Miracle-dz/UniFAR-FTUs huggingface dataset:
A review of the Eviotazebrina complex, with descriptions of four new species (Teleostei, Gobiidae)

Abstract The Eviotazebrina complex includes eight species of closely-related dwarfgobies, four of which are herein described as new. The complex is named for Eviotazebrina Lachner & Karnella, 1978, an Indian Ocean species with the holotype from the Seychelles Islands and also known from the Maldives, which was once thought to range into the Gulf of Aqaba and the Red Sea eastward to the Great Barrier Reef of Australia. Our analysis supports the recognition of four genetically distinct, geographically non-overlapping, species within what was previously called E.zebrina, with E.zebrina being restricted to the Indian Ocean, E.marerubrumsp. nov. described from the Red Sea, E.longirostrissp. nov. described from western New Guinea, and E.pseudozebrinasp. nov. described from Fiji. The caudal fin of all four of these species is crossed by oblique black bars in preservative, but these black bars are absent from the four other species included in the complex. Two of the other species within the complex, E.tetha and E.gunawanae are morphologically similar to each other in having the AITO cephalic-sensory pore positioned far forward and opening anteriorly. Eviotatetha is known from lagoonal environments in Cenderawasih Bay and Raja Ampat, West Papua, and E.gunawanae is known only from deeper reefs (35–60 m) from Fakfak Regency, West Papua. The final two species are E.cometa which is known from Fiji and Tonga and possesses red bars crossing the caudal fin (but lost in preservative) and a 9/8 dorsal/anal-fin formula, and E.oculineatasp. nov., which is described as new from New Guinea and the Solomon Islands, and possesses an 8/7 dorsal/anal-fin formula and lacks red caudal bars. Eviotaoculineata has been confused with E.cometa in the past.
Individual differences in COVID-19 mitigation behaviors: The roles of age, gender, psychological state, and financial status

The present study examined individual characteristics potentially associated with changes in mitigation behaviors (social distancing and hygiene) recommended by the Centers for Disease Control and Prevention. Analysis of online survey responses from 361 adults, ages 20–78, with US IP addresses, identified significant correlates of adaptive behavioral changes, with implications for preventive strategies and mental health needs. The extent to which individuals changed their mitigation behaviors was unrelated to self-rated health or concern regarding the personal effects of COVID-19 but was related to concern regarding the effects of the pandemic on others. Thus, mitigation behaviors do not appear to be primarily motivated by self-protection. Importantly, adaptive changes in mitigation behaviors increased with age. However, these changes, particularly those related to the frequency of close proximity encounters, appear to be due to age-related decreases in anxiety and depression. Taken together, the present results argue against over-reliance on ‘fear appeals’ in public health messages as they may increase anxiety and depression. Instead, the present findings argue for more appeals to people’s concern for others to motivate mitigation as well as indicating an immediate need to address individual mental health concerns for the sake of society as a whole.
Pathogenesis of autoimmune hepatitis

Autoimmune hepatitis (AIH) is a chronic progressive liver disease whose etiology and pathogenesis are not yet clear. It is currently believed that the occurrence of AIH is closely related to genetic susceptibility and immune abnormalities, and other factors such as environment, viral infection and drugs that may cause immune dysfunction. This article reviews the pathogenesis of AIH and describes the latest research results in the past 5 years.
Multiparametric MRI for the improved diagnostic accuracy of Alzheimer’s disease and mild cognitive impairment: Research protocol of a case-control study design

