Miracle-dz/UniFAR-FTUs huggingface dataset:
Livestock phenomics and genetic evaluation approaches in Africa: current state and future perspectives

The African livestock sector plays a key role in improving the livelihoods of people through the supply of food, improved nutrition and consequently health. However, its impact on the economy of the people and contribution to national GDP is highly variable and generally below its potential. This study was conducted to assess the current state of livestock phenomics and genetic evaluation methods being used across the continent, the main challenges, and to demonstrate the effects of various genetic models on the accuracy and rate of genetic gain that could be achieved. An online survey of livestock experts, academics, scientists, national focal points for animal genetic resources, policymakers, extension agents and animal breeding industry was conducted in 38 African countries. The results revealed 1) limited national livestock identification and data recording systems, 2) limited data on livestock production and health traits and genomic information, 3) mass selection was the common method used for genetic improvement with very limited application of genetic and genomic-based selection and evaluation, 4) limited human capacity, infrastructure, and funding for livestock genetic improvement programmes, as well as enabling animal breeding policies. A joint genetic evaluation of Holstein-Friesian using pooled data from Kenya and South Africa was piloted. The pilot analysis yielded higher accuracy of prediction of breeding values, pointing to possibility of higher genetic gains that could be achieved and demonstrating the potential power of multi-country evaluations: Kenya benefited on the 305-days milk yield and the age at first calving and South Africa on the age at first calving and the first calving interval. The findings from this study will help in developing harmonized protocols for animal identification, livestock data recording, and genetic evaluations (both national and across-countries) as well as in designing subsequent capacity building and training programmes for animal breeders and livestock farmers in Africa. National governments need to put in place enabling policies, the necessary infrastructure and funding for national and across country collaborations for a joint genetic evaluation which will revolutionize the livestock genetic improvement in Africa.
Altered metabolic-functional coupling in the epileptogenic network could predict surgical outcomes of mesial temporal lobe epilepsy

Objective To investigate the relationship between glucose metabolism and functional activity in the epileptogenic network of patients with mesial temporal lobe epilepsy (MTLE) and to determine whether this relationship is associated with surgical outcomes. Methods 18F-FDG PET and resting-state functional MRI (rs-fMRI) scans were performed on a hybrid PET/MR scanner in 38 MTLE patients with hippocampal sclerosis (MR-HS), 35 MR-negative patients and 34 healthy controls (HC). Glucose metabolism was measured using 18F-FDG PET standardized uptake value ratio (SUVR) relative to cerebellum; Functional activity was obtained by fractional amplitude of low-frequency fluctuation (fALFF). The betweenness centrality (BC) of metabolic covariance network and functional network were calculated using graph theoretical analysis. Differences in SUVR, fALFF, BC and the spatial voxel-wise SUVR-fALFF couplings of the epileptogenic network, consisting of default mode network (DMN) and thalamus, were evaluated by Mann-Whitney U test (using the false discovery rate [FDR] for multiple comparison correction). The top ten SUVR-fALFF couplings were selected by Fisher score to predict surgical outcomes using logistic regression model. Results The results showed decreased SUVR-fALFF coupling in the bilateral middle frontal gyrus (PFDR = 0.0230, PFDR = 0.0296) in MR-HS patients compared to healthy controls. Coupling in the ipsilateral hippocampus was marginally increased (PFDR = 0.0802) in MR-HS patients along with decreased BC of metabolic covariance network and functional network (PFDR = 0.0152; PFDR = 0.0429). With Fisher score ranking, the top ten SUVR-fALFF couplings in regions from DMN and thalamic subnuclei could predict surgical outcomes with the best performance being a combination of ten SUVR-fALFF couplings with an AUC of 0.914. Conclusion These findings suggest that the altered neuroenergetic coupling in the epileptogenic network is associated with surgical outcomes of MTLE patients, which may provide insight into their pathogenesis and help with preoperative evaluation.
Exploring Staff Attitudes Towards Unspecified Kidney Donors in the United Kingdom: Results From the BOUnD Study

