Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Whole-Genome Sequencing Data for O-Specific Antigen Analysis and In Silico Serotyping of Pseudomonas aeruginosa Isolates

ABSTRACT Accurate typing methods are required for efficient infection control. The emergence of whole-genome sequencing (WGS) technologies has enabled the development of genome-based methods applicable for routine typing and surveillance of bacterial pathogens. In this study, we developed the Pseudomonas aeruginosa serotyper (PAst) program, which enabled in silico serotyping of P. aeruginosa isolates using WGS data. PAst has been made publically available as a web service and aptly facilitates high-throughput serotyping analysis. The program overcomes critical issues such as the loss of in vitro typeability often associated with P. aeruginosa isolates from chronic infections and quickly determines the serogroup of an isolate based on the sequence of the O-specific antigen (OSA) gene cluster. Here, PAst analysis of 1,649 genomes resulted in successful serogroup assignments in 99.27% of the cases. This frequency is rarely achievable by conventional serotyping methods. The limited number of nontypeable isolates found using PAst was the result of either a complete absence of OSA genes in the genomes or the artifact of genomic misassembly. With PAst, P. aeruginosa serotype data can be obtained from WGS information alone. PAst is a highly efficient alternative to conventional serotyping methods in relation to outbreak surveillance of serotype O12 and other high-risk clones, while maintaining backward compatibility to historical serotype data.
Resveratrol Improves Vascular Function and Mitochondrial Number but Not Glucose Metabolism in Older Adults

Background Resveratrol, a plant-derived polyphenol, has been reported to improve glucose metabolism and vascular function and to extend life span in animal models, but studies in humans have been inconclusive. Methods In a randomized, double-blind crossover study, we treated older glucose-intolerant adults (n = 30) with resveratrol (2-3 g/daily) or placebo, each for 6 weeks. A standard mixed-meal test was used to assess insulin sensitivity (Matsuda index) and secretion (C-peptide deconvolution) and vascular function by reactive hyperemia peripheral arterial tonometry. Skeletal muscle samples were obtained for gene expression using RNA-Seq analysis and to assess mitochondrial morphology. Results There were no changes in glucose tolerance, insulin sensitivity, weight, blood pressure, or lipid profile following resveratrol treatment. Fasting reactive hyperemia index improved with resveratrol (2.02 ± 0.2 vs 1.76 ± 0.02, p = .002). RNA-Seq analysis yielded 140 differentially expressed transcripts (corrected p-value ≤ .05), predominantly associated with mitochondrial genes and noncoding RNA. Ingenuity Pathway Analysis confirmed that mitochondrial dysfunction (p = 2.77 × 10-12) and oxidative phosphorylation (p = 1.41 × 10-11) were the most significantly perturbed pathways. Mitochondrial number, but not size, was increased. Conclusions Resveratrol treatment of older adults with impaired glucose regulation may have beneficial effects on vascular function, but not glucose metabolism or insulin sensitivity. Changes in gene expression suggest effects similar to those observed with caloric restriction, which has been shown to increase life and health span in animal models, although its significance for humans is uncertain. Future human studies should address the appropriate dose range and low bioavailability of resveratrol.
Factors That Influence The Change Of Mangrove Forest In South Sulawesi, Indonesia

North Belopa located in Luwu regency, South Sulawesi, Indonesia has a mangrove forest area. This study aims to analyze the factors that causes the reduction of mangrove ecosystem in North Belopa. This mangrove forest has undergone extensive changes. In some areas, there are additional mangrove forests however in other areas, there is a reduction. This change is caused by either the physical or anthropogenic factors. The physical factors consist of climate, rainfall, air temperature, humidity, wind, wave and current. Meanwhile, the anthropogenic factors are the conversion of mangrove forests into ponds and garbage disposal. This anthropogenic factor leads to more reduction and dominant than physical factors. As a result, the area of mangrove forest in North Belopa experienced large transformation.
Value-centred interaction design methods

