Miracle-dz/UniFAR-FTUs huggingface dataset:
Sperm Selection Using Zona Pellucida–Binding Enhanced Embryo Morphokinetic and Clinical Outcomes in ICSI: A Sibling Oocytes Study

The objective was to investigate the embryo morphokinitics using a time-lapse monitoring (TLM) system and assessment of clinical outcomes following intracytoplasmic sperm injection (ICSI) with zona pellucida (ZP)-bound sperm selection and conventional methods. A total of 371 metaphase II (MII) oocytes from 50 ICSI cycles were studied. Sibling oocytes were randomly divided into control (n = 199) and ZP-bound group (n = 172). All resulting zygotes were cultured and monitored in the TLM system up to Day 3 after ICSI. Fertilization rate, early embryo development, and clinical outcomes were evaluated. No significant differences were found in fertilization rate, time-lapse qualitative and quantitative measures, pronuclear fading time (PNF) t2, t3, t4, t5, t6, and t7 (times of cleavage to 2, 3, 4, 5, 6, and 7 cells), respectively. However, the t8 (time of cleavage to eight cells) and cc3 (duration of third cell cycle) revealed a significant difference between control and ZP-bound groups (p < .05). A significant difference between the two groups (p < .05) in the rates of Grade A embryos (according to Basile algorithm), chemical pregnancy, clinical pregnancy, and implantation was observed. Sperm selection using biological materials, such as ZP, improved both embryo quality and pregnancy outcomes, despite not affecting the early embryo development and morphokinetic parameters up to t8. This prospective randomized sibling oocyte trial was registered in October 2020 to January 2022 (IRCT20200705048021N1).
Interactions between classic psychedelics and serotonergic antidepressants: Effects on the acute psychedelic subjective experience, well-being and depressive symptoms from a prospective survey study

Background: There is growing evidence for the therapeutic effects of psychedelics. However, it is still uncertain how these drugs interact with serotonergic antidepressants (serotonin reuptake inhibitors (SRIs)). Objective: This study explores the interaction between psychedelics and SRIs in terms of therapeutic effects. The objective is to compare acute psychedelic effects and subsequent changes in well-being and depressive symptoms among ‘SRI −’ individuals (not on psychiatric medication) and ‘SRI +’ individuals (undergoing SRI treatment). Methods: Using prospective survey data, the study employs multivariate analysis of covariance (MANCOVA) and linear mixed effect models to analyse subjective differences and changes in well-being and depressive symptoms pre- and post-psychedelic experiences. Results: Results indicate that ‘SRI −’ participants experience significantly more intense subjective effects compared to ‘SRI +’ participants (F = 3.200, p = 0.016) in MANCOVA analysis. Further analysis reveals ‘SRI –’ individuals report stronger mystical (18.2% higher, p = 0.048), challenging (50.9% higher, p = 0.001) and emotional breakthrough experiences (31.9% higher, p = 0.02) than ‘SRI +’ individuals. No differences are observed in drug-induced visual effects (p = 0.19). Both groups exhibited similar improvements in well-being and depressive symptoms after the psychedelic experience. Conclusion: Individuals presumed to be on serotonergic antidepressants during psychedelic use display reduced subjective effects but similar antidepressant effects compared to those not undergoing SRI treatment. Further controlled research is needed to comprehend the interplay between serotonergic antidepressants and psychedelics, illuminating potential therapeutic benefits and limitations in clinical contexts.
Decreased retinal thickness in patients with Alzheimer’s disease is correlated with disease severity

