Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptation to full weight‐bearing following disuse in rats: The impact of biological sex on musculoskeletal recovery

Abstract With the technological advances made to expand space exploration, astronauts will spend extended amounts of time in space before returning to Earth. This situation of unloading and reloading influences human physiology, and readaptation to full weight‐bearing may significantly impact astronauts' health. On Earth, similar situations can be observed in patients who are bedridden or suffer from sport‐related injuries. However, our knowledge of male physiology far exceeds our knowledge of female's, which creates an important gap that needs to be addressed to understand the sex‐based differences regarding musculoskeletal adaptation to unloading and reloading, necessary to preserve health of both sexes. Using a ground‐based model of total unloading for 14 days and reloading at full weight‐bearing for 7 days rats, we aimed to compare the musculoskeletal adaptations between males and females. Our results reveal the existence of significant differences. Indeed, males experienced bone loss both during the unloading and the reloading period while females did not. During simulated microgravity, males and females showed comparable muscle deconditioning with a significant decline in rear paw grip strength. However, after 7 days of recovery, muscle strength improved. Additionally, sex‐based differences in myofiber size existing at baseline are significantly reduced or eliminated following unloading and recovery.
Global observations of cloud‐sensitive aerosol loadings in low‐level marine clouds

Aerosol‐cloud interaction is a key component of the Earth's radiative budget and hydrological cycle, but many facets of its mechanisms are not yet fully understood. In this study, global satellite‐derived aerosol and cloud products are used to identify at what aerosol loading cloud droplet size shows the greatest sensitivity to changes in aerosol loading (ACSmax). While, on average, cloud droplet size is most sensitive at relatively low aerosol loadings, distinct spatial and temporal patterns exist. Possible determinants for these are identified with reanalysis data. The magnitude of ACSmax is found to be constrained by the total columnar water vapor. Seasonal patterns of water vapor are reflected in the seasonal patterns of ACSmax. Also, situations with enhanced turbulent mixing are connected to higher ACSmax, possibly due to intensified aerosol activation. Of the analyzed aerosol species, dust seems to impact ACSmax the most, as dust particles increase the retrieved aerosol loading without substantially increasing the concentration of cloud condensation nuclei.
Regional dimensions of innovative activity in outer Western Sydney

iii Abstract The aim of this research is to understand the socio-economic development of a metropolitan region in Sydney through an analysis of regional innovative activity. South West Sydney, a major growth region within Sydney, includes the Local Government Areas (LGAs) of Liverpool, Campbelltown, Camden and Wollondilly. This region has absorbed 25% of Sydney’s population growth in the period from 1991-2001. Although South West Sydney has experienced rapid population growth, this has not been matched by associated employment growth. In some sectors such as business services employment growth has been minimal in the previous decade, this is particularly the case in Liverpool, the regional centre of South West Sydney. Population growth is estimated to continue at the current rate (in excess of 5% per annum) for at least the next fifteen years. In this environment, local government authorities in the region are seeking ways in which to develop the regional economy of South West Sydney and increase the amount of sustainable employment commensurably with current population and labour force increases. The role of innovative activity has a central place in economic development. This thesis uses a ‘systems of innovation’ (SI) approach to examine innovative activity in the South West Sydney region. SI understands innovation as a socially embedded process of transforming ideas and knowledge into novel products, processes and services through the processes of learning and searching. The approach recognises that innovative activity is determined by various actors (firms and institutions) and the interactivity between these actors and the cumulative base of knowledge in which they operate. The Regional Innovations Systems (RIS) framework develops from an acknowledgement that innovation is primarily a geographically bounded phenomenon. The RIS approach sees that specific local resources are important in determining and encouraging the innovative activities carried out by local firms and hence, the competitiveness of these areas. The RIS literature provides two fields of understanding of what constitutes a regional innovation system. The first takes the global examples of highly innovative regions such as Silicon Valley and Route 128 in the United States of America (Saxenian 1994), South West England (Cooke and Morgan 1998), Baden Wurttemberg in Germany (Cooke 2001; Braczyk, Cooke et al. 2004), Northern Italy (Piore and Sabel 1984) and in Australia, the North Ryde corridor (Searle and Pritchard 2005). These regions represent ‘ideal’ or ‘star’ RIS, with highly specialised and networked clusters of firms, many
Effect of the NFIB rs28379954 T>C polymorphism on CYP2D6‐catalyzed metabolism of solanidine

