Soil Loss Estimation Using the Revised Universal Soil Loss Equation (RUSLE): A Coarse Resolution Dataset in the Indian Himalayan Region

Soil erosion is considered a very critical environmental issue that has repercussions for almost every aspect of the world. In developing countries, such as India, soil erosion continues to be a major limitation. A prediction and assessment of erosion prone areas is of utmost importance for soil fertility and water management. Recent technological advancements have provided useful models through which remotely-sensed data for a large scale area can be analyzed and interpreted. This study aims to adopt an erosion model that is unique to the physiography, biological and climatic conditions of the Indian Himalayan Region. The Revised Universal Soil Loss Equation (RUSLE) model estimates the average annual soil loss A in tonnes ha-1 year-1. Recognizing the conditions of the region, the RUSLE developed by Renard et al., (1997) was applied in conjunction with Geographic Information System (GIS) for estimating soil loss. All parameters of the model were thoroughly studied, starting from reviews and research papers on soil erosion assessment at national and catchment levels. The study follows the RUSLE soil model in estimating the rate of soil erosion at state and district level. The model was developed around coarse resolution data requirements, with practicality in providing annual soil loss rate for a large study area. It provides a means to describe specific districts that are vulnerable to soil erosion, rendering immediate action for soil conservation practices. To determine the spatial distribution of average annual soil erosion within the study area, cell-based parameters of the RUSLE were multiplied in the specified 500m x 500m spatial resolution using the raster calculator function in ArcGIS 10.0 software. The spatial pattern of soil erosion indicates that maximum erosion takes place in the north, north-western and eastern regions of the study area while the areas with low erosion rates are located in the eastern-most part of the study area.Keywords: Soil erosion, Remote sensing, Geographic information system, Soil erodibilit

Structural and non-coding variants increase the diagnostic yield of clinical whole genome sequencing for rare diseases

BACKGROUND: Whole genome sequencing is increasingly being used for the diagnosis of patients with rare diseases. However, the diagnostic yields of many studies, particularly those conducted in a healthcare setting, are often disappointingly low, at 25-30%. This is in part because although entire genomes are sequenced, analysis is often confined to in silico gene panels or coding regions of the genome.METHODS: We undertook WGS on a cohort of 122 unrelated rare disease patients and their relatives (300 genomes) who had been pre-screened by gene panels or arrays. Patients were recruited from a broad spectrum of clinical specialties. We applied a bioinformatics pipeline that would allow comprehensive analysis of all variant types. We combined established bioinformatics tools for phenotypic and genomic analysis with our novel algorithms (SVRare, ALTSPLICE and GREEN-DB) to detect and annotate structural, splice site and non-coding variants.RESULTS: Our diagnostic yield was 43/122 cases (35%), although 47/122 cases (39%) were considered solved when considering novel candidate genes with supporting functional data into account. Structural, splice site and deep intronic variants contributed to 20/47 (43%) of our solved cases. Five genes that are novel, or were novel at the time of discovery, were identified, whilst a further three genes are putative novel disease genes with evidence of causality. We identified variants of uncertain significance in a further fourteen candidate genes. The phenotypic spectrum associated with RMND1 was expanded to include polymicrogyria. Two patients with secondary findings in FBN1 and KCNQ1 were confirmed to have previously unidentified Marfan and long QT syndromes, respectively, and were referred for further clinical interventions. Clinical diagnoses were changed in six patients and treatment adjustments made for eight individuals, which for five patients was considered life-saving.CONCLUSIONS: Genome sequencing is increasingly being considered as a first-line genetic test in routine clinical settings and can make a substantial contribution to rapidly identifying a causal aetiology for many patients, shortening their diagnostic odyssey. We have demonstrated that structural, splice site and intronic variants make a significant contribution to diagnostic yield and that comprehensive analysis of the entire genome is essential to maximise the value of clinical genome sequencing.</p

Multi-beam RF aperture using multiplierless FFT approximation

Multiple independent radio-frequency (RF) beams find applications in communications, radio astronomy, radar and microwave imaging. An N-point fast Fourier transform (FFT) applied spatially across an array of receiver antennas provides N-independent RF beams at N/2 log(2) N multiplier complexity. Here, a low-complexity multiplierless approximation for the 8-point FFT is presented for RF beamforming, using only 26 additions. The algorithm provides eight beams that closely resemble the antenna array patterns of the traditional FFT-based beam-former albeit without using multipliers. The proposed FFT-like algorithm was verified on-chip using a Xilinx Virtex-6 Lx240T field programmable gate array (FPGA) device. The FPGA implementation indicated bandwidth of 369 MHz for each of the independent receive-mode RF beams

Genetic predictors of the maximum doses patients receive during clinical use of the anti-epileptic drugs carbamazepine and phenytoin

