Miracle-dz/UniFAR-FTUs huggingface dataset:
A multidimensional resource model for dynamic resource matching in internet of things

With the development of Internet of Things (IoT), many middleware solutions have been proposed for the integration of physical world with the Web. However, most leading middleware solutions provide only limited resource matching and selection functionality. When significant amounts of resources are available, selecting the appropriate resources becomes challenging and time‐consuming. This paper addresses the growing issues of resource matching and selection in IoT solutions. In this paper, we propose a novel resource model to describe the IoT resources in a multidimensional manner. Based on the resource model, a resource matching algorithm that selects the well‐matched resources by matching the similarity between resources is proposed. Moreover, we constructed a combined resource set and used it and OWLS‐TC to evaluate our work comprehensively. Experimental results show that the proposed resource matching approach is more effective and efficient than existing approaches. Copyright © 2013 John Wiley & Sons, Ltd.
Modified user-dependent perfect-scheduling multiple access protocol for WLAN

Based on effective contention access and perfect scheduling transmission, an enhanced multiple access protocol UPMA++ protocol is proposed. It allocates the resource dynamically according to the actual traffic requirement and does not require the synchronisation in the whole network. This protocol adjusts the length of contention access period and polling period effectively to resolve the conflicts between active stations and transmission stations. By analysis, the optimal contention access period length for the given polling period length is found, and the performance evaluations show that UPMA++ protocol has a high throughput, low average message delay and small average message dropping probability.
An inverse-class-F CMOS VCO with intrinsic-high-Q 1st- and 2nd-harmonic resonances for 1/f2-to-1/f3 phase-noise suppression achieving 196.2dBc/Hz FOM

Second-harmonic common-mode (CM) resonance has been explored for LC oscillators to improve their phase noise (PN) in the past. Its implementation evolves from an explicit design [1] that relies on an extra tail tank, to a recent implicit design [2], where the resonator itself offers a CM impedance peak at 2x the oscillation frequency (F<inf>LO</inf>): Explicit design (Fig. 23.5.1-upper): a high-Q tail tank (Q<inf>TAIL</inf>) is desirable to raise its impedance |Z<inf>TAIL</inf>| at 2FLO and to prevent the loss of L<inf>TAIL</inf> from penalizing the PN in the 1/f<sup>2</sup> region [3]. To compare with the theoretical limit (FOM<inf>MAX</inf>), the FOM in the 1/f<sup>2</sup> PN region is plotted against L<inf>TAIL</inf> at different QTAIL. Closing the gap between FOM<inf>MAX</inf> and FOM imposes an excessive Q<inf>TAIL</inf> of 20 or beyond, which can hardly be achieved and maintained over a wide tuning range.
Structure Learning in a Sensorimotor Association Task

Learning is often understood as an organism's gradual acquisition of the association between a given sensory stimulus and the correct motor response. Mathematically, this corresponds to regressing a mapping between the set of observations and the set of actions. Recently, however, it has been shown both in cognitive and motor neuroscience that humans are not only able to learn particular stimulus-response mappings, but are also able to extract abstract structural invariants that facilitate generalization to novel tasks. Here we show how such structure learning can enhance facilitation in a sensorimotor association task performed by human subjects. Using regression and reinforcement learning models we show that the observed facilitation cannot be explained by these basic models of learning stimulus-response associations. We show, however, that the observed data can be explained by a hierarchical Bayesian model that performs structure learning. In line with previous results from cognitive tasks, this suggests that hierarchical Bayesian inference might provide a common framework to explain both the learning of specific stimulus-response associations and the learning of abstract structures that are shared by different task environments.
Maintenance of Sperm Variation in a Highly Promiscuous Wild Bird

Postcopulatory sexual selection is an important force in the evolution of reproductive traits, including sperm morphology. In birds, sperm morphology is known to be highly heritable and largely condition-independent. Theory predicts, and recent comparative work corroborates, that strong selection in such traits reduces intraspecific phenotypic variation. Here we show that some variation can be maintained despite extreme promiscuity, as a result of opposing, copulation-role-specific selection forces. After controlling for known correlates of siring success in the superb fairy-wren (Malurus cyaneus), we found that (a) lifetime extra-pair paternity success was associated with sperm with a shorter flagellum and relatively large head, and (b) males whose sperm had a longer flagellum and a relatively smaller head achieved higher within-pair paternity. In this species extrapair copulations occur in the same morning, but preceding, pair copulations during a female's fertile period, suggesting that shorter and relatively larger-headed sperm are most successful in securing storage (defense), whereas the opposite phenotype might be better at outcompeting stored sperm (offense). Furthermore, since cuckolding ability is a major contributor to differential male reproductive output, stronger selection on defense sperm competition traits might explain the short sperm of malurids relative to other promiscuous passerines.
Smoking Is Associated with, but Does Not Cause, Depressed Mood in Pregnancy – A Mendelian Randomization Study

