Miracle-dz/UniFAR-FTUs huggingface dataset:
Encoding folding paths of RNA switches

RNA co-transcriptional folding has long been suspected to play an active role in helping proper native folding of ribozymes and structured regulatory motifs in mRNA untranslated regions (UTRs). Yet, the underlying mechanisms and coding requirements for efficient co-transcriptional folding remain unclear. Traditional approaches have intrinsic limitations to dissect RNA folding paths, as they rely on sequence mutations or circular permutations that typically perturb both RNA folding paths and equilibrium structures. Here, we show that exploiting sequence symmetries instead of mutations can circumvent this problem by essentially decoupling folding paths from equilibrium structures of designed RNA sequences. Using bistable RNA switches with symmetrical helices conserved under sequence reversal, we demonstrate experimentally that native and transiently formed helices can guide efficient co-transcriptional folding into either long-lived structure of these RNA switches. Their folding path is controlled by the order of helix nucleations and subsequent exchanges during transcription, and may also be redirected by transient antisense interactions. Hence, transient intra- and inter-molecular base pair interactions can effectively regulate the folding of nascent RNA molecules into different native structures, provided limited coding requirements, as discussed from an information theory perspective. This constitutive coupling between RNA synthesis and RNA folding regulation may have enabled the early emergence of autonomous RNA-based regulation networks.
On Constant-Size Graphs That Preserve the Local Structure of High-Girth Graphs

Let G=(V,E) be an undirected graph with maximum degree d. The k-disc of a vertex v is defined as the rooted subgraph that is induced by all vertices whose distance to v is at most k. The k-disc frequency vector of G, freq(G), is a vector indexed by all isomorphism types of k-discs. For each such isomorphism type Gamma, the k-disc frequency vector counts the fraction of vertices that have k-disc isomorphic to Gamma. Thus, the frequency vector freq(G) of G captures the local structure of G. A natural question is whether one can construct a much smaller graph H such that H has a similar local structure. N. Alon proved that for any epsilon>0 there always exists a graph H whose size is independent of |V| and whose frequency vector satisfies ||freq(G) - freq(G)||_1 <= epsilon. However, his proof is only existential and neither gives an explicit bound on the size of H nor an efficient algorithm. He gave the open problem to find such explicit bounds. In this paper, we solve this problem for the special case of high girth graphs. We show how to efficiently compute a graph H with the above properties when G has girth at least 2k+2 and we give explicit bounds on the size of H.
Benevolent Competence and Integrity-Based Trust in Knowledge Transfer: A Look at Software Reuse

The potential of software reuse to reduce costs and time while increasing quality in a new project development, make it an important issue in the management of information systems. The question of how to increase software reuse in new application development has often been asked, but mostly from a managerial perspective rather than the individual perspective. This research effort attempts to address this limitation by conceptualizing software as a form of knowledge, and intending to examine if trustworthiness of various knowledge transfer mechanisms influences receipt of software through those mechanisms. And if so, which knowledge transfer mechanisms do software developers trust most? We propose and intend to test a model of software reuse in which the links between knowledge transfer mechanisms and receipt of software through those mechanisms is mediated by competence-, integrity-, and benevolence-based trust. Hypotheses are developed to test those links, and appropriate methodology is outlined to conduct the research. With a better understanding of why software developers trust certain mechanisms over others, it will be possible to recommend better administrative policies and procedures that work in conjunction with trust to encourage software reuse.
Variability in Abundance of Temperate Reef Fishes Estimated by Visual Census

