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DDoS attacks have impacted businesses financially and hit their market reputation. Entropy variation and machine learning are two popular measures of DDoS detection in the literature. The entropy-based detection takes fewer resources yet a longer time to detect the attack and produces high false positive rate. Meanwhile, traditional machine learning classifiers churn out more accurate classification, however, need ample resources for processing huge data. Since IoT devices generate large amounts of data generally; therefore training ML classifiers with all data is impractical. This paper presents an overview of practical merits and demerits of entropy-based detection approach and ML-based detection. In this paper, we have proposed a two-tier hybrid approach for IoT networks that employs entropy variation to filter the attack traffic from benign traffic in first tier. Further, the remaining and reduced volume of supposedly benign data is fed to the second tier which is ML-based detection approach. We have utilized the CICDDoS2019 dataset to illustrate our notions, perform evaluation and findings. The proposed approach has yielded 99.99% f1-score in the second cycle of training and prediction. The proposed approach gives the first response in comparatively less duration as compared to the ML classifiers and significantly reduces the false positive rate as compared to entropy-based detection. It is found that the proposed detection process takes fewer resources too. The findings of the analysis were validated on the CICIoT2023 dataset, which resulted in similar performance. The proposed approach is compared with peer IDSs and results indicate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Background: Gallbladder carcinoma (GBC) has a dismal prognosis even after curative resection. The objective of the study was to evaluate the effect of adjuvant chemotherapy in patients with GBC undergoing curative resection in a randomized control trial (RCT). Methods: A single-center open-labeled prospective RCT was done from January 2012 to June 2018. R0 curative resected GBC patients were randomized in 1:1 to either surveillance alone (control group) or adjuvant chemotherapy (gemcitabine and cisplatin (GemCis group)) for 6 cycles. The primary outcome was disease-free survival (DFS), and the secondary outcomes were overall survival (OS) and toxicity profile. Results: On the evaluation of 362 patients with GBC, 50 patients were enrolled in each control or GemCis group. Per protocol (PP), it comprised 96 patients. The demographic and clinical profile was similar between the two groups except in the lower nodal stage where patients were higher in the control group (p = 0.01). Recurrences were similar between groups (control 44% vs GemCis 56%; p = 0.23). On the intention to treat (ITT), analyses of median DFS (not reached vs. 24 months, p = 0.14) and OS (not reached vs. 31 months, p = 0.10) were similar between groups. On PP, analyses of median DFS (not reached vs. 24 months, p = 0.16) and OS (not reached vs. 31 months, p = 0.09) were similar between groups. The common toxicity profile was hematological followed by gastrointestinal symptoms. Conclusions: Adjuvant GemCis therapy for 6 cycles does not improve DFS or OS than R0 surgery alone patients with GBC. Trial Registration: NCT02778308 (https://www.clinicaltrials.gov) [ABSTRACT FROM AUTHOR]

• The Covid-MANet is a single generic multi-task framework for automated lung segmentation, COVID-19 diagnosis, infection region quantification and severity assessment of COVID-19 into more specific levels as mild, moderate, severe and critical. • The Covid-MANet improves generalization and interpretability for COVID-19 classification by introducing segmentation-based cropping and classification by proposed MA-DenseNet201 model outperforming state-of-the-art networks. • Hybrid loss function is used for lung and infection segmentation task whereas weighted loss function for performance improvement in classification problem. • Investigates the class-wise sensitivity analysis at various confidence threshold levels. Based on prior awareness of various class level accuracies, a weighted average ensemble approach (WAE) outperforms state-of-the-art models for all the classes. • Finally, a gradient-weighted class activation mapping (Grad-CAM) is used for explainable diagnosis to generate a localization map for each disease type, investigates model interpretation in addition to COVID-19 infection map. The devastating outbreak of Coronavirus Disease (COVID-19) cases in early 2020 led the world to face health crises. Subsequently, the exponential reproduction rate of COVID-19 disease can only be reduced by early diagnosis of COVID-19 infection cases correctly. The initial research findings reported that radiological examinations using CT and CXR modality have successfully reduced false negatives by RT-PCR test. This research study aims to develop an explainable diagnosis system for the detection and infection region quantification of COVID-19 disease. The existing research studies successfully explored deep learning approaches with higher performance measures but lacked generalization and interpretability for COVID-19 diagnosis. In this study, we address these issues by the Covid-MANet network, an automated end-to-end multi-task attention network that works for 5 classes in three stages for COVID-19 infection screening. The first stage of the Covid-MANet network localizes attention of the model to the relevant lungs region for disease recognition. The second stage of the Covid-MANet network differentiates COVID-19 cases from bacterial pneumonia, viral pneumonia, normal and tuberculosis cases, respectively. To improve the interpretation and explainability, three experiments have been conducted in exploration of the most coherent and appropriate classification approach. Moreover, the multi-scale attention model MA-DenseNet201 proposed for the classification of COVID-19 cases. The final stage of the Covid-MANet network quantifies the proportion of infection and severity of COVID-19 in the lungs. The COVID-19 cases are graded into more specific severity levels such as mild, moderate, severe, and critical as per the score assigned by the RALE scoring system. The MA-DenseNet201 classification model outperforms eight state-of-the-art CNN models, in terms of sensitivity and interpretation with lung localization network. The COVID-19 infection segmentation by UNet with DenseNet121 encoder achieves dice score of 86.15% outperforming UNet, UNet++, AttentionUNet, R2UNet, with VGG16, ResNet50 and DenseNet201 encoder. The proposed network not only classifies images based on the predicted label but also highlights the infection by segmentation/localization of model-focused regions to support explainable decisions. MA-DenseNet201 model with a segmentation-based cropping approach achieves maximum interpretation of 96% with COVID-19 sensitivity of 97.75%. Finally, based on class-varied sensitivity analysis Covid-MANet ensemble network of MA-DenseNet201, ResNet50 and MobileNet achieve 95.05% accuracy and 98.75% COVID-19 sensitivity. The proposed model is externally validated on an unseen dataset, yields 98.17% COVID-19 sensitivity. [ABSTRACT FROM AUTHOR]

