-19 virus and, consequently, was living a global health crisis, many other pandemics were putting humankind in danger. The role of social media is of paramount importance in these kinds of contexts since they help health systems to cope with emergencies by contributing to conducting some activities such as the identification of public concerns, the detection of infections’ symptoms, and the traceability of the virus diffusion. In this paper, we have analyzed comments on events related to cholera, ebola, HIV/AIDS, influenza, malaria, Spanish influenza, swine flu, tuberculosis, typhus, yellow fever, and zika, collecting 369,472 tweets from the 3rd of March to the 15th of September, 2022. Our analysis has started with the collection of comments composed of unstructured texts on which we have applied natural language processing solutions. Afterward, we have employed topic modelling and sentiment analysis techniques to obtain a collection of people’s concerns and attitudes toward these pandemics. According to our findings, people's discussions were mostly about malaria, influenza, and tuberculosis and the focus was on the diseases themselves. As regards emotions, the most popular were fear, trust, and disgust where trust is mainly regarding HIV/AIDS tweets.

In this paper, we present a framework that automatically labels Latent Dirichlet Allocation (LDA) generated topics using sentiment and aspect terms from COVID-19 tweets to help the end-users by minimizing the cognitive overhead of identifying key topics labels. Social media platforms especially Twitter are considered as one of the most influential sources of information for providing public opinion related to a critical situation like the COVID-19 pandemic. LDA is a popular topic modelling algorithm that extracts hidden themes of documents without assigning a specific label. Thus automatic labelling of LDA-generated topics from COVID-19 tweets is a great challenge instead of following the manual labelling approach to get an overview of wider public opinion. To overcome this problem, in this paper, we propose a framework named \texttt{SATLabel} that effectively identifies significant topic labels using \textit{top unigrams features of sentiment terms and aspect terms clusters from LDA generated topics} of COVID-19 related tweets to uncover various issues related to the COVID-19 pandemic. The experimental results show that our methodology is more effective, simpler, and traces better topic labels compare to the manual topic labelling approach.

The dataset reports a collection of earnings call transcripts, the related stock prices, and the related sector index. It contains a total of 188 transcripts, 11970 stock prices, and 1196 sector index values. Furthermore, all of these data originated in the period 2016-2020 and are related to the NASDAQ stock market. The data have been collected using Yahoo Finance and Thomson Reuters Eikon. Specifically, Yahoo Finance offered daily stock prices and traded volume. At the same time, Thomson Reuters Eikon has been used as source for the earnings call transcripts. The dataset can be used as a benchmark for the evaluation of several NLP techniques as well as machine learning algorithms for understanding their potential for financial applications. Moreover, it is also possible to expand the dataset by extending the period in which the data originated following a similar procedure.

The rapidly evolving domain of multimodal sentiment analysis is crucial for unraveling the intricate layers of emotional expression in social media content, customer service interactions, and personal vlogs. This research introduces a cutting-edge Advanced Cross-Modal Gating (ACMG) framework that significantly enhances the precision of sentiment analysis by refining the interplay among textual, auditory, and visual modalities. Our approach addresses three foundational aspects of sentiment analysis: (1) Advanced learning of cross-modal interactions, which focuses on extracting and synthesizing sentiment from varied modal inputs, thus providing a holistic view of expressed emotions; (2) Mastery over the temporal dynamics of multimodal data, enabling the model to maintain context and sentiment continuity over extended interactions; and (3) Deployment of a novel fusion strategy that not only integrates unimodal and cross-modal cues but also dynamically adjusts the influence of each modality based on its contextual relevance to the sentiment being expressed. The exploration of these dimensions reveals that the nuanced modeling of cross-modal interactions is crucial for enhancing model responsiveness and accuracy. By applying the ACMG model to two highly regarded datasets—CMU Multimodal Opinion Level Sentiment Intensity (CMU-MOSI) and CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI)—we achieve groundbreaking accuracies of 83.9\% and 81.1\%, respectively. These results represent significant improvements of 1.6\% and 1.34\% over the current state-of-the-art, showcasing the superior performance and potential of our approach in navigating the complexities of multimodal sentiment analysis.

The world health organisation (WHO) revealed approximately 280 million people in the world suffer from depression. Yet, existing studies on early-stage depression detection using machine learning (ML) techniques are limited. Prior studies have applied a single stand-alone algorithm which are unable to deal with data complexities, prone to overfitting and limited in generalisation. To this end, our paper examined the performance of several ML algorithms for early-stage depression detection using two benchmark social media datasets (D1 and D2). More specifically, we incorporated sentiment indicator to improve our model performance. Our experimental results showed that sentence bidirectional encoder representations from transformers (SBERT) numerical vectors fitted into stacking ensemble model achieved comparable F1 scores of 69% in dataset (D1) and 76% in dataset (D2). Our findings suggest that utilising sentiment indicators as additional feature for depression detection yields an improved model performance and thus, we recommend the development of depressive term corpus for future work.

Sentiment analysis of public opinion expressed in social networks has been developed into various applications, especially in English. Hybrid approaches are potential models for reducing sentiment errors on increasingly complex training data. This paper aims to test some hybrid deep learning models' reliability in some domains' Vietnamese language. Our research questions are to determine whether it is possible to produce hybrid models that outperform the Vietnamese language. Hybrid deep sentiment-analysis learning models are built and tested on reviews and feedback of the Vietnamese language. The hybrid models outperformed the accuracy of Vietnamese sentiment analysis on Vietnamese datasets. It contributes to the growing body of research on Vietnamese NLP, providing insights and directions for future studies in this area.

Communication is a key method of expressing one's thoughts and opinions. Amongst many modalities, speech and writing are the most powerful and common forms of human communication. Analysing what and how people think has inherently been an interesting and progressive research domain. This includes bimodal sentiment analysis which is an emerging area in natural language processing (NLP) and has received a great deal of attention in recent years in a variety of areas including social opinion mining, health care, banking, and so on. At present, there are limited studies on bimodal conversational sentiment analysis as it proves to be a challenging area given the complex nature of the way humans express sentiment cues across various modalities. To address this gap, a comparison of the performance of multiple data modality models has been conducted on the MELD dataset, a widely-used dataset for benchmarking sentiment analysis within the research community. Our work then demonstrates the results of combining acoustic and linguistic representations. Lastly, our proposed neural network-based ensemble learning technique is employed over six transformer and deep learning-based models, achieving a State-Of-The-Art (SOTA) accuracy.

Recommender systems have been applied in a wide range of domains such as e-commerce, media, banking, and utilities. This kind of system provides personalized suggestions based on large amounts of data in order to increase user satisfaction. These suggestions help client select products, while organizations can increase the consumption of a product. In the case of social data, sentiment analysis can help gain better understanding of a user’s attitudes, opinions and emotions, which is beneficial to integrate in recommender systems for achieving higher recommendation reliability. On the one hand, this information can be used to complement explicit ratings given to products by users. On the other hand, sentiment analysis of items that can be derived from online news services, blogs, social media or even from the recommender systems themselves is seen as capable of providing better recommendations to users. In this study, we present and evaluate a recommendation approach that integrates sentiment analysis into collaborative filtering methods. The recommender system proposal is based on an adaptive architecture, which includes improved techniques for feature extraction and deep learning models based on sentiment analysis. The results of the empirical study performed with two popular datasets show that sentiment–based deep learning models and collaborative filtering methods can significantly improve the recommender system’s performance.

