Development of a smart walking stick for visually impaired people

One of the major issue faced by blind people are moving from one place to another. Also they get stress while walking along bad conditions of the road. All the time, they are alerted by others to avoid obstacles, staircases and wet terrain. This paper addresses the above issues of blind people with the intervention of technology. Also proffer the development of an electronic stick for blind person to assist them gain self-sufficiency. In the proposed model, an electronic stick was consolidated with an ultrasonic transducer, water circuit and RF transmitter and receiver module. The ultrasonic sensor will sense the obstacle within the span and notify the blind person with the help of buzzer. The water circuit when acquire with the water, it get short circuited and make buzzer to sound. Also, provides a way to find the misplaced stick in indoor. When the person presses the button in the remote, the stick will notify the blind person to realize the stick. Since this is simple, economical and not substantial, one can hold it easily. The prototype model was implemented and the entire setup functions were controlled by using the Arduino ATMEGA 328-PU microcontroller

Cervical Cancer Prediction using NGBFA Feature Selection Algorithm and Hybrid Ensemble Classifier

Cervical Cancer (CC) is a substantial reason of death midst middle-aged women throughout the world, specifically in developing countries, with approximately 85% of deaths. CC patients can be healed if spotted in the early stages. As no symptoms appear in the initial stages, it has become a challenge for investigators to predict the disease in the early stages. Several machine learning algorithms have been used to predict CC since the last decade. Instead of using a single classifier for the prediction, ensemble methods give accurate results, creating and combining multiple models to produce improved results. In this study, we built a hybrid ensemble classifier, 'A Robust Model Stacking: A Hybrid Ensemble,' in which a homogenous ensemble will be performed on a pool of classifiers in the base level followed by a heterogenous ensemble using the majority voting (soft) algorithm to get the final prediction of the new data. The dataset used in this study contains 858 instances with 32 features built from the risk factors and four targets made from the CC diagnosis tests. We have solved the data imbalance problem using an oversampling technique called SMOTE. The model's efficiency was evaluated based on the accuracy, recall, f1-score, precision, and AUC-ROC curve metrics for all four target variables in the dataset. The proposed Biopsy method's accuracy is 98%, Hinselmann is 97%, Schiller is 96.09%, and Citology is 93%. We implement ensemble learning in this study to increase prediction accuracy and decrease bias and variance. We carried the experiments out using the Python language in Google Colab and Jupyter notebooks. The experimental results revealed that our proposed hybrid ensemble learning records a remarkable accuracy for all four target variables

Application of Lean Tools in different Industries: A review

Many companies around the world were pressured to cut costs and become more mindful of consumer needs after the crisis hit at the turn of the twenty-first century. Industry has long seen Lean Manufacturing as a solution to these problems because it removes waste without requiring extra capital. In the current situation, Lean production principles and methods are commonly used by companies to optimise resource efficiency while minimising waste. Toyota has been recognised as one of the manufacturing techniques in improving efficiency, increasing customer loyalty, and thereby an organization's competitiveness, thanks to the Lean theory, which was developed by a Japanese scientist

A Review on Prediction of Heart Disease based on Machine Learning and Datamining Techniques

Heart is the important organ in human body which supplies blood to all organs of the body. The abnormal situation of heart is considered as heart disease. According to WHO data cardiovascular, respiratory and neonatal conditions are the top three causes of Deaths in the World. In the year 2019 Heart diseases occupies 16%(9 million) of overall deaths happened in World. From two decades there is 4 times increase in the deaths with heart diseases this is because of change in life style, lack of physical activity, food habits, obesity ,stress, cholesterol ,high blood pressure and diabetes.so there is a need  to work on prediction of heart diseases to save many lives because prediction is the only way to prevent the disease. In this paper we will discuss about existing algorithms and existing work done in different machine learning and datamining techniques, which are concentrated more on the classification and prediction. main objective is to evaluate the performance of these algorithms and identify the most accurate and efficient approach for diagnosing heart diseases. Some of the machine learning and data mining techniques are Artificial Neural Network(ANN),Decision Tree, Naive Bayes, SVM(Support Vector Machine),k-Nearest Neighbours (KNN),J48,SMO,Random forest and classification Tree

