julilili42/papers huggingface dataset:
Digital Soil Texture Mapping and Spatial Transferability of Machine Learning Models Using Sentinel-1, Sentinel-2, and Terrain-Derived Covariates
2022-11-22

Soil texture is an important property that controls the mobility of the water and nutrients in soil. This study examined the capability of machine learning (ML) models in estimating soil texture fractions using different combinations of remotely sensed data from Sentinel-1 (S1), Sentinel-2 (S2), and terrain-derived covariates (TDC) across two contrasting agroecological regions in Southwest Germany, Kraichgau and the Swabian Alb. Importantly, we tested the predictive power of three different ML models: the random forest (RF), the support vector machine (SVM), and extreme gradient boosting (XGB) coupled with the remote sensing data covariates. As expected, ML model performance was not consistent regarding the input covariates, soil texture fractions, and study regions. For example, in the Swabian Alb, the SVM model performed the best for the sand content with S2 + TDC (RMSE = 3.63%, R2 = 0.42), and XGB best predicted the clay content with S1 + S2 + TDC (RMSE = 6.84%, R2 = 0.64). In Kraichgau, the best models for sand (RMSE = 7.54%, R2 = 0.79) and clay contents (RMSE = 6.14%, R2 = 0.48) were obtained using XGB and SVM, respectively. Moreover, the results indicated that TDC were critical in estimating soil texture fractions, especially in Kraichgau, which indicated that topography plays an important role in defining the spatial distribution of soil properties. In contrast, the contribution of remote sensing data better predicted the silt and clay content in the Swabian Alb. The transferability of a region-specific model to the other region was low as indicated by poor predictive performance. The resulting soil-texture-fraction maps could be a significant source of information for efficient land resource management and environmental monitoring. Nonetheless, further research to evaluate the added value of the Sentinel imagery and to better analyze the spatial transferability of machine learning models is highly recommended.
A research agenda for nonvascular photoautotrophs under climate change
2022-11-22

Summary Nonvascular photoautotrophs (NVP), including bryophytes, lichens, terrestrial algae, and cyanobacteria, are increasingly recognized as being essential to ecosystem functioning in many regions of the world. Current research suggests that climate change may pose a substantial threat to NVP, but the extent to which this will affect the associated ecosystem functions and services is highly uncertain. Here, we propose a research agenda to address this urgent question, focusing on physiological and ecological processes that link NVP to ecosystem functions while also taking into account the substantial taxonomic diversity across multiple ecosystem types. Accordingly, we developed a new categorization scheme, based on microclimatic gradients, which simplifies the high physiological and morphological diversity of NVP and world‐wide distribution with respect to several broad habitat types. We found that habitat‐specific ecosystem functions of NVP will likely be substantially affected by climate change, and more quantitative process understanding is required on: (1) potential for acclimation; (2) response to elevated CO 2 ; (3) role of the microbiome; and (4) feedback to (micro)climate. We suggest an integrative approach of innovative, multimethod laboratory and field experiments and ecophysiological modelling, for which sustained scientific collaboration on NVP research will be essential.
Fabrication of Sustainable Hybrid MOF/Silica Electrodes for Current Lithium-ion Batteries and Beyond
2022-11-22

Natural abundance and well-explored synthesis of silica are among the main motivations for the impressive evolution of silicon-based electrodes occurring over the last few years. In this work, an effective strategy has been introduced for the realization of silica-based anodes for lithium-ion batteries (LIBs) starting from zeolitic imidazolate framework-67 (ZIF67)/mesopores silica (mSiO2), which has been employed as a precursor. This approach leads to the realization of a hybrid electrode formed by the combination of a carbon nanotube (CNT) grown on the nitrogen-doped graphene-like structure, ultrafine cobalt-based nanoparticles, and silica (SiO2/Co3O4/NGC/CNT). From an electrochemical point of view, the performance of this engineered hybrid silica-based electrode (EHSiE), formed by water and a cellulose-based binder, is evaluated in both LP30 and ether-based electrolyte environments, the latter being particularly attractive in the emerging field of sulfur-based batteries. The EHSiE electrode displays a remarkable stability for 1000 cycles with the high reversible capacity of ∼410 mA h g–1 at 5 A g–1 versus Li/Li+ in the LP30 electrolyte. Moreover, this electrode discloses a good electrochemical behavior when coupled with high mass loading LiFePO4 cathode to design a full LIB. More impressively, a systemic investigation reveals a remarkable compatibility of EHSiE with ether-based electrolytes, providing a specific discharge capacity of 300 mA h g–1 for 500 cycles at 1 A g–1. These results suggest that the engineered electrode can be successfully applied in the field of high-energy and environmentally sustainable lithium-based batteries.
Antarctic Bedmap data: FAIR sharing of 60 years of ice bed, surface and thickness data
2022-11-22

