Best Frequency Domain Filter for Fetal Ultrasound Images

This paper tries to find out the most effective frequency domain filter used for enhancing Ultrasound images. A fetal ultrasound image has been taken and is filtered with different low and high pass filters at different cut off frequencies. The enhancement method is very simple because our aim is to find the most effective filter not a good enhancement technique. To assess the enhancement the metric MSE is used. The experiments are done on MATLAB

Routing Protocols

Wireless sensor are scarce resource so therefore Various Algorithms are there which are described in this paper.This paper contains algorithms which are location based ,hierarchical, data centric etc

Appropriate Contrast Enhancement Measures for Brain and Breast Cancer Images

Medical imaging systems often produce images that require enhancement, such as improving the image contrast as they are poor in contrast. Therefore, they must be enhanced before they are examined by medical professionals. This is necessary for proper diagnosis and subsequent treatment. We do have various enhancement algorithms which enhance the medical images to different extents. We also have various quantitative metrics or measures which evaluate the quality of an image. This paper suggests the most appropriate measures for two of the medical images, namely, brain cancer images and breast cancer images

Effect of Frequency Domain Filters on Bio-Medical Images

This study tries to find out the most effective frequency domain filter used for enhancing biomedical images. Five different biomedical images have been taken and they are filtered with different low and high pass filters at different cut off frequencies. As far as enhancement method is concerned, the method is very simple because the motive here is to find the most effective filter not a good enhancement technique. To assess the enhancement the metric MSE is used. The experiments are done on MATLAB

Choosing Glucose-lowering Therapy: A Collaborative Choice Model

Diabetes care is challenging, and the increasing number of available therapeutic options has made it even more complex. Moreover, with an increasing prevalence across the world, it needs to be managed right from the primary care level to a quaternary care hospital. This calls for an easy-to-use algorithm that can be used by a general practitioner, who is often the first contact of a patient to manage diabetes in many countries. There are multiple models to assist in choice of pharmacotherapy, and these have evolved over time. We propose a user-friendly collaborative choice, as an aid to clinical decision-making. This alliterative framework supplements and strengthens existing guidance, by creating a comprehensive, yet simple, thought process for the diabetes care professional

Selection of Housekeeping Genes and Demonstration of RNAi in Cotton Leafhopper, \u3cem\u3eAmrasca biguttula biguttula\u3c/em\u3e (Ishida)

Amrasca biguttula biguttula (Ishida) commonly known as cotton leafhopper is a severe pest of cotton and okra. Not much is known on this insect at molecular level due to lack of genomic and transcriptomic data. To prepare for functional genomic studies in this insect, we evaluated 15 common housekeeping genes (Tub, B-Tub, EF alpha, GADPH, UbiCF, RP13, Ubiq, G3PD, VATPase, Actin, 18s, 28s, TATA, ETF, SOD and Cytolytic actin) during different developmental stages and under starvation stress. We selected early (1st and 2nd), late (3rd and 4th) stage nymphs and adults for identification of stable housekeeping genes using geNorm, NormFinder, BestKeeper and RefFinder software. Based on the different algorithms, RP13 and VATPase are identified as the most suitable reference genes for quantification of gene expression by reverse transcriptase quantitative PCR (RT-qPCR). Based on RefFinder which comprehended the results of three algorithms, RP13 in adults, Tubulin (Tub) in late nymphs, 28S in early nymph and UbiCF under starvation stress were identified as the most stable genes. We also developed methods for feeding double-stranded RNA (dsRNA) incorporated in the diet. Feeding dsRNA targeting Snf7, IAP, AQP1, and VATPase caused 56.17–77.12% knockdown of targeted genes compared to control and 16 to 48% mortality of treated insects when compared to control

RNA Sequencing, Selection of Reference Genes and Demonstration of Feeding RNAi in \u3cem\u3eThrips tabaci\u3c/em\u3e (Lind.) (Thysanoptera: Thripidae)

Background: Thrips tabaci is a severe pest of onion and cotton. Due to lack of information on its genome or transcriptome, not much is known about this insect at the molecular level. To initiate molecular studies in this insect, RNA was sequenced; de novo transcriptome assembly and analysis were performed. The RNAseq data was used to identify reference and RNAi pathway genes in this insect. Additionally, feeding RNAi was demonstrated in T. tabaci for the first time. Results: From the assembled transcriptome, 27,836 coding sequence (CDS) with an average size of 1236 bp per CDS were identified. About 85.4% of CDS identified showed positive Blast hits. The homologs of most of the core RNAi machinery genes were identified in this transcriptome. To select reference genes for reverse-transcriptase real-time quantitative PCR (RT-qPCR) experiments, 14 housekeeping genes were identified in the transcriptome and their expression was analyzed by (RT-qPCR). UbiCE in adult, 28s in nymphs and SOD under starvation stress were identified as the most stable reference genes for RT-qPCR. Feeding dsSNF7 and dsAQP caused 16.4- and 14.47-fold reduction in SNF7 and AQP mRNA levels respectively, when compared to their levels in dsGFP fed control insects. Feeding dsSNF7 or dsAQP also caused 62 and 72% mortality in T. tabaci. Interestingly, simultaneous feeding of dsRNAs targeting SNF7 or AQP and one of the RNAi pathway genes (Dicer-2/Aubergine/Staufen) resulted in a significant reduction in RNAi of target genes. These data suggest the existence of robust RNAi machinery in T. tabaci. Conclusion: The current research is the first report of the assembled, analyzed and annotated RNAseq resource for T. tabaci, which may be used for future molecular studies in this insect. Reference genes validated across stages and starvation stress provides first-hand information on stable genes in T. tabaci. The information on RNAi machinery genes and significant knockdown of the target gene through dsRNA feeding in synthetic diet confirms the presence of efficient RNAi in this insect. These data provide a solid foundation for further research on developing RNAi as a method to manage this pest

