Miracle-dz/UniFAR-FTUs huggingface dataset:
The Genomic Architecture of a Rapid Island Radiation: Recombination Rate Variation, Chromosome Structure, and Genome Assembly of the Hawaiian Cricket Laupala

Phenotypic evolution and speciation depend on recombination in many ways. Within populations, recombination can promote adaptation by bringing together favorable mutations and decoupling beneficial and deleterious alleles. As populations diverge, crossing over can give rise to maladapted recombinants and impede or reverse diversification. Suppressed recombination due to genomic rearrangements, modifier alleles, and intrinsic chromosomal properties may offer a shield against maladaptive gene flow eroding coadapted gene complexes. Both theoretical and empirical results support this relationship. However, little is known about this relationship in the context of behavioral isolation, where coevolving signals and preferences are the major hybridization barrier. Here we examine the genomic architecture of recently diverged, sexually isolated Hawaiian swordtail crickets (Laupala). We assemble a de novo genome and generate three dense linkage maps from interspecies crosses. In line with expectations based on the species’ recent divergence and successful interbreeding in the laboratory, the linkage maps are highly collinear and show no evidence for large-scale chromosomal rearrangements. Next, the maps were used to anchor the assembly to pseudomolecules and estimate recombination rates across the genome to test the hypothesis that loci involved in behavioral isolation (song and preference divergence) are in regions of low interspecific recombination. Contrary to our expectations, the genomic region where a male song and female preference QTL colocalize is not associated with particularly low recombination rates. This study provides important novel genomic resources for an emerging evolutionary genetics model system and suggests that trait–preference coevolution is not necessarily facilitated by locally suppressed recombination.
MCMC GGUM

The latest advances in psychometric research have concluded that noncognitive measurement is better carried out using unfolding item response theory (IRT) models (Chernyshenko, Stark, Chan, Drasgow, & Williams, 2001; Drasgow, Chernyshenko, & Stark, 2010). Unlike cognitive ability tests, which deal with right/wrong responses, noncognitive measurement involves responses that indicate how well an item describes the respondent’s typical preference or standing on a latent continuum. The Generalized Graded Unfolding IRT Model (GGUM), originally developed by Roberts, Donoghue, and Laughlin (2000), is the most popular unfolding IRT model, and has been widely accepted in both academia and applied industry. Up to now, the GGUM has been primarily estimated in GGUM2004 (Roberts, Fang, Cui, & Wang, 2006), which employs maximum likelihood estimation (MLE) method. This software has played a critical role in the use of unfolding IRT in research and applications. Nevertheless, in some situations, GGUM2004 has been found to produce unusually large location parameter estimates and standard errors (de la Torre, Stark, & Chernyshenko, 2006), which inevitably leads to difficulties when conducting differential item functioning (DIF) analyses with GGUM (Wang, Tay, & Drasgow, 2013). To provide an alternative method of estimating the GGUM, MCMC GGUM, a new computer program that employs Markov chain Monte Carlo (MCMC) estimation method, is developed. This computer program also offers a user-friendly graphical interface.
Multimodal deception detection

Deception detection has received an increasing amount of attention due to the signiﬁcant growth of digital media, as well as increased ethical and security concerns. Earlier approaches to deception detection were mainly focused on law enforcement applications and relied on polygraph tests, which had proven to falsely accuse the innocent and free the guilty in multiple cases. More recent work on deception has expanded to other applications, such as deception detection in social media, interviews, or deception in daily life. Moreover, recent research on deception detection has brought together scientists from ﬁelds as diverse as computational linguistics, speech processing, computer vision, psychology, and physiology, which makes this problem particularly appealing for multimodal processing. This chapter will overview the state-of-the-art in multimodal deception detection, covering physiological (e.g., biosensors and thermal imaging), visual (e.g., facial expressions and gestures), speech (e.g., pitch and pause length), and linguistic modalities. We will describe the features that are typically extracted from each of these modalities, as well as means to combine these modalities into an overall system that can detect deception in multimodal content. We will cover methods that make use of lab recordings, as well as methods that rely on real-life data (e.g., recent work on multimodal deception detection from trial data). In general, a multimodal approach where features from different streams are integrated is found to lead to an improved performance as compared to the use of single modalities.
Screening and assessment of chronic pain among children with cerebral palsy: a process evaluation of a pain toolbox