Background and purpose The loss of retinal ganglion cells observed in Alzheimer’s disease (AD) may be attributable to a neurodegeneration of the neuro-retinal structure. Amnestic mild cognitive impairment (aMCI) has been considered a prodromal stage of AD. We evaluated retinal thicknesses in patients with aMCI and AD compared to healthy controls using spectral-domain optical coherence tomography (OCT) to investigate whether changes in retinal thickness are correlated with the clinical severity of dementia. Methods Patients with aMCI (n = 14), mild to moderate AD (n = 7), severe AD (n = 9), and age-matched controls (n = 17) underwent neuro-ophthalmologic examinations. Global deterioration scale (GDS), clinical dementia rating (CDR), and mini-mental status examination (MMSE) were used to evaluate the clinical overall severity of dementia. The thicknesses of the peripapillary retinal nerve fiber layer (RNFL), total macula, and macular ganglion cell-inner plexiform layer (GC-IPL) were measured using Cirrus HD-OCT. Results The severe AD group had overall significantly thinner GC-IPL, total macula, and peripapillary RNFL compared to the controls (p<0.05). In the mild to moderate AD group, the total macula, average RNFL, and superior RNFL thickness were each significantly reduced compared to controls (p<0.05). The aMCI group had reduced total macula, average RNFL, and inferior RNFL thickness, but there were no significant differences compared to the controls. The GDS and CDR scores had a negative correlation with the thickness of the GC-IPL and the total macula. The MMSE scores had a positive correlation with both the total macular and average RNFL thickness, when adjusted for age (p<0.05). Conclusions This study confirmed that retinal thickness is decreased in AD patients. There is a correlation between reduced retinal thickness and the clinical severity of dementia.
Prophylactic tranexamic acid for reducing intraoperative blood loss during cesarean section in women at high risk of postpartum hemorrhage: A double-blind placebo randomized controlled trial

Background: Postpartum hemorrhage remains a leading cause of maternal mortality especially in developing countries. The majority of previous trials on the effectiveness of tranexamic acid in reducing blood loss were performed in low-risk women for postpartum hemorrhage. A recent Cochrane Systematic Review recommended that further research was needed to determine the effects of prophylactic tranexamic acid for preventing intraoperative blood loss in women at high risk of postpartum hemorrhage. Objective: This study aimed to evaluate the effectiveness and safety of tranexamic acid in reducing intraoperative blood loss when given prior to cesarean delivery in women at high risk of postpartum hemorrhage. Study design: The study is a double-blind randomized controlled trial. Methods: The study consisted of 200 term pregnant women and high-risk preterm pregnancies scheduled for lower-segment cesarean delivery at Enugu State University of Science and Technology, Teaching Hospital, Parklane, Enugu, Nigeria. The participants were randomized into two arms (intravenous 1 g of tranexamic acid or placebo) in a ratio of 1:1. The participants received either 1 g of tranexamic acid or placebo (20 mL of normal saline) intravenously at least 10 min prior to commencement of the surgery. The primary outcome measures were the mean intraoperative blood loss and hematocrit change 48 h postoperatively. Results: The baseline sociodemographic characteristics were similar in both groups. The tranexamic acid group when compared to the placebo group showed significantly lower mean blood loss (442.94 ± 200.97 versus 801.28 ± 258.68 mL; p = 0.001), higher mean postoperative hemoglobin (10.39 + 0.96 versus 9.67 ± 0.86 g/dL; p = 0.001), lower incidence of postpartum hemorrhage (1.0% versus 19.0%; p = 0.001), and lower need for use of additional uterotonic agents after routine management of the third stage of labor (39.0% versus 68.0%; p = 0.001), respectively. However, there was no significant difference in the mean preoperative hemoglobin (11.24 ± 0.88 versus 11.15 ± 0.90 g/dL; p = 0.457), need for other surgical intervention for postpartum hemorrhage (p > 0.05), and reported side effect, respectively, between the two groups. Conclusion: Prophylactic administration of tranexamic acid significantly decreases postpartum blood loss, improves postpartum hemoglobin, decreases the need for additional uterotonics, and prevents postpartum hemorrhage following cesarean section in pregnant women at high risk of postpartum hemorrhage. Its routine use during cesarean section in high-risk women may be encouraged. The trial was registered in the Pan-African Clinical Trial Registry with approval number PACTR202107872851363.
Potential predictability sources of the 2012 U.S. drought in observations and a regional model ensemble

