Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional characterization of pheromone receptor candidates in codling moth Cydia pomonella (Lepidoptera: Tortricidae)

Sex pheromones serve a critical role in Lepidopterans finding mates. Male moths perceive and react to sex pheromones emitted by conspecific females through a delicate pheromone communication system. Pheromone receptors (PRs) are the key sensory elements at the beginning of that process. The codling moth (Cydia pomnonella) is an important pome fruit pest globally and a serious invasive species in China. Pheromone‐based techniques have been used successfully in monitoring and controlling this species. We conducted ribonucleic acid sequencing analysis of the codling moth antennal transcriptome and identified 66 odorant receptors (ORs) in a population from Xinjiang province, China, of which 14 were PRs, including two novel PRs (CpomOR2e and CpomOR73). Four PRs that contain full‐length open reading frames (CpomOR1, OR2a, OR5, OR7) and four PRs with ligands that have not been reported previously (CpomOR1, OR2a, OR5, OR7) were selected to deorphanize in the heterologous Xenopus oocyte expression system. Specifically, we found that CpomOR2a and CpomOR5 responded to (E,E)‐8, 10‐dodecadien‐1‐yl acetate (codlemone acetate). Furthermore, CpomOR5 (EC50 = 1.379 × 10−8 mol/L) was much more sensitive to codlemone acetate than CpomOR2a (EC50 = 1.663 × 10−6 mol/L). Since codlemone acetate is an important component of C. pomonella sex pheromone, our results improve the current understanding of pheromone communication in codling moths and will be helpful for the development of pest management strategies.
Bilateral Asymmetries Assessment in Elite and Sub-Elite Male Futsal Players

This study aimed to investigate morphological, functional, and neuromuscular asymmetries on futsal players’ lower limbs at different competitive levels. Sixteen male elite futsal players from the Spanish National Futsal League and thirteen male sub-elite futsal players from the third division participated in this study. Morphological asymmetry was assessed through bioelectrical impedance (fat-mass (g and %) and lean-mass (g)). Functional asymmetry was assessed by means of a 20-s static unipedal balance test. Finally, neuromuscular asymmetry was assessed using tensiomiography tests on both the rectus femoris (RF) and biceps femoris (BF) of each participant. The three tests conducted did not reveal significant bilateral asymmetries in elite players. On the other hand, sub-elite players showed significant bilateral asymmetry in fat-mass percentage between dominant and non-dominant limbs (+6%; CI95%: 1 to 11; ES: 0.88; p = 0.019). They also showed higher bilateral asymmetry in the delay time of the RF (+13%; CI95%: 7 to 21; ES: 1.3; p < 0.05). However, the static unipedal balance test (p > 0.05) did not evidence asymmetries regardless of the level of the participants. Elite futsal players do not develop bilateral asymmetries in lower limbs in the studied parameters. On the contrary, sub-elite players are likely to develop morphological and neuromuscular asymmetries between their dominant leg and non-dominant leg.
Mass spectrometry imaging and LC/MS reveal decreased cerebellar phosphoinositides in Niemann-Pick type C1-null mice[S]

Niemann-Pick disease type C1 (NPC1) is a lipid storage disorder in which cholesterol and glycosphingolipids accumulate in late endosomal/lysosomal compartments because of mutations in the NPC1 gene. A hallmark of NPC1 is progressive neurodegeneration of the cerebellum as well as visceral organ damage; however, the mechanisms driving this disease pathology are not fully understood. Phosphoinositides are phospholipids that play distinct roles in signal transduction and vesicle trafficking. Here, we utilized a consensus spectra analysis of MS imaging data sets and orthogonal LC/MS analyses to evaluate the spatial distribution of phosphoinositides and quantify them in cerebellar tissue from Npc1-null mice. Our results suggest significant depletion of multiple phosphoinositide species, including PI, PIP, and PIP2, in the cerebellum of the Npc1-null mice in both whole-tissue lysates and myelin-enriched fractions. Additionally, we observed altered levels of the regulatory enzyme phosphatidylinositol 4-kinase type 2α in Npc1-null mice. In contrast, the levels of related kinases, phosphatases, and transfer proteins were unaltered in the Npc1-null mouse model, as observed by Western blot analysis. Our discovery of phosphoinositide lipid biomarkers for NPC1 opens new perspectives on the pathophysiology underlying this fatal neurodegenerative disease.
In Children with Provoked Venous Thromboembolism, Increasing Plasma Coagulability during the First 3 Months Postdiagnosis is Prognostic of Recurrence