Abstract Purpose: Though high numbers of children with cerebral palsy experience chronic pain, it remains under-recognized. This paper describes an evaluation of implementation supports and adoption of the Chronic Pain Assessment Toolbox for Children with Disabilities (the Toolbox) to enhance pain screening and assessment practices within a pediatric rehabilitation and complex continuing care hospital. Methods: A multicomponent knowledge translation strategy facilitated Toolbox adoption, inclusive of a clinical practice guideline, cerebral palsy practice points and assessment tools. Across the hospital, seven ambulatory care clinics with cerebral palsy caseloads participated in a staggered roll-out (Group 1: exclusive CP caseloads, March–December; Group 2: mixed diagnostic caseloads, August–December). Evaluation measures included client electronic medical record audit, document review and healthcare provider survey and interviews. Results: A significant change in documentation of pain screening and assessment practice from pre-Toolbox (<2%) to post-Toolbox adoption (53%) was found. Uptake in Group 2 clinics lagged behind Group 1. Opportunities to use the Toolbox consistently (based on diagnostic caseload) and frequently (based on client appointments) were noted among contextual factors identified. Overall, the Toolbox was positively received and clinically useful. Conclusion: Findings affirm that the Toolbox, in conjunction with the application of integrated knowledge translation principles and an established knowledge translation framework, has potential to be a useful resource to enrich and standardize chronic pain screening and assessment practices among children with cerebral palsy. Implications for Rehabilitation It is important to engage healthcare providers in the conceptualization, development, implementation and evaluation of a knowledge-to-action best practice product. The Chronic Pain Toolbox for Children with Disabilities provides rehabilitation staff with guidance on pain screening and assessment best practice and offers a range of validated tools that can be incorporated in ambulatory clinic settings to meet varied client needs. Considering unique clinical contexts (i.e., opportunities for use, provider engagement, staffing absences/turnover) is required to optimize and sustain chronic pain screening and assessment practices in rehabilitation outpatient settings.
phylotaR: An Automated Pipeline for Retrieving Orthologous DNA Sequences from GenBank in R

The exceptional increase in molecular DNA sequence data in open repositories is mirrored by an ever-growing interest among evolutionary biologists to harvest and use those data for phylogenetic inference. Many quality issues, however, are known and the sheer amount and complexity of data available can pose considerable barriers to their usefulness. A key issue in this domain is the high frequency of sequence mislabeling encountered when searching for suitable sequences for phylogenetic analysis. These issues include, among others, the incorrect identification of sequenced species, non-standardized and ambiguous sequence annotation, and the inadvertent addition of paralogous sequences by users. Taken together, these issues likely add considerable noise, error or bias to phylogenetic inference, a risk that is likely to increase with the size of phylogenies or the molecular datasets used to generate them. Here we present a software package, phylotaR that bypasses the above issues by using instead an alignment search tool to identify orthologous sequences. Our package builds on the framework of its predecessor, PhyLoTa, by providing a modular pipeline for identifying overlapping sequence clusters using up-to-date GenBank data and providing new features, improvements and tools. We demonstrate and test our pipeline’s effectiveness by presenting trees generated from phylotaR clusters for two large taxonomic clades: Palms and primates. Given the versatility of this package, we hope that it will become a standard tool for any research aiming to use GenBank data for phylogenetic analysis.
Brouwer's fixed-point theorem in real-cohesive homotopy type theory

