Miracle-dz/UniFAR-FTUs huggingface dataset:
Applications of monoclonal antibodies for the treatment of hematological malignancies

Background: The introduction of mAbs has changed the clinical approach to patients with lymphoma and leukemia. Objective: To summarize the most significant applications of mAb-based regimens in the treatment of hematological malignancies and explore their possible role in the future management of these patients. Results: Rituximab (anti-CD20) was the first mAb developed for the treatment of B-cell lymphomas. Several randomized studies have demonstrated its efficacy in lymphomas and low toxicity profile; rituximab also has significant activity in chronic lymphocytic leukemia (CLL). Alemtuzumab (anti-CD52) has shown efficacy in previously untreated or refractory CLL patients, while gemtuzumab ozogamicin (anti-CD33) appears to have significant activity in acute myeloid leukemias and myelodysplastic syndromes. Conclusions: In the next few years, investigations will be concentrated on the improvement of the older mAbs, and the development of new mAbs, targeting molecules important for malignant cell cycle and survival in an attempt to further improve patient survival.
Robustness of TAP-based scan networks

It is common to embed instruments when developing integrated circuits (ICs). These instruments are accessed at post-silicon validation, debugging, wafer sort, package test, burn-in, printed circuit board bring-up, printed circuit board assembly manufacturing test, power-on self-test, and operator-driven in-field test. At any of these scenarios, it is of interest to access some but not all of the instruments. IEEE 1149.1-2013 and IEEE 1687 propose Test Access Port based (TAP-based) mechanisms to design flexible scan networks such that any combination of instruments can be accessed from outside of the IC. Previous works optimize TAP-based scan networks for one scenario with a known number of accesses. However, at design time, it is difficult to foresee all needed scenarios and the exact number of accesses to instruments. Moreover, the number of accesses might change due to late design changes, addition/exclusion of tests, and changes of constraints. In this paper, we analyze and compare seven IEEE 1687 compatible network design approaches in terms of instrument access time, hardware overhead, and robustness. Given the similarities between IEEE 1149.1-2013 and IEEE 1687, the conclusions are also applicable to IEEE 1149.1-2013 networks.
Hypoglycaemic actions of a medicinal herb, Rothmannia hispida in diabetic rats

The effect of oral administration of crude extract of Rothmannia hispida on alloxan induced diabetic rats was studied to valuate the claimed antidiabetogenic effects of the herb on the early symptoms of diabetes mellitus. Twenty albino rats of wistar strain weighing 130-180g were used. Thirteen of these were made diabetic by intraperitoneal injection of alloxan. Administration of the herb extract in a dose of 0.36g/100g body weight commenced after 1 week of untreated diabetes and lasted for seven days. The results showed that treatment with R. hispida significantly reduced plasma glycose from 27.53 ± 2.46 mmol/L in untreated diabetic animals to 13.10 ± 1.84 mmol/L after treatment, (p R hispida treatment while no glucose was detected in the control urine. Urine output declined significantly, (p R. hispida treatment, while the control steadily gained weight. These results suggest that crude extract of R. hispida contains active or agents that can possibly reverse diabetic symptoms of hyperglycaemia, polyuria and glycosuria. Keywords : diabetes mellitus, Rothmannia hispida, hypoglycaemic, urine glucose Mary Slessor Journal of Medicine Vol. 5(2) 2005: 21-24
Adjuvant radiation therapy is not necessary in the management of endometrial carcinoma stage I, low‐risk cases

