A STUDY OF FRACTAL ANALYSIS AND SINGULARITY SPECTRUM AND ITS POTENTIAL APPLICATION IN THE OIL AND GAS INDUSTRY

The world is made up of various irregular objects and signals. Although traditional mathematical techniques are not able to analyse these signals, it has been identified that these signals show common features such as singularities at various scales of observation. This indicates the existence of fractals within these signals. In the oil and gas industry, seismic data is a collection of reflected audio signals and is an example of irregular signals that could also have fractal features. Even though we know that global petroleum resources are on the decline, oil and gas still remains the main source of energy throughout the world. This makes seismic exploration activities all the more important. Present indirect hydrocarbon detection techniques using seismic are costly and do not guarantee detection of oil or gas. Therefore, a technological advancement in the field of seismic exploration is evidently needed. Therefore, this study aims to develop a method for direct detection and delineation of hydrocarbons from seismic data. In order to analyse the fractal nature of signals, a collection of mathematical steps known as fractal analysis is applied to generate a singularity spectrum. Although importance has been given on the methods of computing the singularity spectrum, there is little study on the effects of different types of singularities on the singularity spectrum. This study aims to understand how the singularity spectrum is affected by changes applied to input signals. It is by acquiring this knowledge first that the study also intends to develop an algorithm for direct detection of hydrocarbons. The Fraclab toolbox in MATLAB will be extensively used to achieve both of these goals. From the study of the changes to singularity spectrum due to change in signals, it was observed that the square wave is the most irregular signal when compared with sine wave and sawtooth wave. Meanwhile, it was also discovered that a change in the amplitude of the periodic signal does not play a part in the final result of the singularity spectrum. The study has also observed that when two regular waves concatenate, the singularity spectrum produces more than one point due to the existence of a singularity or singularities at the point where the two signals concatenate. In direct comparison, when two periodic signals are added to one another, they only produce a dot on the singularity spectrum indicating that the end signal is still monofractal

Acute electrocardiographic changes during smoking: An observational study

Objective To study the temporal relationship of smoking with electrophysiological changes. Design Prospective observational study. Setting Tertiary cardiac center. Participants Male smokers with atypical chest pain were screened with a treadmill exercise test (TMT). A total of 31 such patients aged 49.8±10.5 years, in whom TMT was either negative or mildly positive were included. Heart rate variability (HRV) parameters of smokers were compared to those of 15 healthy non-smoking participants. Interventions All patients underwent a 24 h Holter monitoring to assess ECG changes during smoking periods. Results Heart rate increased acutely during smoking. Mean heart rate increased from 83.8±13.7 bpm 10 min before smoking, to 90.5±16.4 bpm during smoking, (p <0.0001) and returned to baseline after 30 min. Smoking was also associated with increased ectopic beats (mean of 5.3/h prior to smoking to 9.8/h during smoking to 11.3/h during the hour after smoking; p <0.001). Three patients (9.7%) had significant ST–T changes after smoking. HRV index significantly decreased in smokers (15.2±5.3) as compared to non-smoking controls participants (19.4±3.6; p=0.02), but the other spectral HRV parameters were comparable. Conclusions Heart rate and ectopic beats increase acutely following smoking. Ischaemic ST–T changes were also detected during smoking. Spectral parameters of HRV analysis of smokers remained in normal limits, but more importantly geometrical parameter—HRV index—showed significant abnormality

miR-21 in the Extracellular Vesicles (EVs) of Cerebrospinal Fluid (CSF): A Platform for Glioblastoma Biomarker Development

Glioblastoma cells secrete extra-cellular vesicles (EVs) containing microRNAs (miRNAs). Analysis of these EV miRNAs in the bio-fluids of afflicted patients represents a potential platform for biomarker development. However, the analytic algorithm for quantitative assessment of EV miRNA remains under-developed. Here, we demonstrate that the reference transcripts commonly used for quantitative PCR (including GAPDH, 18S rRNA, and hsa-miR-103) were unreliable for assessing EV miRNA. In this context, we quantitated EV miRNA in absolute terms and normalized this value to the input EV number. Using this method, we examined the abundance of miR-21, a highly over-expressed miRNA in glioblastomas, in EVs. In a panel of glioblastoma cell lines, the cellular levels of miR-21 correlated with EV miR-21 levels (p<0.05), suggesting that glioblastoma cells actively secrete EVs containing miR-21. Consistent with this hypothesis, the CSF EV miR-21 levels of glioblastoma patients (n=13) were, on average, ten-fold higher than levels in EVs isolated from the CSF of non-oncologic patients (n=13, p<0.001). Notably, none of the glioblastoma CSF harbored EV miR-21 level below 0.25 copies per EV in this cohort. Using this cut-off value, we were able to prospectively distinguish CSF derived from glioblastoma and non-oncologic patients in an independent cohort of twenty-nine patients (Sensitivity=87%; Specificity=93%; AUC=0.91, p<0.01). Our results suggest that CSF EV miRNA analysis of miR-21 may serve as a platform for glioblastoma biomarker development