Abstract. Over the past 60 years, scientists have strived to understand the past, present and future of the Antarctic Ice Sheet. One of the key components of this research has been the mapping of Antarctic bed topography and ice thickness parameters that are crucial for modelling ice flow and hence for predicting future ice loss and ensuing sea level rise. Supported by the Scientific Committee on Antarctic Research (SCAR), the Bedmap3 Action Group aims not only to produce new gridded maps of ice thickness and bed topography for the international scientific community, but also to standardize and make available all the geophysical survey data points used in producing the Bedmap gridded products. Here, we document the survey data used in the latest iteration, Bedmap3, incorporating and adding to all of the datasets previously used for Bedmap1 and Bedmap2, including ice-bed, surface and thickness point data from all Antarctic geophysical campaigns since the 1950s. More specifically, we describe the processes used to standardize and make these and future survey and gridded datasets accessible under the ‘Findable, Accessible, Interoperable and Reusable’ (FAIR) data principles. With the goals to make the gridding process reproducible and to allow scientists to re-use the data freely for their own analysis, we introduce the new SCAR Bedmap Data Portal (bedmap.scar.org, last access: 18 October 2022) created to provide unprecedented open access to these important datasets, through a user-friendly webmap interface. We believe that this data release will be a valuable asset to Antarctic research and will greatly extend the life cycle of the data held within it. Data are available from the UK Polar Data Centre: https://data.bas.ac.uk.
Potential of Non-Coding RNA as Biomarkers for Progressive Supranuclear Palsy
2022-11-22

Objective markers for the neurodegenerative disorder progressive supranuclear palsy (PSP) are needed to provide a timely diagnosis with greater certainty. Non-coding RNA (ncRNA), including microRNA, piwi-interacting RNA, and transfer RNA, are good candidate markers in other neurodegenerative diseases, but have not been investigated in PSP. Therefore, as proof of principle, we sought to identify whether they were dysregulated in matched serum and cerebrospinal fluid (CSF) samples of patients with PSP. Small RNA-seq was undertaken on serum and CSF samples from healthy controls (n = 20) and patients with PSP (n = 31) in two cohorts, with reverse transcription-quantitative PCR (RT-qPCR) to confirm their dysregulation. Using RT-qPCR, we found in serum significant down-regulation in hsa-miR-92a-3p, hsa-miR-626, hsa-piR-31068, and tRNA-ValCAC. In CSF, both hsa-let-7a-5p and hsa-piR-31068 showed significant up-regulation, consistent with their changes observed in the RNA-seq results. Interestingly, we saw no correlation in the expression of hsa-piR-31068 within our matched serum and CSF samples, suggesting there is no common dysregulatory mechanism between the two biofluids. While these changes were in a small cohort of samples, we have provided novel evidence that ncRNA in biofluids could be possible diagnostic biomarkers for PSP and further work will help to expand this potential.
Assertion, denial, and the evolution of Boolean operators
2022-11-22

Given current data, only a few binary Boolean operators are expressed in lexically simple fashion in the world's languages: and, or, nor . These do not occur in every combination, for example, nor is not observed by itself. To explain these cross‐linguistic patterns, we propose an encoding of Boolean operators as update procedures to accept or reject information in a context. We define a measure of conceptual simplicity for such updates, on which attested operators are conceptually simpler than the remaining Booleans. Moreover, we show that language evolution selects for the attested lexical inventories by minimizing the complexity of using a lexical inventory compositionally to convey precise information.
Meeting Report: Third International Congress of the International Society of Uterus Transplantation, Tübingen
2022-11-22

