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Single-vesicle molecular profiling of cancer-associated extracellular vesicles (EVs) is increasingly being recognized as a powerful tool for cancer detection and monitoring. Mask and target dual imaging is a facile method to quantify the fraction of the molecularly targeted population of EVs in biofluids at the single-vesicle level. However, accurate and efficient dual imaging vesicle analysis has been challenging due to the interference of false signals on the mask images and the need to analyze a large number of images in clinical samples. In this work, we report a fully automatic dual imaging analysis method based on machine learning and use it with dual imaging single-vesicle technology (DISVT) to detect breast cancer at different stages. The convolutional neural network Resnet34 was used along with transfer learning to produce a suitable machine learning model that could accurately identify areas of interest in experimental data. A combination of experimental and synthetic data were used to train the model. Using DISVT and our machine learning-assisted image analysis platform, we determined the fractions of EpCAM-positive EVs and CD24-positive EVs over captured plasma EVs with CD81 marker in the blood plasma of pilot HER2-positive breast cancer patients and compared to those from healthy donors. The amount of both EpCAM-positive and CD24-positive EVs was found negligible for both healthy donors and Stage I patients. The amount of EpCAM-positive EVs (also CD81-positive) increased from 18% to 29% as the cancer progressed from Stage II to III. No significant increase was found with further progression to Stage IV. A similar trend was found for the CD24-positive EVs. Statistical analysis showed that both EpCAM and CD24 markers can detect HER2-positive breast cancer at Stages II, III, or IV. They can also differentiate individual cancer stages except those between Stage III and Stage IV. Due to the simplicity, high sensitivity, and high efficiency, the DISVT with the AI-assisted dual imaging analysis can be widely used for both basic research and clinical applications to quantitatively characterize molecularly targeted EV subtypes in biofluids. [ABSTRACT FROM AUTHOR]

Extracellular vesicles (EVs) are membrane-enclosed particles that have attracted much attention for their potential in disease diagnosis and therapy. However, the clinical translation is limited by the dosing consistency due to their heterogeneity. Among various characterization techniques, nanoparticle tracking analysis (NTA) offers distinct benefits for EV characterization. In this review, we will discuss the NTA technique with a focus on factors affecting the results; then, we will review the two modes of the NTA techniques along with suitable applications in specific areas of EV studies. EVs are typically characterized by their size, size distribution, concentration, protein markers, and RNA cargos. The light-scattering mode of NTA offers accurate size, size distribution, and concentration information in solution, which is useful for comparing EV isolation methods, storage conditions, and EV secretion conditions. In contrast, fluorescent mode of NTA allows differentiating EV subgroups based on specific markers. The success of fluorescence NTA heavily relies on fluorescent tags (e.g., types of dyes and labeling methods). When EVs are labeled with disease-specific markers, fluorescence NTA offers an effective tool for disease detection in biological fluids, such as saliva, blood, and serum. Finally, we will discuss the limitations and future directions of the NTA technique in EV characterization. [ABSTRACT FROM AUTHOR]

This detailed review describes innovative strategies and current products for gene and cell therapy at different stages of research and development to treat recessive dystrophic epidermolysis bullosa (RDEB) which is associated with the functional deficiency of collagen type VII alpha 1 (C7) caused by defects in the COL7A1 gene. The use of allogenic mesenchymal stem/stromal cells, which can be injected intradermally and intravenously, appears to be the most promising approach in the field of RDEB cell therapy. Injections of genetically modified autologous dermal fibroblasts are also worth mentioning under this framework. The most common methods of RDEB gene therapy are gene replacement using viral vectors and gene editing using programmable nucleases. Ex vivo epidermal transplants (ETs) based on autologous keratinocytes (Ks) have been developed using gene therapy methods; one such ET successively passed phase III clinical trials. Products based on the use of two-layer transplants have also been developed with both types of skin cells producing C7. Gene products have also been developed for local use. To date, significant progress has been achieved in the development of efficient biomedical products to treat RDEB, one of the most severe hereditary diseases. [ABSTRACT FROM AUTHOR]

Vacuolar-type ATPase (v-ATPase) is a multimeric protein complex that regulates H+ transport across membranes and intra-cellular organelle acidification. Catabolic processes, such as endocytic degradation and autophagy, strictly rely on v-ATPase-dependent luminal acidification in lysosomes. The v-ATPase complex is expressed at high levels in the brain and its impairment triggers neuronal dysfunction and neurodegeneration. Due to their post-mitotic nature and highly specialized function and morphology, neurons display a unique vulnerability to lysosomal dyshomeostasis. Alterations in genes encoding subunits composing v-ATPase or v-ATPase-related proteins impair brain development and synaptic function in animal models and underlie genetic diseases in humans, such as encephalopathies, epilepsy, as well as neurodevelopmental, and degenerative disorders. This review presents the genetic and functional evidence linking v-ATPase subunits and accessory proteins to various brain disorders, from early-onset developmental epileptic encephalopathy to neurodegenerative diseases. We highlight the latest emerging therapeutic strategies aimed at mitigating lysosomal defects associated with v-ATPase dysfunction. [ABSTRACT FROM AUTHOR]

This study explored differences in student-athletes' symptoms of depression, anxiety, and stress pre- to post-COVID-19-pandemic. The WHO reported a 25% increase in depression and anxiety rates worldwide, with young people disproportionately affected. Student-athletes face many stressors related to their sporting and academic feats, but what is not known is how the COVID-19 pandemic affected their experiences of symptoms of mental illness. A multiple-cohort cross-sectional study design was employed, and data collected using physical and online surveys. Participants (cohort 1 M age = 20.18 years, SD = 1.52; cohort 2 M age = 19.75 years, SD = 1.45) were recruited from UK universities (N = 807; 427 pre-pandemic cohort, 380 post-pandemic cohort). Results revealed statistically significant differences in mean depression (F (1, 805) = 23.92, p < 0.001), anxiety (F (1, 806) = 20.15, p < 0.001), and stress symptoms (F (1, 805) = 5.24, p = 0.022) scores between cohorts. Scores for the post-pandemic cohort were significantly higher than pre-pandemic, suggesting a worsening of symptom severity. Distributions of student-athletes across categories of symptom severity also worsened for depressive and anxiety symptoms post-pandemic and were skewed towards more severe categories. Symptoms of depression, anxiety, and stress were a concern pre-pandemic. Rates are higher in the post-pandemic cohort, suggesting a worsening of symptoms. These data add to evidence on student-athletes' symptoms of mental illness by exploring a UK sample and comparing scores pre- and post-pandemic. [ABSTRACT FROM AUTHOR]

Mitochondrial dysfunction and genomic instability are key hallmarks of aging. The aim of this study was to evaluate whether maintenance of physical capacities at very old age is associated with key hallmarks of aging. To investigate this, we measured mitochondrial bioenergetics, mitochondrial DNA (mtDNA) copy number and DNA repair capacity in peripheral blood mononuclear cells from centenarians. In addition, circulating levels of NAD+/NADH, brain-derived neurotrophic factor (BDNF) and carbonylated proteins were measured in plasma and these parameters were correlated to physical capacities. Centenarians without physical disabilities had lower mitochondrial respiration values including ATP production, reserve capacity, maximal respiration and non-mitochondrial oxygen-consumption rate and had higher mtDNA copy number than centenarians with moderate and severe disabilities (p < 0.05). In centenarian females, grip strength had a positive association with mtDNA copy number (p < 0.05), and a borderline positive trend for activity of the central DNA repair enzyme, APE 1 (p = 0.075), while a negative trend was found with circulating protein carbonylation (p = 0.07) in the entire cohort. Lastly, a trend was observed for a negative association between BDNF and activity of daily living disability score (p = 0.06). Our results suggest that mechanisms involved in maintaining mitochondrial function and genomic stability may be associated with maintenance of physical function in centenarians. [ABSTRACT FROM AUTHOR]

