Digital Forensic Analysis of Telegram Messenger App in Android Virtual Environment

The paper provides an in-depth analysis of the artifacts generated by the Telegram Messenger application on Android OS which provides secure communications between individuals, groups, and channels. Since the past few years, the application went through major changes and updates and the latest version’s artifacts varied from the previous ones. Our methodology is based on the set of experiments designed to generate the artifacts from various use cases on the virtualized environment. The acquired artifacts such as messages, their location, and data structure how they relate to one another were studied and were then compared to the older versions. By correlating the artifacts of newer version with the older ones, it shows how the application have been upgraded behind the scenes and by incorporating those results can provide investigators better understanding and insight for the certain evidence in a potential cybercrime case

DETRIMENTAL CAUSES AND CONSEQUENCES OF ORGANIZATIONAL INJUSTICE IN THE WORKPLACE: EVIDENCE FROM PUBLIC SECTOR ORGANIZATIONS

This study investigated the causes of organizational injusticeand how this influences employees’ job outcomes in public sectororganizations in Pakistan. Two models were constructed and analyzed to fulfill the research goals. Data were obtained using a simple random sampling technique. Of the sample, 254 employees of public sector organizations filled out self-administered questionnaires. Multiple regression was applied to test direct proposed hypotheses. To evaluatethe organizational injustice’s indirect effect on organizationalperformance due to employees’ job dissatisfaction, the mediation test of Preacher and Hayes (2004) was applied. The results showed that organizational injustice negatively impacts affective commitment and perceived organizational performance. Moreover, job dissatisfaction impacts the relationship in organizational injustice, perceived organizational performance and affective commitment

MOTIVATION ENHANCING HRM PRACTICES’ AND EMPLOYEE DEMOGRAPHICS ON AFFECTIVE COMMITMENT AMONG EMPLOYEES IN TEXTILE MANUFACTURING IN PAKISTAN

The current study is an attempt to probe the relationship of a setof motivation enhancing HRM practices & employee demographics with affective commitment among employees working in textile manufacturing organizations. A well-structured questionnaire tool was used to collect the data from 232 employees working on managerial positions. The Pearson coefficient of correlation and ANNOVA analyses revealed that system consisting of motivation enhancing HRM practices and demographic variable “age” were stronger predictors of employee affective commitment, the education level exhibited association at 0.08 significance level, the employee demographics: gender and job period posed no significant association with employee affective commitment. The findings are in relevance with past researches, practical implications and need for futureresearch are also discussed

Functional, cognitive and psychological outcomes, and recurrent vascular events in Pakistani stroke survivors: a cross sectional study

<p>Abstract</p> <p>Background</p> <p>There is little direct data describing the outcomes and recurrent vascular morbidity and mortality of stroke survivors from low and middle income countries like Pakistan. This study describes functional, cognitive and vascular morbidity and mortality of Pakistani stroke survivors discharged from a dedicated stroke center within a nonprofit tertiary care hospital based in a multiethnic city with a population of more than 20 million.</p> <p>Methods</p> <p>Patients with stroke, aged > 18 years, discharged alive from a tertiary care centre were contacted via telephone and a cross sectional study was conducted. All the discharges were contacted. Patients or their legal surrogate were interviewed regarding functional, cognitive and psychological outcomes and recurrent vascular events using standardized, pretested and translated scales. A verbal autopsy was carried out for patients who had died after discharge. Stroke subtype and risk factors data was collected from the medical records. Subdural hemorrhages, traumatic ICH, subarachnoid hemorrhage, iatrogenic stroke within hospital and all other diagnoses that presented like stroke but were subsequently found not to have stroke were also excluded. Composites were created for functional outcome variable and depression. Data were analyzed using logistic regression.</p> <p>Results</p> <p>309 subjects were interviewed at a median of 5.5 months post discharge. 12.3% of the patients had died, mostly from recurrent vascular events or stroke complications. Poor functional outcome defined as Modified Rankin Score (mRS) of > 2 and a Barthel Index (BI) score of < 90 was seen in 51%. Older age (Adj-OR-2.1, <it>p </it>= 0.01), moderate to severe dementia (Adj-OR-19.1, <it>p </it>< 0.001), Diabetes (Adj-OR-2.1, <it>p </it>= 0.02) and multiple post stroke complications (Adj-OR-3.6, <it>p </it>= 0.02) were independent predictors of poor functional outcome. Cognitive outcomes were poor in 42% and predictors of moderate to severe dementia were depression (Adj-OR-6.86, <it>p </it>< 0.001), multiple post stroke complications (Adj-OR-4.58, <it>p </it>= 0.01), presence of bed sores (Adj-OR-17.13, <it>p </it>= 0.01) and history of atrial fibrillation (Adj-OR-5.12, <it>p </it>< 0.001).</p> <p>Conclusions</p> <p>Pakistani stroke survivors have poor outcomes in the community, mostly from preventable complications. Despite advanced disability, the principal caretakers were family rarely supported by health care personnel, highlighting the need to develop robust home care support for caregivers in these challenging resource poor settings.</p

