Miracle-dz/UniFAR-FTUs huggingface dataset:
Does Finasteride Affect the Severity of Prostate Cancer? A Causal Sensitivity Analysis

In 2003 Thompson and colleagues reported that daily use of finasteride reduced the prevalence of prostate cancer by 25% compared to placebo. These results were based on the double-blind randomized Prostate Cancer Prevention Trial (PCPT), which followed 18,882 men with no prior or current indications of prostate cancer annually for 7 years. Enthusiasm for the risk reduction afforded by the chemopreventative agent and adoption of its use in clinical practice, however, was severely dampened by the additional finding in the trial of an increased absolute number of high-grade (Gleason score ≥ 7) cancers on the finasteride arm. The question arose as to whether this finding truly implied that finasteride increased the risk of more severe prostate cancer or was a study artifact due to a series of possible postrandomization selection biases, including differences among treatment arms in patient characteristics of cancer cases, differences in biopsy verification of cancer status due to increased sensitivity of prostate-specific antigen under finasteride, differential grading by biopsy due to prostate volume reduction by finasteride, and nonignorable dropout. Via a causal inference approach implementing inverse probability weighted estimating equations, this analysis addresses the question of whether finasteride caused more severe prostate cancer by estimating the mean treatment difference in prostate cancer severity between finasteride and placebo for the principal stratum of participants who would have developed prostate cancer regardless of treatment assignment. We perform sensitivity analyses that sequentially adjust for the numerous potential postrandomization biases conjectured in the PCPT.
Proinflammatory Effects of Diesel Exhaust Nanoparticles on Scleroderma Skin Cells

Autoimmune diseases are complex disorders of unknown etiology thought to result from interactions between genetic and environmental factors. We aimed to verify whether environmental pollution from diesel engine exhaust nanoparticulate (DEP) of actually operating vehicles could play a role in the development of a rare immune-mediated disease, systemic sclerosis (SSc), in which the pathogenetic role of environment has been highlighted. The effects of carbon-based nanoparticulate collected at the exhaust of newer (Euro 5) and older (Euro 4) diesel engines on SSc skin keratinocytes and fibroblasts were evaluated in vitro by assessing the mRNA expression of inflammatory cytokines (IL-1α, IL-6, IL-8, and TNF-α) and fibroblast chemical mediators (metalloproteases 2, 3, 7, 9, and 12; collagen types I and III; VEGF). DEP was shown to stimulate cytokine gene expression at a higher extent in SSc keratinocytes versus normal cells. Moreover, the mRNA gene expression of all MMPs, collagen types, and VEGF genes was significantly higher in untreated SSc fibroblasts versus controls. Euro 5 particle exposure increased the mRNA expression of MMP-2, -7, and -9 in SSc fibroblasts in a dose dependent manner and only at the highest concentration in normal cells. We suggest that environmental DEP could trigger the development of SSc acting on genetically hyperreactive cell systems.
A Key Management Scheme for Cluster Based Wireless Sensor Networks

Key management is a major challenge to achieve security in wireless sensor networks. In most of the schemes presented for key management in wireless sensor networks, it is assumed that the sensor nodes have the same capability. The recent research has shown that the survivability of the network can be improved if sensor nodes are grouped in clusters in which a powerful cluster head assigned. However, to gain advantages of clustering in order to find an efficient key management scheme needs more research. In this paper, we investigate the key management in cluster-based wireless sensor networks using both private and public key cryptography. Our goal is to introduce a platform in which public key cryptography is used to establish a secure link between sensor nodes and gateways. Instead of pre-loading a large number of keys into the sensor nodes, each node requests a session key from the gateway to establish a secure link with its neighbors after clustering phase. The security analysis and performance evaluation show that the proposed scheme has significant saving in storage space, transmission overhead, and perfect resilience against node capture.
Neural network implementation of the shortest path algorithm for traffic routing in communication networks