Abstract Prognostic factors for venous thromboembolism (VTE) recurrence following provoked VTE are largely unknown. Using the Clot Formation and Lysis (CloFAL) assay, single institutional research has shown overall improvement in acute hypercoagulability during the first 3 months postpediatric VTE, yet a rise in plasma coagulability in a subgroup of patients. We sought to define the incidence of rise in coagulability during the first 3 months post-provoked VTE, to investigate its relationship with elevated D-dimer, and to test the hypothesis that a marked rise in coagulability is independently prognostic of VTE recurrence. CloFAL and D-dimer assays were performed on plasma at 4 to 6 weeks and 3 months post-VTE in the Johns Hopkins pediatric VTE cohort and National Institutes of Health-sponsored Kids-DOTT trial. Associations of VTE recurrence with D-dimer and CloFAL assay measures were evaluated via logistic regression. Eighty-seven patients were included. Median follow-up was 1 year. Complete veno-occlusion was determined in 12% at 6 weeks. During the first 3 months post-VTE, a marked rise in coagulability was observed by CloFAL assay in 17% of patients, while D-dimer was elevated in 21%. Recurrent VTE occurred in 10% of patients. CloFAL assay, but not D-dimer, was associated with recurrence (odds ratio [OR] 5.87, 95% confidence interval [95% CI], 1.34–25.8]). After adjustment for veno-occlusion, patients with a marked rise in coagulability by CloFAL assay had a 10-fold increased risk of recurrent VTE (OR 10.33 [95% CI, 1.83–58.19]). Future work should seek to elucidate the mechanisms underlying a rise in plasma coagulability following provoked VTE and to substantiate its prognostic utility for recurrent VTE.
Tuning the Properties of Graphdiyne through Introducing Electron Withdrawing/Donating Groups.

Herein, properties of graphdiyne (GDY) such as energy gap, conductivity, morphology, and affinity to alkalis metals are efficiently adjusted through including electron withdrawing/donating groups. The experimental and theoretical results indicate that the push-pull electron ability and size differences of groups play a key role on the property adjusting of as-prepared GDY derivatives named MeGDY, HGDY, and CNGDY. Typically, cyano group with electron withdrawing ability and methyl group with electron donating characteristic have successfully reduced the band gaps and increased the conductivity of the GDY network within an obvious range. Meanwhile, the inclusion of cyano and methyl groups affects the aggregation of GDY, thus providing a higher number of micropores and specific surface area. Moreover, the fascinating properties of these groups endow the original GDY additional advantages in following aspects: the stronger electronegativity of cyano groups increase the affinity of GDY frameworks to lithium atoms; the larger atomic volume of methyl groups increases the interlayer distance and provide more storage space and diffusion tunnels. These results provide an inspiring idea to design and synthesize novel functionalized GDY based materials for high stability, high conductivity and controllable functionality.
Growth rates and age-size relationships of tropical wet forest trees in Costa Rica

