Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of nutraceutical supplementation on semen quality in stallions

Abstract Background The use of reproductive biotechnologies in equine practice has shown that some stallions are subfertile, so ways to improve fertility have been sought. Objective This study aimed to evaluate the effect of nutraceutical supplementation on improving semen quality in Quarter Horse stallions. Methods Semen from six Quarter Horse stallions was assessed for 4 months every 20 days using the computer‐assisted semen analysis system. They were evaluated for 60 days before supplementation; then, the same stallions were re‐evaluated for 60 days with nutraceutical supplementation (30 g/day). Results Volume showed no significant difference (p > 0.05) with nutraceuticals. Sperm concentration (10x6) was significantly higher with supplementation (339.4 ± 17.5 sperm/mL) than without supplementation (224.6 ± 19.9). Sperm abnormalities (%) were significantly (p < 0.05) lower with supplementation (14.3 ± 0.6) than without supplementation (19.1 ± 0.4). Sperm kinematic parameters, total motility (TM), progressive motility (PM), rectilinear velocity (VSL), the velocity of the trajectory (VAP) and curvilinear velocity (VCL), were significantly better with supplementation (p < 0.05). Conclusions Based on the results, it is concluded that nutraceutical supplementation improved semen parameters in Quarter Horse stallions.
ForGATE - A Forest Sector Greenhouse Gas Assessment Tool for Maine: Calibration and Overview

This report describes the background calibration, inputs, and outputs of ForGATE, a forest sector greenhouse gas (GHG) accounting tool designed primarily to communicate information relevant to the evaluation of projected net GHG exchange in the context of Maine's forests, the Northeast forest sector, and alternative national or regional carbon (C) accounting guidelines. It also provides forest managers and policy makers with an easy-to-use tool for examining the relative merit (C credit revenue vs. project cost) of C offset projects and forest sector life cycle GHG accounting. GHG accounts include: 1) storage in aboveground and belowground live biomass and dead organic matter components; 2) storage in forest products in use and in landfill; 3) forest sector emissions by harvest, transport, and mills, or avoided emissions (substitution, bioenergy); as well as 4) landfill methane release and avoided emissions from methane energy capture. Different forest and forest product pools can be included in result summaries to reflect different C accounting guidelines (e.g., Climate Action Reserve, Voluntary Carbon Standard). Results can be compared for baseline and C offset project scenarios. Where possible, the marginal differences between baseline and project scenario performance indicators are calculated. All forest-level emission or storage measures are expressed in tonnes of CO2 equivalents for comparison purposes. Finally, economic indicators such as net present value and benefit-cost ratios for C offset projects can be evaluated using alternative assumptions for the value of stumpage, C credits, and offset project costs. The user enters their own inventory of stand type area by treatment regime data for baseline and offset project scenarios and can quickly adjust many GHG accounting parameters. ForGATE is available without charge from http://www.nrs. fs.fed. us/tools/forgate/.
Influence of Input Features and EMG Type on Ankle Joint Torque Prediction With Support Vector Regression

Reliable and accurate EMG-driven prediction of joint torques are instrumental in the control of wearable robotic systems. This study investigates how different EMG input features affect the machine learning algorithm-based prediction of ankle joint torque in isometric and dynamic conditions. High-density electromyography (HD-EMG) of five lower leg muscles were recorded during isometric contractions and dynamic tasks. Four datasets (HD-EMG, HD-EMG with reduced dimensionality, features extracted from HD-EMG with Convolutional Neural Network, and bipolar EMG) were created and used alone or in combination with joint kinematic information for the prediction of ankle joint torque using Support Vector Regression. The performance was evaluated under intra-session, inter-subject, and inter-session cases. All HD-EMG-derived datasets led to significantly more accurate isometric ankle torque prediction than the bipolar EMG datasets. The highest torque prediction accuracy for the dynamic tasks was achieved using bipolar EMG or HD-EMG with reduced dimensionality in combination with kinematic features. The findings of this study contribute to the knowledge allowing an informed selection of appropriate features for EMG-driven torque prediction.
TDLNet: Transfer Data Learning Network for Cross-Subject Classification Based on Multiclass Upper Limb Motor Imagery EEG