Background Alzheimer’s disease (AD) is a major neurocognitive disorder identified by memory loss and a significant cognitive decline based on previous level of performance in one or more cognitive domains that interferes in the independence of everyday activities. The accuracy of imaging helps to identify the neuropathological features that differentiate AD from its common precursor, mild cognitive impairment (MCI). Identification of early signs will aid in risk stratification of disease and ensures proper management is instituted to reduce the morbidity and mortality associated with AD. Magnetic resonance imaging (MRI) using structural MRI (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and magnetic resonance spectroscopy (1H-MRS) performed alone is inadequate. Thus, the combination of multiparametric MRI is proposed to increase the accuracy of diagnosing MCI and AD when compared to elderly healthy controls. Methods This protocol describes a non-interventional case control study. The AD and MCI patients and the healthy elderly controls will undergo multi-parametric MRI. The protocol consists of sMRI, fMRI, DTI, and single-voxel proton MRS sequences. An eco-planar imaging (EPI) will be used to perform resting-state fMRI sequence. The structural images will be analysed using Computational Anatomy Toolbox-12, functional images will be analysed using Statistical Parametric Mapping-12, DPABI (Data Processing & Analysis for Brain Imaging), and Conn software, while DTI and 1H-MRS will be analysed using the FSL (FMRIB’s Software Library) and Tarquin respectively. Correlation of the MRI results and the data acquired from the APOE genotyping, neuropsychological evaluations (i.e. Montreal Cognitive Assessment [MoCA], and Mini–Mental State Examination [MMSE] scores) will be performed. The imaging results will also be correlated with the sociodemographic factors. The diagnosis of AD and MCI will be standardized and based on the DSM-5 criteria and the neuropsychological scores. Discussion The combination of sMRI, fMRI, DTI, and MRS sequences can provide information on the anatomical and functional changes in the brain such as regional grey matter volume atrophy, impaired functional connectivity among brain regions, and decreased metabolite levels specifically at the posterior cingulate cortex/precuneus. The combination of multiparametric MRI sequences can be used to stratify the management of MCI and AD patients. Accurate imaging can decide on the frequency of follow-up at memory clinics and select classifiers for machine learning that may aid in the disease identification and prognostication. Reliable and consistent quantification, using standardised protocols, are crucial to establish an optimal diagnostic capability in the early detection of Alzheimer’s disease.
The impact of green credit on economic growth—The mediating effect of environment on labor supply

Background The progress of green credit in China is accelerating, but its development is uneven and insufficient in different regions. And whether the issuance of green credit can effectively promote the improvement of the environment and economy is not well understood. Objective Previous research has found that green credit promotes economic growth through improvement of the industrial structure and green technological innovation. However, these studies have not considered the positive externality of environmental improvement even though environmental improvement and economic growth are requirements of the sustainable development concept. Methods We use the chain-mediated model to estimate the impact of green credit issuance on the economic growth of different provinces since the large-scale implementation of green credit in China with data from 2008 to 2016. Results and conclusion This paper shows that the issuance of green credit can improve labor supply rather than labor productivity through the improvement of air quality to achieve regional economic growth. Such a chain-mediated path is different from the economic growth caused by industrial structural adjustment and green technology innovation. At the national level, every 1% increase in green credit issuance relative to industrial loans will increase the per capita gross domestic product (GDP) by approximately 4.6 yuan, or 0.012%, through air quality and labor supply, accounting for 2.875% of the total effect. Heterogeneity analysis indicates that due to regional industrial structure differences and diminishing marginal effects, the impact of green credit is stronger in the western region than in the eastern and central regions. For every 1% increase in the proportion of green credit issuance relative to industrial loans, the per capita GDP growth achieved through the chain-mediated path is approximately 30.17 yuan in the western region, approximately 6.6 times greater than that at the national level. Within a 95% confidence interval of 5000 bootstrap samples, this path is found to be true, and the chain-mediated effect accounts for approximately 12.96% of the total indirect effect. Limitations The limitation of this paper is the measurement of green credit. Although green credit has a large volume, it remains underdeveloped, and there is a lack of perfect indicators. Most existing studies have adopted only alternative or reverse indicators to measure the issuance of green credit. For example, this paper takes the interest expenditure of six high-energy-consuming enterprises as the reverse indicator, which may to a certain extent lead to the overestimation of the issuance of green credit and its impact on the environment and economy. Future research can accurately explore the performance of green credit on the basis of its mature development.
The Effects of Diosgenin on Hypolipidemia and Its Underlying Mechanism: A Review

