Miracle-dz/UniFAR-FTUs huggingface dataset:
Carriage of Supernumerary Sex Chromosomes Decreases the Volume and Alters the Shape of Limbic Structures

Abstract Sex chromosome aneuploidy (SCA) increases risk for several psychiatric disorders associated with the limbic system, including mood and autism spectrum disorders. Thus, SCA offers a genetics-first model for understanding the biological basis of psychopathology. Additionally, the sex-biased prevalence of many psychiatric disorders could potentially reflect sex chromosome dosage effects on brain development. To clarify how limbic anatomy varies across sex and sex chromosome complement, we characterized amygdala and hippocampus structure in a uniquely large sample of patients carrying supernumerary sex chromosomes (n = 132) and typically developing controls (n = 166). After adjustment for sex-differences in brain size, karyotypically normal males (XY) and females (XX) did not differ in volume or shape of either structure. In contrast, all SCAs were associated with lowered amygdala volume relative to gonadally-matched controls. This effect was robust to three different methods for total brain volume adjustment, including an allometric analysis that derived normative scaling rules for these structures in a separate, typically developing population (n = 79). Hippocampal volume was insensitive to SCA after adjustment for total brain volume. However, surface-based analysis revealed that SCA, regardless of specific karyotype, was consistently associated with a spatially specific pattern of shape change in both amygdala and hippocampus. In particular, SCA was accompanied by contraction around the basomedial nucleus of the amygdala and an area crossing the hippocampal tail. These results demonstrate the power of SCA as a model to understand how copy number variation can precipitate changes in brain systems relevant to psychiatric disease.
Complete Genome Sequence of the Marine Hydrocarbon Degrader Alcaligenes aquatilis QD168, Isolated from Crude Oil-Polluted Sediment of Quintero Bay, Central Chile

Alcaligenes aquatilis strain QD168 (= CCUG 69566) is a marine hydrocarbon-degrading bacterium isolated from crude oil-polluted sediment from Quintero Bay, Central Chile. Here, we present the 4.32-Mb complete genome sequence of strain QD168, with 3,892 coding sequences, 58 tRNAs, and a 56.3% G+C content. ABSTRACT Alcaligenes aquatilis strain QD168 (= CCUG 69566) is a marine hydrocarbon-degrading bacterium isolated from crude oil-polluted sediment from Quintero Bay, Central Chile. Here, we present the 4.32-Mb complete genome sequence of strain QD168, with 3,892 coding sequences, 58 tRNAs, and a 56.3% G+C content.
Coleoptera of Canada

Abstract The beetle fauna of Canada was assessed, including estimates of yet unreported diversity using information from taxonomists and COI sequence clusters in a BOLD (Barcode of Life Datasystems) COI dataset comprising over 77,000 Canadian records. To date, 8302 species of Coleoptera have been recorded in Canada, a 23% increase from the first assessment in 1979. A total of 639 non-native beetle species have become established in Canada, with most species in the Staphylinidae (153 spp.), Curculionidae (107 spp.), Chrysomelidae (56 spp.) and Carabidae (55 spp.). Based on estimates from the taxonomic community and our BOLD dataset, we estimate that slightly more than 1000 beetle species remain to be reported from Canada, either as new records or undescribed species. Renewed enthusiasm toward and financial support for surveys, especially in the central and western provinces of Canada will be critical for detecting, documenting and describing these species. The Barcode of Life database is still far from comprehensive for Canadian Coleoptera but substantial progress has been made and the number of Barcode Index Numbers (BINs) (as candidate species) has reached nearly 70% of the number of species reported from Canada. Comparison of BINs to observed species in a group of Canadian Staphylinidae suggests that BINs may provide a good estimate of species diversity within the beetles. Histeridae is a diverse family in Canada that is notably underrepresented in BOLD. Families such as Mordellidae, Scraptiidae, Latridiidae, Ptiliidae and Scirtidae are poorly known taxonomically in Canada and are represented in our BOLD dataset by many more BINs than recorded species.
Complete Genome Sequence of an N-Acyl Homoserine Lactone Producer, Breoghania sp. Strain L-A4, Isolated from Rhizosphere of Phragmites australis in a Coastal Wetland

