Sehaa: A big data analytics tool for healthcare symptoms and diseases detection using Twitter, Apache Spark, and Machine Learning

Smartness, which underpins smart cities and societies, is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner. Healthcare is the prime candidate needing the transformative capability of this smartness. Social media could enable a ubiquitous and continuous engagement between healthcare stakeholders, leading to better public health. Current works are limited in their scope, functionality, and scalability. This paper proposes Sehaa, a big data analytics tool for healthcare in the Kingdom of Saudi Arabia (KSA) using Twitter data in Arabic. Sehaa uses Naive Bayes, Logistic Regression, and multiple feature extraction methods to detect various diseases in the KSA. Sehaa found that the top five diseases in Saudi Arabia in terms of the actual aicted cases are dermal diseases, heart diseases, hypertension, cancer, and diabetes. Riyadh and Jeddah need to do more in creating awareness about the top diseases. Taif is the healthiest city in the KSA in terms of the detected diseases and awareness activities. Sehaa is developed over Apache Spark allowing true scalability. The dataset used comprises 18.9 million tweets collected from November 2018 to September 2019. The results are evaluated using well-known numerical criteria (Accuracy and F1-Score) and are validated against externally available statistics

Thermal comfort standards in the Middle East: Current and future challenges

Cooling energy demand has increased three-fold in the Middle East (ME) over the last 30-years. This is driven by the need to maintain thermal comfort in an extremely hot climate, and supported by rising incomes, falling costs of air-conditioning and growth in the number of buildings. The definition of thermal comfort in these buildings is drawn from “international” standards, which, though empirically derived, have no basis data from this region. Hence, we ask, to what extent do indoor conditions in the ME fall within the standards recommended range of thermal comfort, and when they do, whether they are found to be comfortable by their occupants. We present the first large-scale study of thermal comfort in the ME, consisting of two approaches: (i) a meta-analysis of data from existing studies, (ii) independent field data covering four countries representing 27% of the region's population, 31 air-conditioned buildings of different types, including “green” buildings, and 1,101 subjects. The meta-analysis demonstrates that current thermal comfort standards fail to predict thermal sensation of 94% of occupants. Our own data show that, while indoor conditions are within standards-recommended ranges 58% of the time, only 40% of occupants find these conditions acceptable. We find evidence of overcooling in summers, with 39% occupants expressing cold discomfort. Computer models suggest that this is likely to have increased annual cooling energy demand between 13% and 20%, compared to non-overcooled conditions. These results suggest the necessity of localised thermal comfort standards that mitigate excess cooling energy demand, without compromising occupant thermal comfort.</p

HADAD: A Lightweight Approach for Optimizing Hybrid Complex Analytics Queries

International audienceHybrid complex analytics workloads typically include (i) data management tasks (joins, filters, etc.), easily expressed using relational algebra (RA)-based languages, and (ii) complex analytics tasks (regressions, matrix decompositions, etc.), mostly expressed in linear algebra (LA) expressions. Such workloads are common in a number of areas, including scientific computing, web analytics, business recommendation, natural language processing, speech recognition. Existing solutions for evaluating hybrid complex analytics queriesranging from LA-oriented systems, to relational systems (extended to handle LA operations), to hybrid systems-fail to provide a unified optimization framework for such a hybrid setting. These systems either optimize data management and complex analytics tasks separately, or exploit RA properties only while leaving LA-specific optimization opportunities unexplored. Finally, they are not able to exploit precomputed (materialized) results to avoid computing again (part of) a given mixed (LA and RA) computation. We describe HADAD, an extensible lightweight approach for optimizing hybrid complex analytics queries, based on a common abstraction that facilitates unified reasoning: a relational model endowed with integrity constraints, which can be used to express the properties of the two computation formalisms. Our approach enables full exploration of LA properties and rewrites, as well as semantic query optimization. Importantly, our approach does not require modifying the internals of the existing systems. Our experimental evaluation shows significant performance gains on diverse workloads, from LA-centered ones to hybrid ones

Chamuangone-enriched rice bran oil ameliorates neurodegeneration in haloperidol-induced Parkinsonian rat model via modulation of neuro-inflammatory mediators and suppression of oxidative stress markers