The Herpotrichiellaceae family is an important group of dematiaceous filamentous fungi, associated with a variety of pathogenic fungal species causing chromoblastomycosis (CBM) and phaeohyphomycosis (PHM), both with polymorphic clinical manifestations and worldwide incidence. Currently, the identification of this family and determination of the causative agent is challenging due to the subjectivity of morphological identification methods, necessitating the use of molecular techniques to complement diagnosis. In this context, genetic sequencing of the Internal Transcribed Spacer (ITS) has become the norm due to a lack of alternative molecular tools for identifying these agents. Therefore, this study aimed to develop PCR-Multiplex methodologies to address this gap. Sequences from the ITS and Large Subunit (LSU) of ribosomal DNA were used, and after manual curation and in vitro analyses, primers were synthesized for the identification of the targets. The primers were optimized and validated in vitro, resulting in two PCR-Multiplex methodologies: one for identifying the Herpotrichiellaceae family and the bantiana clade, and another for determining the species Fonsecaea pedrosoi and Fonsecaea monophora. Ultimately, the assays developed in this study aim to complement other identification approaches for these agents, reducing the need for sequencing, improving the management of these infections, and enhancing the accuracy of epidemiological information. [ABSTRACT FROM AUTHOR]

Symbolic music understanding is a critical challenge in artificial intelligence. While traditional symbolic music representations like MIDI capture essential musical elements, they often lack the nuanced expression in music scores. Leveraging the advancements in multimodal pre-training, particularly in visual-language pre-training, we propose a groundbreaking approach: the Score Images as a Modality (SIM) model. This model integrates music score images alongside MIDI data for enhanced symbolic music understanding. We also introduce novel pre-training tasks, including masked bar-attribute modeling and score-MIDI matching. These tasks enable the SIM model to capture music structures and align visual and symbolic representations effectively. Additionally, we present a meticulously curated dataset of matched score images and MIDI representations optimized for training the SIM model. Through experimental validation, we demonstrate the efficacy of our approach in advancing symbolic music understanding. [ABSTRACT FROM AUTHOR]

An automatic composition system that includes music generation and music assessment is designed in this paper. In terms of music generation, we modify the Transformer-XL model for generating music. The Mask mechanism based on Transformer-XL is improved to make the attention of model tend to pay on the bidirectional information, so that the generated popular piano music forms a coherent whole. In terms of music assessment, we combine objective and subjective assessment to judge the generated music in a comprehensive way. Meanwhile, we put forward a new objective assessment method, namely the piano roll classification scoring network. It converts music into pictures and uses classification models in the CV, enabling the network itself to classify and score the generated music. The assessment results from subjective and objective experiments show that by improving the Mask mechanism of Transformer-XL, the model is trained to be better and the generated music could achieve the effect of imitating the real music. [ABSTRACT FROM AUTHOR]

Epidemiological data suggest that moderate hyperoxemia may be associated with an improved outcome after traumatic brain injury. In a prospective, randomized investigation of long-term, resuscitated acute subdural hematoma plus hemorrhagic shock (ASDH + HS) in 14 adult, human-sized pigs, targeted hyperoxemia (200 < PaO2 < 250 mmHg vs. normoxemia 80 < PaO2 < 120 mmHg) coincided with improved neurological function. Since brain perfusion, oxygenation and metabolism did not differ, this post hoc study analyzed the available material for the effects of targeted hyperoxemia on cerebral tissue markers of oxidative/nitrosative stress (nitrotyrosine expression), blood–brain barrier integrity (extravascular albumin accumulation) and fluid homeostasis (oxytocin, its receptor and the H2S-producing enzymes cystathionine-β-synthase and cystathionine-γ-lyase). After 2 h of ASDH + HS (0.1 mL/kgBW autologous blood injected into the subdural space and passive removal of 30% of the blood volume), animals were resuscitated for up to 53 h by re-transfusion of shed blood, noradrenaline infusion to maintain cerebral perfusion pressure at baseline levels and hyper-/normoxemia during the first 24 h. Immediate postmortem, bi-hemispheric (i.e., blood-injected and contra-lateral) prefrontal cortex specimens from the base of the sulci underwent immunohistochemistry (% positive tissue staining) analysis of oxidative/nitrosative stress, blood–brain barrier integrity and fluid homeostasis. None of these tissue markers explained any differences in hyperoxemia-related neurological function. Likewise, hyperoxemia exerted no deleterious effects. [ABSTRACT FROM AUTHOR]

With rapid urbanization, many cities have experienced significant changes in land use and land cover (LULC), triggered urban heat islands (UHI), and increased the health risks of citizens' exposure to UHI. Some studies have recognized residents' inequitable exposure to UHI intensity. However, few have discussed the spatiotemporal heterogeneity in environmental justice and countermeasures for mitigating the inequalities. This study proposed a novel framework that integrates the population-weighted exposure model for assessing adjusted thermal comfort exposure (TCEa) and the spatiotemporal weighted regression (STWR) model for analyzing countermeasures. This framework can facilitate capturing the spatiotemporal heterogeneities in the response of TCEa to three specified land-surface and built-environment parameters (i.e., enhanced vegetation index (EVI), normalized difference built-up index (NDBI), and modified normalized difference water index (MNDWI)). Using this framework, we conducted an empirical study in the urban area of Fuzhou, China. Results showed that high TCEa was mainly concentrated in locations with dense populations and industrial regions. Although the TCEa's responses to various land-surface and built-environment parameters differed at locations over time, the TCEa illustrated overall negative correlations with EVI and MNDWI while positive correlations with NDBI. Many exciting spatial details can be detected from the generated coefficient surfaces: (1) The influences of NDBI on TCEa may be magnified, especially in rapidly urbanizing areas. Still, they diminish to some extent, which may be related to the reduction in building construction activities caused by the COVID-19 epidemic and the gradual improvement of urbanization. (2) The influences of EVI on TCEa decline, which may be correlated with the population increase. (3) Compared with NDBI, the MNDWI had more continuous and stable significant cooling effects on TCEa. Several mitigation strategies based on the spatiotemporal heterogeneous relationships also emanated. The effectiveness of the presented framework was verified. It can help analysts effectively evaluate local thermal comfort exposure inequality and prompt timely mitigation efforts. [ABSTRACT FROM AUTHOR]

