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COVID-19 pandemic has affected the patients to a large extent who are in immediate need of medical care. Now-a-days people rely on online reviews shared on review websites to gather information about hospitals like the availability of beds, availability of ventilators etc. However, these reviews are large in number and unstructured which makes it difficult to interpret them. Hence, the authors have proposed a methodology that will apply an aspect based sentiment analysis on the reviews to gain meaningful insights of the hospital based on different aspects like doctor, staff, facilities etc. For the purpose of empirical validation, a total of 26,071 reviews pertaining to 325 hospitals were scrapped. The authors concluded that the study can be useful for patients as it helps them to select the hospital which best suits them. The study also helps hospital administration to improve the current services according to needs of the patients.

Using a panel of cancer cell lines, we characterized a novel degrader of AKT, MS21. In mutant PI3K–PTEN pathway cell lines, AKT degradation was superior to AKT kinase inhibition for reducing cell growth and sustaining lower signaling over many days. AKT degradation, but not kinase inhibition, profoundly lowered Aurora kinase B (AURKB) protein, which is known to be essential for cell division, and induced G2–M arrest and hyperploidy. PI3K activated AKT phosphorylation of AURKB on threonine 73, which protected it from proteasome degradation. A mutant of AURKB (T73E) that mimics phosphorylation and blocks degradation rescued cells from growth inhibition. Degrader-resistant lines were associated with low AKT phosphorylation, wild-type PI3K/PTEN status, and mutation of KRAS/BRAF. Pan-cancer analysis identified that 19% of cases have PI3K–PTEN pathway mutation without RAS pathway mutation, suggesting that these patients with cancer could benefit from AKT degrader therapy that leads to loss of AURKB.Significance:MS21 depletes cells of phosphorylated AKT (pAKT) and a newly identified AKT substrate, AURKB, to inhibit tumor growth in mice. MS21 is superior to prior agents that target PI3K and AKT due to its ability to selectively target active, pAKT and sustain repression of signaling to deplete AURKB.This article is highlighted in the In This Issue feature, p. 2945

Supplementary Data from Loss of PBRM1 Alters Promoter Histone Modifications and Activates ALDH1A1 to Drive Renal Cell Carcinoma

Supplementary Figure from AKT Degradation Selectively Inhibits the Growth of PI3K/PTEN Pathway–Mutant Cancers with Wild-Type KRAS and BRAF by Destabilizing Aurora Kinase B

Supplementary Table from AKT Degradation Selectively Inhibits the Growth of PI3K/PTEN Pathway–Mutant Cancers with Wild-Type KRAS and BRAF by Destabilizing Aurora Kinase B

Supplementary Figure from Loss of PBRM1 Alters Promoter Histone Modifications and Activates ALDH1A1 to Drive Renal Cell Carcinoma

Supplementary Data from AKT Degradation Selectively Inhibits the Growth of PI3K/PTEN Pathway–Mutant Cancers with Wild-Type KRAS and BRAF by Destabilizing Aurora Kinase B

Figure S1: Expression of GSH synthesis enzymes in ccRCC tumors and adjacent healthy kidney tissue, demonstrating increased GGT1 levels in ccRCC. Figure S2: GGT1 mRNA and protein abundance in ccRCC and renal epithelial cell lines. Figure S3: GGT1 inhibition results in proliferation defect in ccRCC cells. Figure S4: GGT1 knockdown results in cell cycle arrest in ccRCC cell lines. Figure S5: GSH levels and GSH/GSSG ratios are reduced in GGT1 KD tumor xenografts, relative to controls. Figure S6: GSH pathway inhibition leads to proliferation defects in ccRCC cells.

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer. While the localized form of this disease can be treated surgically, advanced and metastatic stages are resistant to chemotherapies. Although more innovative treatments, such as targeted or immune-based therapies, exist, the need for new therapeutic options remains. ccRCC presents unique metabolic signatures and multiple studies have reported a significant increase in levels of reduced glutathione (GSH) and its precursors in ccRCC tumor samples compared with normal kidney tissues. These observations led us to investigate the effects of blocking the GSH pathway, particularly the gamma-glutamyltransferase 1 (GGT1) enzyme, in multiple ccRCC cell lines. In this study, we provide in vitro and in vivo evidence that GGT1/GSH pathway inhibition impacts ccRCC cell growth, through increased cell-cycle arrest. Of note, GGT1 inhibition also impairs ccRCC cell migration. Finally, pharmacologic GSH pathway inhibition decreases ccRCC cell proliferation and increases sensitivity to standard chemotherapy. Our results suggest that GGT1/GSH pathway inhibition represents a new strategy to overcome ccRCC chemoresistance.Implications:GGT1/GSH pathway inhibition represents a promising therapeutic strategy to overcome chemoresistance and inhibit progression of ccRCC tumors.

