Miracle-dz/UniFAR-FTUs huggingface dataset:
Sugammadex and anaphylaxis: An analysis of 33 published cases

In this study, the published sugammadex-induced anaphylaxis reports were reviewed to determine similarities in their presentation during anesthesia. PubMed was searched for sugammadex-induced anaphylaxis without time limitation. Reports were evaluated if they were in English and met the criteria of anaphylaxis determined by the World Allergy Organization. Two independent reviewers extracted and assessed the data using predesigned data collection forms. In total, 23 suitable articles were found and 33 sugammadex-induced anaphylaxis cases were included in the study. The mean age was 43.09 years (from 3–89 years) and 17 (51.5%) of the patients were female. Considering all reported cases, the average onset time of anaphylaxis was 3.08 min, with a median of 3 min (range 1–8 min). The most common signs and symptoms were hypotension, tachycardia, erythema, and desaturation. Of the 20 patients who underwent confirmatory skin testing, 15 had a positive skin reaction for sugammadex. Epinephrine was not given when indicated in about 25% of cases. Sugammadex-induced anaphylaxis onset time was less than 5 min in 92.3% of all the reported cases. Rapid diagnosis and early recognition of signs and symptoms of anaphylaxis are essential for a favorable prognosis. Treatment needs to be started as soon as possible to ensure the best outcome for the patient.
Intravenous magnesium sulfate and isoxsuprine for arrest of preterm labor: A comparative study

Background: Despite advances in obstetrics and neonatal care, the rate of incidence of preterm births continues to increase. Use of tocolytic agents such as magnesium sulfate and isoxsuprine could help in arresting preterm labor. Considering the paucity in studies comparing these two agents, a comparative analysis is obligatory. Objective: The objective of the study was to compare the safety, efficacy, and success rate of magnesium sulfate and isoxsuprine in the arrest of preterm labor. Methodology: Eighty-two antenatal women belonging to 28–37 weeks of gestational age, with regular uterine contractions, cervical dilatation (≤3 cm), and <50% cervical effacement admitted with complaints of preterm labor pain were randomly allocated into two groups with 41 participants in each group. Group 1 received 40 mg isoxsuprine for 24 h and later, isoxsuprine capsule 40 mg was given orally twice a day for 7 days, and Group 2 received 4 g magnesium sulfate for 12 h if uterine quiescence was achieved by the end of 2 h. Demographic data, medical history, and clinical data were collected. The outcome variables measured included side effects and the success rate (effacement% and cervical dilatation). Statistical analysis was performed using R software (Version 3.6.0). Results: The majority of the patients in both the groups with <25% effacement had successful tocolysis (P > 0.05). In Group 2, patients with <1 cm of cervical dilatation had successful tocolysis compared to Group 1 (P < 0.05). A highly significant association was observed between the percentage of effacement and cervical dilation, successful tocolysis (P < 0.001). Tachycardia and hypertension were observed more in Group 1, whereas nausea and vomiting were common in Group 2. The overall success rate was better in Group 2 (85.37%) compared to Group 1 (65.85%). Conclusion: Magnesium sulfate was slightly more effective in arresting preterm labor with lesser side effects as compared to isoxsuprine.
READ , LOEWENSTEIN AND RABIN

When making many choices, a person can broadly bracket them by assessing the consequences of all of them taken together, or narrowly bracket them by making each choice in isolation. We integrate research conducted in a wide range of decision contexts which shows that choice bracketing is an important determinant of behavior. Because broad bracketing allows people to take into account all the consequences of their actions, it generally leads to choices that yield higher utility. The evidence that we review, however, shows that people often fail to bracket broadly when it would be feasible for them to do so. In addition to documenting the diverse effects of bracketing, we also discuss factors that determine whether people bracket narrowly or broadly. We conclude with a discussion of normative aspects of bracketing and argue that there are some situations in which narrower bracketing results in superior decision making. If we do not accustom ourselves to look upon war, and the single campaigns in a war, as a chain which is all composed of battles strung together, one of which always brings on another; if we adopt the idea that the taking of a certain geographical point, the occupation of an undefended province, is in itself anything; then we are very likely to regard it as an acquisition which we may retain; and if we look at it so, and not as a term in the whole series of events, we do not ask ourselves whether this possession may not lead to greater disadvantages hereafter. Clausewitz, On War
How to Attract Physicians to Underserved Areas? Policy Recommendations from a Structural Model