(1) Background: This study investigated the effects of caffeinated chewing gum on the basketball-specific performance of trained basketball players. A double-blind, randomized crossover design was employed. (2) Methods: Fifteen participants (age: 20.9 ± 1.0 years; height: 180.9 ± 5.4 cm; mass: 77.2 ± 7.5 kg; training age: 8.2 ± 0.3 years) were recruited and divided into a caffeine trial (CAF) and placebo trial (PL). The participants in the CAF trial chewed gum containing 3 mg/kg of caffeine for 10 min, while those in the PL trial chewed a placebo gum without caffeine. Following a 15 min rest, all the participants completed basketball-specific performance tests. (3) Results: The free throw accuracy for the CAF trial was significantly higher than that for the PL trial (CAF: 79.0 ± 4.31%; PL: 73.0 ± 9.16%; p = 0.012; Cohen's d = 0.94). Additionally, the CAF trial demonstrated significantly better performance in the 20 m segmented dash (CAF: 2.94 ± 1.12 s; PL: 3.13 ± 0.10 s; p < 0.001; Cohen's d =1.8) and squats (p < 0.05), and exhibited lower fatigue indexes (CAF: 3.6 ± 1.6%; PL: 5.2 ± 1.6%; p = 0.009; Cohen's d =1.0). (4) Conclusions: These findings suggest that chewing gum containing 3 mg/kg of caffeine offers moderate-to-large improvements in key performance aspects relevant to professionally trained basketball players. [ABSTRACT FROM AUTHOR]

Even though algorithmic composition might be considered a centuries-old concept, it has been gaining particular momentum since the introduction of computer-based techniques. The development of artificial intelligence (AI) methods, culminating in the latest achievements of deep learning techniques, has provided tools to automatically compose and even produce music. This paper discusses various aspects of the entire process within a context of designing a system able to automatically generate a score and recordings belonging to selected musical genres. It begins with the idea and design overview, followed by considerations regarding the algorithmic formulation of selected musical rules and principles. The system implements a hybrid approach, combining conventional, i.e., stochastic or rule-based, and AI elements. The latter are applied to facilitate the generation of selected layers of composition and to constitute a classifier with a task of evaluating the generated recordings. Selected stages of music generation are discussed, for example how motifs are processed into phrases and how phrases are used in the context of a whole song. To validate the system operation results, an evaluation of the quality of the produced music recordings was conducted, including a test with a group of listeners. The analysis also touches upon some legal aspects related to the creation of algorithmic compositions. [ABSTRACT FROM AUTHOR]

Cholesterol is an essential molecule of life, and its synthesis can be inhibited by both genetic and nongenetic mechanisms. Hundreds of chemicals that we are exposed to in our daily lives can alter sterol biosynthesis. These also encompass various classes of FDA-approved medications, including (but not limited to) commonly used antipsychotic, antidepressant, antifungal, and cardiovascular medications. These medications can interfere with various enzymes of the post-lanosterol biosynthetic pathway, giving rise to complex biochemical changes throughout the body. The consequences of these short- and long-term homeostatic disruptions are mostly unknown. We performed a comprehensive review of the literature and built a catalogue of chemical agents capable of inhibiting post-lanosterol biosynthesis. This process identified significant gaps in existing knowledge, which fall into two main areas: mechanisms by which sterol biosynthesis is altered and consequences that arise from the inhibitions of the different steps in the sterol biosynthesis pathway. The outcome of our review also reinforced that sterol inhibition is an often-overlooked mechanism that can result in adverse consequences and that there is a need to develop new safety guidelines for the use of (novel and already approved) medications with sterol biosynthesis inhibiting side effects, especially during pregnancy. [ABSTRACT FROM AUTHOR]

This study examined the effect of outdoor-fitness-equipment-based resistance training on the health parameters of middle-aged and older adults, as well as analyzing the effect of age on the results found. A total of 149 volunteers were randomly assigned to the training (TG) and control (CG) groups. The TG performed two weekly sessions of resistance training for 8 weeks using outdoor fitness equipment, while the CG continued with their regular daily activities. Body composition was measured using DXA, and the maximal isometric voluntary contraction in knee extension, elbow flexion, and hand grip were assessed, along with the 4 m walk test, the Timed Up and Go Test, and the Short Form 36 Health Survey Questionnaire. The TG showed a significant increase in the lean mass index (p = 0.002) and maximal isometric voluntary contraction in both legs (p < 0.001) and arms (p < 0.001), as well as in physical functioning (p < 0.001) and the role physical dimension (p = 0.006) of the Short Form 36 Health Survey Questionnaire, compared to the CG, which showed a decrease in all these variables. In addition, the TG showed a greater decrease in fat mass (p < 0.001), fat mass index (p = 0.003), and the Timed Up and Go Test (p < 0.001) than the CG. Age conditioned the evolution of most of the variables analyzed (p < 0.05). In conclusion, resistance training with outdoor fitness equipment may be useful for improving the health of middle-aged and older adults, although age is a factor that could influence the adaptations found. [ABSTRACT FROM AUTHOR]

Composing coherent and structured music is one of the main challenges in symbolic music generation. Our research aims to propose a user-centric framework design that promotes a collaborative environment between users and knowledge agents. The primary objective is to improve the music creation process by actively involving users who provide qualitative feedback and emotional assessments. The proposed framework design constructs an abstract format in which a musical piece is represented as a sequence of musical samples. It consists of multiple agents that embody the dynamics of musical creation, emphasizing user-driven creativity and control. This user-centric approach can benefit individuals with different musical backgrounds, encouraging creative exploration and autonomy in personalized, adaptive environments. To guide the design of this framework, we investigate several key research questions, including the optimal balance between system autonomy and user involvement, the extraction of rhythmic and melodic features through musical sampling, and the effectiveness of topological and hierarchical data representations. Our discussion will highlight the different aspects of the framework in relation to the research questions, expected outcomes, and its potential effectiveness in achieving objectives. Through establishing a theoretical foundation and addressing the research questions, this work has laid the groundwork for future empirical studies to validate the framework and its potential in symbolic music generation. [ABSTRACT FROM AUTHOR]

Wellness-centric proactive healthcare is increasingly sought after, with individuals frequently embracing complementary modalities to achieve this goal. In this six-month study, healthy adult participants (n = 25) received specific therapies, including whole-body cryotherapy, infrared sauna, and photobiomodulation, along with guidance on physical activity, diet, and alcohol intake. Serum biomarkers were measured for all participants, while a subset also received biometric assessments for body composition (n = 10) and heart rate variability (n = 7). Over the course of the study (mean (SD) follow-up days = 174 (130)), participants exhibited significant improvements in health. LDL cholesterol (−9.77 (15.43) md/dL) and hsCRP (−1.75 (2.66) mg/L) decreased significantly (p < 0.05). HbA1c increased slightly (p < 0.05), but the effect size was small (0.12 (0.13)%). The body composition subset lost overall body weight (−3.29 (3.75) kg), primarily body fat, while preserving lean muscle mass (p < 0.05). Heart rate variability increased for those with existing cardiovascular risk factors (p < 0.05). In conclusion, participation in the multimodal Healthspan protocol is associated with substantial improvements in health-related biomarkers and biometrics. [ABSTRACT FROM AUTHOR]