Rearing the Cotton Bollworm, Helicoverpa armigera, on a Tapioca-Based Artificial Diet

The impact of a tapioca-based artificial diet on the developmental rate, life history parameters, and fertility was examined over five consecutive generations for the cotton bollworm, Helicoverpa armigera Hubner (Lepidoptera: Noctuidae), a highly polyphagous pest of many agricultural crops. The study showed that when fed the tapioca-based artificial diet during larval stage, larval and pupal developmental period, percent pupating, pupal weight, emergence rate of male and female, longevity, fecundity and hatching were non-significantly different than that of the control agar-based artificial diet. Moreover, the cost to rear on tapioca-based diet approached 2.13 times less than the cost of rearing on the agar-based artificial diet. These results demonstrate the effectiveness and potential cost savings of the tapioca-based artificial diet for rearing H. armigera

An iris based lungs pre-diagnostic system

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Human lungs are essential respiratory organs. Different Obstructive Lung Diseases (OLD) such as bronchitis, asthma, lungs cancer etc. affects the respiration. Diagnosing OLD in the initial stage is better than diagnosing and curing them later. The delay in diagnosing OLD is due to expensive diagnosing tool and experts requirement. Therefore, a non-invasive diagnosing tool for OLD is required that identifies dysfunctional lungs without the support of expert, complex and expensive diagnosing types of equipment. In this work, we design an Iris based Lungs Pre-diagnostic System (ILPS). The ILPS takes iris images as input and identifies dysfunctional Lungs based on iridology map. While testing with 50 lungs patients, the results confirm that the ILPS identifies dysfunctional lungs patients with the accuracy of 88%.The research leading to these results has received funding from the Higher Education Commission under NRPU 2017/18.Peer ReviewedPostprint (author's final draft

Chemical Characterisation, Antidiabetic, Antibacterial, and In Silico Studies for Different Extracts of Haloxylon stocksii (Boiss.) Benth: A Promising Halophyte

The objective of the study is to evaluate the chemical characterisation, and biological and in silico potential of Haloxylon stocksii (Boiss.) Benth, an important halophyte commonly used in traditional medicine. The research focuses on the roots and aerial parts of the plant and extracts them using two solvents: methanol and dichloromethane. Chemical characterisation of the extracts was carried out using total phenolic contents quantification, GC-MS analysis, and LC-MS screening. The results exhibited that the aerial parts of the plant have significantly higher total phenolic content than the roots. The GC-MS and LC-MS analysis of the plant extracts revealed the identification of 18 bioactive compounds in each. The biological evaluation was performed using antioxidant, antibacterial, and in vitro antidiabetic assays. The results exhibited that the aerial parts of the plant have higher antioxidant and in vitro antidiabetic activity than the roots. Additionally, the aerial parts of the plant were most effective against Gram-positive bacteria. Molecular docking was done to evaluate the binding affinity (BA) of the bioactive compounds characterised by GC-MS with diabetic enzymes used in the in vitro assay. The results showed that the BA of γ-sitosterol was better than that of acarbose, which is used as a standard in the in vitro assay. Overall, this study suggests that the extract from aerial parts of H. stocksii using methanol as a solvent have better potential as a new medicinal plant and can provide a new aspect to develop more potent medications. The research findings contribute to the scientific data of the medicinal properties of Haloxylon stocksii and provide a basis for further evaluation of its potential as a natural remedy

Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images : a retrospective study

Background: Determining the status of molecular pathways and key mutations in colorectal cancer is crucial for optimal therapeutic decision making. We therefore aimed to develop a novel deep learning pipeline to predict the status of key molecular pathways and mutations from whole-slide images of haematoxylin and eosin-stained colorectal cancer slides as an alternative to current tests. Methods: In this retrospective study, we used 502 diagnostic slides of primary colorectal tumours from 499 patients in The Cancer Genome Atlas colon and rectal cancer (TCGA-CRC-DX) cohort and developed a weakly supervised deep learning framework involving three separate convolutional neural network models. Whole-slide images were divided into equally sized tiles and model 1 (ResNet18) extracted tumour tiles from non-tumour tiles. These tumour tiles were inputted into model 2 (adapted ResNet34), trained by iterative draw and rank sampling to calculate a prediction score for each tile that represented the likelihood of a tile belonging to the molecular labels of high mutation density (vs low mutation density), microsatellite instability (vs microsatellite stability), chromosomal instability (vs genomic stability), CpG island methylator phenotype (CIMP)-high (vs CIMP-low), BRAFmut (vs BRAFWT), TP53mut (vs TP53WT), and KRASWT (vs KRASmut). These scores were used to identify the top-ranked titles from each slide, and model 3 (HoVer-Net) segmented and classified the different types of cell nuclei in these tiles. We calculated the area under the convex hull of the receiver operating characteristic curve (AUROC) as a model performance measure and compared our results with those of previously published methods. Findings: Our iterative draw and rank sampling method yielded mean AUROCs for the prediction of hypermutation (0·81 [SD 0·03] vs 0·71), microsatellite instability (0·86 [0·04] vs 0·74), chromosomal instability (0·83 [0·02] vs 0·73), BRAFmut (0·79 [0·01] vs 0·66), and TP53mut (0·73 [0·02] vs 0·64) in the TCGA-CRC-DX cohort that were higher than those from previously published methods, and an AUROC for KRASmut that was similar to previously reported methods (0·60 [SD 0·04] vs 0·60). Mean AUROC for predicting CIMP-high status was 0·79 (SD 0·05). We found high proportions of tumour-infiltrating lymphocytes and necrotic tumour cells to be associated with microsatellite instability, and high proportions of tumour-infiltrating lymphocytes and a low proportion of necrotic tumour cells to be associated with hypermutation. Interpretation: After large-scale validation, our proposed algorithm for predicting clinically important mutations and molecular pathways, such as microsatellite instability, in colorectal cancer could be used to stratify patients for targeted therapies with potentially lower costs and quicker turnaround times than sequencing-based or immunohistochemistry-based approaches. Funding: The UK Medical Research Council

TIAToolbox as an end-to-end library for advanced tissue image analytics

Background: Computational pathology has seen rapid growth in recent years, driven by advanced deep-learning algorithms. Due to the sheer size and complexity of multi-gigapixel whole-slide images, to the best of our knowledge, there is no open-source software library providing a generic end-to-end API for pathology image analysis using best practices. Most researchers have designed custom pipelines from the bottom up, restricting the development of advanced algorithms to specialist users. To help overcome this bottleneck, we present TIAToolbox, a Python toolbox designed to make computational pathology accessible to computational, biomedical, and clinical researchers. Methods: By creating modular and configurable components, we enable the implementation of computational pathology algorithms in a way that is easy to use, flexible and extensible. We consider common sub-tasks including reading whole slide image data, patch extraction, stain normalization and augmentation, model inference, and visualization. For each of these steps, we provide a user-friendly application programming interface for commonly used methods and models. Results: We demonstrate the use of the interface to construct a full computational pathology deep-learning pipeline. We show, with the help of examples, how state-of-the-art deep-learning algorithms can be reimplemented in a streamlined manner using our library with minimal effort. Conclusions: We provide a usable and adaptable library with efficient, cutting-edge, and unit-tested tools for data loading, pre-processing, model inference, post-processing, and visualization. This enables a range of users to easily build upon recent deep-learning developments in the computational pathology literature