Purpose – This paper aims to inform and improve the quality of electronic products and services by outlining an approach to ethically grounded design.Design/methodology/approach – A number of design disciplines were investigated using desk research and also learning from experience in commercial design practice in the mobile phone, Internet and software industries.Findings – It is suggested that design “scripts” specific behaviours with either good or bad results. Scripts have a behavioural element and they define the physical, social and individual impact of products and services. This suggests that designers should be aware of the impact design decisions have throughout the product lifecycle. In order to achieve this, three ethical design principles (situated research, reflexivity and participation) are proposed to frame ethical design practice.Research limitations/implications – It is suggested that measuring impact alone is insufficient to minimise harm caused by electronic products and services. Inst.
Using LLC Resonant Converter for Designing Wide-Range Voltage Source

LLC resonant converter with significant resonant inductance is proposed for designing an adjustable wide-range regulated voltage source. Large resonant inductance increases output voltage adjustment range and conversion efficiency, particularly at light loads. Soft switching is achieved for all power devices under all operating conditions by choosing the dead time and maximum switching frequency properly and operating in the converter's inductive region. Using a power-factor-correction (PFC) converter reduces the resonant converter's output voltage dependence to the variations of the main ac input voltage. Thus, the compensation of the load variations and the wide-range adjustment of the regulated output voltage can be achieved by using small switching frequency variations. The proposed method has been used to implement an adjustable wide-range voltage source (35-165 V dc), as an ion implanter arc power supply. An almost flat efficiency curve with maximum value of 94% has been achieved for the converter. Its output current can change from no load to 3 A dc.
Abundance and Biogeography of Picoprasinophyte Ecotypes and Other Phytoplankton in the Eastern North Pacific Ocean

ABSTRACT Eukaryotic algae within the picoplankton size class (≤2 μm in diameter) are important marine primary producers, but their spatial and ecological distributions are not well characterized. Here, we studied three picoeukaryotic prasinophyte genera and their cyanobacterial counterparts, Prochlorococcus and Synechococcus, during two cruises along a North Pacific transect characterized by different ecological regimes. Picoeukaryotes and Synechococcus reached maximum abundances of 1.44 × 105 and 3.37 × 105 cells · ml−1, respectively, in mesotrophic waters, while Prochlorococcus reached 1.95 × 105 cells · ml−1 in the oligotrophic ocean. Of the picoeukaryotes, Bathycoccus was present at all stations in both cruises, reaching 21,368 ± 327 18S rRNA gene copies · ml−1. Micromonas and Ostreococcus clade OI were detected only in mesotrophic and coastal waters and Ostreococcus clade OII only in the oligotrophic ocean. To resolve proposed Bathycoccus ecotypes, we established genetic distances for 1,104 marker genes using targeted metagenomes and the Bathycoccus prasinos genome. The analysis was anchored in comparative genome analysis of three Ostreococcus species for which physiological and environmental data are available to facilitate data interpretation. We established that two Bathycoccus ecotypes exist, named here BI (represented by coastal isolate Bathycoccus prasinos) and BII. These share 82% ± 6% nucleotide identity across homologs, while the Ostreococcus spp. share 75% ± 8%. We developed and applied an analysis of ecomarkers to metatranscriptomes sequenced here and published -omics data from the same region. The results indicated that the Bathycoccus ecotypes cooccur more often than Ostreococcus clades OI and OII do. Exploratory analyses of relative transcript abundances suggest that Bathycoccus NRT2.1 and AMT2.2 are high-affinity NO3 − and low-affinity NH4 + transporters, respectively, with close homologs in multiple picoprasinophytes. Additionally, in the open ocean, where dissolved iron concentrations were low (0.08 nM), there appeared to be a shift to the use of nickel superoxide dismutases (SODs) from Mn/Fe/Cu SODs closer inshore. Our study documents the distribution of picophytoplankton along a North Pacific ecological gradient and offers new concepts and techniques for investigating their biogeography.
Approximations for Asian Options in Local Volatility Models