Mitral valve prolapse (MVP) is a common valvular disease, affecting 2–3% of the adult human population and is a degenerative condition. A total of 5–10% of the afflicted will develop severe mitral regurgitation, cardiac dysfunction, congestive heart failure, and sudden cardiac death. Naturally occurring myxomatous MVP in dogs closely resembles MVP in humans structurally, and functional consequences are similar. In both species, valvular interstitial cells (VICs) in affected valves exhibit phenotype consistent with activated myofibroblasts with increased alpha-smooth muscle actin (αSMA) expression. Using VICs collected from normal and MVP-affected valves of dogs, we analyzed the miRNA expression profile of the cells and their associated small extracellular vesicles (sEV) using RNA sequencing to understand the role of non-coding RNAs and sEV in MVP pathogenesis. miR-145 was shown to be upregulated in both the affected VICs and sEV, and overexpression of miR-145 by mimic transfection in quiescent VIC recapitulates the activated myofibroblastic phenotype. Concurrently, KLF4 expression was noted to be suppressed by miR-145, confirming the miR-145—KLF4—αSMA axis. Targeting this axis may serve as a potential therapy in controlling pathologic abnormalities found in MVP valves. [ABSTRACT FROM AUTHOR]

Background: Incorporating socio-cognitive mindfulness, which has not received much attention from nursing researchers, this study investigated the structural relationships between socio-cognitive mindfulness, everyday creativity, and clinical competence among nursing students. This study also explored the mediating effect of everyday creativity. Methods: A cross-sectional study was performed with 222 nursing students in South Korea. Students completed the questionnaire evaluating their own socio-cognitive mindfulness, everyday creativity, and clinical competence. Data were analyzed using structural equation modeling and path analysis. Results: Socio-cognitive mindfulness positively influenced everyday creativity (β = 0.791, p < 0.01), and everyday creativity also positively influenced clinical competence (β = 0.470, p < 0.01). However, the relationship between socio-cognitive mindfulness and clinical competence was not significant. Importantly, everyday creativity fully mediated the relationship between socio-cognitive mindfulness and clinical competence (a × b = 0.372, p < 0.01). Conclusions: The results indicate that socio-cognitive mindfulness effectively enhances nursing students' clinical competence by improving their everyday creativity. The mediating result highlights the significance of everyday creativity in nursing education, underscoring the necessity for training programs aimed at cultivating creativity. This research offers a basis for developing programs that concentrate on socio-cognitive mindfulness and everyday creativity, with the goal of enhancing the clinical competence of nursing students. [ABSTRACT FROM AUTHOR]

This study aimed to assess the impact of different resistance training (RT) loads and repetition on muscle damage, intramuscular anabolic signaling, and maximal muscle strength (MMS) in weightlifters. Eighteen male weightlifters were randomly assigned to 8 weeks of supervised RT regimes: high-load, low-repetition (HL), low-load, high-repetition (LH), and combination of HL and LH (COMBI). All groups exhibited a significant increase in skeletal muscle mass (SMM) and growth hormone levels, which ultimately contributed to improvement in MMS as indicated by 1-repetition maximum in the back squat and back muscle strength. Notably, while there were no significant changes in the mTOR protein, the phosphorylation of phosphorylation of p70 ribosomal protein S6 kinase 1 (p70S6K1), eukaryotic initiation factor 4E-binding protein 1 (4E-BP1), and eukaryotic elongation factor 2 (eEF2), which are involved in muscle cell growth, was significantly affected by the different training regimens. More importantly, LH-RT led to a significant reduction in muscle damage markers, creatine kinase (CK) and lactate dehydrogenase (LDH), suggesting reduced recovery time and fatigue. Our results demonstrated that the LH-RT paradigm could be a viable alternative for weightlifters to enhance MMS and muscle hypertrophy similar to HL-RT, while reducing RT-induced muscle damage, ultimately contributing to the enhancement of exercise performance. [ABSTRACT FROM AUTHOR]

Cannabidiol is the first cannabis-derived drug approved for the treatment of Lennox–Gastaut syndrome, Dravet syndrome, and Tuberous Sclerosis Complex. In the current study, we performed a descriptive analysis followed by a disproportionality analysis of potential adverse events caused by CBD extracted from the VigiBase® database. Furthermore, the biological plausibility of the association between CBD and the serotonin 5-HT1A receptor as a possible cause of adverse events was analyzed and discussed. Data were extracted from the VigiBase® database using the VigiLyze® signal detection and signal management tool. Adverse events in VigiBase® reports were coded using MedDRA, version 19 of Preferred Terms (PTs). Data were uploaded into SPSS software and analyzed via a disproportionality analysis. Statistically significant disproportionality signals for CBD were found for "weight decreased" (5.19 (95% CI: 4.54–5.70)), "hypophagia" (3.68 (95% CI: 3.22–5.27)), and "insomnia" (1.6 (95% CI: 1.40–1.83)). Positive IC025 values were found for "weight decreased" (2.2), "hypophagia" (1.3), and "insomnia" (0.5), indicating a surplus of reported cases. CBD's interactions with 5-HT1A serotonin receptors may offer a potential biological explanation for the occurrence of insomnia in patients. It is noteworthy that the risk profiles mentioned in the information for prescribing CBD as an antiepileptic agent by regulatory agencies showed disparities specifically related to the adverse event "insomnia". [ABSTRACT FROM AUTHOR]

In this study, we employed organic and inorganic dyes that have fluorescence under visible or near-infrared light region to stain human umbilical cord (Huc) mesenchymal stem cell (MSC)-, HEK293T cell- and HGC cell-derived small extracellular vesicles (sEVs), and then tracked their fluorescence signals in human gastric cancer xenografted murine models. Several biological characteristics were examined and compared when different dye-stained sEVs in the same tumor model or the same dye-stained sEVs between different tumor models were applied, including sEVs circulation in the blood, biodistribution of sEVs in major organs, and time-dependent tumor accumulation of sEVs. The results demonstrated that distinct tumor accumulation features were presented by sEVs if labeled by different fluorescent dyes, while sEVs derived from different cell lines showed homologous blood circulation and tumor accumulation. To conclude, although fluorescence imaging remains a reliable way to trace sEVs, single staining of sEVs membrane should be obviated in future work when examining the biological fate of sEVs. [ABSTRACT FROM AUTHOR]

Extracellular vesicles (EVs) are emerging mediators of intracellular and inter-organ communications in cardiovascular diseases (CVDs), especially in the pathogenesis of heart failure through the transference of EV-containing bioactive substances. microRNAs (miRNAs) are contained in EV cargo and are involved in the progression of heart failure. Over the past several years, a growing body of evidence has suggested that the biogenesis of miRNAs and EVs is tightly regulated, and the sorting of miRNAs into EVs is highly selective and tightly controlled. Extracellular miRNAs, particularly circulating EV-miRNAs, have shown promising potential as prognostic and diagnostic biomarkers for heart failure and as therapeutic targets. In this review, we summarize the latest progress concerning the role of EV-miRNAs in HF and their application in a therapeutic strategy development for heart failure. [ABSTRACT FROM AUTHOR]

