Review Paper on Opinion Extraction of Drug Reviews Using PAMM

By the drastic reputation of net, the have a look at of on line critiques via blogs, dialogue boards and so on., have become maximum famous manner for the sufferers to have drug treatments for continual diseases. In this various research parameters for opinion extraction of drug opinions and techniques used in it. A probabilistic idea is developed to extract useful information from those opinions called as PAMM(Probabilistic aspect Mining model). PAMM has a particular function that it concentrates on locating opinion aspect associated with one magnificence as opposed to locating element for all instructions simultaneously on each execute on. This reduces the chances of blended standards of other training. The components discovered are also responsible to differentiate a class from other training. The paper offers idea to advocate an efficient EM algorithm to advise opinion aspects for diverse groups of a while. An EM algorithm is used for finding approximate parameters of an underlying distribution from information set when it has missing values

Extracting Features from Textual Data in Class Imbalance Problems

[EN] We address class imbalance problems. These are classification problems where the target variable is binary, and one class dominates over the other. A central objective in these problems is to identify features that yield models with high precision/recall values, the standard yardsticks for assessing such models. Our features are extracted from the textual data inherent in such problems. We use n-gram frequencies as features and introduce a discrepancy score that measures the efficacy of an n-gram in highlighting the minority class. The frequency counts of n-grams with the highest discrepancy scores are used as features to construct models with the desired metrics. According to the best practices followed by the services industry, many customer support tickets will get audited and tagged as contract-compliant whereas some will be tagged as over-delivered . Based on in-field data, we use a random forest classifier and perform a randomized grid search over the model hyperparameters. The model scoring is performed using an scoring function. Our objective is to minimize the follow-up costs by optimizing the recall score while maintaining a base-level precision score. The final optimized model achieves an acceptable recall score while staying above the target precision. We validate our feature selection method by comparing our model with one constructed using frequency counts of n-grams chosen randomly. We propose extensions of our feature extraction method to general classification (binary and multi-class) and regression problems. The discrepancy score is one measure of dissimilarity of distributions and other (more general) measures that we formulate could potentially yield more effective models.Aravamuthan, S.; Jogalekar, P.; Lee, J. (2022). Extracting Features from Textual Data in Class Imbalance Problems. Journal of Computer-Assisted Linguistic Research. 6:42-58. https://doi.org/10.4995/jclr.2022.182004258

Quality measures for ETL processes: from goals to implementation

Extraction transformation loading (ETL) processes play an increasingly important role for the support of modern business operations. These business processes are centred around artifacts with high variability and diverse lifecycles, which correspond to key business entities. The apparent complexity of these activities has been examined through the prism of business process management, mainly focusing on functional requirements and performance optimization. However, the quality dimension has not yet been thoroughly investigated, and there is a need for a more human-centric approach to bring them closer to business-users requirements. In this paper, we take a first step towards this direction by defining a sound model for ETL process quality characteristics and quantitative measures for each characteristic, based on existing literature. Our model shows dependencies among quality characteristics and can provide the basis for subsequent analysis using goal modeling techniques. We showcase the use of goal modeling for ETL process design through a use case, where we employ the use of a goal model that includes quantitative components (i.e., indicators) for evaluation and analysis of alternative design decisions.Peer ReviewedPostprint (author's final draft

Scalable architectures for platform-as-a-service clouds: performance and cost analysis

Scalability is a significant feature of cloud computing, which ad-dresses to increase or decrease the capacities of allocated virtual resources at application, platform, database and infrastructure level on demand. We investigate scalable architecture solutions for cloud PaaS that allow services to utilize the resources dynamically and effectively without directly affecting users. We have implemented scalable architectures with different session state management solutions, deploying an online shopping cart application in a PaaS solution, and measuring the performance and cost under three server-side session state providers: Caching, SQL database and NoSQL database. A commercial solution with its supporting state management components has been used. Particularly when re-architecting software for the cloud, the trade-off between performance, scalability and cost implications needs to be discussed