Synthesizing habitat connectivity analyses of a globally important human-dominated tiger-conservation landscape

As ecological data and associated analyses become more widely available, synthesizing results for effective communication with stakeholders is essential. In the case of wildlife corridors, managers in human-dominated landscapes need to identify both the locations of corridors and multiple stakeholders for effective oversight. We synthesized five independent studies of tiger (Panthera tigris) connectivity in central India, a global priority landscape for tiger conservation, to quantify agreement on landscape permeability for tiger movement and potential movement pathways. We used the latter analysis to identify connectivity areas on which studies agreed and stakeholders associated with these areas to determine relevant participants in corridor management. Three or more of the five studies’ resistance layers agreed in 63% of the study area. Areas in which all studies agree on resistance were of primarily low (66%, e.g., forest) and high (24%, e.g., urban) resistance. Agreement was lower in intermediate resistance areas (e.g., agriculture). Despite these differences, the studies largely agreed on areas with high levels of potential movement: >40% of high average (top 20%) current-flow pixels were also in the top 20% of current-flow agreement pixels (measured by low variation), indicating consensus connectivity areas (CCAs) as conservation priorities. Roughly 70% of the CCAs fell within village administrative boundaries, and 100% overlapped forest department management boundaries, suggesting that people live and use forests within these priority areas. Over 16% of total CCAs’ area was within 1 km of linear infrastructure (437 road, 170 railway, 179 transmission line, and 339 canal crossings; 105 mines within 1 km of CCAs). In 2019, 78% of forest land diversions for infrastructure and mining in Madhya Pradesh (which comprises most of the study region) took place in districts with CCAs. Acute competition for land in this landscape with globally important wildlife corridors calls for an effective comanagement strategy involving local communities, forest departments, and infrastructure planners

Genome-wide association study for type 2 diabetes in Indians identifies a new susceptibility locus at 2q21.

Indians undergoing socioeconomic and lifestyle transitions will be maximally affected by epidemic of type 2 diabetes (T2D). We conducted a two-stage genome-wide association study of T2D in 12,535 Indians, a less explored but high-risk group. We identified a new type 2 diabetes-associated locus at 2q21, with the lead signal being rs6723108 (odds ratio 1.31; P = 3.32 × 10⁻⁹). Imputation analysis refined the signal to rs998451 (odds ratio 1.56; P = 6.3 × 10⁻¹²) within TMEM163 that encodes a probable vesicular transporter in nerve terminals. TMEM163 variants also showed association with decreased fasting plasma insulin and homeostatic model assessment of insulin resistance, indicating a plausible effect through impaired insulin secretion. The 2q21 region also harbors RAB3GAP1 and ACMSD; those are involved in neurologic disorders. Forty-nine of 56 previously reported signals showed consistency in direction with similar effect sizes in Indians and previous studies, and 25 of them were also associated (P < 0.05). Known loci and the newly identified 2q21 locus altogether explained 7.65% variance in the risk of T2D in Indians. Our study suggests that common susceptibility variants for T2D are largely the same across populations, but also reveals a population-specific locus and provides further insights into genetic architecture and etiology of T2D

The impact of different fertiliser management options and cultivars on nitrogen use efficiency and yield for rice cropping in the Indo-Gangetic Plain: two seasons of methane, nitrous oxide and ammonia emissions

