Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonuniformity correction algorithm based on infrared focal plane array readout architecture

A nonuniformity correction algorithm based on infrared focal plane array readout architecture is proposed to correct the aggregate nonuniformity by two separate steps.Firstly,the nonuniformity from the readout amplifiers is corrected using the constant statistics method.Then,the noise resulted from the detectors is decreased with the improved neural network method.The experimental results indicate that the proposed algorithm can converge to a good correction level at the frame 20,while the Mean Absolute Error(MAE) and Peak Signal-to-Noise Ratio(PSNR) of the corrected image are 2.9 and 39.2,respectively.The algorithm is recursive with the advantages of high convergent speed and low computational complexity,so it is easy to be implemented in projects.
Indicadores de fuerza en mujeres jóvenes con diferente tasa de fuerza relativa

Objetivo: Comparar la tasa de fuerza relativa (TFR) con distintos indicadores de fuerza en mujeres jóvenes. Métodos: Se evaluaron a 146 mujeres que se distribuyeron en tres grupos de acuerdo con los resultados de la TFR obtenida en el ejercicio de sentadilla y se compararon los resultados obtenidos en las pruebas de Fuerza prensil de la mano derecha e izquierda (FPMD- FPMI), Fuerza isométrica miembros inferiores (FIMI), Fuerza máxima de pecho (FMP), Fuerza máxima en sentadilla (FMS) Velocidad de desplazamiento sobre treinta metros (V30), altura del salto en (CMJ), potencia de pedaleo (PP) y la velocidad media propulsiva de miembros superiores e inferiores (VMPMS-VMPMI) obtenida al 50%, 60%, 70% y 80% de una repetición máxima en sentadilla. Resultados: Se observaron diferencias significativas (p? 0,01) entre los grupos en la FMS, CMJ, V30, VMP y PP, y la mayoría de las variables presentaban la diferencia entre el G1 y G3 (p? 0,01).
Chance, Orientation, and Interpretation: Max Weber’s Neglected Probabilism and the Future of Social Theory

The image of Max Weber as an “interpretivist” cultural theorist of webs of significance that people use to cope with a meaningless world reigns largely unquestioned today. This article presents a different image of Weber’s sociology, where meaning does not transport actors over an abyss of meaninglessness but rather helps them navigate a world of Chance. Retrieving this concept from Weber’s late writings, we argue that the fundamental basis of the orders sociologists seek to understand is not chaos. Action is rendered interpretable, rather, to the extent persons orient themselves to possibilities and probabilities, which remain real but unknowable. In this framework, interpretation and probability are allies, not antagonists. Weber’s systematic use of the probabilistic notion of Chance as a central resource for concept formation and sociological explanation seriously challenges current understandings of probability as a purely statistical, atheoretical concern. We outline the conceptual difference it makes when basic categories of sociology are understood as rooted in Chance, and we point to the larger implications of Weber’s probabilism for contemporary debates around issues of prediction, action, and interpretation.
Association between Cardiorespiratory Fitness and Metabolic Syndrome in Korean Older Adults

Background: Little is known about the relationship between non-exercise-based estimation of cardiorespiratory fitness (eCRF) and metabolic syndrome (MetS) in Korea. The current study examined the prognostic role of eCRF in the risk stratification of MetS in a representative sample of Korean older adults (1822 men and 3069 women). Methods: The data used in the current study were extracted from the Korea National Health and Nutrition Examination Surveys IV and V. eCRF was obtained using a previously validated algorithm. MetS was defined according to the National Cholesterol Education Program definition with the acceptance of a Korean-specific waist circumference cutoff point. Results: Lower eCRF was significantly correlated with abnormalities in several components of MetS, including abdominal obesity, elevated glucose, elevated triglycerides, and decreased high-density lipoprotein cholesterol. Furthermore, there was an inverse linear relationship between MetS prevalence and eCRF levels; higher eCRF was significantly and independently associated with lower prevalence of MetS. Conclusion: The current findings suggest that eCRF can be adopted as a prognostic measure in determining the risk for MetS for elderly persons.
Behavioural Classification of Cattle Using Neck-Mounted Accelerometer-Equipped Collars

