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Context.--Measurable (minimal) residual disease (MRD) is an independent prognostic factor for survival outcomes in patients with lymphoid and plasma cell malignancies and has been incorporated into consensus criteria regarding treatment response, strategy, and clinical trial endpoints. clonoSEQ (a next-generation sequencing [NGS]-MRD assay) uses multiplex polymerase chain reaction and NGS to identify clonotypic rearrangements at the immunoglobulin (Ig) H, IgK, IgL, T-cell receptor (TCR)-[beta], and TCR-[gamma] loci, as well as translocated B-cell lymphoma 1/IgH and 2/IgH sequences for MRD assessment. Additionally, it can be used to confirm diagnoses of cutaneous T-cell lymphoma (CTCL). Objective.--To review the technical aspects of our experience using the clonoSEQ Assay in routine clinical practice. Design.--In this single-center experience, 390 patients with lymphoid and plasma cell malignancies were assessed with the NGS-MRD Assay at a central laboratory. Results.--Median time from arrival of the shipment to initiation of the assay (defined as captured in Adaptive's secure tracking system) was 2.1 hours. Overall, 317 patients had 1 or more samples submitted for sequence identification. Of these, 290 (91.5%) had trackable sequences identified. The median calibration rate of samples by malignancy (where n [greater than or equal to] 10 samples, excluding CTCL samples) was 88.1%, across a variety of fresh and archived sample sources (177 of 201 samples). TCR-[beta] and/ or TCR-[gamma] clonotypes were identified in 40 of 95 samples (42.1%) from 66 patients with suspected CTCL. Conclusions.--This NGS-MRD Assay is a valuable and sensitive tool for monitoring MRD in patients with plasma cell and lymphoid malignancies and assisting in the diagnosis of CTCL., In the past decade, the clinical utility of assessing measurable (minimal) residual disease (MRD) in patients with plasma cell and lymphoid malignancies has been recognized increasingly. (1) MRD has been [...]

The built environment counts most of the world's primary energy which states approximately 40%, energy is being used for different provisions such as for ventilation, lighting cooling and heating. Renewable energy can be a promising feature for reducing the use of primary energy at large scale with latest efficient techniques. There are various factors involved which are influencing energy consumption. Paper focuses on how to find out various attributes which contributes in overall consumption of energy at neighborhood level. This paper includes number of variables of the built environment. The significance of this investigation is identified with the reality of the energy utilization at neighborhood scale with the help of identification of various tools and GIS mapping for the neighborhoods. The result will aim the focus where policies should be framed to reduce the energy level by finding out the relationship between the variables or independent variables. [ABSTRACT FROM AUTHOR]

In recent years, urban areas like Lucknow have faced increasing challenges related to energy consumption and sustainability. As cities grow, the demand for energy rises, necessitating efficient use of resources to mitigate environmental impact and ensure long-term urban livability. Energy efficiency in urban neighborhoods plays a pivotal role in achieving these goals by reducing energy consumption, promoting sustainable practices, and enhancing the quality of life for residents. Despite the importance of energy efficiency, many urban neighborhoods in Lucknow face significant challenges in implementing effective strategies. Variations in infrastructure, socioeconomic factors, and policy frameworks influence the adoption and success of energy-efficient practices across different neighborhoods. Understanding these variations and identifying successful approaches are crucial steps towards fostering sustainable urban development. Urban areas worldwide face growing challenges related to energy consumption and sustainability, prompting cities like Lucknow to explore strategies for enhancing energy efficiency within their urban fabric. This research conducts a comparative analysis of energy efficiency practices across diverse neighborhoods in Lucknow, aiming to identify effective strategies and contextual factors influencing their implementation. Utilizing a mixed-methods approach, qualitative data from interviews and site observations complement quantitative data on energy usage patterns and infrastructure development. Findings reveal significant variations in energy efficiency initiatives, impacted by local governance, community engagement, and technological adoption. Successful practices emphasize multi-stakeholder collaborations, policy coherence, and tailored interventions that address neighborhood-specific needs. The study contributes empirical insights into urban sustainability by informing policy-makers and urban planners on effective pathways for promoting energy efficiency in Lucknow's neighborhoods, thereby advancing broader goals of environmental sustainability and livability in urban environments. [ABSTRACT FROM AUTHOR]

In real-world anomaly detection tasks such as Credit Card Fraud Detection, Cancer Patients Detection, Phishing Website Detection, etc., the training datasets often suffer from skewed class distribution. But the traditional Machine Learning (ML) classification algorithms assume balanced class distribution and equal misclassification costs. As a result, when class-imbalanced data are presented to the traditional ML algorithms they tend to produce biased and inaccurate predictive ML models. In this study, we propose four novel Phishing Website Classification models namely, SMOTEENN-XGB, SMOTEENN-RF, SMOTEENN-LR, and SMOTEENN-SVM by combining SMOTEENN (SMOTE + ENN) hybrid sampling technique with eXtreme Gradient Boosting (XGB), Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM) classifiers respectively. We propose the use of SMOTEENN hybrid sampling as the novel approach to address the problem of class imbalance in Phishing Website datasets prior to building classification models. To the best of our knowledge and belief, our novel proposed four models SMOTEENN-XGB, SMOTEENN-RF, SMOTEEEN-LR, and SMOTEENN-SVM for Phishing Website Detection based on SMOTEENN hybrid sampling approach have not been published in the existing studies as of now. [ABSTRACT FROM AUTHOR]