An Early Multi-Criteria Risk Assessment Model

Accurate time and budget is an essential estimate for planning software projects correctly. Quite often, the software projects fall into unrealistic estimates and the core reason generally owes to problems with the requirement analysis. For investigating such problems, risk has to identified and assessed at the requirement engineering phase only so that defects do not seep down to other software development phases. This article proposes a multi-criteria risk assessment model to compute risk at a requirement level by computing cumulative risk score based on a weighted score assigned to each criterion. The result of comparison with other approaches and experimentation shows that using this model it is possible to predict the risk at the early phase of software development life cycle with high accuracy.

Relación entre inteligencia emocional y rendimiento académico en estudiantes universitarios peruanos

Emotional intelligence is the set of capacities that allow a student to properly manage their emotions and interpersonal relationships, when subjected to stressful situations, such as the educational environment; in which they have to deal with internal and external conflicts for good academic performance. The research aimed to relate emotional intelligence and academic performance in students of the National Autonomous University of Chota, Peru. The study was descriptive correlational, not cross-sectional experimental, and it worked with 325 university students who signed the informed consent, after knowing the purpose of the research; emotional intelligence was determined with the BarOn ICE Emotional Intelligence Test and academic performance with a data collection sheet. The results show a higher proportion of university students with high emotional intelligence (51.1%) and average academic performance of 12.59 ± 1.17 weighted score, results similar to those reported by various national and international studies. It is concluded that there is a statistically significant relationship between emotional intelligence, the adaptability dimension and the academic performance of university students (p = 0.043 and p = 0.021).

A polygenic risk score for multiple myeloma risk prediction

There is overwhelming epidemiologic evidence that the risk of multiple myeloma (MM) has a solid genetic background. Genome-wide association studies (GWAS) have identified 23 risk loci that contribute to the genetic susceptibility of MM, but have low individual penetrance. Combining the SNPs in a polygenic risk score (PRS) is a possible approach to improve their usefulness. Using 2361 MM cases and 1415 controls from the International Multiple Myeloma rESEarch (IMMEnSE) consortium, we computed a weighted and an unweighted PRS. We observed associations with MM risk with OR = 3.44, 95% CI 2.53–4.69, p = 3.55 × 10−15 for the highest vs. lowest quintile of the weighted score, and OR = 3.18, 95% CI 2.1 = 34–4.33, p = 1.62 × 10−13 for the highest vs. lowest quintile of the unweighted score. We found a convincing association of a PRS generated with 23 SNPs and risk of MM. Our work provides additional validation of previously discovered MM risk variants and of their combination into a PRS, which is a first step towards the use of genetics for risk stratification in the general population.

Evaluation of Deep Neural Network Compression Methods for Edge Devices Using Weighted Score-Based Ranking Scheme

The demand for object detection capability in edge computing systems has surged. As such, the need for lightweight Convolutional Neural Network (CNN)-based object detection models has become a focal point. Current models are large in memory and deployment in edge devices is demanding. This shows that the models need to be optimized for the hardware without performance degradation. There exist several model compression methods; however, determining the most efficient method is of major concern. Our goal was to rank the performance of these methods using our application as a case study. We aimed to develop a real-time vehicle tracking system for cargo ships. To address this, we developed a weighted score-based ranking scheme that utilizes the model performance metrics. We demonstrated the effectiveness of this method by applying it on the baseline, compressed, and micro-CNN models trained on our dataset. The result showed that quantization is the most efficient compression method for the application, having the highest rank, with an average weighted score of 9.00, followed by binarization, having an average weighted score of 8.07. Our proposed method is extendable and can be used as a framework for the selection of suitable model compression methods for edge devices in different applications.

Disrupting Mitohormesis As a Novel Therapeutic Strategy for Multiple Myeloma (MM) Including Those with High Risk Disease and Proteosome Inhibitor Resistance