The Breoghania sp. strain L-A4 was isolated from the rhizosphere of Phragmites australis in the Qinhaungdao coastal wetland in China. ABSTRACT The Breoghania sp. strain L-A4 was isolated from the rhizosphere of Phragmites australis in the Qinhaungdao coastal wetland in China. Here, we present the complete genome sequence of strain L-A4, which consists of a chromosome of 5,029,620 bp with a G+C content of 64.53% and 4,964 coding DNA sequences. This strain was the first detected to produce N-acyl homoserine lactone (AHL) signals in a member of this genus.
Rescue of cone function in cone-only Nphp5 knockout mouse model with Leber congenital amaurosis phenotype

Purpose Recessive mutations in the human IQCB1/NPHP5 gene are associated with Senior-Løken syndrome (SLS), a ciliopathy presenting with nephronophthisis and Leber congenital amaurosis (LCA). Nphp5-knockout mice develop LCA without nephronophthisis. Mutant rods rapidly degenerate while mutant cones survive for months. The purpose of this study was to reinitiate cone ciliogenesis in a Nphp5−/−; Nrl−/− mouse with viral expression of full-length NPHP5 and rescue function. Methods Nphp5−/− mice were mated with Nrl−/− mice to generate Nphp5−/−; Nrl−/− double-knockouts. Nphp5−/−; Nrl−/− mice and Nphp5+/−; Nrl−/− controls were phenotyped with confocal microscopy from postnatal day 10 (P10) until 6 months of age. Nphp5−/−; Nrl−/− mice and Nphp5+/−; Nrl−/− controls were injected at P15 with self-complementary adenoassociated virus 8 (Y733F) (AAV8(Y733F)) expressing GRK1-FL-cNPHP5. Expression of mutant NPHP5 was verified with confocal microscopy and electroretinography (ERG). Results In the Nphp5−/− and cone-only Nphp5−/−; Nrl−/− mice, cone outer segments did not form, but mutant cones continued to express cone pigments in the inner segments without obvious signs of cone cell death. The mutant cone outer nuclear layer (ONL) and the inner segments were stable for more than 6 months in the cone-only Nphp5−/−; Nrl−/− retinas. Viral expression of NPHP5 initiated after eye opening showed that connecting cilia and RP1-positive axonemes were formed. Furthermore, cone pigments and other cone outer segment proteins (cone transducin and cone PDE6) were present in the nascent mutant cone outer segments, and rescued mutant cones exhibited a significant photopic b-wave (30% of Nphp5+/−; Nrl−/− controls). Conclusions Nphp5−/−; Nrl−/− cones persistently express cone pigments in the inner segments without obvious degeneration, providing an extended duration interval for viral gene expression. Viral expression of full-length NPHP5 initiates ciliogenesis between P15 and P60, and mutant cones are, in part, functional, encouraging future retina gene replacement therapy.
Nearly Complete Genome Sequence of a Human Norovirus GII.P17-GII.17 Strain Isolated from Brazil in 2015

Human noroviruses are the most common cause of nonbacterial acute gastroenteritis worldwide. We report here the nearly complete genome sequence (7,551 nucleotides) of a human norovirus GII.P17-GII.17 strain detected in July 2015 in the stool sample from an adult with acute gastroenteritis in Brazil. ABSTRACT Human noroviruses are the most common cause of nonbacterial acute gastroenteritis worldwide. We report here the nearly complete genome sequence (7,551 nucleotides) of a human norovirus GII.P17-GII.17 strain detected in July 2015 in the stool sample from an adult with acute gastroenteritis in Brazil.
The Limitations of Being a Copycat: Learning Golf Putting Through Auditory and Visual Guidance

