Miracle-dz/UniFAR-FTUs huggingface dataset:
Predominance of Viable Spore-Forming Piezophilic Bacteria in High-Pressure Enrichment Cultures from ~1.5 to 2.4 km-Deep Coal-Bearing Sediments below the Ocean Floor

Phylogenetically diverse microorganisms have been observed in marine subsurface sediments down to ~2.5 km below the seafloor (kmbsf). However, very little is known about the pressure-adapted and/or pressure-loving microorganisms, the so called piezophiles, in the deep subseafloor biosphere, despite that pressure directly affects microbial physiology, metabolism, and biogeochemical processes of carbon and other elements in situ. In this study, we studied taxonomic compositions of microbial communities in high-pressure incubated sediment, obtained during the Integrated Ocean Drilling Program (IODP) Expedition 337 off the Shimokita Peninsula, Japan. Analysis of 16S rRNA gene-tagged sequences showed that members of spore-forming bacteria within Firmicutes and Actinobacteria were predominantly detected in all enrichment cultures from ~1.5 to 2.4 km-deep sediment samples, followed by members of Proteobacteria, Acidobacteria, and Bacteroidetes according to the sequence frequency. To further study the physiology of the deep subseafloor sedimentary piezophilic bacteria, we isolated and characterized two bacterial strains, 19R1-5 and 29R7-12, from 1.9 and 2.4 km-deep sediment samples, respectively. The isolates were both low G+C content, gram-positive, endospore-forming and facultative anaerobic piezophilic bacteria, closely related to Virgibacillus pantothenticus and Bacillus subtilis within the phylum Firmicutes, respectively. The optimal pressure and temperature conditions for growth were 20 MPa and 42°C for strain 19R1-5, and 10 MPa and 43°C for strain 29R7-12. Bacterial (endo)spores were observed in both the enrichment and pure cultures examined, suggesting that these piezophilic members were derived from microbial communities buried in the ~20 million-year-old coal-bearing sediments after the long-term survival as spores and that the deep biosphere may host more abundant gram-positive spore-forming bacteria and their spores than hitherto recognized.
Stability of LS-CS-residual and modified-CS for sparse signal sequence reconstruction

In this work, we show the “stability” of two of our recently proposed algorithms, LS-CS-residual (LS-CS) and the noisy version of modified-CS, designed for recursive reconstruc tion of sparse signal sequences from noisy measurements. By “stability” we mean that the number of misses from the current support estimate and the number of extras in it remain bounded by a time-invariant value at all times. The concept is meaningful only if the bound is small compared to the current signal support size. A direct corollary is that the reconstruction errors are also bounded by a time-invariant value.
QuickPeer : A P 2 P Framework for Distributed Application Development

Recently P2P computing has attracted lots of attention from both the distributed computing community and millions and millions of Internet users. This paper describes a pure P2P file sharing system, QuickPeer, which is originally designed to satisfy the requirement for a graduate computer science course. Our QuickPeer system is not only a P2P application, but also a software framework for distributed application development.
A supply modulator with nested structure for wideband envelope tracking power amplifier

In this paper, a novel supply modulator with nested structure for envelope tracking base-station power amplifier was presented, which boosts average modulator efficiency by realizing additional peak efficiency points both in linear amplifier & current source array. Tuneable peak efficiency points make a flexible efficiency adaptation for various wideband signal character besides efficiency boosting.
Reimagining tenure reform from a teacher’s perspective

If we want to attract the best and brightest and those with the biggest hearts into the profession that educates all others, we must show today’s college graduates that they have something to look forward to if they choose to be a teacher. We want them to know that teaching is intrinsically gratifying but that there also are tangible rewards and recognition that teachers matter. This could be achieved if instead of vanquishing teacher tenure we reimagined it as a system that would have these traits: Identifies a distinct role for states with local flexibility; set a minimum for years of experience; requires demonstration of growth and development; revisits tenure determinations; and invests in mid- to late-career development and support.
How Context Influences Predominance during Binocular Rivalry

