Miracle-dz/UniFAR-FTUs huggingface dataset:
Inflammasome Activity in the Skeletal Muscle and Heart of Rodent Models for Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is characterized by wasting of muscles that leads to difficulty moving and premature death, mainly from heart failure. Glucocorticoids are applied in the management of the disease, supporting the hypothesis that inflammation may be driver as well as target. However, the inflammatory mechanisms during progression of cardiac and skeletal muscle dysfunction are still not well characterized. Our objective was to characterize the inflammasomes in myocardial and skeletal muscle in rodent models of DMD. Gastrocnemius and heart samples were collected from mdx mice and DMDmdx rats (3 and 9–10 months). Inflammasome sensors and effectors were assessed by immunoblotting. Histology was used to assess leukocyte infiltration and fibrosis. In gastrocnemius, a tendency towards elevation of gasdermin D irrespective of the age of the animal was observed. The adaptor protein was elevated in the mdx mouse skeletal muscle and heart. Increased cleavage of the cytokines was observed in the skeletal muscle of the DMDmdx rats. Sensor or cytokine expression was not changed in the tissue samples of the mdx mice. In conclusion, inflammatory responses are distinct between the skeletal muscle and heart in relevant models of DMD. Inflammation tends to decrease over time, supporting the clinical observations that the efficacy of anti-inflammatory therapies might be more prominent in the early stage.
Improved Canny edge detection method based on self-adaptive threshold

The traditional Canny operator uses the global threshold method,but when the gray of the input image's background and foreground change largely,the global threshold method will lose some weak edge.Concerning this issue,an improved adaptive Canny operator was put forward.Firstly,the image was divided into blocks according to the gradient variance.Secondly,the threshold of the sub blocks was obtained by Otsu method.Then,the threshold matrix was got by interpolation.Finally,an improved edge connection algorithm was proposed to extract the edge with the threshold matrix.The experimental results show that the improved Canny algorithm has not only a good anti-noise function,but also a very good precision.
Developments of a Cross-Correlation Calculation Algorithm for Gas Temperature Distributions Based on TDLAS

Abstract >> Most of reconstruction algorithms for the calculation of temperature distributions in CT (computedtomography)-TDLAS (tunable diode laser absorption spectroscopy) are based upon two-line thermometry method.This method gives unstable calculation convergence due to signal noise, bias error, and signal mis-matches. Inthis study, a new reconstruction algorithm based on cross-correlation for temperature calculation is proposed. The patterns of the optical signals at all wave lengths were used to reconstruct the temperature distribution. Numericaltest has been made using phantom temperature distributions. Using these phantom temperature data, absorptionspectra for all wave lengths were constructed, and these spectra were regarded as the signals that would be obtainedin an actual experiments. Using these virtually generated experimental signals, temperature distribution was onceagain reconstructed, and was compared with those of the original phantom data. Calculation errors obtained bythe newly proposed algorithm were slightly large at high temperatures with small errors at low temperature.Key words : CT(컴퓨터토모그래피), TDLAS(레이저흡수분광법), Cross-Correlation(상호상관), Numerical Test(수치적 시험), Phantom Temperature(가상 온도)
The course and prognostic value of tumor stiffness detected by ultrasound elastography for transarterial chemoembolization of hepatocellular carcinoma