The goal of this study was to investigate whether sensory cues carrying the kinematic template of expert performance (produced by mapping movement to a sound or visual cue) displayed prior to and during movement execution can enhance motor learning of a new skill (golf putting) in a group of novices. We conducted a motor learning study on a sample of 30 participants who were divided into three groups: a control, an auditory guide and visual guide group. The learning phase comprised of two sessions per week over a period of 4 weeks, giving rise to eight sessions. In each session participants made 20 shots to three different putting distances. All participants had their measurements taken at separate sessions without any guidance: baseline, transfer (different distances) and retention 2 weeks later. Results revealed a subtle improvement in goal attainment and a decrease in kinematic variability in the sensory groups (auditory and visual) compared to the control group. The comparable changes in performance between the visual and auditory guide groups, particularly during training, supports the idea that temporal patterns relevant to motor control can be perceived similarly through either visual or auditory modalities. This opens up the use of auditory displays to inform motor learning in tasks or situations where visual attention is otherwise constrained or unsuitable. Further research into the most useful template actions to display to learners may thus still support effective auditory guidance in motor learning.
Potential energy surfaces of actinide nuclei from a multidimensional constrained covariant density functional theory: Barrier heights and saddle point shapes

The potential energy surfaces of actinide nuclei in the (beta(20), beta(22), beta(30)) deformation space are obtained from a multidimensional constrained covariant density functional theory. With this newly developed theory, we are able to explore the importance of the triaxial and octupole shapes simultaneously along the whole fission path. It is found that aside from the octupole deformation, the triaxiality also plays an important role upon the second fission barriers. Both the outer and the inner barriers are lowered by the triaxial deformation compared with axially symmetric results. This lowering effect for the reflection-asymmetric outer barrier is 0.5 similar to 1 MeV, accounting for 10%similar to 20% of the barrier height. With the inclusion of the triaxial deformation, a good agreement with the data for the outer barriers of actinide nuclei is achieved.
C−X (X = N, O) Cross-Coupling Reactions Catalyzed by Copper-Pincer Bis(N-Heterocyclic Carbene) Complexes

Over the last two decades, N-heterocyclic carbene (NHC)–copper catalysts have received considerable attention in organic synthesis. Despite the popularity of copper complexes containing monodentate NHC ligands and recent development of poly(NHC) platforms, their application in C–C and C–heteroatom cross-coupling reactions has been limited. Recently, we reported an air-assisted Sonogashira-type cross-coupling catalyzed by well-defined cationic copper-pincer bis(NHC) complexes. Herein, we report the application of these complexes in Ullmann-type C–X (X = N, O) coupling of azoles and phenols with aryl halides in a relatively short reaction time. In contrast to other well-defined copper(I) catalysts that require an inert atmosphere for an efficient C–X coupling, the employed Cu(I)-pincer bis(NHC) complexes provide good to excellent yields in air. The air-assisted reactivity, unlike that in the Sonogashira reaction, is also affected by the base employed and the reaction time. With Cs2CO3 and K2CO3, the oxygen-generated catalyst is more reactive than the catalyst formed under argon in a short reaction time (12 h). However, the difference in reactivity is compromised after a 24 h reaction with K2CO3. The efficient pincer Cu-NHC/O2/Cs2CO3 system provides great to excellent cross-coupling yields for electronically diverse aryl iodides and imidazole derivatives. The catalyst scope is controlled by a balance between nucleophilicity, coordinating ability, and the steric hindrance of aryl halides and N-/O-nucleophiles.
Spring-Electrical Models For Link Prediction

We propose a link prediction algorithm that is based on spring-electrical models. The idea to study these models came from the fact that spring-electrical models have been successfully used for networks visualization. A good network visualization usually implies that nodes similar in terms of network topology, e.g., connected and/or belonging to one cluster, tend to be visualized close to each other. Therefore, we assumed that the Euclidean distance between nodes in the obtained network layout correlates with a probability of a link between them. We evaluate the proposed method against several popular baselines and demonstrate its flexibility by applying it to undirected, directed and bipartite networks.
Extracting CCGs for Plan Recognition in RTS Games

