Bacterial ‘Cell’ Phones: Do cell phones carry potential pathogens?

Cell phones are important companions for professionals especially health care workers (HCWs) for better communication in hospital. The present study compared the nature of the growth of potentially pathogenic bacterial flora on cell phones in hospital and community. 75% cell phones from both the categories grew at least one potentially pathogenic organism. Cell phones from HCWs grew significantly more potential pathogens like MRSA (20%), Acinetobacter species (5%), Pseudomonas species (2.5%) as compared to the non HCWs. 97.5% HCWs use their cell phone in the hospital, 57.5% never cleaned their cell phone and 20% admitted that they did not wash their hands before or after attending patients, although majority (77.5%) knows that cell phones can have harmful colonization and act as vector for nosocomial infections. It is recommended, therefore, that cell phones in the hospital should be regularly decontaminated. Moreover, utmost emphasis needs to be paid to hand washing practices among HCWs

Deep Over-sampling Framework for Classifying Imbalanced Data

Class imbalance is a challenging issue in practical classification problems for deep learning models as well as traditional models. Traditionally successful countermeasures such as synthetic over-sampling have had limited success with complex, structured data handled by deep learning models. In this paper, we propose Deep Over-sampling (DOS), a framework for extending the synthetic over-sampling method to exploit the deep feature space acquired by a convolutional neural network (CNN). Its key feature is an explicit, supervised representation learning, for which the training data presents each raw input sample with a synthetic embedding target in the deep feature space, which is sampled from the linear subspace of in-class neighbors. We implement an iterative process of training the CNN and updating the targets, which induces smaller in-class variance among the embeddings, to increase the discriminative power of the deep representation. We present an empirical study using public benchmarks, which shows that the DOS framework not only counteracts class imbalance better than the existing method, but also improves the performance of the CNN in the standard, balanced settings

Comparison of Interactions Between Control and Mutant Macrophages

This paper presents a preliminary study on macrophages migration in Drosophila embryos, comparing two types of cells. The study is carried out by a framework called macrosight which analyses the movement and interaction of migrating macrophages. The framework incorporates a segmentation and tracking algorithm into analysing motion characteristics of cells after contact. In this particular study, the interactions between cells is characterised in the case of control embryos and Shot3 mutants, where the cells have been altered to suppress a specific protein, looking to understand what drives the movement. Statistical significance between control and mutant cells was found when comparing the direction of motion after contact in specific conditions. Such discoveries provide insights for future developments in combining biological experiments to computational analysis

A rare case of isoniazid induced sideroblastic anemia

Sideroblastic anemia is a rare cause of anemia. Most of it accounts for the genetic cause, while drug induced is still uncommon. Our patient, a 20 year old female, is a known case of right frontal tuberculoma on ATT presented with complaints of generalized weakness and loss of appetite. On evaluation, she was found to have severe anemia and bone marrow studies confirmed it to be sideroblastic anemia. On revisiting the history, it was noted that she was not taking pyridoxine supplements as advised along with antitubercular drugs. Our patient is one among the few documented cases of Isoniazid induced sideroblastic anemia.This case needs attention because it is a preventable cause of anemia and the clinicians need to be aware about the compliance of the patient with the supplementary drugs.

An Analysis of Strain in Chip Breaking Using Slipline Field Theory with Adhesion Friction at Chip/Tool Interface

Despite rapid growth in the applications of metal machining in manufacturing, a comprehensive analysis of the problem of chip control has always been a di±cult task. This is because of the complex mechanism of the chip formation process and a lack of knowledge of the factors that in°uence chip form/chip breakability under a given set of input machining conditions such as work material properties, tool geometry, chip breakers and cutting conditions. Consequently, the solution to the problem has been approached empirically with a limited degree of success. In the present investigation, an attempt has been made to examine chip breaking by a step-type chip breaker using the rigid-plastic slip-line ¯eld theory. Orthogonal machining is assumed and the deformation mode is analysed using the solutions pro- posed earlier by Kudo and Dewhurst. The rake face friction is represented by the adhesion friction law suggested by Maekawa et al. The ¯elds are constructed and analysed by the m..

Menstrual health management: Knowledge and practices among adolescent girls

Objectives: To assess the knowledge and attitude of adolescent girls regarding menstruation and menstrual hygiene.Material and Methods: After approval by the ethical committee , the study was conducted on 340 adolescent girls aged 12-19 years, belonging to an urban slum area of Delhi. This was a questionnaire based cross-sectional study conducted over a period of 9 months in a tertiary hospital. Confidentiality of the subjects was ensured.Results: Out of 340 girls 69 % were between 16-19 years. Source of information about menstruation in majority of the cases was mother (60%). Only 48% girls knew menstruation before menarche and 24% girls knew uterus as the organ for menstruation. 71 % girls used sterile sanitary napkins as absorbent. Satisfactory changing of pads (>2pads/day) was done by 68 % and 77% girls cleaned the genitalia satisfactorily (>2 times/day). 41% girls were aware of the fact that unhygienic use of pad could be a source of genital infection and 8% girls had associated vaginal discharge. Social restrictions during menses in the form of religious activities, drop out from school, avoiding certain foods etc were practiced in many families.Conclusion: Although the menstrual practices appear to be satisfactory in major percentage of girls but knowledge regarding menstrual hygiene is worrisome, as maximum girls are unaware of menarche and physiology of menstruation. Inclusion of such information in the school curriculum and wider coverage in mass media will help to bridge this gap.Keywords: Adolescence; hygiene; menstruation healt