Variations in the predominance of an object engaged in binocular rivalry may arise from variations in the durations of dominance phases, suppression phases, or both. Earlier work has shown that the predominance of a binocular rival target is enhanced if that target fits well—via common color, orientation, or motion—with its surrounding objects. In the present experiments, the global context outside of the region of rivalry was changed during rivalry, to learn whether contextual information alters the ability to detect changes in a suppressed target itself. Results indicate that context will maintain the dominance of a rival target, but will not encourage a suppressed target to escape from suppression. Evidently, the fate of the suppressed stimulus is determined by neural events distinct from those responsible for global organization during dominance. To reconcile diverse findings concerning rivalry, it may be important to distinguish between processes responsible for selection of one eye's input for dominance from processes responsible for the implementation and maintenance of suppression.
A phase 2 multicenter study of lenalidomide in relapsed or refractory classical Hodgkin lymphoma.

Relapsed or refractory (rel/ref) classical Hodgkin lymphoma (cHL) remains a clinical challenge, with limited effective treatment options available after stem cell transplantation. In a multicenter phase 2 study, the efficacy of lenalidomide in rel/ref cHL patients was evaluated at a dose of 25 mg/d on days 1-21 of a 28-day cycle. Patients remained on lenalidomide until disease progression or an unacceptable adverse event (AE) occurred. Thirty-eight cHL patients were enrolled with a median of 4 (range, 2-9) prior therapies; 87% had undergone prior stem cell transplantation and 55% of patients did not respond to their last prior therapy. Of 36 evaluable patients, responses were 1 complete remission (CR), 6 partial remissions (PRs), and 5 patients with stable disease (SD) for ≥ 6 months resulting in an International Working Committee (IWC) objective overall response rate (ORR) of 19% and a cytostatic ORR of 33%. Decreased chemokine (CCL17 and CCL22) plasma levels at 2 weeks were associated with a subsequent response. The treatment was well tolerated, and the most common grade 3/4 AEs were neutropenia (47%), anemia (29%), and thrombocytopenia (18%). Four patients discontinued lenalidomide because of rash, elevated transaminases/bilirubin, and cytopenias. We provide preliminary evidence of lenalidomide's activity in patients with rel/ref cHL, and therefore exploration of lenalidomide in combination with other active agents is warranted. This trial is registered at www.ClinicalTrials. gov as NCT00540007.
A VE-cadherin–PAR3–α-catenin complex regulates the Golgi localization and activity of cytosolic phospholipase A2α in endothelial cells

The rapid regulation of phospholipase A2 activity is essential for vascular function. Evidence is found for a VE-cadherin–α-catenin–PAR3 complex regulating the reversible association of cPLA2α with the Golgi apparatus in confluent endothelial cells. This regulation is important for controlling both cPLA2α activity and angiogenesis.
Remote Agriculture Automation Using Wireless Link and IoT Gateway Infrastructure

The publication presents a system architecture for remote agriculture process automation, involving sensors and actuators connected to IoT gateway running OPC UA server. Sensors and actuators are very general and do not need any intelligence related to the process under control. Acquired data processing and control algorithms that produce control stimulus are executed in the gateway. This approach features the advantage of convenient possibilities to change control rules from Cloud services (installing or configuring process controller) without updating firmware of remote sensors/actuators. Throughput of data collection channel (long range radio) and IoT gateway performance are limiting factors for real time control or observation of agriculture processes. Therefore, achievable channel "sensors-OPC UA server" throughput is investigated experimentally. Potential agriculture applications that may benefit from the proposed architecture are identified.
Diagnosis of ADHD using SVM algorithm