Domain-configurable planning and plan recognition approaches such as Hierarchical Task Network and Combinatory Categorial Grammar-based (CCG) planning and plan recognition require a domain expert to handcraft a domain definition for each new domain in which we want to plan or recognize. This paper describes an approach to automatically extracting these definitions from plan traces acquired from Real-Time Strategy (RTS) game replays. Specifically, we present a greedy approach to learning CCGs from sets of plan trace, goal pairs that extends prior work on learning CCGs for plan recognition. We provide an empirical evaluation of our learning algorithm in the challenging domain of RTS games. Our results show that we are able to learn a CCG that represents larger sequences of actions, and use them for plan recognition. Our results also demonstrate how scaling the size of the plan traces affects the size of the learned representation, which paves the road for interesting future work.
Query Segmentation via RNNs Encoder-Decoder Framework

Query segmentation is the task of segmenting a Web query into adjacent phrases, typically keywords that form noun phrases or concepts that are relevant to the search task. In this paper, we describe a research study and some preliminary experiment results for query segmentation via a Recurrent Neural Network encoder-decoder framework on a public benchmark dataset (Webis-QSeC-10). The resulting segmented queries can be used for several downstream tasks such as improving the performance of relevance ranking in search, better understanding of the query intent, and suggesting queries for auto-completion.
Improving reliability and latency of Wireless Sensor Networks using Concurrent Transmissions

Abstract Concurrent Transmissions (CT) based flooding appears as a highly reliable and low latency mechanism to achieve source-to-sink communication of packets within a Wireless Sensor Network (WSN). CT are usually misunderstood, since they are mainly analyzed in the baseband domain. A comprehensive analysis, including the effects of the carrier, demonstrates that they cannot work in simple phase-modulated communication systems due to the beating effect. In contrast, non-coherent frequency receivers offer a very robust behavior. CT applicability in IEEE 802.15. 4 is mainly because of two factors: transmissions are usually demodulated as non-coherent MSK (as opposed to coherent OQPSK) and the Direct Sequence Spread Spectrum (DSSS), that helps minimizing the error rate.
DAPA: The WSDM 2019 Workshop on Deep Matching in Practical Applications

Matching between two information objects is the core of many different information retrieval (IR) applications including Web search, question answering, and recommendation. Recently, deep learning methods have yielded immense success in speech recognition, computer vision, and natural language processing, significantly advancing state-of-the-art of these areas. In the IR community, deep learning has also attracted much attention, and researchers have proposed a large number of deep matching models to tackle the matching problem for different IR applications. Despite the fact that deep matching models have gained significant progress in these areas, there are still many challenges to be addressed when applying these models to real IR scenarios. In this workshop, we focus on the applicability of deep matching models to practical applications. We aim to discuss the issues of applying deep matching models to production systems, as well as to shed some light on the fundamental characteristics of different matching tasks in IR. website : https://wsdm2019-dapa.github. io/index.html
Listwise vs Pagewise: Towards Better Ranking Strategies for Heterogeneous Search Results

As heterogeneous verticals account for more and more in search engines, users' preference of search results is largely affected by their presentations. Apart from texts, multimedia information such as images and videos has been widely adopted as it makes the search engine result pages (SERPs) more informative and attractive. It is more proper to regard the SERP as an information union, not separate search results because they interact with each other. Considering these changes in search engines, we plan to better exploit the contents of search results displayed on SERPs through deep neural networks and formulate the pagewise optimization of SERPs as a reinforcement learning problem.
Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network