Abstract Cytochrome P450 2D6 (CYP2D6) is important for metabolism of 20%–25% of all clinically used drugs. Many known genetic variants contribute to the large interindividual variability in CYP2D6 metabolism, but much is still unexplained. We recently described that nuclear factor 1B (NFIB) regulates hepatic CYP2D6 expression with the minor allele of NFIB rs28379954 T>C significantly increasing CYP2D6‐mediated risperidone metabolism. In this study, we investigated the effect of NFIB T>C on metabolism of solanidine, a dietary CYP2D6 substrate. Analyses of solanidine and metabolites (M414, M416, and M444) were performed by ultra‐high performance liquid chromatography‐high‐resolution mass spectrometry in a cohort of 463 CYP2D6‐genotyped patients of which with 58 (12.5%) carried NFIB TC (n = 56) or CC (n = 2). Increased metabolism of solanidine was found in CYP2D6 normal metabolizers (NMs; n = 258, 55.7%) carrying the NFIB C variant (n = 27, 5.8%) with 2.83‐ and 3.38‐fold higher M416‐to‐solanidine (p = 0.039) and M444‐to‐solanidine (p = 0.046) ratios, respectively, whereas this effect was not significant among intermediate metabolizers (n = 166, 35.9%) (p ≥ 0.09). Importantly, no effect of the NFIB polymorphism on solanidine metabolism was seen in TC or CC carriers lacking CYP2D6 activity (poor metabolizers, n = 30, 6.5%, p ≥ 0.74). Furthermore, the NFIB polymorphism significantly explained variability in solanidine metabolism (M414 p = 0.013, M416 p = 0.020, and M416 and M444 p = 0.009) in multiple linear regression models for each metabolic ratio in the entire population, correcting for covariates (including CYP2D6 genotypes). Thus, the study confirms the effect of NFIB in regulating CYP2D6 activity, suggesting an about 200% increase in CYP2D6‐mediated clearance in NMs being NFIB CT or CC carriers, comprising around 6% of Europeans.
Plasminogen activator inhibitor‐1 serum levels in frontotemporal lobar degeneration

Abstract Plasminogen activator inhibitor‐1 (PAI‐1) impedes brain plasmin synthesis. Reduced plasmin activity facilitates cumulation of amyloid beta (Aβ) in Alzheimer's disease (AD). Since plasmin also regulates the synaptic activity, it is possible that altered PAI‐1 is present in other neurodegenerative disorders. We investigated whether PAI‐1 and its counter‐regulatory tissue plasminogen activator (tPA) are altered in serum of patients with dementia due to frontotemporal lobar degeneration (FTLD). Thirty five FTLD patients (21 in mild cognitive impairment stage (MCI) and 14 in dementia stage) and 10 cognitively healthy controls were recruited. Serum tPA and PAI‐1 protein levels were measured by anova. Correlation between biochemical and demographic data were explored by measuring Pearson correlation coefficient. Serum PAI‐1 levels were elevated in the FTLD dementia group as compared to FTLD MCI and controls. tPA serum levels and PAI‐1/tPA ratio did not significantly differ among groups. There was a negative correlation between PAI‐1 serum levels and disease severity measured by MMSE score. No correlations of tPA serum levels and PAI‐1/tPA ratio with MMSE were found. Increased PAI‐1 serum levels may serve as a marker of dementia in FTLD, suggesting that, besides Aβ pathway, the plasmin system may affect cognition through synaptic activity.
AST-120 improved uremic pruritus by lowering indoxyl sulfate and inflammatory cytokines in hemodialysis patients

