Miracle-dz/UniFAR-FTUs huggingface dataset:
Modulation of dorsal premotor cortex disrupts neuroplasticity of primary motor cortex in young and older adults

Although transcranial magnetic stimulation (TMS) research demonstrates that dorsal premotor cortex (PMd) influences neuroplasticity within primary motor cortex (M1), it is unclear how ageing modifies this communication. The present study investigated the influence of PMd on different indirect (I) wave inputs within M1 that mediate cortical plasticity in young and older adults. 15 young and 15 older participants completed two experimental sessions that examined the effects of intermittent theta burst stimulation (iTBS) to M1 when preceded by iTBS (PMd iTBS-M1 iTBS) or sham stimulation (PMd sham-M1 iTBS) to PMd. Changes in corticospinal excitability post-intervention were assessed with motor evoked potentials (MEP) recorded from right first dorsal interosseous using posterior-anterior (PA) and anterior-posterior (AP) current single-pulse TMS (PA1mV; AP1mV; PA0.5mV, early I-wave; AP0.5mV, late I-wave). Although PA1mV did not change post-intervention (P = 0.628), PMd iTBS-M1 iTBS disrupted the expected facilitation of AP1mV (to M1 iTBS) in young and older adults (P = 0.002). Similarly, PMd iTBS-M1 iTBS disrupted PA0.5mV facilitation in young and older adults (P = 0.030), whereas AP0.5mV facilitation was not affected in either group (P = 0.218). This suggests that while PMd specifically influences the plasticity of early I-wave circuits, this communication is preserved in older adults.
Plasma Oscillation Characterization of NASA's HERMeS Hall Thruster via High Speed Imaging

For missions beyond low Earth orbit, spacecraft size and mass can be dominated by onboard chemical propulsion systems and propellants that may constitute more than 50 percent of the spacecraft mass. This impact can be substantially reduced through the utilization of Solar Electric Propulsion (SEP) due to its substantially higher specific impulse. Studies performed for NASA's Human Exploration and Operations Mission Directorate and Science Mission Directorate have demonstrated that a 50kW-class SEP capability can be enabling for both near term and future architectures and science missions. A high-power SEP element is integral to the Evolvable Mars Campaign, which presents an approach to establish an affordable evolutionary human exploration architecture. To enable SEP missions at the power levels required for these applications, an in-space demonstration of an operational 50kW-class SEP spacecraft has been proposed as a SEP Technology Demonstration Mission (TDM). In 2010 NASA's Space Technology Mission Directorate (STMD) began developing high-power electric propulsion technologies. The maturation of these critical technologies has made mission concepts utilizing high-power SEP viable.
Uncovering structural features that control substrate specificity in a Lactobacillus chlorogenic acid esterase

The structural determinants of chlorogenic acid esterase (CE) substrate specificity are poorly understood. Here, we establish how a Lactobacillus helveticus CE selects for its substrate, chlorogenic acid (CGA). We determine that a Lys residue in an extended loop over the active site imparts substrate specificity by hydrogen bonding to CGA. Mutation of the Lys residue abolishes CGA specificity. Comparison with other bacterial CEs reveals that the extended loop is not conserved. However, the hydrogen bonding functionality to CGA is preserved thanks to other residues. Structural comparison with ferulic acid esterases (FAEs), a related enzyme class, shows that CEs feature a more restricted active site, reflecting the fact that they hydrolyze smaller substrates compared to FAEs.
Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity

Learning dynamical latent state models for multimodal spiking and field potential activity can reveal their collective low-dimensional dynamics and enable better decoding of behavior through multimodal fusion. Toward this goal, developing unsupervised learning methods that are computationally efficient is important, especially for real-time learning applications such as brain-machine interfaces (BMIs). However, efficient learning remains elusive for multimodal spike-field data due to their heterogeneous discrete-continuous distributions and different timescales. Here, we develop a multiscale subspace identification (multiscale SID) algorithm that enables computationally efficient modeling and dimensionality reduction for multimodal discrete-continuous spike-field data. We describe the spike-field activity as combined Poisson and Gaussian observations, for which we derive a new analytical subspace identification method. Importantly, we also introduce a novel constrained optimization approach to learn valid noise statistics, which is critical for multimodal statistical inference of the latent state, neural activity, and behavior. We validate the method using numerical simulations and spike-LFP population activity recorded during a naturalistic reach and grasp behavior. We find that multiscale SID accurately learned dynamical models of spike-field signals and extracted low-dimensional dynamics from these multimodal signals. Further, it fused multimodal information, thus better identifying the dynamical modes and predicting behavior compared to using a single modality. Finally, compared to existing multiscale expectation-maximization learning for Poisson-Gaussian observations, multiscale SID had a much lower computational cost while being better in identifying the dynamical modes and having a better or similar accuracy in predicting neural activity. Overall, multiscale SID is an accurate learning method that is particularly beneficial when efficient learning is of interest.
BCL6 is a context-dependent mediator of the glioblastoma response to irradiation therapy

Glioblastoma is a rapidly fatal brain cancer with no cure. The resistance of glioblastoma tumours to available therapies means that more effective treatments are desperately needed. Previous research showed that the transcriptional repressor protein BCL6 is upregulated by chemo– and radiotherapy in glioblastoma and that inhibition of BCL6 enhances the effectiveness of these therapies. Therefore, BCL6 is a promising target to improve the efficacy of available treatments for glioblastoma. BCL6 is known as a transcriptional repressor in germinal centre B cells and is an oncogene in lymphoma, as well as in other cancers. However, previous research indicated that BCL6 induced by chemotherapy or irradiation in glioblastoma may not act as a transcriptional repressor. This study aimed to clarify the role of BCL6 in the response of glioblastoma to irradiation. The effect of BCL6 inhibition on the whole proteome response of glioblastoma cells to fractionated and acute irradiation treatment was investigated. Acute irradiation appeared to cause BCL6 to switch from a repressor of the DNA damage response to a promoter of stress response signalling. Rapid immunoprecipitation mass spectrometry of endogenous proteins enabled identification of proteins associated with BCL6 in untreated and irradiated glioblastoma cells. BCL6 associated with transcriptional coregulators in untreated glioblastoma and its association with the corepressor NCOR2 was validated using proximity ligation assays. However, the association of BCL6 with transcriptional regulatory proteins was lost in response to acute irradiation. This was accompanied by the irradiation-induced association of BCL6 with synaptic and plasma membrane proteins. Overall, these results reveal that the activity of BCL6 in glioblastoma therapy responses is context-dependent and may be mediated by the intensity of cellular stress.
Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder by Pretraining Deep Learning Models with Single Channel Sleep Stage Data

As the field of deep learning has grown in recent years, its application to the domain of raw resting-state electroencephalography (EEG) has also increased. Relative to traditional machine learning methods or deep learning methods applied to extracted features, there are fewer methods for developing deep learning models on small raw EEG datasets. One potential approach for enhancing deep learning performance in this case is the use of transfer learning. In this study, we propose a novel EEG transfer learning approach wherein we first train a model on a large publicly available sleep stage classification dataset. We then use the learned representations to develop a classifier for automated major depressive disorder diagnosis with raw multichannel EEG. We find that our approach improves model performance, and we further examine how transfer learning affected the representations learned by the model through a pair of explainability analyses. Our proposed approach represents a significant step forward for the domain raw resting-state EEG classification. Furthermore, it has the potential to expand the use of deep learning methods across more raw EEG datasets and lead to the development of more reliable EEG classifiers. Clinical Relevance The proposed approach takes the field of deep learning in EEG a step closer to the robustness needed for clinical implementation.
A pilot study of a modification EAT-26 questionnaire for screening pathological eating behavior in competitive athletes