Smokers have a higher prevalence of major depressive episodes and depressive symptoms than the general population, but whether this association is causal, or is due to confounding or reverse causation is uncertain because of the problems inherent in some epidemiological studies. Mendelian randomization, in which a genetic variant is used as a surrogate for measuring exposure, is an approach which may be used to better understand this association. We investigated the rs1051730 single nucleotide polymorphism in the nicotine acetylcholine receptor gene cluster (CHRNA5-CHRNA3-CHRNB4), associated with smoking phenotypes, to determine whether women who continued to smoke were also more likely to report a low mood during pregnancy. We found among women who smoked pre-pregnancy, those with the 1051730 T allele smoked more and were less likely to quit smoking during pregnancy, but were also less likely to report high levels of depressed mood at 18 weeks of pregnancy (per allele OR = 0.84, 95%CI 0.72 to 0.99, p = 0.034). The association between genotype and depressed mood was limited to women who were smokers prior to pregnancy, with weak evidence of an interaction between smoking status and genotype (p = 0.07). Our results do not support a causal role of smoking on depressed mood, but are consistent with a self-medication hypothesis, whereby smoking is used to alleviate symptoms of depression. A replication study using multiple genetic variants which influence smoking via different pathways is required to confirm these findings and provide evidence that the genetic variant is reflecting the effect of quitting smoking on depressed mood, and is not directly affecting mood.
High-Throughput Single-Cell Kinetics of Virus Infections in the Presence of Defective Interfering Particles

ABSTRACT Defective interfering particles (DIPs) are virus mutants that lack essential genes for growth. In coinfections with helper virus, the diversion of viral proteins to the replication and packaging of DIP genomes can interfere with virus production. Mounting cases of DIPs and DIP-like genomes in clinical and natural isolates, as well as growing interest in DIP-based therapies, underscore a need to better elucidate how DIPs work. DIP activity is primarily measured by its inhibition of virus infection yield, an endpoint that masks the dynamic and potentially diverse individual cell behaviors. Using vesicular stomatitis virus (VSV) as a model, we coinfected BHK cells with VSV DIPs and recombinant helper virus carrying a gene encoding a red fluorescent protein (RFP) whose expression correlates with the timing and level of virus release. For single cells within a monolayer, 10 DIPs per cell suppressed the reporter expression in only 1.2% of the cells. In most cells, it slowed and reduced viral gene expression, manifested as a shift in mean latent time from 4 to 6 h and reduced virus yields by 10-fold. For single cells isolated in microwells, DIP effects were more pronounced, reducing virus yields by 100-fold and extending latent times to 12 h, including individual instances above 20 h. Together, these results suggest that direct or indirect cell-cell interactions prevent most coinfected cells from being completely suppressed by DIPs. Finally, a gamma distribution model captures well how the infection kinetics quantitatively depends on the DIP dose. Such models will be useful for advancing a predictive biology of DIP-associated virus growth and infection spread. IMPORTANCE During the last century, basic studies in virology have focused on developing a molecular mechanistic understanding of how infectious viruses reproduce in their living host cells. However, over the last 10 years, the advent of deep sequencing and other powerful technologies has revealed in natural and patient infections that viruses do not act alone. Instead, viruses are often accompanied by defective virus-like particles that carry large deletions in their genomes and fail to replicate on their own. Coinfections of viable and defective viruses behave in unpredictable ways, but they often interfere with normal virus growth, potentially enabling infections to evade host immune surveillance. In the current study, controlled levels of defective viruses are coinfected with viable viruses that have been engineered to express a fluorescent reporter protein during infection. Unique profiles of reporter expression acquired from thousands of coinfected cells reveal how interference acts at multiple stages of infection.
Clustering Coefficients for Correlation Networks

Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly correlated with and therefore may be confounded by the node's connectivity. The proposed methods are expected to help us to understand clustering and lack thereof in correlational brain networks, such as those derived from functional time series and across-participant correlation in neuroanatomical properties.
The A1 Subunit of Shiga Toxin 2 Has Higher Affinity for Ribosomes and Higher Catalytic Activity than the A1 Subunit of Shiga Toxin 1