The review explores the therapeutic potential of mangrove plants as sources of bioactive compounds, particularly focusing on their anti-inflammatory properties. Mangroves, found in tropical and sub-tropical tidal coasts, exhibit unique ecological and biological activities. The review compiles and examines 136 research findings published between 1982 and 2018, with 41 studies specifically addressing the anti-inflammatory properties of mangroves in Indian coastal regions, including the Andaman and Nicobar Islands. It summarizes the chemical structures, molecular weights, formulas, solvents, and biological activities of these mangroves, as reported by various researchers over time. The data underscore the significant potential of tropical Indian mangroves in anti-inflammatory applications. However, a notable research gap exists regarding the extraction of bioactive compounds from mangrove species in the Andaman and Nicobar Islands compared to other regions such as Gujarat, Maharashtra, Goa, Karnataka, Kerala, Tamil Nadu, Andhra Pradesh, Odisha, West Bengal, and Puducherry. Overall, the compilation provides valuable insights into mangrove-based studies for drug discovery research, emphasizing the need for further exploration and exploitation of these natural resources.

Background: Interleukins play dual roles in breast cancer, acting as both promoters and 11 inhibitors of tumorigenesis within the tumor microenvironment, which is shaped by their 12 inflammatory functions. This study analyzed the subtype-specific prognostic significance 13 of anti-inflammatory and pro-inflammatory signatures using microarray-based gene ex- 14 pression analysis. Correlations between these interleukin signatures and immune cell 15 markers (CD8, IgKC, CD20) and immune checkpoints (PD-1) were also evaluated. 16 Materials and Methods: The study investigated the prognostic significance of an anti- 17 inflammatory interleukin signature (IL-12, IL-21, IFN-γ) and a pro-inflammatory interleu- 18 kin signature (IL-4, IL-5, IL-10, IL-13, IL-17, CXCL1) for metastasis-free survival (MFS) 19 using Kaplan-Meier curves and Cox regression analyses in a cohort of 461 patients with 20 early breast cancer. Correlations were analyzed using the Spearman-Rho correlation coef- 21 ficient. 22 Results: Kaplan-Meier curves revealed that the prognostic significance of the anti-inflam- 23 matory signature was specifically pronounced in the basal-like subtype (p=0.004, Log 24 Rank). This signature retained independent prognostic significance in multivariate Cox 25 regression analysis (HR 0.463, 95% CI 0.290-0.741; p=0.001). Higher expression of the anti- 26 inflammatory interleukin signature was associated with longer MFS. The pro-inflamma- 27 tory interleukin signature showed a significant prognostic effect in the whole cohort, with 28 higher expression associated with shorter MFS (p=0.034). Strong correlations were found 29 between the anti-inflammatory signature and CD8 expression (ρ=0.391; p<0.001) and be- 30 tween the pro-inflammatory signature and PD-1 expression (ρ=0.627; p<0.001).

Small interfering RNA (siRNA) holds significant therapeutic potential by silencing target genes through RNA interference. Current clinical applications of siRNA have been primarily limited to liver diseases, while achievements in delivery methods are expanding its applications to various organs, including the lungs. Cholesterol-conjugated siRNA emerges as a promising delivery approach due to its low toxicity and high efficiency. This study focuses on developing a cholesterol-conjugated anti-Il6 siRNA and the evaluation of its potency for the potential treatment of inflammatory diseases using the example of acute lung injury (ALI). The biological activities of different Il6-targeted siRNAs containing chemical modifications were evaluated in J774 cells in vitro. The lead cholesterol-conjugated anti-Il6 siRNA after intranasal instillation demonstrated dose-dependent therapeutic effects in a mouse model of ALI induced by lipopolysaccharide (LPS). The treatment significantly reduced Il6 mRNA levels, inflammatory cell infiltration, and the severity of lung inflammation. IL6 silencing by cholesterol-conjugated siRNA proves to be a promising strategy for treating inflammatory diseases, with potential applications beyond the lungs.

Objective To analyze the influence of HIV in penitentiary institutions through an exhaustive lexiconmetric and sentiment analysis of news in the Spanish written press related to HIV and prison. Material an method observational, descriptive, and retrospective design based on news published in Spanish newspapers from 1981 to 2020, linking HIV infection with penitentiary institutions. Natural language processing and lexiconmetry techniques were used, using the Python programming language and ATLAS Ti v.9 software. Result: 318 articles were analyzed, with 10,502 distinct words. The sentiment of news was evaluated in three different periods: the discovery of HIV, and the periods before and after the existence of antiretroviral treatments. N-gram analysis and word cloud creation were carried out for each period. In the initial phase, ignorance predominated, and prominent terms such as "homosexual," "isolate," "test," "trial," or "confirm" were observed. In the period before antiretroviral treatments, news reflected negative feelings and criticism of prison policy, with keywords such as "give birth," "drug," "carrier," or "disease." With the arrival of treatments, news showed an evolution towards a more positive tone, highlighting terms such as "treatment," "release," or "syringes”.Conclusions: The presence of HIV in Spanish prisons has experienced a significant evolution from an alarming situation to an improvement in the care of inmates, driven by institutional adaptation and advances in antiretroviral treatments.

The quest for youthful, healthy skin and full, vibrant hair has long been a driving force in the dermocosmetics field. However, traditional approaches often struggle to address the underlying causes of aging, damage, and hair loss. Regenerative cosmetics, powered by skin tissue engineering, offer a transformative alternative. This review explores the emerging field of using engineered skin tissues for cosmetic purposes, focusing specifically on their potential for anti-aging, repair, and hair restoration applications. We discuss how these technologies aim to rejuvenate aging skin by promoting collagen production, reducing wrinkles, and improving overall skin function. Additionally, the use of engineered skin for wound healing and scar reduction is examined, highlighting their potential to improve the appearance and functionality of damaged skin. Finally, we advance the exciting prospects of utilizing skin tissue engineering techniques to regenerate hair follicles, potentially offering solutions for hair loss and promoting denser hair growth.

In this follow-up study, we aimed to assess the effectiveness of the “I do not smoke, I exercise”, anti-smoking preventive health education program. The program was based on the Theory of Planned Behavior supplemented with life-skills teaching and targeted at high school students. The intervention comprised ten one-hour online sessions, administered by physical education instructors. The study cohort comprised 222 students (109 boys, 113 girls) from 11 secondary schools, with an average age of 16.42 ± 1.36 years. Data collection involved pre- and post-intervention self-assessment questionnaires. The examined variables included attitudes towards smoking, intention to smoke, subjective norm, perceived behavioral control (PBC), knowledge about smoking, smoking behavior, exercise behavior, attitudes toward the program's implementation, and satisfaction with the program. Separate paired samples t-test revealed a significant improvement in students' knowledge about smoking, perceived behavioral control, and overall program satisfaction following the intervention. However, no significant changes were observed in the remaining variables. These findings suggest that the health education smoking prevention intervention incorporates techniques and strategies that influence the Perceived Behavioral Control variable, emphasizing students' strong interest in educationally theorized programs integrating technology into their design. Future studies should consider further examination of tobacco control strategies within the high school context.