We combine homotopy type theory with axiomatic cohesion, expressing the latter internally with a version of ‘adjoint logic’ in which the discretization and codiscretization modalities are characterized using a judgemental formalism of ‘crisp variables. ’ This yields type theories that we call ‘spatial’ and ‘cohesive,’ in which the types can be viewed as having independent topological and homotopical structure. These type theories can then be used to study formally the process by which topology gives rise to homotopy theory (the ‘fundamental ∞-groupoid’ or ‘shape’), disentangling the ‘identifications’ of homotopy type theory from the ‘continuous paths’ of topology. In a further refinement called ‘real-cohesion,’ the shape is determined by continuous maps from the real numbers, as in classical algebraic topology. This enables us to reproduce formally some of the classical applications of homotopy theory to topology. As an example, we prove Brouwer's fixed-point theorem.
Automatic Predictions Using LDA for Learning through Social Networking Services

Social Networking Services can serve as a great platform for learning. As such, the use of Facebook in learning contexts can be proved beneficial. Following this direction, this paper presents a prototype Facebook application for learning which is supported by Latent Dirichlet allocation (LDA). LDA is a generative model that allows sets of observations to be explained by unobserved groups that clarify why some parts of the data are similar. Hence, making automatic predictions about the interests of students can be made by collecting their preferences and characteristics. Hence, by tracking user interests, accurate recommendations can be made. The experimental results, presented in this paper, reinforce the view that automatic predictions using LDA to students in social networks can be a powerful idea in personalizing instruction.
Versatile Model for Activity Recognition: Sequencelet Corpus Model

In this paper, we propose a Sequencelet Corpus Model (SCM) that is quite versatile for human activity recognition in video. A 'sequencelet', a sequence of actional words, is automatically learned using a data mining approach without any prior knowledge about activity structure and describes a representative partial structure of an activity. We model an activity as a combination of sequencelets in the 'sequencelet corpus' that is a set of learned sequencelets. The SCM does not depend on the type of low-level feature as well as can perform various activity recognition tasks. We evaluate our model on the UT-Interaction dataset and the ActivityNet dataset for activity classification from both fully- and partially-observed video, untrimmed video classification and measuring semantic similarity between activities. The SCM achieved state-of-theart results in activity classification even though the activity is partially observed and promising results in untrimmed video classification, Moreover, by constructing the hierarchy of activities from the similarity computed using the SCM, we demonstrate that the SCM can be used a metric for measuring semantic similarity between activities; these results indicate that the SCM has potential to be an all-around activityrecognition model.
Structural Rounding: Approximation Algorithms for Graphs Near an Algorithmically Tractable Class

We develop a new framework for generalizing approximation algorithms from the structural graph algorithm literature so that they apply to graphs somewhat close to that class (a scenario we expect is common when working with real-world networks) while still guaranteeing approximation ratios. The idea is to $\textit{edit}$ a given graph via vertex- or edge-deletions to put the graph into an algorithmically tractable class, apply known approximation algorithms for that class, and then $\textit{lift}$ the solution to apply to the original graph. We give a general characterization of when an optimization problem is amenable to this approach, and show that it includes many well-studied graph problems, such as Independent Set, Vertex Cover, Feedback Vertex Set, Minimum Maximal Matching, Chromatic Number, ($\ell$-)Dominating Set, Edge ($\ell$-)Dominating Set, and Connected Dominating Set. To enable this framework, we develop new editing algorithms that find the approximately-fewest edits required to bring a given graph into one of several important graph classes (in some cases, also approximating the target parameter of the family). For bounded degeneracy, we obtain a bicriteria $(4,4)$-approximation which also extends to a smoother bicriteria trade-off. For bounded treewidth, we obtain a bicriteria $(O(\log^{1.5} n), O(\sqrt{\log w}))$-approximation, and for bounded pathwidth, we obtain a bicriteria $(O(\log^{1.5} n), O(\sqrt{\log w} \cdot \log n))$-approximation. For treedepth $2$ (also related to bounded expansion), we obtain a $4$-approximation. We also prove complementary hardness-of-approximation results assuming $\mathrm{P} \neq \mathrm{NP}$: in particular, these problems are all log-factor inapproximable, except the last which is not approximable below some constant factor ($2$ assuming UGC).
Interaction and Threshold Effects of Appraisal on Componential Patterns of Emotion: A Study Using Cross-Cultural Semantic Data