MaaSim: A Liveability Simulation for Improving the Quality of Life in Cities

Urbanism is no longer planned on paper thanks to powerful models and 3D simulation platforms. However, current work is not open to the public and lacks an optimisation agent that could help in decision making. This paper describes the creation of an open-source simulation based on an existing Dutch liveability score with a built-in AI module. Features are selected using feature engineering and Random Forests. Then, a modified scoring function is built based on the former liveability classes. The score is predicted using Random Forest for regression and achieved a recall of 0.83 with 10-fold cross-validation. Afterwards, Exploratory Factor Analysis is applied to select the actions present in the model. The resulting indicators are divided into 5 groups, and 12 actions are generated. The performance of four optimisation algorithms is compared, namely NSGA-II, PAES, SPEA2 and eps-MOEA, on three established criteria of quality: cardinality, the spread of the solutions, spacing, and the resulting score and number of turns. Although all four algorithms show different strengths, eps-MOEA is selected to be the most suitable for this problem. Ultimately, the simulation incorporates the model and the selected AI module in a GUI written in the Kivy framework for Python. Tests performed on users show positive responses and encourage further initiatives towards joining technology and public applications.Comment: 16 page

A matter of words: NLP for quality evaluation of Wikipedia medical articles

Automatic quality evaluation of Web information is a task with many fields of applications and of great relevance, especially in critical domains like the medical one. We move from the intuition that the quality of content of medical Web documents is affected by features related with the specific domain. First, the usage of a specific vocabulary (Domain Informativeness); then, the adoption of specific codes (like those used in the infoboxes of Wikipedia articles) and the type of document (e.g., historical and technical ones). In this paper, we propose to leverage specific domain features to improve the results of the evaluation of Wikipedia medical articles. In particular, we evaluate the articles adopting an "actionable" model, whose features are related to the content of the articles, so that the model can also directly suggest strategies for improving a given article quality. We rely on Natural Language Processing (NLP) and dictionaries-based techniques in order to extract the bio-medical concepts in a text. We prove the effectiveness of our approach by classifying the medical articles of the Wikipedia Medicine Portal, which have been previously manually labeled by the Wiki Project team. The results of our experiments confirm that, by considering domain-oriented features, it is possible to obtain sensible improvements with respect to existing solutions, mainly for those articles that other approaches have less correctly classified. Other than being interesting by their own, the results call for further research in the area of domain specific features suitable for Web data quality assessment

Class Balanced Similarity-Based Instance Transfer Learning for Botnet Family Classification

The use of Transfer Learning algorithms for enhancing the performance of machine learning algorithms has gained attention over the last decade. In this paper we introduce an extension and evaluation of our novel approach Similarity Based Instance Transfer Learning (SBIT). The extended version is denoted Class Balanced SBIT (or CB-SBIT for short) because it ensures the dataset resulting after instance transfer does not contain class imbalance. We compare the performance of CB-SBIT against the original SBIT algorithm. In addition, we compare its performance against that of the classical Synthetic Minority Over-sampling Technique (SMOTE) using network tra ffic data. We also compare the performance of CB-SBIT against the performance of the open source transfer learning algorithm TransferBoost using text data. Our results show that CB-SBIT outperforms the original SBIT and SMOTE using varying sizes of network tra ffic data but falls short when compared to TransferBoost using text data

SARS-CoV-2: comparison of IgG levels at 9 months post second dose of vaccination in COVID-survivor and COVID-naïve healthcare workers

Background: Natural (asymptomatic/symptomatic COVID-19 infection) and artificial (vaccination) exposure to the pathogen represent two modes of acquiring active immunity. No definitive guidelines exist regarding whether COVID-survivors (with infection/re-infection/re-re-infection in the three COVID-19 waves) require a modified vaccination schedule. Most countries are offering a third vaccine dose and many are contemplating a fourth dose. Our aim was to gauge the IgG-antibody levels 9m post second vaccination in healthcare workers (HCW) and compare these with IgG-levels 1m post-vaccination in the same cohort for any decline, and to compare the post-vaccination IgG-levels in COVID-survivors and COVID-naïve HCW at 9m.Methods: This prospective observational single-centric cohort study included 63 HCW of either sex, aged 18-70y who completed 9m post-vaccination. The IgG-titre was tested at 9-10m post second vaccination in COVID-survivors and COVID-naïve HCW.Results: At 1m and 9m post-vaccination IgG-levels in COVID-survivors (23.097±4.58 and 15.103±4.367 respectively; p<0.0001) and COVID-naïve HCW (16.277±6.36 and 9.793±6.928 respectively; p=0.0013) had unequal variance (Welsch test; p=0.0022 at 9m). 9/31 COVID-naïve HCW but none of the 32 COVID-survivors tested COVID-positive in the second wave post second vaccination. 11/31 and 3/32 HCW belonging to the former and latter groups developed COVID-19 in the third wave consequently deferring their third/precautionary vaccination.Conclusions: Although HCW with IgG-levels in all brackets developed COVID-19, the severity of symptoms corresponded with the IgG-levels. COVID-19 is here to stay, but in peaceful co-existence in endemic proportions. Considering evidence that immunity acquired by vaccination/natural infection is ephemeral, re-invention of vaccines to match the ever-mutating virus is foreseen.