Background and hypothesis: Pruritus is a common and distressing symptom that affects patients with chronic kidney disease. The concentration of protein bounded uremic toxin was associated with the uremic pruritus. The aim is to assess the efficacy of AST-120 for uremic pruritus in hemodialysis patients. Materials and Methods: The participants were enrolled and then divided into the AST-120 treatment group and control group with a ratio of 2:1. All participants underwent pre-observation screenings two weeks before the study with three visits. In the treatment phase (week 1 to week 4), the treatment group added 6g/day of AST-120 along with routine anti-pruritic treatment. Visual analog scale (VAS) and biochemical parameters were measured. Results: The VAS score began to be lower in the AST-120 treatment group after the 5th visiting (p < 0.05). The reduction in indoxyl sulfate (IS) at 5th week along with TNF-alpha. The reduction ratio of indoxyl sulfate correlated with reduction of parathyroid hormone. Conclusion: This study has demonstrated that the four-week treatment of AST-120 decreased the severity of uremic pruritus in patients with ESRD. The concentration of IS and TNF-alpha decreased in the AST-120 treatment group. The reduction of iPTH correlated with the reduction of IS in the AST-120 treatment.
Quadrupedal Robots Whole-Body Motion Control 
Based on Centroidal Momentum Dynamics

In this paper, we demonstrate a method for quadruped dynamic locomotion based oncentroidal momentum control. Our method relies on a quadratic program that solves an optimalcontrol problem to track the reference rate of change of centroidal momentum as closely as possiblewhile satisfying the dynamic, input, and contact constraints of the full quadruped robot dynamics.Given the desired footstep positions, the according reference rate of change of the centroidalmomentum is formulated as a feedback control task derived from the CoM motions of a simplifiedmodel (linear inverted pendulum) based on Capture Point dynamics. The joint accelerations and theGround Reaction Forces(GRFs) outputted from the quadratic program solver are used to calculatethe desired joint torques using an inverse dynamics algorithm. The performance of the proposedmethod is tested in simulation and on real hardware.
Functional connectivity characteristics of the brain network involved in prickle perception of single fiber stimulation

Abstract Background The complex network connections, information transmission and organization play key roles in brain cognition on sensory stimulation. Previous studies showed that several brain regions of somatosensory, motor, emotional, cognitive, etc. are linked to fabric‐evoked prickle. But the functional connectivity characteristics of the brain network involved in prickle perception is still unclear. Materials and Methods In the present study, resting state fMRI (functional magnetic resonance imaging) with functional connectivity analysis was adopted to build the initial brain functional network, and task fMRI with psychophysiological interaction analysis was employed to investigate modulation features of prickling task to functional connections in the brain network. Results The results showed that, in resting state, six groups or sub‐networks can be identified in the prickle network, and when the subjects performed the prickling task, functional connectivity strength between some seed regions (e.g., somatosensory regions and precuneus, emotional regions and the prefrontal cortex, etc.) in the network increased. Conclusion Combining resting‐state fMRI with task fMRI is a feasible and promising method to study functional connectivity characteristics of the brain network involved in prickle perception. It is inferred that the “itch” ingredient of prickle sensation was transmitted from somatosensory cortices to precuneus, and emotional attribute (e.g., pain) from somatosensory cortices to the prefrontal cortex and at last to emotional regions.
Tubulointerstitial Nephritis and Uveitis Syndrome During the COVID-19 Pandemic: A Case Series