Unspecified kidney donation (UKD) has made substantial contributions to the UK living donor programme. Nevertheless, some transplant professionals are uncomfortable with these individuals undergoing surgery. This study aimed to qualitatively explore the attitudes of UK healthcare professionals towards UKD. An opportunistic sample was recruited through the Barriers and Outcomes in Unspecified Donation (BOUnD) study covering six UK transplant centres: three high volume and three low volume centres. Interview transcripts were analysed using inductive thematic analysis. The study provided comprehensive coverage of the UK transplant community, involving 59 transplant professionals. We identified five themes: staff’s conception of the ethics of UKD; presence of the known recipient in the donor-recipient dyad; need for better management of patient expectations; managing visceral reactions about the “typical” unspecified kidney donor; complex attitudes toward a promising new practice. This is the first in-depth qualitative study of attitudes of transplant professionals towards UKD. The data uncovered findings with strong clinical implications for the UKD programme, including the need for a uniform approach towards younger candidates that is adhered to by all transplant centres, the need to equally extend the rigorous assessment to both specified and unspecified donors, and a new approach to managing donor expectations. Graphical Abstract
Outcomes of IncobotulinumtoxinA Injection on Myalgia and Arthralgia in Patients Undergoing Temporomandibular Joint Arthroscopy: A Randomized Controlled Trial

Background: Several studies have considered Botulinum Neurotoxin Type A injections effective in treating temporomandibular joint disorder (TMD) symptoms. A double-blind, randomized, controlled clinical trial investigated the benefit of complementary incobotulinumtoxinA (inco-BoNT/A) injections in the masticatory muscles of patients submitted to bilateral temporomandibular joint (TMJ) arthroscopy. Methods: Fifteen patients with TMD and an indication for bilateral TMJ arthroscopy were randomized into inco-BoNT/A (Xeomin, 100 U) or placebo groups (saline solution). Injections were carried out five days before TMJ arthroscopy. The primary outcome variable was a Visual Analogue Scale for TMJ arthralgia, and secondary outcomes were the myalgia degree, maximum mouth opening, and joint clicks. All outcome variables were assessed preoperatively (T0) and postoperatively (T1—week 5; T2—6-month follow-up). Results: At T1, the outcomes in the inco-BoNT/A group were improved, but not significantly more than in the placebo group. At T2, significant improvements in the TMJ arthralgia and myalgia scores were observed in the inco-BoNT/A group compared to the placebo. A higher number of postoperative reinterventions with further TMJ treatments were observed in the placebo group compared to inco-BoNT/A (63% vs. 14%). Conclusions: In patients submitted to TMJ arthroscopy, statistically significant long-term differences were observed between the placebo and inco-BoNT/A groups.
Focusing the partly polarized light

The paper presents the mathematical technique for calculation of three dimensional intensity distribution near a focal point of an optical system in case of partly polarized light. The proposed technique considers a high aperture optical system that focuses a partly polarized parallel beam. The principal idea is based on Huygens-Fresnel principle: a spherical wave at an exit pupil of an optical system is considered as a numerous set of secondary light point sources. Each source emits a partly polarized spherical wave. The polarization orientation of each wave can be calculated using angular pupil coordinates. Modulation of amplitude, phase or polarization can be introduced depending on these pupil coordinates. The total intensity is defined as superposition of complex wave amplitudes taking into account polarization orientation, degree of polarization and orientation of detector aperture. The paper presents the intensity distributions calculated for beams with various types and degrees of polarization.
THE ROLE OF CORPORATE SUSTAINABILITY DISCLOSURE IN PROMOTING FIRM VALUE OF LISTED MANUFACTURING FIRMS IN NIGERIA