In an attempt to create uniform nationwide guidelines for the management of all stages of endometrial carcinoma, and to limit the use of adjuvant radiation therapy in stage I disease to high-risk patients only, a protocol was developed by the Danish Endometrial Cancer group (DEMCA). From September 1986 through August 1988, 1214 women in Denmark with newly diagnosed carcinoma of the endometrium have been treated according to this protocol. This figure represents all endometrial carcinomas diagnosed in Denmark during this 2-year period. The primary treatment was total abdominal hysterectomy and bilateral salpingo-oophorectomy and no preoperative radiation therapy was delivered. In 1039 cases no macroscopic residual tumor and/or microscopic tumor tissue in the resection margins was found following surgery. Based on surgery and histopathology, these patients were classified as: P-stage I low-risk (grade 1 & 2 and ≤ 50% myometrial invasion), P-stage I high-risk (grade 1 & 2 and > 50% myometrial invasion, and grade 3), P-stage II and P-stage III (Group 1). Distribution was as follows: P-I low-risk 641 patients, P-I high-risk 235, P-II 105 and P-III (Group 1) 58 patients. No postoperative radiation therapy was given to P-I low-risk cases. P-I high-risk, P-II, and P-III (Group 1) cases received external radiation therapy. Recurrence rate at 68-92 months follow-up was 45/641 (7%) in P-I low-risk, 36/235 (15%) in P-I high-risk, 30/105 (29%) in P-II, and 27/58(47%) in P-III (Group 1) cases. Fifteen of 17 vaginal recurrences in P-I low-risk cases were salvaged (mean observation time 61 months).
Open Access to High-Content Clonogenic Analysis

Image-processing programs are used to identify and classify eukaryotic cell colonies as spots following seeding at low density on dishes or in multiwell plates. The output from such approaches, however, is generally limited to 1–2 parameters, and there is no ability to extract phenotypic information at the single colony level. Furthermore, there is a lack of user-friendly pipelines for analysis of clonogenicity in the context of high-content analysis. This article describes an experimental and multiparametric image analysis workflow for clonogenic assays in multiwell format, named the Colony Assay Toolbox (CAT). CAT incorporates a cellular-level resolution of individual colonies and facilitates the extraction of phenotypic information, including the number and size of colonies and nuclei, as well as morphological parameters associated with each structure. Furthermore, the pipeline is capable of discriminating between colonies composed of senescent and nonsenescent cells. We demonstrate the accuracy and flexibility of CAT by interrogating the effects of 2 preclinical compounds, Nutlin-3a and ABT-737, on the growth of human osteosarcoma cells. CAT is accessible to virtually all laboratories because it uses common wide-field fluorescent microscopes, the open-source CellProfiler program for colony image analysis, and a single fluorescent dye for all the segmentation steps.
Cost Efficient Resource Management in Fog Computing Supported Medical Cyber-Physical System

With the recent development in information and communication technology, more and more smart devices penetrate into people’s daily life to promote the life quality. As a growing healthcare trend, medical cyber-physical systems (MCPSs) enable seamless and intelligent interaction between the computational elements and the medical devices. To support MCPSs, cloud resources are usually explored to process the sensing data from medical devices. However, the high quality-of-service of MCPS challenges the unstable and long-delay links between cloud data center and medical devices. To combat this issue, mobile edge cloud computing, or fog computing, which pushes the computation resources onto the network edge (e.g., cellular base stations), emerges as a promising solution. We are thus motivated to integrate fog computation and MCPS to build fog computing supported MCPS (FC-MCPS). In particular, we jointly investigate base station association, task distribution, and virtual machine placement toward cost-efficient FC-MCPS. We first formulate the problem into a mixed-integer non-linear linear program and then linearize it into a mixed integer linear programming (LP). To address the computation complexity, we further propose an LP-based two-phase heuristic algorithm. Extensive experiment results validate the high-cost efficiency of our algorithm by the fact that it produces near optimal solution and significantly outperforms a greedy algorithm.
Khat use and Its Determinants among University students in Northwest Ethiopia: A multivariable analysis -

