Miracle-dz/UniFAR-FTUs huggingface dataset:
Candidate genes for tick resistance in cattle: a systematic review combining post-GWAS analyses with sequencing data

ABSTRACT Rhipicephalus microplus causes huge losses in cattle. Host genetic background greatly affects the immune efficiency in resistance or susceptibility to tick infestation, which is one of the many factors that play a role on that trait. We performed a systematic review of genome-wide association studies (GWAS) for tick resistance in cattle resulting in 1353 candidate genes for post-GWAS analyses. From those, genes showing possible structural variants from the bovine genome were classified by the Variant Effect Predictor from Ensembl. Ninety-two candidate genes showed potential structural variants in 5′ UTR and coding region and were used for functional annotation. Enriched biological processes (e.g. regulation of eosinophil chemotaxis, RIG-I signalling pathway and monocyte differentiation) and candidate genes (e.g. DAPK2, PUM1, ACIN1, INPP5D) linked with immune system function were identified and thus associated with tick resistance. Besides, gene-transcription factors (TFs) networks were obtained from TFs associated with immune system (FOXO3, PPARG, STAT3, NFKB1, GATA3 and ARNT) and the candidate genes associated with tick resistance in cattle highlighted (e.g. OR4L1, PNP, LRRIQ1, GIMAP8, MYO6, MEP1A and LRFN2). Thus, promising candidate genes with a possible functional role for tick resistance in cattle are presented for further in vitro and/or in vivo analyses.
A Lip Reading Method Based on 3D Convolutional Vision Transformer

Lip reading has received increasing attention in recent years. It judges the content of speech based on the movement of the speaker’s lips. The rapid development of deep learning has promoted progress in lip reading. However, due to lip reading needs to process the information of continuous video frames, it is necessary to consider the correlation information between adjacent images and the correlation between long-distance images. Moreover, lip reading recognition mainly focuses on the subtle changes of lips and their surrounding environment, and it is necessary to extract the subtle features of small-size images. Therefore, the performance of machine lip reading is generally not high, and the research progress is slow. In order to improve the performance of machine lip reading, we propose a lip reading method based on 3D convolutional vision transformer (3DCvT), which combines vision transformer and 3D convolution to extract the spatio-temporal feature of continuous images, and take full advantage of the properties of convolutions and transformers to extract local and global features from continuous images effectively. The extracted features are then sent to a Bidirectional Gated Recurrent Unit (BiGRU) for sequence modeling. We proved the effectiveness of our method on large-scale lip reading datasets LRW and LRW-1000 and achieved state-of-the-art performance.
Evaluation of Mesoporous TiO2 Layers as Glucose Optical Sensors

Porous materials are currently the basis of many optical sensors because of their ability to provide a higher interaction between the light and the analyte, directly within the optical structure. In this study, mesoporous TiO2 layers were fabricated using a bottom-up synthesis approach in order to develop optical sensing structures. In comparison with more typical top-down fabrication strategies where the bulk constitutive material is etched in order to obtain the required porous medium, the use of a bottom-up fabrication approach potentially allows increasing the interconnectivity of the pore network, hence improving the surface and depth homogeneity of the fabricated layer and reducing production costs by synthesizing the layers on a larger scale. The sensing performance of the fabricated mesoporous TiO2 layers was assessed by means of the measurement of several glucose dilutions in water, estimating a limit of detection even below 0.15 mg/mL (15 mg/dL). All of these advantages make this platform a very promising candidate for the development of low-cost and high-performance optical sensors.
The effects of Monetary Policy on Capital Flows: A Meta-Analysis