Monitoring and classification of dairy cattle behaviours is essential for optimising milk yields. Early detection of illness, days before the critical conditions occur, together with automatic detection of the onset of oestrus cycles is crucial for obviating prolonged cattle treatments and improving the pregnancy rates. Accelerometer-based sensor systems are becoming increasingly popular, as they are automatically providing information about key cattle behaviours such as the level of restlessness and the time spent ruminating and eating, proxy measurements that indicate the onset of heat events and overall welfare, at an individual animal level. This paper reports on an approach to the development of algorithms that classify key cattle states based on a systematic dimensionality reduction process through two feature selection techniques. These are based on Mutual Information and Backward Feature Elimination and applied on knowledge-specific and generic time-series extracted from raw accelerometer data. The extracted features are then used to train classification models based on a Hidden Markov Model, Linear Discriminant Analysis and Partial Least Squares Discriminant Analysis. The proposed feature engineering methodology permits model deployment within the computing and memory restrictions imposed by operational settings. The models were based on measurement data from 18 steers, each animal equipped with an accelerometer-based neck-mounted collar and muzzle-mounted halter, the latter providing the truthing data. A total of 42 time-series features were initially extracted and the trade-off between model performance, computational complexity and memory footprint was explored. Results show that the classification model that best balances performance and computation complexity is based on Linear Discriminant Analysis using features selected through Backward Feature Elimination. The final model requires 1.83 ± 1.00 ms to perform feature extraction with 0.05 ± 0.01 ms for inference with an overall balanced accuracy of 0.83.
An Approach to Improve the Spatial Resolution and Accuracy of AMSR2 Passive Microwave Snow Depth Product Using Machine Learning in Northeast China

Snow cover plays a highly critical role in the global water cycle and energy exchange. Accurate snow depth (SD) data are important for research on hydrologic processes, climate change, and natural disaster prediction. However, existing passive microwave (PMW) SD products have high uncertainty in Northeast China owing to their coarse spatial resolution. Surface environment parameters should also be considered to reduce errors in existing SD products. Otherwise, it is difficult to accurately capture snow spatiotemporal variations, especially in a complex environment (e.g., mountain or forests areas). To improve the inversion accuracy and spatial resolution of existing SD products in Northeast China, a multifactor SD downscaling model was developed by combining PMW SD data from the AMSR2 sensor, optical snow cover extent data, and surface environmental parameters to produce fine scale (500 m × 500 m) and high precision SD data. Validations at 98 ground meteorological stations show that the developed model greatly improved the spatial resolution and inversion accuracy of the raw AMSR2 SD product; its root-mean-square error (RMSE) reduced from 26.15 cm of the raw product to 7.58 cm, and the correlation coefficient (R) increased from 0.39 to 0.53. For other SD products (WESTDC and FY), the multifactor SD downscaling model still has good applicability, it could further improve the performance of the WESTDC and FY SD products in time and space and achieve better inversion accuracy than raw SD products. Furthermore, the proposed model exhibited good agreement with the observed SD data in a field quadrat (3 km × 2 km) within the fine scale, with an error ranging between −2 and 2 cm. Compared with the existing downscaling methods, the proposed model presented the best performance.
Long Non-Coding RNA-Based Functional Prediction Reveals Novel Targets in Notch-Upregulated Ovarian Cancer