We develop approximate formulae expressed in terms of elementary functions for the density, the price and the Greeks of path dependent options of Asian style, in a general local volatility model. An algorithm for computing higher order approximations is provided. The proof is based on a heat kernel expansion method in the framework of hypoelliptic, not uniformly parabolic, partial differential equations.
DK1 Induces Apoptosis via Mitochondria-Dependent Signaling Pathway in Human Colon Carcinoma Cell Lines In Vitro

Extensive research has been done in the search for innovative treatments against colon adenocarcinomas; however, the incidence rate of patients remains a major cause of cancer-related deaths in Malaysia. Natural bioactive compounds such as curcumin have been substantially studied as an alternative to anticancer drug therapies and have been surmised as a potent agent but, nevertheless, remain deficient due to its poor cellular uptake. Therefore, efforts now have shifted toward mimicking curcumin to synthesize novel compounds sharing similar effects. A synthetic analog, (Z)-3-hydroxy-1-(2-hydroxyphenyl)-3-phenylprop-2-ene-1-one (DK1), was recently synthesized and reported to confer improved bioavailability and selectivity toward human breast cancer cells. This study, therefore, aims to assess the anticancer mechanism of DK1 in relation to the induction of in vitro cell death in selected human colon cancer cell lines. Using the3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide(MTT) assay, the cytotoxicity of DK1 towards HT29 and SW620 cell lines were investigated. Acridine orange/propidium iodide (AO/PI) dual-staining assay and flow cytometry analyses (cell cycle analysis, Annexin/V-FITC and JC-1 assays) were incorporated to determine the mode of cell death. To further determine the mechanism of cell death, quantitative real-time polymerase chain reaction (qRT-PCR) and proteome profiling were conducted. Results from this study suggest that DK1 induced changes in cell morphology, leading to a decrease in cell viability and subsequent induction of apoptosis. DK1 treatment inhibited cell viability and proliferation 48 h post treatment with IC50 values of 7.5 ± 1.6 µM for HT29 cells and 14.5 ± 4.3 µM for SW620 cells, causing cell cycle arrest with increased accumulation of cell populations at the sub-G0/G1phaseof 74% and 23%, respectively. Flow cytometry analyses showed that DK1 treatment in cancer cells induced apoptosis, as indicated by DNA fragmentation and depolarization of the mitochondrial membrane. qRT-PCR results show significant upregulation in the expression of caspase-9 in both HT29 and SW620 cell lines, further supporting that cell death induction by DK1 is via an intrinsic pathway. These outcomes, therefore, demonstrate DK1 as a potential anticancer agent for colon adenocarcinoma due to its anti-apoptotic attributes.
Artificial Neural Network Based Prediction Hardness of Al2024-Multiwall Carbon Nanotube Composite Prepared by Mechanical Alloying

In this study, artificial neural network was used to predict the microhardness of Al2024-multiwall carbon nanotube(MWCNT) composite prepared by mechanical alloying. Accordingly, the operational condition, i.e., the amount of reinforcement, ball to powder weight ratio, compaction pressure, milling time, time and temperature of sintering as well as vial speed were selected as independent input and the mean micro-hardness of composites was selected as model output. To train the model, a Multilayer perceptron neural network structure and feed-forward back propagation algorithm has been employed. After testing many different ANN architectures an optimal structure of the model i.e. 7-25-1 is obtained. The predicted results, with a correlation relation between 0.982 and 0.9952 and 3.26% mean absolute error, show a very good agreement with the experimental values. Furthermore, the ANN model was subjected to a sensitivity analysis and determined the significant inputs affecting hardness of the samples.
Attentional Bias in Human Category Learning: The Case of Deep Learning