Phenytoin and carbamazepine are effective and inexpensive anti-epileptic drugs (AEDs). As with many AEDs, a broad range of doses is used, with the final “maintenance” dose normally determined by trial and error. Although many genes could influence response to these medicines, there are obvious candidates. Both drugs target the α-subunit of the sodium channel, encoded by the SCN family of genes. Phenytoin is principally metabolized by CYP2C9, and both are probable substrates of the drug transporter P-glycoprotein. We therefore assessed whether variation in these genes associates with the clinical use of carbamazepine and phenytoin in cohorts of 425 and 281 patients, respectively. We report that a known functional polymorphism in CYP2C9 is highly associated with the maximum dose of phenytoin (P = 0.0066). We also show that an intronic polymorphism in the SCN1A gene shows significant association with maximum doses in regular usage of both carbamazepine and phenytoin (P = 0.0051 and P = 0.014, respectively). This polymorphism disrupts the consensus sequence of the 5′ splice donor site of a highly conserved alternative exon (5N), and it significantly affects the proportions of the alternative transcripts in individuals with a history of epilepsy. These results provide evidence of a drug target polymorphism associated with the clinical use of AEDs and set the stage for a prospective evaluation of how pharmacogenetic diagnostics can be used to improve dosing decisions in the use of phenytoin and carbamazepine. Although the case made here is compelling, our results cannot be considered definitive or ready for clinical application until they are confirmed by independent replication

Automated Kidney and Liver Segmentation in MR Images in Patients with Autosomal Dominant Polycystic Kidney Disease: A Multicenter Study

Background Imaging-based total kidney volume (TKV) and total liver volume (TLV) are major prognostic factors in autosomal dominant polycystic kidney disease (ADPKD) and end points for clinical trials. However, volumetry is time consuming and reader dependent in clinical practice. Our aim was to develop a fully automated method for joint kidney and liver segmentation in magnetic resonance imaging (MRI) and to evaluate its performance in a multisequence, multicenter setting. Methods The convolutional neural network was trained on a large multicenter dataset consisting of 992 MRI scans of 327 patients. Manual segmentation delivered ground-truth labels. The model's performance was evaluated in a separate test dataset of 93 patients (350 MRI scans) as well as a heterogeneous external dataset of 831 MRI scans from 323 patients. Results The segmentation model yielded excellent performance, achieving a median per study Dice coefficient of 0.92-0.97 for the kidneys and 0.96 for the liver. Automatically computed TKV correlated highly with manual measurements (intraclass correlation coefficient [ICC]: 0.996-0.999) with low bias and high precision (-0.2%+/- 4% for axial images and 0.5%+/- 4% for coronal images). TLV estimation showed an ICC of 0.999 and bias/precision of -0.5%+/- 3%. For the external dataset, the automated TKV demonstrated bias and precision of -1%+/- 7%. Conclusions Our deep learning model enabled accurate segmentation of kidneys and liver and objective assessment of TKV and TLV. Importantly, this approach was validated with axial and coronal MRI scans from 40 different scanners, making implementation in clinical routine care feasible

Distinct HLA associations of LGI1 and CASPR2-antibody diseases.

The recent biochemical distinction between antibodies against leucine-rich, glioma-inactivated-1 (LGI1), contactin-associated protein-2 (CASPR2) and intracellular epitopes of voltage-gated potassium-channels (VGKCs) demands aetiological explanations. Given established associations between human leucocyte antigen (HLA) alleles and adverse drug reactions, and our clinical observation of frequent adverse drugs reactions in patients with LGI1 antibodies, we compared HLA alleles between healthy controls (n = 5553) and 111 Caucasian patients with VGKC-complex autoantibodies. In patients with LGI1 antibodies (n = 68), HLA-DRB1*07:01 was strongly represented [odds ratio = 27.6 (95% confidence interval 12.9-72.2), P = 4.1 × 10-26]. In contrast, patients with CASPR2 antibodies (n = 31) showed over-representation of HLA-DRB1*11:01 [odds ratio = 9.4 (95% confidence interval 4.6-19.3), P = 5.7 × 10-6]. Other allelic associations for patients with LGI1 antibodies reflected linkage, and significant haplotypic associations included HLA-DRB1*07:01-DQA1*02:01-DQB1*02:02, by comparison to DRB1*11:01-DQA1*05:01-DQB1*03:01 in CASPR2-antibody patients. Conditional analysis in LGI1-antibody patients resolved further independent class I and II associations. By comparison, patients with both LGI1 and CASPR2 antibodies (n = 3) carried yet another complement of HLA variants, and patients with intracellular VGKC antibodies (n = 9) lacked significant HLA associations. Within LGI1- or CASPR2-antibody patients, HLA associations did not correlate with clinical features. In silico predictions identified unique CASPR2- and LGI1-derived peptides potentially presented by the respective over-represented HLA molecules. These highly significant HLA associations dichotomize the underlying immunology in patients with LGI1 or CASPR2 antibodies, and inform T cell specificities and cellular interactions at disease initiation.10.1093/brain/awy109_video1awy109media15796480660001