The spatial characteristics of cracks are significant indicators to assess and evaluate the health of existing buildings and infrastructures. However, the current manual crack description method is time consuming and labor consuming. To improve the efficiency of crack inspection, advanced computer vision‐based techniques have been utilized to detect cracks automatically at image level and grid‐cell level. But existing crack detections are of (high specificity) low generality and inefficient, in terms that conventional approaches are unable to identify and measure diverse cracks concurrently at pixel level. Therefore, this research implements a novel deep learning technique named fully convolutional network (FCN) to address this problem. First, FCN is trained by feeding multiple types of cracks to semantically identify and segment pixel‐wise cracks at different scales. Then, the predicted crack segmentations are represented by single‐pixel width skeletons to quantitatively measure the morphological features of cracks, providing valuable crack indicators for assessment in practice, such as crack topology, crack length, max width, and mean width. To validate the prediction, the predicted segmentations are compared with recent advanced method for crack recognition and ground truth. For crack segmentation, the accuracy, precision, recall, and F1 score are 97.96%, 81.73%, 78.97%, and 79.95%, respectively. For crack length, the relative measurement error varies from −48.03% to 177.79%, meanwhile that ranges from −13.27% to 24.01% for crack width. The results show that FCN is feasible and sufficient for crack identification and measurement. Although the accuracy is not as high as CrackNet because of three types of errors, the prediction has been increased to pixel level and the training time has been dramatically decreased to several per cents of previous methods due to the novel end‐to‐end structure of FCN, which combines typical convolutional neural networks and deconvolutional layers.
An NMF Solution to the TTC 2018 Social Media Case

This paper presents a solution to the Social Media benchmark, used as live contest at the Transformation Tool Contest (TTC) 2018. We demonstrate how the implicit incrementalization abilities of NMF can be used to automatically obtain an incremental algorithm for the presented case. We evaluate the solution against the reference solution and show that the incremental change propagation is faster than batch recomputation by multiple orders of magnitude for large models.
Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Computer vision‐based displacement measurement for structural monitoring has grown popular. However, tracking natural low‐contrast targets in low‐illumination conditions is inevitable for vision sensors in the field measurement, which poses challenges for intensity‐based vision‐sensing techniques. A new edge‐enhanced‐matching (EEM) technique improved from the previous orientation‐code‐matching (OCM) technique is proposed to enable robust tracking of low‐contrast features. Besides extracting gradient orientations from images as OCM, the proposed EEM technique also utilizes gradient magnitudes to identify and enhance subtle edge features to form EEM images. A ranked‐segmentation filtering technique is also developed to post‐process EEM images to make it easier to identify edge features. The robustness and accuracy of EEM in tracking low‐contrast features are validated in comparison with OCM in the field tests conducted on a railroad bridge and the long‐span Manhattan Bridge. Frequency domain analyses are also performed to further validate the displacement accuracy.
A Content-based Recommender System for E-commerce Offers and Coupons

Reward-based e-commerce companies expose thousands of online o ers and coupons every day. Customers who signed up for online coupon services either receive a daily digest email with selected o ers or select speci c o ers on the company website front-page. High-quality online discounts are selected and delivered through these two means by applying a manual process that involves a team of experts who are responsible for evaluating recency, product popularity, retailer trends, and other business-related criteria. Such a process is costly, time-consuming, and not customized on users’ preferences or shopping history. In this work, we propose a contentbased recommender system that streamlines the coupon selection process and personalizes the recommendation to improve the clickthrough rate and, ultimately, the conversion rates. When compared to the popularity-based baseline, our content-based recommender system improves F-measures from 0.21 to 0.85 and increases the estimated click-through rate from 1.20% to 7.80%. The experimental system is currently scheduled for A/B testing with real customers.
Investment Recommendation System for Low-Liquidity Online Peer to Peer Lending (P2PL) Marketplaces

Online P2PL systems allow lending and borrowing between peers without the need for intermediaries such as banks. Convenience and high rate of returns have made P2PL systems very popular. Recommendation systems have been developed to help lenders make wise investment decisions, lowering the chances of overall default. However, P2PL marketplace suffers from low financial liquidity, i.e., loans of different grades are not always available for investment. Moreover, P2PL investments are long term (usually a few years), hence, incorrect investment cannot be liquidated easily. Overall, the state-of-the-art recommendation systems do not account for the low market liquidity and hence, can lead to unwise investment decisions. In this paper we remedy this shortcoming by building a recommendation framework that builds an investment portfolio, which results in the highest return and the lowest risk along with a statistical measure of the number of days required for the amount to be completely funded. Our recommendation system predicts the grade and number of loans that will appear in the future when constructing the investment portfolio. Experimental results show that our recommendation engine outperforms the current state-of-the-art techniques. Our recommendation system can increase the probability of achieving the highest return with the lowest risk by ~ 69%.