Many anti-tumor effects of group-specific component-derived macrophage-activating factors (GcMAFs) have been reported; however, the specific mechanisms remain unclear. Controlling tumor-associated macrophages (TAMs) in the tumor microenvironment is essential for cancer treatment. This study assessed the role of GcMAF in macrophage activation, elucidated the mechanisms by which it exerts its anti-tumor effects, and determined its effects on TAMs in the tumor microenvironment. GcMAF-stimulated RAW264.7 macrophages and EMT6 breast tumor cells were co-cultured in a 0.4-µm pore cell culture insert, and gene and protein expression and cell viability were evaluated. DNA microarray analysis of GcMAF-stimulated RAW264.7 cells was conducted. The induction of M2 RAW264.7 cells by interleukin (IL)-4 and IL-13 was analyzed. GcMAF stimulation increased the tumor necrosis factor-α and inducible nitric oxide synthase mRNA and protein levels in RAW264.7 cells but decreased the viability of co-cultured EMT6 cells. These results were confirmed in the M2 RAW264M2.7 cells, suggesting that GcMAF exerts anti-tumor effects by inducing the differentiation of macrophages into the M1 type and reprogramming M2 macrophages to the M1 type. The anti-tumor activity of GcMAF via M2-to-M1 macrophage reprogramming could aid in developing novel cancer immunotherapies.

Objective: This research was aimed to estimate the anti-fatigue effects of arecoline and elucidate its underlying mechanisms using a murine model of central fatigue precipitated by sleep deprivation (SD). Methods: Seventy-two male C57BL/6 mice were randomly allocated into six groups: a control group, a SD-induced fatigue model group, a group receiving Rhodiola Rosea capsules (2.5 mg/kg), and arecoline groups administered low, medium, and high doses (10, 20, and 40 mg/kg, respectively). Following 28 days of continuous administrations, the effects of arecoline on the mouse fatigue-related behaviors was assessed by the behavioral tests, including grip strength, rotarod performance, and weight-bearing swimming endurance. Enzyme-linked immunosorbent assays (ELISAs) were utilized to measure the levels of the relevant biochemical markers. Western blotting was employed to quantify the expression of nuclear factor erythroid 2-related factor (Nrf2), Kelch-like ECH-associated protein 1 (Keap1), heme oxygenase 1 (HO-1), sequestosome-1 (p62), and NADPH quinone oxidoreductase 1 (NQO1) in the gastrocnemius muscle. Results: Arecoline administration notably enhanced grip strength, delayed the onset of fatigue as evidenced by extended latencies in rotarod tests, and increased the duration of weight-bearing swimming in mice. In the elevated plus maze, arecoline significantly reduced both the number of entries and the total distance traveled in the open arms. Arecoline markedly decreased serum levels of creatine kinase, blood urea nitrogen, lactate dehydrogenase, triglycerides, and cholesterol, while elevated total testosterone, lactate dehydrogenase, and immunoglobulin G levels. Furthermore, it significantly increased superoxide dismutase(SOD), catalase(CAT), and glutathione peroxidase activity in the gastrocnemius muscle, reduced malondialdehyde levels, augmented hippocampal SOD and CAT activity, and elevated glycogen stores in both liver and muscle tissues. Neurotransmitter levels were significantly increased, cytokine levels were markedly reduced, and the expression of Nrf2, Keap1, NQO1, p62, and HO-1 in brain tissue was significantly upregulated. Conclusion: The current study for the first time demonstrated that arecoline has significant anti-fatigue activity, and its mechanism of action is related to elevate the levels of glucose and lipid metabolism, relieve oxidative stress damage, inhibit neuroinflammatory response, regulate neurotransmitter levels and Keap1/Nrf2/HO-1 signaling pathway. The current study provides a new insight into the potential of arecoline in prevention and improvement of fatigue.

Nowadays and in the web era, there is a huge amount of information available due to automatically and easily generated websites. This information has many shared opinions from people toward different entities. Over the last decade there was a significant huge interest to determine the sentiment of any type of text especially on twitter. Twitter provides unstructured text that has many misspelling words, slang words and shortened forms. More complexity and challenges have come to the sentiment analysis researchers in the context of twitter. In this paper we conducted a literature review of the recent research of Twitter sentiment analysis on different languages. After that, we investigated challenges related to twitter sentiment analysis of Arabic language. Then we found a new solution for Sentiment analysis of Arabic twitter. So, we designed the main component of the proposed system and presented its algorithm. Finally, we performed many experiments under two kinds of datasets. The system achieves highest accuracy and shows many important results..

This research investigates the application of deep learning in sentiment analysis of Canadian maritime case law. It offers a framework for improving maritime law and legal analytics policy-making procedures. The automation of legal document extraction takes center stage, underscoring the vital role sentiment analysis plays at the document level. Therefore, this study introduces a novel strategy for sentiment analysis in Canadian maritime case law, combining sentiment case law approaches with state-of-the-art deep learning techniques. The overarching goal is to systematically unearth hidden biases within case law and investigate their impact on legal outcomes. Employing Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) based models, this research achieves a remarkable accuracy of 98.05% for categorizing instances. In contrast, Conventional Machine Learning techniques such as Support Vector Machines (SVM) yield an accuracy rate of 52.57%, Naive Bayes at 57.44 %, and Logistic Regression at 61.86%. The superior accuracy of the CNN and STM model combination underscores its usefulness in legal sentiment analysis, offering promising future applications in diverse fields like legal analytics and policy design. These findings mark a significant choice for AI-powered legal tools, presenting more sophisticated and sentiment-aware options for the legal profession.

This article presents a dataset of tweets in the Urdu language. There are 1,140,824 tweets in the dataset, collected from Twitter for September and October 2020. This large-scale corpus of tweets is generated by performing pre-processing which includes removing columns containing user information, retweet’s count, followers information, duplicate tweets, removing unnecessary punctuation, links, symbols, and spaces, and finally extracting emojis if present in the tweet text. In the final dataset each tweet record contains columns for tweet id, text, and emoji extracted from the text with a sentiment score. Emojis are extracted to validate Machine Learning models used for the multilingual sentiment and behavior analysis. These are extracted using a Python script that searches for an emoji from the list of 751 most frequently used emojis. If an emoji is present in the text, a column with the emoji description and sentiment score is added.

The sentiment of a word varies based on its context of usage: the words used around it and the part-of-speech it is used as. This paper proposes a technique to suggest the sentiment of a word by combining its part-of-speech and the semantic similarities of its co-occurrences with both context-specific and pre-trained embeddings to achieve powerful and fast results. A study was conducted across domains and sub-domains to measure variance of sentiment by switching domains and switching context within the same domain. Re-scoring a commonly used polarity lexicon showed that 10% of words changed scores while switching domains and 8% changed scores within domains while switching context. Part of Speech analysis on 65,353 commonly used sentiment lexicons showed that 81% of sentiment bearing (non-neutral) lexicons were of the tags NN (Common Noun), JJ (Adjective) or NNS (Proper Noun).