Objectives: To report the ocular findings, laboratory results, and management of patients with tubulointerstitial nephritis and uveitis syndrome (TINU), whose numbers increased during the 2019 coronavirus disease (COVID-19) pandemic. Materials and Methods: Demographic characteristics, ophthalmic examination findings, laboratory results including polymerase chain reaction (PCR) test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), serum SARS-CoV-2 immunoglobulin G (IgG) antibody, and treatment of patients diagnosed with TINU between March 2020 and March 2022 were evaluated retrospectively. Results: The study included 19 eyes of 10 patients (6 female/4 male). The mean age was 13.5±2.4 years (range: 8-16 years). The mean follow-up duration was 13.5±6.1 months (range: 6-24 months). All patients presented with anterior uveitis. Anterior uveitis was bilateral in 9 patients (90%) and unilateral in 1 patient (10%). Posterior segment findings were normal in 8 patients (80%), and bilateral optic disc edema was observed in only 2 patients (20%). None of the patients had a previous SARS-CoV-2 infection and/or vaccination history. The SARS-CoV-2 PCR test was negative in all patients at presentation. The SARS-CoV-2 IgG antibody test was reactive in 7 patients (70%). Recurrent uveitis developed in 8 patients (80%) during follow-up. Systemic immunomodulatory therapy was required for the control of ocular inflammation in 7 patients (70%) with severe uveitis flare-ups. Conclusion: TINU is a multisystemic autoimmune disease, especially in response to environmental triggering factors such as viral infections. Although TINU is a rare disease, the number of cases increased during the COVID-19 pandemic. SARS-CoV-2 antibodies were detected at a significant rate of 70% in these patients, who did not have a history of SARS-CoV-2 infection and vaccination. Previous asymptomatic SARS-CoV-2 infection in children may be a triggering factor in the development of TINU.
Seasonal Variation of Proximate Composition of Common Seaweeds in Indian Sundarbans

We conducted a first order analysis on the proximate composition (protein, carbohydrate, fat and astaxanthin) of three dominant seaweed species viz. Enteromorpha intestinalis, Ulva lactuca and Catenella repens inhabiting Indian Sundarbans. The study was conducted at three stations (Gosaba, Bali Island and Jharkhali) during premonsoon, monsoon and postmonsoon of 2014-15. The relevant hydrological parameters (surface water temperature, salinity, pH, dissolved oxygen and dissolved nutrients) were monitored simultaneously during the tenure of the work. ANOVA carried out on the observed data reflects pronounced variations of all hydrological parameters except surface water temperature and salinity between stations. Pronounced seasonal variations were observed for all the selected hydrological parameters. In the domain of proximate composition, ANOVA results exhibit pronounced variations between stations and seasons (except carbohydrate in U. lactuca and C. repens between stations and astaxanthin in U. lactuca between seasons).
An Error-Matching Exclusion Method for Accelerating Visual SLAM

In Visual SLAM, achieving accurate feature matching consumes a significant amount of time, severely impacting the real-time performance of the system. This paper proposes an accelerated method for Visual SLAM by integrating GMS (Grid-based Motion Statistics) with RANSAC (Random Sample Consensus) for the removal of mismatched features. The approach first utilizes the GMS algorithm to estimate the quantity of matched pairs within the neighborhood and ranks the matches based on their confidence. Subsequently, the Random Sample Consensus (RANSAC) algorithm is employed to further eliminate mismatched features. To address the time-consuming issue of randomly selecting all matched pairs, this method transforms it into the problem of prioritizing sample selection from high-confidence matches. This enables the iterative solution of the optimal model. Experimental results demonstrate that the proposed method achieves a comparable accuracy to the original GMS-RANSAC while reducing the average runtime by 24.13% on the KITTI, TUM desk, and TUM doll datasets.
Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation

Average treatment effect estimation is the most central problem in causal inference with application to numerous disciplines. While many estimation strategies have been proposed in the literature, the statistical optimality of these methods has still remained an open area of investigation, especially in regimes where these methods do not achieve parametric rates. In this paper, we adopt the recently introduced structure-agnostic framework of statistical lower bounds, which poses no structural properties on the nuisance functions other than access to black-box estimators that achieve some statistical estimation rate. This framework is particularly appealing when one is only willing to consider estimation strategies that use non-parametric regression and classification oracles as black-box sub-processes. Within this framework, we prove the statistical optimality of the celebrated and widely used doubly robust estimators for both the Average Treatment Effect (ATE) and the Average Treatment Effect on the Treated (ATT), as well as weighted variants of the former, which arise in policy evaluation.
Extention of Bagging MARS with Group LASSO for Heterogeneous Treatment Effect Estimation