A natural bioactive compound chamuangone extracted from Thai salad Garcinia cowa leaves exhibited robust medicinal properties, targeting central oxidative stress pathways, and having neuroprotective potential. Chamuangone-enriched rice bran oil (CERBO), with 1.97 mg/mL chamuangone, was obtained through green extraction. The study was designed to evaluate the anti-Parkinson’s activity of CERBO in the haloperidol-induced Parkinsonian rat model. Animals were categorized into six groups as control, disease control and treatment groups. Parkinson’s disease (PD)-like symptoms were induced by administration of haloperidol 1 mg/kg, intraperitoneally; CERBO treatment groups received 2.5, 5, and 7.5 mg/kg orally before the administration of haloperidol for 21 days. Neurobehavioral, biochemical, neurochemical, and histopathological studies along with gene expression analysis were performed at the completion of the study. CERBO markedly recover the motor and non-motor PD-like symptoms in treatment groups dose-dependently. The levels of antioxidant enzymes, such as catalase, superoxide dismutase, reduced glutathione, and glutathione peroxidase, increased, while malondialdehyde levels decreased dose-dependently in CERBO-treated groups. CERBO dose-dependent elevations were observed in neurotransmitters (dopamine, serotonin, and noradrenaline). PD-associated specific biomarker (α-synuclein) decreased dose-dependently with downregulation in messenger RNA expression of neuro-inflammatory mediators (interleukin α, interleukin 1β, and tumor necrosis factor-α). Histopathological studies revealed recovery in neuronal loss, formation of Lewy’s bodies, and neurofibrillary tangles in the treatment groups. It was concluded from the data that CERBO possessed good anti-Parkinson’s activity and could be a novel, safe, and effective remedy for the treatment of PD.peer-reviewe

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

A first update on mapping the human genetic architecture of COVID-19

peer reviewe

Semantic Optimizations in Modern Hybrid Stores

In recent years, big data applications often involve dealing with diverse datasets in terms of structure: relations flat or nested, complex-structure graphs, documents (JSON or XML), poorly structured logs, or even text data. To handle the heterogeneity of the data, application designers usually rely on several data stores used side-by-side, each supporting a different data model, associated query language (or data access API), and very efficient for some, but not all, kinds of processing on the data. Systems capable of querying disparate data in this fashion are advocated by the database community under terms such as hybrid- or poly-stores.These systems provide no support for semantic query optimizations, which include (i) exploiting possible data redundancy when the same data may be accessible (with different performance) from distinct data stores; (ii) taking advantage of partial query results (in the style of materialized views), which may be available in the stores; and (iii) reasoning semantically about various data models and query operations’ properties, which can enhance the hybrid workload performance. Motivated by these optimization opportunities, this dissertation makes the following two main contributions:We design and demonstrate ESTOCADA, an extensible lightweight framework for providing semantic query optimization on top of hybrid stores without modifying their internals. ESTOCADA transparently enables each query to benefit from the best combination of stored data and available processing capabilities. It leverages recent advances in the area of view-based query rewriting under constraints, which we use to describe various data models and stored data. We demonstrate the effectiveness of our approach with an experimental evaluation using the MIMIC real-world dataset and show significant performance gains achieved by ESTOCADA.Going beyond query workloads covering a variety of data models (relational, JSON, Graph, XML) in hybrid stores, modern applications increasingly need to blend querying and learning on the data, which is primarily expressed using a mix of relational algebra (RA)- and linear algebra (LA)-based languages. Existing specialized solutions for evaluating such hybrid analytical tasks either optimize RA and LA tasks separately, exploiting only RA properties while leaving LA-specific optimizations unexploited, or focus heavily on physical optimizations, leaving semantic query optimization opportunities unexplored. In our second contribution, we take a major step towards filling this gap by proposing HADAD. The novelty of HADAD is to extend the benefits of semantic query rewriting and view-based optimization introduced in ESTOCADA to LA computations, crucial for ML hybrid analytical tasks. Our solution can be naturally and portably applied on top of pure LA and hybrid RA-LA platforms. An extensive empirical evaluation shows that HADAD yields significant performance gains on diverse workloads, ranging from LA-centered to hybrid RA-LA workloads

Dataset for "Thermal comfort standards in the Middle East: Current and future challenges"

This dataset consists of 1,101 valid thermal comfort votes and 787 incomplete thermal comfort records that were collected between 2017 and 2019, during summer and winter. Each record includes measurement of four physical parameters affecting thermal sensation: air temperature (Ta), mean radiant temperature (Tr), relative humidity (RH), and air movement speed (Va). The measurements were coincident with the time of each individual survey of thermal sensation vote (TSV), to be compared to each other. Data were collected in 31 air-conditioned buildings in Amman, Doha, Dubai, and Jeddah. Measurements were done in four occupancy types: offices, schools, hospital, and mosques.Four physical parameters affecting thermal sensation – Air Temperature (°C), Mean Radiant Temperature (°C), Relative Humidity (%), Air Movement Speed (m s⁻¹) – were measured in all surveyed buildings coincident with the time of each individual survey.Data were collected using the following instruments: 1. SWEMA and HD 32.3, both compliant with ISO 7726 and ISO 7730 standards. 2. Extech HT200 heat stress wet bulb globe thermometer, used to monitor Air Temperature, Mean Radiant Temperature, Relative Humidity. 3. ATP uni-directional hot wire thermo-anemometer, used to simultaneously measure Air Movement Speed