For monitoring of chimeric antigen receptor (CAR) T-cell therapies antigen-based CAR detection methods are usually applied. However, for each target-antigen a separate detection system is required. Furthermore, when monitoring CAR T-cells in the blood of patients treated with bispecific antibodies or T-cell engagers (bsAbs/BiTEs) recognize the same antigen, these methods producing false-positive results in clinical diagnostics. Anti-CAR-linker monoclonal antibodies (mAbs) targeting the linker sequence between the variable domains of the antigen binding CAR fragment, promise an universal and unbiased CAR detection. To test this, we analyzed clinical specimens of all BCMA- and CD19 targeting CAR T-cell products currently approved for clinical use. We found a highly specific and sensitive CAR detection by using anti-CAR-linker mAb in blood cells from patients treated with Ide-cel, Tisa-cel, Axi-cel, Brexu-cel, and Liso-cel. For Ide-cel and Tisa-cel the sensitivity was significant lower compared to antigen-based CAR detection assays. Strikingly, the specificity of anti-CAR linker mAb was not affected by the simultaneous presence of bispecific blinatumomab or teclistamab for Axi-cel, Brexu-cel, Liso-cel or Ide-cel, respectively. Cilta-cel (containing an monomeric G4S-CAR linker) could not be detected by anti-CAR linker mAb. In conclusion, anti-CAR-linker mAbs are highly specific and useful for CAR T-cell monitoring but not universal applicable.

Chagas disease (CD) is a neglected tropical parasitic disease caused by the protozoan Trypanosoma cruzi. Unfortunately, the current etiological treatment for CD is unsatisfactory, due to the high toxicity and low cure efficacy of compounds, mainly during the chronic phase of this disease. Thus, the discovery of new drugs for the treatment of CD is urgent. In the present study, we report the synthesis of a series of guanidines and its evaluation in in vitro assays to determine the trypanocidal effects on the Tulahuen T. cruzi strain, transfected with a β-galactosidase reporter gene, in comparison to the cytotoxic effects on vertebrate cells. The most potent and selective compounds LQOF-G1 and LQOF-G29 were considered good drug prototypes and candidates for in vivo tests using mice. Both compounds possess an electron withdrawing group on the aniline moiety, namely a NO2 and Br group, respectively, which effectively decrease the electron density. LQOF-G29 stands out among the investigated compounds due to the replacement of the benzyl group by methyladamantane.

Marine microalgae are a rich reservoir of natural compounds, including bioactives. Nonetheless, these organisms remain fairly unexplored despite their potential biotechnological applications. Culture collections with diverse taxonomic groups and lifestyles are a good source to unlock this potential and discover new molecules for multiple applications such as the treatment of human pathologies or the production of aquaculture species. In the present work extracts from 33 strains (including 20 dinoflagellates, 4 diatoms and 9 strains from other 7 algal classes), cultivated under identical conditions were examined for antiviral, antibacterial, anti-inflammatory and anti-cancer activities. Among these, antiviral and anti-inflammatory activities were detected in a few strains while antibacterial tests showed positive results in most assays. In turn, most trials did not show any anti-cancer activity. Wide differences were observed between species within the same class, in particular dinoflagellates which were better represented in this study. These preliminary findings pave the way for in-depth characterization of extracts with highest signals in each test, identification of compounds responsible of the biological activities found, and a further screening of the CCVIEO culture collection.

Fungal infections caused by Candida spp. are responsible for high hospital morbidity and mortality rates. Therefore, there is a continuous search for new antifungal medicines, particularly ones with anti-Candida activity. This review analyzed articles published between 2002 and 2023 considering the anti-Candida activity of chemical compounds identified or isolated in plants of the Anacardiaceae family. In addition, the in-silico prediction of the isolated compounds described was performed. The analysis of 35 studies showed that extracts, essential oils, and compounds from the anti-Candida activity were frequently determined in vitro using the minimum inhibitory concentration (MIC). The most commonly tested species were Candida albicans, C. tropicalis, C. parapsilosis, C. glabrata, C. krusei, and C. guilliermondii, respectively. Essential oils were the most used form (37% of the studies). The isolated compounds with antifungal effects include cardanol, estragole, trans-anethole, β-caryophyllene, myrcene, catechin-3-o-rhamnoside, β-sitosterol-3-O-glucoside, 24Z-isomasticadienolic acid, oleanolic acid, and oleanolic aldehyde. The in-silico evaluation of those isolated compounds revealed the compounds' drug-likeness and possible antifungal activity. However, some of them showed high toxicity. In conclusion, compounds from the Anacardiaceae family show promise for developing new therapeutic antifungal drugs, especially in combination with conventional antifungals.

Variable flow emitters are a new type of emitter for underground drip irrigation that are used in locations where soil moisture is difficult to transport to the surface, which leads to crop emer-gence difficulties. The flow channel structure parameters of emitters affect the hydraulic per-formance of the emitter and its anticlogging ability. In this study, the variable flow emitter at the conventional low–flow water supply stage is taken as the research object. A combination of CFD numerical simulation of a two–phase flow of water and sand and clear water testing of test samples is applied to study the influence laws of 10 groups of variable flow emitter structural combinations on the effluent flow rate (q), flow index (x), particle passage rate and flow field inside the flow channel. The results show that the Realizable k–ε turbulence model can be used to simulate the flow field inside the variable flow emitter flow channel. The nRMSE between the measured and simulated values of q is 11.23%, and the relative error between the measured and simulated values of x is 4.66%, which gives a high simulation accuracy. The polar analysis shows that the tooth angle A has the smallest effect on the effluent flow rate q0.1 under 0.1 MPa, x, and particle passage rate of the variable flow emitter. The depth of the flow channel D, the spacing of the teeth B, and the height of the teeth E have a different order of precedence in the influence of the three indices, which are D > B > E > A, B > E > D > A, and E > B > D > A, respectively. The value of particle passage rate is positively correlated with the mean flow velocity (v) and the mean turbulent kinetic energy (k) in the flow channel, and particle passage rate tends to increase and then decrease with the increase of x. The retention time (t) of the particles in the flow channel is closely related to the magnitude of v and k. Three multivariate linear regression equations (R2 = 0.883 ~ 0.995) were constructed for q0.1, x, and particle passage rate versus the flow channel structural parameters. Using the scipy.optimize.minimize function in Python, we de-termined the three optimal combinations of flow channel structural parameters corresponding to better hydraulic performance: q0.1 is 1.5 or 2 L/h (objective function y = min x), better clogging resistance (y = max particle passage rate), and both (y = min (0.5 x - 0.5 particle passage rate)). The research results can provide a ref-erence for the optimal design of variable flow emitters.

Now when the whole world is still under COVID-19 pandemic, many schools have transferred the teaching from physical classroom to online platforms. It is highly important for schools and online learning platforms to investigate the feedback to get valuable insights about online teaching process so that both platforms and teachers are able to learn which aspect they can improve to achieve better teaching performance. But handling reviews expressed by students would be a pretty laborious work if they were handled manually as well as it is unrealistic to handle large-scale feedback from e-learning platform. In order to address this problem, both machine learning algorithms and deep learning models are used in recent research to automatically process students' review getting the opinion, sentiment and attitudes expressed by the students. Such studies may play a crucial role in improving various interactive online learning platforms by incorporating automatic analysis of feedback. Therefore, we conduct an overview study of sentiment analysis in educational field presented in recent research, to help people grasp an overall understanding of the sentiment analysis research. Besides, according to the literature review, we identify three future directions that researchers can focus on in automatically feedback processing: high-level entity extraction, multi-lingual sentiment analysis, and handling of figurative language.

