Ayurvedic Treatment in the Rehabilitation of Ischemic Stroke Patients in India: A Randomized Controlled Trial Study Protocol.

In patients with ischemic stroke, motor and sensory impairments are common and are associated with functional disability. Conventional physiotherapy (CP) is the primary modality of rehabilitation for post-stroke sensorimotor dysfunction. Ayurveda is a commonly practiced alternative system of medicine that offers unique rehabilitative measures for post-stroke recovery. We hypothesize that Ayurvedic rehabilitative treatment (ART) is superior to similar duration CP in improving the sensorimotor recovery of patients with ischemic stroke at 90 days after enrollment. AyuRvedic TrEatment in the Rehabilitation of Ischemic STrOke Patients in India: A Randomized controllEd trial (RESTORE) is an investigator-initiated, multicenter, prospective, randomized, controlled, parallel-arm, blinded outcome assessment trial being conducted under the Indian Stroke Clinical Trial (INSTRuCT) Network across the four comprehensive stroke centers in India. Consecutive hemodynamically stable adult patients with their first acute ischemic stroke between 1 and 3 months from stroke onset are being randomized (1:1) into two treatment groups to receive either 1 month of ART or 1 month of CP. The primary outcome measure is the Fugl Meyer Assessment-upper extremity for physical performance at 90 days. The secondary outcomes are the modified Rankin Scale, Barthel Index, Berg Balance, and SF-36 at 90 days. The safety outcomes include a composite of irreversible morbidity and mortality. A sample size of 140 (70 in each group) patients with ischemic stroke will allow us to detect a minimal clinical important difference of 9.4 (standard deviation) with superiority margin of 5, an attrition rate of 10%, alpha of 5%, and power of 80%. This randomized trial will systematically assess the efficacy and safety of traditional ART compared to CP. The trial has been registered in the Clinical Trial Registry India (CTRI/2018/04/013379).

A Methodology for Integrating Population Health Surveys Using Spatial Statistics and Visualizations for Cross-Sectional Analysis.

Large-scale population surveys are beneficial in gathering information on the performance indicators of public well-being, including health and socio-economic standing. However, conducting national population surveys for low and middle-income countries (LMIC) with high population density comes at a high economic cost. To conduct surveys at low-cost and efficiently, multiple surveys with different, but focused, goals are implemented through various organizations in a decentralized manner. Some of the surveys tend to overlap in outcomes with spatial, temporal or both scopes. Mining data jointly from surveys with significant overlap gives new insights while preserving their autonomy. We propose a three-step workflow for integrating surveys using spatial analytic workflow supported by visualizations. We implement the workflow on a case study using two recent population health surveys in India to study malnutrition in children under-five. Our case study focuses on finding hotspots and coldspots for malnutrition, specifically undernutrition, by integrating the outcomes of both surveys. Malnutrition in children under-five is a pertinent global public health problem that is widely prevalent in India. Our work shows that such an integrated analysis is beneficial alongside independent analyses of such existing national surveys to find new insights into national health indicators.

Communities and Cliques in Functional Brain Network Using Multiscale Consensus Approach.

The modular organization of the functional brain connectome implies its functional segregation. Correlation matrices extracted from fMRI data are used as adjacency matrices of the connectome, i.e., the functional connectivity network (FCN). The modular organization of FCN is widely solved using node-community detection methods, albeit with a requirement of edge filtering, mostly. However, network sparsification potentially leads to the loss of correlation information. With no ideal threshold values for edge filtering in literature, there is growing interest in finding communities in the complete weighted network. To address this requirement, we propose the use of exploratory factor analysis (EFA), thus, exploiting the semantics of the correlation matrix. In our recent work on using EFA for FCN analysis, we have proposed a novel consensus-based algorithm using a multiscale approach, where the number of factors nF is treated as the scale. The consensus procedure is employed for transforming the network before performing community detection. Here, we propose a novel extension to our multiscale EFA for finding relevant cliques. We use an ensemble of experiments and extensive quantitative analysis of its outcomes to identify the optimal set of scales for efficient node-partitioning. We perform case studies of datasets of FCN of the human brain at resting state, with different sizes and parcellation atlases (AAL, Schaefer). Our results of consensus communities and cliques correspond to relevant brain activity in its resting state, thus showing the effectiveness of consensus-based multiscale EFA.

HCNM: Heterogeneous Correlation Network Model for Multi-level Integrative Study of Multi-omics Data for Cancer Subtype Prediction.

