Miracle-dz/UniFAR-FTUs huggingface dataset:
Sustainable animal production: exploring the benefits of sperm sexing technologies in addressing critical industry challenges

The sex of the animals is of paramount importance in many animal production systems. This is particularly evident in the production of milk or in breeding programs focused on the production of female animals. In some cases, slaughter or euthanasia of animals of the unwanted sex becomes the only solution, highlighting ethical and economic concerns. As global demand for food continues to rise, the importance of addressing these issues becomes more evident. Reproductive technologies, such as sperm sexing techniques, may hold the key to addressing both animal welfare and the sustainability of animal production. The use of semen enriched with sperm capable of producing offspring of the desired sex can serve as a valuable tool for producers to exert greater control over production outcomes, not only helping to mitigate welfare issues related to the unnecessary premature death of unwanted offspring but also providing a possible ally in the face of stricter animal welfare guidelines. In addition, sexed semen can also contribute to financial gains and reduce greenhouse gas emissions and food waste associated with the less profitable part of the herd. This paper explores the positive impacts that sperm sexing can have on animal welfare, economy, and environment. It also discusses currently available options and strategies for more successful implementation of sexed semen. Partnerships between companies and scientists will be essential to find innovative ways to adapt current production systems and develop sperm sexing technologies that apply to most livestock industries.
Dominican Republic Wind Energy Resource Atlas Development

A wind resource analysis and mapping study was conducted for the Dominican Republic. The purpose of this study was to identify most favorable wind resource areas and quantify the value of that resource within those areas. This was a major study and the first of its kind undertaken for the Dominican Republic. The key to the successful completion of the study was an automated wind resource mapping program recently developed at the National Renewable Energy Laboratory (NREL), a US Department of Energy (DOE) national laboratory.
A2A adenosine receptor-driven cAMP signaling in olfactory bulb astrocytes is unaffected in experimental autoimmune encephalomyelitis

Introduction The cyclic nucleotide cyclic adenosine monophosphate (cAMP) is a ubiquitous second messenger, which is known to play an important anti-inflammatory role. Astrocytes in the central nervous system (CNS) can modulate inflammation but little is known about the significance of cAMP in their function. Methods We investigated cAMP dynamics in mouse olfactory bulb astrocytes in brain slices prepared from healthy and experimental autoimmune encephalomyelitis (EAE) mice. Results The purinergic receptor ligands adenosine and adenosine triphosphate (ATP) both induced transient increases in cAMP in astrocytes expressing the genetically encoded cAMP sensor Flamindo2. The A2A receptor antagonist ZM241385 inhibited the responses. Similar transient increases in astrocytic cAMP occurred when olfactory receptor neurons were stimulated electrically, resulting in ATP release from the stimulated axons that increased cAMP, again via A2A receptors. Notably, A2A-mediated responses to ATP and adenosine were not different in EAE mice as compared to healthy mice. Discussion Our results indicate that ATP, synaptically released by afferent axons in the olfactory bulb, is degraded to adenosine that acts on A2A receptors in astrocytes, thereby increasing the cytosolic cAMP concentration. However, this pathway is not altered in the olfactory bulb of EAE mice.
Cadastral Mapping of Forestlands in Greece: Current and Future Challenges

The Hellenic Cadastre Program (HCP) of Greece aims at developing a modern cadastral system for the first time in the Hellenic history. This paper is focused on the issues related to cadastral forestlands digital mapping, an indispensable part of HCP. Mapping the forestlands is a challenge of multiple disciplines. It includes photogrammetry, photointerpretation, Geographic Information Systems (GIS), and a clear understanding of the current institutional and legislative setting. The process requires both historical and current information pertaining to land-cover in order to identify forestland changes over time. Historical and current digital orthoimagery is generated through photogrammetric operations. Forestlands are delineated in a spatiotemporal environment; state property rights in forestlands are allocated and land ownership is established within the framework of HCP. This paper demonstrates that the integration of airborne remote sensing and collateral data with a GIS is an effective approach for cadastral forest mapping. The produced GIS databases and large-scale Forest Maps may serve as a data foundation towards a land register of forests.
Scattering Characteristic-Aware Fully Polarized SAR Ship Detection Network Based on a Four-Component Decomposition Model