Inclusion body myositis (IBM) is an acquired inflammatory myopathy affecting proximal and distal muscles that leads to weakness in patients over 50. It is diagnosed based on clinical and histological findings in muscle related to inflammation, degeneration, and mitochondria. In relation to IBM, a shortage of validated disease models and a lack of biomarkers and effective treatments constitute an unmet medical need. To overcome these hurdles, we performed an omics analysis of multiple samples from IBM patients (saliva, fibroblasts, urine, plasma, and muscle) to gain insight into the pathophysiology of IBM. Degeneration was evident due to the presence of amyloid β peptide 1–42 (Aβ1–42) in the saliva of the analyzed IBM patients. The presence of metabolic disarrangements in IBM was indicated by an imbalanced organic acid profile in fibroblasts and urine. Specifically, abnormal levels of L-pyroglutamic and orotic acid were supported by the abnormal expression of related metabolites in plasma and urine (glutathione and pyrimidines) and the aberrant expression of upstream gene regulators (L2HGDH, IDH2, OPLAH, and ASL) in muscle. Combined levels of L-pyroglutamic and orotic acid displayed an outstanding biomarker signature in urine with 100% sensitivity and specificity. The confirmation of systemic metabolic disarrangements in IBM and the identification of novel biomarkers reported herein unveil novel insights that require validation in larger cohorts. [ABSTRACT FROM AUTHOR]

Infections caused by biofilm-forming agents have important implications for world health. Mixed infections, caused by more than one etiological agent, are also an emerging problem, especially regarding the standardization of effective diagnosis and treatment methods. Cases of mixed onychomycosis (OM) have been reported; however, studies on the microbial interactions between the different fungi in biofilms formed on nails are still scarce. We describe a case of mixed OM caused by the dermatophyte Trichophyton rubrum and the black yeast-like fungus Rhinocladiella similis. Identical growths of both fungi were observed in more than 50 cultures from different nail samples. Additionally, both species were able to form organized single and mixed biofilms, reinforcing the participation of both fungi in the etiology of this OM case. R. similis seemed to grow faster during the process, suggesting that T. rubrum benefits from biofilm development when in combination. Moreover, the biofilm of the Rhinocladiella isolate exhibited exacerbated production of the extracellular matrix, which was not observed with that of a Rhinocladiella reference strain, suggesting that the isolate had natural abilities that were possibly perfected during development in the nail of the patient. [ABSTRACT FROM AUTHOR]

In this paper, a new method that computes the aesthetics of a melody fragment is proposed, starting from dissonances. While music generated with artificial intelligence applications may be produced considerably more quickly than human-composed music, it has the drawback of not being appreciated like a human composition, being many times perceived by humans as artificial. For achieving supervised machine learning objectives of improving the quality of the great number of generated melodies, it is a challenge to ask humans to grade them. Therefore, it would be preferable if the aesthetics of artificial-intelligence-generated music is calculated by an algorithm. The proposed method in this paper is based on a neural network and a mathematical formula, which has been developed with the help of a study in which 108 students evaluated the aesthetics of several melodies. For evaluation, numerical values generated by this method were compared with ratings provided by human listeners from a second study in which 30 students participated and scores were generated by an existing different method developed by psychologists and three other methods developed by musicians. Our method achieved a Pearson correlation of 0.49 with human aesthetic scores, which is a much better result than other methods obtained. Additionally, our method made a distinction between human-composed melodies and artificial-intelligence-generated scores in the same way that human listeners did. [ABSTRACT FROM AUTHOR]

Autophagy dysregulation is commonplace in the pathogenesis of several invalidating diseases, such as musculoskeletal diseases. Polyamines, as spermidine and spermine, are small aliphatic cations essential for cell growth and differentiation, with multiple antioxidant, anti-inflammatory, and anti-apoptotic effects. Remarkably, they are emerging as natural autophagy regulators with strong anti-aging effects. Polyamine levels were significantly altered in the skeletal muscles of aged animals. Therefore, supplementation of spermine and spermidine may be important to prevent or treat muscle atrophy. Recent in vitro and in vivo experimental studies indicate that spermidine reverses dysfunctional autophagy and stimulates mitophagy in muscles and heart, preventing senescence. Physical exercise, as polyamines, regulates skeletal muscle mass inducing proper autophagy and mitophagy. This narrative review focuses on the latest evidence regarding the efficacy of polyamines and exercise as autophagy inducers, alone or coupled, in alleviating sarcopenia and aging-dependent musculoskeletal diseases. A comprehensive description of overall autophagic steps in muscle, polyamine metabolic pathways, and effects of the role of autophagy inducers played by both polyamines and exercise has been presented. Although literature shows few data in regard to this controversial topic, interesting effects on muscle atrophy in murine models have emerged when the two "autophagy-inducers" were combined. We hope these findings, with caution, can encourage researchers to continue investigating in this direction. In particular, if these novel insights could be confirmed in further in vivo and clinical studies, and the two synergic treatments could be optimized in terms of dose and duration, then polyamine supplementation and physical exercise might have a clinical potential in sarcopenia, and more importantly, implications for a healthy lifestyle in the elderly population. [ABSTRACT FROM AUTHOR]

Background: A massage is a tool that is frequently used in sports and exercise in general for recovery and increased performance. In this review paper, we aimed to search and systemize current literature findings relating to massages' effects on sports and exercise performance concerning its effects on motor abilities and neurophysiological and psychological mechanisms. Methods: The review has been written following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analysis) guidelines. One hundred and fourteen articles were included in this review. Results: The data revealed that massages, in general, do not affect motor abilities, except flexibility. However, several studies demonstrated that positive muscle force and strength changed 48 h after the massage was given. Concerning neurophysiological parameters, the massage did not change blood lactate clearance, muscle blood flow, muscle temperature, or activation. However, many studies indicate pain reduction and delayed onset muscle soreness, which are probably correlated with the reduction of the level of creatine kinase enzyme and psychological mechanisms. In addition, the massage treatment led to a decrease in depression, stress, anxiety, and the perception of fatigue and an increase in mood, relaxation, and the perception of recovery. Conclusion: The direct usage of massages just for gaining results in sport and exercise performance seems questionable. However, it is indirectly connected to performance as an important tool when an athlete should stay focused and relaxed during competition or training and recover after them. [ABSTRACT FROM AUTHOR]

Since introducing the Transformer model, it has dramatically influenced various fields of machine learning. The field of time series prediction has also been significantly impacted, where Transformer family models have flourished, and many variants have been differentiated. These Transformer models mainly use attention mechanisms to implement feature extraction and multi-head attention mechanisms to enhance the strength of feature extraction. However, multi-head attention is essentially a simple superposition of the same attention, so they do not guarantee that the model can capture different features. Conversely, multi-head attention mechanisms may lead to much information redundancy and computational resource waste. In order to ensure that the Transformer can capture information from multiple perspectives and increase the diversity of its captured features, this paper proposes a hierarchical attention mechanism, for the first time, to improve the shortcomings of insufficient information diversity captured by the traditional multi-head attention mechanisms and the lack of information interaction among the heads. Additionally, global feature aggregation using graph networks is used to mitigate inductive bias. Finally, we conducted experiments on four benchmark datasets, and the experimental results show that the proposed model can outperform the baseline model in several metrics. [ABSTRACT FROM AUTHOR]

As a non-homogeneous porous medium, the structural complexity of coal directly affects pore structure parameters and gas percolation characteristics, which in turn determine the fractal dimension of coal samples. Among them, the specific surface area of coal largely determines the complexity of the pore structure and is closely related to coal and gas protrusion hazards. To investigate the relationship between the fractal dimension of coal and its specific surface area, we analyzed the pore structure of coal samples using low-temperature nitrogen adsorption, the mercury pressure method, and X-ray micro-computed tomography (CT) experiments. By calculating the fractal dimension of coal and reconstructing it in three dimensions, the morphological characteristics and distribution of pores can be described qualitatively and quantitatively. The fractal dimension of coal samples was found to increase exponentially with the specific surface area based on measurements of large pores and mesopores via the mercury pressure method and those of small pores and micropores using the nitrogen adsorption method. X-ray micro-CT experiments revealed that the fractal dimension of large pores (i.e., >2 μm) conformed to this pattern. [ABSTRACT FROM AUTHOR]