Leishmaniasis are neglected diseases that affect regions such as South Asia, South Africa and Latin America, less developed regions. Current treatment, for cutaneous, mucocutaneous and visceral leishmaniosis, which are the main forms of the disease, is based on the administration of pentavalent antimonial and amphotericin B. However, these drugs have low efficacy and high toxicity. For this reason, the search for new treatments for this disease is necessary, based on studies of new molecules and their conformations. The research proposed the conformational study of brominated guanidine compounds with potential antileishmanial activity using Nuclear Magnetic Resonance (NMR) and X-ray diffraction (XRD) techniques. The present study involves the bro-minated molecules, LQOF-G2, LQOF-G30, LQOF-G35 and LQOF-G35-Br. The later was synthesized by the reaction of LQOF-G35 with NBS under IR irradiation at 120 Watts of potency and dichloro-methane as solvent by 12-h of exposition. The obtained results demonstrated the efficiency of the bromination method, since two bromines atoms entered the molecule. Furthermore, NMR analysis showed the occurrence of a conformational change from Z to E when compound LQOF-G35 was brominated to LQOF-G35-Br. This feature was well confirmed by comparative DRX study of the LQOF-G35 and LQOF-G35-Br compounds. The antileishmanial activity of LQOF-G2 e LQOF-G35 motivated the synthesis of new brominated compounds LQOF-G30 e LQOF-G35-Br.

The combination of several therapeutic strategies is often seen as a good way to decrease the resistance rates, since bacteria overcome better single-drug treatments than multi-drug ones. This strategy is especially attractive when several targets and subpopulations are affected, as it is the case of Klebsiella pneumoniae persister cells, a subpopulation of bacteria able to transiently survive antibiotic exposures. This work aims to evaluate the potential of a repurposed anticancer drug, mitomycin C, combined with the K. pneumoniae lytic phage vB_KpnM-VAC13 in vitro and the safety in an in vivo murine model against two clinical isolates of this pathogen, one of them exhibiting an imipenem-persister phenotype. At the same time, we verified the absence of toxicity of mitomycin C at the concentration using the human chondrocyte cell line T/C28a2. The viability of these human cells was checked both by cytotoxicity assays and flow cytometry.

Monitoring of clients living with HIV encompasses a specific array of activities that routinely check on various aspects of ART following its initiation depending on the availability of resources in that particular state, among other factors as per guidelines from the World Health Organisation. This is a significant practice as it promotes positive health outcomes for patients and efficient utilisation of ART regimens to reduce resistance to the medication – hence it is a measure to guard avoidable costs. It also forms a basis on which decisions are made from. It can be done clinically by assessing for signs e.g., skin rash and symptoms e.g., nausea and vomiting, checking vital organ function and measuring weights across all populations, or can be also be done by assessing specimens for their respective viral loads, white blood cell count, pregnancy status or other infections such as hepatitis B. There have not been any special indications required for one to be eligible for monitoring during ART in developing countries; all qualify for routine monitoring even though some may be more frequent than others in cases of comorbidities such as renal failure where medications can be attributed to exacerbating the condition.

The aim of this study was to investigate the physicochemical characterization and biological activities of chitosan (CH) based composite films with the incorporation of chestnut flower essential oil (CFEO). The composite films were prepared by casting method and characterized in terms of structural, morphological and mechanical properties via FT-IR, XRD, UV, SEM and TGA. The results proved the significantly increased water vapor permeability (WVP), heat resistance, antibacterial and antioxidant capabilities of CH-CFEO films. The incorporation of CH and CFEO enhanced the UV blocking, which made the film shield almost all UV light. Films with the tensile strength of 6.37 ± 0.41 MPa and elongation at break of 22.57 ± 0.35% were got with 6 mg mL-1 CFEO. Subsequently, the fruit preservation experiments also confirmed that the composite films could effectively extend the shelf life of bananas through their excellent water vapor and oxygen barrier. These desirable performances enable our newly developed composite films to be a remarkable packaging material to solve the fresh fruit preservation dilemma.

As the adoption of deep learning models continues to surge across various applications, the need for efficient deployment architectures becomes increasingly critical. This paper presents a novel approach to enhance the deployment of deep learning models by leveraging serverless architecture. Serverless computing has been popular for its auto-scaling, cost-effectiveness, and simplified management characteristics. However, the intense resource demands of deep learning models pose challenges in maintaining low response times and effective load balancing within serverless environments. The proposed architecture addresses these challenges by integrating principles from both deep learning model optimization and serverless computing. Through systematic experimentation and analysis, we demonstrate that by appropriately designing and tuning the deployment architecture, significant improvements in response time, performance, resource utilization, and load distribution can be achieved.

Cellulose nanocrystals (CNCs) are cellulose-derived nanomaterials that can be easily obtained, e.g., from vegetable wastes produced by circular economies. They show promising antimicrobial activity and the absence of side effects and toxicity. This study investigated the ability of CNCs to reduce microbial adherence and biofilm formation using in vitro microbiological models reproducing the oral environment. Microbial adherence by microbial strains of oral interest, Streptococcus mutans and Candida albicans was evaluated on the surfaces of salivary pellicle-coated enamel disks in the presence of different aqueous solutions of CNCs. The antibiofilm activity of the same CNC solutions was tested against S. mutans and an oral microcosm model based on mixed plaque inoculum using a continuous-flow bioreactor. Results showed an excellent anti-adherent activity of the CNCs against the tested strains from the lowest concentration tested (0.032 wt.%). Such activity was significantly higher against S. mutans than C. albicans, suggesting a selective anti-adherent activity against pathogenic strains. At the same time, there was a minimal albeit significant antibiofilm activity (at least 0.5 and 4 wt.% CNC solution for S. mutans and oral microcosm, respectively). This makes CNCs particularly interesting as anticaries agents, encouraging their use in the oral field.

Counterfeiting and theft have always been problems that incur high costs and results in considerable losses for the international markets. In this research paper, we will address the issue of counterfeiting while using RFID technology in retailer systems or other industries by presenting a new anti-counterfeiting and anti-theft system for the retailer market. This system will address the two above mentioned issues and provide a solution that can save the retailer systems millions of dollars yearly. This proposed system will achieve the objective of preventing or minimising the counterfeiting and theft of tagged products. At the same time, it will provide a strong indication for suspiciously sold or obtained items. Furthermore, we conducted a security analysis to prove the correctness of our protocol on the basis of the strand spaces.

This paper presents a novel approach to sentiment analysis specifically customized for predicting stock market movements, bypassing the need for external dictionaries which are often unavailable for many languages. Our methodology directly analyzes textual data, with a particular focus on context-specific sentiment words within neural network models. This specificity ensures our sentiment analysis is both relevant and accurate in identifying trends in the stock market. We employ sophisticated mathematical modeling techniques to enhance both the precision and interpretability of our models. Through meticulous data handling and advanced machine learning methods, we leverage large datasets from Twitter and financial markets to examine the impact of social media sentiment on financial trends. We achieved an accuracy exceeding 75%, highlighting the effectiveness of our modeling approach, which we further refined into a convolutional neural network model. This achievement contributes valuable insights into sentiment analysis within the financial domain, thereby improving the overall clarity of forecasting in this field.