Identifying sources of sampling variation and quantifying their magnitude is critical to the interpretation of ecological field data. Yet, most monitoring programs of reef fish populations based on underwater visual censuses (UVC) consider only a few of the factors that may influence fish counts, such as the diver or census methodology. Recent studies, however, have drawn attention to a broader range of processes that introduce variability at different temporal scales. This study analyzes the magnitude of different sources of variation in UVCs of temperate reef fishes off Patagonia (Argentina). The variability associated with time-of-day, tidal state, and time elapsed between censuses (minutes, days, weeks and months) was quantified for censuses conducted on the five most conspicuous and common species: Pinguipes brasilianus, Pseudopercis semifasciata, Sebastes oculatus, Acanthistius patachonicus and Nemadactylus bergi. Variance components corresponding to spatial heterogeneity and to the different temporal scales were estimated using nested random models. The levels of variability estimated for the different species were related to their life history attributes and behavior. Neither time-of-day nor tidal state had a significant effect on counts, except for the influence of tide on P. brasilianus. Spatial heterogeneity was the dominant source of variance in all but one species. Among the temporal scales, the intra-annual variation was the highest component for most species due to marked seasonal fluctuations in abundance, followed by the weekly and the instantaneous variation; the daily component was not significant. The variability between censuses conducted at different tidal levels and time-of-day was similar in magnitude to the instantaneous variation, reinforcing the conclusion that stochastic variation at very short time scales is non-negligible and should be taken into account in the design of monitoring programs and experiments. The present study provides baseline information to design and interpret results from visual census programs in temperate reefs.
TREC Genomics Track at UIC

We considered that a query of this year’s Genomics track consists of two parts, target and qualification. A target refers to any instance of a certain entity type and the qualification refers to the condition that the target needs to satisfy in order to be qualified as an answer to the query. For example, the target and qualification of the query “What [ANTIBODIES] have been used to detect protein TLR4? ” are “an instance of ANTIBODIES” and “have been used to detect protein TLR4”, respectively. The relevance of a document to a query was measured by to what degree the document contains a target and satisfies the qualification.
The AIDA Autotransporter System Is Associated with F18 and Stx2e in Escherichia coli Isolates from Pigs Diagnosed with Edema Disease and Postweaning Diarrhea

ABSTRACT Pathogenic Escherichia coli strains are known to cause edema disease (ED) and postweaning diarrhea (PWD) in piglets. Although the exact mechanisms of pathogenicity that lead to ED-PWD remain to be elucidated, E. coli-borne Shiga-like toxin and adhesion-mediating virulence factors such as F18 adhesin or F4 fimbriae are believed to play a central role in ED-PWD. In light of these observations we investigated whether another E. coliadhesin, the plasmid-encoded AIDA (adhesin involved in diffuse adherence) might also be present in ED-PWD-causing E. coli isolates. For rapid screening for the AIDA system in large numbers of isolates, a multiplex PCR method along with a duplex Western blot procedure was developed. When screening 104 strains obtained from pigs with or without ED-PWD, we observed a high prevalence of the AIDA operon in porcine E. coli isolates, with over 25% of all strains being AIDA positive, and we could demonstrate a significant association of the intact AIDA gene (orfB) with ED-PWD, while defects in orfB were associated with the absence of disease. Although our data hint toward a contribution of AIDA to ED-PWD, further studies will be necessary since the presence of the AIDA genes was also associated with the presence of the Shiga-like toxin and F18 adhesin genes, two reported virulence factors for ED-PWD.
Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains

Submodular continuous functions are a category of (generally) non-convex/non-concave functions with a wide spectrum of applications. We characterize these functions and demonstrate that they can be maximized efficiently with approximation guarantees. Specifically, i) We introduce the weak DR property that gives a unified characterization of submodularity for all set, integer-lattice and continuous functions; ii) for maximizing monotone DR-submodular continuous functions under general down-closed convex constraints, we propose a Frank-Wolfe variant with $(1-1/e)$ approximation guarantee, and sub-linear convergence rate; iii) for maximizing general non-monotone submodular continuous functions subject to box constraints, we propose a DoubleGreedy algorithm with $1/3$ approximation guarantee. Submodular continuous functions naturally find applications in various real-world settings, including influence and revenue maximization with continuous assignments, sensor energy management, multi-resolution data summarization, facility location, etc. Experimental results show that the proposed algorithms efficiently generate superior solutions compared to baseline algorithms.
High-strain rate compressive behavior of multi-walled carbon nanotube dispersed thermoset epoxy resin

