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Klebsiella, a gram-negative, rod-shaped bacteria that can cause different types of infections like enteritis, meningitis, urinary tract infections, bacteraemia, septicaemia etc. In humans Klebsiella pneumoniae is among the foremost common bacteria combatted by medical practitioners worldwide which are often resistant to several antibiotics and doesn’t get cured easily. To study the antibiotic resistance among patients, total 856 clinical isolates were collected from different clinical specimens of patients admitted in different wards of C. U. Shah Hospital, Surendranagar, Gujarat, India for a period of 3 years (2016 to 2018). The specimens were processed at Department of Microbiology, C. U. Shah Hospital, Surendranagar, with an objective to study the antibiotic resistance pattern of the Klebsiella Species. Antimicrobial resistance not only increases the healthcare costs, but it is also linked with the severity and death rates from certain infections.

With the increase in adoption of westernization and sedentary life style in low- and middle-income countries like India, there is a significant increase in breast cancer incidence rate. The exact cause of breast cancer is yet unknown and there are limited studies available on the risk factors associated with breast cancer. Thus, this research work is carried out with an aim to study the region-specific contributing risk factors playing role in breast cancer incidence and their prognosis. We also aimed to study rural-urban disparity in risk factors associated with female breast cancer patients of Saurastra region of Gujarat. This Cross Sectional, Single-Centre, Hospital-based study was conducted in Cancer Hospital at Rajkot, Gujarat India. One-to-one In-person interview of total 174 primary breast cancer patients were carried out and details were collected in pre-tested structured questionnaire. Data obtained from the patients were further analysed using Bürkner’s BRMS package (R) interface and SPSS. In our study, majority of patients are from rural background and its subsequent relationship with other relevant parameters is found to be significant (p less than 0.03). The age of patients ranged from 22 to 76 years and the mean age at diagnosis of the breast cancer was 51.5 years. A positive association is observed between risk of breast cancer and BMI for both pre- and post-menopausal rural and urban women. 37% females were having premenopausal status and 62% females were postmenopausal. Out of total postmenopausal patients, 31% had attained late menopause at the age of 51 years, while 13% females had experienced menarche at less than 12 years of age. Our data is first of its kind studying risk factors playing role in breast cancer incidence in Saurashtra region of Gujarat. Even though having limited patients representing the population this region-specific study is useful for cancer projection and to understand treatment modalities.

Breast cancer is the second most common malignancy related mortality observed in women worldwide. It is multifactorial in nature having many factors including genetics, lifestyle factors, socioeconomical etc. playing role in variable occurance and heterogeneity towards treatment. Routine diagnosis for breast cancer is usually done by mammography and histopathology, but recent adavnces suggests the role of certain biomarkers such as tumour specic hormones, serum proteins, serum enzymes etc as a supplementary parameters to predict overall prognosis of the patients. In the present study, relative analysis of the levels of ALP (alkaline phosphatase), LDH (lactate dehydrogenase) and Lipase enzyme is determined among breast cancer patients at the time of diagnosis using semi automated clinical biochemistry analyzer. further correlation is done with other socio-demographic factors prevalent among the patients. For this purpose, preoperative blood samples of breast cancer patients were collected from Rajkot Cancer Hospital and study concludes that higher expression of LDH was found in 50% of breast cancer patients, while 31.81% of breast cancer patients had higher expression levels of lipase and ALP enzyme among the study population.

Neurohormonal blockade (NHB) is the mainstay of therapy for patients with systolic heart failure (HF). However, the efficacy in patients with left ventricular assist devices (LVADs) remains unknown. Of all, 114 LVAD patients (57 [48, 65] years old and 78% male) were enrolled and followed during the early period (6 months after index discharge), and 98 were followed during the late period (6-12 months following index discharge). Of them, 46% were on beta-blocker (BB), 49% on angiotensin-converting enzyme inhibitor (ACEi) and/or angiotensin II receptor blocker (ARB), and 51% on aldosterone antagonist at baseline. Prevalence of BB and ACEi/ARB use increased during the study period. During the early period, similar event rates were found irrespective of the NHB uses. During the late period, BB was associated with reduced HF readmission, and ACEi/ARB was associated with reduced HF readmission and gastrointestinal bleeding (p < 0.05 for all). In conclusion, BB and ACEi/ARB use during the late period was associated with a reduction in HF recurrence in LVAD patients. Further prospective randomized control trials are warranted to clarify the utility of NHB therapy in LVAD patients.