Simple Summary The study contributes to our understanding of the role of lncRNAs in the regulation of Notch signaling and their target genes in ovarian cancer. These lncRNAs were identified with in silico analyses using The Cancer Genome Atlas and the results were validated using our transcriptome data from the in vitro NOTCH1/3 silencing experiments and the qRT-PCR analysis, using tissue samples from patients with ovarian tumors. Finally, to improve our understanding of the complexity of Notch signaling, we identified master transcriptional factors which might orchestrate the upregulation of Notch-related lncRNAs and coding-genes in ovarian cancer. Abstract Notch signaling is a druggable target in high-grade serous ovarian cancers; however, its complexity is not clearly understood. Recent revelations of the biological roles of lncRNAs have led to an increased interest in the oncogenic action of lncRNAs in various cancers. In this study, we performed in silico analyses using The Cancer Genome Atlas data to discover novel Notch-related lncRNAs and validated our transcriptome data via NOTCH1/3 silencing in serous ovarian cancer cells. The expression of novel Notch-related lncRNAs was down-regulated by a Notch inhibitor and was upregulated in high-grade serous ovarian cancers, compared to benign or borderline ovarian tumors. Functionally, Notch-related lncRNAs were tightly linked to Notch-related changes in diverse gene expressions. Notably, genes related to DNA repair and spermatogenesis showed specific correlations with Notch-related lncRNAs. Master transcription factors, including EGR1, CTCF, GABPα, and E2F4 might orchestrate the upregulation of Notch-related lncRNAs, along with the associated genes. The discovery of Notch-related lncRNAs significantly contributes to our understanding of the complex crosstalk of Notch signaling with other oncogenic pathways at the transcriptional level.
A Contemplation on Music Recommendation Systems Based on Emotion Detection

Rise of music streaming platforms have attracted large number of users. This increase in the userbase has given birth to competitive market and competition to pull more number of users by providing quality service. Quality of service on these streaming platforms can be achieved by sensing the user needs and customizing the dashboards or playlist as per their need. This responsibility of customized recommendation lies on the recommendor system, an integral part of streaming platforms. In the absence of an effective recommender system, users have to waste lot of time in finding what they want, sometimes this is very frustrating and may lead to loss in revenue. It is found that “Emotion” play an important role in user music preferences, yet there is very little work done in this sector. In this paper, we have discussed taxonomy of a recommender system, critically analyzed the prominent existing models and have proposed a new hybrid model. The proposed model is an amalgamation of emotion detected from face, lyrical recommendation system and users history.
Impact of Molecule Concentration, Diffusion Rates and Surface Passivation on Single-Molecule Fluorescence Studies in Solution

For single-molecule studies in solution, very small concentrations of dye-labelled molecules are employed in order to achieve single-molecule sensitivity. In typical studies with confocal microscopes, often concentrations in the pico-molar regime are required. For various applications that make use of single-molecule Förster resonance energy transfer (smFRET) or two-color coincidence detection (TCCD), the molecule concentration must be set explicitly to targeted values and furthermore needs to be stable over a period of several hours. As a consequence, specific demands must be imposed on the surface passivation of the cover slides during the measurements. The aim of having only one molecule in the detection volume at the time is not only affected by the absolute molecule concentration, but also by the rate of diffusion. Therefore, we discuss approaches to control and to measure absolute molecule concentrations. Furthermore, we introduce an approach to calculate the probability of chance coincidence events and demonstrate that measurements with challenging smFRET samples require a strict limit of maximal sample concentrations in order to produce meaningful results.
Magnetic Nano-Platform Enhanced iPSC-Derived Trabecular Meshwork Delivery and Tracking Efficiency

