Miracle-dz/UniFAR-FTUs huggingface dataset:
Progress of polymer-based strategies in fungal disease management: Designed for different roles

Fungal diseases have posed a great challenge to global health, but have fewer solutions compared to bacterial and viral infections. Development and application of new treatment modalities for fungi are limited by their inherent essential properties as eukaryotes. The microorganism identification and drug sensitivity analyze are limited by their proliferation rates. Moreover, there are currently no vaccines for prevention. Polymer science and related interdisciplinary technologies have revolutionized the field of fungal disease management. To date, numerous advanced polymer-based systems have been developed for management of fungal diseases, including prevention, diagnosis, treatment and monitoring. In this review, we provide an overview of current needs and advances in polymer-based strategies against fungal diseases. We high light various treatment modalities. Delivery systems of antifungal drugs, systems based on polymers’ innate antifungal activities, and photodynamic therapies each follow their own mechanisms and unique design clues. We also discuss various prevention strategies including immunization and antifungal medical devices, and further describe point-of-care testing platforms as futuristic diagnostic and monitoring tools. The broad application of polymer-based strategies for both public and personal health management is prospected and integrated systems have become a promising direction. However, there is a gap between experimental studies and clinical translation. In future, well-designed in vivo trials should be conducted to reveal the underlying mechanisms and explore the efficacy as well as biosafety of polymer-based products.
Dynamic Emotional Transition Sampling and Emotional Guidance of Individuals Based on Conversation

Emotion is the vital path to achieving strong artificial intelligence. Therefore, it is significant to study the emotional guiding and controlling theory to enhance system intelligence. Conversational datasets in social media contain useful information, including individuals exhibiting continuously changing emotion states in response to external stimuli, and are the foundation for research on artificial emotions. We define dialog-based emotional guidance in reinforcement learning to research the emotional guidance of the discrete emotion model. This article proposes three strategies to obtain the optimal policy: 1) given the current emotional transition matrix, use the emotional Markov decision process (E-MDP) algorithm to calculate the optimal stimulus policy for each target emotion; 2) given the emotional transition sequences, use the emotional Monte Carlo algorithm to calculate the optimal stimulus policy; and 3) given the emotional transition sequences, use the $Q$ -learning algorithm to calculate the optimal stimulus policy. Besides, we improve the Markov chain Monte Carlo algorithm to sample emotional transition sequences and design a metric to evaluate the effectiveness of policies. Experimental results on three datasets show that these methods can more effectively guide emotion than traditional methods. Particularly, E-MDP achieves the best results while others can be more widely used in real-world scenarios.
Mechanisms of multiple resistance to herbicides in Conyza sp. complex

Reports of weed resistance to herbicides have increased in recent years and differentiation in resistance mechanisms is considered to be a concern for the future of weed science. The aim of this work was to characterize the mechanisms of resistance to herbicides associated with Conyza sp. complex and analyze their implications. Aspects of the action of herbicides commonly used in their control will be addressed, in addition to a description of the mechanisms involved in multiple resistance in Conyza species.
Bayesian analysis for emerging infectious diseases

Infectious diseases both within human and animal populations often pose serious health and socioeconomic risks. From a statistical perspective, their prediction is complicated by the fact that no two epidemics are identical due to changing contact habits, mutations of infectious agents, and changing human and animal behaviour in response to the presence of an epidemic. Thus model param- eters governing infectious mechanisms will typically be unknown. On the other hand, epidemic control strategies need to be decided rapidly as data accumulate. In this paper we present a fully Bayesian methodology for performing inference and online prediction for epidemics in structured populations. Key features of our approach are the development of an MCMC- (and adaptive MCMC-) based methodology for parameter estimation, epidemic prediction, and online assessment of risk from currently unobserved infections. We illustrate our methods using two complementary studies: an analysis of the 2001 UK Foot and Mouth epidemic, and modelling the potential risk from a possible future Avian In∞uenza epidemic to the UK Poultry industry.
Temperature influences glyphosate efficacy on glyphosate-resistant and -susceptible goosegrass (Eleusine indica)

