Miracle-dz/UniFAR-FTUs huggingface dataset:
From Approximate to Optimal Solutions: Constructing Pruning and Propagation Rules

At the heart of many optimization procedures are powerful pruning and propagation rules. This paper presents a case study in the construction of such rules. We develop a new algorithm, Complete Decreasing Best Fit, that finds the optimal packing of objects into bins. The algorithm use a branching rule based on the well known Decreasing Best Fit approximation algorithm. In addition, it includes a powerful pruning rule derived from a bound on the solution to the remaining subproblem. The bound is constructed by using modular arithmetic to decompose the numerical constraints. We show that the pruning rule adds essentially a constant factor overhead to runtime, whilst reducing search significantly. On the hardest problems, runtime can be reduced by an order of magnitude. Finally we demonstrate how propagation rules can be built by adding lookahead to pruning rules. This general approach -optimization procedures built from branching rules based on good approximation algorithms, and pruning and propagation rules derived from bounds on the remaining subproblem-may be effective on other NP-complete problems.
Identification and characterization of rodent ABCA 1 in isolated type II pneumocytes

Anna E. Bortnick,1,* Elda Favari,2,* Jian-Qin Tao,3 Omar L. Francone,4 Muredach Reilly,5 Yuzhen Zhang,5 George H. Rothblat,1 and Sandra R. Bates3 1Children’s Hospital of Philadelphia, 5University of Pennsylvania Medical Center, and 3Institute for Environmental Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania 19104; 2Institute of Pharmacological and Biological Sciences, University of Parma, Parma, Italy; and 4Pfizer, Incorporated, Groton, Connecticut 06340
DhARO4 induction and tyrosine nitration in response to reactive radicals generated by salt stress in Debaryomyces hansenii

It has been previously reported that growth of Debaryomyces hansenii in 2 m NaCl induced the expression of ARO4. This gene codifies for DhAro4p, involved in the synthesis of the amino acid tyrosine. In this work we studied the activity of DhAro4p upon salt stress; a higher activity was observed in cells grown with 2 m NaCl, but tyrosine levels were not increased. On the other hand, the addition of tyrosine to the saline medium significantly enhanced the growth of D. hansenii. It was found that the oxidized form of tyrosine, 3‐nitrotyrosine, increased in the presence of salt. Since NaCl protects against oxidative stress in D. hansenii (Navarrete et al., 2009), we propose that a protective pathway is the de novo synthesis of tyrosine and its immediate oxidation to 3‐nitrotyrosine to counteract oxidative stress generated by salt stress, so we measured the production of reactive oxygen species (ROS) and nitric oxide (NO−) in D. hansenii after growing in 2 m NaCl. Results showed the presence of NO− and the increased production of ROS; this is probably due to an increased respiratory activity in the cells grown in the presence of salt. Our results demonstrate that upon salt stress D hansenii responds to oxidative stress via the transcriptional activation of specific genes such as DhARO4. Copyright © 2011 John Wiley & Sons, Ltd.
Antenatal factors predicting subsequent need for insulin treatment in women with gestational diabetes.

OBJECTIVE To identify characteristics indicative of subsequent requirement of insulin in patients with gestational diabetes (GDM). METHODS Identified from a database were patients with GDM not receiving insulin or oral hypoglycemic agents at enrollment for outpatient education and surveillance. Maternal characteristics were compared between patients achieving glycemic control with diet and those requiring insulin. Cox proportional hazards regression was used to assess multiple effects of significant univariate factors. RESULTS Data from 2365 patients were analyzed. Patients requiring insulin were more likely to be multiparous, obese, have a history of GDM, be diagnosed at <28 weeks of gestation, and have a fasting blood glucose of >95 mg/dL, a glucose tolerance test 3-hour blood glucose of >140 mg/dL, and a glycosylated hemoglobin (A1c) of >or=6% at diagnosis of GDM. CONCLUSIONS Laboratory values at diagnosis of GDM were the strongest indicators of subsequent need for insulin treatment. Patients with fasting blood glucose of >95 mg/dL and A1c values >or=6% at diagnosis of GDM should receive close surveillance of daily blood glucose.
The Evolution of Age-Dependent Plasticity

