A handy tool for forecasting population to aid estimation of water demand

1587-1592Estimation of demographic variations for societal or infrastructural development policies requires accurate prediction of futuristic population for a given locality. Economic viability as well as sustainability at large, of the engineering designs in urban development activities depend on the variations in projected populations for a given design period. Considering the uncertainties in existing calculation practices to derive an average value, present study offers a computationally efficient program capable of forecasting the future population based on the existing past population data using three well-known population forecasting methods, namely, arithmetic increase method, geometric increase method and incremental increase method. The results proved that when compared to manual calculation, the predictions were accurate, precise and computationally efficient. This user-friendly tool will be highly beneficial for various service providers where population forecasting is inevitable. The robustness of the computer code has been demonstrated using six decades of real time census data of Coimbatore city

Domain generalization for prostate segmentation in transrectal ultrasound images: A multi-center study

Prostate biopsy and image-guided treatment procedures are often performed under the guidance of ultrasound fused with magnetic resonance images (MRI). Accurate image fusion relies on accurate segmentation of the prostate on ultrasound images. Yet, the reduced signal-to-noise ratio and artifacts (e.g., speckle and shadowing) in ultrasound images limit the performance of automated prostate segmentation techniques and generalizing these methods to new image domains is inherently difficult. In this study, we address these challenges by introducing a novel 2.5D deep neural network for prostate segmentation on ultrasound images. Our approach addresses the limitations of transfer learning and finetuning methods (i.e., drop in performance on the original training data when the model weights are updated) by combining a supervised domain adaptation technique and a knowledge distillation loss. The knowledge distillation loss allows the preservation of previously learned knowledge and reduces the performance drop after model finetuning on new datasets. Furthermore, our approach relies on an attention module that considers model feature positioning information to improve the segmentation accuracy. We trained our model on 764 subjects from one institution and finetuned our model using only ten subjects from subsequent institutions. We analyzed the performance of our method on three large datasets encompassing 2067 subjects from three different institutions. Our method achieved an average Dice Similarity Coefficient (Dice) of 94.0±0.03 and Hausdorff Distance (HD95) of 2.28 mm in an independent set of subjects from the first institution. Moreover, our model generalized well in the studies from the other two institutions (Dice: 91.0±0.03; HD95: 3.7 mm and Dice: 82.0±0.03; HD95: 7.1 mm). We introduced an approach that successfully segmented the prostate on ultrasound images in a multi-center study, suggesting its clinical potential to facilitate the accurate fusion of ultrasound and MRI images to drive biopsy and image-guided treatments

Structure Based Design and Synthesis of Peptide Inhibitor of Human LOX-12: In Vitro and In Vivo Analysis of a Novel Therapeutic Agent for Breast Cancer

Human breast cancer cell proliferation involves a complex interaction between growth factors, steroid hormones and peptide hormones. The interaction of growth factors, such as epidermal growth factor (EGF), with their receptors on breast cancer cells can lead to the hydrolysis of phospholipids and release of fatty acid such as arachidonic acid, which can be further metabolized by cyclooxygenase (COX) and lipoxygenase (LOX) pathways to produce prostaglandins. The high concentration of prostaglandins has been associated with chronic inflammatory diseases and several types of human cancers. This is due to the over expression COX, LOX and other inflammatory enzymes. Ten peptides were designed and synthesized by solid phase peptide synthesis and analyzed in vitro for enzyme inhibition. Out of these peptides, YWCS had shown significant inhibitory effects. The dissociation constant (KD) was determined by surface plasmon resonance (SPR) analysis and was found to be 3.39×10−8 M and 8.6×10−8 M for YWCS and baicalein (positive control), respectively. The kinetic constant Ki was 72.45×10−7 M as determined by kinetic assay. The peptide significantly reduced the cell viability of estrogen positive MCF-7 and estrogen negative MDA-MB-231 cell line with the half maximal concentration (IC50) of 75 µM and 400 µM, respectively. The peptide also induced 49.8% and 20.8% apoptosis in breast cancer cells MCF-7 and MDA-MB-231, respectively. The YWCS was also found to be least hemolytic at a concentration of 358 µM. In vivo studies had shown that the peptide significantly inhibits tumor growth in mice (p<0.017). This peptide can be used as a lead compound and complement for ongoing efforts to develop differentiation therapies for breast cancer

Evaluation of integrin αvβ6 cystine knot PET tracers to detect cancer and idiopathic pulmonary fibrosis.