The aim of this paper is to examine the role sustainability disclosure plays in creating additional value for firms in Nigeria, particularly firms belonging to the Nigerian manufacturing sector. The data were sourced using content analysis on annual reports of 45 listed manufacturing firms for eleven years (2010-2020) collected from Nigerian Stock Exchange. The data were analysed using Generalized Least Square (GLS) regression analysis. The study hypothesized and empirically found that environmental disclosure and social disclosure positively and significantly influences firm value of manufacturing firms in Nigeria. The study concludes that the findings reinforce the general argument of the study that firm’s sustainability practices can be a value creation mechanism to companies that involve in its practices and disclosure. The study recommends that manufacturing firms in Nigeria should disclose more on their sustainability-related information as it helps the stakeholders to make valuable decisions which improves their market share. This is a novel approach to the study of sustainability reporting, as most studies focus on the corporate governance factors leading to sustainability disclosure rather than on the value creation of sustainability reporting practices. This study also offers valuable insights to policy makers interested in improving disclosure practices in the economy of Nigeria.
Machine Learning for Precision Agriculture Using Imagery from Unmanned Aerial Vehicles (UAVs): A Survey

Unmanned aerial vehicles (UAVs) are increasingly being integrated into the domain of precision agriculture, revolutionizing the agricultural landscape. Specifically, UAVs are being used in conjunction with machine learning techniques to solve a variety of complex agricultural problems. This paper provides a careful survey of more than 70 studies that have applied machine learning techniques utilizing UAV imagery to solve agricultural problems. The survey examines the models employed, their applications, and their performance, spanning a wide range of agricultural tasks, including crop classification, crop and weed detection, cropland mapping, and field segmentation. Comparisons are made among supervised, semi-supervised, and unsupervised machine learning approaches, including traditional machine learning classifiers, convolutional neural networks (CNNs), single-stage detectors, two-stage detectors, and transformers. Lastly, future advancements and prospects for UAV utilization in precision agriculture are highlighted and discussed. The general findings of the paper demonstrate that, for simple classification problems, traditional machine learning techniques, CNNs, and transformers can be used, with CNNs being the optimal choice. For segmentation tasks, UNETs are by far the preferred approach. For detection tasks, two-stage detectors delivered the best performance. On the other hand, for dataset augmentation and enhancement, generative adversarial networks (GANs) were the most popular choice.
It’s a Sure Win! Experimental evidence on overconfidence in betting behavior

We conduct an experiment with regular sports bettors in Tanzania to investigate how they value their bets and form expectations about winning probabilities. By comparing a sports bet to a neutral urn-and-balls lottery with identical odds, we find that subjects under the sports framing assign higher subjective values (certainty equivalents) to their bets and are significantly more optimistic about their chances of winning, even though, in fact, they are not more likely to win. This is consistent with bettors being overconfident in their ability to predict sports outcomes. Coupled with data on betting frequency and motives, our results suggest that, by leveraging gamblers’ overconfidence, sports betting magnifies their financial losses
THE USE OF DIGITAL FOOTPRINTS TO CREATE PSYCHOGRAPHIC PORTRAITS FOR INCREASED EFFICIENCY IN ADVERTISING MESSAGES

: This article describes the process of studying users’ behaviour in the digital space and creating psychographic portraits using an automated system inside the VKontakte (VK) social network and collecting users’ information. Within the framework of this study, the authors attempted to prove the possibility of implementing these data for small and medium-sized businesses’ goals and to offer the use of psychographic segmentation to create more personalized brand communication with consumers. The study is based on the analysis of data from an application of the VK social network (TIPI Psychological Test) that was developed by the authors and implemented as an online survey of 261 respondents from Nizhny Novgorod to identify their psychographic profile, as well as a database (MySQL) analysis of the activities of these people in the VK social network (their group subscriptions). Based on a comparison of the results of using these two methods, the authors conclude that it is possible to develop a system that uses users’ digital traces to create psychographic portraits and set up more effective targeted advertising for a specific consumer segment.
Automatic measurements of the corpus callosum in the follow-up of preterm children: Methodology and validation

