Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolvability of Drought Response in Four Native and Non-native Conifers: Opportunities for Forest and Genetic Resource Management in Europe

Intraspecific genetic variation in drought response is expected to play an important role in determining the persistence of tree populations in global change as it (1) allows for spontaneous selection and local adaptation of tree populations, (2) supports assisted seed transfer of less-drought-sensitive provenance, and (3) enables the integration of drought-sensitivity traits into tree breeding. Estimating the potential of such adaptation options requires quantitative genetic knowledge of drought sensitivity across significant parts of species distributions and a comparative assessment of genetic variation within economically and ecologically important tree species. We quantified genetic variation within and among populations of four conifers growing within common garden experiments in the drought-prone eastern Austria. This region experienced three strong drought periods between 1980 and 2010 that resulted in significant reductions in radial growth. Among the four tested species, Douglas-fir revealed the highest resistance during drought and silver fir the best recovery after drought, while European larch and Norway spruce showed the lowest resistance. High genetic variation among populations and phenotypic stability across all three drought events was found for Norway spruce and silver fir, but not for the other species. Heritability and evolvability of drought traits, both approximated via genetic repeatability, revealed strong differences among populations of all four species. Repeatability and evolvability for resistance were highest in Norway spruce and, for recovery, highest in European larch. Our comparison indicates that the mean drought sensitivity of a species is not related to the intraspecific genetic variation in drought response. Thus, also highly drought-sensitive species, such as Norway spruce and European larch, harbor significant genetic variation in drought response within and among populations to justify targeted tree breeding, assisted gene flow, and supportive forest management to foster local adaptations to future conditions.
Real-World Effectiveness of Natalizumab in Korean Patients With Multiple Sclerosis

Background and Purpose: Natalizumab is a highly efficacious disease-modifying therapy for relapsing-remitting multiple sclerosis (MS). Data on the efficacy and safety profile of natalizumab in Asian patients with MS are limited. This study assessed the efficacy and safety of natalizumab in Korean patients with MS in a real-world setting. Methods: This study enrolled consecutive Korean patients with active relapsing-remitting MS who were treated with natalizumab for at least 6 months between 2015 and 2021. To evaluate the therapeutic outcome of natalizumab, we used the Expanded Disability Status Scale (EDSS) scores and brain magnetic resonance imaging; adverse events were assessed at regular intervals. No evidence of disease activity (NEDA) was defined as no clinical relapse, no worsening of EDSS score, and no radiological activities. Results: Fourteen subjects with MS were included in the study. The mean age at initiation of natalizumab therapy was 32 years. All patients were positive for anti-John Cunningham virus antibodies before natalizumab administration. The mean annual relapse rate was markedly reduced from 2.7 ± 3.2 before natalizumab therapy to 0.1 ± 0.4 during natalizumab therapy (p = 0.001). Disability was either improved or stabilized after natalizumab treatment in 13 patients (93%). During the 1st year and 2 years after initiating natalizumab, NEDA-3 was achieved in 11/12 (92%) and 9/11 (82%) patients, respectively. No progressive multifocal leukoencephalopathy or other serious adverse events leading to the discontinuation of natalizumab were observed. Conclusions: Natalizumab therapy showed high efficacy in treating Korean patients with active MS, without unexpected safety problems.
Estimating the Minimal Number of Repeated Examinations for Random Responsiveness With the Coma Recovery Scale—Revised as an Example

Objective: The aim of this study was to develop a general method to estimate the minimal number of repeated examinations needed to detect patients with random responsiveness, given a limited rate of missed diagnosis. Methods: Basic statistical theory was applied to develop the method. As an application, 100 patients with disorders of consciousness (DOC) were assessed with the Coma Recovery Scale–Revised (CRS-R). DOC patients were supposed to be examined for 13 times over 20 days, while anyone who was diagnosed as a minimally conscious state (MCS) in a round would no longer be examined in the subsequent rounds. To test the validation of this method, a series of the stochastic simulation was completed by computer software under all the conditions of possible combinations of three kinds of distributions for p, five values of p, and four sizes of the sample and repeated for 100 times. Results: A series of formula was developed to estimate the probability of a positive response to a single examination given by a patient and the minimal number of successive examinations needed based on the numbers of patients detected in the first i (i =1, 2,.) rounds of repeated examinations. As applied to the DOC patients assessed with the CRS-R, with a rate of missed diagnosis < 0.0001, the estimate of the minimal number of examinations was six in traumatic brain injury patients and five in non-traumatic brain injury patients. The outcome of the simulation showed that this method performed well under various conditions possibly occurring in practice. Interpretation: The method developed in this paper holds in theory and works well in application and stochastic simulation. It could be applied to any other kind of examinations for random responsiveness, not limited to CRS-R for detecting MCS; this should be validated in further research.
Diagnostic Accuracy of Machine Learning Models to Identify Congenital Heart Disease: A Meta-Analysis