Purpose Transplantation of stem cells to remodel the trabecular meshwork (TM) has become a new option for restoring aqueous humor dynamics and intraocular pressure homeostasis in glaucoma. In this study, we aimed to design a nanoparticle to label induced pluripotent stem cell (iPSC)-derived TM and improve the delivery accuracy and in vivo tracking efficiency. Methods PLGA-SPIO-Cypate (PSC) NPs were designed with polylactic acid-glycolic acid (PLGA) polymers as the backbone, superparamagnetic iron oxide (SPIO) nanoparticles, and near-infrared (NIR) dye cypate. In vitro assessment of cytotoxicity, iron content after NPs labeling, and the dual-model monitor was performed on mouse iPSC-derived TM (miPSC-TM) cells, as well as immortalized and primary human TM cells. Cell function after labeling, the delivery accuracy, in vivo tracking efficiency, and its effect on lowering IOP were evaluated following miPSC-TM transplantation in mice. Results Initial in vitro experiments showed that a single-time nanoparticles incubation was sufficient to label iPSC-derived TM and was not related to any change in both cell viability and fate. Subsequent in vivo evaluation revealed that the use of this nanoparticle not only improves the delivery accuracy of the transplanted cells in live animals but also benefits the dual-model tracking in the long term. More importantly, the use of the magnet triggers a temporary enhancement in the effectiveness of cell-based therapy in alleviating the pathologies associated with glaucoma. Conclusion This study provided a promising approach for enhancing both the delivery and in vivo tracking efficiency of the transplanted cells, which facilitates the clinical translation of stem cell-based therapy for glaucoma.
Comparative Study of ZnO Nanomaterials Synthesized by Green and Electrospinning Methods

This article presents a comparative study between the ZnO nanocone and nanofibers. ZnO nanocones were synthesized through the green route, using Azadirachta indica leaf extract media, and ZnO nanofibers were synthesized by the electrospinning process. The microstructural parameters of the prepared nanomaterials were investigated using powder X-ray diffractometer (XRD) and Rietveld refinement analysis. The XRD patterns confirmed the formation of single-phase ZnO with hexagonal wurtzite structure having an average crystallite size of 21 nm and 54 nm for the conical and fibrous nanoparticles, respectively. The field emission scanning electron microscopy revealed that the mean radius of nanofibers was 25-30 nm while the mean height and mean base radius of nanocones were 181 nm and 91 nm respectively. Elastic properties were estimated using elastic compliances S11 (6.0678×10-12 m2N-1), S12 (-2.2602×10-12 m2N-1), S13 (-1.3579×10-12 m2N-1), S33 (5.5196×10-12 m2N-1) and S44 (22.6833×10-12 m2N-1) which were calculated using the THERMO-PW code, based on the density functional theory. The calculated elastic constants of the two nanostructures yielded similar values as expected. However, the elastic limit of the two nanostructures differs due to their morphological anomaly. Moreover, the optical bandgap of nanofibrous ZnO was lower than that of nanoconical ZnO.
In Silico Multi-Target Approach Revealed Potential Lead Compounds as Scaffold for the Synthesis of Chemical Analogues Targeting SARS-CoV-2

Simple Summary COVID-19 is an infectious disease caused by SARS-CoV-2. The virus has rapidly spread to humans, causing the ongoing coronavirus pandemic. Enormous progress in finding therapies has been made, but an effective therapy is still absent. In this study, we propose a computational strategy aimed at identifying novel multi-target scaffolds against the virus. The proposed study was performed using bioinformatics methods, such as homology modeling, virtual screening and classical molecular dynamics, where 3D structures of targets were reconstructed and compounds with potential inhibitory activity against different virus targets were identified. Furthermore, a potential mechanism of action was proposed. Our study could provide new insights and approaches for the rapid identification of novel multi-target inhibitors of SARS-CoV-2. Abstract Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), an infectious disease that spreads rapidly in humans. In March 2020, the World Health Organization (WHO) declared a COVID-19 pandemic. Identifying a multi-target-directed ligand approach would open up new opportunities for drug discovery to combat COVID-19. The aim of this work was to perform a virtual screening of an exclusive chemical library of about 1700 molecules containing both pharmacologically active compounds and synthetic intermediates to propose potential protein inhibitors for use against SARS-CoV-2. In silico analysis showed that our compounds triggered an interaction network with key residues of the SARS-CoV-2 spike protein (S-protein), blocking trimer formation and interaction with the human receptor hACE2, as well as with the main 3C-like protease (3CLpro), inhibiting their biological function. Our data may represent a step forward in the search for potential new chemotherapeutic agents for the treatment of COVID-19.
The Relationship between Alexithymia and Mental Health Is Fully Mediated by Anxiety and Depression in Patients with Psoriasis

