Performance Improvement and Feature Enhancements of Legacy Machine Data System

Implementing Hadoop for data processing and analytics of large dataset helps to store and process large amount of wireless engine data collected from large number of vehicles. This project is mainly focusing on collecting data from Data logger, Telematics and Insite data sources. This project explains us what type of engine data and parameters are getting collected every day. It also describes how this large data is transformed and stored on IBM Big-Insight cluster and how map-reduce framework is used to generate the report. Furthermore, it gives us an idea about how fault codes are analyzed and status is updated to the customers in the form of notifications. This project eventually helps us to understand how Hadoop is effective and faster than MATLAB in terms of speed and volume to process the data from above explained data sources

The Impact of a Marketing Information System: A Case Study of SMART-Baltimore

The purpose of this study is to use existing theories of technology and organizational change to assess the impact of technology implementation within the context of the tourism industry. The framework is applied as a case study to analyze the perceived implications of implementing a destination marketing information system by the Baltimore Area Convention and Visitors Bureau. The results of the study indicate that the most important value of the system is the richness and timeliness of information. The key informants perceive that this system will not only impact the marketing activities at the organization but will not influence the overall organizational activities. Finally, this article discusses the importance of these findings for destination marketing

Xanthogranulomatous orchitis: a case series

Xanthogranulomatous orchitis (XGO) is a sporadic disorder that has no definite aetiology and is a mimicker of several conditions, most significant of which are testicular neoplasms. We present a case series of three cases. The first case presented with swelling in his scrotum. The second case presented with similar symptoms and had prior history of trauma to the scrotum. Both cases were clinically diagnosed with testicular tumour. The third case was a referred case of left chronic orchitis with sinus tract. All three patients underwent high inguinal orchidectomy. Regardless of clinical work-up and diagnoses, upon histopathological evaluation, all three cases were diagnosed with XGO. This study explores the variety of risk factors and aetiologies that may result in XGO

Taxonomic Features and Comparison of the Gut Microbiome from Two Edible Fungus-Farming Termites (Macrotermes falciger, M. natalensis) Harvested in the Vhembe District of Limpopo, South Africa

Background Termites are an important food resource for many human populations around the world, and are a good supply of nutrients. The fungus-farming ‘higher’ termite members of Macrotermitinae are also consumed by modern great apes and are implicated as critical dietary resources for early hominins. While the chemical nutritional composition of edible termites is well known, their microbiomes are unexplored in the context of human health. Here we sequenced the V4 region of the 16S rRNA gene of gut microbiota extracted from the whole intestinal tract of two Macrotermes sp. soldiers collected from the Limpopo region of South Africa. Results Major and minor soldier subcastes of M. falciger exhibit consistent differences in taxonomic representation, and are variable in microbial presence and abundance patterns when compared to another edible but less preferred species, M. natalensis. Subcaste differences include alternate patterns in sulfate-reducing bacteria and methanogenic Euryarchaeota abundance, and differences in abundance between Alistipes and Ruminococcaceae. M. falciger minor soldiers and M. natalensissoldiers have similar microbial profiles, likely from close proximity to the termite worker castes, particularly during foraging and fungus garden cultivation. Compared with previously published termite and cockroach gut microbiome data, the taxonomic representation was generally split between termites that directly digest lignocellulose and humic substrates and those that consume a more distilled form of nutrition as with the omnivorous cockroaches and fungus-farming termites. Lastly, to determine if edible termites may point to a shared reservoir for rare bacterial taxa found in the gut microbiome of humans, we focused on the genus Treponema. The majority of Treponemasequences from edible termite gut microbiota most closely relate to species recovered from other termites or from environmental samples, except for one novel OTU strain, which clustered separately with Treponema found in hunter-gatherer human groups. Conclusions Macrotermes consumed by humans display special gut microbial arrangements that are atypical for a lignocellulose digesting invertebrate, but are instead suited to the simplified nutrition in the fungus-farmer diet. Our work brings to light the particular termite microbiome features that should be explored further as avenues in human health, agricultural sustainability, and evolutionary research

Exploring the Impact of Knowledge and Social Environment on Influenza Prevention and Transmission in Midwestern United States High School Students

We used data from a convenience sample of 410 Midwestern United States students from six secondary schools to develop parsimonious models for explaining and predicting precautions and illness related to influenza. Scores for knowledge and perceptions were obtained using two-parameter Item Response Theory (IRT) models. Relationships between outcome variables and predictors were verified using Pearson and Spearman correlations, and nested [student within school] fixed effects multinomial logistic regression models were specified from these using Akaike’s Information Criterion (AIC). Neural network models were then formulated as classifiers using 10-fold cross validation to predict precautions and illness. Perceived barriers against taking precautions lowered compliance with the CDC recommended preventative practices of vaccination, hand washing quality, and respiratory etiquette. Perceived complications from influenza illness improved social distancing. Knowledge of the influenza illness was a significant predictor for hand washing frequency and respiratory etiquette. Ethnicity and gender had varying effects on precautions and illness severity, as did school-level effects: enrollment size, proficiency on the state’s biology end-of-course examination, and use of free or reduced lunch. Neural networks were able to predict illness, hand hygiene, and respiratory etiquette with moderate success. Models presented may prove useful for future development of strategies aimed at mitigation of influenza in high school youths. As more data becomes available, health professionals and educators will have the opportunity to test and refine these models