Uterus transplantation (UTx) is currently the only available treatment for absolute uterine factor infertility. The International Society of Uterus Transplantation (ISUTx) was formally founded in 2017 and joined the Transplantation Society as a formal section in 2021. The Third International Congress of the ISUTx was held in Tübingen, Germany, in October 2021, as a hybrid meeting, attended virtually by about 450 delegates and in person by 35 delegates. This report summarizes the Tübingen meeting and complementary topics of relevance presented at the Second ISUTx state-of-the-art webinar meeting, held in Prague, in October 2020. Main topics covered included surgical considerations, including dissection of veins in living donors and the pros and cons of minimally invasive surgery; managing immune risks; UTx during the COVID-19 pandemic; lessons learnt in the areas of imaging and cytomegalovirus infection; long-term psychological outcomes; opportunities to increase organ availability; and new horizons in UTx, including potential reuse of transplants and the utilization of robotic approaches. Implementation of an International UTx Registry was discussed and considered crucial to assure quality, safety, and further progress in UTx. Attempts made thus far have been promising.
Initial heritable genome editing: mapping a responsible pathway from basic research to the clinic
2022-11-22

Abstract Following the Second Summit on Human Gene Editing in Hong Kong in 2018, where the birth of two girls with germline genome editing was revealed, the need for a responsible pathway to the clinical application of human germline genome editing has been repeatedly emphasised. This paper aims to contribute to the ongoing discussion on research ethics issues in germline genome editing by exploring key issues related to the initial applications of CRISPR in reproductive medicine. Following an overview of the current discussion on bringing germline genome editing into clinical practice, we outline the specific challenges associated with such interventions and the features that distinguish them from conventional clinical testing of new medical treatments. We then review proposed ethical requirements for initial heritable genome editing, such as the absence of reasonable alternatives, the existence of sufficient and reliable preclinical data, appropriate informed consent, requirements related to safety, and long-term follow-up.
Evaluating the Effects of Virtual Human Animation on Students in an Immersive VR Classroom Using Eye Movements
2022-11-22

Virtual humans presented in VR learning environments have been suggested in previous research to increase immersion and further positively influence learning outcomes. However, how virtual human animations affect students' real-time behavior during VR learning has not yet been investigated. This work examines the effects of social animations (i.e., hand raising of virtual peer learners) on students' cognitive response and visual attention behavior during immersion in a VR classroom based on eye movement analysis. Our results show that animated peers that are designed to enhance immersion and provide companionship and social information elicit different responses in students (i.e., cognitive, visual attention, and visual search responses), as reflected in various eye movement metrics such as pupil diameter, fixations, saccades, and dwell times. Furthermore, our results show that the effects of animations on students differ significantly between conditions (20%, 35%, 65%, and 80% of virtual peer learners raising their hands). Our research provides a methodological foundation for investigating the effects of avatar animations on users, further suggesting that such effects should be considered by developers when implementing animated virtual humans in VR. Our findings have important implications for future works on the design of more effective, immersive, and authentic VR environments.
Learning Groundwater Contaminant Diffusion‐Sorption Processes With a Finite Volume Neural Network
2022-11-22