Background: With the dearth of trained care providers to diagnose congenital heart disease (CHD) and a surge in machine learning (ML) models, this review aims to estimate the diagnostic accuracy of such models for detecting CHD. Methods: A comprehensive literature search in the PubMed, CINAHL, Wiley Cochrane Library, and Web of Science databases was performed. Studies that reported the diagnostic ability of ML for the detection of CHD compared to the reference standard were included. Risk of bias assessment was performed using Quality Assessment for Diagnostic Accuracy Studies-2 tool. The sensitivity and specificity results from the studies were used to generate the hierarchical Summary ROC (HSROC) curve. Results: We included 16 studies (1217 participants) that used ML algorithm to diagnose CHD. Neural networks were used in seven studies with overall sensitivity of 90.9% (95% CI 85.2–94.5%) and specificity was 92.7% (95% CI 86.4–96.2%). Other ML models included ensemble methods, deep learning and clustering techniques but did not have sufficient number of studies for a meta-analysis. Majority (n=11, 69%) of studies had a high risk of patient selection bias, unclear bias on index test (n=9, 56%) and flow and timing (n=12, 75%) while low risk of bias was reported for the reference standard (n=10, 62%). Conclusion: ML models such as neural networks have the potential to diagnose CHD accurately without the need for trained personnel. The heterogeneity of the diagnostic modalities used to train these models and the heterogeneity of the CHD diagnoses included between the studies is a major limitation.
Non-Lactobacillus-Dominated Vaginal Microbiota Is Associated With a Tubal Pregnancy in Symptomatic Chinese Women in the Early Stage of Pregnancy: A Nested Case–Control Study

The features of the vaginal microbiota (VM) community can reflect health status, and they could become new biomarkers for disease diagnosis. During pregnancy, domination of bacteria of the genus Lactobacillus in the VM community is regarded as a keystone because they stabilize the VM by producing antimicrobial compounds and competing adhesion. An altered VM composition provides a marker for adverse pregnancy outcomes. This nested case–control study aimed to characterize the VM in women with a tubal pregnancy (TP) presenting with pain and/or uterine bleeding in early pregnancy. Chinese women with a symptomatic early pregnancy of unknown location were the study cohort. 16S rDNA gene-sequencing of V3–V4 variable regions was done to assess the diversity, structures, taxonomic biomarkers, and classification of the VM community. The primary outcome was the location of the early pregnancy. The VM community in women with a TP showed higher diversity (PD-whole-tree, median: 8.26 vs. 7.08, P = 0.047; Shannon Diversity Index, median: 1.43 vs 0.99, P = 0.03) and showed different structures to those in women with an intrauterine pregnancy (IUP) (R = 0.23, P < 0.01). Bacteria of the genus Lactobacillus were significantly enriched in the IUP group, whereas bacteria of the genera Gardnerella and Prevotella were significantly enriched in the TP group. Lactobacillus abundance could be used to classify the pregnancy location (AUC = 0.81). Non-Lactobacillus-dominated microbiota (≤ 0.85% Lactobacillus) was significantly associated with a TP (adjusted odds ratio: 4.42, 95% confidence interval: 1.33 to 14.71, P = 0.02). In conclusion, among women with a symptomatic early pregnancy, a higher diversity and lower abundance of Lactobacillus in the VM is associated with a TP.
Modeling Small Scale Impacts of Multi-Purpose Platforms: An Ecosystem Approach