We investigate whether central banks are able to attract or redirect capital flows, by bringing together the entire empirical literature into the first quantitative meta-analysis on the subject. We dissect policy effects by the type of flow and by the origin of the monetary shock. Further, we assess whether policy effects depend on factors that drive investors to either search for yields or fly to safety. Our findings indicate a mean effect size of inflows in the amount of 0.09% of quarterly GDP in response to either a 100 basis point (bp) increase in the domestic policy rate or a 100bp reduction in the external rate. However, the effect size under a random effect specification is much lower (0.01%). Factors that significantly attract inflows include foreign exchange reserves, output growth, and financial openness, while factors that deter flows include foreign debt, capital controls, and departures from the uncovered interest rate parity. Also, both local and global risks matter (global risks exerting a larger pressure). Finally, we shed light on differences across the different types of flows: banking flows being the most responsive to monetary policy, while foreign direct investment being the least responsive.
Increased Floral Rewards due to Local Adaptation Drives Plant Ecological Speciation via Learned Preferences of Pollinators

In animal-pollinated plants, the growth environment and pollination environment are two important agents of natural selection. However, their simultaneous effects on plant speciation remain underexplored. Here, we report a theoretical finding that if plants’ local adaptation to the growth environment increases their floral rewards for pollinators, it can strongly facilitate ecological speciation in plants. We consider two evolving plant traits, vegetative and floral signal traits, in a population genetic model for two plant populations under divergent selection from different growth environments. The vegetative trait determines plants’ local adaptation. Locally adapted plants reward pollinators better than maladapted plants. By associative learning, pollinators acquire learned preferences for floral signal traits expressed by better-rewarding plants. If pollinators’ learned preferences become divergent between populations, floral signal divergence occurs and plants develop genetic associations between vegetative and floral signal traits, leading to ecological speciation via a two-allele mechanism. Interestingly, speciation is contingent on whether novel floral signal variants arise before or after plant populations become locally adapted to the growth environment. Our results suggest that simultaneous selection from growth and pollination environments might be important for the ecological speciation of animal-pollinated plants.
Determinants of Purchase Intention among Live Streaming Shoppers: The Roles of Technology Readiness, Social Presence, and Perceived Value

ABSTRACT The potential for business opportunities live streaming services has attracted attention from individuals and businesses. Limited studies have explored the factors associated with the purchase intention of shoppers in live-streaming contexts. The purpose of this study was to examine whether social media technology readiness, perceived value, and social presence influence shoppers’ purchase intention in live-streaming contexts. The results indicated that social media technology readiness and perceived value had a significant and positive effect on purchase intention. A thorough discussion and future research suggestions are provided to obtain a better understanding of the behavioral intention of shoppers in live-streaming contexts.
Seepage Characteristics Study of Single Rough Fracture Based on Numerical Simulation

Fracture seepage is an important aspect of groundwater research, but due to the closure of fractures and the randomness of wall surface roughness, it is a challenge to carry out relevant research. Numerical simulation serves as a good way to solve this problem. As such, the water flow in single fracture with different shapes and densities of roughness elements (various bulges/pits on fracture wall surfaces) on wall surface was simulated by Fluent software. The results show that, in wider rough fractures, the flow rate mainly depends on fracture aperture, while, in narrow and close rough fracture medium, the surface roughness of fracture wall is the main factor of head loss of seepage; there is a negative power exponential relation between the hydraulic gradient index and the average fracture aperture, i.e., with increase of rough fracture aperture, both the relative roughness of fracture and the influence of hydraulic gradient decrease; in symmetrical-uncoupled rough fractures, there is a super-cubic relation between the discharge per unit width and average aperture; the rough fracture permeability coefficient K is not a constant which is affected by the scale effect and the density of roughness elements. Results found provide further understanding of rough fracture seepage.
Structural and Morphological Characterization of the Chromium-doped β-Spodumene to Ceramic Pigment