The Covid pandemic has become a serious public health challenge for people across India and other nations. Nowadays, people rely on the online reviews being shared on different review sites to gather information about hospitals like the availability of beds, availability of ventilators, etc. However, since these reviews are large in number and are unstructured, patients struggle to get accurate and reliable information about the hospitals, due to which they end up taking admission into a hospital which might not be appropriate for the specific treatment they require. This paper employs the use of sentiment analysis to understand various online reviews of hospitals and provide valuable information to the patients. Approximately 30,000 + reviews were collected from more than 500 hospitals. The broad objective of the study is to give the patients a comprehensive and descriptive rating of the hospitals based on the online reviews given by different patients. In addition to providing a comprehensive summary, the study has conducted aspect-based analysis where it compares the hospitals based on four different aspects of the hospital viz. “Doctors’ services”, “Staff’s services”, “Hospital facilities”, and “Affordability”. The database containing aspect-based ratings of the hospitals will be of great value to the patients by allowing them to compare and select the best hospital based on the optimum fit of the aspects of their preference. [ FROM AUTHOR] Copyright of New Generation Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The need of the customers to be connected to the network at all times has led to the evolution of mobile technology. Operating systems play a vitol role when we talk of technology. Nowadays, Android is one of the popularly used operating system in mobile phones. Authors have analysed three stable versions of Android, 6.0, 7.0 and 8.0. Incorporating a change in the version after it is released requires a lot of rework and thus huge amount of costs are incurred. In this paper, the aim is to reduce this rework by identifying certain parts of a version during early phase of development which need careful attention. Machine learning prediction models are developed to identify the parts which are more prone to changes. The accuracy of such models should be high as the developers heavily rely on them. The high dimensionality of the dataset may hamper the accuracy of the models. Thus, the authors explore four dimensionality reduction techniques, which are unexplored in the field of network and communication. The results concluded that the accuracy improves after reducing the features.

Using a panel of cancer cell lines, we characterized a novel degrader of AKT, MS21. In mutant PI3K–PTEN pathway cell lines, AKT degradation was superior to AKT kinase inhibition for reducing cell growth and sustaining lower signaling over many days. AKT degradation, but not kinase inhibition, profoundly lowered Aurora kinase B (AURKB) protein, which is known to be essential for cell division, and induced G2–M arrest and hyperploidy. PI3K activated AKT phosphorylation of AURKB on threonine 73, which protected it from proteasome degradation. A mutant of AURKB (T73E) that mimics phosphorylation and blocks degradation rescued cells from growth inhibition. Degrader-resistant lines were associated with low AKT phosphorylation, wild-type PI3K/PTEN status, and mutation of KRAS/BRAF. Pan-cancer analysis identified that 19% of cases have PI3K–PTEN pathway mutation without RAS pathway mutation, suggesting that these patients with cancer could benefit from AKT degrader therapy that leads to loss of AURKB. Significance: MS21 depletes cells of phosphorylated AKT (pAKT) and a newly identified AKT substrate, AURKB, to inhibit tumor growth in mice. MS21 is superior to prior agents that target PI3K and AKT due to its ability to selectively target active, pAKT and sustain repression of signaling to deplete AURKB. This article is highlighted in the In This Issue feature, p. 2945

The study was conducted to evaluate the clinical outcomes of various glaucoma surgeries (trabeculectomy, trabeculectomy with mitomycin-C and glaucoma valve implantation) in cases of refractory glaucoma. When it becomes obvious that medication will not help then alternatives should be tried.These alternatives can be in form of :- Filtration Surgery Termed as trabeculectomy allows drainage of aqueous humor from within the eye to underneath the conjunctiva where it is absorbed. Trabeculectomy with mitomycin C-The use of antifibrotic agents such as mitomycin-C and 5-fluorouracil, along with releasable sutures or laser suture lysis, enhances the longevity of guarded procedures, Drainage Implants Glaucoma drainage devices are designed to divert aqueous humor from the anterior chamber to an external reservoir, where a fibrous capsule forms about 4-6 weeks after surgery and regulates flow. Aims of Study 1. Study the clinical outcomes of trabeculectomy,trabeculetomy with mitomycin-C and glaucoma valve implantation in management of refractory glaucoma patients. 2.Evaluate the complications of glaucoma valve surgery. 3.Study the complications of trabeculectomy,trabeculectomy with mitomycin-C. The proposed study was conducted in upgraded department of ophthalmology,LLRM Medical College,Meerut. Population characteristics were number of eyes treated,diagnosed and follow up.The study was a prospective study with random selection of patients done from the OPD.The randomly selected patients were then divided into three groups, one group was subjected to trabeculectomy, second with trabeculectomy with mitomycin-C and the other group was subjected to glaucoma valve implantation. CRITERIA FOR FAILURE OF SURGERY: Ÿ IOP > 21 mm hg at end of 1 year or Ÿ IOP not reduced by 20% from baseline at end of 1 year or Ÿ IOP

Huge and reputed software industries are expected to deliver quality products. However, industry suffers from a loss of approximately $500 billion due to shoddy software quality. The quality of the product in terms of its accuracy, efficiency, and reliability can be revamped through testing by focusing attention on testing the product through effective test case generation and prioritization. The authors have proposed a test-case generation technique based on iterative listener genetic algorithm that generates test cases automatically. The proposed technique uses its adaptive nature and solves the issues like redundant test cases, inefficient test coverage percentage, high execution time, and increased computation complexity by maintaining the diversity of the population which will decrease the redundancy in test cases. The performance of the technique is compared with four existing test-case generation algorithms in terms of computational complexity, execution time, coverage, and it is observed that the proposed technique outperformed.