A recent focus in academic library design is as a third place: a home from home. Research has yet to interrogate what it means to be “at home,, and if academic libraries are treated like, and feel like home to students. Seamon’s model provides a framework for understanding the qualities associated with homeness, across the five dimensions of rootedness, appropriation, regeneration, at-easeness, and warmth. Using this framework observations were made in two libraries using categories relating to “homeness.” It was found that students do act and feel at home in the library. Newer library designs did not facilitate homeness more than older designs. It is concluded that new library designs have the opportunity to make students feel at home by offering flexible spaces to make their own, places to retreat from their desk, and basic amenities.

Circular trading of goods is a carefully designed scam ubiquitous among fraudulent business dealers all around the world. Dealers involved in this scheme create an artificial trading network by issuing doctored sales-invoices amongst themselves without any movement of goods. In practice, it is observed that almost all cases of circular trade involve two or three dealers. Here, we work towards predicting circular trade involving three dealers. For the same, we built four different classification models consisting of feature variables tailored for predicting any plausible circular trade amongst three dealers. In particular, the logistic regression model gave the best performance among all the four different models with a prediction accuracy of 80%. Interestingly, we observe that a feature variable formed by using the personalised PageRank technique significantly improves the model over the state of the art link prediction variables. Predicting a future circular trade from a huge network of sales-transactions data is of significant importance to the tax enforcement officers. In addition to automating the process of detecting circular trading, which is manually impossible, this model helps them to target on a set of plausible evaders and take appropriate preventive measures. This model have been developed for the Commercial Taxes Department, Government of Telangana, India, using their first two quarter’s tax returns dataset.

Indirect taxation is a significant source of income for any nation. Tax evasion hinders the progress of a nation. It causes a substantial loss to the revenue of a country. We design a model based on variational graph autoencoders and clustering to identify taxpayers who are evading indirect tax by providing false information in their tax returns. We derive six correlation parameters (features) and three ratio parameters from the data submitted by taxpayers in their returns. We derive four latent features from these nine features using variational graph autoencoder and cluster taxpayers using these four latent features. We identify taxpayers located at the boundary of each cluster by using kernel density estimation, which is further investigated to single out tax evaders. We applied our method to the iron and steel taxpayers data set provided by the Commercial Taxes Department, the government of Telangana, India.

This paper discusses chain ratio type estimator for estimation of population mean in double sampling. The developed estimator uses two auxiliary variates associated with study variate in order to increases its efficiency. The developed estimator has been compared with usual unbiased estimator and other existing estimators. The expression for the bias and mean squared error of the developed estimator is obtained under large sample approximation. We have considered the natural population data set to examine the merits of the developed estimator and carried out the empirical study in support of theoretical findings. Numerical illustration shows that the proposed estimator is more efficient.

Tax evasion is most common in several nations. Taxpayers evade tax by using thoughtful and well-considered techniques, which hinders the economic progress of the nation. Delaying the filing of returns by taxpayers is the most primitive form of tax evasion. Taxpayers who delay the filing of returns are called return defaulters. It is the most brazen form of tax evasion. To tackle this problem, we introduce an example-dependent cost-sensitive deep learning model to identify potential return defaulters. This model takes example-dependent costs into account and makes predictions that aim to minimize the overall cost instead of minimizing the total number of misclassifications. Applying our method, we show cost savings of about 55%. This work is designed and implemented for the Commercial Taxes Department Government of Telangana, India.

Indirect taxation is a significant source of livelihood for any nation. Tax evasion inhibits the economic growth of a nation. It creates a substantial loss of much needed public revenue. We design a method to single out taxpayers who evade indirect tax by dodging their tax returns. Towards this, we derive six correlation parameters (features), three ratio parameters from tax return statements submitted by taxpayers, and another parameter based on the business interactions among taxpayers using the TrustRank algorithm. Then we perform spectral clustering on taxpayers using these ten parameters (features). We identify taxpayers located at the boundary of each cluster by using kernel density estimation, which are further investigated to single out tax evaders. We applied our method on the iron and steel taxpayer’s data set provided by the Commercial Taxes Department, Government of Telangana, India.