Advances in precision molecular imaging promise to transform our ability to detect, diagnose and treat disease. Here, we describe the engineering and validation of a new cystine knot peptide (knottin) that selectively recognizes human integrin αvβ6 with single-digit nanomolar affinity. We solve its 3D structure by NMR and x-ray crystallography and validate leads with 3 different radiolabels in pre-clinical models of cancer. We evaluate the lead tracer's safety, biodistribution and pharmacokinetics in healthy human volunteers, and show its ability to detect multiple cancers (pancreatic, cervical and lung) in patients at two study locations. Additionally, we demonstrate that the knottin PET tracers can also detect fibrotic lung disease in idiopathic pulmonary fibrosis patients. Our results indicate that these cystine knot PET tracers may have potential utility in multiple disease states that are associated with upregulation of integrin αvβ6

The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19

A bibliography of parasites and diseases of marine and freshwater fishes of India

With the increasing demand for fish as human food, aquaculture both in freshwater and salt water is rapidly developing over the world. In the developing countries, fishes are being raised as food. In many countries fish farming is a very important economic activity. The most recent branch, mariculture, has shown advances in raising fishes in brackish, estuarine and bay waters, in which marine, anadromous and catadromous fishes have successfully been grown and maintained

Magnetic Resonance - Ultrasound Fusion of the Prostate: Imaging for Cancer Diagnosis

Methods to diagnosis prostate cancer, a disease affecting approximately 240,000 men in the U.S. annually, have remained largely unchanged in the last several decades. An increased level of prostate specific antigen (PSA) is the usual initiating event followed by an ultrasound-guided biopsy. Such biopsies are performed in a systematic, but blind manner, and tumor discovery is often fortuitous. Furthermore, such biopsies often cannot differentiate between serious, potentially lethal forms of prostate cancer and insignificant, indolent forms. This inadequate method of diagnosis has led to over-treatment of indolent disease, a major concern due to the quality-of-life issues of impotence and incontinence associated with curative treatment.Targeted biopsy utilizing multi-parametric magnetic resonance (MR) imaging may comprise an important advance in prostate cancer diagnosis. MR-guided biopsies, while effective, suffer from high cost, limited availability, and long procedure times. MR-Ultrasound (MR-US) fusion, marrying the predictive accuracy of MR and the real-time capabilities of ultrasound, offers an alternative that can be performed in most outpatient settings, while potentially retaining the cancer detection accuracy of MR-guided biopsy. This thesis presents comprehensive research studies that validate targeted biopsy using MR-US fusion. We found that the use of image fusion in targeted prostate biopsy yielded an improved cancer detection rate in a low-risk population. Further, we discover that fusion is appropriate for men with prior negative biopsies and elevated levels of prostate specific antigen (PSA), some of whom may be screened using MRI. In men undergoing repeat biopsy to rule out cancer, we observed a cancer detection rate of almost four times that usually reported (37% vs. 10%).We also discover that significant components to errors in targeting are volume accuracy and registration between MR and TRUS. To this end, this thesis presents a novel method of real-time 3D prostate imaging suitable for image fusion, transurethral ultrasound (TUUS). A number of engineering challenges have been addressed to bring this concept to realization: a catheter-based transducer theoretically capable of volumetric imaging of the prostate was fabricated and evaluated; reconfigurable hardware was designed to provide flexibility in imaging techniques; and image reconstruction techniques were developed and implemented for MR-US fusion