This study presents detailed crop and gas flux data from two years of rice production at the experimental farm of the ICAR-Indian Agricultural Research Institute, New Delhi, India. In comparing 4 nitrogen (N) fertiliser regimes across 4 rice cultivars (CRD 310, IR-64, MTU 1010, P-44), we have added to growing evidence of the environmental costs of rice production in the region. The study shows that rice cultivar can impact yields of both grain, and total biomass produced in given circumstances, with the CRD 310 cultivar showing consistently high nitrogen use efficiency (NUE) for total biomass compared with other tested varieties, but not necessarily with the highest grain yield, which was P-44 in this experiment. While NUE of the rice did vary depending on experimental treatments (ranging from 41% to 73%), 73%), this did not translate directly into the reduction of emissions of ammonia (NH3) and nitrous oxide (N2O). Emissions were relatively similar across the different rice cultivars regardless of NUE. Conversely, agronomic practices that reduced total N losses were associated with higher yield. In terms of fertiliser application, the outstanding impact was of the very high methane (CH4) emissions as a result of incorporating farmyard manure (FYM) into rice paddies, which dominated the overall effect on global warming potential. While the use of nitrification and urease inhibiting substances decreased N2O emissions overall, NH3 emissions were relatively unaffected (or slightly higher). Overall, the greatest reduction in greenhouse gas (GHG) emissions came from reducing irrigation water added to the fields, resulting in higher N2O, but significantly less CH4 emissions, reducing net GHG emission compared with continuous flooding. Overall, genetic differences generated more variation in yield and NUE than agronomic management (excluding controls), whereas agronomy generated larger differences than genetics concerning gaseous losses. This study suggests that a mixed approach needs to be applied when attempting to reduce pollution and global warming potential from rice production and potential pollution swapping and synergies need to be considered. Finding the right balance of rice cultivar, irrigation technique and fertiliser type could significantly reduce emissions, while getting it wrong can result in considerably poorer yields and higher pollution

A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems

Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed. This critical gap may be addressed through the development of "Lifelong Learning" systems that are capable of 1) Continuous Learning, 2) Transfer and Adaptation, and 3) Scalability. Unfortunately, efforts to improve these capabilities are typically treated as distinct areas of research that are assessed independently, without regard to the impact of each separate capability on other aspects of the system. We instead propose a holistic approach, using a suite of metrics and an evaluation framework to assess Lifelong Learning in a principled way that is agnostic to specific domains or system techniques. Through five case studies, we show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems. We highlight how the proposed suite of metrics quantifies performance trade-offs present during Lifelong Learning system development - both the widely discussed Stability-Plasticity dilemma and the newly proposed relationship between Sample Efficient and Robust Learning. Further, we make recommendations for the formulation and use of metrics to guide the continuing development of Lifelong Learning systems and assess their progress in the future.Comment: To appear in Neural Network

Common variants in CLDN2 and MORC4 genes confer disease susceptibility in patients with chronic pancreatitis

A recent Genome-wide Association Study (GWAS) identified association with variants in X-linked CLDN2 and MORC4 and PRSS1-PRSS2 loci with Chronic Pancreatitis (CP) in North American patients of European ancestry. We selected 9 variants from the reported GWAS and replicated the association with CP in Indian patients by genotyping 1807 unrelated Indians of Indo-European ethnicity, including 519 patients with CP and 1288 controls. The etiology of CP was idiopathic in 83.62% and alcoholic in 16.38% of 519 patients. Our study confirmed a significant association of 2 variants in CLDN2 gene (rs4409525—OR 1.71, P = 1.38 x 10-09; rs12008279—OR 1.56, P = 1.53 x 10-04) and 2 variants in MORC4 gene (rs12688220—OR 1.72, P = 9.20 x 10-09; rs6622126—OR 1.75, P = 4.04x10-05) in Indian patients with CP. We also found significant association at PRSS1-PRSS2 locus (OR 0.60; P = 9.92 x 10-06) and SAMD12-TNFRSF11B (OR 0.49, 95% CI [0.31–0.78], P = 0.0027). A variant in the gene MORC4 (rs12688220) showed significant interaction with alcohol (OR for homozygous and heterozygous risk allele -14.62 and 1.51 respectively, P = 0.0068) suggesting gene-environment interaction. A combined analysis of the genes CLDN2 and MORC4 based on an effective risk allele score revealed a higher percentage of individuals homozygous for the risk allele in CP cases with 5.09 fold enhanced risk in individuals with 7 or more effective risk alleles compared with individuals with 3 or less risk alleles (P = 1.88 x 10-14). Genetic variants in CLDN2 and MORC4 genes were associated with CP in Indian patients