In this paper, we delve into the realm of aspect-level sentiment analysis, a sophisticated analytical task focused on pinpointing and interpreting the sentiment directed towards specific elements within a sentence. Traditional methods in this domain, primarily based on neural networks, have often overlooked the critical role of syntactic structures in sentences. To bridge this gap, we have developed the Syntax-Enhanced Sentiment Graph Network (SentiSyn). This pioneering model represents a significant step forward in aspect-level sentiment analysis, bringing to the forefront the utilization of word dependency relationships to enrich sentiment analysis. SentiSyn stands out by its innovative use of a dependency graph, a tool that meticulously maps out the intricate web of syntactic relationships surrounding a target aspect in a sentence. This approach allows SentiSyn to effectively capture and channel sentiment-related characteristics that are deeply rooted in the syntactic context of the aspect target. By doing so, SentiSyn unlocks a deeper understanding of sentiment dynamics in textual content, enabling a more nuanced and accurate sentiment analysis. Our comprehensive experimental evaluation of SentiSyn showcases its remarkable capabilities. When combined with advanced embedding techniques like GloVe and BERT, SentiSyn demonstrates a superior performance edge over several existing sentiment analysis methods. This performance leap is not just incremental; it represents a significant enhancement in the field of sentiment analysis, underscoring the importance of syntactic context in understanding sentiments. Furthermore, our analysis delves into how SentiSyn effectively leverages these embeddings to gain a more profound and contextually rich insight into sentiment dynamics. The results from our tests indicate that SentiSyn, with its unique approach to integrating syntactic structures and advanced embeddings, sets a new benchmark in aspect-level sentiment analysis, offering both enhanced accuracy and deeper sentiment understanding.

Election outcomes have been predicted in the past with the help of various state-of-the-art language models. Sentiment analysis helps in establishing the opinions of the public about a particular subject, a popular experiment known as opinion mining. Twitter has grown in popularity and proven to be a key tool in mining people’s sentiments concerning election and other trending subjects of interest. The outcome of the just concluded Presidential election in Nigeria shifts the focus on Lagos State governorship election. In this study, we propose a Bidirectional Encoder Representations from Transformers (BERT) model for the sentiment analysis of governorship election in Lagos State Nigeria using Twitter data. A total of 800,000 personal and public tweets were scraped from twitter concerning the three prominent contesting candidates using carefully selected search queries. The tweets were preprocessed to avoid noise and inconsistencies. The preprocessed tweets were passed into the pretrained and finetuned BERT model. The result was analyzed to establish the sentiments of the public about the candidates. The social networks of the candidates were also analyzed. The parameter-tuning yield different results with different learning rates (LR). Results showed that the learning rate at 1e-7 gave the best performance and that the smaller the learning rate, the higher the accuracy but the larger the epoch size, the higher the accuracy.

The work presented in this paper makes multiple scientific contributions with a specific focus on the analysis of misinformation about COVID-19 on YouTube. First, the results of topic modeling performed on the video descriptions of YouTube videos containing misinformation about COVID-19 revealed four distinct themes or focus areas - Promotion and Outreach Efforts, Treatment for COVID-19, Conspiracy Theories regarding COVID-19, and COVID-19 and Politics. Second, the results of topic-specific sentiment analysis revealed the sentiment associated with each of these themes. For the videos belonging to the theme of Promotion and Outreach Efforts, 45.8% were neutral, 39.8% were positive, and 14.4% were negative, for the videos belonging to the theme of Treatment for COVID-19, 38.113% were positive, 31.343% were neutral, and 30.544% were negative, for the videos belonging to the theme of Conspiracy Theories regarding COVID-19, 46.9% were positive, 31.0% were neutral, and 22.1% were negative, and for the videos belonging to the theme of COVID-19 and Politics, 35.70% were positive, 32.86% were negative, and 31.44% were negative. Third, topic-specific language analysis was performed to detect the various languages in which the video descriptions per topic were published on YouTube. This analysis revealed multiple novel insights. For instance, for all the themes, English and Spanish were the most widely used and second-most widely used languages, respectively. Fourth, the patterns of sharing these videos on other social media channels such as Facebook and Twitter were also investigated. The results revealed that videos containing video descriptions in English were shared the highest number of times on Facebook and Twitter. Finally, correlation analysis was performed by taking into account multiple characteristics of these videos. The results revealed that the correlation between the length of the video title and the number of Tweets as well as the correlation between the length of the video title and the number of Facebook posts was statistically significant.

Investigating and classifying sentiments of social media users (e.g., positive, negative) towards an item, situation, and system are very popular among the researchers. However, they rarely discuss the underlying socioeconomic factor associations for such sentiments. This study attempts to explore the factors associated with positive and negative sentiments of the people about reopening the economy, in the United States (US) amidst the COVID-19 global crisis. It takes into consideration the situational uncertainties (i.e., changes in work and travel pattern due to lockdown policies), economic downturn and associated trauma, and emotional factors such as depression. To understand the sentiment of the people about the reopening economy, Twitter data was collected, representing the 51 states including Washington DC of the US. State-wide socioeconomic characteristics of the people (e.g., education, income, family size, and employment status), built environment data (e.g., population density), and the number of COVID-19 related cases were collected and integrated with Twitter data to perform the analysis. A binary logit model was used to identify the factors that influence people toward a positive or negative sentiment. The results from the logit model demonstrate that family households, people with low education levels, people in the labor force, low-income people, and people with higher house rent are more interested in reopening the economy. In contrast, households with a high number of members and high income are less interested to reopen the economy. The accuracy of the model is good (i.e., the model can correctly classify 56.18\% of the sentiments). The Pearson chi2 test indicates that overall this model has high goodness-of-fit. This study provides a clear indication to the policymakers where to allocate resources and what policy options they can undertake to improve the socioeconomic situations of the people and mitigate the impacts of pandemics in the current situation and as well as in the future.

Sentiment analysis is a pivotal tool for gauging the public’s perception and understanding of human communication across digital social media platforms. However, due to linguistic complexities and limited resources, sentiment analysis is not well-represented in many African languages. While benchmark Africa-Centric Pre-trained Language Models (PLMs) have been developed for various Natural Language Processing (NLP) tasks, their applications in eXplainable Artificial Intelligence (XAI) remain unexplored. In this study, we introduce a novel approach that combines Africa-centric PLMs with XAI techniques for sentiment analysis. We demonstrate that applying attention mechanisms and visualisation techniques improves the transformer-based model’s transparency, trustworthiness, and decision-making abilities when making sentiment predictions. We then employ the SAfriSenti—a multilingual sentiment corpus for South African under-resourced languages. We use the corpus to perform various sentiment analysis experiments and also enable comprehensive evaluations, comparing the performance of Africa-centric models against mainstream PLMs. The Afro-XLMR model outperformed all models and achieved an average F1-score performance of 71.04% across the five tested languages and the lowest error rate among the evaluated models. Additionally, we incorporated techniques like Local Interpretive Model-Agnostic Interpretation (LIME) and Shapley Additive Interpretation (SHAP) in the sentiment classifier’s output to enhance the Afro-XLMR model’s interpretability and explainability. As a result, the use of XAI strategies ensures that sentiment predictions are not only accurate and interpretable but also understandable, fostering trust and reliability in the decision-making of AI-driven NLP technologies, particularly in the context of African languages.

Since the outbreak of COVID-19, social media platforms, such as Twitter, have experienced a tremendous increase in conversations related to Long COVID. The term “Long COVID” describes the persistence of symptoms of COVID-19 for several weeks or even months following the initial infection. Recent works in this field have focused on sentiment analysis of Tweets related to COVID-19 to unveil the multifaceted spectrum of emotions, viewpoints, and perspectives held by the Twitter community. However, most of these works did not focus on Long COVID, and the few works that focused on Long COVID have limitations. Furthermore, no prior work in this field has investigated Tweets where individuals self-reported experiencing Long COVID on Twitter. The work presented in this paper aims to address these research challenges by presenting multiple novel findings from a comprehensive analysis of a dataset comprising 1,244,051 Tweets about Long COVID, posted on Twitter between May 25, 2020, and January 31, 2023. First, the analysis shows that the average number of Tweets per month where individuals self-reported Long COVID on Twitter, has been considerably high in 2022 as compared to the average number of Tweets per month in 2021. Second, findings of sentiment analysis using VADER show that the percentage of Tweets with positive, negative, and neutral sentiment were 43.12%, 42.65%, and 14.22%, respectively. Third, the analysis of sentiments associated with these Tweets also shows that the emotion of sadness was expressed in most of these Tweets. It was followed by the emotions of fear, neutral, surprise, anger, joy, and disgust, respectively.