Abstract Improved understanding of complex hydrosystem processes is key to advance water resources research. Nevertheless, the conventional way of modeling these processes suffers from a high conceptual uncertainty, due to almost ubiquitous simplifying assumptions used in model parameterizations/closures. Machine learning (ML) models are considered as a potential alternative, but their generalization abilities remain limited. For example, they normally fail to predict accurately across different boundary conditions. Moreover, as a black box, they do not add to our process understanding or to discover improved parameterizations/closures. To tackle this issue, we propose the hybrid modeling framework FINN (finite volume neural network). It merges existing numerical methods for partial differential equations (PDEs) with the learning abilities of artificial neural networks (ANNs). FINN is applied on discrete control volumes and learns components of the investigated system equations, such as numerical stencils, model parameters, and arbitrary closure/constitutive relations. Consequently, FINN yields highly interpretable results. We demonstrate FINN's potential on a diffusion‐sorption problem in clay. Results on numerically generated data show that FINN outperforms other ML models when tested under modified boundary conditions, and that it can successfully differentiate between the usual, known sorption isotherms. Moreover, we also equip FINN with uncertainty quantification methods to lay open the total uncertainty of scientific learning, and then apply it to a laboratory experiment. The results show that FINN performs better than calibrated PDE‐based models as it is able to flexibly learn and model sorption isotherms without being restricted to choose among available parametric models.
Screening for eukaryotic motifs in Legionella pneumophila reveals Smh1 as bacterial deacetylase of host histones
2022-11-22

<i>Legionella pneumophila</i> (<i>L.p</i>.) is a bacterial pathogen which is a common causative agent of pneumonia. In humans, it infects alveolar macrophages and transfers hundreds of virulence factors that interfere with cellular signalling pathways and the transcriptomic landscape to sustain its own replication. By this interaction, it has acquired eukaryote-like protein motifs by gene transfer events that partake in the pathogenicity of <i>Legionella</i>. In a computational screening approach for eukaryotic motifs in the transcriptome of <i>Legionella</i>, we identified the <i>L.p</i>. strain Corby protein ABQ55614 as putative histone-deacetylase and named it "suppressing modifier of histones 1" (Smh1). During infection, Smh1 is translocated from the <i>Legionella</i> vacuole into the host cytosol. When expressed in human macrophage THP-1 cells, Smh1 was localized predominantly in the nucleus, leading to broad histone H3 and H4 deacetylation, blunted expression of a large number of genes (<i>e.g</i>. IL-1β and IL-8), and fostered intracellular bacterial replication. <i>L.p</i>. with a Smh1 knockdown grew normally in media but showed a slight growth defect inside the host cell. Furthermore, Smh1 showed a very potent histone deacetylation activity <i>in vitro</i>, <i>e.g</i>. at H3K14, that could be inhibited by targeted mutation of the putative catalytic center inferred by analogy with eukaryotic HDAC8, and with the deacetylase inhibitor trichostatin A. In summary, Smh1 displays functional homology with class I/II type HDACs. We identified Smh1 as a new <i>Legionella</i> virulence factor with a eukaryote-like histone-deacetylase activity that moderates host gene expression and might pave the way for further histone modifications.IMPORTANCE<i>Legionella pneumophila (L.p.)</i> is a prominent bacterial pathogen, which is a common causative agent of pneumonia. In order to survive inside the host cell, the human macrophage, it profoundly interacts with host cell processes to advance its own replication. In this study, we identify a bacterial factor, Smh1, with yet unknown function as a host histone deacetylase. The activity of this factor in the host cell leads to attenuated gene expression and increased intracellular bacterial replication.
Heavy quark contribution to the electromagnetic properties of the nucleon
2022-11-22

Quantum chromodynamics (QCD) predicts the existence of both nonperturbative intrinsic and perturbative extrinsic heavy quark contributions to the fundamental structure of hadrons. The existence of intrinsic charm at the 3-standard-deviation level in the proton has recently been established from structure function measurements by the NNPDF Collaboration. Here, we revisit the physics of intrinsic heavy quarks using light-front holographic QCD (LFHQCD)—a novel comprehensive approach to hadron structure which provides detailed predictions for dynamical properties of the hadrons, such as form factors, distribution amplitudes, structure functions, etc. We will extend this nonperturbative light-front QCD approach to study the heavy quark-antiquark contribution to the electromagnetic properties of nucleon. Our framework is based on a study of the eigenfunctions of the QCD light-front Hamiltonian, the frame-independent light-front wave functions (LFWFs) underlying hadron dynamics. We analyze the heavy quark content in the proton, induced either directly by the nonperturbative | + Fock state or by the | +$g \\\\rangle$ Fock state, where the gluon splits into a heavy quark-antiquark pair. The specific form of these LFWFs are derived from LFHQCD. Using these LFWFs, we construct light-front representations for the heavy quark-antiquark asymmetry, the electromagnetic form factors of nucleons induced by heavy quarks, including their magnetic moments and radii.
Sex differences in prenatal development of neural complexity in the human brain
2022-11-22