Tumors exhibit enhancer reprogramming compared to normal tissue. The etiology is largely attributed to cell-intrinsic genomic alterations. Here, using freshly resected primary CRC tumors and patient-matched adjacent normal colon, we find divergent epigenetic landscapes between CRC tumors and cell lines. Intriguingly, this phenomenon extends to highly recurrent aberrant super-enhancers gained in CRC over normal. We find one such super-enhancer activated in epithelial cancer cells due to surrounding inflammation in the tumor microenvironment. We restore this super-enhancer and its expressed gene, PDZK1IP1, following treatment with cytokines or xenotransplantation into nude mice, thus demonstrating cell-extrinsic etiology. We demonstrate mechanistically that PDZK1IP1 enhances the reductive capacity CRC cancer cells via the pentose phosphate pathway. We show this activation enables efficient growth under oxidative conditions, challenging the previous notion that PDZK1IP1 acts as a tumor suppressor in CRC. Collectively, these observations highlight the significance of epigenomic profiling on primary specimens.

Tumors exhibit widespread enhancer landscape reprogramming compared to normal tissue. The etiology is believed to be largely cell-intrinsic in non-hormonal cancers, attributed to such genomic alterations as focal amplification of non-coding regions, aberrant activation of transcription factors, and non-coding mutations creating de novo transcription factor binding sites. Here, using freshly resected primary CRC tumors and patient-matched adjacent normal colon epithelia, we find divergent epigenetic landscapes between primary CRC tumors and CRC cell lines. We identify a unique super-enhancer signature largely absent in cell culture. Intriguingly, this phenomenon extends to highly recurrent aberrant super-enhancers gained in CRC over patient-matched normal epithelium suggesting novel insight into the etiology of enhancer reprogramming in CRC and its downstream relevance to tumor biology. We find one such super-enhancer activated in epithelial cancer cells due to surrounding inflammation in the tumor microenvironment. CRISPR-dcas9-KRAB interference of this super-enhancer identifies PDZK1IP1 as its target gene. PDZK1IP1 is previously observed to be highly up-regulated in CRC. However, the mechanism behind its transcriptional activation is not fully understood. We restore both the super-enhancer and PDZK1IP1 levels following treatment with cytokines or xenotransplantation into nude mice, thus demonstrating its etiology via local tumor microenvironment acquisition. Deletion of inflammatory transcription factors RELA and STAT3 in human CRC cells inhibits PDZK1IP1 induction in xenografts. PDZK1IP1 appears to be critical for CRC growth in the setting of its super-enhancer induction as xenografts, but not in cell culture where the super-enhancer is absent and expression is largely silent. Building on its known role in glucose uptake via SGLT receptors, we demonstrate mechanistically that PDZK1IP1 enhances the reductive capacity CRC cancer cells via the pentose phosphate pathway using polar metabolomic profiling. We show this activation enables efficient growth under oxidative conditions both in vitro and in vivo, challenging the previous notion that PDZK1IP1 acts as a tumor suppressor in CRC. Collectively, these observations highlight the biologic significance of epigenomic profiling on patient-matched primary specimens and identify this microenvironment-acquired super-enhancer as an oncogenic driver in the setting of the inflamed tumor. Citation Format: Royce Zhou, Jia Xu, Tiphaine Martin, Alexis Zachem, John He, Sait Ozturk, Deniz Demircioglu, Ankita Bansal, Andrew Trotta, Bruno Giotti, Berkley Gryder, Yao Shen, Saul Carcamo, Xuewei Wu, Kaitlyn Bosch, Benjamin Hopkins, Alexander Tsankov, Randolph Steinhagen, Drew Jones, John Asara, Jerry Chipuk, Rachel Brody, Steven Itzkowitz, Iok In Christine Chio, Dan Hasson, Emily Bernstein, Ramon Parsons. A local tumor microenvironment acquired super-enhancer induces an oncogenic driver in colorectal carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3481.

