Miracle-dz/UniFAR-FTUs huggingface dataset:
Biochemical and Nutritional Impact of Celery and Turnip Leaves on Induced Obese by High Fat Diet (HFD)

The high fiber diet is reported to have substantial health benefits such as anti-obesity. So, this study evaluated the effect of celery and turnip leaves on high fat diet (HFD) induced-obese rats. Sprague-Dawley male rats aged 4 weeks old divided 6 groups: G1, fed on basal diet and G2 fed on HFD during the nutritional experimental period, G3 fed on HFD with oral administrated water extract of celery, G4 fed on HFD with oral administrated water extract of turnip leaves, G5 and G6 fed on HFD containing fresh blanch of celery and turnip leaves (as 5% fiber content), respectively for 7 weeks. Finally, at the end of experimental period the blood samples were collected. Rats were weighted, killed and organs were removed. Histopathological and adipose tissue tests were evaluated. Results: Generally, the results showed that the treated rats by celery and turnip leaves had significant decrease in body weight gain and feed intake compared to positive control. Also, celery and turnip leaves had significant decrease in TG, TC, LDL-C and VLDL-C.Similarly, celery and turnip leaves caused reductions in the atherogenic index and coronary risk index (AI and CRI). The liver and kidney functions were decreased when rats fed on HFD with celery and turnip leaves compared to positive control. And rats fed on celery and turnip leaves diets had significant increase in fasting insulin concentration compared to positive control. Concerning histopathological findings; the HFD group had a high changes in liver and kidney. The rats fed on HFD with water extract of both celery and turnip leaves had normal aorta, while some changes in liver and kidney were detected. The rats fed on HFD with 5% as fiber from turnip leaves had a few changes in liver and kidney. The rats feeding on HFD with fresh blanch celery had the lowest weight of total adipose tissue mass and no. of pad cell. Conclusion: using vegetables caused decrease in serum lipid profile, loss body weight, reductions in AI, CRIand prevent the accumulation of white adipose tissue (WAT) in rats. Also, it is a good sources of fiber and bioactive compounds which using as functional compounds.
A simple way to estimate the median time and compare survival distributions in analgesic trials under informative censoring.

We discuss the problem of estimating the median time and comparison of survival curves when data are nonrandomly censored in analgesic trials. In these trials patients experience post-surgical pain at the time of randomization. Time to onset of analgesia is measured by patient-administered stopwatches. An effective analgesic is one for which the median time to onset is “short. ” The study design allows patients to remedicate if their pain persists, and this remedication prior to pain relief censors the time-to-onset measures. The time to onset for patients who remedicate is nonrandomly censored. Assuming noninformative censoring yields misleading results with the Kaplan-Meier method (for estimation of median time) and the log-rank test (for comparison of survival curves). This assumption can also obscure the superior effect of an effective analgesic over an ineffective one. We propose a simple and intuitive way to handle the nonrandomly censored data in analgesic trials in order to (a) estimate the median .
Automated Task-Informed Document Retrieval on the COVID-19 Open Research Dataset Using Topic Modeling

The COVID-19 Open Research Dataset (CORD-19), a fast growing collection of biomedical research literature, was made available in March 2020 as an effort to facilitate research addressing the COVID-19 pandemic, along with 17 tasks specifying the research questions of interest. We propose an automated, task-informed document retrieval framework for CORD-19 that leverages latent factors learned through topic models to select a set of research articles most relevant to the tasks at hand. Compared to na¨ıve keyword-based approaches, our approach broadens the qualiﬁcation of relevance from the presence of speciﬁc terms to the activity of latent topics. We show that our approach provides an overlapping yet notably different set of selections, as the latent factors account for meaningful document-wise co-occurrences that individual keywords fail to capture. Upon both qualitative and quantitative examination of retrieval results, we further provide recommendations regarding task creation intended for unsupervised document retrieval in large, heterogeneous, natural language datasets.
A metabolic life table exemplified by sockeye salmon