When organisms encounter environments that are heterogeneous in time, phenotypic plasticity is often favored by selection. The degree of such plasticity can vary during an organism’s lifetime, but the factors promoting differential plastic responses at different ages or life stages remain poorly understood. Here we develop and analyze an evolutionary model to investigate how environmental information is optimally collected and translated into phenotypic adjustments at different ages. We demonstrate that plasticity must often be expected to vary with age in a nonmonotonic fashion. Early in life, it is generally optimal to delay phenotypic adjustments until sufficient information has been collected about the state of the environment to warrant a costly phenotypic adjustment. Toward the end of life, phenotypic adjustments are disfavored as well because their beneficial effects can no longer be fully reaped before death. Our analysis clarifies how patterns of age-dependent plasticity are shaped by the interplay of environmental uncertainty, the accuracy of perceived information, and the costs of phenotypic adjustments with life-history determinants such as the relative strengths of fecundity and viability selection experienced by the organism over its lifetime. We conclude by comparing our results with expectations for alternative mechanisms, including developmental constraints, that promote age-dependent plasticity.
A Case-Control Study and Meta-Analysis Reveal BDNF Val66Met Is a Possible Risk Factor for PTSD

Posttraumatic stress disorder (PTSD) is a debilitating condition that develops in some people after exposure to a traumatic event. Brain-derived neurotrophic factor (BDNF) is highly expressed in the mammalian brain and is thought to be involved in learning and memory processes. A nonsynonymous polymorphism in the BDNF gene, rs6265 (Val66Met), has been hypothesised to be associated with PTSD. Association studies examining the Val66Met polymorphism and PTSD have been inconclusive, likely due to the variability in type of trauma exposure analysed. Vietnam veterans (n = 257) screened for PTSD and controlled for trauma exposure were genotyped for BDNF Val66Met. The association was not significant so we incorporated our data into a meta-analysis to obtain greater statistical power. A comprehensive search of more than 1237 articles revealed eight additional studies suitable for meta-analysis (n = 3625). A random-effects meta-analysis observed a potential protective factor of the Val/Val genotype. After removing two studies with violation of Hardy-Weinberg equilibrium, findings for the Val/Val genotype reached significance. Subgroup analyses confirmed a trend for this finding. Limitations of some studies that inform this meta-analysis include poorly screened controls and a lack of examination of population stratification. Effectively designed studies should inform this line of research in the future.
Privacy-preserving Paralinguistic Tasks

Speech is one of the primary means of communication for humans. It can be viewed as a carrier for information on several levels as it conveys not only the meaning and intention predetermined by a speaker, but also paralinguistic and extralinguistic information about the speaker’s age, gender, personality, emotional state, health state and affect. This makes it a particularly sensitive biometric, that should be protected. In this work we intent to explore how Leveled Homomorphic Encryption can be combined with a Neural Network to create a privacy-preserving machine learning framework for speech-based health-related tasks. In particular, we will apply this framework to the detection and assessment of a Cold, Depression and Parkinson’s Disease. Moreover, we will show how using a Quantized Neural Network, with discretized weights, allows us to apply a Leveled Homomorphic Encryption technique called batching that can be utilized to reduce the effective computational cost of this framework.
Trace driven registration of neuron confocal microscopy stacks

Active research in the area of 3-D neurite tracing has predominantly focused on single sections. Ultimately, however, neurobiologists desire to study the long range connectivity of the brain, which requires tracing axons across multiple serially-cut sections. Registration of axonal sections is challenging due to several factors, such as sparseness of the axons and complications of the sectioning process, including tissue deformation and loss. This paper investigates a method for registering sections using centerline traces which provide the locations of axons at section boundaries and the angles at which the axons approach the boundaries. This information is used to determine correspondences between two serial sections. Both global and local differences are accounted for using rigid and non-rigid transforms. Results show that utilizing information from traced axons allows axon continuity across sections to be restored.
Solar Irradiance Forecasts by Mesoscale Numerical Weather Prediction Models with Different Horizontal Resolutions