Subunits of SWI/SNF chromatin remodeling complexes are frequently mutated in human malignancies. The PBAF complex is composed of multiple subunits, including the tumor-suppressor protein PBRM1 (BAF180), as well as ARID2 (BAF200), that are unique to this SWI/SNF complex. PBRM1 is mutated in various cancers, with a high mutation frequency in clear cell renal cell carcinoma (ccRCC). Here, we integrate RNA-seq, histone modification ChIP-seq, and ATAC-seq data to show that loss of PBRM1 results in de novo gains in H3K4me3 peaks throughout the epigenome, including activation of a retinoic acid biosynthesis and signaling gene signature. We show that one such target gene, ALDH1A1, which regulates a key step in retinoic acid biosynthesis, is consistently upregulated with PBRM1 loss in ccRCC cell lines and primary tumors, as well as non-malignant cells. We further find that ALDH1A1 increases the tumorigenic potential of ccRCC cells. Using biochemical methods, we show that ARID2 remains bound to other PBAF subunits after loss of PBRM1 and is essential for increased ALDH1A1 after loss of PBRM1, whereas other core SWI/SNF components are dispensable, including the ATPase subunit BRG1. In total, this study uses global epigenomic approaches to uncover novel mechanisms of PBRM1 tumor suppression in ccRCC. Implications: This study implicates the SWI/SNF subunit and tumor-suppressor PBRM1 in the regulation of promoter histone modifications and retinoic acid biosynthesis and signaling pathways in ccRCC and functionally validates one such target gene, the aldehyde dehydrogenase ALDH1A1.

Recently, blockchains have found a place in all sorts of industries, ranging from healthcare to the government sector, finance, real estate and utilities. Blockchain has proved to be advantageous to the applications which previously required a central authority and a trusted intermediary, as now blockchain allows them to operate in a decentralised manner and helps them to achieve the same functionality much more efficiently. Transactions in blockchains are trustless, that is, the involved parties without knowing each other and without establishing any trust relations, sit in any corner of the world and such transactions thus occur without any trusted middlemen. For authorisation of these transactions, strong cryptographic principles, which form an essential part of the blockchain infrastructure, are used. Smart contracts basically add a layer of logic and computation to the already existing trust infrastructure of the blockchain, and thus allows for appropriate, heavily automated and distributed workflows, to function efficiently. In this chapter, we outline the art of using smart contracts and blockchain technology to implement a decentralized, secure, transparent yet anonymous digital voting system.

Tumors exhibit widespread enhancer landscape reprogramming compared to normal tissue. The etiology is believed to be largely cell-intrinsic in non-hormonal cancers, attributed to such genomic alterations as focal amplification of non-coding regions, aberrant activation of transcription factors, and non-coding mutations creating de novo transcription factor binding sites. Here, using freshly resected primary CRC tumors and patient-matched adjacent normal colon epithelia, we find divergent epigenetic landscapes between primary CRC tumors and CRC cell lines. We identify a unique super-enhancer signature largely absent in cell culture. Intriguingly, this phenomenon extends to highly recurrent aberrant super-enhancers gained in CRC over patient-matched normal epithelium suggesting novel insight into the etiology of enhancer reprogramming in CRC and its downstream relevance to tumor biology. We find one such super-enhancer activated in epithelial cancer cells due to surrounding inflammation in the tumor microenvironment. We restore this super-enhancer and its expressed gene, PDZK1IP1, following treatment with cytokines or xenotransplantation into nude mice, thus demonstrating its etiology via local tumor microenvironment acquisition. Building on its known role in glucose uptake via SGLT receptors, we demonstrate mechanistically that PDZK1IP1 enhances the reductive capacity CRC cancer cells via the pentose phosphate pathway using polar metabolomic profiling. We show this activation enables efficient growth under oxidative conditions both in vitro and in vivo, challenging the previous notion that PDZK1IP1 acts as a tumor suppressor in CRC. Collectively, these observations highlight the biologic significance of epigenomic profiling on patient-matched primary specimens and identify this microenvironment-acquired super-enhancer as an oncogenic driver in the setting of the inflamed tumor.

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer. While the localized form of this disease can be treated surgically, advanced and metastatic stages are resistant to chemotherapies. Although more innovative treatments, such as targeted or immune-based therapies, exist, the need for new therapeutic options remains. ccRCC presents unique metabolic signatures and multiple studies have reported a significant increase in levels of reduced glutathione (GSH) and its precursors in ccRCC tumor samples compared with normal kidney tissues. These observations led us to investigate the effects of blocking the GSH pathway, particularly the gamma-glutamyltransferase 1 (GGT1) enzyme, in multiple ccRCC cell lines. In this study, we provide in vitro and in vivo evidence that GGT1/GSH pathway inhibition impacts ccRCC cell growth, through increased cell-cycle arrest. Of note, GGT1 inhibition also impairs ccRCC cell migration. Finally, pharmacologic GSH pathway inhibition decreases ccRCC cell proliferation and increases sensitivity to standard chemotherapy. Our results suggest that GGT1/GSH pathway inhibition represents a new strategy to overcome ccRCC chemoresistance. Implications: GGT1/GSH pathway inhibition represents a promising therapeutic strategy to overcome chemoresistance and inhibit progression of ccRCC tumors.