Background Transarterial chemoembolization (TACE) is recommended as the first-line treatment in intermediate-stage patients with hepatocellular carcinoma (HCC) or as a palliative treatment modality in advanced patients. However, tumor control usually requires multiple TACE interventions due to the presence of residual and recurrent lesions. Elastography can provide information about tumor stiffness (TS) to predict tumor residual or recurrence. In this study, we aimed to analyze the effects of TACE on HCC stiffness using ultrasound elastography (US-E). We investigated whether quantifying TS using US-E could predict the recurrence of HCC. Methods This retrospective cohort study included 116 patients undergoing TACE to treat HCC. US-E was performed to measure the tumor’s elastic modulus within 3 days before TACE, in the 2 days after the intervention, and at the 1-month follow-up. The known prognostic factors of HCC were also analyzed. Results The average TS before TACE was 40.1±14.36 kPa, and the average TS 1 month after TACE was 19.3±9.80 kPa. The mean progression-free survival (PFS) was 39.129 months, and the 1-, 3-, and 5-year PFS rates were 81.0%, 56.9%, and 37.9%, respectively. The mean overall survival (OS) was 48.552 months, and the 1-, 3-, and 5-year OS rates of patients with malignant hepatic tumors were 95.7%, 75.0%, and 49.1%, respectively. Tumor number, tumor location, TS before TACE, and TS 1 month after TACE were significant predictive factors for OS (P=0.02, P=0.03, P<0.001, and P<0.001, respectively). Rank correlation analysis and linear regression revealed that a higher TS before or 1 month after TACE was negatively associated with PFS. The reduction ratio in TS before and 1 month after therapy was positively associated with PFS. The optimal cutoff TS value was set at 46 and 24.5 kPa before and 1 month after TACE according to the optimal Youden index. Kaplan-Meier survival analyses demonstrated that the 2 groups had significant differences in OS and PFS and that a higher TS was positively correlated with OS and PFS. Conclusions Our results verify that US-E provides additional information to characterize the tumoral stiffness of HCC. These findings indicate that US-E is a valuable tool for evaluating the tumor response after TACE therapy in patients. TS can also be an independent prognostic factor. Patients with a high TS had a higher risk of recurrence and a worse survival time.
Resumption of postpartum reproductive cyclicity and pregnancy rates after treatment with CIDR plus PGF2 α in normally calved and retained fetal membranes affected water buffaloes

An experiment was conducted to compare the estrus induction response, estrus pattern, endometrial regeneration and pregnancy rates following treatment with CIDR plus PGF2 α between normally calved (NC) and retained fetal membranes (RFM) affected water buffaloes. A total of 32 water buffaloes including 16 treated for RFM and 16 NC were selected and divided into 4 groups as I and II, and III and IV respectively. Buffaloes of group I and III were dewormed with Albendazole followed by the administration of mineral mixture in feed for 15 days. Buffaloes of all the groups were treated with CIDR intravaginally for 9 days and they received PGF2 α at 24 hours prior to CIDR withdrawal. After CIDR removal, the buffaloes of all the groups were artificially inseminated during induced estrus. Those buffaloes returned to estrus following breeding at induced estrus, were artificially inseminated twice at an interval of 24 hours. The estrus response following CIDR withdrawal was 100%. The RFM affected buffaloes had significantly longer interval to onset of estrus and shorter duration of estrus than NC buffaloes. More pronounced intensity of estrus following CIDR treatment was observed in NC buffaloes than in RFM affected buffaloes. The mean pregnancy rates obtained in NC and RFM affected buffaloes were 81.25% and 50%. Complete loss of epithelial layer, degenerative changes of endometrium and atrophic changes in the endometrial glands were observed in RFM affected buffaloes before estrus induction. After treatment, RFM affected buffaloes showed clear regeneration of endometrium and its glandular acini. It is concluded that estrus induction using CIDR plus PGF2 α might help to augment the fertility in NC and RFM affected buffaloes at field level.
Real-World Treatment Intensity and Patterns in Patients With Myopic Choroidal Neovascularization: Common Data Model in Ophthalmology