Slow cycling intestinal stem cell and Paneth cell responses to Trichinella spiralis infection

There is limited information regarding responses by slow cycling stem cells during T. spiralis-induced T-cell mediated intestinal inflammation and how such responses may relate to those of Paneth cells. Transgenic mice, in which doxycycline induces expression of histone 2B (H2B)-green fluorescent protein (GFP), were used. Following discontinuation of doxycycline (“chase” period), retention of H2B-GFP enabled the identification of slow cycling stem cells and long-lived Paneth cells. Inflammation in the small intestine (SI) was induced by oral administration of T. spiralis muscle larvae. Epithelial retention of H2B-GFP per crypt cell position (cp) was studied following immunohistochemistry and using the Score and Wincrypts program. Compared to non-infected controls, there was significant reduction in the number of H2B-GFP-retaining stem cells in T. spiralis-infected small intestines. H2B-GFP-retaining stem cells peaked at around cp 4 in control sections, but smaller peaks at higher cell positions (>10) were seen in sections of inflamed small intestines. In the latter, there was a significant increase in the total number of Paneth cells, with significant reduction in H2B-GFP-retaining Paneth cells, but a marked increase in unlabelled (H2B-GFP-negative) Paneth cells. In conclusion, following T. spiralis-infection, putative slow cycling stem cell numbers were reduced. A marked increase in newly generated Paneth cells at the crypt base led to higher cell positions of the remaining slow cycling stem cells

Earthquake trend prediction using long short-term memory RNN

The prediction of a natural calamity such as earthquakes has been an area of interest for a long time but accurate results in earthquake forecasting have evaded scientists, even leading some to deem it intrinsically impossible to forecast them accurately. In this paper an attempt to forecast earthquakes and trends using a data of a series of past earthquakes. A type of recurrent neural network called Long Short-Term Memory (LSTM) is used to model the sequence of earthquakes. The trained model is then used to predict the future trend of earthquakes. An ordinary Feed Forward Neural Network (FFNN) solution for the same problem was done for comparison. The LSTM neural network was found to outperform the FFNN. The R^2 score of the LSTM is better than the FFNN’s by 59%

Can Subjective Pain Be Inferred From Objective Physiological Data? Evidence From Patients With Sickle Cell Disease

Patients with sickle cell disease (SCD) experience lifelong struggles with both chronic and acute pain, often requiring medical interventMaion. Pain can be managed with medications, but dosages must balance the goal of pain mitigation against the risks of tolerance, addiction and other adverse effects. Setting appropriate dosages requires knowledge of a patient\u27s subjective pain, but collecting pain reports from patients can be difficult for clinicians and disruptive for patients, and is only possible when patients are awake and communicative. Here we investigate methods for estimating SCD patients\u27 pain levels indirectly using vital signs that are routinely collected and documented in medical records. Using machine learning, we develop both sequential and non-sequential probabilistic models that can be used to infer pain levels or changes in pain from sequences of these physiological measures. We demonstrate that these models outperform null models and that objective physiological data can be used to inform estimates for subjective pain. Author summary: Understanding subjective human pain remains a major challenge. If objective data could be used in place of reported pain levels, it could reduce patient burdens and enable the collection of much larger data sets that could deepen our understanding of causes of pain and allow for accurate forecasting and more effective pain management. Here we apply two machine learning approaches to data from patients with sickle cell disease, who often experience debilitating pain crises. Using vital sign data routinely collected in hospital settings including respiratory rate, heart rate, and blood pressure and amidst the real-world challenges of irregular timing, missing data, and inter-patient variation, we demonstrate that these models outperform baseline models in estimating subjective pain, distinguishing between typical and atypical pain levels, and detecting changes in pain. Once trained, these types of models could be used to improve pain estimates in real time in the absence of direct pain reports

DNA Damage Repair Genes and Noncoding RNA in High-Grade Gliomas and Its Clinical Relevance

Gliomas are the most common malignant tumors originating from the glial cells in the central nervous system. Grades III and IV, considered high-grade gliomas occur at a lower incidence (1.5%) but have higher mortality. Several genomic alterations like IDH mutation, MGMT mutation, 1p19q Codeletion, and p53 mutations have been attributed to its pathogenicity. Recently, several noncoding RNAs have also been identified to alter the expression of crucial genes. Current chemotherapeutic drugs include temozolomide targeting hypermethylated MGMT, a DNA repair protein; or bevacizumab, which targets VEGF. This book chapter delves deeper into the DNA damage repair pathway including its correlation with survival and the regulation of these genes by noncoding RNAs. Novel therapeutic drugs being developed are also highlighted