Summary form only given, as follows. A neural network computation algorithm is introduced to solve the optimal traffic routing in a general N-node communication network. The algorithm chooses multilink paths for node-to-node traffic which minimize a certain cost function (e.g. expected delay). Unlike the algorithm introduced earlier in this area, the knowledge about the number of links (hops) between each origin-destination pair is not required by the algorithm, therefore it can be applied to a more general network. The neural network structure for implementing the algorithm is a modification of the one used by the traveling salesman algorithm. Computer simulations in a nine-node grid network show that the algorithm performs well. <<ETX>>
Layered space-time codes with iterative receiver and space-time soft-output decoding in a Rayleigh fading environment

We propose a modified BLAST architecture which employs space-time trellis codes at each layer as constituent codes, and an iterative parallel interference canceller (PIC) at the receiver. The PIC performs both interference regeneration and cancellation; and furthermore, the scheme employs maximum-likelihood soft-output decoders, based on the Viterbi algorithm. These decoders calculate the a posteriori probabilities of the codewords in order to produce a soft output. Comparisons are drawn against the original BLAST architecture and simulation results are obtained. A significant gain is achieved in the case of Horizontal BLAST and a similar gain for the case of Diagonal BLAST, at a reduced computational complexity.
Headaches and Stress in Schoolchildren: An Epidemiological Study

Headache parameters, personality variables and stress factors were assessed in a sample of about 2,300 school pupils between 10 and 17 years of age in Amsterdam. More than 15% of the subjects reported headaches occurring weekly. Fear of failure and school problems, but not achievement motivation, had significant positive correlations with headache complaints after correction for differences in sex and age. Menarche and social class had little bearing in relation to the headache variables. From all reported causes of headache, stress was mentioned most frequently in both elementary and secondary schools.
Redundancy control in large scale sensor networks via compressive sensing

In wireless sensor networks for smart city or smart planet applications, massive volumes of real-time sensory data are being generated in every second, which pose great challenges to the power-limited sensor nodes, bandwidth-limited transmission links, and require high data storage and management costs. To deal with these challenges, compressive sensing (CS) converts the the spatially and temporally correlated information to sparse signals in some transformed domains (Such as DCT and FFT), and conducts cost-efficient, low-rank sensing. This paper presents a cost-centric comparison between recent compressive sensing solutions, i.e., Compressive Data Gathering (CDG) and Compressive Sparse Function (CSF), with traditional sensing technologies, in the means of sensing, transmission, storage and computation costs. It shows by a city temperature collection example that CDG performs similarly to CSF, both of which can prolong the network lifetime for almost one magnitude than traditional multi-hop sensing, while providing enough information for recovering the temperature distributions.
Diminished ROS Production in Neonatal Polymorphonuclear Leukocytes Stimulated with Escherichia coli

Bacterial infection is an important cause of death and long-term morbidity in neonates, especially in immature preterm neonates. The present study clarified the defense mechanism in neonates by measuring superoxide anion (one of reactive oxygen species: ROS) production and phagocytic activity of polymorphonuclear leukocytes (PMNs) caused by live bacterial stimulation in whole blood. In neonatal PMNs, superoxide anion production was significantly diminished when stimulated with an appropriate amount of Escherichia coli (E. coli). There was no significant difference in ROS production with stimulation of Staphylococcus aureus (S. aureus) between adult and neonatal PMNs. Both adult and neonatal PMNs showed equal activity in E. coli phagocytosis. The expression of Toll-Like Receptor 2 (TLR2), Toll-Like Receptor 4 (TLR4) and CD14 on the PMNs showed no difference between neonate and adult blood. The p38 mitogen-activated protein kinase (p38 MAPK) was also activated equally with the stimulation of live bacteria in both PMNs. Since p38 MAPK is a key molecule in lipopolysaccharide binding protein (LBP)-TLR4 mediating intracellular pathway, the receptor mediated signal transduction pathways have no difference between adult and neonatal PMNs. Diminished superoxide production of neonatal PMNs with E. coli stimulation was reconstituted, when neonatal PMNs were stimulated in adult plasma. This phenomenon was abolished by the heat inactivation of adult plasma but not by the addition of anti-interferon γ or anti-tumor necrosis factor α blocking antibody. These data suggest that some heat-labile factors in whole blood might have a role in augmentation of the ROS production of neonatal PMNs, responding to E. coli.
Lack of effect of a low-fat, high-fruit, -vegetable, and -fiber diet on serum prostate-specific antigen of men without prostate cancer: results from a randomized trial.