ABSTRACT Diameter growth rates and age-size relationships are reported for 45 abundant tree species and one liana in tropical wet forest at La Selva, Costa Rica. Thirteen-year increments in each species were analysed using growth simulation, a stochastic technique which projects growth trajectories. Median growth rates ranged from 0.35 mm yr-1 (Anaxagorea crassipetala) to 13.41 mm yr-1) (Stryphnodendron excelsum). Maximum ranges ranged from 0.95 mm yr-1 (Quararibea brac-teolosa) to 14.62 mm yr-1 (Hernandia didymanthera). Minimum rates ranged from zero growth (Capparis pittieri, Colubrina spinosa, Doliocarpus spp.) to 7.45 mm yr-1 (Stryphno dendron excelsum). Projected lifespan (from 100 mm dbh to the maximum dbh for the species) varied from 52 years (Anaxagorea crassipetala, Guatteria inuncta) to 442 years (Carapa guianensis). The mean longevity among the 45 tree species studied is 190 years. Four main patterns of growth behaviour are recognized, based upon longevity and growth rates: (1) understorey species have slow maximum growth rates and short lifespans; (2) shade-tolerant subcanopy trees live around twice as long as understorey trees and grow at approxi-mately the same maximum rates; (3) canopy and subcanopy trees that are shade-tolerant but respond opportunistically to increased light levels have long lifespans and fast maximum growth rates; (4) shade-intolerant canopy and subcanopy species are short-lived and have fast maximum growth rates. Understorey species intergrade with shade-tolerant subcanopy species in terms of growth behaviour; shade-tolerant subcanopy species with opportunistic, shade-tolerant species; and opportunistic, shade-tolerant with shade-intolerant species. Intraspecific variation in growth rates is lower in short-lived trees (understorey species with uniformly slow growth and shade-intolerant species with uniformly rapid growth) than in the two long-lived groups. These patterns are discussed in the context of tree ecophysiology and forest light environments.
A Wireless Angle and Position Tracking Concept for Live Data Control of Advanced, Semi-Automated Manufacturing Processes

Despite recent industrial automation advances, small series production still requires a considerable amount of manual work, and training, and monitoring of workers is consuming a significant amount of time and manpower. Adopting live monitoring of the stages in manual production, along with the comprehensive representation of production steps, may help resolve this problem. For ergonomic live support, the overall system presented in this paper combines localization, torque control, and a rotation counter in a novel approach to monitor of semi-automated manufacturing processes. A major challenge in this context is tracking, especially hand-guided tools, without the disruptions and restrictions necessary with rigid position encoders. In this paper, a promising measurement concept involving wireless wave-based sensors for close-range position tracking in industrial surroundings is proposed. By using simple beacons, the major share of processing is transferred to fixed nodes, allowing for reduced hardware size and power consumption for the wireless mobile units. This requires designated localization approaches relying on only relative phase information, similar to the proposed Kalman-filter-based-beam-tracking approach. Measurement results show a beam-tracking accuracy of about 0.58∘ in azimuth and 0.89∘ in elevation, resulting in an overall tracking accuracy of about 3.18 cm.
The Functional Spatio-Temporal Statistical Model with Application to O3 Pollution in Beijing, China

In recent years, with rapid industrialization and massive energy consumption, ground-level ozone (O3) has become one of the most severe air pollutants. In this paper, we propose a functional spatio-temporal statistical model to analyze air quality data. Firstly, since the pollutant data from the monitoring network usually have a strong spatial and temporal correlation, the spatio-temporal statistical model is a reasonable method to reveal spatial correlation structure and temporal dynamic mechanism in data. Secondly, effects from the covariates are introduced to explore the formation mechanism of ozone pollution. Thirdly, considering the obvious diurnal pattern of ozone data, we explore the diurnal cycle of O3 pollution using the functional data analysis approach. The spatio-temporal model shows great applicational potential by comparison with other models. With application to O3 pollution data of 36 stations in Beijing, China, we give explanations of the covariate effects on ozone pollution, such as other pollutants and meteorological variables, and meanwhile we discuss the diurnal cycle of ozone pollution.
Studying Onboarding in Distributed Software Teams: A Case Study and Guidelines