Category learning performance is influenced by both the nature of the category's structure and the way category features are processed during learning. Shepard (1964, 1987) showed that stimuli can have structures with features that are statistically uncorrelated (separable) or statistically correlated (integral) within categories. Humans find it much easier to learn categories having separable features, especially when attention to only a subset of relevant features is required, and harder to learn categories having integral features, which require consideration of all of the available features and integration of all the relevant category features satisfying the category rule (Garner, 1974). In contrast to humans, a single hidden layer backpropagation (BP) neural network has been shown to learn both separable and integral categories equally easily, independent of the category rule (Kruschke, 1993). This “failure” to replicate human category performance appeared to be strong evidence that connectionist networks were incapable of modeling human attentional bias. We tested the presumed limitations of attentional bias in networks in two ways: (1) by having networks learn categories with exemplars that have high feature complexity in contrast to the low dimensional stimuli previously used, and (2) by investigating whether a Deep Learning (DL) network, which has demonstrated humanlike performance in many different kinds of tasks (language translation, autonomous driving, etc.), would display human-like attentional bias during category learning. We were able to show a number of interesting results. First, we replicated the failure of BP to differentially process integral and separable category structures when low dimensional stimuli are used (Garner, 1974; Kruschke, 1993). Second, we show that using the same low dimensional stimuli, Deep Learning (DL), unlike BP but similar to humans, learns separable category structures more quickly than integral category structures. Third, we show that even BP can exhibit human like learning differences between integral and separable category structures when high dimensional stimuli (face exemplars) are used. We conclude, after visualizing the hidden unit representations, that DL appears to extend initial learning due to feature development thereby reducing destructive feature competition by incrementally refining feature detectors throughout later layers until a tipping point (in terms of error) is reached resulting in rapid asymptotic learning.
A Broad Learning Approach for Context-Aware Mobile Application Recommendation

With the rapid development of mobile apps, the availability of a large number of mobile apps in application stores brings challenges to locate appropriate apps for users. Providing accurate mobile app recommendation for users becomes an imperative task. Conventional approaches mainly focus on learning users' preferences and app features to predict the user-app ratings. However, most of them did not consider the interactions among the context information of apps. To address this issue, we propose a broad learning approach for Context-Aware app recommendation with Tensor Analysis (CATA). Specifically, we utilize a tensor-based framework to effectively integrate app category information and multi-view features on users and apps, respectively, to facilitate the performance of rating prediction. The multidimensional structure is employed to capture the hidden relationships among the app categories and the multiview features. We develop an efficient factorization method which applies Tucker decomposition to learn the full-order interactions among the app categories and features. Furthermore, we employ a group ℓ1-norm regularization to learn the group-wise feature importance of each view with respect to each app category. Experiments on a real-world mobile app dataset demonstrate the effectiveness of the proposed method.
Silver in urban wastewater

As the use of consumer products is increasing, a similar increase of hazardous substances can be seen in sewage sludge in Swedish wastewater treatment plants (WWTPs). Silver (Ag) is a heavy metal with a high accumulation rate in soils when applying sewage sludge as fertilizer on arable land. Silver is therefore prioritized in REVAQ, a certificate system for Swedish WWTPs. The levels of Ag in the incoming water to the WWTP are today too high and must be reduced. This study aims to identify major diffuse emission sources of Ag and map out the inflow to, the stock (accumulated amount in the technosphere) and the outflow from Stockholm during 2012. In 2012, 62 kg silver was measured in the sludge in Henriksdal WWTP in Stockholm. Sources explaining 56 % of the measured amount of silver have been identified in this study, corresponding to 46 mg Ag per person and year. The sources with the largest contribution of silver are estimated to be textiles (19 %), urine and faeces (from for example amalgam fillings and food) (15 %) as well as cleaning products (7 %). The inflow of Ag to Stockholm during 2012 was estimated to 3 200 kg and the stock to 105 tons, where electric goods and appliances as well as silverware and jewellery together explain 94 % of the inflow and 98 % of the stock.
Expression of muscle‐related genes and two MyoD genes during amphioxus notochord development

