Thermophoresis effects on non-Darcy MHD mixed convective heat and mass transfer past a porous wedge in the presence of suction/injection

An analysis is presented to investigate the effect of thermophoresis particle deposition and variable viscosity on non-Darcy MHD mixed convective heat and mass transfer of a viscous, incompressible and electrically conducting fluid past a porous wedge in the presence of suction/injection. The wall of the wedge is embedded in a uniform non-Darcian porous medium in order to allow for possible fluid wall suction or injection. The governing partial differential equations of the problem, subjected to their boundary conditions, are solved numerically by applying an efficient solution scheme for local nonsimilarity boundary layer analysis. Numerical calculations are carried out for different values of dimensionless parameter in the problem and an analysis of the results obtained show that the flow field is influenced appreciably by the applied magnetic field. The results are compared with those known from the literature and excellent agreement between the results is obtained

Investigating hookworm genomes by comparative analysis of two Ancylostoma species

Background Hookworms, infecting over one billion people, are the mostly closely related major human parasites to the model nematode Caenorhabditis elegans. Applying genomics techniques to these species, we analyzed 3,840 and 3,149 genes from Ancylostoma caninum and A. ceylanicum. Results Transcripts originated from libraries representing infective L3 larva, stimulated L3, arrested L3, and adults. Most genes are represented in single stages including abundant transcripts like hsp-20 in infective L3 and vit-3 in adults. Over 80% of the genes have homologs in C. elegans, and nearly 30% of these were with observable RNA interference phenotypes. Homologies were identified to nematode-specific and clade V specific gene families. To study the evolution of hookworm genes, 574 A. caninum / A. ceylanicum orthologs were identified, all of which were found to be under purifying selection with distribution ratios of nonsynonymous to synonymous amino acid substitutions similar to that reported for C. elegans / C. briggsae orthologs. The phylogenetic distance between A. caninum and A. ceylanicum is almost identical to that for C. elegans / C. briggsae. Conclusion The genes discovered should substantially accelerate research toward better understanding of the parasites' basic biology as well as new therapies including vaccines and novel anthelmintics

Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis.

Radiomics is an emerging technology for imaging biomarker discovery and disease-specific personalized treatment management. This paper aims to determine the benefit of using multi-modality radiomics data from PET and MR images in the characterization breast cancer phenotype and prognosis. Eighty-four features were extracted from PET and MR images of 113 breast cancer patients. Unsupervised clustering based on PET and MRI radiomic features created three subgroups. These derived subgroups were statistically significantly associated with tumor grade (p = 2.0 × 10-6), tumor overall stage (p = 0.037), breast cancer subtypes (p = 0.0085), and disease recurrence status (p = 0.0053). The PET-derived first-order statistics and gray level co-occurrence matrix (GLCM) textural features were discriminative of breast cancer tumor grade, which was confirmed by the results of L2-regularization logistic regression (with repeated nested cross-validation) with an estimated area under the receiver operating characteristic curve (AUC) of 0.76 (95% confidence interval (CI) = [0.62, 0.83]). The results of ElasticNet logistic regression indicated that PET and MR radiomics distinguished recurrence-free survival, with a mean AUC of 0.75 (95% CI = [0.62, 0.88]) and 0.68 (95% CI = [0.58, 0.81]) for 1 and 2 years, respectively. The MRI-derived GLCM inverse difference moment normalized (IDMN) and the PET-derived GLCM cluster prominence were among the key features in the predictive models for recurrence-free survival. In conclusion, radiomic features from PET and MR images could be helpful in deciphering breast cancer phenotypes and may have potential as imaging biomarkers for prediction of breast cancer recurrence-free survival

Elucidating the role of Staphylococcus epidermidis serine-aspartate repeat protein G in platelet activation.

BACKGROUND: Staphylococcus epidermidis is a commensal of the human skin that has been implicated in infective endocarditis and infections involving implanted medical devices. S. epidermidis induces platelet aggregation by an unknown mechanism. The fibrinogen-binding protein serine-aspartate repeat protein G (SdrG) is present in 67-91% of clinical strains. OBJECTIVES: To determine whether SdrG plays a role in platelet activation, and if so to investigate the role of fibrinogen in this mechanism. METHODS: SdrG was expressed in a surrogate host, Lactococcus lactis, in order to investigate its role in the absence of other staphylococcal components. Platelet adhesion and platelet aggregation assays were employed. RESULTS: L. lactis expressing SdrG stimulated platelet aggregation (lag time: 2.9 +/- 0.5 min), whereas the L. lactis control did not. L. lactis SdrG-induced aggregation was inhibited by alpha(IIb)beta3 antagonists and aspirin. Aggregation was dependent on both fibrinogen and IgG, and the platelet IgG receptor FcgammaRIIa. Preincubation of the bacteria with Bbeta-chain fibrinopeptide inhibited aggregation (delaying the lag time six-fold), suggesting that fibrinogen acts as a bridging molecule. Platelets adhered to L. lactis SdrG in the absence of fibrinogen. Adhesion was inhibited by alpha(IIb)beta3 antagonists, suggesting that this direct interaction involves alpha(IIb)beta3. Investigation using purified fragments of SdrG revealed a direct interaction with the B-domains. Adhesion to the A-domain involved both a fibrinogen and an IgG bridge. CONCLUSION: SdrG alone is sufficient to support platelet adhesion and aggregation through both direct and indirect mechanisms