Abstract Hyperlipidemia is a disorder of lipid metabolism, which is a major cause of coronary heart disease. Although there has been considerable progress in hyperlipidemia treatment, morbidity and risk associated with the condition continue to rise. The first-line treatment for hyperlipidemia, statins, has multiple side effects; therefore, development of safe and effective drugs from natural products to prevent and treat hyperlipidemia is necessary. Diosgenin is primarily derived from fenugreek (Trigonella foenum graecum) seeds, and is also abundant in medicinal herbs such as Dioscorea rhizome, Dioscorea septemloba, and Rhizoma polygonati, is a well-known steroidal sapogenin and the active ingredient in many drugs to treat cardiovascular conditions. There is abundant evidence that diosgenin has potential for application in correcting lipid metabolism disorders. In this review, we evaluated the latest evidence related to diosgenin and hyperlipidemia from clinical and animal studies. Additionally, we elaborate the pharmacological mechanism underlying the activity of diosgenin in treating hyperlipidemia in detail, including its role in inhibition of intestinal absorption of lipids, regulation of cholesterol transport, promotion of cholesterol conversion into bile acid and its excretion, inhibition of endogenous lipid biosynthesis, antioxidation and lipoprotein lipase activity, and regulation of transcription factors related to lipid metabolism. This review provides a deep exploration of the pharmacological mechanisms involved in diosgenin-hyperlipidemia interactions and suggests potential routes for the development of novel drug therapies for hyperlipidemia.
Network Pharmacology Analysis of the Identification of Phytochemicals and Therapeutic Mechanisms of Paeoniae Radix Alba for the Treatment of Asthma

Background Paeoniae Radix Alba (PRA), the root of the plant Paeonia lactiflora Pall. , has been suggested to play an important role for the treatment of asthma. A biochemical understanding of the clinical effects of Paeoniae Radix Alba is needed. Here, we explore the phytochemicals and therapeutic mechanisms via a systematic and comprehensive network pharmacology analysis. Methods Through TCMSP, PubChem, GeneCards database, and SwissTargetPrediction online tools, potential targets of active ingredients from PRA for the treatment of asthma were obtained. Cytoscape 3.7. 2 was used to determine the target of active ingredients of PRA. Target protein interaction (PPI) network was constructed through the STRING database. The Gene Ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genes (KEGG) pathway enrichment analysis were analyzed through the biological information annotation database (DAVID). Results Our results indicate that PRA contains 21 candidate active ingredients with the potential to treat asthma. The enrichment analysis of GO and KEGG pathways found that the treatment of asthma by PRA may be related to the process of TNF (tumor necrosis factor) release, which can regulate and inhibit multiple signaling pathways such as ceramide signaling. Conclusions Our work provides a phytochemical basis and therapeutic mechanisms of PRA for the treatment of asthma, which provides new insights on further research on PRA.
Predicting Individual Risk of Emergency Hospital Admissions – A Retrospective Validation Study

Purpose A high number of hospital admissions may indicate poor general health and less than optimal health care across sectors. To prevent hospital admissions, previous studies have focused on predicting readmissions relating to a defined index admission and specific condition, whereas generic models suited for community-dwelling persons are lacking. The aim of this study was to validate a generic model that predicted risk of emergency hospital admission within the following three months and to investigate regional variation. Materials and Methods This study is an observational register-based validation study of a prediction model. The prediction model was based on a population of frail elderly, persons with non-communicable diseases, and persons with three emergency hospital admissions using information about diagnoses and hospital contacts. The prediction model consisted of two stages. In the first stage, covariate associations to admissions are estimated from observed data in one year. In the second stage, admissions are predicted in the coming three months based on observed estimations from the first stage. The validity of the model was calculated by comparing predicted and observed admissions from August 1st to October 31st, 2016. Results The study included 112,026 persons. In nationwide data, area under the curve (AUC) was 0.7742 (95% CI 0.7698–0.7786), and the positive predictive value was 52% for the 99th percentile (the top 1%). AUC varied between regions from 0.6914 in Southern Denmark (95% CI 0.6779–0.7049) to 0.8224 (95% CI 0.8064–0.8384) in North Denmark. AUC was higher with nationwide data compared to regional. Conclusion The model performed satisfactorily in predicting individual risk of emergency hospital admission.
Cancer-related anxiety, COVID-19, and the oncologist: the formation of a ‘Balint’ process group