BACKGROUND: Despite routine use of echocardiographic parameters to evaluate the severity of cardiac amyloidosis (CA), this methodology has not been well validated. We developed a histopathologic schema for quantifying CA burden and evaluated its relationship with clinical outcomes. Additionally, echocardiographic parameters were tested as potential noninvasive indices of CA burden. METHODS: We retrospectively studied 59 patients with CA (17 light chain, 42 transthyretin) who underwent endomyocardial biopsies. Light microscopy with staining was used to categorize CA burden as mild-to-moderate (

This article details a piece of small‐scale action‐based research in a school for students with learning difficulties. It focusses on the use of Drama as a means whereby confidence of those students who are at a point of transition to employment can be enhanced. Using qualitative‐based mixed methods of data generation, a range of issues are highlighted relating to the challenges for students with SEND moving into employment. The value of Drama activities both intrinsically within a whole school curriculum, as well as a cross‐curricular intervention, is also noted. Finally, the article reflects on some of the key opportunities in undertaking research of this kind within a school‐based context. [ABSTRACT FROM AUTHOR]

Background Food allergies are increasingly prevalent in the pediatric population. Balancing allergen avoidance with the promotion of healthy eating behaviors can be challenging for families. Objective To characterize mealtime behaviors among parents of young children with food allergy. Methods Seventy-four parents of young children with food allergies (≤7 years of age) completed measures of mealtime behavior, perceptions of food allergy risk and severity, pediatric parenting stress, and food allergy–related quality of life. Mealtime behavior reports were compared with published data regarding typically developing children, young children with type 1 diabetes, and children with diagnosed feeding disorders (with or without related medical factors). Results Parents of young children with food allergies reported frequent mealtime concerns. Specifically, they reported significantly more mealtime behavioral concerns than typically developing peers, comparable mealtime behavioral concerns to young children with type 1 diabetes, and significantly fewer mealtime behavioral concerns than children with diagnosed feeding disorders. Parental mealtime concerns were positively correlated with other parent perceptions of food allergy, such as risk of allergen exposure, illness-related parenting stress, and food allergy–related quality of life. Conclusion Young children with food allergy and their parents are more likely to exhibit mealtime behavioral concerns than typically developing peers and their parents. Future research should investigate the effect of food allergies and maladaptive mealtime behaviors on children's nutrition to provide clinical guidelines for parents who may benefit from psychosocial and/or nutritional support.

Between March 21 and May 18 2020 we admitted 98 patients with COVID-19 to our ICU of whom 72 required invasive mechanical ventilation Of these, 3 patients required extracorporeal support: One V-V ECMO for respiratory failure, one V-V ECMO for tracheoesophageal fistula, and one V-AV ECMO for cardiogenic shock due to pulmonary thromboembolism, to which we had to add an extra return cannula to improve oxygenation Coagulation abnormalities have been described in COVID-19 with an increase in D-dimer, a modest decrease in platelet count, and delay in the prothrombin time (1) Pro-coagulant states however, may not be highlighted by a standard coagulation essay that's why we decided to assess them by ROTEM Even with an ACT level kept between 180-200s, we found a normal value of aPTT and a ROTEM profile consistent with hypercoagulability: An acceleration of blood clot formation (CFT below the lower limit in INTEM and EXTEM) and a significantly higher clot strength (MCF above the upper limit in INTEM and EXTEM in two patients and in FIBTEM in all patients) For this reason, we titrate heparin infusion to maintain INTEM CT in a range between normal and 100 sec longer for all the ECMO run, without thrombotic complications Despite the superiority of ROTEM to monitor anticoagulation in ECMO is not yet proven in literature, this test could help us to understand the coagulation profile of each patient and to titrate heparin where the coagulation function is greatly altered by the virus and by the circuit

Tax evasion is pervasive in most of the nations. It impedes the economic progress of the nation. In this paper, we devise a technique that detects groups of people who commit evasion in the goods and services tax by manipulating their sales and (or) purchases with the motivation to reduce their tax liability. In particular, we perform clustering analysis on certain sensitive parameters pertaining to each of the dealers. Then by analyzing the number of dealers across the different clusters, we identified the suspicious set of dealers who are further investigated by looking at other sensitive parameters. This work is designed and implemented for the Government of Telangana, India.