Studies that investigated the relation between appraisal and emotion have largely focused on the linear effect of appraisal criteria on subjective feelings (e.g., the effect of appraised goal obstruction on anger). Emotional responding can be extended to include more than just feelings, however. Componential definitions of emotion also add motivation, physiology, and expression. Moreover, a linear model is not compatible with the idea held by many appraisal theorists that appraisal criteria interact to produce emotional responding. In the present study, we modeled adaptive nonlinear interaction effects of appraisal criteria on motivation, expression, and physiology simultaneously. We applied a combination of principal component analysis for data reduction and multivariate adaptive regression splines (MARS) for automatic interaction identification. Data were obtained from a large-scale cross-cultural study on emotion concepts conducted in 27 countries, which represented semantic profiles of component information in 24 common emotion words. Results of modeling indicated that (a) appraisal of relevance, familiarity, goal compatibility, coping potential, and suddenness showed main effects on component responses; (b) appraisals of agency and norm compatibility uniquely showed interaction effects on component responses; (c) interaction effects explained significant variance only in some component responses but not all; and (d) the emotion patterns simulated by the fitted MARS model could be clustered according to qualitative emotion categories.
Development and evaluation of a gaze feedback system integrated into eyetrace

A growing field of studies in eye-tracking is the use of gaze data for realtime feedback to the subject. In this work, we present a software system for such experiments and validate it with a visual search task experiment. This system was integrated into an eye tracking analysis tool. Our aim was to improve subject performance in this task by employing saliency features for gaze guidance. This realtime feedback system can be applicable within many realms, such as learning interventions, computer entertainment, or virtual reality.
Imaging-based molecular barcoding with pixelated dielectric metasurfaces

Metasurfaces for molecular detection Although mid-infrared (mid-IR) spectroscopy is a mainstay of molecular fingerprinting, its sensitivity is diminished somewhat when looking at small volumes of sample. Nanophotonics provides a platform to enhance the detection capability. Tittl et al. built a mid-IR nanophotonic sensor based on reflection from an all-dielectric metasurface array of specially designed scattering elements. The scattering elements could be tuned via geometry across a broad range of wavelengths in the mid-IR. The approach successfully detected and differentiated the absorption fingerprints of various molecules. The technique offers the prospect of on-chip molecular fingerprinting without the need for spectrometry, frequency scanning, or moving mechanical parts. Science, this issue p. 1105 A pixelated dielectric metasurface is used for the mid-infrared detection of molecular fingerprints. Metasurfaces provide opportunities for wavefront control, flat optics, and subwavelength light focusing. We developed an imaging-based nanophotonic method for detecting mid-infrared molecular fingerprints and implemented it for the chemical identification and compositional analysis of surface-bound analytes. Our technique features a two-dimensional pixelated dielectric metasurface with a range of ultrasharp resonances, each tuned to a discrete frequency; this enables molecular absorption signatures to be read out at multiple spectral points, and the resulting information is then translated into a barcode-like spatial absorption map for imaging. The signatures of biological, polymer, and pesticide molecules can be detected with high sensitivity, covering applications such as biosensing and environmental monitoring. Our chemically specific technique can resolve absorption fingerprints without the need for spectrometry, frequency scanning, or moving mechanical parts, thereby paving the way toward sensitive and versatile miniaturized mid-infrared spectroscopy devices.
Design of integrated navigation system using IMU and multiple ranges from in-flight rotating hexacopter system

This paper introduces a new integrated navigation algorithm using sensor measurements from INS and multiple range sensors. A simple INS model is employed in design with states of position, velocity and attitude. From a geometric interpretation on line of sight vectors, the measurement model of the multiple range sensors is derived, and then linearized to design the extended Kalman filter. To verify the proposed algorithm, a comprehensivesimulator is designed, and the results are compared between flight scenarios. For practical performance demonstration, experiment result with a multicopter platform is presented. As a result of the experiment, the suggested algorithm is proven to be valid for an indoor hexarotor flight.
Zinc Oxide Nanostructures: Synthesis and Characterization