Attention Deficit Hyperactivity Disorder (ADHD) is a Disruptive Behaviour Disorder characterized by the presence of a set of chronic and impairing behaviour patterns that display abnormal levels of inattention, hyperactivity, or their combination. Since most individuals especially children display these behaviours from time to time, it is be difficult to differentiate behaviours that reflect ADHD from those that are a normal part of growing up which makes the diagnosis a tricky job. In this paper, we apply a well known artificial intelligence technique, the SVM algorithm, for the diagnosis of the disorder. The major advantage of using SVM is that it helps in controlling the complexity of the problem of diagnosing. There has not been much development or research on ADHD using SVM algorithm. Hence this is the first attempt at diagnosing the problems using the algorithm. To improve on the overall identification accuracy; we also make use of the GA-based, Feature Selection Algorithm. Genetic algorithms are known to give good solution to very complex problems. In conclusion, we expect that AI techniques like SVM will certainly play an essential role in future ADHD diagnosis applications.
International Journal of Hospitality Management Testing the Stressor–strain–outcome Model of Customer-related Social Stressors in Predicting Emotional Exhaustion, Customer Orientation and Service Recovery Performance

Keywords: Customer-related social stressors Emotional exhaustion Customer orientation Service recovery performance Stressor–strain–outcome framework Conservation of resources theory a b s t r a c t The integrated perspective of human resource management (HRM) and service marketing (SM) on the emotional labor issue provides company managers with critical insight into how to create customer equity through effective management of their human capital. The objective of this study is to investigate the structural relationships among customer-related social stressors (CSSs) as job stressors, emotional exhaustion (EE) as a job strain, and both customer orientation (CO) and service recovery performance (SRP) as job outcomes using data from 1014 frontline service providers (tour guides, frontline tourist hotel employees and frontline tourist restaurant employees) employed in the three major sectors (travel agency, tourist hotel and tourist restaurant) of the Korean tourism industry. Specifically, this study incorporates HRM issues (CSSs and EE) and SM concerns (CO and SRP) into a single model using Koeske and Koeske's (1993) stressor–strain–outcome framework, which is useful for understanding the work-stress process. This study uses a structural equation modeling to investigate the research model and hypotheses. Results showed that CSSs (ambiguous customer expectations, disliked customers, and customer verbal aggression, with the exception of disproportionate customer expectations) significantly and positively influence EE. EE then significantly and negatively influences CO and SRP. In particular, the effect of customer verbal aggression on EE is stronger than the effects of ambiguous customer expectations and disliked customers. The results also showed that CO has a highly influential positive effect on SRP. The study concludes with a discussion of the empirical findings, theoretical contributions, managerial implications, and suggestions for future research.
Optical Communications Comparison of hard-decision and soft-decision channel coded M-ary PPM performance over free space optical links

Error control coded M-ary pulse position modulation (PPM) can be used over free space optical links to mitigate the atmospheric attenuations and the turbulence induced fading. In this paper, we present performance analysis for coded free space optics (FSO) communication systems operating over various atmospheric conditions. We justify the selection of higher state PPM as an appropriate modulation scheme for the links and provide a comparative analysis between hard decision decoded (Reed-Solomon, RS) and soft decision decoded (Turbo) channel coded PPM over varying link conditions. Simulation results strongly indicate RS-coded PPM as a robust and well-performing coded-modulation scheme for the FSO links.
Towards System Viewpoints to Specify Adaptation Models at Runtime

Current research focuses on the specification, analysis, and realisation of systems with self-* properties. Such systems are characterised by the fact that they react on changes in their environment by altering their own structure in order to maintain their non-functional properties. However, there is no standard engineering method to model such systems. In this paper, we propose a set of new system viewpoints and their views. Additionally we describe initial ideas of the properties we want to check based on the new system views.
A Systematic Wavelength Assign Algorithm for Multicast in WDM Networks with Sparse Conversion Nodes