The limited number of brain-computer interface based on motor imagery (MI-BCI) instruction sets for different movements of single limbs makes it difficult to meet practical application requirements. Therefore, designing a single-limb, multi-category motor imagery (MI) paradigm and effectively decoding it is one of the important research directions in the future development of MI-BCI. Furthermore, one of the major challenges in MI-BCI is the difficulty of classifying brain activity across different individuals. In this article, the transfer data learning network (TDLNet) is proposed to achieve the cross-subject intention recognition for multiclass upper limb motor imagery. In TDLNet, the Transfer Data Module (TDM) is used to process cross-subject electroencephalogram (EEG) signals in groups and then fuse cross-subject channel features through two one-dimensional convolutions. The Residual Attention Mechanism Module (RAMM) assigns weights to each EEG signal channel and dynamically focuses on the EEG signal channels most relevant to a specific task. Additionally, a feature visualization algorithm based on occlusion signal frequency is proposed to qualitatively analyze the proposed TDLNet. The experimental results show that TDLNet achieves the best classification results on two datasets compared to CNN-based reference methods and transfer learning method. In the 6-class scenario, TDLNet obtained an accuracy of 65%±0.05 on the UML6 dataset and 63%±0.06 on the GRAZ dataset. The visualization results demonstrate that the proposed framework can produce distinct classifier patterns for multiple categories of upper limb motor imagery through signals of different frequencies. The ULM6 dataset is available at https://dx.doi. org/10.21227/8qw6-f578.
Space Techniques Used to Measure Change in Terrestrial Waters

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Copyright Space Techniques Used to Measure Change in Terrestrial Waters Alexandre-Brice Cazenave, P.C. D. Milly, H. Douville, J. Benveniste, Pascal Kosuth, D. Lettenmaier
Venous Thromboembolism and Estrogen-Containing Gender-Affirming Hormone Therapy

Gender-affirming therapy involves the use of hormones to develop the physical characteristics of the identified gender and suppressing endogenous sex hormone production. Venous thromboembolism (VTE) is a known risk of exogenous estrogen therapy, and while evidence of VTE risk among transgender women using modern gender-affirming hormone therapy (GAHT) is still emerging, it is thought to affect up to 5% of transgender women. Historically, GAHT was associated with a high risk of VTE however modern preparations are less thrombogenic mainly due to significantly lower doses used as well as different preparations. This review presents the available literature regarding the following 4 topics: 1) risk of VTE among transgender women receiving estradiol GAHT, 2) how the route of administration of estradiol affects the VTE risk, 3) perioperative management of GAHT, 4) VTE risk among children and adolescents on GAHT. There is a need for large, longitudinal studies of transgender women using GAHT to further characterise VTE risk and how this is affected by factors such as patient age, duration of GAHT use, tobacco use, BMI, and comorbidities. Future studies in these areas could inform the development of clinical guidelines to improve the care of transgender people.
A deep supervised transformer U-shaped full-resolution residual network for the segmentation of breast ultrasound image.

PURPOSE Breast ultrasound (BUS) is an important breast imaging tool. Automatic BUS image segmentation can measure the breast tumor size objectively and reduce doctors' workload. In this article, we proposed a deep supervised transformer U-shaped full-resolution residual network (DSTransUFRRN) to segment BUS images. METHODS In the proposed method, a full-resolution residual stream and a deep supervision mechanism were introduced into TransU-Net. The residual stream can keep full resolution features from different levels and enhance features fusion. Then, the deep supervision can suppress gradient dispersion. Moreover, the transformer module can suppress irrelevant features and improve feature extraction process. Two datasets (dataset A and B) were used for training and evaluation. The dataset A included 980 BUS image samples and the dataset B had 163 BUS image samples. RESULTS Cross-validation was conducted. For the dataset A, the proposed DSTransUFRRN achieved significantly higher Dice (91.04 ± 0.86%) than all compared methods (p < 0.05). For the dataset B, the Dice was lower than that for the dataset A due to the small number of samples, but the Dice of DSTransUFRRN (88.15% ± 2.11%) was significantly higher than that of other compared methods (p < 0.05). CONCLUSIONS In this study, we proposed DSTransUFRRN for BUS image segmentation. The proposed methods achieved significantly higher accuracy than the compared previous methods.
Functional Characterization of Four Olive Squalene Synthases with Respect to the Squalene Content of the Virgin Olive Oil

The release of new olive cultivars with an increased squalene content in their virgin olive oil is considered an important target in olive breeding programs. In this work, the variability of the squalene content in a core collection of 36 olive cultivars was first studied, revealing two olive cultivars, 'Dokkar' and 'Klon-14', with extremely low and high squalene contents in their oils, respectively. Next, four cDNA sequences encoding squalene synthases (SQS) were cloned from olive. Sequence analysis and functional expression in bacteria confirmed that they encode squalene synthases. Transcriptional analysis in distinct olive tissues and cultivars indicated that expression levels of these four SQS genes are spatially and temporally regulated in a cultivar-dependent manner and pointed to OeSQS2 as the gene mainly involved in squalene biosynthesis in olive mesocarp and, therefore, in the olive oil. In addition, the biosynthesis of squalene appears to be transcriptionally regulated in water-stressed olive mesocarp.
Examining attitudes toward a proposed sugar-sweetened beverage tax among urban Indigenous adults: a qualitative study using a decolonizing lens