Background: Khat (Catha edulis) chewing is becoming a significant public health problem, and it becomes a common practice among high school, college, and university students. Objective: This study was conducted to assess the prevalence and determinants of khat chewing among Gondar University students in Northwest Ethiopia. Materials and Methods: The study was conducted among 872 randomly selected students in April 2014. A structured self-administered questionnaire was used to collect the data. Descriptive statistics and logistic regression were performed to examine the prevalence and predictors of current use of khat. Results: The lifetime and current prevalence of khat chewing among respondents was 17.9% (95% CI: 15.3–20.5) and 13.6% (95% CI: 11.4–15.9), respectively. Mental distress (adjusted odds ratio (AOR): 1.66 (95% CI: 1.05–2.61)), having friends who chewed khat (AOR: 3.9 (95% CI: 1.65–5.21)), cigarette smoking (AOR: 18 (95% CI: 9.5–18)), increased classwork load (AOR: 1.9 (95% CI: 1.12–3.2)), and religious practice (AOR: 0.4 (95% CI: 0.24–0.71)) were found to be independent predictors of chewing. Conclusion: The current prevalence of khat chewing was found to be high. Actions targeting those determinants are essential to effectively reduce khat use among university students.
Optical flooding cluster switching (OFCS)

This paper proposes a novel framework of bandwidth provisioning based on optical burst switching (OBS), called optical flooding cluster switching (OFCS), which is developed to achieve a better resource allocation and efficiency by fully exploiting the potential characteristics of data-dominated traffic with a high self-similarity and burstiness in the modern communication networks. The concept of flooding clusters is first defined, which serves as basis of the study. The design objectives and the working principles of the OFCS scheme are introduced, where the corresponding advantages against the pure OBS, pure OCS (optical circuit switching), and the hybrid OBS/OCS architectures, are discussed. A comprehensive analytical model on the burst drop rate is formulated for the proposed scheme. To verify the analytical model and compare with the other counterparts, extensive simulation efforts have been addressed. The results reveal that OFCS can overcome the non-predictiveness of traffic self-similarity, where the flooding clusters are set up for a partial prediction of the burst arrival. We conclude that the flooding effect of data-dominated traffic can be successfully smoothed out by the proposed OFCS scheme
An Evolutionary Algorithm to Find Associations in Dense Genetic Maps

Discovering the genetic basis of common human diseases will be assisted by large-scale association studies with a large number of individuals and genetic markers, such as single-nucleotide polymorphisms (SNPs). The potential size of the data and the resulting model space require the development of efficient methodology to unravel associations between epidemiological outcomes and SNPs in dense genetic maps. We apply an evolutionary algorithm (EA) to construct models consisting of logic trees. These trees are Boolean expressions involving nodes that contain strings of SNPs in high linkage disequilibrium (LD), that is, SNPs that are highly correlated with each other. At each generation of the algorithm, a population of logic tree models is modified using selection, crossover, and mutation moves. Logic trees are selected for the next generation using a fitness function based on the marginal likelihood in a Bayesian regression framework. Mutation and crossover moves use LD measures to propose changes to the trees, and facilitate the movement through the model space. We demonstrate our method on data from a candidate gene study of quantitative genetic variation.
On distributed space-time filtering

Distributed space-time filtering (DSTF) has been recently proposed as a means for node cooperation in wireless networks. In this paper, we derive a new optimization criterion for the node signature filter vectors (SFVs) used in DSTF and we provide two practical design methods for sets of SFVs. We show that for the special case of a frequency-nonselective channel and two active nodes the SFV optimization problem is related to the design of Grassmannian frames. This relation allows us to derive a lower bound for the performance penalty incurred by the distributed implementation. We show that SFV sets designed with the proposed methods perform close to this lower bound and achieve a significant performance gain over previously proposed designs. Simulation results confirm the excellent performance of the proposed SFV sets in frequency-nonselective and frequency-selective channels
A Territory Defining Multiobjective Evolutionary Algorithms and Preference Incorporation

We have developed a steady-state elitist evolutionary algorithm to approximate the Pareto-optimal frontiers of multiobjective decision making problems. The algorithms define a territory around each individual to prevent crowding in any region. This maintains diversity while facilitating the fast execution of the algorithm. We conducted extensive experiments on a variety of test problems and demonstrated that our algorithm performs well against the leading multiobjective evolutionary algorithms. We also developed a mechanism to incorporate preference information in order to focus on the regions that are appealing to the decision maker. Our experiments show that the algorithm approximates the Pareto-optimal solutions in the desired region very well when we incorporate the preference information.
Identification of Chemical Compounds That Inhibit Protein Synthesis in Pseudomonas aeruginosa