Summary The notochord is one of the diagnostic features of the phylum Chordata. Despite the similarities in the early morphogenetic patterns of the notochords of various chordates, they are strikingly distinct from one another at the histological level. The amphioxus notochord is one example of an evolutionary novelty because it is made up of muscle cells. Our previous expressed sequence tag analysis, targeting messenger RNAs expressed in the adult amphioxus notochord, demonstrated that many muscle‐related genes are expressed there. To characterize amphioxus notochord cells and to gain insights into the myogenic program in the notochord, we determined the spatial and temporal expre‐ssion patterns of these muscle‐related genes during amphioxus development. We found that BbNA1 (notochord actin), Amphi‐Trop I (troponin I), Amphi‐TPmyosin (tropo‐myosin), Amphi‐MHC2 (myosin heavy chain), Amphi‐nMRLC (notochord‐specific myosin regulatory light chain), Amphi‐nTitin/MLCK (notochord‐specific titin/myosin light chain kinase), Amphi‐MLP/CRP3 (muscle LIM protein), and Amphi‐nCalponin (notochord‐specific calponin) are expres‐sed with characteristic patterns in notochord cells, including the central cells, dorsally located cells, and ventrally located cells, suggesting that each notochord cell has a unique molecular architecture that may reflect its function. In addition, we characterized two MyoD genes (Amphi‐MyoD1 and Amphi‐MyoD2) to gain insight into the genetic circuitry governing the formation of the notochord muscle. One of the MyoD genes (Amphi‐MyoD2) is expressed in the central notochord cells, and the coexistence of Amphi‐MyoD2 transcripts along with the Amphi‐MLP/CRP3 transcripts implies the participation of Amphi‐MyoD2 in the myogenic program in the notochord muscle.
Chemically functionalized 3D graphene hydrogel for high performance gas sensing

The performance of chemical sensors can be optimized not only by material structural design, but also via functionalization of sensing materials. For the first time, we develop a high performance, cost-effective NO2 and CO2 sensor by exploiting a 3D chemically functionalized reduced graphene oxide hydrogel (FRGOH). The self-assembly and chemical modification of the 3D FRGOH are realized using hydroquinone molecules in a simple, one-step hydrothermal synthesis process. Compared with an unmodified RGOH counterpart, the chemically derived FRGOH sensor not only displays twofold higher sensitivity in both NO2 and CO2 sensing, but also exhibits significantly faster recovery and a lower limit of detection (LOD). Thus, a theoretical LOD of 57 ppb NO2 is obtained and a low concentration of 200 ppb NO2 is detected experimentally by the FRGOH sensor. Importantly, an integrated microheater is employed not only to significantly improve the selectivity of NO2 sensing, but also to accelerate the response and recovery. This work sheds light on improving the performance of a graphene-based gas sensor by simultaneous chemical functionalization, 3D structural design and temperature modulation.
Why do Individuals Differ in Viral Susceptibility? A Story Told by Model Organisms

Viral susceptibility and disease progression is determined by host genetic variation that underlies individual differences. Genetic polymorphisms that affect the phenotype upon infection have been well-studied for only a few viruses, such as HIV-1 and Hepatitis C virus. However, even for well-studied viruses the genetic basis of individual susceptibility differences remains elusive. Investigating the effect of causal polymorphisms in humans is complicated, because genetic methods to detect rare or small-effect polymorphisms are limited and genetic manipulation is not possible in human populations. Model organisms have proven a powerful experimental platform to identify and characterize polymorphisms that underlie natural variations in viral susceptibility using quantitative genetic tools. We summarize and compare the genetic tools available in three main model organisms, Mus musculus, Drosophila melanogaster, and Caenorhabditis elegans, and illustrate how these tools can be applied to detect polymorphisms that determine the viral susceptibility. Finally, we analyse how candidate polymorphisms from model organisms can be used to shed light on the underlying mechanism of individual variation. Insights in causal polymorphisms and mechanisms underlying individual differences in viral susceptibility in model organisms likely provide a better understanding in humans.