This study deals with pathological forms of eating behavior and disorders of athletes with the aim to verify a newly created questionnaire method focused on their screening. First, a detailed analysis of one of the most frequently used methods, EAT-26, was carried out, which was subsequently reworked into a newly created questionnaire that should meet the criteria for application to a group of competitive athletes. This new questionnaire was then verified on a group of athletes in risky sports disciplines. It was distributed among athletes of aesthetic sports, specifically among aerobics (gymnastic, sport, and fitness), gymnastics (modern and sport), professional dance, figure skating, and bodybuilding/fitness (classic bodybuilding, bikini fitness, and men’s physique). In total, 100 respondents, 79 women and 21 men, with 20 persons from each sport branch, aged 16–26, participated in the research. The main results of the research investigation were determined using factor analysis, which yielded positive results. Five strong factors (dietary control, body weight control, training obsession, appetite, and calorie counting) were identified, which can be defined as common and well-known characteristics in the eating and training regime of competitive athletes. At the same time, the factors found can be defined as essential factors influencing the emergence of disturbed eating behavior or the later development of an eating disorder. Compared to the original EAT-26, the point score was adjusted, and its critical value was determined at 57 points. Of the respondents, 33%, i.e., 33 out of a possible 100, achieved this value or above. Respondents with a point score of 57 and above were found in every sport tested. Of the 33 respondents reaching this point limit, 6% were in aerobics, 24% in gymnastics, 15% in professional dance, 27% in figure skating, and 27% in bodybuilding/fitness. Respondents from the bodybuilding and fitness sports sector achieved the highest number of points on average and those were the only ones who exceeded the threshold of 57 points on average. The results of the work correspond with the opinion of many experts that the sports environment is ideal for covering up disturbed eating behavior or eating disorders, and diagnosis in this environment is difficult.
Metagenomic analysis reveals the virome profiles of Aedes albopictus in Guangzhou, China

Introduction Aedes albopictus is an aggressive invasive mosquito species widely distributed around the world, and it is also a known vector of arboviruses. Virus metagenomics and RNA interference (RNAi) are important in studying the biology and antiviral defense of Ae. albopictus. However, the virome and potential transmission of plant viruses by Ae. albopictus remain understudied. Methods Mosquito samples of Ae. albopictus were collected from Guangzhou, China, and small RNA sequencing was performed. Raw data were filtered, and virus-associated contigs were generated using VirusDetect. The small RNA profiles were analyzed, and maximum-likelihood phylogenetic trees were constructed. Results The small RNA sequencing of pooled Ae. albopictus revealed the presence of five known viruses, including Wenzhou sobemo-like virus 4, mosquito nodavirus, Aedes flavivirus, Hubei chryso-like virus 1, and Tobacco rattle virus RNA1. Additionally, 21 new viruses that had not been previously reported were identified. The mapping of reads and contig assembly provided insights into the viral diversity and genomic characteristics of these viruses. Field survey confirmed the detection of the identified viruses in Ae. albopictus collected from Guangzhou. Discussion The comprehensive analysis of the virus metagenomics of Ae. albopictus in this study sheds light on the diversity and prevalence of viruses in mosquito populations. The presence of known and novel viruses highlights the need for continued surveillance and investigation into their potential impact on public health. The findings also emphasize the importance of understanding the virome and potential transmission of plant viruses by Ae. albopictus. Conclusion This study provides valuable insights into the virome of Ae. albopictus and its potential role as a vector for both known and novel viruses. Further research is needed to expand the sample size, explore additional viruses, and investigate the implications for public health.
A COMMON FIXED POINT THEOREM IN

In the present work, we introduce two types of compatible maps in non- Archimedean Menger PM-spaces and obtain a common fixed point theorem for six maps. 1. Introduction and Preliminaries In 1997, Cho et al. (2) introduced the concepts of compatible maps and compatible maps of type (A) in non-Archimedean Menger probabilistic met- ric spaces and gave some fixed point theorems for these maps. In this paper, we introduce the concept of compatible maps of type (A-1) and type (A-2), show that they are equivalent to compatible maps under certain conditions and illustrating with an example, prove a common fixed point theorem for such maps in the spaces which generalizes, extends and fuzzifies several fixed point theorems for contractive type maps on metric spaces and fuzzy metric spaces.
Normal glucose tolerant women with low glycemia during the oral glucose tolerance test have a higher risk to deliver a low birth weight infant

