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Pathogenic variants in GEMIN4 have recently been linked to an inherited autosomal recessive neurodevelopmental disorder characterized with microcephaly, cataracts, and renal abnormalities (NEDMCR syndrome). This report provides a retrospective review of 16 patients from 11 unrelated Saudi consanguineous families with GEMIN4 mutations. The cohort comprises 11 new and unpublished clinical details from five previously described patients. Only two missense, homozygous, pathogenic variants were found in all affected patients, suggesting a founder effect. All patients shared global developmental delay with variable ophthalmological, renal, and skeletal manifestations. In addition, we knocked down endogenous Drosophila GEMIN4 in neurons to further investigate the mechanism of the functional defects in affected patients. Our fly model findings demonstrated developmental defects and motor dysfunction suggesting that loss of GEMIN4 function is detrimental in vivo; likely similar to human patients. To date, this study presents the largest cohort of patients affected with GEMIN4 mutations. Considering that identifying GEMIN4 defects in patients presenting with neurodevelopmental delay and congenital cataract will help in early diagnosis, appropriate management and prevention plans that can be made for affected families.

Dysfunction of RNA-binding proteins are often linked to a wide range of human disease in general, and particularly with neurological conditions. Gemin5 is a member of the survival of motor neuron (SMN) complex, a ribosome-binding protein and a translation reprogramming factor. Recently, pathogenic mutations in Gemin5 protein have been reported but the functional consequences of these variants remain elusive. Here we report functional and structural deficiencies associated with compound heterozygosity variants within theGemin5gene found in patients with neurodevelopmental disorders. These clinical variants are located in key domains of Gemin5, the tetratricopeptide repeat (TPR)-like dimerization module and the non-canonical RNA-binding site 1 (RBS1). We show that the TPR-like variants disrupt protein dimerization while the RBS1 variant confers protein instability. All mutants are defective in the interaction with protein networks involved in translation and RNA-driven pathways. Importantly, the TPR-like variants fail to associate with native ribosomes, abolishing the cap-dependent and selective translation control of Gemin5, and establishing a functional difference with the wild type protein. Our study provides a molecular basis of disease associated with malfunction of Gemin5 protein.

Although learning is often viewed as a unique feature of organisms with complex nervous systems, single-celled organisms also demonstrate basic forms of learning. The giant ciliate Stentor coeruleus responds to mechanical stimuli by contracting into a compact shape, presumably as a defense mechanism. When a Stentor cell is repeatedly stimulated at a constant level of force, it will learn to ignore that stimulus but will still respond to stronger stimuli. Prior studies of habituation in Stentor reported a graded response, suggesting that cells transition through a continuous range of response probabilities. By analyzing single cells using an automated apparatus to deliver calibrated stimuli, we find that habituation occurs via a single step-like switch in contraction probability within each cell, with the graded response in a population arising from the random distribution of switching times in individual cells. This step-like response allows Stentor behavior to be represented by a simple two-state model whose parameters can be estimated from experimental measurements. We find that transition rates depend on stimulus force and also on the time between stimuli. The ability to measure the behavior of the same cell to the same stimulus allowed us to quantify the functional heterogeneity among single cells. Together, our results suggest that the behavior of Stentor is governed by a two-state stochastic machine whose transition rates are sensitive to the time series properties of the input stimuli.

Deepa Rajan,1 Vinod G Pillai,2 Patsy Varghese1 1Department of Obstetrics and Gynecology, Believers Church Medical, College Hospital, Thiruvalla, Kerala, India; 2Department of Surgery, Believers, Church Medical College Hospital, Thiruvalla, Kerala, IndiaCorrespondence: Vinod G PillaiDepartment of Surgery, Believers Church Medical College Hospital, Thiruvalla, Kerala, 689103, IndiaTel +91 469 3503100Email drvinodpillai@gmail.com View the original paper by Dr Rajan and colleagues This is in response to the Letter to the Editor

Deepa Rajan,1 Vinod G Pillai,2 Patsy Varghese1 1Department of Obstetrics and Gynecology, Believers Church Medical College Hospital, Thiruvalla, Kerala, India; 2Department of Surgery, Believers Church Medical College Hospital, Thiruvalla, Kerala, IndiaCorrespondence: Vinod G PillaiDepartment of Surgery, Believers Church Medical College Hospital, Thiruvalla, Kerala, 689103, IndiaTel/Fax +91 469 3503100Email drvinodpillai@gmail.comIntroduction: The Covid pandemic and social distancing has adversely impacted the conventional apprenticeship method of postgraduate training in laparoscopic surgery. Social media may be a useful adjunct for laparoscopic training, but its utility in developing countries like India has not been studied carefully. This paper describes an observational, cross-sectional study on the educational utility of Facebook groups based in India and which focus on laparoscopic gynecologic surgery.Methods: The most popular Facebook groups involving Indians and focusing on laparoscopic gynecology were identified using appropriate search terms as well as inclusion and exclusion criteria. Demographic data related to the groups, the authors of posts as well as descriptive statistics of all the posts during the study period were collected and appropriate statistical analysis was performed.Results: All the groups in this study were large and growing steadily. Posts related to laparoscopy were more likely to be videos, dealing with operative techniques and having educational value for postgraduate residents (p value < 0.001) compared to posts unrelated to laparoscopic surgery. The majority of posts (88.2%) presented original content created by group members rather than material shared from other sources. Members preferred to share laparoscopic content using links to their personal YouTube channels rather than using institutional YouTube channels, dedicated websites for laparoscopic surgery or direct posts on Facebook. Group members liked educational content and laparoscopic surgery-related content significantly more than other content. Only 16.7% of the laparoscopic surgeons could be identified to be working in academic institutes.Conclusion: Social media for medical education has inherent advantages and disadvantages. This article provides objective data regarding its utilisation in a developing country in the midst of the Covid pandemic, and provides a guide for further research and development of innovative teaching methods.Keywords: online learning, distance learning, surgical education, laparoscopic gynecology, Covid pandemic, social media