Background: Sugar-sweetened beverage taxation has been proposed as a public health policy to reduce consumption, and compared with other ethnic or racialized groups in Canada, off-reserve Indigenous populations consume sugar-sweetened beverages at higher frequencies and quantities. We sought to explore the acceptability and anticipated outcomes of a tax on sugar-sweetened beverages among Indigenous adults residing in an inner-city Canadian neighbourhood. Methods: Using a community-based participatory research approach, we conducted semistructured interviews (November 2019–August 2020) with urban Indigenous adults using purposive sampling. Interviews were audio-recorded, transcribed verbatim and analyzed using theoretical thematic analysis. Results: All 20 participants (10 female, 8 male and 2 two-spirit) consumed sugar-sweetened beverages on a regular, daily basis at the time of the interview or at some point in their lives. Most participants were opposed to and concerned about the prospect of sugar-sweetened beverage taxation owing to 3 interconnected themes: government is not trustworthy, taxes are ineffective and lead to inequitable outcomes, and Indigenous self-determination is critical. Participants discussed government’s mismanagement of previous taxes and lack of prioritization of their community’s specific needs. Most participants anticipated that Indigenous people in their community would continue to consume sugar-sweetened beverages, but that a tax would result in fewer resources for other necessities, including foods deemed healthy. Interpretation: Low support for the tax among urban Indigenous people is characterized by distrust regarding the tax, policy-makers and its perceived effectiveness. Findings underscore the importance of self-determination in informing health policies that are equitable and nonstigmatizing.
Novel HPD mutation p.A244V compound with p.T219M causing tyrosinemia type III in a Chinese girl and review of the genotype–phenotype spectrum

Abstract Background Hereditary tyrosinemia type III (HT III) is an extremely rare form of tyrosinemia, characterized by autosomal recessive inheritance and biallelic mutations in the HPD gene. The clinical presentation of HT III is variable and poorly understood, with symptoms ranging from developmental delay and intellectual impairment to seizures and intermittent ataxia. This study aimed to provide further insights into the clinical and genetic characteristics of HT III. Methods A 3‐year‐old girl, identified through newborn screening, was diagnosed with HT III using targeted next‐generation sequencing. A comprehensive literature review was conducted, and the clinical, biochemical, and genetic findings of previously reported HT III patients were summarized and analyzed. Results The genetic analysis of the proband revealed compound heterozygous mutations in the HPD gene such as c.731C>T (p.A244V) and c.656C>T (p.T219M). Notably, the HPD p.A244V mutation had not been previously documented in public databases or the scientific literature. Bioinformatics analysis classified both variants as pathogenic variants. The patient exhibited persistent tyrosinemia, elevated levels of related metabolite derivatives, confirming the diagnosis of HT III. The review of previously published cases contributed to a better understanding of the clinical and genetic characteristics associated with HT III. Conclusion Early diagnosis and prompt treatment in infancy are crucial for managing HT III effectively. Dietary therapy, particularly during childhood, plays a significant role in disease management. The findings from this study enhance our understanding of the genotype–phenotype associations in HT III and emphasize the importance of early intervention for improved patient outcomes.
Clinicopathologic characteristics of neuroendocrine tumors with assessment by digital image analysis for Ki-67 index with a focus on the gastroenteropancreatic tract: a single-center study.

OBJECTIVES Neuroendocrine tumors (NETs) are a heterogeneous group of tumors that arise at various sites throughout the body. The gastroenteropancreatic (GEP) tract is the most common site of NETs. We investigated the clinicopathologic features of patients with GEP-NETs and the utility of digital image analysis, which was compared to eyeball estimation, a conventional method used to determine the Ki-67 labeling index. METHODS The clinicopathologic data of GEP-NET patients at Gangnam Severance Hospital from January 2008 to October 2019 were retrospectively analyzed. Each case was reclassified according to the 2019 World Health Organization classification system, to which the classification of grade 3 was added. Comparisons between eyeball estimation and the digital image analysis method for Ki-67 index assessment were performed by calculating Cohen's kappa (k) coefficient. RESULTS In total, 345 patients with GEP-NETs were enrolled. The mean age was 49.3 (range 13-79) years, with more male (61.1%) than female patients. The primary tumor sites were the rectum (70.1%), pancreas (12.5%), stomach (6.7%), and duodenum (5.8%). Overall, 298 (86.4%), 35 (10.1%), 2 (0.6%), and 10 (2.9%) patients exhibited grade 1, 2, and 3 and neuroendocrine carcinoma, respectively. Statistical analysis revealed that age > 50 years, tumor size > 2 cm, and presence of lymphovascular invasion, nodal metastasis, and distant metastasis were significantly associated with short overall survival. Additionally, 283 patients underwent digital image analysis of the Ki-67 index, and substantial agreement was found between the two methods (κ value: 0.765). CONCLUSIONS Eyeball estimation revealed non-inferior results compared with digital image analysis. Further research is needed to evaluate the possibility of using digital image analysis as an alternative analysis method.
Accelerating Monte Carlo Tree Search with Probability Tree State Abstraction