Brain injury in preterm infants is associated with a high risk of neurodevelopmental disability. One of the most frequent forms of brain injury is white matter injury. The largest white matter structure is the corpus callosum and measurements of this structure have been associated with white matter volume. Consequently, quantification of the corpus callosum could provide an insight into the white matter injury related to preterm birth. However, manual measurements require an experienced rater, are highly time-consuming and suffer from high inter- and intra-rater variability. In this paper, we present an automated method for measuring the corpus callosum on T1-weighted images of children, and we evaluate the model in terms of accuracy performance. Automatic measurements of the anterior area, posterior area and length of the corpus callosum have a good intraclass correlation coefficient while relatively low absolute error compared to the same measurement performed manually by an expert child neurologist.
Attention-based Row Selecting Networks for Lane Detection

Most mainstream methods mainly regard lane detection as a pixel-by-pixel segmentation task, resulting in high computational cost and time-consuming, and the accuracy is inﬂuenced by severe occlusion and extreme lighting conditions. To tackle these issues, we propose a novel Attention-based Row Selecting Networks(ARS-Net), which utilizes the row selecting method based on global features to detect lanes, greatly improves the detection speed. At the same time, channel and spatial attention mechanisms are integrated into ResNet as the backbone to focus on important features and suppress unimportant ones, so as to adjust feature weights and reduce information loss. Besides, group normalization is employed to replace batch normalization, which enhances the stability of accuracy. We carry out immense amounts of experiments on two international public datasets TuSimple and CULane, the experimental results show that our method achieves the state-of-the-art performance in both accuracy and speed and signiﬁcantly outperforms other methods for real-time and eﬃcient lane detection in real-world applications.
Differentiation Strategy and Cost Leadership Strategy: Their Contribution to Achieving Sustainable Financial Performance

This research aims to study the inﬂuence of differentiation and cost-leadership strategy on sustainable ﬁnancial performance with innovation as the moderator variable. This research uses the last 5 years ﬁnancial report of go-public companies in South-East Asia, with a total of 250 ﬁrm years. In the early stage of the research, the companies are grouped into their respective strategy. From that grouping, each strategy is then measured. The researcher uses 5 measurements to test the strategies, 2 for differentiation strategy and 3 for cost-leadership strategy. The ﬁnding of this research is that differentiation positively inﬂuences sustainable ﬁnancial performance, with innovation as the moderator variable. However, these ﬁndings are not proved in cost leadership strategy. Innovation shows partially inﬂuences to the achievement of sustainable ﬁnancial performance. This research contributes to adding the innovation factor that strengthens the relationship between the successes of a strategy with the sustainable ﬁnancial performance so that the result can be more consistent.
Acquiring and Processing Data Using Simpliﬁed EEG-based Brain-Computer Interface for the Purpose of Detecting Emotions

—The aim of the paper was to analyze how to acquire and process EEG data with a simpliﬁed, commercially applicable EEG interface and to check whether it is possible to recognize human emotions with it. The EEG data gathering station was built and the data was gathered from the subjects. Then, the data was processed to apply it to the classiﬁer training. The AutoML software was used to ﬁnd the best ML model, and it was also built manually to prove the output accuracy was reliable and there was no overﬁtting. The AutoML experiment has shown that the best classiﬁer was the boosted decision tree algorithm, and building it manually resulted in an accuracy of recognizing four distinct emotions equal to 99.80%.
LOCAL POLYNOMIAL REGRESSION WITH SELECTION BIASED DATA