Compressive high-strain rate behavior of thermoset epoxy resin dispersed with multi-walled carbon nanotubes is presented. The present study investigates the effect of strain rate as well as effect of multi-walled carbon nanotube dispersion on the compressive stress–compressive strain behavior of thermoset epoxy resin. The amount of multi-walled carbon nanotubes dispersion was varied up to 3% by weight. The studies were carried out on compressive split Hopkinson pressure bar apparatus in the strain rate range of 800–3700/s. Processing of multi-walled carbon nanotubes-dispersed thermoset epoxy resin, testing, damage, and energy absorbing mechanisms and key findings are reported. It is observed that the compressive strength of thermoset epoxy resin increases up to 147% at high-strain rate compared with that at quasi-static loading. The corresponding increase for multi-walled carbon nanotubes-dispersed thermoset epoxy resin is up to 140%. It is also observed that the compressive strength and energy-absorption capability of thermoset epoxy resin increase with the addition of multi-walled carbon nanotubes.
Lymphatic endothelium

The lymphatic microvasculature is uniquely adapted for the continuous removal of interstitial fluid and proteins, and is an important point of entry for leukocytes and tumor cells. The traditional view that lymphatic capillaries are passive participants in these tasks is currently being challenged. This overview highlights recent advances in our understanding of the molecular mechanisms underlying the formation and function of lymphatic vessels.
Causal Discovery Using Regression-Based Conditional Independence Tests

Conditional independence (CI) testing is an important tool in causal discovery. Generally, by using CI tests, a set of Markov equivalence classes w.r. t. the observed data can be estimated by checking whether each pair of variables x and y is d-separated, given a set of variables Z. Due to the curse of dimensionality, CI testing is often difficult to return a reliable result for high-dimensional Z. In this paper, we propose a regression-based CI test to relax the test of x ⊥ y|Z to simpler unconditional independence tests of x − f(Z) ⊥ y−g(Z), and x−f(Z) ⊥ Z or y−g(Z) ⊥ Z under the assumption that the data-generating procedure follows additive noise models (ANMs). When the ANM is identifiable, we prove that x − f(Z) ⊥ y − g(Z) ⇒ x ⊥ y|Z. We also show that 1) f and g can be easily estimated by regression, 2) our test is more powerful than the state-of-the-art kernel CI tests, and 3) existing causal learning algorithms can infer much more causal directions by using the proposed method.
Development of a Native Escherichia coli Induction System for Ionic Liquid Tolerance

The ability to solubilize lignocellulose makes certain ionic liquids (ILs) very effective reagents for pretreating biomass prior to its saccharification for biofuel fermentation. However, residual IL in the aqueous sugar solution can inhibit the growth and function of biofuel-producing microorganisms. In E. coli this toxicity can be partially overcome by the heterologous expression of an IL efflux pump encoded by eilA from Enterobacter lignolyticus. In the present work, we used microarray analysis to identify native E. coli IL-inducible promoters and develop control systems for regulating eilA gene expression. Three candidate promoters, PmarR’, PydfO’, and PydfA’, were selected and compared to the IPTG-inducible PlacUV5 system for controlling expression of eilA. The PydfA’ and PmarR’ based systems are as effective as PlacUV5 in their ability to rescue E. coli from typically toxic levels of IL, thereby eliminating the need to use an IPTG-based system for such tolerance engineering. We present a mechanistic model indicating that inducible control systems reduce target gene expression when IL levels are low. Selected-reaction monitoring mass spectrometry analysis revealed that at high IL concentrations EilA protein levels were significantly elevated under the control of PydfA’ and PmarR’ in comparison to the other promoters. Further, in a pooled culture competition designed to determine fitness, the strain containing pPmarR’-eilA outcompeted strains with other promoter constructs, most significantly at IL concentrations above 150 mM. These results indicate that native promoters such as PmarR’ can provide effective systems for regulating the expression of heterologous genes in host engineering and simplify the development of industrially useful strains.
Effect of Temperature on the Physico-Chemical Properties of a Room Temperature Ionic Liquid (1-Methyl-3-pentylimidazolium Hexafluorophosphate) with Polyethylene Glycol Oligomer