Recent years, large scale clinical data like patient surveys and medical record data are playing an increasing role in medical data science. These large-scale clinical data, collectively referred to as"real-world data (RWD)". It is expected to be widely used in large-scale observational studies of specific diseases, personal medicine or precise medicine, finding the responder of drugs or treatments. Applying RWD for estimating heterogeneous treat ment effect (HTE) has already been a trending topic. HTE has the potential to considerably impact the development of precision medicine by helping doctors make more informed precise treatment decisions and provide more personalized medical care. The statistical models used to estimate HTE is called treatment effect models. Powers et al. proposed a some treatment effect models for observational study, where they pointed out that the bagging causal MARS (BCM) performs outstanding compared to other models. While BCM has excellent performance, it still has room for improvement. In this paper, we proposed a new treatment effect model called shrinkage causal bagging MARS method to improve their shared basis conditional mean regression framework based on the following points: first, we estimated basis functions using transformed outcome, then applied the group LASSO method to optimize the model and estimate parameters. Besides, we are focusing on pursing better interpretability of model to improve the ethical acceptance. We designed simulations to verify the performance of our proposed method and our proposed method superior in mean square error and bias in most simulation settings. Also we applied it to real data set ACTG 175 to verify its usability, where our results are supported by previous studies.
Random-Order Online Interval Scheduling and Geometric Generalizations

In the Maximum Independent Set of Hyperrectangles problem, we are given a set of $n$ (possibly overlapping) $d$-dimensional axis-aligned hyperrectangles, and the goal is to find a subset of non-overlapping hyperrectangles of maximum cardinality. For $d=1$, this corresponds to the classical Interval Scheduling problem, where a simple greedy algorithm returns an optimal solution. In the offline setting, for $d$-dimensional hyperrectangles, polynomial time $(\log n)^{O(d)}$-approximation algorithms are known. However, the problem becomes notably challenging in the online setting, where the input objects (hyperrectangles) appear one by one in an adversarial order, and on the arrival of an object, the algorithm needs to make an immediate and irrevocable decision whether or not to select the object while maintaining the feasibility. Even for interval scheduling, an $\Omega(n)$ lower bound is known on the competitive ratio. To circumvent these negative results, in this work, we study the online maximum independent set of axis-aligned hyperrectangles in the random-order arrival model, where the adversary specifies the set of input objects which then arrive in a uniformly random order. Starting from the prototypical secretary problem, the random-order model has received significant attention to study algorithms beyond the worst-case competitive analysis. Surprisingly, we show that the problem in the random-order model almost matches the best-known offline approximation guarantees, up to polylogarithmic factors. In particular, we give a simple $(\log n)^{O(d)}$-competitive algorithm for $d$-dimensional hyperrectangles in this model, which runs in $\tilde{O_d}(n)$ time. Our approach also yields $(\log n)^{O(d)}$-competitive algorithms in the random-order model for more general objects such as $d$-dimensional fat objects and ellipsoids.
Moonwalk: Inverse-Forward Differentiation

Backpropagation, while effective for gradient computation, falls short in addressing memory consumption, limiting scalability. This work explores forward-mode gradient computation as an alternative in invertible networks, showing its potential to reduce the memory footprint without substantial drawbacks. We introduce a novel technique based on a vector-inverse-Jacobian product that accelerates the computation of forward gradients while retaining the advantages of memory reduction and preserving the fidelity of true gradients. Our method, Moonwalk, has a time complexity linear in the depth of the network, unlike the quadratic time complexity of na\"ive forward, and empirically reduces computation time by several orders of magnitude without allocating more memory. We further accelerate Moonwalk by combining it with reverse-mode differentiation to achieve time complexity comparable with backpropagation while maintaining a much smaller memory footprint. Finally, we showcase the robustness of our method across several architecture choices. Moonwalk is the first forward-based method to compute true gradients in invertible networks in computation time comparable to backpropagation and using significantly less memory.