Sentiment analysis is a process of extracting opinions into the positive, negative, or neutral categories from a pool of text using Natural Language Processing (NLP). In the recent era, our society is swiftly moving towards virtual platforms by joining virtual communities. Social media such as Facebook, Twitter, WhatsApp, etc are playing a very vital role in developing virtual communities. A pandemic situation like COVID-19 accelerated people's involvement in social sites to express their concerns or views regarding crucial issues. Mining public sentiment from these social sites especially from Twitter will help various organizations to understand the people's thoughts about the COVID-19 pandemic and to take necessary steps as well. To analyze the public sentiment from COVID-19 tweets is the main objective of our study. We proposed a deep learning architecture based on Bidirectional Gated Recurrent Unit (BiGRU) to accomplish our objective. We developed two different corpora from unlabelled and labeled COVID-19 tweets and use the unlabelled corpus to build an improved labeled corpus. Our proposed architecture draws a better accuracy of 87% on the improved labeled corpus for mining public sentiment from COVID-19 tweets.

An increase in the use of smartphones has laid to the use of the internet and social media platforms. The most commonly used social media platforms are Twitter, Facebook, WhatsApp and Instagram. People are sharing their personal experiences, reviews, feedbacks on the web. The information which is available on the web is unstructured and enormous. Hence, there is a huge scope of research on understanding the sentiment of the data available on the web. Sentiment Analysis (SA) can be carried out on the reviews, feedbacks, discussions available on the web. There has been extensive research carried out on SA in the English language, but data on the web also contains different other languages which should be analyzed. This paper aims to analyze, review and discuss the approaches, algorithms, challenges faced by the researchers while carrying out the SA on Indigenous languages.

In recent years, online social media information has been subject of study in several data science fields due to its impact on users as a communication and expression channel. Data~gathered from online platforms such as Twitter has the potential to facilitate research over social phenomena based on sentiment analysis, which usually employs Natural Language Processing and Machine Learning techniques to interpret sentimental tendencies related to users opinions and make predictions about real events. Cyber attacks are not isolated from opinion subjectivity on online social networks. Various security attacks are performed by hacker activists motivated by reactions from polemic social events. In this paper, a methodology for tracking social data that can trigger cyber attacks is developed. Our main contribution lies in the monthly prediction of tweets with content related to security attacks and the incidents detected based on ℓ1 regularization.

Cacalol (C), a sesquiterpene isolated from Psacalium decompositum, has demonstrated anti-inflammatory and antioxidant activities. Its cytotoxic, antiproliferative, and pro-apoptotic effects have been previously shown in an in vitro breast cancer model. A derivative, Cacalol Acetate (CA), shows potential in regulating these processes, which had not been previously reported. This study focused on an in vitro cervical cancer model, assessing CA's anti-proliferative, pro-apoptotic, cytostatic, and anti-migratory activities using the HeLa cell line. The natural anticancer agent I3C was used as a control for comparison. CA demonstrated significant antitumor activities, including inhibiting cell growth, inducing apoptosis, arresting cells in the G2 phase of the cell cycle, and inhibiting cell migration. These effects were notably greater compared to I3C. I3C, while following a similar trend, did not induce Cas-3 expression, suggesting a different apoptotic pathway. Neither CA nor I3C increased p62 and LC3B levels, indicating they do not stimulate autophagy marker expression. Both compounds inhibited HeLa cell migrate on and induced cell cycle arrest. Despite both holding promise as anticancer agents for cervical cancer, CA's lower cytotoxicity and stronger regulation of tumor phenotypes make it a more promising agent compared to I3C.

Background and Objectives: Age-related macular degeneration (AMD) is one of the major causes of central vision loss in the older age group and its dry form accounts for the majority of cases. Although several causes and mechanisms for the development and progression of AMD have previously been identified, the pathogenesis of this complex disease is still not entirely understood. As the inflammation and involvement of the immune system are strongly suggested to have a central role to promote the degenerative process and to stimulate the onset of complications, we aimed to analyze the prevalence of serum anti-retinal (ARA) and anti-endothelial cell antibodies (AECA) in patients with dry AMD and to determine their relationship with the clinical features of the disease, notably area of geographic atrophy (GA). Materials and Methods: We enrolled 41 patients with dry AMD and 50 sex- and age-matched healthy controls without AMD. The presence of ARA was determined by indirect immunofluorescence using monkey retina as an antigen substrate, while the presence of AECA was determined using cultivated human umbilical vein endothelial cells and primate skeletal muscle. Results: ARA were detected 36 (87.8%) AMD patients (titers range from 1:20 to 1:320) and in 16 (39.0%) (titers range from 1:10 to 1:40) of controls (p=0.0000). Twenty of the 41 patients (48.8%) were positive for AECA, while in a control group AECA were present only in 5 sera (10.0%). The titers of AECA in AMD patients ranged from 1:100 to 1:1000 and in the control group the AECA titers were 1:100 (p=0.0001). There were no significant correlations between the presence of AECA and disease activity. Conclusions: This study provides evidence for the possible involvement of an autoimmune process against retina and retinal vessel antigens in the pathogenesis of geographic atrophy associated with dry AMD.

Aiming at the APF algorithm in the collision-free path planning and LQR algorithm tracking process, there are problems such as unreachable target points, local optimization, low safety coefficient of autonomous lane change collision avoidance tracking control and poor stability, this paper proposes an optimized APF method, which solves the defects of the traditional artificial potential field method by adding the distance adjustment factor, the safety distance protection model and the sub-target virtual potential field, and at the same time generates the autonomous driving car collision-free path. The path tracking control system is designed based on feed-forward control and DLQR controller, in which the vehicle dynamics state space model is used as the basis, the model is linearized and discretized, and the corresponding constraint variables are set to minimize the control command objective cost function under the constraints, and the objective of the DLQR controller.The objective is to accurately determine the steering angle in order to achieve path tracking and eliminate the lateral error between the reference path and the actual path caused by external interference.Finally, the simulink model and CarSim parameter configuration joint simulation platform are built, and different obstacle scenarios and different speeds are selected for collision-free path planning and tracking motion simulation research and analysis, which verifies the accuracy and practicability of the algorithm, and shows more obvious advantages in obstacle avoidance ability, path smoothing degree, and collision avoidance safety stability. The ideas proposed in this paper are expected to be further extended and popularized to actual path planning problems, which has important practical significance and engineering value.

