Does trade liberalization promote regional disparities? Evidence from a multiregional CGE model of India

YesOver last few decades, there has been a growing interest among researchers in understanding the link between trade liberalization and regional disparities within the context of an individual country. In this study, we develop the first ever single-country multiregional Computable General Equilibrium (CGE) model for the Indian economy to investigate this linkage. Overall our results suggest that, in the short run, trade liberalization has a beneficial impact on the rich and fast-growing middle-income states and a marginal or negative impact on the poor states

Raw transcriptomics data to gene specific SSRs: a validated free bioinformatics workflow for biologists

Recent advances in next-generation sequencing technologies have paved the path for a considerable amount of sequencing data at a relatively low cost. This has revolutionized the genomics and transcriptomics studies. However, different challenges are now created in handling such data with available bioinformatics platforms both in assembly and downstream analysis performed in order to infer correct biological meaning. Though there are a handful of commercial software and tools for some of the procedures, cost of such tools has made them prohibitive for most research laboratories. While individual open-source or free software tools are available for most of the bioinformatics applications, those components usually operate standalone and are not combined for a user-friendly workflow. Therefore, beginners in bioinformatics might find analysis procedures starting from raw sequence data too complicated and time-consuming with the associated learning-curve. Here, we outline a procedure for de novo transcriptome assembly and Simple Sequence Repeats (SSR) primer design solely based on tools that are available online for free use. For validation of the developed workflow, we used Illumina HiSeq reads of different tissue samples of\ua0Santalum album\ua0(sandalwood), generated from a previous transcriptomics project. A portion of the designed primers were tested in the lab with relevant samples and all of them successfully amplified the targeted regions. The presented bioinformatics workflow can accurately assemble quality transcriptomes and develop gene specific SSRs. Beginner biologists and researchers in bioinformatics can easily utilize this workflow for research purposes

Slim-YOLO: A Simplified Object Detection Model for the Detection of Pigmented Iris Freckles as a Potential Biomarker for Cutaneous Melanoma

Melanomas are the most dangerous form of skin cancer, accounting for a majority of mortality among all skin cancers. As melanomas tend to go unnoticed without constant supervision, it is important that steps are taken to identify and prevent their spread before they reach more severe stages. Many recent clinical trials and research have identified various indicators of melanoma that may be used for early detection. In this work, we explore the use of Convolutional Neural Networks (CNN) to localize and detect one such indicator - a strong correlation between the number of pigmented freckles in a person's iris and their risk of developing melanoma on the skin. We model this task of detecting pigmented iris freckles as a single-class, one-sized object detection problem. For this, we propose Slim-YOLO, a lighter and simpler object detection model based on YOLOv3. The simplifications of Slim-YOLO are introduced through reducing the model computations by removing the need for multiple detection scales and classification. We also remove the constraints applied by anchor boxes. The experimental results show that Slim-YOLO is capable of achieving comparable performance (90.7% in mAP) with YOLOv3 (93.7%) while yielding a smaller model size of two-third the size of YOLOv3. These may prove highly beneficial to better facilitate deployment of the model on mobile devices in the future. Thus, we automate the iris freckle detection process successfully to help provide insights to practitioners, and contribute to the use of deep learning methods in detection of anomalies in medical imaging.</p

Universal barcoding regions, rbcL, matK and trnH-psbA do not discriminate Cinnamomum species in Sri Lanka.

The genus Cinnamomum consists of about 250 species spread globally. Out of these, C. verum (C. zeylanicum), also known as true cinnamon or Ceylon cinnamon, has gained worldwide attention due to its culinary uses and medicinal values. Sri Lanka is the largest true cinnamon producer in the world and accounts for about 80-90% of global production. Other than the cultivated species, Sri Lankan natural vegetation is home to seven endemic wild species of the genus Cinnamomum. While these are underutilized, proper identification and characterization are essential steps in any sustainable conservation and utilization strategies. Currently, species identification is purely based on morphological traits, and intraspecific diversity has made it more challenging. In this study, all the eight Cinnamomum species found in Sri Lanka, C. capparu-coronde, C. citriodorum C. dubium, C. litseifolium, C. ovalifolium, C. rivulorum, C. sinharajaense, and C. verum were collected in triplicates and identified using typical morphological traits. DNA extracted with the same collection was assessed with universal barcoding regions, rbcL, matK, and trnH-psbA. While no intraspecific sequence differences were observed in C. citriodorum, C. rivulorum, and C. verum, the others had polymorphic sites in one, two, or all regions assessed. Interestingly, two individuals of C. sinharajaense had identical barcodes to the cultivated species C. verum, while the other one had one variable cite in matK region and three cites in trnH-psbA reigon. Further, one C. dubium and one C. capparu-coronde accession each had identical, rbcL, and trnH-psbA sequences while those had only a single nucleotide variation observed in matK region. Overall, the phylogeny of Cinnamomum species found in Sri Lanka could not be completely resolved with DNA barcoding regions studied