IoT and Big Data Integration for Real-Time Agricultural Monitoring

The integration of Internet of Things (IoT) and Big Data technologies has emerged as a transformative force in modern agriculture. This review paper provides a comprehensive examination of the implications and applications of this integration for real-time agricultural monitoring. The paper begins by emphasizing the critical role of agriculture in global food security and economic stability, underscoring the need for innovative solutions to address the challenges facing the sector. The review delves into the key components of the integration, starting with a detailed exploration of the diverse range of IoT devices and sensors instrumental in gathering real-time data. It further emphasizes the importance of robust data handling and transmission mechanisms to facilitate timely decision-making. The significance of data fusion and aggregation processes in distilling meaningful insights from the voluminous data generated is thoroughly examined, along with the pivotal role of data analytics in driving data-driven decision-making and optimizing agricultural operations. Acknowledging the challenges associated with the integration, the review highlights the critical need for scalable systems to accommodate the evolving needs of farms. Additionally, it emphasizes the importance of prudent cost assessment for a sustainable and economically viable implementation. This review paper provides a comprehensive overview of the integration of IoT and Big Data in agricultural monitoring. By synthesizing these technologies, farmers are poised to embark on a new era of data-driven agriculture, marked by increased efficiency, resource optimization, and ultimately, enhanced global food security

Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0.

COVLIAS 1.0: an automated lung segmentation was designed for COVID-19 diagnosis. It has issues related to storage space and speed. This study shows that COVLIAS 2.0 uses pruned AI (PAI) networks for improving both storage and speed, wiliest high performance on lung segmentation and lesion localization.ology: The proposed study uses multicenter ∼9,000 CT slices from two different nations, namely, CroMed from Croatia (80 patients, experimental data), and NovMed from Italy (72 patients, validation data). We hypothesize that by using pruning and evolutionary optimization algorithms, the size of the AI models can be reduced significantly, ensuring optimal performance. Eight different pruning techniques (i) differential evolution (DE), (ii) genetic algorithm (GA), (iii) particle swarm optimization algorithm (PSO), and (iv) whale optimization algorithm (WO) in two deep learning frameworks (i) Fully connected network (FCN) and (ii) SegNet were designed. COVLIAS 2.0 was validated using "Unseen NovMed" and benchmarked against MedSeg. Statistical tests for stability and reliability were also conducted.Pruning algorithms (i) FCN-DE, (ii) FCN-GA, (iii) FCN-PSO, and (iv) FCN-WO showed improvement in storage by 92.4%, 95.3%, 98.7%, and 99.8% respectively when compared against solo FCN, and (v) SegNet-DE, (vi) SegNet-GA, (vii) SegNet-PSO, and (viii) SegNet-WO showed improvement by 97.1%, 97.9%, 98.8%, and 99.2% respectively when compared against solo SegNet. AUC > 0.94 (p 0.86 (p < 0.0001) on NovMed data set for all eight EA model. PAI <0.25 s per image. DenseNet-121-based Grad-CAM heatmaps showed validation on glass ground opacity lesions.Eight PAI networks that were successfully validated are five times faster, storage efficient, and could be used in clinical settings

Drug discovery for Diamond-Blackfan anemia using reprogrammed hematopoietic progenitors

Diamond-Blackfan anemia (DBA) is a congenital disorder characterized by the failure of erythroid progenitor differentiation, severely curtailing red blood cell production. Because many DBA patients fail to respond to corticosteroid therapy, there is considerable need for therapeutics for this disorder. Identifying therapeutics for DBA requires circumventing the paucity of primary patient blood stem and progenitor cells. To this end, we adopted a reprogramming strategy to generate expandable hematopoietic progenitor cells from induced pluripotent stem cells (iPSCs) from DBA patients. Reprogrammed DBA progenitors recapitulate defects in erythroid differentiation, which were rescued by gene complementation. Unbiased chemical screens identified SMER28, a small-molecule inducer of autophagy, which enhanced erythropoiesis in a range of in vitro and in vivo models of DBA. SMER28 acted through autophagy factor ATG5 to stimulate erythropoiesis and up-regulate expression of globin genes. These findings present an unbiased drug screen for hematological disease using iPSCs and identify autophagy as a therapeutic pathway in DBA.National Institute of Diabetes and Digestive and Kidney Diseases (U.S.) (Grant R24-DK092760)National Institute of Diabetes and Digestive and Kidney Diseases (U.S.) (Grant R24-DK49216)National Institute of Diabetes and Digestive and Kidney Diseases (U.S.) (Grant U54DK110805)National Heart, Lung, and Blood Institute (Grant UO1-HL100001)National Heart, Lung, and Blood Institute (Grant U01HL134812)National Heart, Lung, and Blood Institute (Grant R01HL04880)National Institutes of Health (U.S.) (Grant R24OD017870-01