Coronary heart disease (CHD) is the leading cause of death worldwide. However, current diagnostic tools for CHD, such as coronary computed tomography angiography (CCTA), are poorly suited for monitoring treatment response. Recently, we have introduced an artificial-intelligence-guided integrated genetic–epigenetic test for CHD whose core consists of six assays that determine methylation in pathways known to moderate the pathogenesis of CHD. However, whether methylation at these six loci is sufficiently dynamic to guide CHD treatment response is unknown. To test that hypothesis, we examined the relationship of changes in these six loci to changes in cg05575921, a generally accepted marker of smoking intensity, using DNA from a cohort of 39 subjects undergoing a 90-day smoking cessation intervention and methylation-sensitive digital PCR (MSdPCR). We found that changes in epigenetic smoking intensity were significantly associated with reversion of the CHD-associated methylation signature at five of the six MSdPCR predictor sites: cg03725309, cg12586707, cg04988978, cg17901584, and cg21161138. We conclude that methylation-based approaches could be a scalable method for assessing the clinical effectiveness of CHD interventions, and that further studies to understand the responsiveness of these epigenetic measures to other forms of CHD treatment are in order. [ABSTRACT FROM AUTHOR]

Simple Summary: A subgroup of cancers that arises in the zone of the head and neck (HNCs) are caused by some types of human papillomavirus (HPV), so called high-risk (HR)-HPVs. HR-HPVs promote signaling pathways alterations involved in the initiation and progression of cancer. Among them, phosphatidyl inositol 3-kinase (PI3K)/AKT/mTOR signaling is involved in increasing cell proliferation, migration, and invasion. In this review, we dissect the role of PI3K/AKT/mTOR in HR-HPV-associated HNCs development and the impact as a potential therapeutic target. High-risk human papillomaviruses (HR-HPVs) are the causal agents of cervical, anogenital and a subset of head and neck carcinomas (HNCs). Indeed, oropharyngeal cancers are a type of HNC highly associated with HR-HPV infections and constitute a specific clinical entity. The oncogenic mechanism of HR-HPV involves E6/E7 oncoprotein overexpression for promoting cell immortalization and transformation, through the downregulation of p53 and pRB tumor suppressor proteins, among other cellular targets. Additionally, E6/E7 proteins are involved in promoting PI3K/AKT/mTOR signaling pathway alterations. In this review, we address the relationship between HR-HPV and PI3K/AKT/mTOR signaling pathway activation in HNC with an emphasis on its therapeutic importance. [ABSTRACT FROM AUTHOR]

At present it is well-defined that autophagy is a fundamental process essential for cell life but its pro-viral and anti-viral role has been stated out with the COVID pandemic. However, viruses in turn have evolved diverse adaptive strategies to cope with autophagy driven host defense, either by blocking or hijacking the autophagy machinery for their own benefit. The mechanisms underlying autophagy modulation are presented in the current review which summarizes the accumulated knowledge on the crosstalk between autophagy and viral infections, with a particular emphasizes on SARS-CoV-2. The different types of autophagy related to infections and their molecular mechanisms are focused in the context of inflammation. In particular, SARS-CoV-2 entry, replication and disease pathogenesis are discussed. Models to study autophagy and to formulate novel treatment approaches and pharmacological modulation to fight COVID-19 are debated. The SARS-CoV-2—autophagy interplay is presented, revealing the complex dynamics and the molecular machinery of autophagy. The new molecular targets and strategies to treat COVID-19 effectively are envisaged. In conclusion, our finding underline the importance of development new treatment strategies and pharmacological modulation of autophagy to fight COVID-19. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic led to tremendously use of antimicrobial due to the lack of proper treatment strategies, raising concerns about emergence of antimicrobial resistance (AMR). This study aimed at determining the prevalence and antibiotic resistance pattern of selected bacteria isolates in 02 referral health facilities in Yaoundé before and during the COVID-19 pandemic era. We conducted a retrospective study over a period of 03 years (from 1 January 2019 to 31 December 2021) in the bacteriology units of the Central and General Hospitals of Yaoundé, Cameroon. Data on bacteria genera (Streptococcus, Staphylococcus, Neisseria meningitidis and Enterobacteriaceae) as well as their corresponding specifics antibiotics: Cefixime, azythromycin and erythromycin were obtained from laboratory records. The global resistance rate of bacteria as well as their correlation with antibiotics according to COVID-19 pandemic era was determined and compared. For p < 0.05, the difference was statistically significant. In all, 426 bacterial strains were included. It appeared that the highest number of bacteria isolates and lowest rate of bacterial resistance were recorded during the pre-COVID-19 period in 2019 (160 isolates vs. 58.8% resistance rate). Conversely, lower bacteria strains but greater resistance burden were recorded during the pandemic era (2020 and 2021) with the lowest bacteria amount and peak of bacteria resistance registered in 2020, the year of COVID-19 onset (120 isolates vs. 70% resistance in 2020 and 146 isolates vs. 58.9% resistance in 2021). In contrast to almost all others groups of bacteria where the resistance burden was quite constant or decreasing over years, the Enterobacteriaceae exhibited greater resistance rate during the pandemic period [60% (48/80) in 2019 to 86.9% (60/69) in 2020 and 64.5% (61/95) in 2021)]. Concerning antibiotics, unlike erythromycin, azythromycin related resitance increased during the pandemic period and the resistance to Cefixim tends to decrease the year of the pandemic onset (2020) and re-increase one year therafter. A significant association was found between resistant Enterobacteriaceae strains and cefixime (R = 0.7; p = 0.0001) and also, between resistant Staphylococcus strains and erythromycin (R = 0.8; p = 0.0001). These retrospective data showed a herogeneous MDR bacteria rate and antibiotic resistance pattern over time before and during the COVID-19 pandemic era suggesting that antimicrobial resistance needs to be more closely monitored. [ABSTRACT FROM AUTHOR]

Artificial intelligence, particularly machine learning, has begun to permeate various real-world applications and is continually being explored in automatic music generation. The approaches to music generation can be broadly divided into two categories: rule-based and data-driven methods. Rule-based approaches rely on substantial prior knowledge and may struggle to handle large datasets, whereas data-driven approaches can solve these problems and have become increasingly popular. However, data-driven approaches still face challenges such as the difficulty of considering long-distance dependencies when handling discrete-sequence data and convergence during model training. Although the diffusion model has been introduced as a generative model to solve the convergence problem in generative adversarial networks, it has not yet been applied to discrete-sequence data. This paper proposes a transformer-based diffusion model known as MelodyDiffusion to handle discrete musical data and realize chord-conditioned melody generation. MelodyDiffusion replaces the U-nets used in traditional diffusion models with transformers to consider the long-distance dependencies using attention and parallel mechanisms. Moreover, a transformer-based encoder is designed to extract contextual information from chords as a condition to guide melody generation. MelodyDiffusion can automatically generate diverse melodies based on the provided chords in practical applications. The evaluation experiments, in which Hits@k was used as a metric to evaluate the restored melodies, demonstrate that the large-scale version of MelodyDiffusion achieves an accuracy of 72.41% (k = 1). [ABSTRACT FROM AUTHOR]

