Miracle-dz/UniFAR-FTUs huggingface dataset:
Ectopic expression of Hoxb1 induces cardiac and craniofacial malformations

Members of the large family of Hox transcription factors are encoded by genes whose tightly regulated expression in development and in space within different embryonic tissues confer positional identity from the neck to the tips of the limbs. Many structures of the face, head and heart develop from cell populations expressing few or no Hox genes. Hoxb1 is the member of its chromosomal cluster expressed in the most rostral domain during vertebrate development, but never by the multipotent neural crest cell population anterior to the cerebellum. We have developed a novel floxed transgenic mouse line, CAG-Hoxb1,-EGFP (CAG-Hoxb1), which upon recombination by Cre recombinase conditionally induces robust Hoxb1 and eGFP over-expression. When induced within the neural crest lineage, pups die at birth. A variable phenotype develops from E11.5 on, associating frontonasal hypoplasia/aplasia, micrognathia/agnathia, major ocular and forebrain anomalies, and cardiovascular malformations. Neural crest derivatives in the body appear unaffected. Transcription of effectors of developmental signaling pathways (Bmp, Shh, Vegfa) and transcription factors (Pax3, Sox9) is altered in mutants. These outcomes emphasize that repression of Hoxb1, along with other paralog group 1 and 2 Hox genes, is strictly necessary in anterior cephalic NC for craniofacial, visual, auditory and cardiovascular development.
Diagnosing Performance Fluctuations of High-Throughput Software for Multi-core CPUs

Performance fluctuations are common in various software such as databases and software networking stacks. A fluctuation refers different performance (latency, throughput) for similar or identical data-items (e.g. requests, queries, packets) due to non-functional states such as cache warmth. While tail latency caused by fluctuations badly affect user experiences, diagnosing them is difficult as reproducing non-functional states in a controlled environment is not feasible. To this end, we estimate elapsed time of each function for each data-item individually to observe a single fluctuation occurrence online so that reproducing non-functional states is no longer needed. The issue is that instrumentation-based tracing methods are too heavy because a function takes a few micro seconds in high-throughput software systems for the multi-core age. We propose a hybrid approach of instrumentation and hardware-based sampling. It enables to diagnose performance fluctuations of high-throughput software systems with acceptable and adjustable overhead. Our evaluations show that it can clearly show a performance fluctuation that occurs by different cache-warmth in a sample application, and that it can be also applied to realistic software.
The application of implementation science theories for population health: A critical interpretive synthesis

Background and Purpose Over the last decade, the field of implementation science (IS) has yielded an array of theoretical approaches to clarify and understand how factors influence the application and scaling-up of evidence-based practice in health care. These developments have led to questions about whether IS theories and frameworks might be of value to population health researchers and decision makers. The purpose of this research was to conduct a critical interpretive synthesis to explore, if, and how, key IS theories and frameworks might inform population health interventions aimed at reducing the burden of illness across populations. Methods An initial list of theories and frameworks was developed based on previous published research and narrowed to focus on theories considered as formative for the field of IS. A standardized data extraction form was used to gather key features of the theories and critically appraise their relevance to population health interventions. Results Ten theories were included in the review and six deemed most applicable to population health based on their consideration of broader contextual and system-level factors. The remaining four were determined to have less relevant components for population health due to their limited consideration of macro-level factors, often focusing on micro (individual) and meso (organizational) level factors. Conclusions Theories and frameworks are important to guide the implementation and sustainability of population health interventions. The articulation of meso level factors common in IS theories may be of value to interventions targeted at the population level. However, some of the reviewed theories were limited in their consideration of broader contextual factors at the macro level (community, policy or societal). This critical interpretive synthesis also found that some theories lacked provision of practical guidance to address interventions targeting structural factors such as key social determinants of health (e.g., housing, income).
Misclassification Cost-Sensitive Software Defect Prediction