Model-based decomposition methods are widely used in full-polarization synthetic aperture radar (SAR), for the inversion and interpretation of ground features and constitute an important approach for understanding the behavior of backscattering. However, due to the substantial differences between land and marine environments, different man-made and natural vegetation scattering structures render existing decomposition models unable to reasonably characterize scatterers on ships. Moreover, the combination of polarization decomposition models and neural networks for ship detection has rarely been investigated. Therefore, this study proposes a four-component decomposition model (Ship-4SD) suitable for describing the scattering characteristics of ships based on the surface scattering, double-bounce scattering, ± 45° oriented dipole, and asymmetric scattering components. Furthermore, based on the differences in the scattering properties exhibited by different scattering components in ships and the powerful feature extraction capability of convolutional neural networks (CNNs), a scattering characteristic-aware fully polarized SAR ship detection network (SCANet) was designed to make full use of the scattering components in the decomposition model. Finally, the experimental results on a large amount of GF-3 fully polarized SAR (PolSAR) data validated the reasonability and superiority of Ship-4SD and SCANet. The Ship-4SD can better distinguish ship and clutter pixels compared to other four-component models and has a higher target-clutter ratio with respect to the multicomponent models. SCANet proposed in this article achieved an average precision (AP) of 94.43% and 96.56% on the GF-3 and SSDD datasets, respectively, which is better than that of other competitive CNN algorithms.
Successful treatment of Caparinia tripilis infestation with ivermectin in an African pygmy hedgehog

Acariasis is a prevalent disease in Atelerix albiventris or African pygmy hedgehogs, with Caparinia tripilis being the main aetiologic agent. This case report describes a single instance of C. tripilis mange in a 16-month-old African pygmy hedgehog detected through clinical and microscopic skin scraping investigations. The owner brought the hedgehog to the IPB University Veterinary Teaching Hospital, complaining about inappetence, intense pruritus, diarrhoea, and crusts in the patient. Faecal native and flotation showed no helminthic or protozoan parasites. The treatment comprised subcutaneous ivermectin application at a dose of 0.4 mg/kg body weight. The control was made 14 and 28 days after the first hospital visit, with ivermectin applications at the same dose (three doses in total and two weeks apart from each dose). Microscopic examination of the skin scrapings was performed on days 14 and 28, and C. tripilis was not observed in these examinations. Complete clinical improvement was observed during the two control examinations. This is the first report to describe the use of a three-dose (two weeks apart) of ivermectin to treat capariniasis in an African pygmy hedgehog in Indonesia.
Validation of Novel Reference Genes in Different Rice Plant Tissues through Mining RNA-Seq Datasets

Reverse transcription quantitative real-time PCR (RT-qPCR) is arguably the most prevalent and accurate quantitative gene expression analysis. However, selection of reliable reference genes for RT-qPCR in rice (Oryza sativa) is still limited, especially for a specific tissue type or growth condition. In this study, we took the advantage of our RNA-seq datasets encompassing data from five rice varieties with diverse treatment conditions, identified 12 novel candidate reference genes, and conducted rigorous evaluations of their suitability across typical rice tissues. Comprehensive analysis of the leaves, shoots, and roots of two rice seedlings subjected to salt (30 mmol/L NaCl) and drought (air-dry) stresses have revealed that OsMED7, OsACT1, and OsOS-9 were the robust reference genes for leaf samples, while OsACT1, OsZOS3-23, and OsGDCP were recommended for shoots and OsMED7, OsOS-9, and OsGDCP were the most reliable reference genes for roots. Comparison results produced by different sets of reference genes revealed that all these newly recommended reference genes displayed less variation than previous commonly used references genes under the experiment conditions. Thus, selecting appropriate reference genes from RNA-seq datasets leads to identification of reference genes suitable for respective rice tissues under drought and salt stress. The findings offer valuable insights for refining the screening of candidate reference genes under diverse conditions through the RNA-seq database. This refinement serves to improve the accuracy of gene expression in rice under similar conditions.
An overview of simultaneous localisation and mapping: towards multi-sensor fusion

Simultaneous localisation and mapping (SLAM) systems have been widely studied over the past 30 years and extensively applied in various fields such as mobile robotics, augmented reality, and virtual reality. The goal of the SLAM technique is to simultaneously map the surrounding environment and obtain the ego-motion of the sensing platform. As the number of application scenarios of SLAM systems increases and related tasks become more complex, SLAM systems based on a single sensor are no longer sufficient to meet the demands, thus the trend for SLAM systems based on multi-sensor fusion has emerged. In this paper, we review the SLAM systems from the perspective of various configurations of heterogeneous sensors. These configurations include visual-inertial, lidar-inertial, lidar-visual, lidar-visual-inertial, and other multi-sensor fusion systems. In addition, the advantages and disadvantages of each configuration are also given. Based on the review, several open issues for further research are discussed at the end of this paper.
Comparing efficacy of first-line treatment of metastatic castration resistant prostate cancer: a network meta-analysis of randomized controlled trials