The structural and morphological properties of the novel chromium-doped β-spodumene based ceramic pigment were evaluated aiming to study its chemical and thermal stability in industrial applications. The pigment samples were synthesized by the proteic sol-gel method using gelatin as a ligand. X-ray diffraction (XRD) and scanning electron microscopy (SEM) were performed. Rietveld refinement of experimental diffractograms and average crystallites size by the Scherrer’s method analysis were carried out. XRD results confirmed that the crystal structure of lattice corresponded to β-spodumene and the Rietveld refinement indicated the absence of structural changes with doping up to 3% of chromium. Scherrer’s method indicates that there are no trends changes in the average size of crystallites in the analyzed doping contents. SEM analysis indicated that there are no morphological changes in the chromium-doped particles. The present study concluded that structural or morphological alterations are not noticed for β-spodumene samples produced with chromium doping up to 3% by weight, and that this pigment possibly preserves the chemical and thermal stability of pure β-spodumene. The absence of changes in these properties by doping with low chromium contents were attributed to possible substitutions of Al3+ by Cr3+ ions in the β-spodumene crystal lattice.
Chemical evaluation and biological activity of bioactive compounds from Campomanesia xanthocarpa Berg

Campomanesia xanthocarpa Berg. (Myrtaceae), also known as “guabiroba”, is widely distributed in the South and Southern regions of Brazil and has been used by local families as folk medicine to treat several diseases. This study aimed to evaluate the bioactive compounds of guabiroba fruits and leaves, such as flavonoids, carotenoids, chlorophyll, total phenolics, tannins, and coumarins. The biological effect of different parts of C. xanthocarpa (leaf and pulp) was also investigated for the immunomodulatory activity on RAW 264.7 macrophages, and anti-cancer effects on H295R adrenocortical cells under different concentrations and extraction protocols. Extracts from guabiroba leaves and pulp using 70% ethanol and n-hexane as a solvent by magnetic stirring, as well as ethanolic extracts from leaves and pulp obtained by heat reflux extraction were produced. Most of the bioactive compounds were found in the leaves rather than the fruit pulp. The in vitro assays showed that the leaf extraction with hexane affected the viability assay in the adrenocortical cell line H295R, while the pulp extracted with hexane activated the macrophage cell line RAW 264.7. Therefore, C. xanthocarpa leaves and pulp have proven to affect macrophage and adrenocortical cell lines in a dose-dependent manner.
RECONFIGURING TEACHER EDUCATION IN SOUTH AFRICAN UNIVERSITIES TO ADDRESS LEARNER BEHAVIOURAL CONDUCTS IN SCHOOLS: CHANGED REALITY

"The purpose of the paper is to argue for the need to reconfigure the training of Bachelor of Education Degree student teachers as a strategy to empower them in dealing with the changed reality in secondary schools. The purpose is achieved by looking at the relevancy of the education or training offered to undergraduate teacher trainee students at universities in Eastern Cape Province of South Africa regarding learner behavioural problems being experienced in secondary schools. The paper was guided by a situated learning theory. The paper adopted a qualitative approach and a case study design. The study purposively selected 3 universities, 9 lecturers and 15 teacher trainees as participants. Data were collected through use of interviews and focus group discussions. Thematic frames were used to analyse data that were discussed concurrently with the findings. The study established a mismatch between how the teachers’ training and actual reality in schools as a challenge that needs to be addressed. The implication drawn is that failure to move with speed to reconfigure the training of initial teacher education increases frustration and tension between teachers and learners. The paper concludes that the current way of training of teachers lacks reality of how teacher trainees should deal with learner behavioural problems in schools. Universities should redesign an initial teacher education programme that capacitates teacher trainees to handle and speak to the current generation of secondary school learners. "
For Whom Do Unstructured Activities Matters? The Interaction Between Unstructured and Structured Activities in Delinquency and Cannabis Use: A National Self-Report Study

This study examines whether unstructured and structured activities interact in their association with delinquency and cannabis use. We hypothesize that unstructured activities are more strongly associated with delinquency and cannabis use for those who are less engaged in structured activities. Data are drawn from three nationally representative self-report studies conducted between 2005 and 2011 in Sweden, and include 19,644 adolescents. The results support the hypothesis that unstructured activities interact with structured activities in the association with delinquency and cannabis use. The association between unstructured activities and these outcomes is stronger for those with lower levels of structured activities. Sporting activities constitute the structured activity that most clearly interacts with unstructured activities in the association with delinquency and cannabis use.
Large-Scale Profiling on lncRNAs in Human Platelets: Correlation with Platelet Reactivity

