•Low contrast haze reduction approach is proposed for contrast enhancement and noise removal.•A thresholding function is defined for segmentation of disease region where LAB image is utilized as ...input.•Canonical correlation-based features fusion is performed.•NCA based irrelevant features are reduced.•A disease-based fair comparison is conducted at the end.
Efficient and fast segmentation of fruit symptoms is one of the major businesses nowadays in the agro-economy. Manual segmentation and recognition of fruit symptoms is a hard job because of many aspects such like time-consuming and waste of money. Lately, several scholars came up with image processing and pattern recognition based methods for segmentation and recognition of fruit symptoms based on their features such as color, texture, and shape. In this article, we proposed an automated system for segmentation and recognition of grape leaf diseases. The proposed system comprises of four main steps. In first step, a local contrast haze reduction (LCHR) enhancement technique is proposed for increasing the local contrast of symptoms. Thereafter, LAB color transformation is held in the second step and the best channel is selected based on the pixels information that is later utilized into thresholding function. Color, texture, and geometric features are extracted and fused by canonical correlation analysis (CCA) approach. At the time of features fusion, a noise is added in the form of irrelevant and redundant features that are removed by Neighborhood Component Analysis (NCA). The classification of final reduced features is then performed by M-class SVM. The introduced system is assessed on Plant Village dataset of three types of grape leaf diseases such as black measles, black rot, and leaf blight including healthy. The proposed method acquired an average segmentation accuracy rate of 90% and classification accuracy is above 92% which is superior in contrast of existing techniques.
Drawing on moral disengagement theory, this study examined the interplay of perceived market competition threat, fear of failure, moral relativism, and moral disengagement. Perceived competitive ...threat was proposed to be positively related to moral disengagement, and fear of failure was proposed to mediate the relationship between perceived competitive threat and moral disengagement. Furthermore, moral relativism was anticipated to have a moderating effect on the direct and indirect relationships (via fear of failure) between perceived competitive threat and moral disengagement. The hypothesized relationships were examined using self-report survey data collected from 393 employees in the financial sector in Khyber Pakhtunkhwa Province, Pakistan. Structural equation modeling and bootstrap analysis techniques were used to test the articulated hypotheses. The results were consistent with our proposed hypotheses. The findings offer theoretical and practical implications that are discussed at the end.
Application of natural dyes has increased interest in the past few years due to the eco-friendly behavior of these dyes. The present research is concerned with the effect of UV on dyeing behavior of ...cotton using marigold as source of natural Lutein dye. This is colorant lutein which imparts greenish yellow color to cotton fabric. The dye powder and cotton fabric were exposed to UV-radiation for different time intervals prior to dyeing and dyeing was performed at different dyeing variables. International Standard Organization (ISO) methods were employed to evaluate the color fastness properties, such as color fastness to light, washing and rubbing. It is found that 90 min exposure of UV radiations was the optimum condition for surface modification and dyeing of 70 min at 40°C give excellent results using 4 g/L salt to achieve maximum exhaustion. For improvement of color fastness, tannic acid (8%) as pre- and 6% as post-mordant is the best condition. It is found that UV ray treatment can be used to other fabrics followed by dyeing using extracts of dye yielding plants without any physical characteristics damage.
Pancreatic ductal adenocarcinoma is an aggressive cancer that interacts with stromal cells to produce a highly inflammatory tumor microenvironment that promotes tumor growth and invasiveness. The ...precise interplay between tumor and stroma remains poorly understood. TLRs mediate interactions between environmental stimuli and innate immunity and trigger proinflammatory signaling cascades. Our finding that TLR7 expression is upregulated in both epithelial and stromal compartments in human and murine pancreatic cancer led us to postulate that carcinogenesis is dependent on TLR7 signaling. In a mouse model of pancreatic cancer, TLR7 ligation vigorously accelerated tumor progression and induced loss of expression of PTEN, p16, and cyclin D1 and upregulation of p21, p27, p53, c-Myc, SHPTP1, TGF-β, PPARγ, and cyclin B1. Furthermore, TLR7 ligation induced STAT3 activation and interfaced with Notch as well as canonical NF-κB and MAP kinase pathways, but downregulated expression of Notch target genes. Moreover, blockade of TLR7 protected against carcinogenesis. Since pancreatic tumorigenesis requires stromal expansion, we proposed that TLR7 ligation modulates pancreatic cancer by driving stromal inflammation. Accordingly, we found that mice lacking TLR7 exclusively within their inflammatory cells were protected from neoplasia. These data suggest that targeting TLR7 holds promise for treatment of human pancreatic cancer.
A novel method is presented for the instantaneous frequency estimation of multi-component signals with crossing signatures in the time-frequency domain. The proposed method uses a combination of ...Eigen decomposition of time-frequency distributions and time-frequency filtering to recursively extract signal components from the original mixture and estimate their instantaneous frequencies. The proposed algorithm outperforms other algorithms of similar complexity in terms of mean square error accuracy.