Background Data are limited on pregnancy outcomes of normal glucose tolerant (NGT) women with a low glycemic value measured during the 75g oral glucose tolerance test (OGTT). Our aim was to evaluate maternal characteristics and pregnancy outcomes of NGT women with low glycemia measured at fasting, 1-hour or 2-hour OGTT. Methods The Belgian Diabetes in Pregnancy-N study was a multicentric prospective cohort study with 1841 pregnant women receiving an OGTT to screen for gestational diabetes (GDM). We compared the characteristics and pregnancy outcomes in NGT women according to different groups [(<3.9mmol/L), (3.9-4.2mmol/L), (4.25-4.4mmol/L) and (>4.4mmol/L)] of lowest glycemia measured during the OGTT. Pregnancy outcomes were adjusted for confounding factors such as body mass index (BMI) and gestational weight gain. Results Of all NGT women, 10.7% (172) had low glycemia (<3.9 mmol/L) during the OGTT. Women in the lowest glycemic group (<3.9mmol/L) during the OGTT had compared to women in highest glycemic group (>4.4mmol/L, 29.9%, n=482), a better metabolic profile with a lower BMI, less insulin resistance and better beta-cell function. However, women in the lowest glycemic group had more often inadequate gestational weight gain [51.1% (67) vs. 29.5% (123); p<0.001]. Compared to the highest glycemia group, women in the lowest group had more often a birth weight <2.5Kg [adjusted OR 3.41, 95% CI (1.17-9.92); p=0.025]. Conclusion Women with a glycemic value <3.9 mmol/L during the OGTT have a higher risk for a neonate with birth weight < 2.5Kg, which remained significant after adjustment for BMI and gestational weight gain.
Gut microbiota-derived metabolite trimethylamine-N-oxide and stroke outcome: a systematic review

Introduction The relationship between baseline trimethylamine N-oxide (TMAO) levels and stroke outcomes remains unclear. Therefore, this systematic review aimed to summarize the existing relevant research. Methods We searched for studies on the association between baseline plasma levels of TMAO and stroke outcomes in the PubMed, EMBASE, Web of Science, and Scopus databases from their inception to 12 October 2022. Two researchers independently reviewed the studies for inclusion and extracted the relevant data. Results Seven studies were included in the qualitative analysis. Among them, six studies reported the outcome of acute ischemic stroke (AIS) and one study of intracerebral hemorrhage (ICH), respectively. Furthermore, no study reported the outcome of subarachnoid hemorrhage. Among patients with AIS, high baseline TMAO levels were associated with unfavorable functional outcomes or mortality at 3 months, as well as a high hazard ratio of mortality, recurrence, or major adverse cardiac event. Moreover, TMAO levels showed predictive utility for unfavorable functional outcomes or mortality at 3 months. Among patients with ICH, high TMAO levels were associated with unfavorable functional outcomes at 3 months, regardless of whether the TMAO value was considered a continuous or a categorical variable. Conclusion Limited evidence indicates that high baseline plasma levels of TMAO may be associated with poor stroke outcomes. Further studies are warranted to confirm the relationship between TMAO and stroke outcomes.
Nanocarriers for methotrexate delivery/codelivery in the frame of cancer diagnostics and treatment: a review

Cancer is one of the most life-threatening family of diseases that cause death worldwide. As a highly researched and successful therapeutic agent, methotrexate (MTX) treats many solid tumours, hematologic malignancies, and autoimmune illnesses. Despite many benefits, methotrexate induces drug resistance and limits plasma half-life due to its poor pharmacokinetics. The variable biological availability have prompted researchers to investigate innovative delivery strategies for enhancing its therapeutic qualities. To develop more suitable methotrexate formulations, nanoparticles (NPs) have recently gained a significant interest. A wide range of nanoparticles, including polymer-based nanoparticles, carbon-based nanoparticles, lipid-based nanoparticles, as well as inorganic nanoparticles, can be deliver cancer chemotherapeutics such as methotrexate. Loading methotrexate into NPs can provide a delivery system that has shown great promise to carcinoma therapy. In this review, we will describe the feasibility of NP-based strategies to deliver methotrexate in cancer therapy, outlining the current state of the art and the challenges/promises for the future.
Floristic composition, life history traits and phytogeographic distribution of forest vegetation in the Western Himalaya