Abstract This paper exploits location choices of all generalist physicians who graduated in Brazil between 2001 and 2013 to study policies aimed at increasing the supply of physicians in underserved areas. We set up and estimate a supply and demand model for physicians. We estimate physicians' locational preferences using a random coefficients discrete choice model. The demand has private establishments competing for physicians with private and public facilities around the country. Policy counterfactuals indicate that quotas in medical schools for students born in underserved areas and the opening of vacancies in medical schools in deprived areas are more cost-effective than financial incentives.
Stroke Lesion Outcome Prediction Based on 4D CT Perfusion Data Using Temporal Convolutional Networks

Acute ischemic stroke is caused by a blockage in the cerebral arteries, resulting in long-term disability and sometimes death. To determine the optimal treatment strategy, a patient-speciﬁc assessment is often based on advanced neuroimaging data, such as spatio-temporal (4D) CT Perfusion (CTP) imaging. To date, perfusion maps are typically calculated from 4D CTP data and then thresholded to localize and quantify the stroke lesion core and tissue-at-risk. A few studies have recently developed deep learning methods to predict stroke lesion outcomes from perfusion maps. The basic idea of these is to train a model, using perfusion maps acquired at baseline and their corresponding follow-up images acquired several days after treatment, to automatically estimate the ﬁnal lesion location and volume in new patients. Nevertheless, model training based on the original 4D CTP scans might be desirable, as they could contain more valuable information not directly represented in perfusion maps. Therefore, we aimed to develop and evaluate a temporal convolutional neural network (TCN) to predict stroke lesion outcomes directly from 4D CTP datasets acquired at admission, without computing any perfusion maps. Using a total of 176 CTP scans, we investigated the impact of the time window size by training the proposed TCN on various numbers of CTP frames: 8, 16, and 32 time points. For comparison purposes, we also trained a convolutional neural network based on perfusion maps. The results show that the model trained on 32 time points yielded signiﬁcantly higher Dice values (0.33 ± 0.21) than the models trained on 8 time points (0.25 ± 0.20; P < 0.05), 16 time points (0.28 ± 0.21; P < 0.001), and perfusion maps (0.23 ± 0.18; P < 0.05). These experiments demonstrate that the proposed model eﬀectively extracts spatio-temporal data from CTP scans to predict stroke lesion outcomes, which leads to better results than using perfusion maps.
Cell Line by Phorbol Ester and Hormones Expression in a Chemically Transformed Rat Hepatic Epithelial Messenger RNA α Regulation of Transforming Growth Factor Updated

Transforming growth factor a (TGF-a) is produced by many trans formed cells, but little is known about the regulation of its expression. We examined TGF-a mRNA levels in a set of cloned neoplastic cell lines derived by chemical transformation of a normal rat liver epithelial cell. The untransformed parental cell line, WB-344, did not express a detect able level of TGF-a mRNA, whereas GPÃ3ac,a transformed line capable of autonomous growth in soft agar, expressed TGF-a. When GP6ac cells were treated with agents thought to regulate protein kinase C activity, e.g., the tumor promoter, 12-O-tetradecanoylphorbol-13-acetate (TPA), TGF-a mRNA levels increased by 8to 11-fold. The induction of TGFÂ»mRNA was detectable at 2 h, was maximal at 8-12 h, and declined by 24 h. Angiotensin, bradykinin, epinephrine, and epidermal growth factor also increased TGF-a mRNA by 2to 5-fold. In contrast, parental WB cells neither expressed TGF-a mRNA, nor responded to TPA. TPA also increased epidermal growth factor receptor mRNA in GP6ac cells but the effect was less prolonged; maximal levels were seen at 4 h after TPA exposure and returned to control levels by 12 h. TPA increased TGF-a mRNA in GP6ac cells, in part, by increasing transcription of the TGF'-a gene as measured by run-on transcription rates in isolated nuclei. In addition, the induction of TGF-a by TPA was blocked by concurrent incubation with agents that inhibit protein synthe sis. However, if TPA was present for at least 2 h, subsequent addition of cycloheximide enhanced the effect of TPA. This indicates that the induction of TGF-a in GP6ac cells is comprised of at least two phases demarcated by the requirement for protein synthesis. The time course of induction and the sensitivity to inhibition of protein synthesis distinguish the effect of TPA on TGF'-a mRNA from that of other genes regulated by TPA, e.g., c-myc and c-fos. These data also suggest that chemical transformation of rat liver epithelial cells leads to expression of TGF-a mRNA, and that once expressed, TGF-a mRNA can be modulated in a protein kinase C-dependent manner.
DISCOURSE DISCOURSE MONOLOGUE DIALOGUE

