Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel animal model of neuropathic corneal pain–the ciliary nerve constriction model

Introduction Neuropathic pain arises as a result of peripheral nerve injury or altered pain processing within the central nervous system. When this phenomenon affects the cornea, it is referred to as neuropathic corneal pain (NCP), resulting in pain, hyperalgesia, burning, and photoallodynia, severely affecting patients’ quality of life. To date there is no suitable animal model for the study of NCP. Herein, we developed an NCP model by constriction of the long ciliary nerves innervating the eye. Methods Mice underwent ciliary nerve constriction (CNC) or sham procedures. Safety was determined by corneal fluorescein staining to assess ocular surface damage, whereas Cochet-Bonnet esthesiometry and confocal microscopy assessed the function and structure of corneal nerves, respectively. Efficacy was assessed by paw wipe responses within 30 seconds of applying hyperosmolar (5M) saline at Days 3, 7, 10, and 14 post-constriction. Additionally, behavior was assessed in an open field test (OFT) at Days 7, 14, and 21. Results CNC resulted in significantly increased response to hyperosmolar saline between groups (p < 0.0001), demonstrating hyperalgesia and induction of neuropathic pain. Further, animals that underwent CNC had increased anxiety-like behavior in an open field test compared to controls at the 14- and 21-Day time-points (p < 0.05). In contrast, CNC did not result in increased corneal fluorescein staining or decreased sensation as compared to sham controls (p > 0.05). Additionally, confocal microscopy of corneal whole-mounts revealed that constriction resulted in only a slight reduction in corneal nerve density (p < 0.05), compared to naïve and sham groups. Discussion The CNC model induces a pure NCP phenotype and may be a useful model for the study of NCP, recapitulating features of NCP, including hyperalgesia in the absence of ocular surface damage, and anxiety-like behavior.
An Untethered Soft Robotic Dog Standing and Fast Trotting with Jointless and Resilient Soft Legs

Soft robots are compliant, impact resistant, and relatively safe in comparison to hard robots. However, the development of untethered soft robots is still a major challenge because soft legs cannot effectively support the power and control systems. Most untethered soft robots apply a crawling or walking gait, which limits their locomotion speed and mobility. This paper presents an untethered soft robot that can move with a bioinspired dynamic trotting gait. The robot is driven by inflatable soft legs designed on the basis of the pre-charged pneumatic (PCP) actuation principle. Experimental results demonstrate that the developed robot can trot stably with the fastest speed of 23 cm/s (0.97 body length per second) and can trot over different terrains (slope, step, rough terrain, and natural terrains). The robotic dog can hold up to a 5.5 kg load in the static state and can carry up to 1.5 kg in the trotting state. Without any rigid components inside the legs, the developed robotic dog exhibits resistance to large impacts, i.e., after withstanding a 73 kg adult (46 times its body mass), the robotic dog can stand up and continue its trotting gait. This innovative robotic system has great potential in equipment inspection, field exploration, and disaster rescue.
Distributed Resilient Filtering for Power Systems Subject to Denial-of-Service Attacks

This paper addresses the distributed resilient filtering problem for a class of power systems subject to denial-of-service (DoS) attacks. A novel distributed filter is first constructed to practically reflect the impact from both cyber-attacks and gain perturbations. For all possible occurrence of DoS attacks and gain perturbations, an upper bound of filtering error covariance is derived by resorting to some typical matrix inequalities. Furthermore, the desired filter gain relying on the solution of two Riccati-like difference equations is obtained with the help of the gradient-based approach and the mathematical induction. The developed algorithm with a recursive form is independent of the global information and thus satisfies the requirements of scalability and distributed implementation online. Finally, a benchmark simulation test is exploited to check the usefulness of the designed filter.
Vibration Reduction Control of Switched Reluctance Machine