The 2012 drought was the most severe and extensive summertime U.S. drought in half a century with substantial economic loss and impacts on food security and commodity prices. A unique aspect of the 2012 drought was its rapid onset and intensification over the Southern Rockies, extending to the Great Plains during late spring and early summer, and the absence of known precursor large‐scale patterns. Drought prediction therefore remains a major challenge. This study evaluates relationships among snow, soil moisture, and precipitation to identify sources of potential predictability of the 2012 summer drought using observations and a Weather Research and Forecasting model multiphysics ensemble experiment. Although underestimated in intensity, the drought signal is robust to the way atmospheric physical processes are represented in the model. For the Southern Rockies, soil moisture exhibits stronger persistence than precipitation in observations and the ensemble experiment. Correlations between winter/spring snowmelt and concurrent and following season soil moisture, and between soil moisture and concurrent and following season precipitation, in both observations and the model ensemble, suggest potential predictability beyond 1 and 2 month lead‐time reside in the land surface conditions for apparent flash droughts such as the 2012 drought.
Comparison of the Capacity of Several Machine Learning Tools to Assist Immunofluorescence-Based Detection of Anti-Neutrophil Cytoplasmic Antibodies

The success of artificial intelligence and machine learning is an incentive to develop new algorithms to increase the rapidity and reliability of medical diagnosis. Here we compared different strategies aimed at processing microscope images used to detect anti-neutrophil cytoplasmic antibodies, an important vasculitis marker: (i) basic classifier methods (logistic regression, k-nearest neighbors and decision tree) were used to process custom-made indices derived from immunofluorescence images yielded by 137 sera. (ii) These methods were combined with dimensional reduction to analyze 1733 individual cell images. iii) More complex models based on neural networks were used to analyze the same dataset. The efficiency of discriminating between positive and negative samples and different fluorescence patterns was quantified with Rand-type accuracy index, kappa index and ROC curve. It is concluded that basic models trained on a limited dataset allowed positive/negative discrimination with an efficiency comparable to that obtained by conventional analysis performed by humans (0.84 kappa score). More extensive datasets may be required for efficient discrimination between different fluorescence patterns generated by different auto-antibody species.
UMBRELLA: A One-Stop Shop Bridging the Gap From Lab to Real-World IoT Experimentation

UMBRELLA (A Living Laboratory: https://www.umbrellaiot.com/) is an open, large-scale IoT ecosystem deployed across South Gloucestershire, UK. It is intended to accelerate innovation across multiple technology domains. UMBRELLA is built to bridge the gap between existing specialised testbeds and address holistically real-world technological challenges in a System-of-Systems (SoS) fashion. UMBRELLA provides open access to real-world devices and infrastructure, enabling researchers and the industry to evaluate solutions for Smart Cities, Robotics, Wireless Communications, Edge Intelligence, and more. Key features include over 200 multi-sensor nodes installed on public infrastructure, a robotics arena with 20 mobile robots, a 5G network-in-a-box solution, and a unified backend platform for management, control and secure user access. The heterogeneity of hardware components, including diverse sensors, communication interfaces, and GPU-enabled edge devices, coupled with tools like digital twins, allows for comprehensive experimentation and benchmarking of innovative solutions unviable in lab environments. This paper provides a comprehensive overview of UMBRELLA’s multi-domain architecture and capabilities, making it an ideal playground for Internet of Things (IoT) and Industrial IoT (IIoT) innovation. It discusses the challenges in designing, developing and operating UMBRELLA as an open, sustainable testbed and shares lessons learned to guide similar future initiatives. With its unique openness, heterogeneity, realism and tools, UMBRELLA aims to continue accelerating cutting-edge technology research, development and translation into real-world progress.
A Framework for Assurance Audits of Algorithmic Systems