Software defect prediction helps developers focus on defective modules for efficient software quality assurance. A common goal shared by existing software defect prediction methods is to attain low classification error rates. These proposals suffer from two practical problems: (i) Most of the prediction methods rely on a large number of labeled training data. However, collecting labeled data is a difficult and expensive task. It is hard to obtain classification labels over new software projects or existing projects without historical defect data. (ii) Software defect datasets are highly imbalanced. In many real-world applications, the misclassification cost of defective modules is generally several times higher than that of non-defective ones. In this paper, we present a misclassification Cost-sensitive approach to Software Defect Prediction (CSDP). The CSDP approach is novel in two aspects: First, CSDP addresses the problem of unlabeled software detect datasets by combining an unsupervised sampling method with a domain specific misclassification cost model. This preprocessing step selectively samples a small percentage of modules through estimating their classification labels. Second, CSDP builds a cost-sensitive support vector machine model to predict defect-proneness of the rest of modules with both overall classification error rate and domain specific misclassification cost as quality metrics. CSDP is evaluated on four NASA projects. Experimental results highlight three interesting observations: (1) CSDP achieves higher Normalized Expected Cost of Misclassification (NECM) compared with state-of-art supervised learning models under imbalanced training data with limited labeling. (2) CSDP outperforms state-of-art semi-supervised learning methods, which disregards classification costs, especially in recall rate. (3) CSDP enhanced through unsupervised sampling as a preprocessing step prior to training and prediction outperforms the baseline CSDP without the sampling process.
Men’s Coercive Condom Use Resistance: The Roles of Sexual Aggression History, Alcohol Intoxication, and Partner Condom Negotiation

Condom use resistance (CUR) through coercive tactics is a significant public health concern. The present study investigated CUR risk factors through an alcohol administration experiment using a sexual risk analog with a community sample of male nonproblem drinkers (N = 321). Utilizing a path analysis framework, results demonstrated that men with more severe sexual aggression histories displayed stronger in-the-moment power and control responses, which was associated with greater coercive CUR and unprotected sex intentions. A significant interaction between sexual aggression history, risk rationale, and alcohol condition also predicted coercive CUR intentions. These findings emphasize the relationship between sexual aggression and sexual risk behaviors and highlight the importance of targeting these constructs in intervention and prevention efforts.
SWDE : A Sub-Word And Document Embedding Based Engine for Clickbait Detection

In order to expand their reach and increase website ad revenue, media outlets have started using clickbait techniques to lure readers to click on articles on their digital platform. Having successfully enticed the user to open the article, the article fails to satiate his curiosity serving only to boost click-through rates. Initial methods for this task were dependent on feature engineering, which varies with each dataset. Industry systems have relied on an exhaustive set of rules to get the job done. Neural networks have barely been explored to perform this task. We propose a novel approach considering different textual embeddings of a news headline and the related article. We generate sub-word level embeddings of the title using Convolutional Neural Networks and use them to train a bidirectional LSTM architecture. An attention layer allows for calculation of significance of each term towards the nature of the post. We also generate Doc2Vec embeddings of the title and article text and model how they interact, following which it is concatenated with the output of the previous component. Finally, this representation is passed through a neural network to obtain a score for the headline. We test our model over 2538 posts (having trained it on 17000 records) and achieve an accuracy of 83.49% outscoring previous state-of-the-art approaches.
MIDAS: Multilinear Detection at Scale

We focus on two classes of problems in graph mining: (1) finding trees and (2) anomaly detection in complex networks using scan statistics. These are fundamental problems in a broad class of applications. Most of the parallel algorithms for such problems are either based on heuristics, which do not scale very well, or use techniques like color coding, which have a high memory overhead. In this paper, we develop a novel approach for parallelizing both these classes of problems, using an algebraic representation of subgraphs as monomials—this methodology involves detecting multilinear terms in multivariate polynomials. Our algorithms show good scaling over a large regime, and they run on networks with close to half one billion edges. The resulting parallel algorithm for trees is able to scale to subgraphs of size 18, which has not been done before, and it significantly outperforms the best prior color coding based method (FASCIA) by more than two orders of magnitude. Our algorithm for network scan statistics is the first such parallelization, and it is able to handle a broad class of scan statistics functions with the same approach.
Envelope-Domain Analysis and Modeling of Super-Regenerative Oscillators

An envelope-domain methodology for the numerical modeling of super-regenerative oscillators (SROs) is presented. The main advantage is its generality of application to transistor-based oscillators with arbitrary topology. Initially, a stability analysis of the nonoscillatory steady-state solution, forced by the quench signal, is performed. It is based on the calculation of a linear-time-variant (LTV) transfer function, obtained by linearizing the circuit envelope-domain equations about the nonoscillatory regime. Under moderate quench frequencies, it will be possible to estimate the SRO normalized envelope and sensitivity function from the detected dominant pair of complex-conjugate poles. In the general case, the SRO oscillatory response is modeled with a numerical method, valid under linear operation with respect to the input signal. This is based on the calculation of the LTV impulse response from a time–frequency transfer function obtained under a small-signal sinusoidal excitation. The LTV impulse response enables a straightforward determination of the sensitivity time interval and time distance to the envelope maximum. An integral expression, in terms of the LTV transfer function, will provide the SRO response to any small-signal input with any arbitrarily carrier frequency and modulation. The methodology has been successfully validated through its application to an SRO at 2.7 GHz, which has been manufactured and measured.
Efficient Purely Convolutional Text Encoding