The management of anxiety and distress in patients with cancer is stressful for the oncology clinicians who treat them [1]. Unfortunately, psychosocial care for patients with cancer is not universally available or standardized [2]. Referrals from oncology services to psychological serves are often not initiated early enough, may not be encouraged from medicine or surgical services, and are subsequently foregone or patients do not follow up beyond a single appointment [3]. As purveyors of cancer-related information, oncologists often find themselves in situations where their patients are reluctant to engage psychosocial care for various reasons (e.g., stigma, additional appointments, or just prioritizing oncology care] but remain highly symptomatic, which can be disruptive to their quality of life, their families, and following through with their oncology care [4,5]. The consequence is detrimental to their overall quality of life and cancer related mortality and places undue stresses on primary oncology services who are not equipped to manage complicated psychological stressors of their patients [6]. Inadvertently, distress is easily transmitted to cancer care teams and oncologists who are caring for them [7,8].
Micellization of Cetylpyridinium Chloride in Aqueous Lithium Chloride, Sodium Chloride and Potassium Chloride Media

Surface tension and electrical conductance of cetylpyridinium chloride (CPC) in water were measured in the presence of lithium chloride, sodium chloride and potassium chloride at 295, 298 and 303 K. From these experimental data, critical micelle concentration, surface density, counter-ion binding constant and standard free energy of micellization of CPC were determined and no effect of co-ion on these parameters is found. Critical micelle concentration has a minimum around 298 K. Surface density of CPC at the air - water interface increased by the addition of electrolyte. The surface area per molecule of CPC at the air - water interface is about 63 % more compared to that of sodium dodecyl sulfate molecule and hence the pyridine ring of the adsorbed CPC molecule appears to reside at the interface. Aggregation numbers of CPC were estimated from the electrical conductance data by applying the mixed electrolyte model. In the presence of more than 5.2 × 10 -4 mol kg -1 NaCl, aggregation number of CPC is found to be higher than that in the presence of LiCl and KCl thereby indicating the possibility of co-ion effect on the aggregation number.
Data-Driven Design of Novel Halide Perovskite Alloys

The great tunability of the properties of halide perovskites presents new opportunities for optoelectronic applications as well as significant challenges associated with exploring combinatorial chemical spaces. In this work, we develop a framework powered by high-throughput computations and machine learning for the design and prediction of mixed cation halide perovskite alloys. In a chemical space of ABX3 perovskites with a selected set of options for A, B, and X atoms, pseudo-cubic structures of compounds with B-site mixing are simulated using density functional theory (DFT) and several properties are computed, including stability, lattice constant, band gap, vacancy formation energy, refractive index, and optical absorption spectrum, using both semilocal and hybrid functionals. Neural networks (NN) are used to train predictive models for every property using tabulated elemental properties of A, B, and X site atoms as descriptors. Starting from the DFT dataset of 229 points, we use the trained NN models to predict the structural, energetic, electronic and optical properties of a complete dataset of 17,955 compounds, and perform high-throughput screening in terms of stability, band gap and defect tolerance, to obtain 574 promising compounds that are ranked as potential absorbers according to their photovoltaic figure of merit. Compositional trends in the screened set of attractive mixed cation halide perovskites are revealed and additional computations are performed on selected compounds. The data-driven design framework developed here is promising for designing novel mixed compositions and can be extended to a wider perovskite chemical space in terms of A, B, and X atoms, different kinds of mixing at the A, B, or X sites, non-cubic phases, and other properties of interest.
Frequency-based Ultrasonic Backscatter Modulation for Passive Sensing Applications