Background: Metastatic castration-resistant prostate cancer (mCRPC) presents significant treatment selection challenges due to limited therapeutic options. This study aimed to comprehensively assess the efficacy of multiple treatment regimens for mCRPC through a network meta-analysis (NMA) of randomized controlled trials (RCTs). Methods: A systematically comprehensive search for randomized controlled trials (RCTs) was performed in Pubmed, Cochrane Library, Embase, and Web of Science databases. The network meta-analysis was employed to compare the overall survival (OS), progression-free survival (PFS), and radiographic progression-free survival (rPFS) among different interventions at specific time points. This study was prospectively registered with PROSPERO (CRD42023422823). Results: A total of 29 RCTs, involving 12,706 patients and investigating 16 interventions, were included in the analysis. Chempretarget ((capivasertib or cabozantinib) + docetaxel + prednisone)) and PARP (Olaparib or rucaparib) inhibitors emerged as interventions that significantly improved survival outcomes compared to first-line treatment in mCRPC patients. Chempretarget demonstrated superior overall survival starting from the 12th month, while PARP inhibitors showed a clear advantage in progression-free survival within the 3–18 months range. Notably, chempre ((Docetaxel or Cabazitaxel) + prednisone) exhibited favorable performance in radiographic progression-free survival during the 3–18 month period. Conclusion: Our findings underscore the efficacy of chempretarget, PARP inhibitors, and chempre in enhancing survival outcomes for mCRPC patients. Further head-to-head comparisons are warranted to validate these results. These findings carry important implications for treatment decision-making in mCRPC and may guide the development of more effective therapeutic strategies.
The association between dietary inflammatory index and cognitive function in adults with/without chronic kidney disease

Background and aims Cognitive impairment (CI) is a prevalent condition in patients with chronic kidney disease (CKD), who face an elevated risk of developing cognitive decline. The fundamental mechanism underlying CI is linked to chronic inflammation, which can be gauged by the Dietary Inflammatory Index (DII). The DII is categorized into anti-inflammatory diets with lower scores and pro-inflammatory diets with higher scores. Specifically, pro-inflammatory diets may contribute to chronic inflammation. However, the correlation between the inflammatory potential of diet and cognitive function in patients with CKD has not been explored. This study aims to investigate the connection between the inflammatory potential of diet and cognitive function in individuals with or without chronic kidney disease. Methods Data from the 2011–2012 and 2013–2014 National Health and Nutrition Examination Survey (NHANES) were utilized. Participants under the age of 60 or lacking DII, CI, CKD, and other essential data were excluded. DII was computed based on a 24-h dietary recall interview for each participant. Cognitive performance was evaluated using three cognitive tests: the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) test, the Animal Fluency Test (AFT), and the Digital Symbol Substitution Test (DSST). Logistic regression analysis and subgroup analysis were conducted to assess the independent relationship between DII score and CI in the CKD and non-CKD populations. Results The study included a total of 2069 subjects, with CI prevalence ranging from 21.4 to 23.5%. Multiple regression models showed that after adjusting for all covariates of the three cognitive function tests, higher DII scores were significantly associated with increased risk of CI (CERAD OR = 1.18, 95% CI: 1.1 ~ 1.26, AFT OR = 1.15, 95% CI: 1.08 ~ 1.23, DSST OR = 1.19, 95% CI: 1.11 ~ 1.28). Subgroup analysis indicated that the effect of DII score on CI remained consistent in all subgroups (p > 0.05). Conclusion Higher DII scores were associated with an increased risk of cognitive impairment in people with or without CKD, suggesting that consuming a pro-inflammatory diet may contribute to the impairment of the cognitive function.
A Direct Position Determination Method Using Distributed UAVs With Synchronization Error

A direct position determination (DPD) method based on time difference of arrival (TDOA) using distributed unmanned aerial vehicles (UAVs) equipped with a single antenna is studied, where the sensing data are influenced by the clock synchronization error among the UAVs, and a robust DPD method suitable for the scenario is proposed. First, the received data of the UAVs containing a synchronization error matrix are transformed into the frequency domain. Then, the subspace orthogonality is used to build the cost function for source localization. Next, the change of positions and signal receptions of the UAVs are implemented to solve the rank deficiency of the equations caused by unknown synchronization errors, and the minimized cost function is obtained by fusing all the cost functions. Compared to other DPD methods, the proposed method can achieve higher positioning accuracy in the scenario where synchronization error exists. Simulation results demonstrate the superiority of the proposed method to other TDOA-based methods.
Sexual Health and Psychological Well-Being of Women: A Systematic Review