Monte Carlo Tree Search (MCTS) algorithms such as AlphaGo and MuZero have achieved superhuman performance in many challenging tasks. However, the computational complexity of MCTS-based algorithms is influenced by the size of the search space. To address this issue, we propose a novel probability tree state abstraction (PTSA) algorithm to improve the search efficiency of MCTS. A general tree state abstraction with path transitivity is defined. In addition, the probability tree state abstraction is proposed for fewer mistakes during the aggregation step. Furthermore, the theoretical guarantees of the transitivity and aggregation error bound are justified. To evaluate the effectiveness of the PTSA algorithm, we integrate it with state-of-the-art MCTS-based algorithms, such as Sampled MuZero and Gumbel MuZero. Experimental results on different tasks demonstrate that our method can accelerate the training process of state-of-the-art algorithms with 10%-45% search space reduction.
CAT-RRT: Motion Planning that Admits Contact One Link at a Time

Current motion planning approaches rely on binary collision checking to evaluate the validity of a state and thereby dictate where the robot is allowed to move. This approach leaves little room for robots to engage in contact with an object, as is often necessary when operating in densely cluttered spaces. In this work, we propose an alternative method that considers contact states as high-cost states that the robot should avoid but can traverse if necessary to complete a task. More specifically, we introduce Contact Admissible Transition-based Rapidly exploring Random Trees (CAT-RRT)11Supplementary video and open source code [1]. , a planner that uses a novel per-link cost heuristic to find a path by traversing high-cost obstacle regions. Through extensive testing, we find that state-of-the-art optimization planners tend to over-explore low-cost states, which leads to slow and inefficient convergence to contact regions. Conversely, CAT-RRT searches both low and high-cost regions simultaneously with an adaptive thresholding mechanism carried out at each robot link. This leads to paths with a balance between efficiency, path length, and contact cost.
Empower Nested Boolean Logic via Self-Supervised Curriculum Learning

Beyond the great cognitive powers showcased by language models, it is crucial to scrutinize whether their reasoning capabilities stem from strong generalization or merely exposure to relevant data. As opposed to constructing increasingly complex logic, this paper probes into the boolean logic, the root capability of a logical reasoner. We find that any pre-trained language models even including large language models only behave like a random selector in the face of multi-nested boolean logic, a task that humans can handle with ease. To empower language models with this fundamental capability, this paper proposes a new self-supervised learning method \textit{Curriculum Logical Reasoning} (\textsc{Clr}), where we augment the training data with nested boolean logic chain step-by-step, and program the training from simpler logical patterns gradually to harder ones. This new training paradigm allows language models to effectively generalize to much harder and longer-hop logic, which can hardly be learned through naive training. Furthermore, we show that boolean logic is a great foundation for improving the subsequent general logical tasks.
Effective Slogan Generation with Noise Perturbation

Slogans play a crucial role in building the brand's identity of the firm. A slogan is expected to reflect firm's vision and the brand's value propositions in memorable and likeable ways. Automating the generation of slogans with such characteristics is challenging. Previous studies developed and tested slogan generation with syntactic control and summarization models which are not capable of generating distinctive slogans. We introduce a novel approach that leverages pre-trained transformer T5 model with noise perturbation on newly proposed 1:N matching pair dataset. This approach serves as a contributing factor in generating distinctive and coherent slogans. Furthermore, the proposed approach incorporates descriptions about the firm and brand into the generation of slogans. We evaluate generated slogans based on ROUGE-1, ROUGE-L and Cosine Similarity metrics and also assess them with human subjects in terms of slogan's distinctiveness, coherence, and fluency. The results demonstrate that our approach yields better performance than baseline models and other transformer-based models.
Whispering LLaMA: A Cross-Modal Generative Error Correction Framework for Speech Recognition

We introduce a new cross-modal fusion technique designed for generative error correction in automatic speech recognition (ASR). Our methodology leverages both acoustic information and external linguistic representations to generate accurate speech transcription contexts. This marks a step towards a fresh paradigm in generative error correction within the realm of n-best hypotheses. Unlike the existing ranking-based rescoring methods, our approach adeptly uses distinct initialization techniques and parameter-efficient algorithms to boost ASR performance derived from pre-trained speech and text models. Through evaluation across diverse ASR datasets, we evaluate the stability and reproducibility of our fusion technique, demonstrating its improved word error rate relative (WERR) performance in comparison to n-best hypotheses by relatively 37.66%. To encourage future research, we have made our code and pre-trained models open source at https://github.com/Srijith-rkr/Whispering-LLaMA.