A systematic study of the effect of composition on the thermo-physical properties of the binary mixtures of 1-methyl-3-pentyl imidazolium hexafluorophosphate [MPI][PF6] with poly(ethylene glycol) (PEG) [Mw = 400] is presented. The excess molar volume, refractive index deviation, viscosity deviation, and surface tension deviation values were calculated from these experimental density, ρ, refractive index, n, viscosity, η, and surface tension, γ, over the whole concentration range, respectively. The excess molar volumes are negative and continue to become increasingly negative with increasing temperature; whereas the viscosity and surface tension deviation are negative and become less negative with increasing temperature. The surface thermodynamic functions, such as surface entropy, enthalpy, as well as standard molar entropy, Parachor, and molar enthalpy of vaporization for pure ionic liquid, have been derived from the temperature dependence of the surface tension values.
Multi-Label Prediction via Compressed Sensing

We consider multi-label prediction problems with large output spaces under the assumption of output sparsity - that the target (label) vectors have small support. We develop a general theory for a variant of the popular error correcting output code scheme, using ideas from compressed sensing for exploiting this sparsity. The method can be regarded as a simple reduction from multi-label regression problems to binary regression problems. We show that the number of subproblems need only be logarithmic in the total number of possible labels, making this approach radically more efficient than others. We also state and prove robustness guarantees for this method in the form of regret transform bounds (in general), and also provide a more detailed analysis for the linear prediction setting.
Recent activities in flame retardancy of wood-plastic composites at the Forest Products Laboratory

For a variety of reasons, wood-plastic composite (WPC) products are widely available for some building applications. In applications such as outdoor decking, WPCs have gained a significant share of the market. As an option to improve the efficient use of wood fiber, the USDA Forest Service, Forest Products Laboratory (FPL), has an extensive research program on WPCs. Although often ignored in discussions of WPCs, the flammability of WPCs can be a significant factor in certain building applications. This paper reviews past, present, and planned cooperative FPL research projects on fire-retardant treatment of WPCs and related topics.
Functional and direct interaction between the RNA binding protein HuD and active Akt1

The RNA binding protein HuD plays essential roles in neuronal development and plasticity. We have previously shown that HuD stimulates translation. Key for this enhancer function is the linker region and the poly(A) binding domain of HuD that are also critical for its function in neurite outgrowth. Here, we further explored the underlying molecular interactions and found that HuD but not the ubiquitously expressed HuR interacts directly with active Akt1. We identify that the linker region of HuD is required for this interaction. We also show by using chimeric mutants of HuD and HuR, which contain the reciprocal linker between RNA-binding domain 2 (RBD2) and RBD3, respectively, and by overexpressing a dominant negative mutant of Akt1 that the HuD–Akt1 interaction is functionally important, as it is required for the induction of neurite outgrowth in PC12 cells. These results suggest the model whereby RNA-bound HuD functions as an adapter to recruit Akt1 to trigger neurite outgrowth. These data might also help to explain how HuD enhances translation of mRNAs that encode proteins involved in neuronal development.
Crystal Structure and Pyridoxal 5-Phosphate Binding Property of Lysine Decarboxylase from Selenomonas ruminantium