An increasing number of regulations propose the notion of AI audits as an enforcement mechanism for achieving transparency and accountability for AI systems. Despite some converging norms around various forms of AI auditing, auditing for the purpose of compliance and assurance currently have little to no agreed upon practices, procedures, taxonomies, and standards. We propose the criterion audit as an operationalizable compliance and assurance external audit framework. We model elements of this approach after financial auditing practices, and argue that AI audits should similarly provide assurance to their stakeholders about AI organizations' ability to govern their algorithms in ways that mitigate harms and uphold human values. We discuss the necessary conditions for the criterion audit, and provide a procedural blueprint for performing an audit engagement in practice. We illustrate how this framework can be adapted to current regulations by deriving the criteria on which bias audits for hiring algorithms can be performed, as required by the recently effective New York City Local Law 144 of 2021. We conclude by offering critical discussion on the benefits, inherent limitations, and implementation challenges of applying practices of the more mature financial auditing industry to AI auditing where robust guardrails against quality assurance issues are only starting to emerge. Our discussion as informed by experiences in performing these audits in practice highlights the critical role that an audit ecosystem plays in ensuring the effectiveness of such methodology.
Self-interacting dark matter solves the final parsec problem of supermassive black hole mergers

Evidence for a stochastic gravitational wave (GW) background, plausibly originating from the merger of supermassive black holes (SMBHs), is accumulating with observations from pulsar timing arrays. An outstanding question is how inspiraling SMBHs get past the"final parsec"of separation, where they have a tendency to stall before GW emission alone can make the binary coalesce. We argue that dynamical friction from the dark matter (DM) spike surrounding the black holes is sufficient to resolve this puzzle, if the DM has a self-interaction cross section of order $0.2-5\,$cm$^2$/g. The same effect leads to a softening of the GW spectrum at low frequencies as suggested by the current data. For collisionless cold DM, the friction deposits so much energy that the spike is disrupted and cannot bridge the final parsec, while for self-interacting DM, the isothermal core of the halo can act as a reservoir for the energy liberated from the SMBH orbits. A realistic velocity dependence, such as generated by the exchange of a massive mediator like a dark photon, is favored to give a good fit to the GW spectrum while providing a large enough core. A similar velocity dependence has been advocated for solving the small-scale structure problems of cold DM.
SparseCoder: Identifier-Aware Sparse Transformer for File-Level Code Summarization

Code summarization aims to generate natural language descriptions of source code, facilitating programmers to understand and maintain it rapidly. While previous code summarization efforts have predominantly focused on method-level, this paper studies file-level code summarization, which can assist programmers in understanding and maintaining large source code projects. Unlike method-level code summarization,file-level code summarization typically involves long source code within a single file, which makes it challenging for Transformer-based models to understand the code semantics for the maximum input length of these models is difficult to set to a large number that can handle long code input well, due to the quadratic scaling of computational complexity with the input sequence length. To address this challenge, we propose SparseCoder, an identifier-aware sparse transformer for effectively handling long code sequences. Specifically, the SparseCoder employs a sliding window mechanism for self-attention to model short-term dependencies and leverages the structure message of code to capture long-term dependencies among source code identifiers by introducing two types of sparse attention patterns named global and identifier attention. To evaluate the performance of SparseCoder, we construct a new dataset FILE-CS for file-level code summarization in Python. Experimental results show that our SparseCoder model achieves state-of-the-art performance compared with other pre-trained models, including full self-attention and sparse models. Additionally, our model has low memory overhead and achieves comparable performance with models using full self-attention mechanism.
Off-Policy Primal-Dual Safe Reinforcement Learning