Background A paucity of data addressing real-world treatment of myopic choroidal neovascularization (mCNV) in the era of anti-vascular endothelial growth factor (VEGF) drugs led us to investigate real-world treatment intensity and treatment patterns in patients with mCNV. Methods This is a retrospective, observational study using the Observational Medical Outcomes Partnership-Common Data Model database of treatment-naïve patients with mCNV over the 18-year study period (2003–2020). Outcomes were treatment intensity (time trends of total/average number of prescriptions, mean number of prescriptions in the first year and the second year after initiating treatment, proportion of patients with no treatment in the second year) and treatment patterns (subsequent patterns of treatment according to the initial treatment). Results Our final cohort included 94 patients with at-least 1-year observation period. Overall, 96.8% of patients received anti-VEGF drugs as first-line treatment, with most of injections from bevacizumab. The number of anti-VEGF injections in each calendar year showed an increasing trend over time; however, there was a drop in the mean number of injections in the second year compared to the first year from 2.09 to 0.47. About 77% of patients did not receive any treatment in their second year of treatment regardless of drugs. Most of patients (86.2%) followed non-switching monotherapy only and bevacizumab was the most popular choice either in the first-line (68.1%) or in the second-line (53.8%) of treatment. Aflibercept was increasingly used as the first-line treatment for patients with mCNV. Conclusion Anti-VEGF drugs have become the treatment of choice and second-line treatment for mCNV over the past decade. Anti-VEGF drugs are effective for the treatment of mCNV as the non-switching monotherapy is the main treatment regimen in most cases and the number of treatments decreases significantly in the second year of treatment.
Development of Sharing Economy Business Model for Company Sustainability: A Case Study on Gojek

The definition of the sharing economy is still unclear and causes inconsistent research contributions and the application of the concept of the sharing economy business model. The research focused on developing sharing economy business model that supported sharing economy for company sustainability. The research approach was a qualitative research, the object was data related to the sharing economy business model on company sustainability. The sample data were collected from consumers’ comments and reviews on the Gojek application on Google Playstore. The data used were 23.888 reviews. Then, Bigram was used as text processing and language developed by the system to identify topics in the comment data using Latent Dirichlet Allocation (LDA) topic modeling to determine trending topics. The results show that the sharing economy business model have an important role in company sustainability. There are important elements, such as type of platform, shared practice, place of interaction, review system, and revenue stream. The research also provides additional contributions to scientific development in the managerial field. It is expected to support the sharing economy theory and business model. Last, the research results can support and prove that the sustainable company is good at implementing the sharing economy business model for company sustainability.
Excitons and Cooper Pairs: Two Composite Bosons in Many-Body Physics

PART I: EXCITONS PART II: COOPER PAIRS PART III: PARTICLES RELATED TO EXCITONS PART IV: BOSONIC CONDENSATION APPENDIX A : SOME MATHEMATICAL RESULTS APPENDIX B : THE SECOND QUANTIZATION FORMALISM APPENDIX C : THE HAMILTONIAN FOR WANNIER EXCITONS APPENDIX D : THE VALENCE ELECTRON OPERATOR VERSUS THE HOLE OPERATOR APPENDIX E : "THE COBOSON BIBLE" APPENDIX F : DIRECT COULOMB SCATTERING FOR WANNIER EXCITONS APPENDIX G : CONCERNING N GROUND-STATE WANNIER EXCITONS APPENDIX H : PHOTON-SEMICONDUCTOR INTERACTION APPENDIX I : PHOTON-EXCITON INTERACTION REFERENCES INDEX
Fanning In Honey Bees—a Comparison BetweenMeasurement And Calculation Of Non-stationaryAerodynamic Forces

Fanning bees (Apis mellifica carnica) stand on the landing platform at the entrance to their hive and, by beating their wings, produce a current of air that serves to air-condition the hive. They remain up to 5 min on the same spot, so that their regular wing movements present a state of ‘flight’ suitable for study. The three-dimensional kinematic data and the velocity of airflow as well as the produced lift and thrust can be measured directly from individual fanning bees without having to alter their natural behaviour by fixing them. These experimental results allow an assessment of the aerodynamic model calculations which take measured parameters of movement and airflow into account. The reduced frequency, the high amplitude of the wing beat and the form of the trajectory suggest non-stationary aerodynamics. Thus a quasi-stationary and an non-stationary model were tested. The drag and lift coefficients required for quasi-stationary calculations were obtained from the polars of fresh specimens. Nevertheless the quasi-stationary results show that this formulation is not sufficient to explain the resultant aerodynamic forces. The Improved Method of Discrete Vortices was applied for non-stationary calculations. It was possible to model the non-stationary airflow around the virtual oscillating wing (2D), the wake and the resulting aerodynamic forces. The results correspond satisfactorily to the measured values. Based on the simulated vortex pattern and the way the lines of equal pressure and velocities develop it was possible to analyse the effect of inertia, vortices in the wake and the circulation around the wings.
Efficiently enumerating frequent itemsets using combinatorial number system