In this paper, a new vibration reduction control of the switched reluctance machine (SRM) is proposed, which takes into consideration the undesirable effects of the torque ripple. The control approach aims to reduce the variation of the sum of radial forces while optimizing the control parameters of the machine to achieve the torque ripple minimization. Thus, a reference current adapter is used in order to handle the tradeoff between the vibration reduction and the torque ripple reduction. It produces an auto-tuning reference current that limits the variation of the torque and of the total radial force. The effectiveness of the proposed control strategy is proved by simulation and experimental results. Comparisons with conventional control methods investigating vibrations, noise, and torque ripple are provided, which show the advantages of the control approach based on an auto-tuning reference current.
A New Successive Time Balancing Time-to-Digital Conversion Method

This paper presents a new self-clocked time-to-digital conversion method based on a binary successive approximation (SA) algorithm. Its novelty consists in combining fully clockless operation with direct conversion of the measured time interval. The lack of any reference clock makes the presented method potentially predisposed to low-power solutions. Furthermore, its circuit representation is extremely simple, thereby the ability to direct conversion of time intervals is not burdened by a significant amount of components. The method is intended to measure relatively long time intervals, i.e., hundreds of microseconds. Therefore, it is suitable for e.g., biomedical applications using time-mode signal processing.
Immune Cells in the Tumor Microenvironment of Soft Tissue Sarcomas

Simple Summary Soft tissue sarcomas represent a large heterogeneous group of malignant neoplasms that are characterized by a poor disease outcome and being poorly studied. Recently, more and more studies have been directed to the role of tumor microenvironment components in establishing the aggressive potential of the tumor. Many antitumor drugs, in addition to their direct effect on tumor cells, modify the tumor microenvironment; in particular, they act on the immune component, which may be the reason for their effectiveness in soft tissue sarcoma. Immune cells themselves are now considered as promising antitumor strategies for the treatment of soft tissue sarcomas. Understanding the mechanisms of interaction between tumor cells and immune cells in different types of soft tissue sarcomas will allow us to divide patients into those for whom immunotherapy will be highly effective and those for whom this type of treatment will be ineffective. Abstract Soft tissue sarcomas (STSs) are a rare heterogeneous group of malignant neoplasms characterized by their aggressive course and poor response to treatment. This determines the relevance of research aimed at studying the pathogenesis of STSs. By now, it is known that STSs is characterized by complex relationships between the tumor cells and immune cells of the microenvironment. Dynamic interactions between tumor cells and components of the microenvironment enhance adaptation to changing environmental conditions, which provides the high aggressive potential of STSs and resistance to antitumor therapy. Today, active research is being conducted to find effective antitumor drugs and to evaluate the possibility of using therapy with immune cells of STS. The difficulty in assessing the efficacy of new antitumor options is primarily due to the high heterogeneity of this group of malignant neoplasms. Studying the role of immune cells in the microenvironment in the progression STSs and resistance to antitumor therapies will provide the discovery of new biomarkers of the disease and the prediction of response to immunotherapy. In addition, it will help to initially divide patients into subgroups of good and poor response to immunotherapy, thus avoiding wasting precious time in selecting the appropriate antitumor agent.
The Diagnostic Accuracy of a Fecal Immunochemical Test in Detecting Colorectal Cancer and Advanced Precancerous Colorectal Neoplasia in Patients with Iron Deficiency: A Protocol for Systematic Review and Meta-Analysis

