Optimizing NEURON Simulation Environment Using Remote Memory Access with Recursive Doubling on Distributed Memory Systems

Increase in complexity of neuronal network models escalated the efforts to make NEURON simulation environment efficient. The computational neuroscientists divided the equations into subnets amongst multiple processors for achieving better hardware performance. On parallel machines for neuronal networks, interprocessor spikes exchange consumes large section of overall simulation time. In NEURON for communication between processors Message Passing Interface (MPI) is used. MPI_Allgather collective is exercised for spikes exchange after each interval across distributed memory systems. The increase in number of processors though results in achieving concurrency and better performance but it inversely affects MPI_Allgather which increases communication time between processors. This necessitates improving communication methodology to decrease the spikes exchange time over distributed memory systems. This work has improved MPI_Allgather method using Remote Memory Access (RMA) by moving two-sided communication to one-sided communication, and use of recursive doubling mechanism facilitates achieving efficient communication between the processors in precise steps. This approach enhanced communication concurrency and has improved overall runtime making NEURON more efficient for simulation of large neuronal network models

The Pakistan risk of myocardial infarction study: A resource for the study of genetic, lifestyle and other determinants of myocardial infarction in south Asia

The burden of coronary heart disease (CHD) is increasing at a greater rate in South Asia than in any other region globally, but there is little direct evidence about its determinants. The Pakistan Risk of Myocardial Infarction Study (PROMIS) is an epidemiological resource to enable reliable study of genetic, lifestyle and other determinants of CHD in South Asia. By March 2009, PROMIS had recruited over 5,000 cases of first-ever confirmed acute myocardial infarction (MI) and over 5,000 matched controls aged 30-80 years. For each participant, information has been recorded on demographic factors, lifestyle, medical and family history, anthropometry, and a 12-lead electrocardiogram. A range of biological samples has been collected and stored, including DNA, plasma, serum and whole blood. During its next stage, the study aims to expand recruitment to achieve a total of about 20,000 cases and about 20,000 controls, and, in subsets of participants, to enrich the resource by collection of monocytes, establishment of lymphoblastoid cell lines, and by resurveying participants. Measurements in progress include profiling of candidate biochemical factors, assay of 45,000 variants in 2,100 candidate genes, and a genomewide association scan of over 650,000 genetic markers. We have established a large epidemiological resource for CHD in South Asia. In parallel with its further expansion and enrichment, the PROMIS resource will be systematically harvested to help identify and evaluate genetic and other determinants of MI in South Asia. Findings from this study should advance scientific understanding and inform regionally appropriate disease prevention and control strategies

Fully Automated Skull Stripping from Brain Magnetic Resonance Images Using Mask RCNN-Based Deep Learning Neural Networks

This research comprises experiments with a deep learning framework for fully automating the skull stripping from brain magnetic resonance (MR) images. Conventional techniques for segmentation have progressed to the extent of Convolutional Neural Networks (CNN). We proposed and experimented with a contemporary variant of the deep learning framework based on mask region convolutional neural network (Mask–RCNN) for all anatomical orientations of brain MR images. We trained the system from scratch to build a model for classification, detection, and segmentation. It is validated by images taken from three different datasets: BrainWeb; NAMIC, and a local hospital. We opted for purposive sampling to select 2000 images of T1 modality from data volumes followed by a multi-stage random sampling technique to segregate the dataset into three batches for training (75%), validation (15%), and testing (10%) respectively. We utilized a robust backbone architecture, namely ResNet–101 and Functional Pyramid Network (FPN), to achieve optimal performance with higher accuracy. We subjected the same data to two traditional methods, namely Brain Extraction Tools (BET) and Brain Surface Extraction (BSE), to compare their performance results. Our proposed method had higher mean average precision (mAP) = 93% and content validity index (CVI) = 0.95%, which were better than comparable methods. We contributed by training Mask–RCNN from scratch for generating reusable learning weights known as transfer learning. We contributed to methodological novelty by applying a pragmatic research lens, and used a mixed method triangulation technique to validate results on all anatomical modalities of brain MR images. Our proposed method improved the accuracy and precision of skull stripping by fully automating it and reducing its processing time and operational cost and reliance on technicians. This research study has also provided grounds for extending the work to the scale of explainable artificial intelligence (XAI)

Regulation of Phosphorus and Zinc Uptake in Relation to Arbuscular Mycorrhizal Fungi for Better Maize Growth