Vitamin D is involved in the pathophysiology of anemia. This cross-sectional study was conducted using the Nationwide Nutrition and Health Survey in Pregnant Women in Taiwan database. We investigated associations among dietary patterns (DPs), vitamin D, and iron-related biomarkers in pregnant women. The principal component analysis revealed four DPs. Linear and logistic regression analyses were performed to investigate the association of DPs with anemia-related biomarkers. Plant-based, carnivore, and dairy and nondairy alternatives DPs were positively associated with serum vitamin D levels. After adjusting covariates, the pregnant women consuming plant-based DPs at the mid-tertile (T2) were associated with reduced risks of low serum folate and vitamin D levels, and those consuming carnivore DPs at higher tertiles (T2 and/or T3) were correlated with an increased risk of low serum iron levels but decreased risks of low serum transferrin saturation, vitamin B12, and vitamin D levels. The pregnant women consuming dairy and nondairy alternatives DPs at the highest tertile (T3) were associated with reduced risks of low serum folate and vitamin B12 levels. However, the processed food DP was not correlated with anemia-related biomarkers. Thus, plant-based, carnivore, and dairy and nondairy alternatives DPs were associated with the risk of low-serum-anemia-related variables. [ABSTRACT FROM AUTHOR]

We demonstrated a narrow-linewidth high-power Yb-doped polarization-maintaining (PM) fiber laser with near-diffraction-limited beam. The laser system consisted of a phase-modulated single-frequency seed source and four-stage amplifiers in the master oscillator power amplifier configuration. A quasi-flat-top pseudo random binary sequence (PRBS) phase-modulated single-frequency laser with a linewidth of 8 GHz was injected into the amplifiers for suppressing stimulated Brillouin scattering. The quasi-flat-top PRBS signal was readily generated from the conventional PRBS signal. The maximum output power was 2.01 kW with polarization extinction ratio (PER) of ~15 dB. The beam quality (M2) was less than 1.3 over the power scaling range. [ABSTRACT FROM AUTHOR]

Rigosertib (ON-01910.Na) is a small-molecule member of the novel synthetic benzyl-styryl-sulfonate family. It is currently in phase III clinical trials for several myelodysplastic syndromes and leukemias and is therefore close to clinical translation. The clinical progress of rigosertib has been hampered by a lack of understanding of its mechanism of action, as it is currently considered a multi-target inhibitor. Rigosertib was first described as an inhibitor of the mitotic master regulator Polo-like kinase 1 (Plk1). However, in recent years, some studies have shown that rigosertib may also interact with the PI3K/Akt pathway, act as a Ras–Raf binding mimetic (altering the Ras signaling pathway), as a microtubule destabilizing agent, or as an activator of a stress-induced phospho-regulatory circuit that ultimately hyperphosphorylates and inactivates Ras signaling effectors. Understanding the mechanism of action of rigosertib has potential clinical implications worth exploring, as it may help to tailor cancer therapies and improve patient outcomes. [ABSTRACT FROM AUTHOR]

Language models based on deep learning showed promising results for artistic generation purposes, including musical generation. However, the evaluation of symbolic musical generation models is mostly based on low-level mathematical metrics (e.g., the result of the loss function) due to the inherent difficulty in measuring the musical quality of a given performance due to the subjective nature of music. This work sought to measure and evaluate musical excerpts generated by deep learning models from a human perspective, limited to the scope of classical piano music generation. In this assessment, a population of 117 people performed blind tests with musical excerpts of human composition and musical excerpts generated using artificial intelligence models, including the models PerformanceRNN, Music Transformer, MuseNet and a custom model based on GRUs. The experiments demonstrated that musical excerpts generated using models based on the Transformer neural network architecture obtained the greatest receptivity within the tested population, surpassing the results of human compositions. In addition, the experiments also demonstrated that people with greater musical sensitivity and musical experience were more able to identify the compositional origin of the excerpts heard. [ABSTRACT FROM AUTHOR]

Iron is a transition metal that plays a crucial role in several physiological processes. It can also exhibit toxic effects on cells, due to its role in the formation of free radicals. Iron deficiency and anemia, as well as iron overload, are the result of impaired iron metabolism, in which a number of proteins, such as hepcidin, hemojuvelin and transferrin, take part. Iron deficiency is common in individuals with renal and cardiac transplants, while iron overload is more common in patients with hepatic transplantation. The current knowledge about iron metabolism in lung graft recipients and donors is limited. The problem is even more complex when we consider the fact that iron metabolism may be also driven by certain drugs used by graft recipients and donors. In this work, we overview the available literature reports on iron turnover in the human body, with particular emphasis on transplant patients, and we also attempt to assess the drugs' impact on iron metabolism, which may be useful in perioperative treatment in transplantology. [ABSTRACT FROM AUTHOR]

Inertial localisation is an important technique as it enables ego-motion estimation in conditions where external observers are unavailable. However, low-cost inertial sensors are inherently corrupted by bias and noise, which lead to unbound errors, making straight integration for position intractable. Traditional mathematical approaches are reliant on prior system knowledge, geometric theories and are constrained by predefined dynamics. Recent advances in deep learning, which benefit from ever-increasing volumes of data and computational power, allow for data-driven solutions that offer more comprehensive understanding. Existing deep inertial odometry solutions rely on estimating the latent states, such as velocity, or are dependent on fixed-sensor positions and periodic motion patterns. In this work, we propose taking the traditional state estimation recursive methodology and applying it in the deep learning domain. Our approach, which incorporates the true position priors in the training process, is trained on inertial measurements and ground truth displacement data, allowing recursion and learning both motion characteristics and systemic error bias and drift. We present two end-to-end frameworks for pose invariant deep inertial odometry that utilises self-attention to capture both spatial features and long-range dependencies in inertial data. We evaluate our approaches against a custom 2-layer Gated Recurrent Unit, trained in the same manner on the same data, and tested each approach on a number of different users, devices and activities. Each network had a sequence length weighted relative trajectory error mean ≤ 0.4594 m, highlighting the effectiveness of our learning process used in the development of the models. [ABSTRACT FROM AUTHOR]

Machine learning is widely used in various practical applications with deep learning models demonstrating advantages in handling huge data. Treating music as a special language and using deep learning models to accomplish melody recognition, music generation, and music analysis has proven feasible. In certain music-related deep learning research, recurrent neural networks have been replaced with transformers. This has achieved significant results. In traditional approaches with recurrent neural networks, input sequences are limited in length. This paper proposes a method to generate chord progressions for melodies using a transformer-based sequence-to-sequence model, which is divided into a pre-trained encoder and decoder. A pre-trained encoder extracts contextual information from melodies, whereas a decoder uses this information to produce chords asynchronously and finally outputs chord progressions. The proposed method addresses length limitation issues while considering the harmony between chord progressions and melodies. Chord progressions can be generated for melodies in practical music composition applications. Evaluation experiments are conducted using the proposed method and three baseline models. The baseline models included the bidirectional long short-term memory (BLSTM), bidirectional encoder representation from transformers (BERT), and generative pre-trained transformer (GPT2). The proposed method outperformed the baseline models in Hits@k (k = 1) by 25.89, 1.54, and 2.13 %, respectively. [ABSTRACT FROM AUTHOR]