Background. Iron deficiency (ID) is a common micronutrient deficiency and the leading cause of anemia worldwide. ID can be caused by chronic occult blood loss from colorectal neoplasia including colorectal cancer (CRC) and advanced precancerous colorectal lesions. Current guidelines recommend colonoscopy in both men and postmenopausal women presenting with ID anemia (IDA). However, there is controversy on the investigation of patients presenting with a lower risk of CRC including younger women with ID and those with nonanemic ID (NAID). There is a need for a triaging tool to identify which ID patients may benefit from colonoscopy. The fecal immunochemical test (FIT) is sensitive for CRC screening in an asymptomatic population, but its role in ID patients is unclear. The aim of this study is to conduct a systematic review to determine the diagnostic accuracy of FIT for detecting CRC and advanced precancerous neoplasia in individuals presenting with ID with or without anemia. Methods and Analysis. This protocol conforms with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols and Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. A comprehensive search of the MEDLINE, Embase, and Web of Science databases will be undertaken for studies published after 2010 which involve patients with ID, who completed a FIT in the 6 months prior to colonoscopy, with FIT sensitivity and specificity calculated against the reference standard colonoscopy. The search will be limited to studies conducted after 2010 to reduce variability in colonoscopy quality. Risk of bias assessment will be conducted using the Quality Assessment of Diagnostic Accuracy Studies version 2. FIT sensitivity and specificity will be the primary measure of diagnostic accuracy, and data will be analysed using a random effects meta-analysis. Discussion. This review and meta-analysis will be the first to systematically explore the value of the FIT as a triaging tool for patients with ID. This trial is registered with CRD42022367162.
Tryptophan metabolism enzymes are potential targets in ovarian clear cell carcinoma

As the second most prevalent subtype of epithelial ovarian cancers, ovarian clear cell carcinoma (OCCC) is known for its chemoresistance to conventional platinum‐based therapy. In this work, we examined the tryptophan (Trp) metabolism enzymes' differential expression in patients with OCCC to assess the potential for personalised treatment.
OCEANOGRAPHIC AND ATMOSPHERIC RETRIEVALS FROM AVIRIS HYPERSPECTRAL DATA

The ocean and coastal zone are particularly challenging environments for using highaltitude, hyperspectral data. To discern ocean characteristics, atmospheric effects must be compensated for; not only does absorption by atmospheric gases affect a large portion of the reflective spectrum, but atmospheric path scattering contributes a much greater percentage of the measured radiance than that which emanates from the surface and water column. Additionally, the water surface has a strong bi-reflectance distribution function (BRDF) that changes as a function of wind and wave conditions. These effects, too, must be compensated for to make full use of hyperspectral signatures of water column and surface phenomena, an area rich in potential for hyperspectral applications.
Analysis of Human Development Index, Government Expenditure, Exports and Imports on Economic Growth in ASEAN-5

This study aims to analyze the Human Development Index, Government Expenditure, Exports and Imports on Economic Growth in Singapore, Philippines, Malaysia, Thailand, and Indonesia. Using secondary data the period 2010-2021. The method used is the Quantitative method using Panel Data Regression. Results of panel data regression, export variable product has a positive and significant effect on economic growth. These results reveal that to increase economic growth, it is necessary to increase export performance. Increasing export performance can be done in various ways, one of which is by improving the export administration system, increasing research and product development, improving infrastructure facilities and infrastructure, exchange rate stability and expanding non-traditional markets
Test-Taker Engagement in AI Technology-Mediated Language Assessment

ABSTRACT In language assessment, AI technology has been incorporated in task design, assessment delivery, automated scoring of performance-based tasks, score reporting, and provision of feedback. AI technology is also used for collecting and analyzing performance data in language assessment validation. Research has been conducted to investigate the efficiency and functionality of assessment technologies, but empirical explorations on test-taker engagement in AI technology-mediated language assessment are remarkably scarce. In this commentary, we first examine the impact of AI technology on test takers, in terms of both the benefits and the challenges that it poses in the critical stages of language assessment development and validation. Next, we propose a conceptual model to facilitate the implementation and evaluation of test-taker engagement in AI technology-mediated language assessment. The model delineates two forms of test-taker engagement: test takers’ participation in technology-mediated assessment activities and their perceptions of technological innovations in language assessment. We then review the articles in this special issue based on this model and discuss directions for future research. We conclude by offering some guidance for maximizing test-taker engagement in technological innovations, thereby promoting learning-oriented and equity-minded language assessment.
Phase Angle as a Prognostic Predictor for Physical Function in Fragile Osteoporotic Fracture Patients