Zinc (Zn) is an important micronutrient for plants, whose deficiency in alkaline soils creates hurdles in the achievement of optimum crop growth. Moreover, overuse of phosphorus (P) fertilizers often causes Zn immobilization in the soil. The employment of arbuscular mycorrhizal fungi (AMF) could be potentially environmentally friendly technology in this regard. Therefore, a pot experiment was conducted to assess the beneficial role of AMF (Glomus species) on maize under low and high P and Zn levels. Seven levels of Zn (0, 20, 40, 60, 80, 100 and 120 mg Zn kg−1 soil ZnSO4·7H2O) and three levels of P (0, 14.5, 29 and 58 kg ac−1 as single superphosphate) were applied with (M+) and without AMF (M−). The results showed that a high application rate of Zn (100 and 120 mg Zn kg−1 soil) restricted P translocation in plants and vice versa. Moreover, the nutritional status of mycorrhizal plants (AM) was better than non-mycorrhizal (NM) plants. AM plants showed a maximum positive response at 20 mg Zn kg−1 soil, or 29 kg P ac−1. In response to 20 mg Zn kg−1 soil, root colonization was maximum, which enhanced the maize nutrient concentration in shoots. In conclusion, AMF inoculation (M+) with P (29 kg ac−1) and Zn (20 mg kg−1) is efficacious for improving maize’s growth and nutrition. More investigations are suggested at the field level under different agroclimatic zones to ascertain whether P (29 kg ac−1) or Zn (20 mg kg−1) with AMF is the best treatment for maize growth optimization

Correlation of Soil Characteristics and Citrus Leaf Nutrients Contents in Current Scenario of Layyah District

Soil with low fertility is a big problem for achieving citrus productivity. In this regard, the management of macro and micronutrients is essential. Macro and micronutrient deficiency decreased the yield and the quality of citrus fruit. It is the need of the hour to classify the soil fertility status under changing climatic scenarios. The current soil fertility survey was conducted to examine the macro and micronutrient status in the citrus production area. In soil, three depths (0–15, 15–30, and 30–45 cm) were taken for sampling. For leaves, 4–6-months-old non-bearing twigs were sampled from 20 trees per orchard at breast height. Results showed that soil pH (7.1–8.4) was slightly alkaline, electrical conductivity (EC) was non-saline (<4 dSm−1), soil organic matter (SOM) was deficient (<0.86%), and calcium carbonate (CaCO3) was slight calcareous (<8%), at 0–15, 15–30, and 30–45 cm depths. The majority of soil samples were low in nitrogen (N) contents at all depths, i.e., (<0.043) 0–15 (85%), 15–30 (97%), and 30–45 (100%) cm depths. Phosphorus (P) was medium (7–15 mg kg−1) at 0–15 cm (60%) but low (<7 mg kg−1) at 15–30 (63%) and 30–45 cm (82%) depths. Potassium (K) was medium (80–180 mg kg−1) at 0–15 (69%), 15–30 (69%), and 30–45 cm (10%) depths. Boron (B) and manganese (Mn) were medium, and Cu was high in 0.15 cm, but all were low at 15–30 and 30–45 cm depths. Iron (Fe) and zinc (Zn) were low at depths of 0–15, 15–30, and 30–45 cm. Most citrus leaves were deficient in N (94%), Fe (76%), Zn (67%), and B (67%). In conclusion, soil fertilization is not sufficient for optimum citrus yield because of alkaline pH and slight calcareous soil conditions in this region. Foliar application of nutrients is suggested instead of only soil fertilization, for better nutrient management in citrus orchards

Correlation of Soil Characteristics and Citrus Leaf Nutrients Contents in Current Scenario of Layyah District

Soil with low fertility is a big problem for achieving citrus productivity. In this regard, the management of macro and micronutrients is essential. Macro and micronutrient deficiency decreased the yield and the quality of citrus fruit. It is the need of the hour to classify the soil fertility status under changing climatic scenarios. The current soil fertility survey was conducted to examine the macro and micronutrient status in the citrus production area. In soil, three depths (0–15, 15–30, and 30–45 cm) were taken for sampling. For leaves, 4–6-months-old non-bearing twigs were sampled from 20 trees per orchard at breast height. Results showed that soil pH (7.1–8.4) was slightly alkaline, electrical conductivity (EC) was non-saline (−1), soil organic matter (SOM) was deficient (3) was slight calcareous (−1) at 0–15 cm (60%) but low (−1) at 15–30 (63%) and 30–45 cm (82%) depths. Potassium (K) was medium (80–180 mg kg−1) at 0–15 (69%), 15–30 (69%), and 30–45 cm (10%) depths. Boron (B) and manganese (Mn) were medium, and Cu was high in 0.15 cm, but all were low at 15–30 and 30–45 cm depths. Iron (Fe) and zinc (Zn) were low at depths of 0–15, 15–30, and 30–45 cm. Most citrus leaves were deficient in N (94%), Fe (76%), Zn (67%), and B (67%). In conclusion, soil fertilization is not sufficient for optimum citrus yield because of alkaline pH and slight calcareous soil conditions in this region. Foliar application of nutrients is suggested instead of only soil fertilization, for better nutrient management in citrus orchards