(1) Background: Psychological well-being (PWB) and female sexual health are two important areas for women’s quality of life and research, and they are closely related. The aim of this study was to conduct a systematic review of the existing literature to explore the association between PWB and sexual health in women. (2) Methods: This review was carried out following the PRISMA checklist. The inclusion criteria were studies with samples of adult women that evaluated and associated sexual functioning and psychological well-being. Scientific articles were identified on Web of Science, Scopus, EBSCO (PsycInfo, PsycArticles, and Psicodoc), ProQuest, and PubMed. The search was limited to years between 2010 and 2023. The methodological quality of the studies was assessed using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies (QATOCCS). (3) Results: 14 selected articles were analyzed, in which population samples and variables related to psychological and sexual health were examined. In total, 42.9% of the studies included clinical samples, 71.4% focused on anxiety and depression as the main psychological variables, and 50% examined female sexual functioning as a sexual health variable. (4) Conclusions: This review provides more up-to-date information about valuable insights into the possible determinants of female sexual health. An association between PWB and female sexual health has been demonstrated.
Impact of ethical leadership in HRM on employee's turnover intention post Covid-19

Goal: The paper aims to identify ethical leadership's efficacy in HRM and differentiate it from negative HRM leadership. Pandemic and post-pandemic challenges are evident, which can destabilize an organization, so we need to develop a business-ethics perspective on leadership for HRM. Method: For the study, the data of 120 manufacturing companies' employees from Uttar Pradesh has been collected. The analysis of data has been done with the help of simple linear regression and T-test through SPSS. Results: The research on the concerned issue and critically reflects on negative leadership issues that arise post-Covid-19. The analysis results have concluded that there is a positive impact of ethical leadership on employee turnover intention post-covid-19 in Uttar Pradesh, and there is no significant difference in the perception of male and female respondents towards turnover intentions post-covid-19. Limitations: The study is a small-scale study consisting of 120 employees from manufacturing companies; for future research, the data can be collected for conducting a large-scale study. Pratical implication: The study will be helpful for organizations to know about the impact of covid-19 on HRM and will lead to creating an understanding of the need of developing ethical leadership in HRM to deal with the turnover intentions of employees at the time of crisis. Originility: There has been various previous studies which talk about the employees turnover intentions during covid-19, while there is no previous literature available which states about the turnover intentions of employees post covid-19 in Uttar Pradesh, India. Moreover, the study is discussing the situation from an ethical perspective which is an addition to the existing literature.
Characterization of traffic accidents for urban road safety

Transit crashes are a serious social problem for any country, with a significant loss of human lives and economic consequences that are difficult to quantify. This article proposes a characterization of the transit crash rate for urban road safety using time series. A quantitative descriptive study was conducted, characterizing the variables of each crash extracted from the National Traffic Agency of Ecuador (NTA); the data were processed at a descriptive and predictive level for the city of Guayaquil. The first step was an exploration of the scientific interest of the topic with the processing of bibliographic data taken from Scopus and Web of Science articles. Among the results obtained, there is a growing trend of research related to the evaluation of traffic crash through applied statistics. Every day, approximately 155 people die as a result of a traffic crash. In addition, traffic crashes are analyzed based on three indicators: number of crashes, injuries and onsite fatalities. Finally, an adequate performance is found, with very few differences in the forecast of incidents using three times series models, autoregressive integrated moving average (ARIMA). It is expected that this study will be valuable for data analysts and decision makers at the security level to reduce human losses related to these events in urban cities with similar characteristics to the analyzed cases.
Firmulti Fuzzer: Discovering Multi-process Vulnerabilities in IoT Devices with Full System Emulation and VMI

With the growth of Internet of Things devices, the number and complexity of these devices are increasing rapidly. Nevertheless, many IoT products are developed without sufficient consideration for security, leaving them vulnerable to exploitation by malware. To proactively address these vulnerabilities before they are discovered by malicious attackers, information security researchers use both static and dynamic analysis techniques to identify vulnerabilities and propose firmware updates. Due to the variety of IoT firmware architectures, conducting fuzzing tests directly on firmware using a general personal computer is challenging. As a solution, emulation techniques are commonly applied to create virtual environments for vulnerability detection. However, existing emulation-based fuzzing test tools often prioritize efficiency and avoid utilizing full-system emulation. These tools are limited to detecting vulnerabilities in individual programs and are unable to identify deep-seated vulnerabilities that arise from interactions across multiple processes. To solve this challenge, we have proposed Firmulti Fuzzer, a fuzzing framework leverages full system emulation. In our approach, we do emulation for two times. The first emulation utilizes the existing emulation system to acquire the full system emulation configuration of the firmware. Next, the second emulation uses an emulator with virtual machine introspection (VMI) function to monitor the entire system environment. With Firmulti Fuzzer, we can track the execution status of all programs within the environment and generate notifications upon detecting exceptions, thereby identifying vulnerabilities stemming from interactions among multiple processes. Experiments have shown the effectiveness of Firmulti Fuzzer in detecting both general vulnerabilities and multi-process vulnerabilities. Most importantly, Firmulti Fuzzer outperforms other fuzzers in identifying multi-process vulnerabilities. Firmulti Fuzzer holds promising potential as a tool for enhancing the security of IoT devices and mitigating the exploitation of vulnerabilities by malicious attackers.