ABSTRACT In this paper we establish novel theoretical framework for frequent itemset mining problem, as well as new and efficient join-based algorithm. The algorithm maintains one-dimensional array rank, starting from k=2nd iteration. The algorithm uses combinatorial number schema to map k-candidates to indexes of array. At the index r of the array, the algorithm stores RANKC of rth candidate in the lexicographic order, so , where is the rth candidate in lexicographic order in iteration k. Having in hand rank array, the algorithm will join two candidates iff their ranks are equal, making join an operation. Also, we believe that candidate ranking by combinatorial number system can be effectively integrated into pattern-growth algorithms, that are state of the art in frequent itemset mining, and additionally improve their performances.
Distortion measures for speech processing

Several properties, interrelations, and interpretations are developed for various speech spectral distortion measures. The principle results are 1) the development of notions of relative strength and equivalence of the various distortion measures both in a mathematical sense corresponding to subjective equivalence and in a coding sense when used in minimum distortion or nearest neighbor speech processing systems; 2) the demonstration that the Itakura-Saito and related distortion measures possess a property similar to the triangle inequality when used in nearest neighbor systems such as quantization and cluster analysis; and 3) that the Itakura-Saito and normalized model distortion measures yield efficient computation algorithms for generalized centroids or minimum distortion points of groups or clusters of speech frames, an important computation in both classical cluster analysis techniques and in algorithms for optimal quantizer design. We also argue that the Itakura-Saito and related distortions are well-suited computationally, mathematically, and intuitively for such applications.
J. A. C. Brown: Early Economic Modelling and Applied Econometrics in the UK

Abstract This paper describes J. A. C. (Alan) Brown’s contribution to early economic modelling and applied econometrics in the UK. This involved pioneering work, including the early application of programmable computers to linear programming and optimal diets, demand analysis (including Engel curves and probit analysis), and large-scale economic modelling which integrates National Income accounting methods, input-output matrices, and demand projections. His joint book on the lognormal distribution, its application to a range of economic contexts, and associated estimation problems, written with John Aitchison, continues to be widely cited. His influence, through his undergraduate teaching and graduate supervision, is also shown to be of much value.
Estimating Soil Salinity Using Multiple Spectral Indexes and Machine Learning Algorithm in Songnen Plain, China

Soil salinization is one of the most critical threats to land degradation in arid and semiarid regions. This study is based on machine learning using Landsat 8 operational land imager (OLI) imagery to estimate soil salinity in Da'an City. A total of 19 spectral indexes, including 15 salinity indexes, 3 vegetation indexes, and a brightness index, were calculated using the blue, green, red, and near-infrared bands of Landsat 8 OLI images. Four machine learning regression algorithms, namely Cubist, support vector regression, random forest regression, and extreme gradient boosting regression, were used on the basis of the 19 aforementioned indexes to estimate soil salinity. Results demonstrated that the Cubist model has the highest prediction accuracy (RMSE = 0.31 mS/cm). Thus, the spatial distribution of soil salinity based on the Cubist model best meets the expectations of the authors. Moreover, the canopy salinity index correlated the most with the measured electrical conductivity, demonstrating a correlation coefficient of −0.44. After using the random forest method for variable screening, the Cubist model based on nine spectral indexes still achieved satisfactory prediction accuracy with the RMSE of 0.34 mS/cm. Thus, the Cubist method is recommended for soil salinity monitoring in arid and semiarid areas.
Cancer survival in the United States 2007–2016: Results from the National Program of Cancer Registries