PURPOSE To determine whether a diet low in fat and high in fruits, vegetables, and fiber may be protective against prostate cancer by having an impact on serial levels of serum prostate-specific antigen (PSA). METHODS Six hundred eighty-nine men were randomized to the intervention arm and 661 to the control arm. The intervention group received intensive counseling to consume a diet low in fat and high in fiber, fruits, and vegetables. The control group received a standard brochure on a healthy diet. PSA in serum was measured at baseline and annually thereafter for 4 years, and newly diagnosed prostate cancers were recorded. RESULTS The individual PSA slope for each participant was calculated, and the distributions of slopes were compared between the two groups. There was no significant difference in distributions of the slopes (P =.99). The two groups were identical in the proportions of participants with elevated PSA at each time point. There was no difference in the PSA slopes between the two groups (P =.34) and in the frequencies of elevated PSA values for those with elevated PSA at baseline. Incidence of prostate cancer during the 4 years was similar in the two groups (19 and 22 in the control and intervention arms, respectively). CONCLUSION Dietary intervention over a 4-year period with reduced fat and increased consumption of fruits, vegetables, and fiber has no impact on serum PSA levels in men. The study also offers no evidence that this dietary intervention over a 4-year period affects the incidence of prostate cancer during the 4 years.
Grounding Systems Modeling Including Soil Ionization

This paper presents a hybrid frequency-time domain methodology for the modeling of grounding systems considering the nonlinear effect of soil ionization. The method is validated by analyzing two typical grounding systems under high soil ionization effects. It is clear from the gotten results the reduction effect of the so-called grounding system ldquoequivalent impedancerdquo when soil ionization takes place.
Entity Extraction, Linking, Classification, and Tagging for Social Media: A Wikipedia-Based Approach

Many applications that process social data, such as tweets, must extract entities from tweets (e.g., "Obama" and "Hawaii" in "Obama went to Hawaii"), link them to entities in a knowledge base (e.g., Wikipedia), classify tweets into a set of predefined topics, and assign descriptive tags to tweets. Few solutions exist today to solve these problems for social data, and they are limited in important ways. Further, even though several industrial systems such as OpenCalais have been deployed to solve these problems for text data, little if any has been published about them, and it is unclear if any of the systems has been tailored for social media. In this paper we describe in depth an end-to-end industrial system that solves these problems for social data. The system has been developed and used heavily in the past three years, first at Kosmix, a startup, and later at WalmartLabs. We show how our system uses a Wikipedia-based global "real-time" knowledge base that is well suited for social data, how we interleave the tasks in a synergistic fashion, how we generate and use contexts and social signals to improve task accuracy, and how we scale the system to the entire Twitter firehose. We describe experiments that show that our system outperforms current approaches. Finally we describe applications of the system at Kosmix and WalmartLabs, and lessons learned.
Efficient Simulation from the Multivariate Normal and Student-t Distributions Subject to Linear Constraints and the Evaluation of Constraint Probabilities

John GewekeDepartment of EconomicsUniversity of MinnesotaMinneapolis, MN 55455First draft: April, 1991Phone: (612)625-7563 Fax: (612)624-0209E-mail: geweke@atlas.socsci. umn.eduAbstractThe construction and implementation of a Gibbs sampler for efficient simulation from thetruncated multivariate normal and Student-t distributions is described. It is shown how theaccuracy and convergence of integrals based on the Gibbs sample may be constructed, andhow an estimate of the probability of the constraint set under the unrestricted distributionmay be produced.Keywords: Bayesian inference; Gibbs sampler; Monte Carlo; multiple integration;truncated normalThis paper was prepared for a presentation at the meeting Computing Science and Statistics:the Twenty-Third Symposium on the Interface, Seattle, April 22-24, 1991. Researchassistance from Zhenyu Wang and financial support from National Science FoundationGrant SES-8908365 are gratefully acknowledged. The software for the examples may berequested by electronic mail, and will be returned by that medium.
Spectral clustering in telephone call graphs