Homozygous loss-of-function variants in FILIP1 cause autosomal recessive arthrogryposis multiplex congenita with microcephaly

Arthrogryposis multiplex congenita forms a broad group of clinically and etiologically heterogeneous disorders characterized by congenital joint contractures that involve at least two different parts of the body. Neurological and muscular disorders are commonly underlying arthrogryposis. Here, we report five affected individuals from three independent families sharing an overlapping phenotype with congenital contractures affecting shoulder, elbow, hand, hip, knee and foot as well as scoliosis, reduced palmar and plantar skin folds, microcephaly and facial dysmorphism. Using exome sequencing, we identified homozygous truncating variants in FILIP1 in all patients. FILIP1 is a regulator of filamin homeostasis required for the initiation of cortical cell migration in the developing neocortex and essential for the differentiation process of cross-striated muscle cells during myogenesis. In summary, our data indicate that bi-allelic truncating variants in FILIP1 are causative of a novel autosomal recessive disorder and expand the spectrum of genetic factors causative of arthrogryposis multiplex congenita

Multicentric osteolytic syndromes represent a phenotypic spectrum defined by defective collagen remodeling

Frank-Ter Haar syndrome (FTHS), Winchester syndrome (WS), and multicentric osteolysis, nodulosis, and arthropathy (MONA) are ultra-rare multisystem disorders characterized by craniofacial malformations, reduced bone density, skeletal and cardiac anomalies, and dermal fibrosis. These autosomal recessive syndromes are caused by homozygous mutation or deletion of respectively SH3PXD2B (SH3 and PX Domains 2B), MMP14 (matrix metalloproteinase 14), or MMP2. Here, we give an overview of the clinical features of 63 previously reported patients with an SH3PXD2B, MMP14, or MMP2 mutation, demonstrating considerable clinical overlap between FTHS, WS, and MONA. Interestingly, the protein products of SH3PXD2B, MMP14, and MMP2 directly cooperate in collagen remodeling. We review animal models for these three disorders that accurately reflect the major clinical features and likewise show significant phenotypical similarity with each other. Furthermore, they demonstrate that defective collagen remodeling is central in the underlying pathology. As such, we propose a nosological revision, placing these SH3PXD2B, MMP14, and MMP2 related syndromes in a novel “defective collagen-remodelling spectrum (DECORS)”. In our opinion, this revised nosology better reflects the central role for impaired collagen remodeling, a potential target for pharmaceutical intervention

Screening, production and biochemical characterization of a new fibrinolytic enzyme produced by Streptomyces sp. (Streptomycetaceae) isolated from Amazonian lichens

Thrombosis is a pathophysiological disorder caused by accumulation of fibrin in the blood. Fibrinolytic proteases with potent thrombolytic activity have been produced by diverse microbial sources. Considering the microbial biodiversity of the Amazon region, this study aimed at the screening, production and biochemical characterization of a fibrinolytic enzyme produced by Streptomyces sp. isolated from Amazonian lichens. The strain Streptomyces DPUA1576 showed the highest fibrinolytic activity, which was 283 mm2. Three variables at two levels were used to assess their effects on the fibrinolytic production. The parameters studied were agitation (0.28 - 1.12 g), temperature (28 - 36 ºC) and pH (6.0 - 8.0); all of them had significant effects on the fibrinolytic production. The maximum fibrinolytic activity (304 mm2) was observed at 1.12 g, 28 ºC, and pH of 8.0. The crude extract of the fermentation broth was used to assess the biochemical properties of the enzyme. Protease and fibrinolytic activities were stable during 6 h, at a pH ranging from 6.8 to 8.4 and 5.8 to 9.2, respectively. Optimum temperature for protease activity ranged between 35 and 55 °C, while the highest fibrinolytic activity was observed at 45 ºC. Proteolytic activity was inhibited by Cu2+ and Co2+ ions, phenylmethylsulfonyl fluoride (PMSF) and pepstatin A, which suggests that the enzyme is a serine protease. Enzymatic extract cleaved fibrinogen at the subunits A-chain, A-chain, and -chain. The results indicated that Streptomyces sp. DPUA 1576 produces enzymes with fibrinolytic and fibrinogenolytic activity, enzymes with an important application in the pharmaceutical industry.The authors grateful acknowledge the financial support of Fundação de Amparo a Pesquisa do Estado de Pernambuco (FACEPE, Pernambuco, Brazil, N. 0158-2.12/11), CNPq/ RENORBIO (National Counsel of Technological and Scientific Development, N.55146/2010-3) and National Council for the Improvement of Higher Education (CAPES, Brazil) for the scholarship. The author thanks editor and reviewers for their review and comments.info:eu-repo/semantics/publishedVersio