Zinc oxide (ZnO) is a wide band gap semiconductor with an energy gap of 3.37 eV at room temperature. It has been used considerably for its catalytic, electrical, optoelectronic, and photochemical properties. ZnO nanomaterials, such as quantum dots, nanorods, and nanowires, have been intensively investigated for their important properties. Many methods have been described in the literature for the production of ZnO nanostructures, such as laser ablation, hydrothermal methods, electrochemical deposition, sol–gel methods, Chemical Vapour Deposition, molecular beam epitaxy, the common thermal evaporation method, and the soft chemical solution method. The present Special Issue is devoted to the Synthesis and Characterization of ZnO nanostructures with novel technological applications.
Diagnosis and repair of cells (DRC) responsible for power-supply-noise violations

This paper proposes an automatic method to diagnose risky cells that are responsible for PSN (Power Supply Noise) violations. We can also find ECO (Engineering Change Order) method (resize or move) to perform on risky cells to repair PSN violations. We apply superposition principle to model the diagnosis and repair as a linear programming problem. We consider not only PSN constraints but also timing constraints to make sure that we can repair all PSN violations without causing new timing violations. With our diagnosis result, designers can perform ECO with very little effort. The experimental results show that we can repair all PSN violations with only less than 2% of all candidate cells.
BMP and FGF signaling interact to pattern mesoderm by controlling basic helix-loop-helix transcription factor activity

The mesodermal germ layer is patterned into mediolateral subtypes by signaling factors including BMP and FGF. How these pathways are integrated to induce specific mediolateral cell fates is not well understood. We used mesoderm derived from post-gastrulation neuromesodermal progenitors (NMPs), which undergo a binary mediolateral patterning decision, as a simplified model to understand how FGF acts together with BMP to impart mediolateral fate. Using zebrafish and mouse NMPs, we identify an evolutionarily conserved mechanism of BMP and FGF-mediated mediolateral mesodermal patterning that occurs through modulation of basic helix-loop-helix (bHLH) transcription factor activity. BMP imparts lateral fate through induction of Id helix loop helix (HLH) proteins, which antagonize bHLH transcription factors, induced by FGF signaling, that specify medial fate. We extend our analysis of zebrafish development to show that bHLH activity is responsible for the mediolateral patterning of the entire mesodermal germ layer.
Dynamic action of the Sec machinery during initiation, protein translocation and termination

Protein translocation across cell membranes is a ubiquitous process required for protein secretion and membrane protein insertion. This is mediated, for the majority of proteins, by the highly conserved Sec machinery. The bacterial translocon – SecYMKEG – resides in the plasma membrane, where translocation is driven through rounds of ATP hydrolysis by the cytoplasmic SecA ATPase, and the proton motive force (PMF). We have used single molecule Förster resonance energy transfer (FRET) alongside a combination of confocal and total internal reflection microscopy to gain access to SecY pore dynamics and translocation kinetics on timescales spanning milliseconds to minutes. This allows us to dissect and characterise the translocation process in unprecedented detail. We show that SecA, signal sequence, pre-protein and ATP hydrolysis each have important and specific roles in unlocking and opening the Sec channel, priming it for transport. After channel opening, translocation proceeds in two phases: an initiation phase independent of substrate length, and a length-dependent transport phase with an intrinsic translocation rate of ~ 40 amino acids per second for the model pre-protein substrate proOmpA. The initiation and translocation phases are both coupled to ATP hydrolysis while termination is ATP-independent. Distributions of translocation rates reflect the stochastic nature of the translocation process and are consistent with the recently proposed Brownian ratchet model [Allen et al. doi: 10.7554/eLife.15598]. The results allow us unparalleled access to the kinetics of the complex reaction and provide a framework for understanding the molecular mechanism of protein secretion.