Primal-dual safe RL methods commonly perform iterations between the primal update of the policy and the dual update of the Lagrange Multiplier. Such a training paradigm is highly susceptible to the error in cumulative cost estimation since this estimation serves as the key bond connecting the primal and dual update processes. We show that this problem causes significant underestimation of cost when using off-policy methods, leading to the failure to satisfy the safety constraint. To address this issue, we propose \textit{conservative policy optimization}, which learns a policy in a constraint-satisfying area by considering the uncertainty in cost estimation. This improves constraint satisfaction but also potentially hinders reward maximization. We then introduce \textit{local policy convexification} to help eliminate such suboptimality by gradually reducing the estimation uncertainty. We provide theoretical interpretations of the joint coupling effect of these two ingredients and further verify them by extensive experiments. Results on benchmark tasks show that our method not only achieves an asymptotic performance comparable to state-of-the-art on-policy methods while using much fewer samples, but also significantly reduces constraint violation during training. Our code is available at https://github.com/ZifanWu/CAL.
Semantic Sensitivities and Inconsistent Predictions: Measuring the Fragility of NLI Models

Recent studies of the emergent capabilities of transformer-based Natural Language Understanding (NLU) models have indicated that they have an understanding of lexical and compositional semantics. We provide evidence that suggests these claims should be taken with a grain of salt: we find that state-of-the-art Natural Language Inference (NLI) models are sensitive towards minor semantics preserving surface-form variations, which lead to sizable inconsistent model decisions during inference. Notably, this behaviour differs from valid and in-depth comprehension of compositional semantics, however does neither emerge when evaluating model accuracy on standard benchmarks nor when probing for syntactic, monotonic, and logically robust reasoning. We propose a novel framework to measure the extent of semantic sensitivity. To this end, we evaluate NLI models on adversarially generated examples containing minor semantics-preserving surface-form input noise. This is achieved using conditional text generation, with the explicit condition that the NLI model predicts the relationship between the original and adversarial inputs as a symmetric equivalence entailment. We systematically study the effects of the phenomenon across NLI models for \text{\emph{in-}} and \text{\emph{out-of-}} domain settings. Our experiments show that semantic sensitivity causes performance degradations of 12.92% and 23.71% average over \text{\emph{in-}} and \text{\emph{out-of-}} domain settings, respectively. We further perform ablation studies, analysing this phenomenon across models, datasets, and variations in inference and show that semantic sensitivity can lead to major inconsistency within model predictions.
Understanding Disparities in Post Hoc Machine Learning Explanation

Previous work has highlighted that existing post-hoc explanation methods exhibit disparities in explanation fidelity (across 'race' and 'gender' as sensitive attributes), and while a large body of work focuses on mitigating these issues at the explanation metric level, the role of the data generating process and black box model in relation to explanation disparities remains largely unexplored. Accordingly, through both simulations as well as experiments on a real-world dataset, we specifically assess challenges to explanation disparities that originate from properties of the data: limited sample size, covariate shift, concept shift, omitted variable bias, and challenges based on model properties: inclusion of the sensitive attribute and appropriate functional form. Through controlled simulation analyses, our study demonstrates that increased covariate shift, concept shift, and omission of covariates increase explanation disparities, with the effect pronounced higher for neural network models that are better able to capture the underlying functional form in comparison to linear models. We also observe consistent findings regarding the effect of concept shift and omitted variable bias on explanation disparities in the Adult income dataset. Overall, results indicate that disparities in model explanations can also depend on data and model properties. Based on this systematic investigation, we provide recommendations for the design of explanation methods that mitigate undesirable disparities.
DAM: Diffusion Activation Maximization for 3D Global Explanations

In recent years, the performance of point cloud models has been rapidly improved. However, due to the limited amount of relevant explainability studies, the unreliability and opacity of these black-box models may lead to potential risks in applications where human lives are at stake, e.g. autonomous driving or healthcare. This work proposes a DDPM-based point cloud global explainability method (DAM) that leverages Point Diffusion Transformer (PDT), a novel point-wise symmetric model, with dual-classifier guidance to generate high-quality global explanations. In addition, an adapted path gradient integration method for DAM is proposed, which not only provides a global overview of the saliency maps for point cloud categories, but also sheds light on how the attributions of the explanations vary during the generation process. Extensive experiments indicate that our method outperforms existing ones in terms of perceptibility, representativeness, and diversity, with a significant reduction in generation time. Our code is available at: https://github.com/Explain3D/DAM