A metabolic life table (MLT) is a combination of energy budget and life table that quantifies metabolism and life history over an entire life cycle. It provides a conceptual framework for integrating data on physiology, demography and ecology that are usually the subject of discipline- and taxon-specific studies. Our MLT for sockeye salmon revives John Brett’s classic data on metabolism, growth, survival and reproduction to provide a synthetic analysis of metabolic performance and its life history consequences. The MLT quantifies the energy budget of an average female over her life cycle. The early stages in fresh water have low rates of growth and mortality. Then juveniles enter the ocean, feed voraciously, grow rapidly, and accumulate a store of biomass; More than 98% of the total lifetime assimilation and growth occurs in the ocean. Maturing adults stop feeding, return to fresh water, expend stored body energy to fuel migration and reproduction, and leave a clutch of eggs and a depleted carcass. The MLT also quantifies the energetic contribution of salmon to freshwater and marine ecosystems. Salmon are very efficient: of the food energy in their zooplankton prey, 47% is expended on respiration to fuel activity, 23% is allocated to growth and reproduction to produce biomass, and 30% is excreted in feces. Although 96% of lifetime biomass production occurs in the ocean, about 29% is transported into fresh water in the bodies of maturing adults, where their carcasses and gametes provide an important “marine subsidy” to the energy and nutrient budgets of freshwater and terrestrial ecosystems. The MLT highlights some features of the salmon metabolism – variation in rates of assimilation, respiration and production with body size and temperature – that are qualitatively similar to other ectotherms and predictions of metabolic theory. But because of the unusual physiology, life history, ecosystem impacts and socio-economic importance of wild-caught salmon, reanalyzing Brett’s data in the context of a MLT has additional broad applications for basic and applied ecology.
A combined pipeline for quantitative analysis of human brain cytoarchitecture

The 3D analysis of the human brain architecture at cellular resolution is still a big challenge. In this work, we propose a pipeline that solves the problem of performing neuronal mapping in large human brain samples at micrometer resolution. First, we introduce the SWITCH/TDE protocol: a robust methodology to clear and label human brain tissue. Then, we implement the 2.5D method based on a Convolutional Neural Network, to automatically detect and segment all neurons. Our method proved to be highly versatile and was applied successfully on specimens from different areas of the cortex originating from different subjects (young, adult and elderly, both healthy and pathological). We quantitatively evaluate the density and, more importantly, the mean volume of the thousands of neurons identified within the specimens. In conclusion, our pipeline makes it possible to study the structural organization of the brain and expands the histopathological studies to the third dimension.
Contributions to Multivariate Analysis due to C. R. Rao and Associated Developments

C. R. Rao has made various significant contributions to multivariate analysis. Among them, we consider the following topics: (i) Rao’s U -statistic in discriminant analysis, (ii) MANOVA tests, (iii) Rao’s F approximation, (iv) Growth curve analysis, and (v) Information criteria for the selection of variables. Some of these were introduced at the dawn of multivariate analysis. Under topic (v), we also discuss recent developments on the selection of variables in discriminant analysis. AMS 2000 Subject Classification: primary 62H15; secondary 62H10
Regularized Training of Convolutional Autoencoders using the Rényi-Stratonovich Value of Information

We propose an information-theoretic cost function or the regularized training of convolutional auto encoders that imposes an organization on the bottleneck-layer-projected samples so as to facilitate discrimination. This function is based on a continuous-space, Rényi-mutual-information version of Stratonovich’s value of information. It quantifies the maximum benefit that can be obtained for a given bottleneck-layer representation compression amount. The compression amount is controlled by a single hyperparameter that trades off between the autoencoder reconstruction quality and the hidden-layer representation uncertainty.
Spectroscopic classification of X-ray selected stars in the ρ Ophiuchi star-forming region and vicinity

AbstractWe present intermediate-resolution (FWHM∼1.5–2.6 A) optical spectra of 106 candidate optical counterparts of 77 X-ray sources detected in four pointed (t exp ≥ 8700 s) ROSAT PSPC observations of the ρ Ophiuchi star-forming region and vicinity. Using the spectral types and equivalent widths of Hα and Li iλ670. 8 nm obtained from our spectra, we applied spectroscopic criteria in order to classify our sample in different pre-main-sequence subtypes: ‘classical’ T Tauri stars (CTTS); ‘weak’ T Tauri stars (WTTS), and ‘post’ T Tauri stars (PTTS). A total of 10 CTTS, 43 WTTS and 6 PTTS were found among the PSPC-selected stars. Our results more than double the number of pre-main-sequence stars spectroscopically identified in the ρ Ophiuchi region.We considered regions with different molecular cloud properties: the central core and the outer ring of the ρ Ophiuchi dark cloud (L1688); the ‘streamers’ (L1709); the R7 clump; and the ‘smoke rings’. In the inner field of L1688, the ratio of WTTS over CTTS is ∼1:1, significantly smaller than in the other regions (4:1 in the outer ring of L1688 and 5:1 in the smoke rings). The WTTS/CTTS ratio in the R7 field is the highest of our survey (10:1). We argue that this could be a result of the UV radiation from the nearby massive binary ρ Oph AB, and/or of winds from the Upper Sco OB association, which might shorten the lifetime of the circumstellar discs of the low-mass stars.We find no PTTS in the inner field of our L1688 PSPC image, and only 3 PTTS in the outer ring despite the high sensitivity of our X-ray observations in this region. This result confirms that the central region of L1688 is extremely young (age < 5 Myr), as suggested by near-infrared surveys. The presence of a small number of PTTS scattered around the ρ Ophiuchi molecular clouds suggests that star formation may have been going on for 10 to 30 Myr, very slowly at first, but at a much higher rate for the last ∼10 Myr. We provide rough estimates of the star formation rate for the main ρ Ophiuchi molecular cloud complex.
Rolling Bearing Fault Diagnosis Based on Sensitive Feature Transfer Learning and Local Maximum Margin Criterion under Variable Working Condition