Gracie: Oh yeah. and then Mr. and Mrs. Jones were having mat rimonial trouble, and my brother was hired to watch Mrs. Jone s. George: Well, I imagine she was a very attractive woman. Gracie: She was, and my brother watched her day and night for s ix months. George: Well, what happened? Gracie: She finally got a divorce. George: Mrs. Jones? Gracie: No, my brother’s wife. George Burns and Gracie Allen in The Salesgirl
Holistic Diagnosis Tool for Early Detection of Breast Cancer

Globally, of all cancer diseases, breast cancer is the number one killer in women. The diseases commonly occur in high income countries, but recently there is rapid increase of breast cancer in middle and low income countries in Asia, Latin America and Africa. This is due to increase in life expectancy, increased urbanization and adoption of western cultures. Although, some strategies to reduce the risks of occurrence of breast cancer are being implemented in developed countries, the case in middle and low income countries is that majority of breast cancer patients are affected by the disease due to diagnosis at late stages of the disease. Therefore, early detection of breast cancer is needed to overcome this problem. In this paper, a holistic diagnosis tool for early detection of breast cancer is proposed. The tool is software based utilizing a novel prediction model for breast cancer survivability developed by using available data mining (DM) technologies. Specifically, five popular data mining algorithms (logistic regression, decision tree, support vector machine, K nearest neighbors and random forest) were used to develop the prediction tool using Wisconsin breast cancer data set. In the paper, prediction tool training and test set results are reported. Achieved from the reported work of training sets are classification accuracies of 100% (Decision Tree); 99.8046% (Random Forest); 97.46% (Logistic Regression and Support Vector Machine); 97.07% (K Nearest Neighbors) and for testing sets are classification accuracies of 93.5672% (Decision Tree); 92.9% (Random Forest); 92.39% (Logistic Regression, Support Vector Machine and K Nearest Neighbors). These results are much better than those reported in the literature. The results show that the proposed DM disease prediction tool has potential to greatly impact on current patient management, care and future interventions against the breast cancer disease and through customization even against other deadly diseases.
Influence of Water Flotation Technique on Seed and Seedling Quality Characteristics of Sesamum indicum.

The present Studies made with gingelly to upgrade the quality of seeds based on that specific gravity using water floatation technique seeds revealed that specific gravity grading using water improved the seed quality of gingelly with respect to seed recovery and seed quality characters. I. Introduction Sesame seed is an important source of edible oil. It is often considered as the "queen" of vegetable oils. More than 80 per cent of the crop is cultivated in kharif and a small quantity is cultivated in rabi . Therefore, sesame seed is available throughout the year in the country. Homoginising the heterogeneous seed lot is the prime aim of any post harvest seed management techniques. The homogenisation is mainly based on physical characters, as it could be easily adoptable. Sesamum is a crop expressing all possible variation in physical character such as weight, colour and size necessitating the need for processing the seeds before sowing.
Characterization of Turkish-Style Boiled Coffee Aroma by Gas Chromatography and Mass Spectrometry and Descriptive Analysis Techniques