Background Cancer survival has improved for the most common cancers. However, less improvement and lower survival has been observed in some groups perhaps due to differential access to cancer care including prevention, screening, diagnosis, and treatment. Methods To further understand contemporary relative cancer survival (one- and five- year), we used survival data from CDC’s National Program of Cancer Registries (NPCR) for cancers diagnosed during 2007–2016. We examined overall relative cancer survival by sex, race and ethnicity, age, and county-level metropolitan and non-metropolitan status. Relative cancer survival by metropolitan and non-metropolitan status was further examined by sex, race and ethnicity, age, and cancer type. Results Among persons with cancer diagnosed during 2007–2016 the overall one-year and five-year relative survival was 80.6% and 67.4%, respectively. One-year relative survival for persons living in metropolitan counties was 81.1% and 77.8% among persons living in non-metropolitan counties. We found that persons who lived in non-metropolitan counties had lower survival than those who lived in metropolitan counties, and this difference persisted across sex, race and ethnicity, age, and most cancer types. Conclusion Further examination of the differences in cancer survival by cancer type or other characteristics might be helpful for identifying potential interventions, such as programs that target screening and early detection or strategies to improve access to high quality cancer treatment and follow-up care, that could improve long-term outcomes. Impact This analysis provided a high-level overview of contemporary cancer survival in the United States.
COVID‐19 in Morocco's region: Observational study of prevalence in symptomatic adults using the PANBIOS® rapid antigen test September 2021

Rapid antigen tests have emerged to deal with the COVID‐19 pandemic. Rapid diagnosis of SARS‐CoV‐2 infection is essential to reduce the spread of the disease. The aim of this study was to estimate the prevalence of COVID‐19 infection and test the sensitivity and specificity in Temara‐Skhirat in symptomatic adults through PANBIOS® test.
Rapid physiological and transcriptomic changes associated with oxygen delivery in larval anemonefish suggest a role in adaptation to life on hypoxic coral reefs

Connectivity of coral reef fish populations relies on successful dispersal of a pelagic larval phase. Pelagic larvae must exhibit high swimming abilities to overcome ocean and reef currents, but once settling onto the reef, larvae transition to endure habitats that become hypoxic at night. Therefore, coral reef fish larvae must rapidly and dramatically shift their physiology over a short period of time. Taking an integrative, physiological approach, using swimming respirometry, and examining hypoxia tolerance and transcriptomics, we show that larvae of cinnamon anemonefish (Amphiprion melanopus) rapidly transition between “physiological extremes” at the end of their larval phase. Daily measurements of swimming larval anemonefish over their entire early development show that they initially have very high mass-specific oxygen uptake rates. However, oxygen uptake rates decrease midway through the larval phase. This occurs in conjunction with a switch in haemoglobin gene expression and increased expression of myoglobin, cytoglobin, and neuroglobin, which may all contribute to the observed increase in hypoxia tolerance. Our findings indicate that critical ontogenetic changes in the gene expression of oxygen-binding proteins may underpin the physiological mechanisms needed for successful larval recruitment to reefs.
Factors associated with mortality in patients hospitalized for COVID-19 admitted to a tertiary hospital in Lambayeque, Peru, during the first wave of the pandemic

Introduction COVID-19 caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has spread worldwide, becoming a long-term pandemic. Objectives To analyze the factors associated with mortality in patients hospitalized for COVID-19 in a tertiary hospital in the Lambayeque region of Peru. Methods A retrospective cohort study of patients with a diagnosis of COVID-19, hospitalized in a hospital in northern Peru, was conducted from March to September 2020. Results Of the 297 patients studied, 69% were women, the mean age was 63.99 years (SD = ±15.33 years). Hypertension was the most frequent comorbidity (36.67%), followed by diabetes mellitus (24.67%) and obesity (8.33%). The probability of survival at 3 days of ICU stay was 65.3%, at 7 days 24.2%, and 0% on day 14. Risk factors associated with mortality in patients hospitalized for COVID-19 are age, male sex, tachypnea, low systolic blood pressure, low peripheral oxygen saturation, impaired renal function, elevated IL-6 and elevated D-dimer. Conclusions Mortality in hospitalized patients with COVID-19 was 51.18 per 100 persons, Mortality was found to be associated with hypertension, type of infiltrating, and sepsis.