Aquaculture and marine renewable energy are two expanding sectors of the Blue Economy in Europe. Assessing the long-term environmental impacts in terms of eutrophication and noise is a priority for both the EU Water Framework Directive and the Marine Strategy Framework Directive, and cumulative impacts will be important for the Maritime Spatial Planning under the Integrated Maritime Policy. With the constant expansion of aquaculture production, it is expected that farms might be established further offshore in more remote areas, as high-energy conditions offer an opportunity to generate more power locally using Marine Renewable Energy (MRE) devices. A proposed solution is the co-location of MRE devices and aquaculture systems using Multi-Purpose Platforms (MPPs) comprising offshore wind turbines (OWTs) that will provide energy for farm operations as well as potentially shelter the farm. Disentangling the impacts, conflicts and synergies of MPP elements on the surrounding marine ecosystem is challenging. Here we created a high-resolution spatiotemporal Ecospace model of the West of Scotland, in order to assess impacts of a simple MPP configuration on the surrounding ecosystem and how these impacts can cascade through the food web. The model evaluated the following specific ecosystem responses: (i) top-down control pathways due to distribution changes among top-predators (harbor porpoise, gadoids and seabirds) driven by attraction to the farming sites and/or repulsion/killing due to OWT operations; (ii) bottom-up control pathways due to salmon farm activity providing increasing benthic enrichment predicated by a fish farm particle dispersal model, and sediment nutrient fluxes to the water column by early diagenesis of organic matter (recycled production). Weak responses of the food-web were found for top-down changes, whilst the results showed high sensitivity to increasing changes of bottom-up drivers that cascaded through the food-web from primary producers and detritus to pelagic and benthic consumers, respectively. We assessed the sensitivity of the model to each of these impacts and the cumulative effects on the ecosystem, discuss the capabilities and limitations of the Ecospace modeling approach as a potential tool for marine spatial planning and the impact that these results could have for the Blue Economy and the EU’s New Green Deal.
Perceived Barriers to Blood Flow Restriction Training

Blood flow restriction (BFR) training is increasing in popularity in the fitness and rehabilitation settings due to its role in optimizing muscle mass and strength as well as cardiovascular capacity, function, and a host of other benefits. However, despite the interest in this area of research, there are likely some perceived barriers that practitioners must overcome to effectively implement this modality into practice. These barriers include determining BFR training pressures, access to appropriate BFR training technologies for relevant demographics based on the current evidence, a comprehensive and systematic approach to medical screening for safe practice and strategies to mitigate excessive perceptual demands of BFR training to foster long-term compliance. This manuscript attempts to discuss each of these barriers and provides evidence-based strategies and direction to guide clinical practice and future research.
HIV-1 Hijacking of Host ATPases and GTPases That Control Protein Trafficking

The human immunodeficiency virus (HIV-1) modifies the host cell environment to ensure efficient and sustained viral replication. Key to these processes is the capacity of the virus to hijack ATPases, GTPases and the associated proteins that control intracellular protein trafficking. The functions of these energy-harnessing enzymes can be seized by HIV-1 to allow the intracellular transport of viral components within the host cell or to change the subcellular distribution of antiviral factors, leading to immune evasion. Here, we summarize how energy-related proteins deviate from their normal functions in host protein trafficking to aid the virus in different phases of its replicative cycle. Recent discoveries regarding the interplay among HIV-1 and host ATPases and GTPases may shed light on potential targets for pharmacological intervention.
Heart Valve Biomechanics: The Frontiers of Modeling Modalities and the Expansive Capabilities of Ex Vivo Heart Simulation

The field of heart valve biomechanics is a rapidly expanding, highly clinically relevant area of research. While most valvular pathologies are rooted in biomechanical changes, the technologies for studying these pathologies and identifying treatments have largely been limited. Nonetheless, significant advancements are underway to better understand the biomechanics of heart valves, pathologies, and interventional therapeutics, and these advancements have largely been driven by crucial in silico, ex vivo, and in vivo modeling technologies. These modalities represent cutting-edge abilities for generating novel insights regarding native, disease, and repair physiologies, and each has unique advantages and limitations for advancing study in this field. In particular, novel ex vivo modeling technologies represent an especially promising class of translatable research that leverages the advantages from both in silico and in vivo modeling to provide deep quantitative and qualitative insights on valvular biomechanics. The frontiers of this work are being discovered by innovative research groups that have used creative, interdisciplinary approaches toward recapitulating in vivo physiology, changing the landscape of clinical understanding and practice for cardiovascular surgery and medicine.
The Wild Sugarcane and Sorghum Kinomes: Insights Into Expansion, Diversification, and Expression Patterns