Novel subtype of mucopolysaccharidosis caused by arylsulfatase K (ARSK) deficiency

BACKGROUND: Mucopolysaccharidoses (MPS) are monogenic metabolic disorders that significantly affect the skeleton. Eleven enzyme defects in the lysosomal degradation of glycosaminoglycans (GAGs) have been assigned to the known MPS subtypes (I-IX). Arylsulfatase K (ARSK) is a recently characterised lysosomal hydrolase involved in GAG degradation that removes the 2-O-sulfate group from 2-sulfoglucuronate. Knockout of Arsk in mice was consistent with mild storage pathology, but no human phenotype has yet been described. METHODS: In this study, we report four affected individuals of two unrelated consanguineous families with homozygous variants c.250C>T, p.(Arg84Cys) and c.560T>A, p.(Leu187Ter) in ARSK, respectively. Functional consequences of the two ARSK variants were assessed by mutation-specific ARSK constructs derived by site-directed mutagenesis, which were ectopically expressed in HT1080 cells. Urinary GAG excretion was analysed by dimethylene blue and electrophoresis, as well as liquid chromatography/mass spectrometry (LC-MS)/MS analysis. RESULTS: The phenotypes of the affected individuals include MPS features, such as short stature, coarse facial features and dysostosis multiplex. Reverse phenotyping in two of the four individuals revealed additional cardiac and ophthalmological abnormalities. Mild elevation of dermatan sulfate was detected in the two subjects investigated by LC-MS/MS. Human HT1080 cells expressing the ARSK-Leu187Ter construct exhibited absent protein levels by western blot, and cells with the ARSK-Arg84Cys construct showed markedly reduced enzyme activity in an ARSK-specific enzymatic assay against 2-O-sulfoglucuronate-containing disaccharides as analysed by C18-reversed-phase chromatography followed by MS. CONCLUSION: Our work provides a detailed clinical and molecular characterisation of a novel subtype of mucopolysaccharidosis, which we suggest to designate subtype X

SCUBE3 loss-of-function causes a recognizable recessive developmental disorder due to defective bone morphogenetic protein signaling

Signal peptide-CUB-EGF domain-containing protein 3 (SCUBE3) is a member of a small family of multifunctional cell surface-anchored glycoproteins functioning as co-receptors for a variety of growth factors. Here we report that bi-allelic inactivating variants in SCUBE3 have pleiotropic consequences on development and cause a previously unrecognized syndromic disorder. Eighteen affected individuals from nine unrelated families showed a consistent phenotype characterized by reduced growth, skeletal features, distinctive craniofacial appearance, and dental anomalies. In vitro functional validation studies demonstrated a variable impact of disease-causing variants on transcript processing, protein secretion and function, and their dysregulating effect on bone morphogenetic protein (BMP) signaling. We show that SCUBE3 acts as a BMP2/BMP4 co-receptor, recruits the BMP receptor complexes into raft microdomains, and positively modulates signaling possibly by augmenting the specific interactions between BMPs and BMP type I receptors. Scube3(-/-) mice showed craniofacial and dental defects, reduced body size, and defective endochondral bone growth due to impaired BMP-mediated chondrogenesis and osteogenesis, recapitulating the human disorder. Our findings identify a human disease caused by defective function of a member of the SCUBE family, and link SCUBE3 to processes controlling growth, morphogenesis, and bone and teeth development through modulation of BMP signaling.Genetics of disease, diagnosis and treatmen