In this paper, we propose a systematic scheme of wavelength assignment for multicast in WDM network with sparse wavelength conversion nodes. The proposed scheme considers the generalized network environment when comparing to the literature [8], which assumed that all network nodes have no wavelength conversion capability. By using the concept of segmentation and grouping, the proposed scheme can be generally applied for the wavelength assignment of multicast in WDM network. Based on the foundation of the proposed scheme, three approaches, Minimum-Effect-First (MEF), First-fit, and Random, are provided for the selection of wavelength. The efficiency of the arrangement of wavelengths is measured by its influences on the available capacity of the network. The performance of the proposed scheme is examined through exhaustive simulations and compared with the upper bound. Our experimental results indicate that the proposed scheme can effectively minimize the affected bandwidth and the MEF approach is superior to the other two approaches.
Genetic Complementary Learning for Translation Initialization Sites Prediction

Accurate prediction of the translation initiation sites (TIS) in eukaryotes is paramount for better understanding of the translation process, gene structure, as well as protein coding, and for more reliable amino acid prediction, etc. However, detecting TIS is not a simple task. Hence, computational biology is adopted to assist in the detection. Unfortunately, some computational biology tools do not provide means for facilitating the knowledge extraction or system validation. Also, they have neither biological interpretation nor human-like reasoning process. Realizing that, a novel Genetic Complementary Learning (GCL) fuzzy neural network, which based on gene selection process, is proposed. GCL inherits some advantageous traits from three worlds: the dynamics from genetic algorithm, the good pattern recognition performance from complementary learning, as well as the interpretable, autonomous, and human-like operations from fuzzy neural network From experimental result, GCL demonstrates itself as a competent tool for TIS prediction.
On preprosessing of protein sequences for neural network prediction of polyproline type II secondary structure

Motivation: Polyproline type II stretches are rather rare among proteins, and, therefore, it is a very challenging task to try to find them computationally. In the present study our aim was to consider especially the preprocessing phase, which is important for any machine learning method. Preprocessing includes selection of relevant data from Protein Data Bank and investigation of learnability properties. These properties show whether the material is suitable for neural network computing. In addition, algorithms in connection with data selection and other preprocessing steps were considered. Results: We found that feedforward perceptron neural networks were appropriate for the prediction of polyproline type II as well as relatively effective in this task. The problem is very difficult because of high similarity of the two classes present in the classification. Still neural networks were able to recognize and predict about 75 % of secondary structures. Contact: Markku.Siermala@uta.fi
Laser range data scan-matching algorithm for mobile robot indoor self-localization

This paper deals with a Scan-Matching algorithm for 2D laser range data developed to perform Self-Localization of mobile robots. The proposed algorithm is based on Features-Maps extracted from laser range data through Split and Merge features-extraction algorithm. A data association method, based on simultaneous observation of features from two consecutive scans, is used to determine correct features associations. Common features extracted from each scan are used as landmarks of the environment to estimate relative robot position in an unknown environment without considering any kinematic model of the robot and, moreover, without using odometry.
Towards Scalable Grid Replica Optimization Framework

Data grids are becoming a popular infrastructure for distributed, data intensive scientific application. Data access optimization is one of the key features of such systems, as the transfer and access to the huge amounts of data are often the bottleneck of the data intensive tasks. Concept of replication, creation of multiple copies of a data source across multiple grid nodes, was adopted by grid community to increase data availability. In this paper, we present the ongoing work on the grid replica optimization framework that addresses both short-term and long-term replica optimization problems. We describe high level architecture of the optimization framework and discuss the promising results achieved by prototype implementation of several system components
Diversity, Loss, and Gain of Malaria Parasites in a Globally Invasive Bird