The characteristic volatiles of light, medium, and dark roasted Turkish-style boiled coffee brews were studied by headspace solid-phase micro extraction/gas chromatography-mass spectrometry and sensory flavor profile analysis. Sixty-five volatile compounds, including furans, pyrazines, pyrroles, phenols, pyridines, ketones, oximes, alcohols, aldehydes, thiophenes, benzenes, and terpenes, were isolated from Turkish-style boiled coffee samples. The flavor profile analysis showed Turkish coffee brews as roasted/burnt, spicy, bitter, acidic, sweet, salty, astringent (dry), woody, fermented, earthy, and tobacco-like flavor characteristics. It was concluded that Turkish coffee was rich in furans, pyrazines, pyrroles, and phenols giving its roasted/burnt, spicy, woody, and fermented flavor characteristics.
MERCURY, ZINC, CADMIUM UPTAKE ABILITY AND POLYPEPTIDE PROFILE OF Cr-RESISTANT PSEUDOMONAS Spp UNDER THESE STRESSES

Three chromium resistant strains Pseudomonas CrT-8, Pseudomonas Crm-8, and Pseudomonas S-3, isolated by [1]Faisal and Hasnain (1998) were used to check their resistance against mercury, zinc and cadmium and their uptake ability of these metals was evaluated. Mercury, zinc and cadmium was estimated in pellet (take up/accumulation), washing of pellet (loosely bound/adsorbed) and supernatant (left over/unavailable) of these strains. Mercury, zinc and cadmium uptake ability of these strains was checked. Mercury uptake ability of Pseudomonas CrT-8 and Pseudomonas S-3 was greater than Pseudomonas Crm-8. All the strains have great ability to volatilize the mercury except Pseudomonas CrT-8. These strains can uptake zinc. Pseudomonas CrT-8 and Pseudomonas S-3 can uptake cadmium but in low amount. Pseudomonas Crm-8 cannot tolerate CdCl2. Protein analysis of these strains under metallic stresses revealed that they synthesized new polypeptides with different metals. In Pseudomonas CrT-8 polypeptides of 36KD, 30KD, 26KD and 8KD with Zn and polypeptides of 48KD, 44KD and 15KD with Hg were synthesized. Pseudomonas Crm-8 two new protein bands of 45KD and 30KD were appeared.
Learning Interclass Relations for Intravenous Contrast Phase Classification in CT

In classiﬁcation, categories are typically treated as independent of one-another. In many problems, however, this neglects the natural relations that exist between categories, which are often dictated by an underlying biological or physical process. In this work, we propose novel formulations of the classiﬁcation problem, aimed at reintroducing class relations into the training process. We demonstrate the beneﬁt of these approaches for the classiﬁcation of intravenous contrast enhancement phase in CT images, an important task in the medical imaging domain. First, we propose manual ways reintroduce knowledge about problem-speciﬁc interclass relations into the training process. Second, we propose a general approach to jointly learn categorical label representations that can implicitly encode natural interclass relations, alleviating the need for strong prior assumptions or knowledge. We show that these improvements are most signiﬁcant for smaller training sets, typical in the medical imaging domain where access to large amounts of labelled data is often not trivial.
A Literature Survey on Recommendation Systems for Scientific Articles

Researchers consume a lot of time searching for relevant resources for their research. This slows down the research process and reduces the researchers’ productivity. Nowadays, Recommender Systems play an important role in facilitating to the users finding what they are looking for. This study discusses the use of Recommender Systems to enhance the research process. Relying on the fact that research topics in a specified field are related, we use semantics to represent their relationships in terms of their relevance to the field. The objective is to develop a framework for semantic-based Recommender System for scientific articles, integrating the concepts of Semantic web (Ontologies) and Recommender Systems (semantic-based). To fulfill this objective, we first mention the problems that face the research nowadays and then we show a survey of the existing recommender systems in different fields and in the field of Recommendation of scientific articles.
SWNet: Surgical Workflow Recognition with Deep Convolutional Network