Glyphosate has been widely used to control Eleusine indica and other weeds in South China for many years. Among the most troublesome weeds in South China, E. indica can remain alive all year round. However, the influence of temperature on glyphosate efficacy on E. indica, especially under days with fluctuating temperature, is unknown. This study evaluated the influence of two temperature regimes on glyphosate efficacy on glyphosate-resistant (R) and -susceptible (S) E. indica biotypes. Plants of the R and S biotypes were cultivated under two temperature regimes (high: 30°C/20°C day/night; low: 20°C/15°C day/night). Dose-response experiments showed improved efficacy of glyphosate at the low temperature compared with that at the high temperature for both biotypes. Based on the LD50 values, the R biotype was 8.9 times more resistant to glyphosate than the S biotype at the high temperature; however, the resistance index (R/S) decreased to 3.1 at the low temperature. At 4 days after glyphosate application, shikimic acid accumulation was greater at the low temperature than at the high temperature in plants of both biotypes, and the increase was higher in plants of the R biotype than in the S biotype. At a sublethal glyphosate dose (R: 400 g ai ha−1; S: 200 g ai ha−1), plants grown at the low temperature showed a strong decrease in leaf chlorophyll content and Fv/Fm value compared with those of plants grown at the high temperature and the untreated control. At 3 days after treatment, glyphosate absorption was similar between biotypes at the high temperature, but absorption decreased to 64.9% and 53.1% at the low temperature for the R and S biotypes, respectively. For both biotypes, glyphosate translocation from the leaf to the remainder of the plant was reduced at the low temperature compared with that at the high temperature. No differences in glyphosate translocation were observed between biotypes within each temperature regime. This is the first report on the effect of temperature on glyphosate efficacy on E. indica, and provides important insights for glyphosate application and resistance management.
A dynamic approach to quadrilateral definitions

This study examined 18 prospective secondary mathematics teachers' understanding of the nature of definitions, and their use of the dynamic geometry software Sketchpad to improve not only their understanding of definitions but also their ability to define geometric concepts themselves. Results indicated that the evaluation of definitions by accurate construction and measurement enabled students to achieve a better understanding of necessary and sufficient conditions, as well as the ability to more readily find counter-examples, and to recognise and improve on uneconomical definitions.
The Influence of Sensory Characteristics of Game Meat on Consumer Neuroperception: A Narrative Review

Game meat contains bioactive compounds that directly influence the formation of a rich reservoir of flavor precursors that produce specific sensory properties. Quality is considered one of the most influential determinants of consumer behavior, but the interpretation of this concept differs between consumers. Although recognized for its quality, its unique sensory characteristics (smell, taste, aroma) may have a major impact on consumer perception. The aim of this review is to describe the consumer behavior regarding game meat through elements of neuroperception, using methods of analysis, observation, and interpretation of scientific information from the literature. Following the analysis of published papers on this topic, it was shown that external factors influencing the biological basis of behavior could provide explanations for the acceptance or rejection of this type of meat and solutions. Neuroperception can explain the mechanism behind consumer decision-making. The influence of extrinsic factors (environment, mood, emotions, stress) shapes the perception of the quality attributes of game meat, the unique sensory characteristics of game meat passing through a primary filter of sensory receptors (eyes, nose, tongue, etc). Game meat is darker and tougher (compared to meat from domestic animals), and the taste and smell have the power to trigger memories and change the mood, influencing consumer behavior. Understanding consumer attitudes towards game meat in relation to quality attributes and the physiology of sensory perception can provide important insights for food industry professionals, processors, sensory evaluators, and researchers.
Editorial: Interpretable and explainable machine learning models in oncology

COPYRIGHT © 2023 Hrinivich, Wang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. TYPE Editorial PUBLISHED 22 March 2023 DOI 10.3389/fonc.2023. 1184428
Combinatory Effects of Training and Nutritive Administration of Carbohydrates and Protein via Food on Strength in Postmenopausal Women, and Old Men and Women

The age-related loss of muscle mass promotes many impairments. Training and protein supplementation are suggested to prevent muscle wasting, but recommendations for all populations are not based on scientific evidence. This study combines protein/carbohydrate supplementation (PCS) and training for seniors and postmenopausal women. Project A: 51 postmenopausal women (PMW, 57.3 ± 3.0 years old) underwent health-oriented training (12 weeks, moderate-strength training + moderate-endurance training). The intervention group (IG) additionally received 110 g sour milk cheese (SMC) and toast. Project B: 25 women and 6 men (65.9 ± 4.9 years old) performed intense sling training (12 weeks). The IG additionally received 110 g SMC, toast, and buttermilk. Strength was tested before and after in both studies. Project A: there was significant increase in strength, no additional effect of PCS, and a reduction in body fat in the controls. Project B: there was significant increase in strength, significant additional effects of PCS for trunk strength, and a significant reduction in body weight. Combining training and PCS may counteract strength loss. Combined endurance/resistance training is recommended to PMW for whom the benefits of PCS are restricted. Aged subjects may benefit from PCS when training intensely, but these benefits may be strongly individual.
MERTIS: understanding Mercury's surface composition from mid-infrared spectroscopy