Four inhibitory compounds were identified using a poly-uridylic acid (polyU) mRNA-directed aminoacylation/translation (A/T) protein synthesis system composed of phenylalanyl-tRNA synthetases (PheRS), ribosomes, and ribosomal factors from Pseudomonas aeruginosa in an in vitro screen of a synthetic compound library. The compounds were specific for inhibition of bacterial protein synthesis. In enzymatic assays, the compounds inhibited protein synthesis with IC50 values ranging from 20 to 60 μM. Minimum inhibitory concentrations (MICs) were determined in cultures for a panel of pathogenic organisms, including Enterococcus faecalis, Escherichia coli, Haemophilus influenzae, P. aeruginosa, Staphylococcus aureus, and Streptococcus pneumoniae. All the compounds were observed to have broad-spectrum activity and inhibited an efflux pump mutant strain of P. aeruginosa with MICs of 0.5–16 μg/mL. The molecular target of two compounds was determined to be PheRS. These two compounds were bacteriostatic against both Gram-positive and Gram-negative pathogens. In competition assays, they were not observed to compete with the natural substrates ATP or phenylalanine for active site binding. The other two compounds directly inhibited the ribosome and were bactericidal against both Gram-positive and Gram-negative pathogens. In cytotoxicity MTT testing in human cell lines, the compounds were shown to be from 2500- to 30,000-fold less active than the control staurosporine.
Optimal Cycle Program of Traffic Lights With Particle Swarm Optimization

Optimal staging of traffic lights, and in particular optimal light cycle programs, is a crucial task in present day cities with potential benefits in terms of energy consumption, traffic flow management, pedestrian safety, and environmental issues. Nevertheless, very few publications in the current literature tackle this problem by means of automatic intelligent systems, and, when they do, they focus on limited areas with elementary traffic light schedules. In this paper, we propose an optimization approach in which a particle swarm optimizer (PSO) is able to find successful traffic light cycle programs. The solutions obtained are simulated with simulator of urban mobility, a well-known microscopic traffic simulator. For this study, we have tested two large and heterogeneous metropolitan areas with hundreds of traffic lights located in the cities of Bahía Blanca in Argentina (American style) and Málaga in Spain (European style). Our algorithm is shown to obtain efficient traffic light cycle programs for both kinds of cities. In comparison with expertly predefined cycle programs (close to real ones), our PSO achieved quantitative improvements for the two main objectives: 1) the number of vehicles that reach their destination and 2) the overall journey time.
Religiousness as a Cultural Adaptation of Basic Traits: A Five-Factor Model Perspective

Individual differences in religiousness can be partly explained as a cultural adaptation of two basic personality traits,Agreeableness and Conscientiousness. This argument is supported by a meta-analysis of 71 samples (N = 21,715) from 19 countries and a review of the literature on personality and religion. Beyond variations in effect magnitude as a function of moderators, the main personality characteristics of religiousness (Agreeableness and Conscientiousness) are consistent across different religious dimensions, contexts (gender, age, cohort, and country), and personality measures, models, and levels, and they seem to predict religiousness rather than be influenced by it. The copresence of Agreeableness and Conscientiousness sheds light on other explanations of religiousness, its distinctiveness from related constructs, its implications for other domains, and its adaptive functions.
How Should the Internal Structure of Personality Inventories Be Evaluated?