This study examines the performance of radiation processes (shortwave and longwave radiations) using numerical weather prediction models (NWPs). NWP were calculated using four different horizontal resolutions (5, 2 and 1 km, and 500 m). Validation results on solar irradiance simulations with a horizontal resolution of 500 m indicated positive biases for direct normal irradiance dominate for the period from 09 JST (Japan Standard Time) to 15 JST. On the other hand, after 15 JST, negative biases were found. For diffused irradiance, weak negative biases were found. Validation results on upward longwave radiation found systematic negative biases of surface temperature (corresponding to approximately −2 K for summer and approximately −1 K for winter). Downward longwave radiation tended to be weak negative biases during both summer and winter. Frequency of solar irradiance suggested that the frequency of rapid variations of solar irradiance (ramp rates) from the NWP were less than those observed. Generally, GHI distributions between the four different horizontal resolutions resembled each other, although horizontal resolutions also became finer.
Incremental Deployment Strategies for Effective Detection and Prevention of BGP Origin Hijacks

A variety of solutions have been proposed for detecting and preventing IP hijack attacks. Despite potentially serious consequences these solutions have not been widely deployed, partially because many ISPs do not view their risk as large enough to warrant investment. Nevertheless, a number of organizations such as critical national infrastructure are at a very high risk level and require a deployed solution. Is it possible for these sites to be protected despite the majority apathy, given that a critical mass of ISPs is generally required to participate in the solution? We examine this conflict by presenting an approach which determines AS vulnerability based on topological location. We next examine the effectiveness of incremental security deployment. We separately examine BGP hijack detection which, if improperly peered, may completely miss a hijack. Finally, we address a pessimistic view with respect to deployment and propose an approach in which an autonomous system can act in its own self-interest to determine a minimal threshold for hijack detection or prevention.
Modulation of Dnmt3b function in vitro by interactions with Dnmt3L, Dnmt3a and Dnmt3b splice variants

DNA methylation, an essential regulator of transcription and chromatin structure, is established and maintained by the coordinated action of three DNA methyltransferases: DNMT1, DNMT3A and DNMT3B, and the inactive accessory factor DNMT3L. Disruptions in DNMT3B function are linked to carcinogenesis and genetic disease. DNMT3B is also highly alternatively spliced in a tissue- and disease-specific manner. The impact of intra-DNMT3 interactions and alternative splicing on the function of DNMT3 family members remains unclear. In the present work, we focused on DNMT3B. Using a panel of in vitro assays, we examined the consequences of DNMT3B splicing and mutations on its ability to bind DNA, interact with itself and other DNMT3's, and methylate DNA. Our results show that, while the C-terminal catalytic domain is critical for most DNMT3B functions, parts of the N-terminal region, including the PWWP domain, are also important. Alternative splicing and domain deletions also influence DNMT3B’s cellular localization. Furthermore, our data reveal the existence of extensive DNMT3B self-interactions that differentially impact on its activity. Finally, we show that catalytically inactive isoforms of DNMT3B are capable of modulating the activity of DNMT3A–DNMT3L complexes. Our studies therefore suggest that seemingly ‘inactive’ DNMT3B isoforms may influence genomic methylation patterns in vivo.
Structured Prediction with Test-time Budget Constraints

We study the problem of structured prediction under test-time budget constraints. We propose a novel approach applicable to a wide range of structured prediction problems in computer vision and natural language processing. Our approach seeks to adaptively generate computationally costly features during test-time in order to reduce the computational cost of prediction while maintaining prediction performance. We show that training the adaptive feature generation system can be reduced to a series of structured learning problems, resulting in efficient training using existing structured learning algorithms. This framework provides theoretical justification for several existing heuristic approaches found in literature. We evaluate our proposed adaptive system on two real-world structured prediction tasks, optical character recognition (OCR) and dependency parsing. For OCR our method cuts the feature acquisition time by half coming within a 1% margin of top accuracy. For dependency parsing we realize an overall runtime gain of 20% without significant loss in performance.
Workshop on recommendation utility evaluation: beyond RMSE -- RUE 2012