This paper explains an application of big data analytics to detect illegitimate transactions performed by fraudulent communities of people who are engaged in a notorious tax evasion practice called circular trading. We designed and implemented this technique for the commercial taxes department, government of Telangana, India. This problem is solved in two steps. In step one, the problem is formulated as detecting fraudulent communities in a social network, where the vertices correspond to dealers and edges correspond to sales transactions. In step two, specific type of cycles are removed from each fraudulent community, which were identified in step one, to detect the illegitimate transactions. We used RHadoop framework for implementing this technique.

The problem of tax evasion is as old as taxes itself. Tax evasion causes several problems that affects the growth of a nation. In this paper, we present our work in controlling tax evasion by using big data analytics, Android applications, and information technology. We implemented this work for the commercial taxes department, government of Telangana, India. Here we developed a complete software framework for scrutiny of suspicious accounts. This system detects suspicious dealers using certain sensitive parameters and standardizes the process of scrutiny of accounts. We used sophisticated statistical and machine learning tools to predict suspicious dealers. To increase the compliance levels, we developed a regression model for identifying return defaulters and user-friendly Android applications to assist the officers in collecting the tax. The other aspect we explored is the detection and analysis of a tax evasion mechanism, known as circular trading, using advanced algorithmic and social-network analytic techniques.

Tax evasion is an illegal activity where a taxpayer avoids paying his/her tax liability. Any taxpayer has to file their tax return statements periodically at regular intervals. Avoiding to file or delaying the filing of the tax return statement is one among the most basic methods of tax evasion. The taxpayers who are not filing returns or delaying the filing of returns are called return defaulters. Financial loss to the Government due to avoiding to file or delayed filing of returns varies between taxpayers. While designing any statistical model to predict return defaulters, we have to take into account the real financial loss associated with the misclassification. In this paper, we constructed an example dependent cost - sensitive logistic regression model that predicts whether a taxpayer is a potential return defaulter for the upcoming tax-filing period. While designing the model, we studied the effect of business interactions among the taxpayers on return filing behavior. We developed this model for the commercial taxes department, Government of Telangana, India. Applying our method to tax data, we show significant cost saving.

A book review of Aston, S. and Walsh, A. (eds). 2021. Library pedagogies: Personal reflections from library practitioners. Huddersfield: Innovative Libraries Press. Published in: Journal of Information Literacy, 15(2), pp.193-194.

Gas sensors based on polymeric membrane using cellulose acetate and amino acid ionic liquids (AAIL) were prepared using the phase inversion method. Initially, AAIL was prepared by the neutralisation method of amino acids L-Proline and L-Phenylalanine with the IL EMIM OH (base) to form ProIL and PheIL. The prepared membranes were characterized by Fourier transform infrared spectroscopy, scanning electron microscopy, thermogravimetric analysis, differential scanning calorimetry, atomic force microscopy. Asymmetric nature and finger-like macrovoids was confirmed using SEM and AFM. The synthesized cellulose acetate membrane incorporated with IL and AAILs was tested for the gas sensing by quantifying the change in resistance, in the absence and presence of the test gas inside a chamber. CA membranes with CA-ProIL displayed the best ammonia sensing with lowest value of 1 ppm detection and response and recovery times of 60 s and 78 s respectively. The results suggest that the CA membranes have great potential for gas sensing application and by incorporating IL, AAILs into the matrix it is further increased by a significant amount. Further, NMR analysis suggests the possible mechanism of adduct formation of ammonia with ProIL.

Tax evasion is as old as tax itself. In this paper, we devise a technique to predict the amount of tax-revenue lost by the state due to unscrupulous actions from a particular set of suspicious dealers. For the same, we build a regression model using the tax-return information of genuine business dealers and predict the amount of tax evaded by suspicious business dealers. Dealers are classified as genuine or suspicious by applying Benford's analysis on the different group of dealers formed after running k-medoids clustering algorithm over a set of dealers. In addition to getting an estimate on the loss of tax-revenue, results obtained from this work aid the tax enforcement officers on taking precautionary measures against tax evasion. The dataset used in the work is provided by the commercial tax department of Telangana state, India.