In this work, we focus on a lightweight convolutional architecture that creates fixed-size vector embeddings of sentences. Such representations are useful for building NLP systems, including conversational agents. Our work derives from a recently proposed recursive convolutional architecture for auto-encoding text paragraphs at byte level. We propose alternations that significantly reduce training time, the number of parameters, and improve auto-encoding accuracy. Finally, we evaluate the representations created by our model on tasks from SentEval benchmark suite, and show that it can serve as a better, yet fairly low-resource alternative to popular bag-of-words embeddings.
Geometry Analysis in Screen-Printed Stretchable Interconnects

Deformability of interconnects and conductors as the skeleton of soft electronic devices plays an important role in the stretchability of the whole system. To make the interconnects stretchable, either employing deformable materials or tailoring the design are the primary approaches. It is also possible to combine the two strategies. In advanced stretchable electronic circuits, there is a frequent need for the interconnects to transform from a narrow to a wide geometry. Therefore, this paper assesses three different geometries that accommodate a narrow-to-wide transition along the interconnects. First, the geometry is modeled using finite-element (FE) analysis. Second, in order to verify the accuracy of the FE model, screen-printed interconnects are fabricated accordingly by using silver flake ink on a deformable substrate. The geometrical modification shows a considerable improvement in the stretchability of the whole system. In addition, the effect of encapsulation with thermoplastic polyurethane on the performance of stretchable interconnects is investigated.
Evaluating Insider Threat Detection Workflow Using Supervised and Unsupervised Learning

Insider threat is a prominent cyber-security danger faced by organizations and companies. In this research, we study and evaluate an insider threat detection workflow using supervised and unsupervised learning algorithms. To this end, we study data exploration and analysis, anomaly detection and malicious behaviour classification on a publicly available data set. We evaluate several supervised and unsupervised learning algorithms - HMM, SOM, and DT - using this workflow.
Synthesis, characterization, and SAR of arylated indenoquinoline‐based cholinesterase and carbonic anhydrase inhibitors

We report the synthesis of bromoindenoquinolines (15a–f) by Friedlander reactions in low yields (13–50%) and the conversion of the corresponding phenyl‐substituted indenoquinoline derivatives 16–21 in high yields (80–96%) by Suzuki coupling reactions. To explore the structure–activity relationship (SAR), their inhibition potentials to inhibit acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and human carbonic anhydrase cyctosolic (hCA I and II) enzymes were determined. Monophenyl (16–18) indenoquinolines significantly inhibited the AChE and BChE enzymes in ranges of IC50 37–57 nM and 84–93 nM, respectively, compared with their starting materials 15a–c and reference compounds (galanthamine and tacrine). On the other hand, these novel arylated indenoquinoline‐based derivatives were effective inhibitors of the BChE, hCA I and II, BChE and AChE enzymes with Ki values in the range of 37 ± 2.04 to 88640 ± 1990 nM for AChE, 120.94 ± 37.06 to 1150.95 ± 304.48 nM for hCA I, 267.58 ± 98.05 to 1568.16 ± 438.67 nM for hCA II, and 84 ± 3.86 to 144120 ± 2910 nM for BChE. As a result, monophenyl indenoquinolines 16–18 may have promising anti‐Alzheimer drug potential and 3,8‐dibromoindenoquinoline amine (15f) can be novel hCA I and hCA II enzyme inhibitors.
S-Band Spline-Shaped Aperture-Stacked Patch Antenna for Air Traffic Control Applications

The design of the radiating elements of a $S$ -band planar array for air traffic control applications is addressed. Toward this end, a novel aperture-stacked patch (ASP) is proposed that, unlike the state-of-the-art layouts, exploits a spline shape on the top layer of the ASP to give the designer more degrees-of-freedom to fit user-defined requirements over the whole operative bandwidth. The elementary radiator is synthesized by optimizing the key indicators of the radiation performance of the final planar arrangement instead of, as generally done, the single-element features. The analysis of both the single element and the array outcomes is carried out by means of full-wave simulations to assess the effectiveness of the proposed design as well as the matching with the design constraints.
Partition-Aware Scalable Outlier Detection Using Unsupervised Learning