Lysine decarboxylase (LDC) is a crucial enzyme for acid stress resistance and is also utilized for the biosynthesis of cadaverine, a promising building block for bio-based polyamides. We determined the crystal structure of LDC from Selenomonas ruminantium (SrLDC). SrLDC functions as a dimer and each monomer consists of two distinct domains; a PLP-binding barrel domain and a sheet domain. We also determined the structure of SrLDC in complex with PLP and cadaverine and elucidated the binding mode of cofactor and substrate. Interestingly, compared with the apo-form of SrLDC, the SrLDC in complex with PLP and cadaverine showed a remarkable structural change at the PLP binding site. The PLP binding site of SrLDC contains the highly flexible loops with high b-factors and showed an open-closed conformational change upon the binding of PLP. In fact, SrLDC showed no LDC activity without PLP supplement, and we suggest that highly flexible PLP binding site results in low PLP affinity of SrLDC. In addition, other structurally homologous enzymes also contain the flexible PLP binding site, which indicates that high flexibility at the PLP binding site and low PLP affinity seems to be a common feature of these enzyme family.
SUper Team at SemEval-2016 Task 3: Building a Feature-Rich System for Community Question Answering

We present the system we built for participating in SemEval-2016 Task 3 on Community Question Answering. We achieved the best results on subtask C, and strong results on subtasks A and B, by combining a rich set of various types of features: semantic, lexical, metadata, and user-related. The most important group turned out to be the metadata for the question and for the comment, semantic vectors trained on QatarLiving data and similarities between the question and the comment for subtasks A and C, and between the original and the related question for Subtask B.
Reducing the training set using semi-supervised self-training algorithm for segmenting the left ventricle in ultrasound images

Statistical pattern recognition models are one of the core research topics in the segmentation of the left ventricle of the heart from ultrasound data. The underlying statistical model usually relies on a complex model for the shape and appearance of the left ventricle whose parameters can be learned using a manually segmented data set. Unfortunately, this complex requires a large number of parameters that can be robustly learned only if the training set is sufficiently large. The difficulty in obtaining large training sets is currently a major roadblock for the further exploration of statistical models in medical image analysis. In this paper, we present a novel semi-supervised self-training model that reduces the need of large training sets for estimating the parameters of statistical models. This model is initially trained with a small set of manually segmented images, and for each new test sequence, the system re-estimates the model parameters incrementally without any further manual intervention. We show that state-of-the-art segmentation results can be achieved with training sets containing 50 annotated examples.
Identification of Fungal DNA Barcode Targets and PCR Primers Based on Pfam Protein Families and Taxonomic Hierarchy

DNA barcoding is the application of DNA sequences of standardized genetic markers for the identification of eukaryotic organisms. We attempted to identify alternative candidate barcode gene targets for the fungal biota from available fungal genomes using a taxonomy-aware processing pipeline. Putative-protein coding sequences were matched to Pfam protein families and aligned to reference Pfam accessions. Conserved sequence blocks were identified in the resulting alignments and degenerate primers were designed. The processing pipeline is described and the resulting candidate gene targets are discussed. The pipeline allows analysis of subsets at various hierarchical, taxonomic levels (selectable by GenBank taxonomy ID or scientific name) of the available reference data, allowing discrete taxonomic groups to be combined into a single subset, or for subordinate taxa to be excluded from the analysis of higher-level taxa. Putative degenerate primer pairs were designed as high as the superkingdom rank for the set of organisms included in the analysis. The identified targets have essential housekeeping functions, like the well known phylogenetic or barcode markers, and most have a better resolution potential to differentiate species among fully sequenced genomes than the most presently used markers. Some of the commonly used species-level phylogenetic markers for fungi, especially tef1-� and rpb2, were not recovered in our analysis because of their existence in multiple copies in single organisms, and because Pfam families do not always correlate with complete proteins.
Voice activity detection based on fusion of audio and visual information

In this paper, we propose a multi-modal voice activity detection system (VAD) that uses audio and visual information. Audioonly VAD systems typically are not robust to (acoustic) noise. Incorporating visual information, for example information extracted from mouth images, can improve the robustness since the visual information is not affected by the acoustic noise. In multi-modal (speech) signal processing, there are two methods for fusing the audio and the visual information: concatenating the audio and visual features, and employing audio-only and visual-only classifiers, then fusing the unimodal decisions. We investigate the effectiveness of these methods and also compare model-based and model-free methods for VAD. Experimental results show feature fusion methods to generally be more effective, and decision fusion methods generally perform better using model-free methods.