ABSTRACT Objectives: This study aimed to determine whether the phase angle is associated with physical function at discharge and discharge destination in patients with osteoporotic fragile fractures. Methods: This retrospective cohort study included patients with fragile osteoporotic fractures who were admitted to a convalescent rehabilitation ward. The phase angle was calculated using a body composition meter and bioelectrical impedance analysis. The primary outcome was the Functional Independence Measure motor (FIM-motor) score at discharge, and the secondary outcome was discharge to home. Multivariate analysis was used to determine the association between phase angle and FIM-motor scores at discharge and discharge to home. Results: The study included 127 patients (108 women, age 81.2 ± 9.7 years). The median phase angle on admission was 4.1° for men and 3.6° for women. The median FIM-motor score at discharge was 83, with 92 (72.4%) patients discharged home and 35 (27.6%) discharged to a destination other than home. Multiple regression analysis adjusted for confounders revealed a significant independent association between the phase angle and FIM-motor score at discharge (β=0.262, P=0.019). However, no significant association was found between phase angle and discharge destination (odds ratio, 1.350; 95% confidence interval: 0.680–2.670, P=0.391). Conclusions: Phase angle was independently associated with physical function at discharge in patients with fragile osteoporotic fractures. For patients with a reduced phase angle on admission, a multidisciplinary approach, including exercise, nutrition, oral health, and medication, should be implemented to maximize improvement in physical function.
Hydrogen spillover enhances alkaline hydrogen electrocatalysis on interface-rich metallic Pt-supported MoO3

Efficient and cost-effective electrocatalysts for the hydrogen oxidation/evolution reaction (HOR/HER) are essential for commercializing alkaline fuel cells and electrolyzers. The sluggish HER/HOR reaction kinetics in base is the key issue that requires resolution so that commercialization may proceed. It is also quite challenging to decrease the noble metal loading without sacrificing performance. Herein, we report improved HER/HOR activity as a result of hydrogen spillover on platinum-supported MoO3 (Pt/MoO3-CNx-400) with a Pt loading of 20%. The catalyst exhibited a decreased over-potential of 66.8 mV to reach 10 mA cm−2 current density with a Tafel slope of 41.2 mV dec−1 for the HER in base. The Pt/MoO3-CNx-400 also exhibited satisfactory HOR activity in base. The mass-specific exchange current density of Pt/MoO3-CNx-400 and commercial Pt/C are 505.7 and 245 mA mgPt−1, respectively. The experimental results suggest that the hydrogen binding energy (HBE) is the key descriptor for the HER/HOR. We also demonstrated that the enhanced HER/HOR performance was due to the hydrogen spillover from Pt to MoO3 sites that enhanced the Volmer/Heyrovsky process, which led to high HER/HOR activity and was supported by the experimental and theoretical investigations. The work function value of Pt [Φ = 5.39 eV) is less than that of β-MoO3 (011) [Φ = 7.09 eV], which revealed the charge transfer from Pt to the β-MoO3 (011) surface. This suggested the feasibility of hydrogen spillover, and was further confirmed by the relative hydrogen adsorption energy [ΔGH] at different sites. Based on these findings, we propose that the H2O or H2 dissociation takes place on Pt and interfaces to form Pt–Had or (Pt/MoO3)–Had, and some of the Had shifted to MoO3 sites through hydrogen spillover. Then, Had at the Pt and interface, and MoO3 sites reacted with H2O and HO− to form H2 or H2O molecules, thereby boosting the HER/HOR activity. This work may provide valuable information for the development of hydrogen-spillover-based electrocatalysts for use in various renewable energy devices.
Efficacies of S-nitrosoglutathione (GSNO) and GSNO reductase inhibitor in SARS-CoV-2 spike protein induced acute lung disease in mice