Recently, long noncoding RNAs (lncRNAs) have been key regulators for both mRNAs and proteins in nucleated cells. However, the expression profiles of lncRNAs in non-nucleated cells such as platelets are currently unclear. In this study, we determined the expression profiles of lncRNAs in human platelets. We found that 6109 lncRNAs were expressed in human platelets. Interestingly, 338 lncRNAs were differentially expressed in hyperreactive and hyporeactive platelets. Bioinformatics’ analysis revealed that these aberrantly expressed lncRNAs might be related to platelet activity and other platelet functions. To provide a proof of concept, we measured the expression levels of PARLncRNA-1, a down-regulated lncRNA of hyperreactive platelets, in platelets from 12 patients with acute myocardial infarction and their controls. We found that the lncRNA was also significantly down-regulated in platelets from patients, which was partially reversed by treatment with aspirin a known antiplatelet drug. LncRNAs may represent a novel class of modulators for platelet functions.
Non‐alternating stochastic
 K
 ‐means based on probabilistic representation of solution space

A non-alternating (NA) form of K-means is proposed to improve the performance for the most fundamental, yet highly non-convex clustering problem. The motivation of this Letter is that the non-convex nature of K-means can be better handled by stochastic optimisation. However, the alternating update of the Lloyd's algorithm prohibits an effective stochastic optimisation. In order to fully realise the idea, a probabilistic representation is provided for the solution space of K-means, which leads to a simple yet efficient NA update. Experiments show that the proposed method outperforms the existing variants, especially for large numbers of clusters, with reasonable time complexity.
AE-Net:Adjoint Enhancement Network for Efficient Action Recognition in Video Understanding

Action recognition in video understanding is a challenging task, largely because of the complexity and difficulty in temporal modeling, making it suffer from motion information loss and misalignment of temporal attention in spatial dimensions. To overcome these difficulties, we propose a novel temporal modeling method called Adjoint Enhancement Network (AE-Net), which can fully explore clues of motion and time in the long-range structure. The AE-Net mainly consists of two new modules: the Initial Adjoint Enhancement Module (IAE-Module), which deals with shallow features; and the Global Adjoint Enhancement Module (GAE-Module), which deals with global features. With a novel mechanism of parallel spatio-temporal convolution and difference fusion, the IAE-Module is to enhance the degree of motion transformation in shallow network features, exciting the potential of motion flow and avoiding motion information loss. The GAE-Module is proposed to improve the local temporal representation in long-range structures by feeding the enhanced feature differences into a spatial cascade module with residuals to resolve the misalignment of temporal attention in the spatial dimension.The experimental results show that our AE-Net can achieve state-of-the-art results in Something-Something V1, UCF-101 and HMDB-51 datasets.
High Figure of Merit SPR Sensor Based on Raspberry-Like Silica

To address the problems that the surface plasmon resonance (SPR) sensor is not sensitive enough to detect biomolecules in the refractive index range (RI $=1.33$ –1.34) and the traditional methods to enhance the sensitivity cause the sensor poor bioaffinity, this article proposed a raspberry-like silica-assisted high figure of merit (FOM) fiber-optic SPR sensor. The finite element method results show that when the raspberry-like silica is introduced to the SPR sensor, the resonant electric field intensity on the sensor surface is 2.97 times stronger than that of SPR sensors only. Raspberry-like silica particles composed of different noble metal nanoparticles (AuNPs, PtNPs, and CuNPs) were prepared to compare and analyze the effects of different nanoparticles on the sensing performance. The sensing experiments showed that AuNPs raspberry-like silica had the most excellent performance, enabling the sensor to achieve high sensitivity of 5264.3 nm/RIU, which is 2.65 times higher than the Au film SPR sensor. The fitting calculations showed that the AuNPs raspberry-like silica-assisted SPR sensor had a high FOM of 42.9 RIU $^{-1}$ , 1.9 times more than the Au film SPR sensor. In addition, the sensor has good temperature stability, repeatability, and generality after the corresponding test. The proposed raspberry-like structure provides a new approach to obtain both high sensitivity and high FOM for the SPR sensor, which makes the sensor potential for biosensing.
De la investigación al aula: orientaciones didácticas para diseñar e implementar tareas probabilísticas en Educación Primaria.