ABSTRACT Shiga toxin (Stx)-producing Escherichia coli (STEC) infections can lead to life-threatening complications, including hemorrhagic colitis (HC) and hemolytic-uremic syndrome (HUS), which is the most common cause of acute renal failure in children in the United States. Stx1 and Stx2 are AB5 toxins consisting of an enzymatically active A subunit associated with a pentamer of receptor binding B subunits. Epidemiological evidence suggests that Stx2-producing E. coli strains are more frequently associated with HUS than Stx1-producing strains. Several studies suggest that the B subunit plays a role in mediating toxicity. However, the role of the A subunits in the increased potency of Stx2 has not been fully investigated. Here, using purified A1 subunits, we show that Stx2A1 has a higher affinity for yeast and mammalian ribosomes than Stx1A1. Biacore analysis indicated that Stx2A1 has faster association and dissociation with ribosomes than Stx1A1. Analysis of ribosome depurination kinetics demonstrated that Stx2A1 depurinates yeast and mammalian ribosomes and an RNA stem-loop mimic of the sarcin/ricin loop (SRL) at a higher catalytic rate and is a more efficient enzyme than Stx1A1. Stx2A1 depurinated ribosomes at a higher level in vivo and was more cytotoxic than Stx1A1 in Saccharomyces cerevisiae. Stx2A1 depurinated ribosomes and inhibited translation at a significantly higher level than Stx1A1 in human cells. These results provide the first direct evidence that the higher affinity for ribosomes in combination with higher catalytic activity toward the SRL allows Stx2A1 to depurinate ribosomes, inhibit translation, and exhibit cytotoxicity at a significantly higher level than Stx1A1.
The efficacy and safety of alectinib in the treatment of ALK+ NSCLC: a systematic review and meta-analysis

Background Alectinib is a second-generation anaplastic lymphoma kinase (ALK) inhibitor approved by the US Food and Drug Administration to treat crizotinib-refractory non-small cell lung cancer. We performed this meta-analysis to synthesize the results of different clinical trials to evaluate the efficacy and safety of alectinib. Methods A search of 3 databases, including PubMed, Web of Science, and the Cochrane Library, was performed from the inception of each database through September 5, 2017. We have pooled the overall response rate (ORR), disease control rate, progression-free survival, and intracranial ORR to evaluate the efficacy of alectinib. Discontinuation rate, rate of dose reduction or interruption due to adverse events as well as the incidence of several adverse events were aggregated to evaluate its safety. Results A total of 8 studies with 626 patients have been included in our study. The pooled efficacy parameters are as follows: ORR 70% (95% CI: 57% to 82%), disease control rate 88% (95% CI: 82% to 94%), progression-free survival 9.36 months (95% CI: 7.38% to 11.34%), and intracranial ORR 52% (95% CI: 45% to 59%). ALK inhibitor-naïve patients tend to have better responses than crizotinib-pretreated patients. The aggregate discontinuation rate is 7% (95% CI: 4% to 10%), and the pooled rate of dose reduction or interruption is 33% (95% CI: 24% to 42%). The incidences of most adverse events were relatively low, while the incidences of 2 frequently reported adverse events, myalgia (18%) and anemia (25%), were even higher than with the first-generation ALK inhibitor crizotinib. Conclusion Generally, alectinib is a drug with preferable efficacy and tolerable adverse effects, and it is suitable for the treatment of intracranial metastases.
Family-Environmental Factors Associated with Attention Deficit Hyperactivity Disorder in Chinese Children: A Case-Control Study

Background Attention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric disorders, affecting an estimated 5 to 12% of school-aged children worldwide. From 15 to 19 million Chinese children suffer from ADHD. The aim of this study was to investigate the association between family-environmental factors and ADHD in a sample of Chinese children. Methods A pair-matched, case-control study was conducted with 161 ADHD children and 161 non-ADHD children of matching age and sex, all from 5–18 years of age. The ADHD subjects and the normal controls were all evaluated via structured diagnostic interviews. We examined the association between family-environmental factors and ADHD using the conditional multiple logistic regression with backward stepwise selection to predict the associated factors of ADHD. Results Having experienced emotional abuse and being a single child were both significant factors associated with children diagnosed with ADHD. ADHD subjects were more likely to have suffered from emotional abuse (OR = 11.09, 95% CI = 2.15–57.29, P = 0.004) and have been a single child in the family (OR = 6.32, 95% CI = 2.09–19.14, P = 0.001) when compared to normal controls. The results were not modified by other confounding factors. Conclusion Our findings provide evidence that family-environmental factors are associated with ADHD among children in China. These findings, if confirmed by future research, may help to decrease ADHD by increasing the awareness of the effects of childhood emotional abuse.
Detection of Emerging Vaccine-Related Polioviruses by Deep Sequencing