Let Y and X be real- and R d -valued random variables. We consider the estimation of the nonparametric regression function m(x )= E(Y |X = x) when s ≥ 1 independent selection-biased samples of (Y,X) are observed. This sampling scheme, which arises naturally in biological and epidemiological studies and many other fields, includes stratified samples, length-biased samples and other weighted distributions. A class of local polynomial estimators of m(x) is derived by smoothing Vardi's nonparametric maximum likelihood estimator of the underlying distribution function. Large sample properties, such as asymptotic distributions and asymptotic mean squared risks, are derived explicitly. Unlike local polynomial regression with i.i. d. direct samples, we show here that kernel choices are important and optimal kernel functions may be asymmetric and discontinuous when the weight functions of the biased samples have jumps. A cross-validation criterion is proposed for the selection of data-driven bandwidths. Through a simple comparison, we show that our estimators are superior to other intuitive estimators of m(x).
Berberine Represses b -Catenin Translation Involving 4E-BPs in Hepatocellular Carcinoma Cells s

Aberrant activation of Wnt/ b -catenin axis occurs in several gastrointestinal malignancies due to inactivating mutations of adenomatous polyposis coli (in colorectal cancer) or activating mutations of b -catenin itself [in hepatocellular carcinoma (HCC)]. These lead to b -catenin stabilization, increase in b -catenin/T-cell factor (TCF) – mediated transcriptional activation, and target gene expression, many of which are involved in tumor progression. While studying pharmaceutical agents that can target b -catenin in cancer cells, we observed that the plant compound berberine (BBR), a potent activator of AMP-activated protein kinase (AMPK)
Investigation of Secondary Organic Aerosol Formation during O3 and PM2.5 Episodes in Bangkok, Thailand

In Bangkok, the megacity of Thailand, concentrations of fine particulate matter (PM2.5) have often exceeded the National Ambient Air Quality standards. During severe smog events over Bangkok, the air quality has exhibited moderate to unhealthy atmospheric conditions, according to the air quality index of the United States. To investigate the formation of secondary organic aerosols (SOA), a field campaign to estimate secondary organic carbon (SOC) in Bangkok using the EC tracer method was conducted in January 2021, when the concentrations of PM2.5 were high. The monthly period was classified into three pollution groups, including high pollution, high PM, and low pollution events. The study showed that the correlations between PM2.5 and O3 were negative during both the daytime and night-time. The OC/EC ratios varied from 4.32 to 5.43, while the moderate OC/EC values implied that fossil fuel combustion was the major carbonaceous aerosol in Bangkok. The EC tracer-estimated SOC and POC showed that SOC contributed between 32.5 and 46.4% to OC, while the highest SOC contribution occurred during the low pollution event. The heightened formation of SOA during the low pollution event was perhaps owing to the levels of oxides of nitrogen (NOx). Since Bangkok is more likely to have a NOx-rich photochemical reaction regime, an increase in the NOx level tended to decrease the SOA yield ([NOx] was 21.6 ppb, 20.8 ppb, and 17.1 ppb during the high pollution, high PM, and low pollution events, respectively). Together with the high humidity and high light intensity during the low pollution event, the SOA formation was enhanced. Even though the driving factors of SOA formation over Bangkok remain unclear, the results of this study reveal the significance and urgency of local actions to reduce NOx and O3 towards more habitable and sustainable urban environments.
Encrypted malware detection based on HTTPs financial data communication mode

In recent years, fintech has gained widely and deeply application, and the existing data security problems also keep emerging. Network attacks using advanced local hiding technology are increasing, which has become a serious threat to the financial data security. However, these attacks cannot be normally detected, and some attacks imitate normal data to evade detection. Therefore, a malware detection method representing users' web communication behavior is proposed, which introduces HTTPs data communication mode for classification. Two methods of constructing HTTPs communication descriptors are discussed, a method of large-scale descriptor construction is introduced, and the machine learning algorithm and similarity search methods suitable for the proposed descriptors are analyzed. Experiments are conducted to compare the performance of the two types of descriptors in various classification algorithms and search scenarios. The test results show that the machine learning classification method based on the proposed communication descriptor can effectively detect encrypted malware communications based on HTTPs.