We evaluate various heuristics for hierarchical spectral clustering in large telephone call graphs. Spectral clustering without additional heuristics often produces very uneven cluster sizes or low quality clusters that may consist of several disconnected components, a fact that appears to be common for several data sources but, to our knowledge, not described in the literature. Divide-and-Merge, a recently described postfiltering procedure may be used to eliminate bad quality branches in a binary tree hierarchy. We propose an alternate solution that enables k-way cuts in each step by immediately filtering unbalanced or low quality clusters before splitting them further. Our experiments are performed on graphs with various weight and normalization built based on call detail records. We investigate a period of eight months of more than two millions of Hungarian landline telephone users. We measure clustering quality both by cluster ratio as well as by the geographic homogeneity of the clusters obtained from telephone location data. Although divide-and-merge optimizes its clusters for cluster ratio, our method produces clusters of similar ratio much faster, furthermore we give geographically much more homogeneous clusters with the size distribution of our clusters resembling to that of the settlement structure.
Synthetic beta-solenoid proteins with the fragment-free computational design of a beta-hairpin extension

Significance The development of algorithms to design new proteins with backbone plasticity is a key challenge in computational protein design. In this paper, we describe a class of extensible synthetic repeat protein scaffolds with computationally designed variable loops projecting from the central core. We have developed methods to sample backbone conformations computationally using a coarse-grained potential energy function without using backbone fragments from known protein structures. This procedure was combined with existing methods for sequence design to successfully design a loop at atomic level precision. Given the inherent modular and composable nature of repeat proteins, this approach allows the iterative atomic-resolution design of complex structures with potential applications in novel nanomaterials and molecular recognition. The ability to design and construct structures with atomic level precision is one of the key goals of nanotechnology. Proteins offer an attractive target for atomic design because they can be synthesized chemically or biologically and can self-assemble. However, the generalized protein folding and design problem is unsolved. One approach to simplifying the problem is to use a repetitive protein as a scaffold. Repeat proteins are intrinsically modular, and their folding and structures are better understood than large globular domains. Here, we have developed a class of synthetic repeat proteins based on the pentapeptide repeat family of beta-solenoid proteins. We have constructed length variants of the basic scaffold and computationally designed de novo loops projecting from the scaffold core. The experimentally solved 3.56-Å resolution crystal structure of one designed loop matches closely the designed hairpin structure, showing the computational design of a backbone extension onto a synthetic protein core without the use of backbone fragments from known structures. Two other loop designs were not clearly resolved in the crystal structures, and one loop appeared to be in an incorrect conformation. We have also shown that the repeat unit can accommodate whole-domain insertions by inserting a domain into one of the designed loops.
Inhibition of Myostatin Signaling through Notch Activation following Acute Resistance Exercise

