72 research outputs found

    A review on rainfall forecasting using ensemble learning techniques

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    Significant challenges to human health and life have arisen as a result of heavy rains. Floods and other natural disasters that affect people all over the world every year are caused by prolonged periods of heavy rainfall. Predictions of rainfall must be accurate in countries like India where agriculture is the primary occupation. The non-linearity of rainfall makes machine learning (ML) methods more efficient than many other approaches. In machine learning (ML), individual classifiers are less accurate than ensemble learning (EL) techniques. In order to better understand the various Machine Learning algorithms and Ensemble Learning techniques that researchers employ to predict rainfall, this review paper has been written.This article reviews ensemble learning algorithms for predicting rainfall. In order to increase the accuracy of rainfall forecasts and consequently avoid the negative effects of heavy precipitation, ensemble learning algorithms have gained popularity. This review article examines and makes reference to the development of ensemble approaches, including bagging, boosting, and stacking. The findings of this survey demonstrate that ensemble techniques are much superior to conventional (individual) model learning in terms of rainfall prediction. Additionally, boosting techniques (such enabling, AdaBoost, and extreme gradient boosting) have been applied more frequently and successfully in scenarios involving rainfall forecasting

    Phage vs. Phage: Direct Selections of Sandwich Binding Pairs

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    The sandwich format immunoassay is generally more sensitive and specific than more common assay formats, including direct, indirect, or competitive. A sandwich assay, however, requires two receptors to bind non-competitively to the target analyte. Typically, pairs of antibodies (Abs) or antibody fragments (Fabs) that are capable of forming a sandwiching with the target are identified through a slow, guess-and-check method with panels of candidate binding partners. Additionally, sandwich assays that are reliant on commercial antibodies can suffer from changes to reagent quality outside the researchers' control. This report presents a reimagined and simplified phage display selection protocol that directly identifies sandwich binding peptides and Fabs. The approach yielded two sandwich pairs, one peptide-peptide and one Fab-peptide sandwich for the cancer and Parkinson's disease biomarker DJ-1. Requiring just a few weeks to identify, the sandwich pairs delivered apparent affinity that is comparable to other commercial peptide and antibody sandwiches. The results reported here could expand the availability of sandwich binding partners for a wide range of clinical biomarker assays

    Single-molecule Taq DNA polymerase dynamics.

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    Taq DNA polymerase functions at elevated temperatures with fast conformational dynamics-regimes previously inaccessible to mechanistic, single-molecule studies. Here, single-walled carbon nanotube transistors recorded the motions of Taq molecules processing matched or mismatched template-deoxynucleotide triphosphate pairs from 22° to 85°C. By using four enzyme orientations, the whole-enzyme closures of nucleotide incorporations were distinguished from more rapid, 20-μs closures of Taq's fingers domain testing complementarity and orientation. On average, one transient closure was observed for every nucleotide binding event; even complementary substrate pairs averaged five transient closures between each catalytic incorporation at 72°C. The rate and duration of the transient closures and the catalytic events had almost no temperature dependence, leaving all of Taq's temperature sensitivity to its rate-determining open state

    Two-band conduction as a pathway to non-linear Hall effect and unsaturated negative magnetoresistance in the martensitic compound GdPd2Bi

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    The present work aims to address the electronic and magnetic properties of the intermetallic compound GdPd2_2Bi through a comprehensive study of the structural, magnetic, electrical and thermal transport on a polycrystalline sample, followed by theoretical calculations. Our findings indicate that the magnetic ground state is antiferromagnetic in nature. Magnetotransport data present prominent hysteresis loop hinting a structural transition with further support from specific heat and thermopower measurements, but no such signature is observed in the magnetization study. Temperature dependent powder x-ray diffraction measurements confirm martensitic transition from the high-temperature (HT) cubic Heusler L21L2_1 structure to the low-temperature (LT) orthorhombic PmmaPmma structure similar to many previously reported shape memory alloys. The HT to LT phase transition is characterized by a sharp increase in resistivity associated with prominent thermal hysteresis. Further, we observe robust Bain distortion between cubic and orthorhombic lattice parameters related by aorth=2acuba_{orth} = \sqrt{2}a_{cub}, borth=acubb_{orth} = a_{cub} and corth=acub/2c_{orth} = a_{cub}/\sqrt{2}, that occurs by contraction along cc-axis and elongation along aa-axis respectively. The sample shows an unusual `non-saturating' H2H^2-dependent negative magnetoresistance for magnetic field as high as 150 kOe. In addition, non-linear field dependence of Hall resistivity is observed below about 30 K, which coincides with the sign change of the Seebeck coefficient. The electronic structure calculations confirm robust metallic states both in the LT and HT phases. It indicates complex nature of the Fermi surface along with the existence of both electron and hole charge carriers. The anomalous transport behaviors can be related to the presence of both electron and hole pockets.Comment: 13 pages, 12 figure

    Reengineering the specificity of the highly selective Clostridium botulinum protease via directed evolution.