Personality trait inventories often perform poorly when their structure is evaluated with confirmatory factor analysis (CFA). The authors demonstrate poor CFA fit for several widely used personality measures with documented evidence of criterion-related validity but also show that some measures perform well from an exploratory factor analytic perspective. In light of these results, the authors suggest that the failure of these measures to fit CFA models is because of the inherent complexity of personality, issues related to its measurement, and issues related to the application and interpretation of CFA models. This leads to three recommendations for researchers interested in the structure and assessment of personality traits: (a) utilize and report on a range of factor analytic methods, (b) avoid global evaluations regarding the internal validity of multiscale personality measures based on model fit according to conventional CFA cutoffs, and (c) consider the substantive and practical implications of model modifications designed to improve fit.
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints

Having developed multiobjective optimization algorithms using evolutionary optimization methods and demonstrated their niche on various practical problems involving mostly two and three objectives, there is now a growing need for developing evolutionary multiobjective optimization (EMO) algorithms for handling many-objective (having four or more objectives) optimization problems. In this paper, we recognize a few recent efforts and discuss a number of viable directions for developing a potential EMO algorithm for solving many-objective optimization problems. Thereafter, we suggest a reference-point-based many-objective evolutionary algorithm following NSGA-II framework (we call it NSGA-III) that emphasizes population members that are nondominated, yet close to a set of supplied reference points. The proposed NSGA-III is applied to a number of many-objective test problems with three to 15 objectives and compared with two versions of a recently suggested EMO algorithm (MOEA/D). While each of the two MOEA/D methods works well on different classes of problems, the proposed NSGA-III is found to produce satisfactory results on all problems considered in this paper. This paper presents results on unconstrained problems, and the sequel paper considers constrained and other specialties in handling many-objective optimization problems.
Strategies for Cardiac Regeneration and Repair

Regenerative medicine strategies that enhance heart repair are being pursued to face the growing heart disease epidemic. Heart failure is a growing epidemic caused by cardiomyocyte depletion. Current therapies prolong survival by protecting remaining cardiomyocytes but are unable to overcome the fundamental problem of regenerating lost cardiomyocytes. Several strategies for promoting heart regeneration have emerged from decades of intensive study. Although some of these strategies remain confined to basic research, others are beginning to be tested in humans. We review strategies for cardiac regeneration and summarize progress of related clinical trials.
Temporal Interpersonal Emotion Systems

Emotion is often framed as an intrapersonal system comprised of subcomponents such as experience, behavior, and physiology that interact over time to give rise to emotional states. What is missing is that many emotions occur in the context of social interaction or ongoing relationships. When this happens, the result can be conceptualized as a temporal interpersonal emotion system (TIES) in which the subcomponents of emotion interact not only within the individual but across the partners as well. The present review (a) suggests that TIES can be understood in terms of the characteristics of dynamic systems, (b) reviews examples from diverse research that has investigated characteristics of TIES, (c) attempts to clarify the overlapping terms that have been used to refer to those characteristics by mapping them to the statistical, mathematical, and graphical models that have been used to represent TIES, and (d) offers pragmatic advice for analyzing TIES data.
A Discrete Particle Swarm Optimization for Covering Array Generation

Software behavior depends on many factors. Combinatorial testing (CT) aims to generate small sets of test cases to uncover defects caused by those factors and their interactions. Covering array generation, a discrete optimization problem, is the most popular research area in the field of CT. Particle swarm optimization (PSO), an evolutionary search-based heuristic technique, has succeeded in generating covering arrays that are competitive in size. However, current PSO methods for covering array generation simply round the particle's position to an integer to handle the discrete search space. Moreover, no guidelines are available to effectively set PSOs parameters for this problem. In this paper, we extend the set-based PSO, an existing discrete PSO (DPSO) method, to covering array generation. Two auxiliary strategies (particle reinitialization and additional evaluation of gbest) are proposed to improve performance, and thus a novel DPSO for covering array generation is developed. Guidelines for parameter settings both for conventional PSO (CPSO) and for DPSO are developed systematically here. Discrete extensions of four existing PSO variants are developed, in order to further investigate the effectiveness of DPSO for covering array generation. Experiments show that CPSO can produce better results using the guidelines for parameter settings, and that DPSO can generate smaller covering arrays than CPSO and other existing evolutionary algorithms. DPSO is a promising improvement on PSO for covering array generation.