Introduction Scientific documentation of the qualitative forest vegetation parameters of a biogeographical area provides baseline information to guide conservation strategies and design policies for biodiversity management regulations. Methods We present one of the most comprehensive qualitative vegetation analyses to evaluate the entire structure and function of an ecosystem in the remote northern part of the Kashmir Himalaya, India. Several multivariate ecological community analyses were conducted after determining the presence of plant species in the various habitats using a random sampling technique. Results In total, 155 plant species belonging to 120 genera and 49 families occurred in the area. Asteraceae was the largest family (12% of plant species) followed by Rosaceae (11%). The patterns of species distribution across families were uneven, with 50% of the species belonging to only 7 families, and 23 families being monotypic. In terms of functional groups, the herbaceous growth form dominated. Therophytes were the dominant life form, indicating that the vegetation was disturbed. According to the phytogeographical research, 65% of the species documented in the study area were native, 15% were invasive, 14% naturalized, and 5% being casual. The majority (30%) of exotic species were reported along roadsides. Of all the species found, 39% grew in their natural habitats, such as forests, and 11% were scattered along roadsides. Plant species were grouped in five different clusters based on their floristic similarity. According to the estimated diversity indices, natural forest has the greatest values for Shannon’s and Simpson’s index. We found that the study area serves as the natural habitat for several significant, endangered medicinal plants, including Arnebia benthamii, Bergenia ciliata, Delphinium roylei, Gentiana kurroo, Phytolacca acinosa, Saussurea costus, and Trillium govanianum. Therefore, we recommend that human intervention in natural regeneration efforts be prioritized in these habitats to increase the population of these species. Conclusion Examining species features from the perspective of functional groups contributes to our understanding of the ecological aspects of the flora. It may also be useful in developing management plans to ensure long-term management of forest landscapes in this remote Himalayan region.
Non-suicidal self-injury function: prevalence in adolescents with depression and its associations with non-suicidal self-injury severity, duration and suicide

Background Given that adolescents with depression are at the highest risk for non-suicidal self-injury (NSSI), a thorough understanding of their NSSI functions, as well as associations between functions and severe behavioral consequences, is essential for risk assessment and invention development. Methods Adolescents with depression from 16 hospitals across China, for whom data was available regarding their NSSI function, frequency, number of methods used, time characteristics, and suicide history were included. Descriptive statistical analyses were performed to determine the prevalence of NSSI functions. Regression analyses were conducted to explore the relationship between NSSI functions and behavioral characteristics of NSSI and suicide attempts. Results Affect regulation was the primary function of NSSI and followed by anti-dissociation in adolescents with depression. Females recognized automatic reinforcement functions more frequently than males, while males had a higher prevalence of social positive reinforcement functions. Automatic reinforce functions played the prominent role in associations between NSSI functions and all the severe behavioral consequences. Specifically, functions of anti-dissociation, affect regulation, and self-punishment were all associated with NSSI frequency, while higher levels of endorsements for anti-dissociation and self-punishment were linked to more NSSI methods, and greater level of endorsement for anti-dissociation was related to longer NSSI duration. Only the increase in endorsement of self-punishment was associated with a greater hazard of suicide attempts. Conclusion The dominant functions of NSSI in adolescents with depression was automatic reinforcement, specifically affect regulation. And prevalence of NSSI function differed between males and females. Anti-dissociation and self-punishment seemed to be the most risky factors as they were linked to severe NSSI or suicide behaviors. More attention should be given to these functions in risk evaluation, and the targeted interventions should be developed accordingly in a timely manner.