Anomaly Detection is an important area of research in data science. However, for big data it is challenging due to limited (or no) labeled data during training. This challenge is prevalent in the cyber-security area. There is always a lag before labels of (near) real time data become available. Hence, we would like to develop a new anomaly detection framework without any labeled data (no human intervention). For this, we utilize the density-based spatial clustering of applications with noise (DBSCAN). To address the scalability issues that arise when using DBSCAN, we exploit novel techniques based on partitions with minimum false alarm. We compare our approaches with other unsupervised approaches to demonstrate the effectiveness of our work.
The divergence history of European blue mussel species reconstructed from Approximate Bayesian Computation: the effects of sequencing techniques and sampling strategies

Genome-scale diversity data are increasingly available in a variety of biological systems, and can be used to reconstruct the past evolutionary history of species divergence. However, extracting the full demographic information from these data is not trivial, and requires inferential methods that account for the diversity of coalescent histories throughout the genome. Here, we evaluate the potential and limitations of one such approach. We reexamine a well-known system of mussel sister species, using the joint site frequency spectrum (jSFS) of synonymous mutations computed either from exome capture or RNA-seq, in an Approximate Bayesian Computation (ABC) framework. We first assess the best sampling strategy (number of: individuals, loci, and bins in the jSFS), and show that model selection is robust to variation in the number of individuals and loci. In contrast, different binning choices when summarizing the joint site frequency spectrum, strongly affect the results: including classes of low and high frequency shared polymorphisms can more effectively reveal recent migration events. We then take advantage of the flexibility of ABC to compare more realistic models of speciation, including variation in migration rates through time (i.e. periodic connectivity) and across genes (i.e. genome-wide heterogeneity in migration rates). We show that these models were consistently selected as the most probable, suggesting that mussels have experienced a complex history of gene flow during divergence and that the species boundary is semi-permeable. Our work provides a comprehensive evaluation of ABC demographic inference in mussels based on the coding site frequency spectrum, and supplies guidelines for employing different sequencing techniques and sampling strategies. We emphasize, perhaps surprisingly, that inferences are less limited by the volume of data, than by the way in which they are analyzed.
Improvement of GPSR Protocol in Vehicular Ad Hoc Network

In a vehicular ad hoc network (VANET), vehicles always move in high-speed which may cause the network topology changes frequently. This is challenging for routing protocols of VANET. Greedy Perimeter Stateless Routing (GPSR) is a representative routing protocol of VANET. However, when constructs routing path, GPSR selects the next hop node which is very easily out of the communication range in greedy forwarding, and builds the path with redundancy in perimeter forwarding. To solve the above-mentioned problems, we proposed Maxduration-Minangle GPSR (MM-GPSR) routing protocol in this paper. In greedy forwarding of MM-GPSR, by defining cumulative communication duration to represent the stability of neighbor nodes, the neighbor node with the maximum cumulative communication duration will be selected as the next hop node. In perimeter forwarding of MM-GPSR when greedy forwarding fails, the concept of minimum angle is introduced as the criterion of the optimal next hop node. By taking the position of neighbor nodes into account and calculating angles formed between neighbors and the destination node, the neighbor node with minimum angle will be selected as the next hop node. By using NS-2 and VanetMobiSim, simulations demonstrate that compared with GPSR, MM-GPSR has obvious improvements in reducing the packet loss rate, decreasing the end-to-end delay and increasing the throughput, and is more suitable for VANET.
Programmable Multifunctional RF/Microwave Circuit for Antenna and Filter Operation

This paper describes a multifunctional microwave device that can be reconfigured to behave either as a filter or an antenna. The device is based around a single-resonant element, referred to as a building block element that is connected to four ports. The resonant element is a microstrip square patch working in the 2.45 GHz unlicensed Industrial Scientific and Medical band used for IEEE 802.11 WiFi. The proposed building block is simple in its design and construction. This paper also presents devices composed of three building blocks, which can realize more complex filtering and antenna mode performance. In addition, p-i-n diodes have also been incorporated into the devices in order to facilitate electronic control. Measured results, pertaining to fabricated prototypes, show that filter and antenna operation can be achieved in the same device at a frequency of around 2.45 GHz with acceptable insertion loss and efficiency. In the future, it is anticipated that this could lead to a new paradigm, namely, a device that is analogous to a field programmable gate array operating in the microwave frequency regime.