Advancements in murine modeling systems for ulcerative colitis have diversified our understanding of the pathophysiological factors involved in disease onset and progression. This has fueled the ...identification of molecular targets, resulting in a rapidly expanding therapeutic armamentarium. Subsequently, management strategies have evolved from symptomatic resolution to well-defined objective endpoints, including clinical remission, endoscopic remission and mucosal healing. While the incorporation of these assessment modalities has permitted targeted intervention in the context of a natural disease history and the prevention of complications, studies have consistently depicted discrepancies associated with ascertaining disease status through clinical and endoscopic measures. Current recommendations lack consideration of histological healing. The simultaneous achievement of clinical, endoscopic, and histologic remission has not been fully investigated. This has laid the groundwork for a novel therapeutic outcome termed disease clearance (DC). This article summarizes the concept of DC and its current evidence.
•Identifying proteolytic enzymes like Asparagine peptide lyase (APL) is crucial for understanding their role in virus maturation and virulence.•We developed APLpred, a new computational model that ...accurately predicts APL enzymes directly from protein sequences.•A systematic approach using optimal hybrid encodings trained with an SVM exhibited the best performance on training and independent datasets.•APLpred is now publicly available at https://procarb.org/APLpred/, providing an invaluable tool for predicting APLs.
Asparagine peptide lyase (APL) is among the seven groups of proteases, also known as proteolytic enzymes, which are classified according to their catalytic residue. APLs are synthesized as precursors or propeptides that undergo self-cleavage through autoproteolytic reaction. At present, APLs are grouped into 10 families belonging to six different clans of proteases. Recognizing their critical roles in many biological processes including virus maturation, and virulence, accurate identification and characterization of APLs is indispensable. Experimental identification and characterization of APLs is laborious and time-consuming. Here, we developed APLpred, a novel support vector machine (SVM) based predictor that can predict APLs from the primary sequences. APLpred was developed using Boruta-based optimal features derived from seven encodings and subsequently trained using five machine learning algorithms. After evaluating each model on an independent dataset, we selected APLpred (an SVM-based model) due to its consistent performance during cross-validation and independent evaluation. We anticipate APLpred will be an effective tool for identifying APLs. This could aid in designing inhibitors against these enzymes and exploring their functions. The APLpred web server is freely available at https://procarb.org/APLpred/.
IntroductionA quasi-experimental study was conducted to estimate the impact of sequential emergency department (ED) capacity building interventions on key performance indicators such as patients’ ...length of stay (LOS) and wait time (WT) during the COVID-19 pandemic. This was achieved through augmenting personnel education and head count, space restructuring and workflow reorganisation.Setting and participantsThis study included 268 352 patients presenting from January 2019 to December 2020 at Indus Hospital and Health network Karachi, a philanthropic tertiary healthcare facility in a city of 20 million residents. A follow-up study was undertaken from January to December 2021 with 123 938 participants.Primary and secondary outcome measuresThese included mean and median ED-LOS and WT for participants presenting in different cohorts. The results of the pre-COVID-19 year 2019 (phase 0) were compared with that of the COVID-19 year, 2020 (phases 1–3 corresponding to peaks, and phase 4 corresponding to reduction in caseloads). The follow-up was conducted in 2021 to see the sustainability of the sequential capacity building.ResultsPhases 1, 2 and 3 had a lower mean adjusted LOS (4.42, 3.92 and 4.40 hours) compared with phase 0 (4.78 hours, p<0.05) with the lowest numbers seen in phase 2. The same held true for WT with 45.1, 23.8 and 30.4 min in phases 1–3 compared with 49.9 in phase 0. However, phase 4 had a higher LOS but a lower WT when compared with phase 0 with a p<0.05.ConclusionSequential capacity building and improving the operational flow through stage appropriate interventions can be used to off-load ED patients and improve process flow metrics. This shows that models created during COVID-19 can be used to develop sustainable solutions and investment is needed in ideas such as ED-based telehealth to improve patient satisfaction and outcomes.
This study investigated the potential of chitosan-coated mixed micellar nanocarriers (polyplexes) for codelivery of siRNA and doxorubicin (DOX). DOX-loaded mixed micelles (serving as cores) were ...prepared by thin film hydration method and coated with chitosan (CS, serving as outer shell), and complexed with multidrug resistance (MDR) inhibiting siRNA. Selective targeting was achieved by folic acid conjugation. The polyplexes showed pH-responsive enhanced DOX release in acidic tumor pH, resulting in higher intracellular accumulation, which was further augmented by downregulation of mdr-1 gene after treatment with siRNA-complexed polyplexes. In vitro cytotoxicity assay demonstrated an enhanced cytotoxicity in native 4T1 and multidrug-resistant 4T1-mdr cell lines, compared to free DOX. Furthermore, in vivo, polyplexes codelivery resulted in highest DOX accumulation and significantly reduced the tumor volume in mice with 4T1 and 4T1-mdr tumors as compared to the free DOX groups, leading to improved survival times in mice. In conclusion, codelivery of siRNA and DOX via polyplexes has excellent potential as targeted drug nanocarriers for treatment of MDR cancers.
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