Background: Psoriasis is a common skin disease that affects quality of life, especially mental health. Alexithymia has been considered a relevant feature in psoriasis patients. Moreover, psoriasis was found to be associated with negative psychological health, including anxiety and depression. As the pathways linking alexithymia and mental health remain unclear among patients with psoriasis, we aimed to examine the mediating role of anxiety and depression in the relationship between alexithymia and mental health in these patients. Methods: To explore our variables of interest, we used the Toronto Alexithymia Scale (TAS-20), the 12-Item Short Form Health Survey (SF-12), and the Hospital Anxiety and Depression Scale (HADS). Results: Forty-four percent of patients were alexithymic and reported higher anxiety and depression, and lower quality of life compared to non-alexithymic patients. Alexithymic patients also had lower educational attainment. A correlation analysis showed positive associations between alexithymia and both anxiety and depression, whereas mental and physical health were negatively associated with alexithymia. Moreover, anxiety and depression fully mediated the relationship between alexithymia and mental health. Conclusions: Our findings highlight the importance of assessing alexithymia and psychological distress in clinical practice to identify vulnerable patients and to implement interventions aimed at improving negative emotional states.
Effects of Non-Invasive Radiofrequency Diathermy in Pelvic Floor Disorders: A Systematic Review

Background and Objectives: In recent years, the use of radiofrequency diathermy in pelvic floor disorders has grown proportionally to the interest in this specialty. Despite the common use of this therapy among pelvic floor physiotherapists, little is known about its effects and effectiveness in pelvic floor disorders. For this reason, the aim of the present review is to assess the effects of non-invasive 300 kHz–1 MHz radiofrequency diathermy in the treatment of pelvic floor disorders. Materials and Methods: A literature search was performed in PubMed, Scopus and Web of Science, searching for any type of study that included pelvic floor disorder participants and an experimental group treated with non-invasive nor ablative radiofrequency diathermy. Results: There were a total of 578 studies after removing duplicates. The inclusion and exclusion criteria were applied, resulting in a total of 15 studies, which were methodologically assessed with PEDro and the Newcastle and Ottawa scale. Conclusions: Despite the low quality of most of them, the studies showed improvements in urinary incontinence, pelvic pain conditions, pelvic floor muscles strength and sexual function. These findings must be considered with caution until more randomized clinical trials are performed to solve the biases detected.
Characteristics of SARS-CoV-2 Seropositivity among Emergency Department Healthcare Workers at a Tertiary Care Center in Baltimore

Early in the COVID-19 pandemic (March–July 2020 in Baltimore), emergency department (ED) healthcare workers (HCWs) were considered to be at greater risk of contracting SARS-CoV-2. Limited data existed, however, on the prevalence of SARS-CoV-2 infection and its impact in this workforce population. We enrolled 191 ED HCWs from a tertiary academic center, administered baseline and weekly surveys, and tested them twice (July and December 2020) for serum antibodies against SARS-CoV-2 spike protein. Approximately 6% (11 of 191, 5.8%) of ED HCWs had spike antibodies in July, a prevalence that doubled by December (21 of 174, 12.1%). A positive PCR test was self-reported by 15 of 21 (71%) seropositive and 6 of 153 (4%) seronegative HCWs (p < 0.001). Of the total 27 HCWs who had antibodies and/or were PCR positive, none required hospitalization, 18 (67%) had a self-perceived COVID-19 illness, and 12 of the 18 reported symptoms. The median number of missed workdays was 8.5 (ranging from 2 to 21). While most seropositive ED HCWs who reported symptoms took work absences, none required hospitalization, indicating that COVID-19’s impact on staffing prior to vaccination was not as great as feared.