Myostatin is a TGFβ family member and negative regulator of muscle size. Due to the complexity of the molecular pathway between myostatin mRNA/protein and changes in transcription, it has been difficult to understand whether myostatin plays a role in resistance exercise-induced skeletal muscle hypertrophy. To circumvent this problem, we determined the expression of a unique myostatin target gene, Mighty, following resistance exercise. Mighty mRNA increased by 6 h (82.9±24.21%) and remained high out to 48 h (56.5±19.67%) after resistance exercise. Further examination of the soleus, plantaris and tibialis anterior muscles showed that the change in Mighty mRNA at 6 h correlated with the increase in muscle size associated with this protocol (R2 = 0.9996). The increase in Mighty mRNA occurred both independent of Smad2 phosphorylation and in spite of an increase in myostatin mRNA (341.8±147.14% at 3 h). The myostatin inhibitor SKI remained unchanged. However, activated Notch, another potential inhibitor of TGFβ signaling, increased immediately following resistance exercise (83±11.2%) and stayed elevated out to 6 h (78±16.6%). Electroportion of the Notch intracellular domain into the tibialis anterior resulted in an increase in Mighty mRNA (63±13.4%) that was equivalent to the canonical Notch target HES-1 (94.4±7.32%). These data suggest that acute resistance exercise decreases myostatin signaling through the activation of the TGFβ inhibitor Notch resulting in a decrease in myostatin transcriptional activity that correlates well with muscle hypertrophy.
Behavioral and Neural Correlates of Acute and Scheduled Hunger in C57BL/6 Mice

In rodents, daily feeding schedules induce food anticipatory activity (FAA) rhythms with formal properties suggesting mediation by food-entrained circadian oscillators (FEOs). The search for the neuronal substrate of FEOs responsible for FAA is an active area of research, but studies spanning several decades have yet to identify unequivocally a brain region required for FAA. Variability of results across studies leads to questions about underlying biology versus methodology. Here we describe in C57BL/6 male mice the effects of varying the ‘dose’ of caloric restriction (0%, 60%, 80%, 110%) on the expression of FAA as measured by a video-based analysis system, and on the induction of c-Fos in brain regions that have been implicated in FAA. We determined that more severe caloric restriction (60%) leads to a faster onset of FAA with increased magnitude. Using the 60% caloric restriction, we found little evidence for unique signatures of neuronal activation in the brains of mice anticipating a daily mealtime compared to mice that were fasted acutely or fed ad-libitum–even in regions such as the dorsomedial and ventrolateral hypothalamus, nucleus accumbens, and cerebellum that have previously been implicated in FAA. These results underscore the importance of feeding schedule parameters in determining quantitative features of FAA in mice, and demonstrate dissociations between behavioral FAA and neural activity in brain areas thought to harbor FEOs or participate in their entrainment or output.
Toll-Like Receptor-4 Mediated Inflammation Is Involved in the Cardiometabolic Alterations Induced by Intermittent Hypoxia

Objective. Intermittent hypoxia (IH) is a major component of sleep apnea syndrome as its cardiometabolic complications have been mainly attributed to IH. The pathophysiology is still poorly understood but there are some similarities with the obesity-associated cardiometabolic complications. As the latter results from inflammation involving toll-like receptor-4 (TLR4) signaling, we assessed this pathway in the cardiometabolic consequences of IH. Methods. Lean adult male TLR4-deficient (TLR4−/−) mice and their controls (C57BL/6 mice) were exposed to either IH (FiO2 21-5%, 1 min cycle, 8 h/day) or air (normoxic mice) for 4 weeks. Animals were assessed at 1-week exposure for insulin tolerance test and after 4-week exposure for morphological and inflammatory changes of the epididymal fat and thoracic aorta. Results. IH induced insulin resistance, morphological and inflammatory changes of the epididymal fat (smaller pads and adipocytes, higher release of TNF-α and IL-6) and aorta (larger intima-media thickness and higher NFκB-p50 activity). All these alterations were prevented by TLR4 deletion. Conclusion. IH induces metabolic and vascular alterations that involve TLR4 mediated inflammation. These results confirm the important role of inflammation in the cardiometabolic consequences of IH and suggest that targeting TLR4/NFκB pathway could represent a further therapeutic option for sleep apnea patients.
On Choosing and Bounding Probability Metrics

When studying convergence of measures, an important issue is the choice of probability metric. We provide a summary and some new results concerning bounds among some important probability metrics/distances that are used by statisticians and probabilists. Knowledge of other metrics can provide a means of deriving bounds for another one in an applied problem. Considering other metrics can also provide alternate insights. We also give examples that show that rates of convergence can strongly depend on the metric chosen. Careful consideration is necessary when choosing a metric.