Surgical workﬂow recognition has been playing an essential role in computer-assisted interventional systems for modern operating rooms. In this paper, we present a computer vision-based method named SWNet that focuses on utilizing spatial information and temporal information from the surgical video to achieve surgical workﬂow recognition. As the ﬁrst step, we utilize Interaction-Preserved Channel-Separated Convolutional Network (IP-CSN) to extract features that contain spatial information and local temporal information from the surgical video through segments. Secondly, we train a Multi-Stage Temporal Convolutional Network (MS-TCN) with those extracted features to capture global temporal information from the full surgical video. Finally, by utilizing Prior Knowledge Noise Filtering (PKNF), prediction noise from the output of MS-TCN is ﬁltered. We evaluate SWNet for Sleeve Gastrectomy surgical workﬂow recognition. SWNet achieves 90% frame-level accuracy and reaches a weighted Jaccard Score of 0.8256. This demonstrates that SWNet has considerable potential to solve the surgical workﬂow recognition problem.
Rational Veriﬁcation: A Progress Report 1

We provide a survey of the state of the art of rational veriﬁcation : the prob-8 lem of checking whether a given temporal logic formula ϕ is satisﬁed in some or all 9 game theoretic equilibrium computations of a multi-agent system – that is, whether 10 the system will exhibit the behaviour ϕ represents under the assumption that agents 11 within the system act rationally in pursuit of their preferences. After motivating and 12 introducing the overall framework of rational veriﬁcation, we discuss key results ob-13 tained in the past few years as well as relevant related work in logic, AI, and Computer 14 Science. 15
R & D AND LICENSING . BUILDING VALUE THROUGH INTELLECTUAL ASSETS

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Insights to Existing Techniques of Subspace Clustering in High-Dimensional Data

— With an increasing attention towards the significance of increasing data sizes, the area of clustering is still under the focus of the researchers. There has been extensive research work from more than a decade in this regards and still the problems of understanding the data with a shape of business logic is yet to be seen. Hence, this manuscript gives the snapshots of the problems associated with high-dimensional data and cluster analysis that finally leads to discussion of Subspace clustering problem. It has been seen that large scale of research attempt is dedicated more towards subspace clustering process and quite less for dimensionality reduction process. The manuscript reviews the existing technique and draws a research gap of the existing literatures. Finally, the manuscript highlights a conceptual framework in order to overcome the problems associated with the subspace clustering problem
Flexible Dynamic Linking

Portability of programs between run-time environments is regarded as a desirable feature by programmers and users. In a modern virtual machine like the JVM, portability is supported by large standard libraries. A program that uses an interface from a standard library should be portable to any run-time environment that provides an implementation of the interface. Because different implementations of an interface have different names, a programmer who desires portability must write code to select an implementation. This introduces software engineering costs and type-safety concerns. We believe that dynamic linking is well-suited to providing portability automatically. Linking traditionally requires that types from the compile-time environment are present in the run-time environment. We suggest “flexible” dynamic linking that relaxes a program’s dependencies on compile-time types in a type-safe and language-independent way, thus providing portability to different run-time environments. Bytecode is annotated with type variables rather than type names, and a non-deterministic model substitutes type variables with type names during linking. We interleave linking with execution, as in the JVM, and examine how linking builds a well-formed program. We prove soundness for any substitution strategy, representing execution with an arbitrary degree of eagerness/laziness in linking. We then explore portability in the Common Language Infrastructure (CLI). We formalise its component, linking and execution models to show the flexibility of existing link-time mechanisms, and how linking varies between CLI implementations like . NET, Mono and Rotor. The CLI’s reflection mechanism is often used by programmers to facilitate portability, but reflection introduces many complexities into linking and execution that we formalise accurately. Then, as an alternative to reflection, we design flexible dynamic linking for the CLI and implement it in the Rotor virtual machine. In this scheme, a programmer controls portability by identifying potential substitutions explicitly, thus losing the generality of reflection but ensuring predictability and type-safety.