Abstract The complexity of neural activity is a commonly used read-out of healthy functioning in cortical circuits. Prior work has linked neural complexity to the level of maternal care in preterm infants at risk for developing mental disorders, yet the evolution of neural complexity in early human development is largely unknown. We hypothesized that cortical dynamics would evolve to optimize information processing as birth approaches, thereby increasing the complexity of cortical activity. To test this hypothesis, we conducted the first ever study relating prenatal neural complexity to maturation. MEG recordings were obtained from a sample of fetuses and newborns, including longitudinal data before and after birth. Using cortical responses to auditory irregularities, we computed several entropy measures which reflect the complexity of the MEG signal. Despite our hypothesis, neural complexity significantly decreased with maturation in both fetuses and newborns. Furthermore, we found that complexity decreased significantly faster in male fetuses for most entropy measures. Our surprising results lay the groundwork for the first ever mapping of how neural complexity evolves in early human development, with important implications for future efforts to develop predictive biomarkers of psychiatric disorders based on the complexity of perinatal MEG signals.
A pharmacokinetic–pharmacodynamic model for chemoprotective agents against malaria
2022-11-22

Abstract Chemoprophylactics are a vital tool in the fight against malaria. They can be used to protect populations at risk, such as children younger than the age of 5 in areas of seasonal malaria transmission or pregnant women. Currently approved chemoprophylactics all present challenges. There are either concerns about unacceptable adverse effects such as neuropsychiatric sequalae (mefloquine), risks of hemolysis in patients with G6PD deficiency (8‐aminoquinolines such as tafenoquine), or cost and daily dosing (atovaquone–proguanil). Therefore, there is a need to develop new chemoprophylactic agents to provide more affordable therapies with better compliance through improving properties such as pharmacokinetics to allow weekly, preferably monthly, dosing. Here we present a pharmacokinetic–pharmacodynamic (PKPD) model constructed using DSM265 (a dihydroorotate dehydrogenase inhibitor with activity against the liver schizonts of malaria, therefore, a prophylaxis candidate). The PKPD model mimics the parasite lifecycle by describing parasite dynamics and drug activity during the liver and blood stages. A major challenge is the estimation of model parameters, as only blood‐stage parasites can be observed once they have reached a threshold. By combining qualitative and quantitative knowledge about the parasite from various sources, it has been shown that it is possible to infer information about liver‐stage growth and its initial infection level. Furthermore, by integrating clinical data, the killing effect of the drug on liver‐ and blood‐stage parasites can be included in the PKPD model, and a clinical outcome can be predicted. Despite multiple challenges, the presented model has the potential to help translation from preclinical to late development for new chemoprophylactic candidates.
When yeast cells change their mind: cell cycle “Start” is reversible under starvation
2022-11-23

Abstract Eukaryotic cells decide in late G1 phase of the cell cycle whether to commit to another round of division. This point of cell cycle commitment is termed “Restriction Point” in mammals and “Start” in the budding yeast Saccharomyces cerevisiae . At Start, yeast cells integrate multiple signals such as pheromones and nutrients, and will not pass Start if nutrients are lacking. However, how cells respond to nutrient depletion after the Start decision remains poorly understood. Here, we analyze how post‐Start cells respond to nutrient depletion, by monitoring Whi5, the cell cycle inhibitor whose export from the nucleus determines Start. Surprisingly, we find that cells that have passed Start can re ‐import Whi5 into the nucleus. In these cells, the positive feedback loop activating G1/S transcription is interrupted, and the Whi5 repressor re‐binds DNA. Cells which re‐import Whi5 become again sensitive to mating pheromone, like pre‐Start cells, and CDK activation can occur a second time upon replenishment of nutrients. These results demonstrate that upon starvation, the commitment decision at Start can be reversed. We therefore propose that cell cycle commitment in yeast is a multi‐step process, similar to what has been suggested for mammalian cells.