Class Imbalance Problem is the major issue in machine intelligence producing biased classifiers that work well for the majority class but have a relatively poor performance for the minority class. To ensure the development of accurate prediction models, it is essential to deal with the class imbalance problem. In this paper, the class imbalance problem is handled using focused undersampling techniques viz. Cluster Based, Tomek Link and Condensed Nearest Neighbours which equalize the number of instances of the two types of classes by undersampling the majority class based on some particular criteria. This is in contrast to random undersampling where the data samples are selected randomly from the majority class leading to underfitting and loss of some important datapoints. To fairly compare and evaluate the performance of focused undersampling approaches, prediction models are constructed using popular machine learning classifiers like K-Nearest Neighbor, Decision Tree and Naive Bayes. The results have shown that Decision Tree outperformed other machine learning techniques. Comparing and contrasting the undersampling approaches for Decision Tree concluded Condensed Nearest Neighbours to be best amongst others.

Since the onset of COVID-19 pandemic, officials and several others have been making an effort to form informed decisions and take relevant measures to curb the outbreak. Mostly authorities have used standard statistical models, and epidemiological models to determine the outbreak. Although these models have shown to have accuracy in the past, they seem to be highly ineffective during the COVID-19 pandemic, mostly because of the complexities in it's outbreak. With the lack of studies done on the outbreak, it's imperative for us to determine the attributes which could show correlation with the spread for us to improve upon our predictions. Therefore in this paper the authors have tried to find the attributes which best help in the outbreak modelling and have applied those attributes to the traditional regression models to study their effects on the predictions.

PI3K/AKT/mTOR signaling pathway is one of the most frequently dysregulated pathway in cancer development and the serine/threonine kinase AKT functions as the key node in this pathway to regulate multiple cellular and physiological processes. AKT inhibitors that competitively bind the ATP pocket have been evaluated in clinical trials and have a tolerable toxicity profile with greater efficacy for tumors with pathway mutations; however, many PI3K/AKT pathway mutant tumors remain resistant. Proteolysis targeting chimeras (PROTACs) are a powerful targeted protein degradation technology that hijacks the cellular ubiquitin-proteasome system to induce selective polyubiquitination and degradation of the target proteins. Using this technology, we designed and developed a library of novel small-molecules putative degraders to degrade AKT by recruiting either the cereblon (CRBN) or von Hippel-Lindau (VHL) E3 ligase, that have the ability to degrade AKT in cells and lower downstream signaling to various extents. Though extensive structure-activity relationship studies on various linkers, E3 ligase ligands, and AKT binding moieties, we identified a novel VHL-recruiting AKT degrader, MS21, and characterized it using a panel of 38 cancer cell lines with diverse genotypes and tissue origins. Our results suggest that efficient pharmacologic degradation of AKT phosphorylated on threonine 308 and serine 473 leads to selective inhibition of the growth of tumor cells with alterations of HER2, PIK3CA, PTEN, or AKT1. In these PI3K/PTEN pathway mutant lines, AKT degradation by MS21 was superior to AKT kinase inhibition for reducing cell growth and sustaining lower signaling over many days, and inhibited tumor cell growths through lowering the level of Aurora Kinase B, which we found to be an AKT substrate protein. AKT degradation but not kinase inhibition profoundly lowered Aurora kinase B (AURKB) protein, which is known to be essential for cell division, and induced G2/M arrest and hyperploidy. PI3K activated AKT phosphorylation of AURKB on threonine 73, which protected it from proteasome degradation. A mutant of AURKB (T73E) that mimics phosphorylation and blocks its degradation rescued cells from growth inhibition by MS21. In addition, resistance to MS21 was found to be associated with low levels of baseline AKT phosphorylation in cells as well as mutation of either KRAS or BRAF, and resistance to MS21 could be overcome by the combination treatment of a MEK inhibitor trametinib with MS21, which not only inhibited MEK but also increased AKT phosphorylation and enhanced AKT degradation. Pan-cancer analysis identified that 19% of cases have PI3K/PTEN pathway mutation without RAS pathway mutation, suggesting that these cancer patients could benefit from AKT degrader therapy that leads to loss of AURKB. Citation Format: Jia Xu, Xufen Yu, Tiphaine C. Martin, Ankita Bansal, Kakit Cheung, Abigail Lubin, Elias Stratikopoulos, Kaitlyn M. Cahuzac, Li Wang, Ling Xie, Royce Zhou, Yudao Shen, Xuewei Wu, Shen Yao, Ruifang Qiao, Poulikos I. Poulikakos, Xian Chen, Jing Liu, Jian Jin, Ramon Parsons. AKT degradation selectively inhibits the growth of PI3K/PTEN pathway mutant cancers with wild type KRAS and BRAF by destabilizing Aurora kinase B [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB195.