Invasive species can displace natives, and thus identifying the traits that make aliens successful is crucial for predicting and preventing biodiversity loss. Pathogens may play an important role in the invasive process, facilitating colonization of their hosts in new continents and islands. According to the Novel Weapon Hypothesis, colonizers may out-compete local native species by bringing with them novel pathogens to which native species are not adapted. In contrast, the Enemy Release Hypothesis suggests that flourishing colonizers are successful because they have left their pathogens behind. To assess the role of avian malaria and related haemosporidian parasites in the global spread of a common invasive bird, we examined the prevalence and genetic diversity of haemosporidian parasites (order Haemosporida, genera Plasmodium and Haemoproteus) infecting house sparrows (Passer domesticus). We sampled house sparrows (N = 1820) from 58 locations on 6 continents. All the samples were tested using PCR-based methods; blood films from the PCR-positive birds were examined microscopically to identify parasite species. The results show that haemosporidian parasites in the house sparrows' native range are replaced by species from local host-generalist parasite fauna in the alien environments of North and South America. Furthermore, sparrows in colonized regions displayed a lower diversity and prevalence of parasite infections. Because the house sparrow lost its native parasites when colonizing the American continents, the release from these natural enemies may have facilitated its invasion in the last two centuries. Our findings therefore reject the Novel Weapon Hypothesis and are concordant with the Enemy Release Hypothesis.
Postpartum Vascular Dysfunction in the Reduced Uteroplacental Perfusion Model of Preeclampsia

Preeclampsia is a disorder affecting 2–8% of all pregnancies, characterized by gestational hypertension (≥ 140/90 mmHg) and proteinuria (≥300 mg over 24 hours) diagnosed following the 20th week of pregnancy, and for which there is currently no available treatment. While the precise cause of preeclampsia is unknown, placental ischemia/hypoxia resulting from abnormal trophoblast invasion and maternal endothelial dysfunction are central characteristics. Preeclampsia is a major cause of both maternal and fetal morbidity and mortality in the perinatal period. In addition, women who have experienced preeclampsia are more likely to suffer cardiovascular disease later in life. The cause of this elevation in cardiovascular risk postpartum, however, is unknown. We hypothesize that there may be lasting vascular dysfunction following exposure to reduced uteroplacental perfusion during pregnancy that may contribute to increased cardiovascular risk postpartum. Using the rat reduced utero-placental perfusion pressure (RUPP) model of preeclampsia, blood pressure was assessed in dams at gestational day 20, one and three months postpartum. Mesenteric artery and aortic function were assessed using wire myography. We demonstrated hypertension and increased mesenteric artery responses to phenylephrine at gestational day 20, with the latter due to a decreased contribution of nitric oxide without any change in methylcholine-induced relaxation. At one month postpartum, we demonstrated a small but significant vasoconstrictive phenotype that was due to an underlying loss of basal nitric oxide contribution. At three months postpartum, endothelium-dependent relaxation of the aorta demonstrated sensitivity to oxLDL and mesenteric arteries demonstrated decreased nitric oxide bioavailability with impaired methylcholine-induced relaxation; indicative of an early development of endothelial dysfunction. In summary, we have demonstrated impaired vascular function following exposure to a RUPP pregnancy that continued into the postpartum period; suggesting that a pregnancy complicated by preeclampsia may predispose women to later life cardiovascular disease via ongoing vascular dysfunction.
A Step Forward To Component-based Software Cost Estimation in Object-oriented Environment

Software cost estimation (SCE) of a project is pivotal to the acceptance or rejection of the development of software project. Various SCE techniques have been in practice with their own strengths and limitations. The latest of these is object-oriented one. Currently object-oriented approach for SCE is based on Line of Code (LOC), function points, functions and classes etc. Relatively less attention has been paid to the SCE in component-based software engineering (CBSE). So there is a pressing need to search parameters/variables that have a vital role for the SCE using CBSE which is taken up in this paper. This paper further looks at level of significance of all the parameters/variables thus searched. The time is being used as an independent variable because time is a parameter which is almost, all previous in one. Therefore this approach may be in a way an alternate of all previous approaches. Infact the underlying research ultimately may lead towards SCE of complex systems, using CBSE, in a scientific, systematic and comprehensive way.