Ultrasonic backscatter communication has gained popularity in recent years where piezoceramic resonators are used as acoustic antennas. Currently, backscatter communication revolves around amplitude modulation of the echo signal by the sensor, which can be modelled as a variable shunt impedance. Amplitude based sensing is prone to high levels of noise, motion artifacts, and has low efficiency with respect to power usage and bandwidth. To overcome these shortcomings, we present here, a frequency-based sensing method for ultrasonic backscatter communication. This system exhibits a frequency shift in the ultrasonic sensor’s parallel resonance when its shunt capacitance is varied, similar to an inductive near-field link. The concept is simulated using an equivalent end-to-end model in SPICE that matches well with the experimental observation. To monitor the resonance frequency shift in the experiments a variety of methods are employed including ringdown measurements, chirp spectroscopy, bode analysis and phase locked loop. All the methods provide substantial proof for feasibility of frequency-based sensing in ultrasonic backscatter communication enabling passive sensor motes for sensing applications.
Astronomical Pipeline Provenance: A Use Case Evaluation

In this decade astronomy is undergoing a paradigm shift to handle data from next generation observatories such as the Square Kilometre Array (SKA) or the Vera C. Rubin Observatory (LSST). Producing real time data streams of up to 10 TB/s and data products of the order of 600 Pbytes/year, the SKA will be the biggest civil data producing machine of the world that demands novel solutions on how these data volumes can be stored and analysed. Through the use of complex, automated pipelines the provenance of this real time data processing is key to establish confidence within the system, its final data products, and ultimately its scientific results. The intention of this paper is to lay the foundation for making an automated provenance generation tool for astronomical/data-processing pipelines. We therefore present a use case analysis, specific to the astronomical needs which addresses the issues of trust and reproducibility as well as other ulterior use cases which are of interest to astronomers. This analysis is subsequently used as the basis to discuss the requirements, challenges, and opportunities involved in designing both the tool and the associated provenance model.
Geometric Fabrics: Generalizing Classical Mechanics to Capture the Physics of Behavior

Classical mechanical systems are central to controller design in energy shaping methods of geometric control. However, their expressivity is limited by position-only metrics and the intimate link between metric and geometry. Recent work on Riemannian Motion Policies (RMPs) has shown that shedding these restrictions results in powerful design tools, but at the expense of theoretical stability guarantees. In this work, we generalize classical mechanics to what we call geometric fabrics, whose expressivity and theory enable the design of systems that outperform RMPs in practice. Geometric fabrics strictly generalize classical mechanics forming a new physics of behavior by first generalizing them to Finsler geometries and then explicitly bending them to shape their behavior while maintaining stability. We develop the theory of fabrics and present both a collection of controlled experiments examining their theoretical properties and a set of robot system experiments showing improved performance over a well-engineered and hardened implementation of RMPs, our current state-of-the-art in controller design.
Towards The Automatic Coding of Medical Transcripts to Improve Patient-Centered Communication

This paper aims to provide an approach for automatic coding of physician-patient communication transcripts to improve patient-centered communication (PCC). PCC is a central part of high-quality health care. To improve PCC, dialogues between physicians and patients have been recorded and tagged with predefined codes. Trained human coders have manually coded the transcripts. Since it entails huge labor costs and poses possible human errors, automatic coding methods should be considered for efficiency and effectiveness. We adopted three machine learning algorithms (Naïve Bayes, Random Forest, and Support Vector Machine) to categorize lines in transcripts into corresponding codes. The result showed that there is evidence to distinguish the codes, and this is considered to be sufficient for training of human annotators.