Background: Non-Functional Requirements (NFR) have a direct impact on the architecture of the system, thus it is essential to identify NFRs in the initial phases of software development. Aim: The work is based on extraction of relevant keywords from NFR descriptions by employing text mining steps and thereafter classifying these descriptions into one of the nine types of NFRs. Method: For each NFR type, keywords are extracted from a set of pre-categorized specifications using Information-Gain measure. Then models using 8 Machine Learning (ML) techniques are developed for classification of NFR descriptions. A set of 15 projects (containing 326 NFR descriptions) developed by MS students at DePaul University are used to evaluate the models. Results: The study analyzes the performance of ML models in terms of classification and misclassification rate to determine the best model for predicting each type NFR descriptions. The Naïve Bayes model has performed best in predicting “maintainability” and “availability” type of NFRs. Conclusion: The NFR descriptions should be analyzed and mapped into their corresponding NFR types during the initial phases. The authors conducted cost benefit analysis to appreciate the advantage of using the proposed models.

Feature selection is one of the most significant steps of pre-processing which deals with extracting the pertinent subset of features from the available feature set. This reduces the dimensionality of the dataset which is a key challenge in the field of data mining. In this chapter, three types of search methods are used for feature selection, viz. local search, evolutionary search and metaheuristic search. The goal of this chapter is to evaluate the search ability of metaheuristic methods as they overcome the drawbacks of local search and evolutionary search methods. For this, the authors have conducted an exhaustive comparison amongst the search methods where a collection of nine meta-heuristic search methods (Ant search, Bat search, Bee search, Cuckoo search, Elephant search, Firefly search, Flower search, Harmony search, Wolf search) are compared with three local search and four evolutionary search methods. These search methods are validated against two open source datasets belonging to different domains (allow to provide stronger and reliable conclusion), viz. Pima Indians Diabetes dataset and Hepatocellular carcinoma dataset found in UCI machine learning repository. After extracting the useful features using all the search methods, models are predicted using ensemble classifiers, Random Forest, Logitboost, Adaboost and Bagging. To the best of authors’ knowledge, there is no work in literature which has done such an exhaustive evaluation and comparison of search methods (total 16 search methods are used) and then constructed ensemble models to determine the effectiveness of each search method. The results proved that meta-heuristic search methods are more optimal for feature selection process and are better than local and evolutionary search methods.

With the evolution of tools used for image editing in today’s world, the images can be manipulated with ease. Resizing, cloning, cropping, etc., becomes easy and fast while on the other end, a great challenge in front of the world is determining whether an image has been tempered or not. A very popular manipulation method for tempering images is Copy-move forgery in which a part of an image is duplicated and pasted in some other location of the same image. Major research has been going on in detection of copy-move forgery. A detailed review and analytical discussion has been presented in this paper along with pros and cons of each of the detection techniques used for copy-move forgery detection.

Changes in software systems are inevitable. Identification of change-prone modules can help developers to focus efforts and resources on them. In this article, the authors conduct various intra-project and cross-project change predictions. The authors use distributional characteristics of dataset to generate rules which can be used for successful change prediction. The authors analyze the effectiveness of meta-heuristic decision trees in generating rules for successful cross-project change prediction. The employed meta-heuristic algorithms are hybrid decision tree genetic algorithms and oblique decision trees with evolutionary learning. The authors compare the performance of these meta-heuristic algorithms with C4.5 decision tree model. The authors observe that the accuracy of C4.5 decision tree is 73.33%, whereas the accuracy of the hybrid decision tree genetic algorithm and oblique decision tree are 75.00% and 75.56%, respectively. These values indicate that distributional characteristics are helpful in identifying suitable training set for cross-project change prediction.

Objective Clear cell renal cell carcinoma (ccRCC) is a subtype of kidney cancer defined by robust lipid accumulation, which prior studies have indicated plays an important role in tumor progression. We hypothesized that the peroxisome proliferator-activated receptor gamma (PPARγ), detected in both ccRCC tumors and cell lines, promotes lipid storage in ccRCC and contributes to tumorigenesis in this setting. PPARγ transcriptionally regulates a number of genes involved in lipid and glucose metabolism in adipocytes, yet its role in ccRCC has not been described. The objective of this study was to elucidate endogenous PPARγ function in ccRCC cells. Methods and results Using chromatin immunoprecipitation followed by deep sequencing (ChIP-seq), we found that PPARγ and its heterodimer RXR occupy the canonical DR1 PPAR binding motif at approximately 1000 locations throughout the genome that can be subdivided into adipose-shared and ccRCC-specific sites. CRISPR-Cas9 mediated, loss-of-function studies determined that PPARγ is dispensable for viability, proliferation, and migration of ccRCC cells in vitro and in vivo. Also, surprisingly, PPARγ deletion had little effect on the robust lipid accumulation that typifies the “clear cell” phenotype of kidney cancer. Conclusion Our results suggest that PPARγ plays neither a tumor suppressive nor oncogenic role in advanced ccRCC, and thus single-agent therapeutics targeting PPARγ are unlikely to be effective for the treatment of this disease. The unique cistrome of PPARγ in ccRCC cells demonstrates the importance of cell type in determining the functions of PPARγ., Highlights • PPARγ expression is elevated in ccRCC patient samples and cell lines relative to renal epithelium. • The PPARγ-RXR cistrome is distinct between ccRCC and adipocytes. • PPARγ is dispensable for viability, proliferation, and migration of ccRCC cells in vitro and in vivo. • Lipid storage and triglyceride synthesis occur independently of PPARγ in ccRCC.