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    The botulinum neurotoxin serotype A (BoNT/A) cuts a single peptide bond in SNAP25, an activity used to treat a wide range of diseases. Reengineering the substrate specificity of BoNT/A's protease domain (LC/A) could expand its therapeutic applications; however, LC/A's extended substrate recognition (≈ 60 residues) challenges conventional approaches. We report a directed evolution method for retargeting LC/A and retaining its exquisite specificity. The resultant eight-mutation LC/A (omLC/A) has improved cleavage specificity and catalytic efficiency (1300- and 120-fold, respectively) for SNAP23 versus SNAP25 compared to a previously reported LC/A variant. Importantly, the BoNT/A holotoxin equipped with omLC/A retains its ability to form full-length holotoxin, infiltrate neurons, and cleave SNAP23. The identification of substrate control loops outside BoNT/A's active site could guide the design of improved BoNT proteases and inhibitors

    Predicting COVID-19 Severity with a Specific Nucleocapsid Antibody plus Disease Risk Factor Score.

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    Effective methods for predicting COVID-19 disease trajectories are urgently needed. Here, enzyme-linked immunosorbent assay (ELISA) and coronavirus antigen microarray (COVAM) analysis mapped antibody epitopes in the plasma of COVID-19 patients (n = 86) experiencing a wide range of disease states. The experiments identified antibodies to a 21-residue epitope from nucleocapsid (termed Ep9) associated with severe disease, including admission to the intensive care unit (ICU), requirement for ventilators, or death. Importantly, anti-Ep9 antibodies can be detected within 6 days post-symptom onset and sometimes within 1 day. Furthermore, anti-Ep9 antibodies correlate with various comorbidities and hallmarks of immune hyperactivity. We introduce a simple-to-calculate, disease risk factor score to quantitate each patient's comorbidities and age. For patients with anti-Ep9 antibodies, scores above 3.0 predict more severe disease outcomes with a 13.42 likelihood ratio (96.7% specificity). The results lay the groundwork for a new type of COVID-19 prognostic to allow early identification and triage of high-risk patients. Such information could guide more effective therapeutic intervention.IMPORTANCE The COVID-19 pandemic has resulted in over two million deaths worldwide. Despite efforts to fight the virus, the disease continues to overwhelm hospitals with severely ill patients. Diagnosis of COVID-19 is readily accomplished through a multitude of reliable testing platforms; however, prognostic prediction remains elusive. To this end, we identified a short epitope from the SARS-CoV-2 nucleocapsid protein and also a disease risk factor score based upon comorbidities and age. The presence of antibodies specifically binding to this epitope plus a score cutoff can predict severe COVID-19 outcomes with 96.7% specificity

    Predicting COVID-19 Severity with a Specific Nucleocapsid Antibody plus Disease Risk Factor Score.

    No full text
    Effective methods for predicting COVID-19 disease trajectories are urgently needed. Here, enzyme-linked immunosorbent assay (ELISA) and coronavirus antigen microarray (COVAM) analysis mapped antibody epitopes in the plasma of COVID-19 patients (n = 86) experiencing a wide range of disease states. The experiments identified antibodies to a 21-residue epitope from nucleocapsid (termed Ep9) associated with severe disease, including admission to the intensive care unit (ICU), requirement for ventilators, or death. Importantly, anti-Ep9 antibodies can be detected within 6 days post-symptom onset and sometimes within 1 day. Furthermore, anti-Ep9 antibodies correlate with various comorbidities and hallmarks of immune hyperactivity. We introduce a simple-to-calculate, disease risk factor score to quantitate each patient's comorbidities and age. For patients with anti-Ep9 antibodies, scores above 3.0 predict more severe disease outcomes with a 13.42 likelihood ratio (96.7% specificity). The results lay the groundwork for a new type of COVID-19 prognostic to allow early identification and triage of high-risk patients. Such information could guide more effective therapeutic intervention.IMPORTANCE The COVID-19 pandemic has resulted in over two million deaths worldwide. Despite efforts to fight the virus, the disease continues to overwhelm hospitals with severely ill patients. Diagnosis of COVID-19 is readily accomplished through a multitude of reliable testing platforms; however, prognostic prediction remains elusive. To this end, we identified a short epitope from the SARS-CoV-2 nucleocapsid protein and also a disease risk factor score based upon comorbidities and age. The presence of antibodies specifically binding to this epitope plus a score cutoff can predict severe COVID-19 outcomes with 96.7% specificity

    On the Importance of Pooling Layer Tuning for Profiling Side-Channel Analysis

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    In recent years, the advent of deep neural networks opened new perspectives for security evaluations with side-channel analysis. Profiling attacks now benefit from capabilities offered by convolutional neural networks, such as dimensionality reduction and the inherent ability to reduce the trace desynchronization effects. These neural networks contain at least three types of layers: convolutional, pooling, and dense layers. Although the definition of pooling layers causes a large impact on neural network performance, a study on pooling hyperparameters effect on side-channel analysis is still not provided in the academic community. This paper provides extensive experimental results to demonstrate how pooling layer types and pooling stride and size affect the profiling attack performance with convolutional neural networks. Additionally, we demonstrate that pooling hyperparameters can be larger than usually used in related works and still keep good performance for profiling attacks on specific datasets.</p
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