The protein kinase (PK) superfamily is one of the largest superfamilies in plants and the core regulator of cellular signaling. Despite this substantial importance, the kinomes of sugarcane and sorghum have not been profiled. Here, we identified and profiled the complete kinomes of the polyploid Saccharum spontaneum (Ssp) and Sorghum bicolor (Sbi), a close diploid relative. The Sbi kinome was composed of 1,210 PKs; for Ssp, we identified 2,919 PKs when disregarding duplications and allelic copies, and these were related to 1,345 representative gene models. The Ssp and Sbi PKs were grouped into 20 groups and 120 subfamilies and exhibited high compositional similarities and evolutionary divergences. By utilizing the collinearity between the species, this study offers insights into Sbi and Ssp speciation, PK differentiation and selection. We assessed the PK subfamily expression profiles via RNA-Seq and identified significant similarities between Sbi and Ssp. Moreover, coexpression networks allowed inference of a core structure of kinase interactions with specific key elements. This study provides the first categorization of the allelic specificity of a kinome and offers a wide reservoir of molecular and genetic information, thereby enhancing the understanding of Sbi and Ssp PK evolutionary history.
Leading or Following? Dyadic Robot Imitative Interaction Using the Active Inference Framework

This study investigated how social interaction among robotic agents changes dynamically depending on the individual belief of action intention. In a set of simulation studies, we examine dyadic imitative interactions of robots using a variational recurrent neural network model. The model is based on the free energy principle such that a pair of interacting robots find themselves in a loop, attempting to predict and infer each other's actions using active inference. We examined how regulating the complexity term to minimize free energy determines the dynamic characteristics of networks and interactions. When one robot trained with tighter regulation and another trained with looser regulation interact, the latter tends to lead the interaction by exerting stronger action intention, while the former tends to follow by adapting to its observations. The study confirms that the dyadic imitative interaction becomes successful by achieving a high synchronization rate when a leader and a follower are determined by developing action intentions with strong belief and weak belief, respectively.
The impact of temperature resolution on trajectory modeling of stratospheric water vapour

The impact of temperature resolution on trajectory modeling of stratospheric water vapour T. Wang, A. E. Dessler, M. R. Schoeberl, W. J. Randel, and J.-E. Kim Texas A&M University, College Station, Texas, USA NASA Jet Propulsion Laboratory/California Institute of Technology, Pasadena, California, USA Science and Technology Corporation, Columbia, Maryland, USA National Center for Atmospheric Research, Boulder, Colorado, USA University of Colorado, Boulder, Colorado, USA
An Effective Process for Pretreating Rice Husk To Enhance Enzyme Hydrolysis

Numerous pretreatment strategies that have been developed to degrade or remove lignin from lignocellulosic residues to promote the production of sugars by enzymatic saccharification have been described in the literature. Among them, alkaline hydrogen peroxide (AHP) at atmospheric pressure has been described as an efficient method. In this work, a new pretreatment that combines in one stage a process at high pressure (10–30 bar) and alkaline hydrogen peroxide (HPAHP) has been developed. For experiments performed at atmospheric pressure, the maximum hydrolysis yield (72.91 ± 5.93%) was obtained at 7.5% w/v H2O2, with a reaction time of 2 h. With the proposed high-pressure method, a maximum hydrolysis yield of 98.50 ± 6.28% was reached with a reduced peroxide concentration of 3% w/v, a reaction time of 30 min, and a pressure of 30 bar. These HPAHP pretreatment conditions were optimized using a statistical design of experiments. Results showed that the operation variables with the most significant effects on .
A Binary Translation Framework for Automated Hardware Generation