Purpose This paper has proposed a Fog architecture-based framework, which classifies dengue patients into uninfected, infected and severely infected using a data set built in 2010. The aim of this proposed framework is to developed a latency-aware system for classifying users into different categories based on their respective symptoms using Internet of Things (IoT) sensors and audio and video files. Design/methodology/approach To achieve the aforesaid aim, a smart framework is proposed, which consist of three components, namely, IoT layer, Fog infrastructure and cloud computing. The latency of the system is reduced by using network devices located in the Fog infrastructure. Data generated by IoT layer will first be processed by Fog layer devices which are in closer proximity of the user. Raw data and data generated will later be stored on cloud infrastructure, from where it will be sent to different entities such as user, hospital, doctor and government healthcare agencies. Findings Experimental evaluation proved the hypothesis that using the Fog infrastructure can achieve better response time for latency sensitive applications with the least effect on accuracy of the system. Originality/value The proposed Fog-based architecture can be used with IoT to directly link it with the Fog layer.

Microbial desalination cell (MDC) is a propitious technology towards water desalination by utilizing wastewater as an energy source. In this study, a multi-chambered MDC was used to bioremediate steel plant wastewater using the same wastewater as a fuel for anodic bacteria. A pure culture of Pseudomonas putida MTCC 1194 was isolated and inoculated to remove toxic phenol. Three different inoculum conditions, namely P. putida (INC-A), a mixture of P. putida and activated sludge (INC-B), and activated sludge alone (INC-C) were employed in an anodic chamber to mainly compare the electricity generation and phenol degradation in MDCs. The study revealed the maximum phenol removal of 82 ± 2.4%, total dissolved solids (TDS) removal of 68 ± 1.5%, and power generation of 10.2 mW/m2 using INC-B. The synergistic interactions between microorganisms, can enhance the toxic phenol degradation and also electricity generation in MDC for onsite wastewater application.

CAPTCHA stands for Completely Automated Public Turing Test to Tell Computers and Humans Apart. CAPTCHAs are used as security mechanism in web applications to differentiate between real users and automated users, also known as bots. Text-based CAPTCHAs are the popularly used CAPTCHA schemes due to their simplicity and thus, they are still being used despite the proposal of several attack mechanisms. In this work, the authors have proposed a novel approach to solve CAPTCHA schemes. In this approach, the authors have used Depth First Search algorithm for the extraction of characters from CAPTCHAs and Convolutional Neural Network for recognizing these extracted characters. The proposed approach was validated on 3000+ CAPTCHA schemes and proved to be efficient by providing an average accuracy of more than 92.0% in detecting CAPTCHA schemes.

Ovarian torsion is a common diagnostic challenge constituting 2.7% to 7.4% of all gynaecological emergencies. It commonly occurs in women of reproductive age however pre-pubertal girls and postmenopausal women can also be affected. Ovarian torsion accounts for approximately 3% of all cases of children with acute abdominal pain and requires immediate surgical intervention. Other common acute adnexal pathologies include simple ovarian cysts (OCs) with or without rupture. Owing to the nonspecific clinical presentation and poor specificity of radiologic tests, the diagnosis of OT in girls remains challenging. Here we are reporting a case of acute abdomen with large cyst on ultrasound clinically presented as torsion later on in the post-operative period diagnosed with acute pancreatitis.

Bansal and Simon discuss strategies to block glutathione synthesis and utilization pathways to inhibit tumor propagation and treatment resistance., Glutathione (GSH) is the most abundant antioxidant found in living organisms and has multiple functions, most of which maintain cellular redox homeostasis. GSH preserves sufficient levels of cysteine and detoxifies xenobiotics while also conferring therapeutic resistance to cancer cells. However, GSH metabolism plays both beneficial and pathogenic roles in a variety of malignancies. It is crucial to the removal and detoxification of carcinogens, and alterations in this pathway can have a profound effect on cell survival. Excess GSH promotes tumor progression, where elevated levels correlate with increased metastasis. In this review, we discuss recent studies that focus on deciphering the role of GSH in tumor initiation and progression as well as mechanisms underlying how GSH imparts treatment resistance to growing cancers. Targeting GSH synthesis/utilization therefore represents a potential means of rendering tumor cells more susceptible to different treatment options such as chemotherapy and radiotherapy.