Using Performance Forecasting to Accelerate Elasticity

Cloud computing facilitates dynamic resource provisioning. The automation of resource management, known as elasticity, has been subject to much research. In this context, monitoring of a running service plays a crucial role, and adjustments are made when certain thresholds are crossed. On such occasions, it is common practice to simply add or remove resources. In this paper we investigate how we can predict the performance of a service to dynamically adjust allocated resources based on predictions. In other words, instead of “repairing” because a threshold has been crossed, we attempt to stay ahead and allocate an optimized amount of resources in advance. To do so, we need to have accurate predictive models that are based on workloads. We present our approach, based on the Universal Scalability Law, and discuss initial experiments

CAR T-Cell-Based gene therapy for cancers: new perspectives, challenges, and clinical developments

Chimeric antigen receptor (CAR)-T cell therapy is a progressive new pillar in immune cell therapy for cancer. It has yielded remarkable clinical responses in patients with B-cell leukemia or lymphoma. Unfortunately, many challenges remain to be addressed to overcome its ineffectiveness in the treatment of other hematological and solidtumor malignancies. The major hurdles of CAR T-cell therapy are the associated severe life-threatening toxicities such as cytokine release syndrome and limited anti-tumor efficacy. In this review, we briefly discuss cancer immunotherapy and the genetic engineering of T cells and, In detail, the current innovations in CAR T-cell strategies to improve efficacy in treating solid tumors and hematologic malignancies. Furthermore, we also discuss the current challenges in CAR T-cell therapy and new CAR T-cell-derived nanovesicle therapy. Finally, strategies to overcome the current clinical challenges associated with CAR T-cell therapy are included as well

Comparative analysis of homology models of the Ah receptor ligand binding domain: Verification of structure-function predictions by site-directed mutagenesis of a nonfunctional receptor

The aryl hydrocarbon receptor (AHR) is a ligand-dependent transcription factor that mediates the biological and toxic effects of a wide variety of structurally diverse chemicals, including the toxic environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). While significant interspecies differences in AHR ligand binding specificity, selectivity, and response have been observed, the structural determinants responsible for those differences have not been determined, and homology models of the AHR ligand-binding domain (LBD) are available for only a few species. Here we describe the development and comparative analysis of homology models of the LBD of 16 AHRs from 12 mammalian and nonmammalian species and identify the specific residues contained within their ligand binding cavities. The ligand-binding cavity of the fish AHR exhibits differences from those of mammalian and avian AHRs, suggesting a slightly different TCDD binding mode. Comparison of the internal cavity in the LBD model of zebrafish (zf) AHR2, which binds TCDD with high affinity, to that of zfAHR1a, which does not bind TCDD, revealed that the latter has a dramatically shortened binding cavity due to the side chains of three residues (Tyr296, Thr386, and His388) that reduce the amount of internal space available to TCDD. Mutagenesis of two of these residues in zfAHR1a to those present in zfAHR2 (Y296H and T386A) restored the ability of zfAHR1a to bind TCDD and to exhibit TCDD-dependent binding to DNA. These results demonstrate the importance of these two amino acids and highlight the predictive potential of comparative analysis of homology models from diverse species. The availability of these AHR LBD homology models will facilitate in-depth comparative studies of AHR ligand binding and ligand-dependent AHR activation and provide a novel avenue for examining species-specific differences in AHR responsiveness. © 2013 American Chemical Society

Analysis of gene junction sequences of human parainfluenza virus type 3.

Transcription of the human parainfluenza virus type 3 (hPIV3) genome occurs by a sequential stop-start mechanism which is directed by short conserved sequence elements found at the boundary of the each of the hPIV3 genes, yielding several monocistronic mRNAs. The general aim of this project was to begin to elucidate the role of these cis acting transcription regulatory sequences. A technique called minigenome rescue, which allows the in vitro manipulation and analysis of cDNA representing minigenome analogs of the hPIV3 genome, was used. The requirement of the rule of six for efficient rescue of hPIV3 minigenomes was verified using a series of monocistronic cDNAs. Minigenomes whose total length was a multiple of six nt (6n) were rescued more efficiently than minigenomes that were not 6n nt in length, as assayed by CAT activity. Removal of the eight extra nt from the M/F junction reduced the frequency of transcriptional readthrough to the level seen for the other hPIV3 gene junctions. (Abstract shortened by UMI.