On correctness in RDF stream processor benchmarking

Two complementary benchmarks have been proposed so far for the evaluation and continuous improvement of RDF stream processors: SRBench and LSBench. They put a special focus on different features of the evaluated systems, including coverage of the streaming extensions of SPARQL supported by each processor, query processing throughput, and an early analysis of query evaluation correctness, based on comparing the results obtained by different processors for a set of queries. However, none of them has analysed the operational semantics of these processors in order to assess the correctness of query evaluation results. In this paper, we propose a characterization of the operational semantics of RDF stream processors, adapting well-known models used in the stream processing engine community: CQL and SECRET. Through this formalization, we address correctness in RDF stream processor benchmarks, allowing to determine the multiple answers that systems should provide. Finally, we present CSRBench, an extension of SRBench to address query result correctness verification using an automatic method

SRBench: A streaming RDF/SPARQL benchmark

We introduce SRBench, a general-purpose benchmark primarily designed for streaming RDF/SPARQL engines, completely based on real-world data sets from the Linked Open Data cloud. With the increasing problem of too much streaming data but not enough tools to gain knowledge from them, researchers have set out for solutions in which Semantic Web technologies are adapted and extended for publishing, sharing, analysing and understanding streaming data. To help researchers and users comparing streaming RDF/SPARQL (strRS) engines in a standardised application scenario, we have designed SRBench, with which one can assess the abilities of a strRS engine to cope with a broad range of use cases typically encountered in real-world scenarios. The data sets used in the benchmark have been carefully chosen, such that they represent a realistic and relevant usage of streaming data. The benchmark defines a concise, yet omprehensive set of queries that cover the major aspects of strRS processing. Finally, our work is complemented with a functional evaluation on three representative strRS engines: SPARQLStream, C-SPARQL and CQELS. The presented results are meant to give a first baseline and illustrate the state-of-the-art

Space-optimal Heavy Hitters with Strong Error Bounds

The problem of finding heavy hitters and approximating the frequencies of items is at the heart of many problems in data stream analysis. It has been observed that several proposed solutions to this problem can outperform their worst-case guarantees on real data. This leads to the question of whether some stronger bounds can be guaranteed. We answer this in the positive by showing that a class of "counter-based algorithms" (including the popular and very space-efficient FREQUENT and SPACESAVING algorithms) provide much stronger approximation guarantees than previously known. Specifically, we show that errors in the approximation of individual elements do not depend on the frequencies of the most frequent elements, but only on the frequency of the remaining "tail." This shows that counter-based methods are the most space-efficient (in fact, space-optimal) algorithms having this strong error bound. This tail guarantee allows these algorithms to solve the "sparse recovery" problem. Here, the goal is to recover a faithful representation of the vector of frequencies, f. We prove that using space O(k), the algorithms construct an approximation f* to the frequency vector f so that the L1 error ||f -- f*||[subscript 1] is close to the best possible error min[subscript f2] ||f2 -- f||[subscript 1], where f2 ranges over all vectors with at most k non-zero entries. This improves the previously best known space bound of about O(k log n) for streams without element deletions (where n is the size of the domain from which stream elements are drawn). Other consequences of the tail guarantees are results for skewed (Zipfian) data, and guarantees for accuracy of merging multiple summarized streams.David & Lucile Packard Foundation (Fellowship)Center for Massive Data Algorithmics (MADALGO)National Science Foundation (U.S.). (Grant number CCF-0728645

Quantifying the Influence of Psychosocial Characteristics, Supportive Care Needs and Quality of Life on Breast Cancer Survival.

OBJECTIVE: To identify the contribution of psychosocial characteristics, supportive care needs, or quality of life on breast cancer survival outcomes. METHODS: This study used data from a population-based longitudinal study involving women diagnosed with invasive breast cancer (n = 3326, response rate = 71%) in Queensland, Australia, 2010-2013, and followed up to 2020. Flexible parametric survival models were used to identify which factors were associated with survival outcomes. Model fit was assessed using D and R D 2 ${R}_{D}^{2}$ statistics. RESULTS: Unmet physical and daily living needs, social support, age, stage at diagnosis, tumour grade, clinical subtype and mode of detection explained 39% of survival variability ( R D 2 ${R}_{D}^{2}$ 0.39; 95% CI 0.33-0.44), with a Harrell's C statistic of 0.84 (95% CI 0.81-0.86). Unmet physical and daily living needs and social support, which fall under the categories of supportive care needs and psychosocial characteristics respectively, were identified as key factors that predict breast cancer survival, explaining 3% of survival variability. When compared to women who had less unmet physical needs and adequate social support (5-year survival: 96.6%, 95% CI 92%-99%), those who had more unmet physical needs and limited social support had poorer breast cancer-specific survival (5-year survival: 86.8%, 95% CI 72%-95%). CONCLUSION: The study found that unmet physical and daily living needs and social support play a marginal but significant role in influencing breast cancer outcomes. The findings enhance the current literature regarding the impact of psychosocial characteristics and supportive care needs on breast cancer survival and suggest that integrating psychosocial support and interventions alongside medical treatment may further improve the survival outcomes for women diagnosed with breast cancer

A Comparative Study of the Adsorption Efficiency of the Newly Synthetic Nano Iron Oxide and Commercial Activated Charcoal Towards the Removal of the Nickel(II) Ions

The synthetic nano iron oxide (SNIO) was synthesized by acid base hydrolysis and characterized by the XRD, SEM and EDAX techniques. Batch adsorption experiments were carried out to study the sorption behaviour of SNIO and commercial activated charcoal (CAC) towards Ni(II) ions as a function of initial concentration of the adsorbate, adsorbent dosage, contact time and pH. The adsorption for Ni(II) is found to be better in acidic pH for both SNIO and CAC. The equilibrium adsorption isotherm data have been tested by applying both Freundlich and Langmuir isotherm models. The Separation factor R was found to be between 0 and 1 for both the adsorbent, it clearly indicates the feasibility of adsorption