Identifying and examining the change-prone parts of the software is gaining wide importance in the field of software engineering. This would help software practitioners to cautiously assign the resources for testing and maintenance. Software metrics can be used for constructing various classification models which allow timely identification of change prone classes. There have been various machine learning classification models proposed in the literature. However, due to varying results across studies, more research needs to be done to increase the confidence in the results and provide a valuable conclusion. In this paper, we have used a number of data analysis techniques (14 machine learning techniques and a statistical technique) to construct change prediction models and performed statistical testing to compare the performance of these models. The application of a large number of techniques will allow for fair evaluation and will thus, increase the conclusion validity of the study. The results are validated on five releases of an open source, widely used operating system in mobile phone and tablet computers, ‘Android’. To make the results more generalizable, we have also conducted inter-release and cross-project predictions. The results conclude that the machine learning techniques are effective in predicting change prone classes and thus, should be widely used by researchers and practitioners to reduce maintenance effort and thus efficient and better development of software.

Mycobacterial beta-lactamases are involved in exerting beta-lactam resistance, though many of these proteins remain uncharacterized. Here, we have characterized MSMEG_4455 of Mycobacterium smegmatis as a beta-lactamase using molecular, biochemical and mutational techniques. To elucidate its nature in vivo and in vitro, and to predict its structure-function relationship in silico analysis is done. The MSMEG_4455 is cloned and expressed ectopically in a beta-lactamase deficient Escherichia coli mutant to establish the in vivo beta-lactamase like nature via minimum inhibitory concentration (MIC) determination. Likewise the in vivo results, purified soluble form of MSMEG_4455 showed beta-lactam hydrolysis pattern similar to group 2a penicillinase. In silico analyses of MSMEG_4455 reveal glutamic acid (E)193 and tyrosine (Y)194 of omega-like loop might have importance in strengthening hydrogen bond network around the active-site, though involvement of tyrosine is rare for beta-lactamase activity. Accordingly, these residues are mutated to alanine (A) and phenylalanine (F), respectively. The mutated proteins have partially lost their ability to exert beta-lactamase activity both in vivo and in vitro. The Y194F mutation had more prominent effect on the enzymatic activity. Therefore, we infer that Y194 is the key for beta-lactamase activity of MSMEG_4455.

Software metrics are widely used to predict the classes in a software that are comparatively more change prone than others. Developers construct various prediction models which are used for the prediction of change prone classes. Thus, focussed attention can be laid on such classes leading to saving of lots of resources in terms of money, manpower and cost. But construction and usage of the prediction models using metrics is not always efficient and practical for developers. Thus, the alternative approach can be to identify certain alarming values of metrics above which a class is said to be at high risk. These alarming values are known as thresholds of the metrics. The main focus of this study is to identify metric threshold values using receiver operating characteristic curves. We have used threshold values to identify change prone classes (or classes that may lead to some risk in future) so that developers can carefully examine and pay attention on those classes. The study has used five release...

There are numerous reasons leading to change in software such as changing requirements, changing technology, increasing customer demands, fixing of defects etc. Thus, identifying and analyzing the change-prone classes of the software during software evolution is gaining wide importance in the field of software engineering. This would help software developers to judiciously allocate the resources used for testing and maintenance. Software metrics can be used for constructing various classification models which can be used for timely identification of change prone classes. Search based algorithms which form a subset of machine learning algorithms can be utilized for constructing prediction models to identify change prone classes of software. Search based algorithms use a fitness function to find the best optimal solution among all the possible solutions. In this work, we analyze the effectiveness of hybridized search based algorithms for change prediction. In other words, the aim of this work is to find whether search based algorithms are capable for accurate model construction to predict change prone classes. We have also constructed models using machine learning techniques and compared the performance of these models with the models constructed using Search Based Algorithms. The validation is carried out on two open source Apache projects, Rave and Commons Math. The results prove the effectiveness of hybridized search based algorithms in predicting change prone classes of software. Thus, they can be utilized by the software developers to produce an efficient and better developed software. Used hybridized search based algorithms to identify change prone classes.For empirical validation, two open source projects (Apache Rave,Commons Math) used.Assessed performance of search based algorithms using g-mean and accuracy.Machine learning models constructed and performance compared with hybridised models.Results showed that hybridised models outperformed machine learning models.

As a software system evolves, the changes are introduced at every stage of its development. The timely identification of change prone classes is very important to reduce the costs associated with the maintenance phase. Thus, the author has developed various models which can be used in the design phase (one of the early phases) to identify the parts of software which are more change prone than others. Software metrics along with the change data can be used for developing the models. In this study, the author has investigated the performance of various ensemble learners and a statistical model for identifying the classes which are change prone. The use of ensemble learners gives the researchers an opportunity to analyze and investigate them in the area of change prediction. The empirical validation is carried on five official releases of Android operating system. The overall results of the study indicate that the ensemble learners are capable of effective prediction.