PURPOSE OF STUDY: Advanced uterovaginal prolapse can significantly affect the quality of life in women and usually requires surgical management. McCall’s culdoplasty (M) or sacrospinous fixation (SSF) are done at the time of vaginal hysterectomy with pelvic floor repair (VHPFR) to reduce recurrence, but recurrence rates of 15% and 33% have been reported with these procedures respectively. We hypothesize that combining VH-PFR with both McCall’s culdoplasty and sacrospinous fixation (VH-PFR-M-SSF) may decrease recurrence rates compared to VH-PFR-M without significantly affecting other perioperative outcomes. METHODS: All patients with advanced uterovaginal prolapse and willing for VH-PFR at our institute from January 2015 to March 2018 were included after informed consent, except for medically unfit women and those preferring alternative management. We conducted a case control study comparing VH-PFR-M and VH-PFR-M-SSF with a follow-up period of 24 months. Qualitative and quantitative data were statistically analysed and Odds ratio and 95% Confidence interval was calculated. Kaplan Meier Curve was drawn and Log Rank test was used to compare recurrence. RESULTS: Out of 174 patients who underwent surgery in the study period, 131 patients (75.28%) underwent VH-PFR-M and 43 patients (24.71%) underwent VH-PFR-M-SSF. Both groups were comparable for age, body mass index, parity, postmenopausal status, comorbidities and aggravating factors. Patients with higher stage of prolapse were more in group 2 (p

Road traffic safety is an important perturbation for government transport authorities as well ascommon people. Road accidents are ambivalent and not able to be predict the accidents andtheir survey requires the factors affecting them. Road accidents cause difficulties which are higher at an alarming rate. Controlling the traffic accidents on roads is a crucial task. To give safe driving suggestions, clear and careful study of roadway traffic data is critical. Increasing the number of vehicles from past few years has put lot of pressure on the existing roads and ultimately resulting in increasing the road accidents. A road traffic accident is any harm due to collision originating from, terminating with or involving a vehicle partially or fully on a public road.

Heart disease is a most harmful one that will cause death. It has a serious long term disability. This disease attacks a person so instantly. Medical data is still information rich but knowledge poor. Therefore diagnosing patients correctly on the basis of time is an exigent function for medical support. An invalid diagnosis done by the hospital leads for losing reputation. The precise diagnosis of heart disease is the dominant biomedical issue. The motivation of this paper is to develop an efficacious treatment using data mining techniques that can help remedial situations. Further data mining classification algorithms like decision trees, neural networks, Bayesian classifiers, Support vector machines, Association Rule, K- nearest neighbour classification are used to diagnosis the heart diseases. Among these algorithms Support Vector Machine (SVM) gives best result.

Weather forecasting is a method to predict what the atmosphere will be like in a particular place by using scientific knowledge to make the weather observations. In other words, it’s a way of predicting things like cloud cover, rain, snow, wind speed and temperature before they happen. Perfect weather predictions are needed for daily activities and it was one of the main challenging problem facing throughout the world because it consists of multidimensional and nonlinear data. As per the survey the various methods and algorithms used for weather prediction in the field of data mining are supervised and unsupervised machine learning algorithms, Artificial neural network, Support Vector Machine,FP Growth Algorithm,hadoop with map reduces, K-medoids algorithm, Naive Bayes algorithm and decision tree classification algorithm.

The research on this review paper presents the complicated usage of prescribed drugs which perform under the area of data mining for managing high volume of data and usage of complex function for performing more refined analysis using cloud platform. The aim of this review paper is to understand the innovative and extensive frame that characterize drug abuse using social media. The concept of this review paper is an analytical approach to analyze social media by applying powerful techniques such as cloud computing and Map Reduce model. for acquiring the drug abuse emerge trends. This paper describes how to capture important data to evaluate from networks like Twitter, Facebook, and Instagram. Also, big data techniques are used for analysis of data content.

Tumor segmentation from MRI (Magnetic Resonance Imaging) data is an emerging process in medicine but it is time consuming manual task performed by medical experts. Because of the highdiversity in tumor tissue of different patients, automating this process is a challenging riskMedical resonance imaging is a challenging and innovative field in medical science. The brain tumor detection using MRI images has many applications. Brain tumor detection and segmentationfrom MRI images is being one of the emerging fields in the biomedicine. There are different brain tumor detection and segmentation methods. Detection and segmentation methods are used to detect and segment the brain tumor from the MRI images. Here a brief review about the differentbrain tumor detection and segmentation methods has been discussed. The different methods include watershed method, bilateral method, etc.