Many companies have turned towards globally distributed software development in their quest for access to more development capacity. This paper investigates how a company onboarded distributed teams in a global project, and report experience on how to study such distributed projects. Onboarding is the process of helping new team members adapt to the existing team and ways of working. The goal of the studied onboarding program was to integrate Portuguese developers into two existing Norwegian teams. Further, due to the growing trend in utilizing globally distributed projects, and the challenge of conducting studies in distributed organizations, it is crucial to find good practices for researching such projects. We collected qualitative data from interviews, observations, Slack conversations and documents, and quantitative data on Slack activity. We report experiences on different onboarding practices and techniques, and we suggest guidelines to help other researchers conduct qualitative studies in globally distributed projects.
Learning Graph Neural Networks with Deep Graph Library

Learning from graph and relational data plays a major role in many applications including social network analysis, marketing, e-commerce, information retrieval, knowledge modeling, medical and biological sciences, engineering, and others. In the last few years, Graph Neural Networks (GNNs) have emerged as a promising new supervised learning framework capable of bringing the power of deep representation learning to graph and relational data. This ever-growing body of research has shown that GNNs achieve state-of-the-art performance for problems such as link prediction, fraud detection, target-ligand binding activity prediction, knowledge-graph completion, and product recommendations. The objective of this tutorial is twofold. First, it will provide an overview of the theory behind GNNs, discuss the types of problems that GNNs are well suited for, and introduce some of the most widely used GNN model architectures and problems/applications that are designed to solve. Second, it will introduce the Deep Graph Library (DGL), a new software framework that simplifies the development of efficient GNN-based training and inference programs. To make things concrete, the tutorial will provide hands-on sessions using DGL. This hands-on part will cover both basic graph applications (e.g., node classification and link prediction), as well as more advanced topics including training GNNs on large graphs and in a distributed setting. In addition, it will provide hands-on tutorials on using GNNs and DGL for real-world applications such as recommendation and fraud detection.
Social Attentive Network for Live Stream Recommendation

Live streaming platforms not only provide live videos but also allow social interactions between viewers via real-time chatting. However, none of existing research has studied the social impact for recommending live streams. In this work, we formulate a new personalized recommendation problem by factoring in both video and social contents (chats). Accordingly, we 1) design a new attention network ANSWER to identify viewers’ attention on video and social contents, and 2) rank the channels based on the attentive features. We collect a real dataset from Twitch for evaluation. The experimental results manifest that ANSWER outperforms baselines by at least 26.6% in terms of NDCG@5.
Towards Controllable Explanation Generation for Recommender Systems via Neural Template

It has been commonly agreed that the explanation associated with recommendation can be effective in increasing the recommender systems (RS)’s transparency and thus users’ satisfaction and acceptance. Among the various types of explanation in RS, the commonly used textual explanation can be roughly classified into two categories, i.e., template-based and generation-based. As for the former, the fixed template may lose flexibility, while, though the latter may enrich the explanation, it may produce less useful content due to the lack of controllability. In this work, we combine the advantages of the two types of method by developing a neural generation approach named Neural Template (NETE) whose explanations are not only flexible but also controllable and useful. Our human evaluation results confirm that the explanations from our model are perceived helpful by users. Furthermore, our case study illustrates that the explanation generation process is controllable. To demonstrate the controllability of our model, we present a demo that can be easily viewed on a Web browser.
Experiences from an Interpretative Case Study of Innovative Public Procurement of Digital Systems in the Norwegian Public Sector

We report findings from a case study of innovative public procurement of digital systems. The study uses interviews and workshops as data. Our findings show that 1) the procurer role is changing because of new innovation requirements, 2) Procurement regimes promote buying off-the-shelf products, not building software incrementally, 3) Collaboration among actors is challenging due to different agendas. These findings show that innovating using procurements can be a challenge.
Contour Accentuation for Transfer Learning-Based Ship Recognition Method