Measuring the error in rating prediction has been by far the dominant evaluation methodology in the Recommender Systems literature. Yet there seems to be a general consensus that this criterion alone is far from being enough to assess the practical effectiveness of a recommender system. Information Retrieval metrics have started to be used to evaluate item selection and ranking rather than rating prediction, but considerable divergence remains in the adoption of such metrics by different authors. On the other hand, recommendation utility includes other key dimensions and concerns beyond accuracy, such as novelty and diversity, user engagement, and business performance. While the need for further extension, formalization, clarification and standardization of evaluation methodologies is recognized in the community, this need is still unmet for a large extent. The RUE 2012 workshop sought to identify and better understand the current gaps in recommender system evaluation methodologies, help lay directions for progress in addressing them, and contribute to the consolidation and convergence of experimental methods and practice.
Unconditionally Secure Computation on Large Distributed Databases with Vanishing Cost

Consider a network of k parties, each holding a long sequence of n entries (a database), with minimum vertex-cut greater than t. We show that any empirical statistic across the network of databases can be computed by each party with perfect privacy, against any set of t < k/2 passively colluding parties, such that the worst-case distortion and communication cost (in bits per database entry) both go to zero as n, the number of entries in the databases, goes to infinity. This is based on combining a striking dimensionality reduction result for random sampling with unconditionally secure multi-party computation protocols.
A systematic approach to the analysis and design of electrical machines for automotive traction applications

In the last decades, the electrification process has involved several aspects and activities of modern industrialized societies. In this context, the electrification of the transportation sector still exhibits a significant delay. This is mainly due to the rather competitive oil performances as an energy source from both a technical and economic point of view. However, air pollution considerations as well as energy self-sufficiency policies have recently raised the industrial interest for automotive hybrid and electric powertrains. Moreover, in just a few years, the electrical powertrains have reached the Internal Combustion Engine performances and, thanks to their particular features (such as the overload and the very high efficiency levels), they even outperformed most of the conventional powertrains. This work focuses on the electrical machines used for the new car propulsion and braking, with special regard to the rotor geometry, winding configurations and all the most important trade-offs related to the machine design. The control capabilities over the whole speed range have also been considered carefully. In addition, a new recursive magnetic model has been developed to perform multi-objective rotor parameters optimization and a novel spoke-type geometry is proposed to reduce the machine permanent magnet volume. This thesis is organized in the following way: the introduction gives the general background information about the car electrification topic, the chapter 1 deals with the specific requirements for a traction motor showing that the Interior Permanent Magnet is the motor type which best meets those constraints, in the chapter 2 a deep analysis is carried out on two case study machines by means of both analytic approach and Finite Elements simulations, then chapter 3 performs a critical analysis of the machine main design options and finally, in chapter 4, the resultant guidelines are applied to the design of a new motor prototype for Student Formula races. The design covers not only the typical electric and magnetic aspects of the motor but also the thermal and mechanical properties of the machine.
Guanine Quadruplex Electrochemical Aptasensors

Guanine-rich nucleic acids are able to self-assemble into G-quadruplex four-stranded secondary structures, which are found at the level of telomeric regions of chromosomes, oncogene promoter sequences and other biologically-relevant regions of the genome. Due to their extraordinary stiffness and biological role, G-quadruples become relevant in areas ranging from structural biology to medicinal chemistry, supra-molecular chemistry, nanotechnology and biosensor technology. In addition to classical methodologies, such as circular dichroism, nuclear magnetic resonance or crystallography, electrochemical methods have been successfully used for the rapid detection of the conformational changes from single-strand to G-quadruplex. This review presents recent advances on the G-quadruplex electrochemical characterization and on the design and applications of G-quadruplex electrochemical biosensors, with special emphasis on the G-quadruplex aptasensors and hemin/G-quadruplex peroxidase-mimicking DNAzyme biosensors.
Metagenomics Reveals Planktonic Bacterial Community Shifts across a Natural CO2 Gradient in the Mediterranean Sea