Cloud storage is nowadays trending in case of storage and the cloud service providers (CSP) provides varieties of offers for organisations and made the resources available online. Data integrity checking and regeneration of failed servers nowadays becomes critical and we need to safeguard the delegated data in cloud storage against corruptions. It will become the major delinquent towards the cloud servers. Surviving paper works offers only private auditing and requires data owners to always available and handle auditing, and repairing which may sometimes be unrealistic. Our work-scheme focuses with regeneration and for that cloud user’s first produce their secret keys and the TPA audits the files, in case of file corruption then forwards to a proxy agent by sharing partial private key and regenerates the corrupted files. Here we uses the seed block algorithm for the purpose of regeneration and also introduces an additional agent for auto regeneration, which reduces the downloading time related issues.

To analyse cases of endometrioid epithelial ovarian carcinoma with respect to clinicopathological features, management and outcome at a tertiary cancer referral hospital in western India. This is a retrospective analysis of 46 cases treated at the Gujarat Cancer & Research Institute from January 2008–September 2012. Baseline characteristics, CA-125, RMI, management, staging, grading, residual disease, adjuvant therapy and survival were studied and analysed, using SPSS 22.0 software. Non-parametric tests for statistical significance and Kaplan–Meier survival analysis were applied. Out of 46 women (median age 50 years), 39 % were diagnosed in early stage, 24 % in advanced stage and 37 % were unstaged. Overall survival at 24 months was 87.8 %. It was associated with infertility (28 %), endometriosis (9 %) and synchronous endometrial carcinoma (20 %). R0 resection was achieved in 74 % of patients. The univariate analysis showed that size of tumour, disease stage and R0 resection status were significant prognostic factors for endometrioid subtype of epithelial ovarian cancer. Endometrioid epithelial ovarian cancer was associated with synchronous endometrial carcinoma, nulliparity and younger age at presentation. NACT with interval cytoreduction (in patients with clinically advanced tumours where R0 resection may be unfeasible) can offer survival benefits equivalent to primary cytoreduction (in advanced stage).

The heart is the bodily part that is most necessary or significant. Our body's blood must be combined and controlled by the heart. Around the world, heart disease is a problem that many individuals face. Many individuals die as a result of heart disease. Numerous symptoms are mentioned, including chest discomfort and an irregular heartbeat. A reliable, precise, and useful strategy is required to identify these disorders early enough for appropriate therapy. For medical experts and institutions throughout the world, especially hospitals in India, forecasting and recognizing heart disease has become a challenging challenge. The researchers are targeting the development of software employing machine learning techniques to help physicians with the identification and prognosis of cardiac disease. The algorithms are used based on characteristics to anticipate heart illness. The effectiveness of various models built using such methodologies and techniques is investigated in this study. Models created utilizing supervised learning techniques like Support Vector Machines (SVM), Decision Trees (DT), Random Forest (RF), etc. are frequently preferred by researchers. The primary goal of this study is to use machine learning algorithms to better correctly predict a patient's heart state., {"references":["1.\tArchana Singh, Rakesh Kumar. Heart Disease Prediction Using Machine Learning Algorithms (23rd June,2020).","2.\tCincy Raju, Philipsy E, Siji Chacko, L Padma Suresh, Deepa Rajan S. A Survey on Predicting Heart Disease Using Data Mining Techniques (March, 2018).","3.\tHimanshu Sharma, M A Rizvi. Prediction of Heart Disease using Machine Learning Algorithms (August,2017).","4.\tSaba Bashir, Zain Sikander Khan, Frahan Hassan Khan, Aitzaz Anjum, Khurram Bashir. Improving Heart Disease Prediction Using Feature Selection Technique (March, 2019).","5.\tSonam Nikhar, A.M.Karandikar. Prediction of Heart Disease Using Machine Learning Algorithms (6th June, 2016).","6.\tVijeta Sharma, Shrinkhala Yadav, Manjari Gupta. Heart Disease Prediction Using Machine Learning Techniques (December, 2020).","7.\tV.V.Ramakingam, Ayantan Danapath, M Karthik Raja. Heart Disease Prediction using Machine Learning Techniques(March,2018).","8.\tDeepak, N. R., & Balaji, S. (2016, April). Uplink Channel Performance and Implementation of Software for Image Communication in 4G Network. In Computer Science On-line Conference (pp. 105-115). Springer, Cham.","9.\tThiagarajan, R., Balajivijayan, V., Krishnamoorthy, R., & Mohan, I. (2022). A robust, scalable, and energy-efficient routing strategy for UWSN using a Novel Vector-based Forwarding routing protocol. Journal of Circuits, Systems and Computers.","10.\tNR, D., GK, S., & Kumar Pareek, D. (2022). A Framework for Food recognition and predicting its Nutritional value through Convolution neural network."]}