The Mercury Radiometer and Thermal Infrared Imaging Spectrometer MERTIS on the joint ESA-JAXA mission BepiColombo to Mercury is combining a spectrometer using an uncooled microbolometer in a pushbroom mode with a highly miniaturized radiometer. A full development model of MERTIS is now available. So, after three flybys of Mercury by the MESSENGER mission and with the Planetary Emissivity Laboratory at DLR in Berlin that can routinely obtain infrared emission spectra at high temperatures it is a good time to review the MERTIS science requirements and the performance in perspective of our new knowledge of Mercury.
Modifications of the PI3K/Akt/mTOR axis during FeHV-1 infection in permissive cells

FeHV-1 is the causative agent of infectious rhinotracheitis in cats. The relationship between viral infection and the PI3K/Akt/mTOR pathway, as well as its function in crucial physiological processes like as autophagy, apoptosis or the IFN induction cascade is known for other varicelloviruses. However, there is no information on whether autophagy is activated during FeHV-1 infection nor on how this infection modifies PI3K/Akt/mTOR pathway. In this work, we aim to elucidate the involvement of this pathway during cytolytic infection by FeHV-1 in permissive cell lines. Using a phenotypic approach, the expression of proteins involved in the PI3K/Akt/mTOR pathway was examined by Western blot analysis. The findings demonstrated the lack of modifications in relation to viral dose (except for phospho-mTOR), whereas there were changes in the expression of several markers in relation to time as well as a mismatch in the time of activation of this axis. These results suggest that FeHV-1 may interact independently with different autophagic signaling pathways. In addition, we found an early phosphorylation of Akt, approximately 3 h after infection, without a concomitant decrease in constitutive Akt. This result suggests a possible role for this axis in viral entry. In a second phase, the use of early autophagy inhibitors was examined for viral yield, cytotoxic effects, viral glycoprotein expression, and autophagy markers and resulted in inefficient inhibition of viral replication (12 h post-infection for LY294002 and 48 h post-infection for 3-methyladenine). The same markers were examined during Akt knockdown, and we observed no differences in viral replication. This result could be explained by the presence of a protein kinase in the FeHV-1 genome (encoded by the Us3 gene) that can phosphorylate various Akt substrates as an Akt surrogate, as has already been demonstrated in genetically related viruses (HSV-1, PRV, etc.). For the same reasons, the use of LY294002 at the beginning of infection did not affect FeHV-1-mediated Akt phosphorylation. Our findings highlight changes in the PI3K/Akt/mTOR pathway during FeHV-1 infection, although further research is needed to understand the importance of these changes and how they affect cellular processes and viral propagation.
The Association between Unhealthy Food Consumption and Impaired Glucose Metabolism among Adults with Overweight or Obesity: A Cross-Sectional Analysis of the Indonesian Population

Background It has been shown that dietary patterns are associated with glucose control. However, the association between the types of food consumed and blood glucose in overweight or obese individuals is still unclear. The present study aimed to determine the association between unhealthy food consumption and impaired glucose metabolism in adults with overweight or obesity. Methods The analysis presented in this study was based on the data from a population-based, cross-sectional, nationally representative survey (Indonesian Basic Health Research 2018/RISKESDAS 2018). The body mass index (BMI) was calculated as weight (kg)/height squared (m2) and was determined based on the World Health Organization (WHO) criteria for the Asian population. A validated questionnaire and food card were used to assess the diet. Fasting plasma glucose and 2-hpost-prandial glucose were employed to determine blood glucose markers. Results In total, 8752 adults with overweight or obesity were included in this analysis. We found that consumption of sweet, grilled, and processed foods was associated with impaired fasting plasma glucose (IFG) before and after adjustment (p < 0.05). Consumption of high-fat foods was also associated with impaired glucose tolerance (IGT) for all models tested (p < 0.05). Furthermore, all models showed a link between processed food consumption and combined glucose intolerance (CGI) (p ≤ 0.001). Conclusions Differential food group consumption was associated with IFG, IGT, and CGI in Indonesian adults who were overweight or obese.
Geometry of compactifications of locally symmetric spaces

Pour un espace localement symetrique M nous definissons une compactification M U M(∞) que nous appelons compactification geodesique. Elle est construite en ajoutant des points limites dans M(∞) a certaines geodesiques dans M. La compactification geodesique apparait dans d'autres cas. Les constructions generales de Gromov permettent, dans le cas des espaces symetriques, d'identifier le bord de la compactification de Gromov avec M(∞). De plus M(oo) se construit naturellement avec la theorie des groupes en utilisant l'immeuble de Tits. La compactification geodesique joue deux roles fondamentaux dans l'analyse harmonique de l'espace localement symetrique: 1) c'est la compactification de Martin minimale pour les valeurs negatives du laplacien et 2) elle peut etre utilisee pour parametrer les valeurs propres du laplacien dans le spectre continu sur L 2 .
Possibility of gravitational quantization under the teleparallel theory of gravitation

