Miracle-dz/UniFAR-FTUs huggingface dataset:
Computer simulation study of liquid HF with a new effective pair potential model

A new three-site effective pair potential has been developed to reproduce the properties of liquid hydrogen fluoride. Computer simulations have been performed with the new model at two thermodynamic state points. Thermodynamic properties and pair correlation functions are presented and compared with experimental data. The partial pair correlation functions obtained are compared also with the results of a reverse Monte Carlo simulation using neutron diffraction data. The newly developed potential model is found to be more realistic than the previous models.
Simulated and reconstructed winter temperature in the eastern China during the last millennium

The reconstructed temperature anomalies in the eastern China were compared with the output from a 1000-year model simulation in an attempt to evaluate the model’s regional simulation skills and to understand the causes of climate change in China over the last millennium. The reconstructed data are the winter half-year temperature anomalies in the central region of eastern China (25°–40°N, east of 105°E) for the last 1000 years with a 30-year resolution. The model used is the global atmosphere-ocean coupled climate model, ECHO-G, which was driven by time-varying external forcings including solar radiation, volcanic eruptions, and greenhouse gas concentrations (CO2 and CH4) for the same period. The correlation coefficient between the simulated and reconstructed time series is 0.37, which is statistically significant at a confidence level of 97.5%. The Medieval Warm Period (MWP) during 1000–1300 A.D., the Little Ice Age (LIA) during 1300–1850 A.D. and the modern warming period after 1900 A.D. are all recognizable from both the simulated and reconstructed temperatures. The anomalies associated with the LIA and the modern warming simulated by the model are in good consistency with the reconstructed counterpart. In particular during the Maunder sun-spot minimum (1670–1710 A.D.), both the simulated and reconstructed temperature anomalies reach their minima without any phase difference. But in the earlier MWP, significant discrepancies exist between the simulation and the reconstruction, which might reflect the degrading quality of the reconstruction data. The range of the simulated anomalies (1.62 K) is comparable with that of reconstructed (2.0 K). Diagnosis of the model results indicates that, during the last millennium, variations in solar radiation and volcanic activity are the main controlling factors on regional temperature change, while in the recent 100 years, the change of the concentration of greenhouse gases plays most important role in explaining the rapid temperature rising.
Sexual Experiences of Patients with Acute Onset of Coronary Artery Disease in Korea

Research on the sex life experiences of patients with acute onset of coronary artery disease (CAD) in Korea is limited. This study explored the sex life experiences of Korean patients with acute onset of CAD. In-depth interviews were conducted from April 2019 to March 2020 and responses were analyzed using Colaizzi’s phenomenological method. Participants were nine patients with acute onset of CAD. Five theme clusters were discovered: clearing some doubts about CAD and sex, but remains confused; becoming disheartened after disease onset in a society that considers discussion about sex as taboo; avoiding sexual intercourse after recognizing changes in sexual function; experiencing an imbalance in their relationship with their spouse; and hoping to overcome sex life difficulties and return to their old self. Overall, participants experienced challenges in their sex lives after diagnosis and treatment of CAD. The study results contribute to the nursing knowledge on sexual experiences after CAD.
A Monte Carlo evaluation of growth mixture modeling

Abstract Growth mixture modeling (GMM) and its variants, which group individuals based on similar longitudinal growth trajectories, are quite popular in developmental and clinical science. However, research addressing the validity of GMM-identified latent subgroupings is limited. This Monte Carlo simulation tests the efficiency of GMM in identifying known subgroups (k = 1–4) across various combinations of distributional characteristics, including skew, kurtosis, sample size, intercept effect size, patterns of growth (none, linear, quadratic, exponential), and proportions of observations within each group. In total, 1,955 combinations of distributional parameters were examined, each with 1,000 replications (1,955,000 simulations). Using standard fit indices, GMM often identified the wrong number of groups. When one group was simulated with varying skew and kurtosis, GMM often identified multiple groups. When two groups were simulated, GMM performed well only when one group had steep growth (whether linear, quadratic, or exponential). When three to four groups were simulated, GMM was effective primarily when intercept effect sizes and sample sizes were large, an uncommon state of affairs in real-world applications. When conditions were less ideal, GMM often underestimated the correct number of groups when the true number was between two and four. Results suggest caution in interpreting GMM results, which sometimes get reified in the literature.
Ciliary Rootlet Coiled-Coil 2 (crocc2) Is Associated with Evolutionary Divergence and Plasticity of Cichlid Jaw Shape