Small satellite constellations in multiple-inclination, low-circular orbits around Mars and Venus have the potential to perform a range of high-value science investigations within cost-constrained missions. A major challenge for small satellites is that they require large Δ V to enter low-circular orbits, which can drive up both spacecraft mass and cost. Compared to chemical propulsion, which requires large amounts of propellant, and electric propulsion, which requires large solar arrays and comes with long flight times, aerocapture enables direct access to low-circular orbits at Mars and Venus with minimal Δ V . The study shows how drag-modulation aerocapture, when combined with small B-plane targeting maneuvers, allows the delivery of multiple small satellites to various-inclination, low-circular orbits to establish a constellation. Preliminary cost estimates indicate that by reducing the required Δ V for orbit insertion, aerocapture can potentially reduce the cost of a small satellite going to a low-circular Mars orbit compared to propulsive insertion. The ability of low-cost spacecraft to enter planetary orbits will enable a new paradigm of interplanetary missions using small dedicated launch vehicles and planetary constellations at Mars and Venus. [ABSTRACT FROM AUTHOR]

Organs-on-chips (OoCs) are miniature microfluidic systems that have arguably become a class of advanced in vitro models. Deep learning, as an emerging topic in machine learning, has the ability to extract a hidden statistical relationship from the input data. Recently, these two areas have become integrated to achieve synergy for accelerating drug screening. This review provides a brief description of the basic concepts of deep learning used in OoCs and exemplifies the successful use cases for different types of OoCs. These microfluidic chips are of potential to be assembled as highly potent human-on-chips with complex physiological or pathological functions. Finally, we discuss the future supply with perspectives and potential challenges in terms of combining OoCs and deep learning for image processing and automation designs. [ABSTRACT FROM AUTHOR]

As urbanization expands, it is becoming increasingly important to understand how anthropogenic activity is affecting ecological and evolutionary processes. Few studies have examined how human social patterns within cities can modify eco-evolutionary dynamics. We tested how socioeconomic variation corresponds with changes in trophic interactions and natural selection on prey phenotypes using the classic interaction between goldenrod gall flies (Eurosta solidaginis) and their natural enemies: birds, beetles, and parasitoid wasps. We sampled galls from 84 sites across neighbourhoods with varying socioeconomic levels, and quantified the frequency of predation/parasitism on flies and natural selection by each enemy. We found that bird predation was higher in the highest income neighbourhoods, increasing the strength of selection for smaller galls. Wasp and beetle attack, but not their strength of selection, increased in lower income neighbourhoods. We show that socioeconomic variation in cities can have strong unintended consequences for the ecology and evolution of trophic interactions., (© 2024 The Authors. Ecology Letters published by John Wiley & Sons Ltd.)

In recent decades, extracellular vesicles have been recognized as "very important particles" (VIPs) associated with aging and age-related disease. During the 1980s, researchers discovered that these vesicle particles released by cells were not debris but signaling molecules carrying cargoes that play key roles in physiological processes and physiopathological modulation. Following the International Society for Extracellular Vesicles (ISEV) recommendation, different vesicle particles (e.g., exosomes, microvesicles, oncosomes) have been named globally extracellular vesicles. These vesicles are essential to maintain body homeostasis owing to their essential and evolutionarily conserved role in cellular communication and interaction with different tissues. Furthermore, recent studies have shown the role of extracellular vesicles in aging and age-associated diseases. This review summarizes the advances in the study of extracellular vesicles, mainly focusing on recently refined methods for their isolation and characterization. In addition, the role of extracellular vesicles in cell signaling and maintenance of homeostasis, as well as their usefulness as new biomarkers and therapeutic agents in aging and age-associated diseases, has also been highlighted. [ABSTRACT FROM AUTHOR]

Measures of cardiovascular health (CVH) assessed by a combination of behavioral and biological factors has shown protective associations with all-cause mortality. The mechanisms underlying these associations have not been fully elucidated. In this study, we characterized the plasma proteomics profile of CVH and tested whether specific proteins mediated the associations between CVH and all-cause mortality in participants of the InCHIANTI study. Of the 1301 proteins tested, 92 proteins were associated with CVH (22 positively, 70 negatively). Proteins most strongly associated with CVH included leptin (LEP), fatty acid binding protein 3 (FABP3), Angiopoietin-2 (ANGPT2), and growth-differential factor 15 (GDF15). Of the 92 CVH-associated proteins, 33 proteins significantly mediated the associations between CVH and all-cause mortality, with percent mediation ranging from 5 to 30%. The most significant mediating proteins were GDF15 and insulin-like growth factor 2 (IGFBP2). Proteins associated with better CVH were enriched for proteins that reflect the suppression of the complement coagulation and GH/IGF pathways. [ABSTRACT FROM AUTHOR]

This paper presents recent advances in low-latency, single-channel, deep neural network-based speech enhancement systems. The sources of latency and their acceptable values in different applications are described. This is followed by an analysis of the constraints imposed on neural network architectures. Specifically, the causal units used in deep neural networks are presented and discussed in the context of their properties, such as the number of parameters, the receptive field, and computational complexity. This is followed by a discussion of techniques used to reduce the computational complexity and memory requirements of the neural networks used in this task. Finally, the techniques used by the winners of the latest speech enhancement challenges (DNS, Clarity) are shown and compared. [ABSTRACT FROM AUTHOR]

Thymic stromal lymphopoietin (TSLP) is an epithelium-derived pro-inflammatory cytokine involved in lung inflammatory responses. Previous studies show conflicting observations in blood TSLP in COVID-19, while none report SARS-CoV-2 inducing TSLP expression in bronchial epithelial cells. Our objective in this study was to determine whether TSLP levels increase in COVID-19 patients and if SARS-CoV-2 induces TSLP expression in bronchial epithelial cells. Plasma cytokine levels were measured in patients hospitalized with confirmed COVID-19 and age- and sex-matched healthy controls. Demographic and clinical information from COVID-19 patients was collected. We determined associations between plasma TSLP and clinical parameters using Poisson regression. Cultured human nasal (HNEpC) and bronchial epithelial cells (NHBEs), Caco-2 cells, and patient-derived bronchial epithelial cells (HBECs) obtained from elective bronchoscopy were infected in vitro with SARS-CoV-2, and secretion as well as intracellular expression of TSLP was detected by immunofluorescence. Increased TSLP levels were detected in the plasma of hospitalized COVID-19 patients (603.4 ± 75.4 vs 997.6 ± 241.4 fg/mL, mean ± SEM), the levels of which correlated with duration of stay in hospital (β: 0.11; 95% confidence interval (CI): 0.01–0.21). In cultured NHBE and HBECs but not HNEpCs or Caco-2 cells, TSLP levels were significantly elevated after 24 h post-infection with SARS-CoV-2 (p < 0.001) in a dose-dependent manner. Plasma TSLP in COVID-19 patients significantly correlated with duration of hospitalization, while SARS-CoV-2 induced TSLP secretion from bronchial epithelial cells in vitro. Based on our findings, TSLP may be considered an important therapeutic target for COVID-19 treatment. [ABSTRACT FROM AUTHOR]