As applications move to the edge, efficiency in computing power and power/energy consumption is required. Heterogeneous computing promises to meet these requirements through application-specific hardware accelerators. Runtime adaptivity might be of paramount importance to realize the potential of hardware specialization, but further study is required on workload retargeting and offloading to reconfigurable hardware. This article presents our framework for the exploration of both offloading and hardware generation techniques. The framework is currently able to process instruction sequences from MicroBlaze, ARMv8, and riscv32imaf binaries, and to represent them as Control and Dataflow Graphs for transformation to implementations of hardware modules. We illustrate the framework’s capabilities for identifying binary sequences for hardware translation with a set of 13 benchmarks.
Clinical outcomes of patients with multiple sclerosis treated with ocrelizumab in a US community MS center: an observational study

Background To monitor long-term outcomes of ocrelizumab treatment. Objective To evaluate safety and treatment outcomes of ocrelizumab in a community-based multiple sclerosis (MS) population. Methods Adult patients with MS prescribed ocrelizumab were eligible. Chart reviews were conducted at the start of ocrelizumab treatment and every 6 months thereafter. Results Of the 355 patients enrolled, 71.9% were female; mean (SD) age was 51.8 (12.5) years; 78.3% had relapsing MS (RMS). Median baseline Expanded Disability Status Scale (EDSS) (IQR) was 3.0 (2.0–4.0) for RMS, 6.5 (6.0–7.5) for secondary progressive MS, and 6.5 (6.0–7.0) for primary progressive MS. Respiratory infections occurred in 40.1% and urinary tract infections in 33.1% of patients. There was no difference in the percentage of infections among patients <55 (68.5%, n=122), and those ≥55 of age (67.5%, n=104) (p=0.94). Twenty-five hospitalisations were due to infections; 69.2% of these patients were ≥55 with a mean EDSS of 5.7 (±1.86). Four patients have died. Serum IgM and IgG levels did not predict infection risk. Annualised relapse rate was 0.34 for the patients with RMS in the preceding 2 years and 0.09 in patients who received ≥2 ocrelizumab 600 mg courses. The first on-treatment MRI was stable in 262 (90.0%) patients, 6.9% had new T2 lesions, 2.7% had enlarging T2 lesions and 1.4% had gadolinium-enhancing lesions. Median EDSS at 12 months was unchanged. Conclusion Ocrelizumab effectively controlled relapse risk and disability worsening. Although only 12.1% of patients have discontinued ocrelizumab, infections resulting in hospitalisation are a concern, especially in older and disabled patients.
Preparation of 4A zeolite molecular sieve from Xuzhou coal-measure kaolin

The 4A zeolite molecular sieve [Na12(Al12Si12O48) ·27H2O] was synthesized in a sealed Teflon lined autoclave by hydrothermal reaction of NaOH solution and metakaolinite(the mass ratio of solid/liquid is 1:20) prepared by calcined coal-measure kaolin from Xuzhou at 550℃ for 2h.4A zeolite molecular sieve was characterized by X-ray diffraction(XRD),Fourier transform infrared spectroscopy(FT-IR),scanning electron microscopy(SEM) and N2 adsorption and desorption isotherm plot methods.The results indicated that amorphous anhydrous metakaolinite was obtained by calcination of kaolin at 550℃ for 2h;it was transformed to 4A zeolite molecular sieve by alkali leaching;the best concentration of NaOH solution was 2mol/L;the pore channel of 4A zeolite molecular sieve was long and narrow,but its shape and size were not uniform.
The SMART trial; does it make us wiser in choosing between sirolimus in preference to cyclosporine – A postrenal transplant?

The follow-up analysis of the SMART trial[1] assessed the role of sirolimus (mTOR inhibitor) as an alternative option to cyclosporin A (calcineurin inhibitor) in the renal transplant patients' maintenance regimen in a randomized controlled multicenter trial. Induction was given with anti-thymocyte globulin and methylprednisolone. Cyclosporin A, along with mycophenolate mofetil, was started in all within 24 hours of transplant. Those randomized to sirolimus arm were switched two to three weeks after transplant. The original trial indicated that early conversion to sirolimus might improve renal function; however, is limited by significantly higher adverse effects. The role of donor‐specific antibody (DSA) was not studied.