The deep neural network - BERT model (Bidirectional Encoder Representations from Transformers) and the stocks cumulative abnormal return is used in this article to analyze the sentiment of financial texts. The proposed approach, unlike those used so far, does not require the creation of dictionaries, takes into account the broad context of words and their meaning in financial texts, eliminates the problem of ambiguity of words in various contexts, does not require manual labelling of data and is free from the subjective assessment of the researcher. The sentiment of financial texts in the meaning presented in this paper is directly related to the market reaction to the information contained in these texts. For texts belonging to one of the two classes (positive or negative) with the highest probability the BERT model gives the results of predictions with a precision level of 62.38% for the positive class and 55% for the negative class. The results at this level can be used in event study, market efficiency research, investment strategy development or support of investment analysts using fundamental analysis.

Clinical, pathological, and imaging evidence in multiple sclerosis (MS) shows that inflammation starts early and progresses with age. B cells play a central role in this process, contributing to cytokine production, defective regulatory functions, and abnormal immunoglobulin production, even in the central nervous system. Anti-CD20 (aCD20) therapies, which deplete CD20+ B cells, are effective for both relapsing-remitting (RR) and progressive-relapsing (PR) MS. While effective against MS symptoms and lesions detectable by magnetic resonance imaging, aCD20 therapies can reduce the immune response to COVID-19 vaccination. By using high-parameter flow cytometry, we examined the antigen-specific (Ag+) immune response six months post-third COVID-19 mRNA-vaccination in MS patients with RR and PR forms on aCD20 therapy. Despite lower Ag+ B cell responses and lower levels of anti-SARS-CoV2, both total and neutralizing antibodies, RR and PR patients developed strong Ag+ T cell responses. We observed similar percentages and numbers of Ag+ CD4+ T cells and high proportion of Ag+ CD8+ T cells, with slight differences in T cell phenotype and functionality, that however suggested the presence of differences in immune responses driven by age and disease severity.

The main inhibitory neurotransmitter in the mammalian brain is 4-aminobutyric acid (GABA). Phthalimide acts as a sodium channel antagonist, one of the finest targets for treating epilepsy is the sodium channel and JNK kinase activity is inhibited to treat epilepsy. a novel compound with multiple targets of action for the treatment of epilepsy also anti-oxidant activity has been designed. The compounds are designed in two series aldehydes and aniline. Docking of all the designed compounds has been done with the suitable protein GABA-AT, sodium channel, JNK protein, and anti-oxidant from protein bank. QSAR was done for constructing many linear regression models. In combination with the developed Docking and QSAR outcomes, ADMET has been performed for the active compounds. The result shows that 11 of the compounds have excellent binding energy in docking with all the receptors and also have good QSAR results and follow the ADMET. The structural understanding of human GABA-AT inhibition was obtained from molecular dynamics simulation studies for compound no. 11 at 5 nanoseconds. Synthesis and pharmacological activity also suggest that the designed compound no.11 be considered the best Novel Compound.

Bosentan, an endothelin-receptor antagonist (ERA), has potential anti-atherosclerotic properties. We investigated the complementary effects of bosentan and atorvastatin on the progression and composition of the atherosclerotic lesions in diabetic mice. Forty-eight male apoE-/- mice were fed high-fat diet (HFD) for 14 weeks. At week 8, diabetes was induced with streptozotocin and mice were randomized into 4 groups: 1) Control/COG: no intervention. 2) ΒΟG: bosentan 100 mg/kg/day per os. 3) ATG: atorvastatin 20mg/kg/day per os). 4) BO+ATG: Combined administration of bosentan and atorvastatin. The intra-plaque contents of collagen, elastin, monocyte chemoattractant protein-1 (MCP-1), tumor necrosis factor-a (TNF-a), matrix-metalloproteinases (MMP-2,-3,-9), and TIMP-1 concentrations were determined. The percentage of lumen stenosis significantly decreased across all treated groups: BOG: 19.5±2.2%, ATG: 12.8±4.8%, BO+ATG: 9.1±2.7% compared to controls (24.6±4.8%, p&lt;0.001). Both atorvastatin and bosentan significantly increased collagen content and fibrous cap thickness versus COG (p&lt;0.01). All intervention groups reduced the relative intra-plaque concentrations of MCP-1, MMP-3,-9, and increased TIMP-1 compared to COG (p&lt;0.001). Importantly, bosentan showed modest but additive to atorvastatin effects on the latter parameters compared COG (p&lt;0.05). Bosentan treatment in diabetic, atherosclerotic apoE-/- mice delayed the atherosclerosis progression and enhanced plaques’ stability, showing modest but complementary effects with atorvastatin, which are promising in atherosclerotic cardiovascular diseases.

Mining and analysis of the Big Data of Twitter conversations have been of significant interest to the scientific community in the fields of healthcare, epidemiology, big data, data science, computer science, and their related areas, as can be seen from several works in the last few years that focused on sentiment analysis and other forms of text analysis of Tweets related to Ebola, E-Coli, Dengue, Human papillomavirus (HPV), Middle East Respiratory Syndrome (MERS), Measles, Zika virus, H1N1, influenza-like illness, swine flu, flu, Cholera, Listeriosis, cancer, Liver Disease, Inflammatory Bowel Disease, kidney disease, lupus, Parkinson's, Diphtheria, and West Nile virus. The recent outbreaks of COVID-19 and MPox have served as "catalysts" for Twitter usage related to seeking and sharing information, views, opinions, and sentiments involving both these viruses. While there have been a few works published in the last few months that focused on performing sentiment analysis of Tweets related to either COVID-19 or MPox, none of the prior works in this field thus far involved analysis of Tweets focusing on both COVID-19 and MPox at the same time. With an aim to address this research gap, a total of 61,862 Tweets that focused on Mpox and COVID-19 simultaneously, posted between May 7, 2022, to March 3, 2023, were studied to perform sentiment analysis and text analysis. The findings of this study are manifold. First, the results of sentiment analysis show that almost half the Tweets (the actual percentage is 46.88%) had a negative sentiment. It was followed by Tweets that had a positive sentiment (31.97%) and Tweets that had a neutral sentiment (21.14%). Second, this paper presents the top 50 hashtags that were used in these Tweets. Third, it presents the top 100 most frequently used words that are featured in these Tweets. The findings of text analysis show that some of the commonly used words involved directly referring to either or both viruses. In addition to this, the presence of words such as "Polio", "Biden", "Ukraine", "HIV", "climate", and "Ebola" in the list of the top 100 most frequent words indicate that topics of conversations on Twitter in the context of COVID-19 and MPox also included a high level of interest related to other viruses, President Biden, and Ukraine. Finally, a comprehensive comparative study that involves a comparison of this work with 49 prior works in this field is presented to uphold the scientific contributions and relevance of the same.

The Ebola virus disease (EVD) is an extremely contagious and fatal illness caused by the Ebola virus. Recently, Uganda witnessed an outbreak of EVD, which generated a lot of attention on various social media platforms. To ensure effective communication and implementation of targeted health interventions, it is crucial for stakeholders to comprehend the sentiments expressed in the posts and discussions on these online platforms. In this study, we used deep learning techniques to analyze the sentiments expressed in Ebola-related tweets during the outbreak. We explored the application of three deep learning techniques to classify the sentiments in 8,395 tweets as positive, neutral, or negative. The techniques examined included a 6-layer convolutional neural network (CNN), a 6-layer long-short-term memory model (LSTM), and an 8-layer Bidirectional Encoder Representations from Transformers (BERT) model. The study found that the BERT model outperformed both the CNN and LSTM-based models across all the evaluation metrics, achieving a remarkable classification accuracy of 95\%. These findings confirm the reported effectiveness of transformer-based architectures in tasks related to natural language processing, such as sentiment analysis.