Integrative analysis of multi-omics data is important for biomedical applications, as it is required for a comprehensive understanding of biological function. Integrating multi-omics data serves multiple purposes, such as, an integrated data model, dimensionality reduction of omic features, patient clustering, etc. For oncological data, patient clustering is synonymous to cancer subtype prediction. However, there is a gap in combining some of the widely used integrative analyses to build more powerful tools. To bridge the gap, we propose a multi-level integration algorithm to identify representative integrative subspace and use it for cancer subtype prediction. The three integrative approaches we implement on multi-omics features are, (1) multivariate multiple (linear) regression of the features from a cohort of patients/samples, (2) network construction using different omics features, and (3) fusion of sample similarity networks across the features. We use a type of multilayer network, called heterogeneous network, as a data model to transition between a network-free (NF) regression model and a network-based (NB) model, which uses correlation networks. The heterogeneous networks consist of intra- and inter-layer graphs. Our proposed heterogeneous correlation network model, HCNM, is central to our algorithm for gene-ranking, integrative subspace identification, and tumor-specific subtypes prediction. The genes of our representative integrative subspace have been enriched with gene-ontology and found to exhibit significant gene-disease association (GDA) scores. The subspace in genes which is less than 5% of the total gene-set of each genomic feature is used with NB fusion integrative model to predict sample subtypes. As the identified integrative subspace data of multi-omics is less prone to noise, bias, and outliers, our experiments show that the subtypes in our results agree with previous benchmark studies and exhibit better classification between poor and good survival of patient cohorts.Clinical relevance: Finding significant cancer-specific genes and subtypes of cancer is vital for early prognosis, and personalized treatment; therefore, improves survival probability of a patient.

A Multiscale Consensus Method Using Factor Analysis to Extract Modular Regions in the Functional Brain Network.

The brain functional connectivity network is complex, generally constructed using correlations between the regions of interest (ROIs) in the brain, corresponding to a parcellation atlas. The brain is known to exhibit a modular organization, referred to as "functional segregation." Generally, functional segregation is extracted from edge-filtered, and optionally, binarized network using community detection and clustering algorithms. Here, we propose the novel use of exploratory factor analysis (EFA) on the correlation matrix for extracting functional segregation, to avoid sparsifying the network by using a threshold for edge filtering. However, the direct usability of EFA is limited, owing to its inherent issues of replication, reliability, and generalizability. In order to avoid finding an optimal number of factors for EFA, we propose a multiscale approach using EFA for node-partitioning, and use consensus to aggregate the results of EFA across different scales. We define an appropriate scale, and discuss the influence of the "interval of scales" in the performance of our multiscale EFA. We compare our results with the state-of-the-art in our case study. Overall, we find that the multiscale consensus method using EFA performs at par with the state-of-the-art.Clinical relevance: Extracting modular brain regions allows practitioners to study spontaneous brain activity at resting state.

Case Report: Obsessive compulsive disorder in posterior cerebellar infarction - illustrating clinical and functional connectivity modulation using MRI-informed transcranial magnetic stimulation.

Objectives: We describe atypical and resistant neuropsychiatric clinical manifestations in a young male with posterior cerebellar gliosis. We also attempt to test the mediating role of the cerebellum in the clinical presentation by manipulating the frontal-cerebellar network using MRI-informed transcranial magnetic stimulation (TMS). Methods: A case report of a young adult male describing obsessive-compulsive symptoms, probably secondary to an infarct in the cerebellar right crus II, combined with an examination of behavioral and functional connectivity changes following TMS treatment. Results: Obsessions, compulsions, and pathological slowing were observed in the background of a posterior cerebellar infarct, along with impairments in vigilance, working memory, verbal fluency, visuospatial ability, and executive functions, in the absence of any motor coordination difficulties. These symptoms did not respond to escitalopram. MRI-informed intermittent theta-burst stimulation delivered to the pre-supplementary motor area identified based on its connectivity with the cerebellar lesion in the crus II resulted in partial improvement of symptoms with enhanced within and between-network modularity of the cerebellar network connectivity. Conclusion: We illustrate a case of OCD possibly secondary to a posterior cerebellar infarct, supporting the role of the cerebellum in the pathophysiology of OCD. Functional connectivity informed non-invasive neuromodulation demonstrated partial treatment response. A seriation technique showed extended connectivity of the cerebellar lesion regions following the neuromodulatory treatment.