A partir de los resultados de una investigación previa en la que se han analizado las tareas probabilísticas que propone el profesorado de Educación Primaria para promover la alfabetización probabilística, en este artículo se ofrecen diversas orientaciones didácticas con el propósito de enlazar los resultados de esta investigación con la práctica escolar. En concreto, se abordan los cinco componentes de la dimensión “tareas probabilísticas” del “Instrumento de Observación de Clases de Probabilidad” (IOC-PROB): uso de recursos; contextos probabilísticos; reto cognitivo; procedimientos y estrategias; y significados de la probabilidad. Para cada componente, primero se presenta una breve descripción y una síntesis de los principales resultados obtenidos en el estudio preliminar; y, seguidamente, se ofrecen diversas orientaciones didácticas fundamentadas teórico-metodológicamente, a partir de una actividad en particular, para que el profesorado de Educación Primaria pueda llevar a cabo una planificación y gestión de las tareas probabilísticas más eficaz y ajustada a los resultados de la investigación. Se concluye que la implementación de tareas probabilísticas en el aula alineadas con la investigación sobre este tópico puede contribuir a mejorar el desarrollo de la alfabetización probabilística del alumnado de Educación Primaria.
Recommending Advanced Deep Learning Models for Efficient Insect Pest Detection

Insect pest management is one of the main ways to improve the crop yield and quality in agriculture and it can accurately and timely detect insect pests, which is of great significance to agricultural production. In the past, most insect pest detection tasks relied on the experience of agricutural experts, which is time-consuming, laborious and subjective. In rencent years, various intelligent methods have emerged for detection. This paper employs three frontier Deep Convolutional Neural Network (DCNN) models—Faster-RCNN, Mask-RCNN and Yolov5, for efficient insect pest detection. In addition, we made two coco datasets by ourselves on the basis of Baidu AI insect detection dataset and IP102 dataset, and compared these three frontier deep learning models on the two coco datasets. In terms of Baidu AI insect detection dataset whose background is simple, the experimental results strongly recommend Yolov5 for the insect pest detection, because its accuracy reaches above 99% while Faster-RCNN’s and Mask-RCNN’s reach above 98%. Meanwhile, Yolov5 has the faster computational speed than Faster-RCNN and Mask-RCNN. Comparatively speaking, with regard to the IP102 dataset whose background is complex and categories are abundant, Faster-RCNN and Mask-RCNN have the higher accuracy, reaching 99%, than Yolov5 whose accuracy is about 97%.
Corporate entrepreneurship and employees’ competencies: Do employees’ perceived feasibility and desirability matter?

Abstract The purpose of the study was to investigate the influence of antecedents of corporate entrepreneurship (CE) on competencies of employees of Small and Medium Scale Enterprises (SMEs) in Sekondi-Takoradi Metropolis, considering the respective mediating and moderating roles of employees’ perceived feasibility and desirability. A cross-sectional and descriptive survey design that makes use of quantitative approach was used. A sample of 449 employees of SMEs in the Metropolis was used. Computer method of simple random sampling technique was used to select the firms and the employees. Questionnaire was used to collect the data. Pearson product moment correlation, hierarchical multiple regression, mediation and moderation analyses were used to analyse the data. It was revealed that when employees perceive organisational and environmental factors in positive terms, they are likely to believe that they possess the necessary skills and abilities required to be successful in undertaking a task which will in turn boost the level at which they want to become an intrapreneur. It was recommended to owners/managers of SMEs to ensure that they champion innovative ideas, recognise employees who articulate good ideas, and provide the necessary resources to all employees to help boost their corporate entrepreneurial actions. This study was successful in developing an empirical model for understanding the influence corporate entrepreneurship has on employees’ competencies and the mediating and moderating roles of perceived feasibility and desirability, respectively, on the relationship between CE and employees’ competencies.