Teleparallel gravity (TG) or tele-equivalent general relativity (TEGR) is an alternative gauge theory for gravity. In TG tetrad fields are defined to express gravitational fields and act like gauge potentials in standard gauge theory. The lagrangians for the gravitational field in TG and for the Yang-Mills field in standard gauge theory differ due to different indices that stick on the components of the corresponding fields: two external indices for tetrad field and internal and external indices for the Yang-Mills field. Different types of indices lead to different possible contractions and thus lead to different expression of the lagrangian for the Yang Mills field and for the tetrad field. As TG is a gauge theory it is then natural to quantize gravity in TG by applying the same procedure of quantization as in the standard gauge theory. Here we will discuss on the possibility to quantize gravity, canonically and functionally, under the framework of TG theory.
Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis

A New Way of Analyzing Object Data from a Nonparametric Viewpoint Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis provides one of the first thorough treatments of the theory and methodology for analyzing data on manifolds. It also presents in-depth applications to practical problems arising in a variety of fields, including statistics, medical imaging, computer vision, pattern recognition, and bioinformatics. The book begins with a survey of illustrative examples of object data before moving to a review of concepts from mathematical statistics, differential geometry, and topology. The authors next describe theory and methods for working on various manifolds, giving a historical perspective of concepts from mathematics and statistics. They then present problems from a wide variety of areas, including diffusion tensor imaging, similarity shape analysis, directional data analysis, and projective shape analysis for machine vision. The book concludes with a discussion of current related research and graduate-level teaching topics as well as considerations related to computational statistics. Researchers in diverse fields must combine statistical methodology with concepts from projective geometry, differential geometry, and topology to analyze data objects arising from non-Euclidean object spaces. An expert-driven guide to this approach, this book covers the general nonparametric theory for analyzing data on manifolds, methods for working with specific spaces, and extensive applications to practical research problems. These problems show how object data analysis opens a formidable door to the realm of big data analysis.
Origin and Emergence of Microglia in the CNS—An Interesting (Hi)story of an Eccentric Cell

Microglia belong to tissue-resident macrophages of the central nervous system (CNS), representing the primary innate immune cells. This cell type constitutes ~7% of non-neuronal cells in the mammalian brain and has a variety of biological roles integral to homeostasis and pathophysiology from the late embryonic to adult brain. Its unique identity that distinguishes its “glial” features from tissue-resident macrophages resides in the fact that once entering the CNS, it is perennially exposed to a unique environment following the formation of the blood–brain barrier. Additionally, tissue-resident macrophage progenies derive from various peripheral sites that exhibit hematopoietic potential, and this has resulted in interpretation issues surrounding their origin. Intensive research endeavors have intended to track microglial progenitors during development and disease. The current review provides a corpus of recent evidence in an attempt to disentangle the birthplace of microglia from the progenitor state and underlies the molecular elements that drive microgliogenesis. Furthermore, it caters towards tracking the lineage spatiotemporally during embryonic development and outlining microglial repopulation in the mature CNS. This collection of data can potentially shed light on the therapeutic potential of microglia for CNS perturbations across various levels of severity.
Identification of a Novel Protein-Based Prognostic Model in Gastric Cancers

Gastric cancer (GC) is the third leading cause of cancer-related deaths worldwide. However, there are still no reliable biomarkers for the prognosis of this disease. This study aims to construct a robust protein-based prognostic prediction model for GC patients. The protein expression data and clinical information of GC patients were downloaded from the TCPA and TCGA databases, and the expressions of 218 proteins in 352 GC patients were analyzed using bioinformatics methods. Additionally, Kaplan–Meier (KM) survival analysis and univariate and multivariate Cox regression analysis were applied to screen the prognosis-related proteins for establishing the prognostic prediction risk model. Finally, five proteins, including NDRG1_pT346, SYK, P90RSK, TIGAR, and XBP1, were related to the risk prognosis of gastric cancer and were selected for model construction. Furthermore, a significant trend toward worse survival was found in the high-risk group (p = 1.495 × 10−7). The time-dependent ROC analysis indicated that the model had better specificity and sensitivity compared to the clinical features at 1, 2, and 3 years (AUC = 0.685, 0.673, and 0.665, respectively). Notably, the independent prognostic analysis results revealed that the model was an independent prognostic factor for GC patients. In conclusion, the robust protein-based model based on five proteins was established, and its potential benefits in the prognostic prediction of GC patients were demonstrated.
Study on model of ionosphere fine structure for cognitive radar

It is required for the Cognitive Over-the-Horizon-Radar (COTHR) to deepen the cognition of the ionosphere environment. The current model of the ionosphere structure cannot satisfy this requirement. In this paper, the dispersion effect is introduced into the ionosphere fine structure and the space-time-frequency fine structure model of the ionosphere is proposed. Simulation results testify the validity of the model.