Potential for energy generation from anaerobic digestion of food waste in Australia.

Published national and state reports have revealed that Australia deposits an average of 16 million Mg of solid waste into landfills yearly, of which approximately 12.6% is comprised of food. Being highly biodegradable and possessing high energy content, anaerobic digestion offers an attractive treatment option alternative to landfilling. The present study attempted to identify the theoretical maximum benefit of food waste digestion in Australia with regard to energy recovery and waste diversion from landfills. The study also assessed the scope for anaerobic process to utilize waste for energy projects through various case study scenarios. Results indicated anaerobic digestion of total food waste generated across multiple sites in Australia could generate 558 453 dam(3) of methane which translated to 20.3 PJ of heating potential or 1915 GWe in electricity generation annually. This would contribute to 3.5% of total current energy supply from renewable sources. Energy contribution from anaerobic digestion of food waste to the total energy requirement in Australia remains low, partially due to the high energy consumption of the country. However its appropriateness in low density regions, which are prevalent in Australia, may allow digesters to have a niche application in the country.

Wastewater garden--a system to treat wastewater with environmental benefits to community.

Many communities and villages around the world face serious problems with lack of sanitation especially in disposing of the wastewater-black water and grey water from the houses, or wash outs from animal rearing sheds. Across the world diverting wastewater to the surroundings or to the public spaces are not uncommon. This is responsible for contaminating drinking water sources causing health risks and environmental degradation as they become the breeding grounds of mosquitoes and pathogens. Lack of collection and treatment facilities or broken down sewage systems noticed throughout the developing world are associated with this situation. Diverting the wastewater to trees and vegetable gardens was historically a common practice. However the modern world has an array of problems associated with such disposal such as generation of large quantity of wastewater, unavailability of space for onsite disposal or treatment and increase in population. This paper considers the wastewater garden as a means for wastewater treatment and to improve the vegetation and biodiversity of rural areas. This can also be implemented in urban areas in association with parks and open spaces. This also highlights environmental safety in relation to the nutrient, pathogen and heavy metal content of the wastewater. The possibilities of different types of integration and technology that can be adopted for wastewater gardens are also discussed.

Zinc and copper uptake by silver beet grown in secondary treated effluent.

The study was conducted in a hydroponics set-up to determine the suitability of zinc (Zn) and copper (Cu) contaminated wastewater as a growth medium for edible crops; to identify accumulation of Zn and Cu in different parts of the plants and to understand their effects on plant growth. Silver beet was found to produce adequate yields in the Zn (1.7 mg/L and 2.0mg/L) and Cu (0.35 mg/L and 1.1mg/L) spiked medium but not in the secondary treated domestic effluent. The plants grown in secondary treated effluent showed stunted growth and accumulated Zn and Cu to a high level in their shoots. In all other mediums Zn and Cu accumulated more in the roots. The stunted growth of silver beet in secondary treated wastewater and uptake of Zn in particular was considered to be due to the deficiency of nitrogen and other plant nutrients rather than the effect of heavy metals. The study also revealed that if there was adequate amount of macronutrients available for growth, silver beet could be grown in domestic effluent without the risk of Zn or Cu contamination to the undesired level through hydroponics.

Impact of heavy metals on enzymatic activity of substrate and on composting worms Eisenia fetida.

The effect of heavy metals Cu and Zn on dehydrogenase and protease activity of the substrate during vermicomposting was investigated. Three dosages of Cu and Zn were tested in mesocosm experiments to investigate their bioaccumulation and impact on the reproduction of Eisenia fetida. Cu accumulated within the worm tissues in dosage concentrations up to a maximum level of 213 mg Cu kg(-1). The number of juveniles decreased from the lowest to highest dosages of Cu and Zn after 10 weeks of the experiment. Dehydrogenase showed a strong negative correlation (P < 0.01) with increased dosage of Cu, while protease remained unaffected. An overall reduction on dehydrogenase activity with increasing dosages of Cu and Zn indicated that these metals would impact detrimentally on the soil microbiology and consequently the stabilisation of the dosed media.

Effect of pre-composting on vermicomposting of kitchen waste.

The aim of this work was to test combination of the thermocomposting and vermicomposting to improve the treatment efficiency and assess the optimum period required in each method to produce good quality compost. The results showed that pre-thermocomposting improved vermicomposting of kitchen waste. A 9-day thermocomposting prior to vermicomposting helped in mass reduction, moisture management and pathogen reduction.