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which initially surfaced in late 2019, often triggers severe pulmonary complications, encompassing various disease mechanisms such as intense lung inflammation, vascular dysfunction, and pulmonary embolism. Currently, however, there’s no drug addressing all these mechanisms simultaneously. This study explored the multi-targeting potential of S-nitrosoglutathione (GSNO) and N6022, an inhibitor of GSNO reductase (GSNOR) on markers of inflammatory, vascular, and thrombotic diseases related to COVID-19-induced acute lung disease. For this, acute lung disease was induced in C57BL/6 mice through intranasal administration of recombinant SARS-CoV-2 spike protein S1 domain (SP-S1). The mice exhibited fever, body weight loss, and increased blood levels and lung expression of proinflammatory cytokines (e.g., TNF-α and IL-6) as well as increased vascular inflammation mediated by ICAM-1 and VCAM-1 and lung infiltration by immune cells (e.g., neutrophils, monocytes, and activated cytotoxic and helper T cells). Further, the mice exhibited increased lung hyperpermeability (lung Evans blue extravasation) leading to lung edema development as well as elevated blood coagulation factors (e.g., fibrinogen, thrombin, activated platelets, and von Willebrand factor) and lung fibrin deposition. Similar to the patients with COVID-19, male mice showed more severe disease than female mice, along with higher GSNOR expression in the lungs. Optimization of GSNO by treatment with exogenous GSNO or inhibition of GSNOR by N6022 (or GSNO knockout) protects against SP-S1-induced lung diseases in both genders. These findings provide evidence for the potential efficacies of GSNO and GSNOR inhibitors in addressing the multi-mechanistic nature of SARS-CoV-2 SP-associated acute-lung disease.
Multi-Iteration Approach to Studying Tracer Spreading Using Drifter Data

AbstractA novel multi-iteration statistical method for studying tracer spreading using drifter data is introduced. The approach allows for the best use of the available drifter data by making use of a simple iterative procedure, which results in the statistically probable map showing the likelihood that a tracer released at some source location would visit different geographical regions, along with the associated arrival travel times. The technique is tested using real drifter data in the North Atlantic. Two examples are considered corresponding to sources in the western and eastern North Atlantic Ocean, that is, Massachusetts Bay–like and Irish Sea–like sources, respectively. In both examples, the method worked well in estimating the statistics of the tracer transport pathways and travel times throughout the entire North Atlantic. The role of eddies versus mean flow is quantified using the same technique, and eddies are shown to significantly broaden the spread of a tracer. The sensitivity of the results.
A Frame Work to Extract Accurate Opinion Phrases from Online Product Reviews using Topical Sentiment Analysis

Data mining is considered as an analytical approach designed to discover data where the opinion mining offers with the computational management of opinion, sentiment and subjective in a text. The principal application of opinion mining is gathering the online reports about the product. The research concern is extracting the opinion ambitions and the opinion words and detecting the opinion family members among the many words. Opinion goal is a noun or noun the phrases defined as the object about which person specific their opinions. Opinion phrase is a verb or adjectives used to specific user’s opinion about the object. Opinion analysis has been classified into three phases. The first stage is recorded stage, classifies whether a whole opinion file expresses a confident or poor opinion about the product. The second stage is sentenced level, classifies whether or not every sentence categorical a constructive, poor or impartial opinion. The third level is part level, performs a fine-grained classification of an opinion on the product. Focusing the extraction of opinion goals and the opinion phrases, earlier work more commonly adopted a collective extraction strategy. The instinct represented by this method used to be that in sentences, opinion phrases regularly co-occur with opinion targets, and there are robust change relations and associations among them. Subsequently, a Word Alignment model, a partially supervised alignment model which regards in selecting opinion members of the family as an alignment method was awarded in which, the graph-situated corating algorithm is exploited to estimate the confidence of every candidate where the candidates with greater confidence are extracted as opinion goals or opinion phrases. The technique, in general, excited by detecting opinion relations between opinion pursuits and opinion words and when compared to earlier methods founded on adjacent neighbour rules and syntactic patterns, in utilizing a word alignment mannequin, this approach captures opinion family members extra precisely and for this reason is extra mighty for opinion goal and opinion