Gallbladder cancer (GBC) is an aggressive malignancy usually diagnosed in an advanced stage. We investigated the effects of alterations of the phosphatase and tensin homologue ( PTEN ) gene on the occurrence and development of GBC, which has not been previously reported. A total 141 cases of GBC were analyzed for mutation, expression, and methylation across the nine exons of the PTEN gene. DNA sequencing methods were applied for mutation detection, whereas protein expression and methylation status were evaluated by immunohistochemical and methylation-specific PCR analysis, respectively. Novel PTEN mutations were observed in 6.3% of cases (9/141), and they included two silent mutations. In mutant cases, according to changes in codons, the respective amino acid sequences were also changed, which caused of proteins. A high percentage (72%) of loss of protein expression was observed more often in cases than in control samples. Interestingly, all nine cases with mutations showed loss of PTEN expression, whereas four of these nine cases showed positive promoter methylation. Hypermethylation was significantly more common in older patients than in younger ones ( P  <   0.02). These findings suggest that PTEN mutations and inactivation may play an important role in the development and progression of gallbladder carcinoma. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we give some extensive benchmark results for some dynamic priority clustering algorithms for homogeneous multiprocessor environments. By dynamic priority we mean a priority function that can change with every step of the algorithm. Using dynamic priority can give us more flexibility as compared to static priority algorithms. Our objective in this paper is to compare the dynamic priority algorithms with some well known algorithms from the literature and discuss their strengths and weaknesses. For our study we have selected two recently proposed dynamic priority algorithms: CPPS (Cluster Pair Priority Scheduling algorithm) having complexity and DCCL (Dynamic Computation Communication Load scheduling algorithm) having complexity where is the number of nodes in the task graph, and is the number of edges in the task graph. We have selected a recently proposed randomized algorithm with static priority (RCCL: Randomized Computation Communication Load scheduling algorithm) and converted it into a dynamic priority algorithm: RDCC (Randomized Dynamic Computation Communication load scheduling algorithm) having complexity where a is the number of randomization steps, and b is a limit on the number of clusters formed. We have also selected three well known algorithms from literature: DSC (Dominant Sequence Clustering algorithm) having complexity , EZ (Edge Zeroing algorithm) having complexity , and LC (Linear Clustering algorithm) having complexity . We have compared these algorithms using various comparison parameters including some statistical parameters, and also using various types of task graphs including some synthetic and real task graphs. Our results show that the dynamic priority algorithms give best results for the case of random task graphs, and for the case when the number of available processors are small. [Copyright &y& Elsevier]

B Introduction: b Dilated common bile duct (CBD) [ 8-15 mm] with normal liver function test are seen frequently while evaluating upper abdominal pain. Patients were categorised as: Group A- Dilated CBD with post-cholecystectomy status; Group B- Dilated CBD with cholelithiasis; Group C- Dilated CBD without cholelithiasis. The outcome of patients managed without CBD excision is similar to those with excision. [Extracted from the article]