In real industrial scenarios, the working conditions of bearings are variable, and it is therefore difficult for data-driven diagnosis methods based on conventional machine-learning techniques to guarantee the desirable performance of diagnosis models, as the models assume that the distributions of both the training and testing data are the same. To enhance the performance of the fault diagnosis of bearings under different working conditions, a novel diagnosis framework inspired by feature extraction, transfer learning (TL), and feature dimensionality reduction is proposed in this work, and dual-tree complex wavelet packet transform (DTCWPT) is used for signal processing. Additionally, transferable sensitive feature selection by ReliefF and the sum of mean deviation (TSFSR) is proposed to reduce the redundant information of the original feature set, to select sensitive features for fault diagnosis, and to reduce the difference between the marginal distributions of the training and testing feature sets. Furthermore, a modified feature reduction method, the local maximum margin criterion (LMMC), is proposed to acquire low-dimensional mapping for high-dimensional feature spaces. Finally, bearing vibration signals collected from two test rigs are analyzed to demonstrate the adaptability, effectiveness, and practicability of the proposed diagnosis framework. The experimental results show that the proposed method can achieve high diagnosis accuracy and has significant potential benefits in industrial applications.
Freezing- and Drying-Induced Perturbations of Protein Structure and Mechanisms of Protein Protection by Stabilizing Additives

There are numerous unique, critical applications for proteins in human healthcare [1–3]. However, even the most promising and effective protein therapeutic will not be of benefit if its stability cannot be maintained during packaging, shipping, long-term storage, and administration. For ease of preparation and cost containment by the manufacturer, and ease of handling by the end-user, an aqueous protein solution often is the preferred formulation. However, proteins are readily denatured (often irreversibly) by the numerous stresses arising is solution, e.g., heating, agitation, freezing, pH changes, and exposure to interfaces or denaturants [4]. The result is usually inactive protein molecules and aggregates, which compromise clinical efficacy and increase the risk of adverse side effects (e.g., [5]). Even if its physical stability is maintained, a protein can be degraded by chemical reactions (e.g., hydrolysis and deamidation), many of which are mediated by water. Thus, inherent protein instability and/or the logistics of product
A Survey on Image Classification using Data Mining Techniques

— Image classification is one of the most useful and essential research field in computer vision domain. The growing demands for image classification in computer vision having application such as video surveillance, image and video retrieval, Web content analysis, biometrics etc. has pushed application developers to search ways to manage and classify images more efficiently. Classification includes image pre-processing, object detection, object segmentation, feature extraction and object classification. The main goal of image classification is to classifying image into different classes according to their visual characteristics. Recently there has been new trend to merge two or three field to take the advantage of each field. In this paper presents a survey on various image classifications by data mining techniques and image representation methods.
Is Expressed Mainly in Neurons

The olfactory epithelium is the only neuronal tissue capable of generating new neurons during adult life and hence must express genes responsible for this phenomenon. Therefore, we have used mRNA from immortalized olfactory epithelial cells to search for novel protein tyrosine kinases by polymerase chain reaction, using as primers conserved sequences from the catalytic domain of known kinase genes. A full-length complementary DNA clone corresponding to one such polymerase chain reaction product was isolated and sequenced. This complementary DNA, designated Kiz-1, encodes a protein containing two prominent domains; the NH2-terminal region contains a cysteine/histidinerich moiety previously identified as a zinc-finger domain in proteins of the LIM family, while the COOH-terminus contains a kinase domain. Kiz-1 is expressed mainly in the brain of adult mice but also in a range of cultured cell lines, regardless of their tissue of origin. Immunohistochemical studies on adult mouse brain demonstrated that Kiz-1 is expressed exclusively in neurons, not in astrocytes or oligodendrocytes. In the developing embryo, however, Kiz-1 is expressed in all tissues. In COS cells transfected with Kiz-1 complmentary DNA and in the immortalized olfactory epithelial cells, Kiz-1 was found mainly in the cytoplasm, but in neurons of the adult brain, it resided also in the nucleus. Two Kiz-1 mRNA species are expressed in cell lines as well as in the murine and human brain. One transcript lacks a region of 60 nucleotides, which lies within the catalytic domain of the kinase and is encoded by a separate exon. Our results suggest that Kiz-1 may play distinct roles in dividing cells and in differentiated neurons.
TITLE: Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study. Author Names:

Objective: To examine the capability of MRI texture analysis to differentiate the primary site of origin of brain metastases following a radiomics approach. Methods: Sixty-seven untreated brain metastases (BM) were found in 3D T1-weighted MRI of 38 patients with cancer: 27 from lung cancer, 23 from melanoma and 17 from breast cancer. These lesions were segmented in 2D and 3D to compare the discriminative power of 2D and 3D texture features. The images were quantized using different number of gray-levels to test the influence of quantization. Forty-three rotationinvariant texture features were examined. Feature selection and random forest classification were implemented within a nested cross-validation structure. Classification was evaluated with the area under receiver operating characteristic curve (AUC) considering two strategies: multiclass and one-versus-one. Results: In the multiclass approach, 3D texture features were more discriminative than 2D features. The best results were achieved for images quantized with 32 gray-levels (AUC = 0.873 ± 0.064) using the top four features provided by the feature selection method based on the p-value. In the one-versus-one approach, high accuracy was obtained when differentiating lung cancer BM from breast cancer BM (4 features, AUC = 0.963 ± 0.054) and melanoma BM (8 features, AUC = 0.936 ± 0.070) using the optimal dataset (3D features, 32 gray-levels). Classification of breast cancer and melanoma BM was unsatisfactory (AUC = 0.607 ± 0.180).
THE investigation of the relationship between the coach and teammates and the unity of the team in volleyball

A team, is a group in which have the same goals and priorities for which they work hard to achieve. With their own personal tactics and experiences, a coach is there to help them achieve the goals that are set. Thus, the coach is there to provide the team with influencing leadership. The teams’ unity and the leaders influence upon such a unity comes to be an important role for which we cannot deny. The research; taking place in 2012-2013 seasonal activities of The Volleyball 2 league (Male-Female) group competition of The Turkish University which from these teams 50 male and 44 female athletes have volunteered to participate in. In order to find out certain personal information about them the volunteers they have been given an “Personal Information Form”. Designed by W. Neil Wildmeyer, Lawrence R. Brawley, Albert W. Carron (1985) the “Individual Charm-Task”, “Individual Charm-Social”, “Group Unity-Task” and “Group Unity-Social” have been used as a subdimensional survey for Team Unity. In order to measure the coach’s leaded eristic characteristics the survey by Chelladurai and Saleh (1980), which consists of 40 substances “Leader Characteristics of Sports”, was used. The results was determined on a SSPS program packet level and for which the level of significance (P<0.05) was determined. As a result; the teams in which participated in the research, we can say they are in possession of a coach for which gives great importance to training and education and is in favor of democracy, shows them social support, and rewards their success but also shows authoritative characterizes when needed. Looking at the results of team unity we can say, according to gender and different experiences, understanding of the task group, the team's success is more important to the integrity of "Group integrity-task" higher understanding of the athletes to the success of teams in group shows in the foreground tasks.
Cardiovascular risk factors and clinical pattern in young Nepalese population with acute coronary syndrome presenting to a tertiary care center of Nepal

Background and objectives: Cardiovascular disease is a major health problem reaching epidemic proportions. Although Acute Coronary Syndrome (ACS) is an uncommon entity in the young, it constitutes a rising burden in the socioeconomic status of the country because of its impact on the economically productive age group. Early identification and control of the cardiovascular risk factors helps to prevent cases of Myocardial Infarction (MI) in young resulting in reduced health burden. Therefore, we aimed to assess clinical pattern and the prevalence of cardiovascular risk factors in the young and economically productive population of an underdeveloped country to lower the socioeconomic burden. Methods: A total of 60 patients presenting at the cardiology department of Manmohan Cardiothoracic Vascular and Transplant Center were included in our study after fulfilling the inclusion criteria. ACS was diagnosed by cardiac enzymes, electrocardiography, and echocardiography and coronary intervention was done in the cardiac catheterization room. Patients were followed up at 1 and 3-months and reassessed clinically and by echocardiography. Results: Mean age of presentation was 38.55 + 4.98 (SD) years. Over three-fourth of the patients were male (80%) and nearly three-quarter experienced ST-elevated MI (73%). Smoking was the most prevalent risk factor (67%), followed by dyslipidemia (40%), diabetes (32%), hypertension (30%), and obesity (13%). Most of the patients presented with single-vessel disease (65%), followed by double-vessel disease (18%), triple-vessel disease (12%), left main disease (3%), and minor coronary artery disease (2%). Conclusions: ACS in underdeveloped country is more common in male and single-vessel disease is the most common clinical pattern of ACS and smoking the most prevalent risk factor.
A deeper look at some properties of space-time covariance functions

We present space-time covariance models which satisfy two main features: (a) they may be negative or oscillate between negative and positive values, and (b) they would preferably allow for an easy interpretation. In particular, we show some properties of correlation models which can attain negative values, being at the same time compactly supported.