This study proposes a ship recognition system which includes intelligent bridge piers and a ship recognition server. The ship recognition server can analyse the contour features of ship images from intelligent bridge piers by the proposed contour accentuation method; the ship image with contour accentuation can be adopted as the inputs of transfer learning-based neural network for ship classification by the proposed transfer learning-based ship recognition method. In practical experiments, the results showed that the proposed transfer learning-based ship recognition method with contour accentuation can obtain higher accuracy, and the accuracy of the proposed method was 97.79%.
DNA Replication in Relation to S Phase Duration in Six Angiosperm Annual Species

Using fiber autoradiography, replicon sizes and DNA replication fork rates were studied in 6 diploid angiosperm annual species with different 2C DNA values and S phase durations (Ts). The only correlations were found between Ts and Rs/Ts ratios (positive correlation, p=0.001) and between 2C DNA content and the rate of fork movement (negative correlation, p=0.05). The observations raise the possibility of a connection between the mechanisms involved in DNA chain elongation and the amount of repetitive sequences and between Ts and the functional arrangements of replicons.
Influence of Platform Characteristics on Purchase Intention in Social Commerce: Mechanism of Psychological Contracts

Social commerce is an emerging platform that entails different social features to attract the attention of online consumers. Borrowing insights from the psychological contract theory, this study examines the characteristics of social commerce platforms, such as platform interactivity and rating and reviews that influence the consumers’ purchase intentions. An online survey of 430 consumers was conducted on a popular social commerce site in China. Partial least squares structural equation modeling and fuzzy sets qualitative comparative analysis techniques are used to analyze this mediation model. The results reveal the positive relationships between (1) psychological contract and purchase intention; (2) platform interactivity and relational contract; and (3) rating and reviews and transactional contract. Besides, partial mediating effects of relational contract, on the relationship between platform interactivity and purchase intention, and transactional contract, on the relationship between rating and reviews and purchase intention, are also observed. Moreover, fuzzy sets qualitative comparative analysis validates the robustness of platform characteristics with psychological contracts and purchase intentions. Overall findings suggest that the platform characteristics have the potential to transform consumers’ purchase intentions through psychological contracts. The study provides new insights on consumers' behavior in social commerce milieus, along with its theoretical and practical implications.
The State of the Art on Secure Software Engineering: A Systematic Mapping Study

Secure Software Development (SSD) is becoming a major challenge, due to the increasing complexity, openness and extensibility of Information and Communication Technologies (ICTs). These make the overall security requirements analysis very difficult. Many techniques have been theoretically developed, however, there is a lack of empirical evidence of its application in building secure software system. A Systematic Mapping Study (SMS) has been conducted in this paper to examine the existence of software security frameworks, models and methods. In total, we selected 116 primary studies. After examining the selected studies, we identified 37 Secure Software Engineering (SSE) paradigms/frameworks/models. The results show that the most frequently used SSE frameworks/models are "Microsoft Software Development Life Cycle (MS-SDL)", "Misuse case modeling", "Abuse case modeling", "Knowledge Acquisition for Automated Specification", "System Security Engineering-Capability Maturity Model (SSE-CMM)" and "Secure Tropos Methodology". This work will help organizations in the development of software to better understand existing security initiatives used in the development of secure software. It can also provide researchers with a basis for designing and developing new methods of software security and identifying new axis of research.
A Multiple Case Study of Artificial Intelligent System Development in Industry

There is a rapidly increasing amount of Artificial Intelligence (AI) systems developed in recent years, with much expectation on its capacity of innovation and business value generation. However, the promised value of AI systems in specific business contexts might not be understood, and further integrated into the development processes. We wanted to understand how software engineering processes and practices can be applied to develop AI systems in a fast-faced, business-driven manner. As the first step, we explored contextual factors of AI development and the connections between AI developments to business opportunities. We conducted 12 semi-structured interviews in seven companies in Brazil, Norway and Southeast Asia. Our investigation revealed different types of AI systems and different AI development approaches. However, it is common that business opportunities involving with AI systems are not validated and there is lack of business-driven metrics that guide the development of AI systems. The findings have implications for future research on business-driven AI development and supporting tools and practices.