ABSTRACT Oral poliovirus vaccine can mutate to regain neurovirulence. To date, evaluation of these mutations has been performed primarily on culture-enriched isolates by using conventional Sanger sequencing. We therefore developed a culture-independent, deep-sequencing method targeting the 5′ untranslated region (UTR) and P1 genomic region to characterize vaccine-related poliovirus variants. Error analysis of the deep-sequencing method demonstrated reliable detection of poliovirus mutations at levels of <1%, depending on read depth. Sequencing of viral nucleic acids from the stool of vaccinated, asymptomatic children and their close contacts collected during a prospective cohort study in Veracruz, Mexico, revealed no vaccine-derived polioviruses. This was expected given that the longest duration between sequenced sample collection and the end of the most recent national immunization week was 66 days. However, we identified many low-level variants (<5%) distributed across the 5′ UTR and P1 genomic region in all three Sabin serotypes, as well as vaccine-related viruses with multiple canonical mutations associated with phenotypic reversion present at high levels (>90%). These results suggest that monitoring emerging vaccine-related poliovirus variants by deep sequencing may aid in the poliovirus endgame and efforts to ensure global polio eradication.
Automated Phenotyping System for Energy Crops

Background. Biofuels and biopower are a significant source of renewable energy. Our team has built a high-throughput (HTP) robotic phenotyping system in field environments which takes pictures of plants everyday and a software will take these pictures and output 3D point clouds of plants. We would like to use these 3D point clouds from the field to estimate the phenotypes. Aim. Building an automated system that takes a noisy 3D point cloud of a plant as input and output its phenotypes which include leaf area, leaf length, leaf width, angle between leaf stem, etc. Data. The data we will use has a small amount (∼ 50) 3D point clouds of plants with measured phenotypes and a large amount of simulated plants (∼ 10, 000) to help us train the prediction model. Methods. We use geometric and learning based methods to simulate plants and use 3D Deep Convolutional Neural Network for phenotype estimation. Results. The generated plants are similar to the real plants. The estimation algorithm achieves significant better result than the naive method (∼ 25% relative root mean squared error). Conclusions. We validate the effectiveness of 3D convolutional neural network that only trained on simulated data for plant phenotyping. Intellectual merit. Our proposed methods combined geometric knowledge of the target objects and modern day deep statistical models. This idea may be applied to other vision problems. Broader impacts. This automated phenotyping system can be directly used by biologists to extract useful phenotypes from photos of plants. It will also draw computer scientists’ attention to the problem of extracting useful information from noisy field data.
A mitogenic lectin from Rhizoctonia bataticola arrests growth, inhibits metastasis, and induces apoptosis in human colon epithelial cancer cells

The correlation between colorectal cancer (CRC) progression and altered expression of N‐glycans can be considered in search for new biomarkers and anticancer agents to control CRC. Earlier N‐glycan specific mitogenic lectin from Rhizoctonia bataticola (RBL) has been reported which has growth inhibitory and apoptotic effect on human ovarian and leukemic cells, but mitogenic effect on normal PBMCs revealing its clinical potential. Here, we report the effect of RBL on human colon cancer HT 29, SW480, and SW620 cell growth and its differential binding to human normal colon and cancer tissues. RBL has strong binding to both primary and metastatic colon cancer cells with MFI of 403, 404, and 192, respectively for HT 29, SW480, and SW620 cells. RBL shows dose and time dependent growth inhibitory effect with IC50 of 5, 6.4, and 6.8 µg/mL, respectively for HT 29, SW480, and SW620 cells. RBL inhibited the clonogenicity of colon carcinoma cells. RBL arrests metastatic SW620 cell growth at S phase, increased hypodiploid population by 6.1%, 14.3%, and 23.2%, respectively at 12, 24, and 36 h. Further, RBL induces SW620 cell apoptosis in time dependent manner, showed increased release of ROS and nuclear degradation compared to lectin untreated control. Adhesion, wound healing, invasion, and migration assays demonstrated anti‐metastasis effect of RBL in SW620 cells apart from its growth inhibitory effect. Anti angiogenic effect of RBL was demonstrated by CAM assay. All these results show the promising potential of RBL both as diagnostic and therapeutic agent.
The IEEE 1857 Standard: Empowering Smart Video Surveillance Systems

The IEEE 1857 Standard for Advanced Audio and Video Coding was released as IEEE 1857-2013 in June 2013. Despite consisting of several different groups, the most significant feature of IEEE 1857-2013 is its Surveillance Groups, which can not only achieve at least twice the coding efficiency on surveillance videos as H.264/AVC High Profile, but it's the most analysis-friendly video coding standard. This article presents an overview of IEEE 1857 Surveillance Groups, highlighting background model-based coding technology and analysis-friendly functionalities. IEEE 1857-2013 will present new opportunities and drive research in smart video surveillance communities and industries.