Background: Moderate mitochondrial stress induced by multiple mediators but most notably ROS can lead to activation of persistent mito-protective mechanisms termed "Mitohormesis". As a result of massive protein synthesis, malignant plasma cells (PCs) from MM patients (pts) undergo substantial ER stress but in addition high rates of Ig synthesis contributes to overproduction of ROS. We hypothesized that MM cells exploit mitohormesis to maintain ROS in the hometic zone, thereby increasing mitochondrial fitness to avoid apoptosis. We therefore set out to determine if the processes of mitohormesis are activated in MM and whether unmitigated mitochondrial stress can be exploited as a therapeutic strategy in MM. Results: Protective stress mechanisms of mitohormesis include the activation of the mitochondrial UPR (UPR MT),a mitochondrial-to-nuclear signaling pathway mediated by CHOP and ATF5 that upregulates mitochondrial import proteins, chaperones and proteases to maintain mitochondrial proteastasis. We first demonstrated that UPR MT activation occurs with progression from precursor to overt MM. Using a UPR MTgene signature derived from published gene-sets we observed upregulation of UPR MT genes in single-cell RNA sequencing (scRNA-seq) data generated from PCs derived from Vκ*MYC mice (a transgenic mouse model of MM) spanning the spectrum of the disease. UPR MT gene signature scores in PCs from mice increased with disease progression with the highest levels found in late-MM> int-MM> early MM>wild type mice. Similarly, analysis of publicly available gene expression datasets (GSE6477) that includes normal donors, MGUS and newly diagnosed MM (NDMM) revealed higher expression of UPR MT genes in the majority of NDMM, weak expression in MGUS and absence in normal PCs. To assess the impact of UPR MT expression on pt outcomes we calculated a UPR MT index score derived from the median expression of 12 mtUPR classifier genes across the MMRF CoMMpass dataset of NDMM pts. Stratifying pts by UPR MT expression score we found that pts in the top quartile had a significantly shorter PFS and OS compared to pts with the lowest quartile weighted score. Next, we postulated that perturbation of the mitochondrial import protein, Translocase of the Inner Membrane 23 (TIM23) would exaggerate mitochondrial stress as mitochondrial import efficiency is a key regulator of the UPR MT. First, we demonstrated that TIM23 complex genes are enriched in pts from the CoMMpass dataset with poor risk (1q gain and PR gene signature) and that shorter PFS and OS is associated with a higher weighted score of TIM23 complex genes. We then demonstrated that genetic (shRNA) knockdown or pharmacologic inhibition of TIM23 with MB-10, a small molecule inhibitor of TIM23 induced apoptosis of MM cell lines and primary pt PCs. Further non-transformed cell lines, CD138 - non-MM cells and normal donor hematopoietic progenitor cells were less susceptible to the effects of MB-10. Consistent with activation of the UPR MT, treatment of MM cells resulted in increased cytosolic ATF4, CHOP and a shift of ATF5 to the nuclear fraction. Activation of the CHOP-dependent branch of the UPR MT resulted in in upregulation of mitochondrial-targeted proteins, cpn10 and ClpP. Interestingly, MB-10 also induced XBP1 splicing demonstrating that inhibition of TIM23 complex can simultaneously activate the IRE1/XBP1 branch of integrated stress response (ISR), This led us to hypothesize that targeting TIM23 as an alternative means of activating the ISR could overcome acquired resistance to proteosome inhibitors (PIs). Indeed, PI-resistant and parental isogenic cell lines were equally susceptible to MB-10 as measured by IC50 values of cell growth. Finally, we demonstrated that doxycycline inducible knockdown of TIM23 in a mouse xenograft model induced tumor regression with significantly small tumor volumes at the end of 17 days of doxycycline treatment compared to tumors expressing an inducible control vector. Conclusions: These data demonstrate that mitohormesis and UPR MT activation is associated with MM progression and worse clinical outcomes. Further we show that disrupting mitochondrial protein import results in unmitigated mitochondrial stress that switches the UPR MT from an adaptive cytoprotective to cytotoxic proapoptotic response. Thus, targeting mitochondrial import proteins such as TIM23 may represent novel therapeutic targets for MM. Disclosures Schimmer: Takeda Pharmaceuticals: Consultancy, Research Funding; Medivir AB: Research Funding; Novartis: Consultancy, Honoraria; Jazz: Consultancy, Honoraria; Otsuka Pharmaceuticals: Consultancy, Honoraria; UHN: Patents & Royalties. Trudel: Janssen: Honoraria, Research Funding; GlaxoSmithKline: Consultancy, Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Roche: Consultancy; Sanofi: Honoraria; Pfizer: Honoraria, Research Funding; Genentech: Research Funding; BMS/Celgene: Consultancy, Honoraria, Research Funding.

DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions

We present Descending from Stochastic Clustering Variance Regression (DiSCoVeR), a Python tool for identifying high-performing, chemically unique compositions relative to existing compounds using a combination of a chemical distance metric, density-aware dimensionality reduction, and clustering. We introduce several new metrics for materials discovery and validate DiSCoVeR on Materials Project bulk moduli using compound-wise and cluster-wise validation methods. We visualize these via multiobjective Pareto front plots and assign a weighted score to each composition where this score encompasses the trade-off between performance and density-based chemical uniqueness. We explore an additional uniqueness proxy related to property gradients in chemical space. We demonstrate that DiSCoVeR can successfully screen materials for both performance and uniqueness in order to extrapolate to new chemical spaces.

DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions

We present Descending from Stochastic Clustering Variance Regression (DiSCoVeR), a Python tool for identifying high-performing, chemically unique compositions relative to existing compounds using a combination of a chemical distance metric, density-aware dimensionality reduction, and clustering. We introduce several new metrics for materials discovery and validate DiSCoVeR on Materials Project bulk moduli using compound-wise and cluster-wise validation methods. We visualize these via multiobjective Pareto front plots and assign a weighted score to each composition where this score encompasses the trade-off between performance and density-based chemical uniqueness. We explore an additional uniqueness proxy related to property gradients in chemical space. We demonstrate that DiSCoVeR can successfully screen materials for both performance and uniqueness in order to extrapolate to new chemical spaces.

DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions

We present Descending from Stochastic Clustering Variance Regression (DiSCoVeR), a Python tool for identifying high-performing, chemically unique compositions relative to existing compounds using a combination of a chemical distance metric, density-aware dimensionality reduction, and clustering. We introduce several new metrics for materials discovery and validate DiSCoVeR on Materials Project bulk moduli using compound-wise and cluster-wise validation methods. We visualize these via multiobjective Pareto front plots and assign a weighted score to each composition where this score encompasses the trade-off between performance and density-based chemical uniqueness. We explore an additional uniqueness proxy related to property gradients in chemical space. We demonstrate that DiSCoVeR can successfully screen materials for both performance and uniqueness in order to extrapolate to new chemical spaces.

An analysis of Teachers’ Behaviour for the Academic Performance of School Students in Faisalabad, Pakistan

Instructors’ attitudes can help or hurt student enthusiasm, achievement, and well-being. Recent studies found that negative instructor attitudes can prejudice academic achievement and escalation students' psychological syndromes and physical indications of stress. Instructors who use degradation or sarcasm can leave a child feeling demeaned. Discipline by fear and intimidation can harm the student's future achievement. Teachers who are strict in their display of authority or indifferent toward their students or lessons can leave a lingering feeling of negativity. Teachers facilitate the students for improving their performance and always are available to solve their glitches and give courage to the students for innovative thinking. But teaching occupation is seen as poorly managed in recent situations. The main focused area is to recognize students’ difficulties concerning learning. For this purpose, well-trained teachers are required to guide the students. The study's primary objective was to analyse teachers’ behavior for the academic performance of school students in Tehsil Faisalabad, Pakistan. The study population was all the teachers and students of Govt. sector schools, of Tehsil Faisalabad (From Pakistan). The study was conducted at the Institute of Agri. Extension, Education and Rural Development, University of Agriculture, Faisalabad, Punjab, Pakistan. From (88) total number of schools, six schools (3 schools from urban and 3 schools from rural areas) were selected on a random basis 5 students were selected from 6th standard, 5 from 7th standard, 5 from 8th standard) making a sample of 90 students. Three teachers were selected from the selected standards (1 from 6th standard, 1 from 7th standard, 1 from 8th standard). The selected teachers’ samples were eighteen. Two questionnaires were used with a five-point rating scale to collect the views of school teachers and students about the teachers’ behavior regarding the students' academic achievement. The researcher personally visited schools and collected views regarding the behavior of teachers on the academic achievements of students. Quantitative data were analyzed through SPSS. The result also demonstrates that teachers’ behavior directly affects students' academic performance. The analysis of data showed a significant association between teachers’ behavior and students’ academic achievements. It was recommended that teachers behave positively with students and show professionalism so their students will pay attention to their study through such kind of motivation. Most of the students were agreed that they are appreciated by their teacher (a weighted score of 25.00). Most of the students were agreed that communication between teacher and student helps the teacher to understand students' feelings with a weighted score of 31.00. Most of the students were agreed that the students consider their teacher as a role model with a weighted score of 36.00. The study's major conclusions were that teachers felt honoured to be teachers, adjusted themselves with the predominant situation and environments, and used different motivational teaching techniques. Students were found to be pleased with the positive behaviour of their teachers. The relationship between the teachers’ behaviour and corresponding academic success (marks) revealed a highly positive significant correlation.