ABSTRACT Bacterial communities at a CO2 vent (pH 6.7) were compared with those at control (pH 8.0) and transition sites (pH 7.6) using 16S rRNA metagenomics. Firmicutes and unclassified bacteria dominated across all sites, Proteobacteria, especially Gammaproteobacteria, declined, and Epsilonproteobacteria increased in the vent with an increase in Bacteroidetes at both the vent and transition sites.
The Middle Fragment of Helicobacter pylori CagA Induces Actin Rearrangement and Triggers Its Own Uptake into Gastric Epithelial Cells

Cytotoxin-associated gene product A (CagA) is a major virulence factor secreted by Helicobacter pylori. CagA activity in the gastric epithelium is associated with higher risk of gastric cancer development. Bacterial type IV secretion system (T4SS)-mediated translocation of CagA into the cytosol of human epithelial cells occurs via a poorly understood mechanism that requires CagA interaction with the host membrane lipid phosphatidylserine (PS) and host cell receptor integrin α5β1. Here we have characterized the isolated recombinant middle fragment of CagA (CagA-M) that contains the positively-charged PS-binding region (aa 613–636) and a putative β1 integrin binding site, but lacks the EPIYA region, secretion signal peptide and the CagA multimerization motif. We show that CagA-M, when immobilized on latex beads, is capable of binding to, and triggering its own uptake into, gastric epithelial cells in the absence of infection with cagA-positive H. pylori. Using site-directed mutagenesis, fluorescent and electron microscopy, and highly-specific inhibitors, we demonstrate that the cell-binding and endocytosis-like internalization of CagA-M are dependent on (1) binding to PS; (2) β1 integrin activity; and (3) actin dynamics. Interaction of CagA-M with the host cells is accompanied by the development of long filopodia-like protrusions (macrospikes). This novel morphology is different from the hummingbird phenotype induced by the translocation of full-length CagA. The determinants within CagA-M and within the host that are important for endocytosis-like internalization into host cells are very similar to those observed for T4SS-mediated internalization of full-length CagA, suggesting that the latter may involve an endocytic pathway.
ERG Expression Levels in Prostate Tumors Reflect Functional Status of the Androgen Receptor (AR) as a Consequence of Fusion of ERG with AR Regulated Gene Promoters

Expression of the ERG proto-oncogene, is activated in 50-70% of prostate tumors by androgen receptor (AR) mediated signals due to the fusion of AR regulated promoters (primarily TMPRSS2 and to a lesser extent SLC45A3 and NDRG1) to the ERG protein coding sequence. Our previous studies of quantitative expression levels of ERG or TMPRSS2-ERG fusion transcripts have noted that relatively low or no ERG expression in prostate tumors significantly associated with progressive disease. Here, we have tested the hypothesis that ERG expression levels in prostate tumor cells reflect AR transcriptional regulatory function in a given biological context of the tumor progression. Therefore, tumors with lower ERG may represent a subset with attenuated AR signaling. Expression of ERG and other AR regulated genes were evaluated in a GeneChip dataset obtained from a panel of laser capture micro-dissected well/moderately differentiated (WD) or poorly differentiated (PD) tumor cells derived from primary tumors of patients, who had no prior androgen ablation treatment. Overall, ERG expression pattern was similar to that of other AR regulated genes. Strikingly low frequency of ERG expression was noted in PD tumor cells (30%) in comparison to WD tumor cells (80%), suggesting for subdued AR function in a significant fraction of tumors with genomic alterations of ERG. By integrating ERG into a panel of defined AR target genes, we developed a cumulative AR Function Index (ARFI), which if validated may have future potential in stratifying patients for targeted therapy on the basis of overall AR functional status in primary tumors.