Abstract Cichlid fishes exhibit rapid, extensive, and replicative adaptive radiation in feeding morphology. Plasticity of the cichlid jaw has also been well documented, and this combination of iterative evolution and developmental plasticity has led to the proposition that the cichlid feeding apparatus represents a morphological “flexible stem. ” Under this scenario, the fixation of environmentally sensitive genetic variation drives evolutionary divergence along a phenotypic axis established by the initial plastic response. Thus, if plasticity is predictable then so too should be the evolutionary response. We set out to explore these ideas at the molecular level by identifying genes that underlie both the evolution and plasticity of the cichlid jaw. As a first step, we fine-mapped an environment-specific quantitative trait loci for lower jaw shape in cichlids, and identified a nonsynonymous mutation in the ciliary rootlet coiled-coil 2 (crocc2), which encodes a major structural component of the primary cilium. Given that primary cilia play key roles in skeletal mechanosensing, we reasoned that this gene may confer its effects by regulating the sensitivity of bone to respond to mechanical input. Using both cichlids and zebrafish, we confirmed this prediction through a series of experiments targeting multiple levels of biological organization. Taken together, our results implicate crocc2 as a novel mediator of bone formation, plasticity, and evolution.
Characterization and Distribution of Agar-degrading Steroidobacter agaridevorans sp. nov., Isolated from Rhizosphere Soils

The environment of plant rhizosphere soil differs from that of non-rhizosphere soil due to the secretion of mucilage polysaccharides from the roots. This environment is regarded as one of the preferential habitats for agar-degrading bacteria. In a previous study, agar-degrading Steroidobacter agariperforans KA5-BT was isolated from agar-enriched agricultural soil using diffusible metabolites from Rhizobiales bacteria. Based on the hypothesis that similar characteristic bacteria still exist in the rhizosphere, isolation was performed using rhizosphere soils. Agar-degrading SA29-BT and YU21-B were isolated from onion and soybean rhizosphere soils. The 16S rRNA genes of these strains showed ≥98.7% identities with the most closely related strain KA5-BT. However, differences were noted in polysaccharide utilization, and average nucleotide identities were <95–96% against strain KA5-BT, indicating that they are different species from S. agariperforans KA5-BT. To investigate the distribution of bacterial sequences affiliated with novel strains, a primer set was designed and a meta-analysis of the 16S rRNA gene was performed. Sequences were widely distributed in rhizospheres throughout Japan, but varied in plant- and region-dependent manners. Regarding phenotypic characterization, distinguishable features were observed in growth temperatures, pH, and dominant fatty acids. SA29-BT and YU21-B grew at 15–40°C and pH 6.0–12 and contained C16:0 as the dominant cell fatty acid, whereas KA5-BT showed no growth at 40°C and pH 12 and contained a moderate amount of C16:0. Based on these characteristics, SA29-BT (JCM 333368T=KCTC 72223T) and YU21-B (JCM 333367=KCTC 72222) represent novel species in the genus Steroidobacter, for which the name Steroidobacter agaridevorans sp. nov. is proposed.
Sphingolipid long-chain base hydroxylation influences plant growth and callose deposition in Physcomitrium patens.

Sphingolipids are enriched in microdomains in the plant plasma membrane (PM). Hydroxyl groups in the characteristic long-chain base (LCB) moiety might be essential for the interaction between sphingolipids and sterols during microdomain formation. Investigating LCB hydroxylase mutants in Physcomitrium patens might therefore reveal the role of certain plant sphingolipids in the formation of PM subdomains. Physcomitrium patens mutants for the LCB C-4 hydroxylase S4H were generated by homologous recombination. Plants were characterised by analysing their sphingolipid and glycosylated sterol (SG) profiles and by investigating different gametophyte stages. s4h mutants lost the hydroxyl group at the C-4 position of their LCB moiety. Loss of this hydroxyl group caused global changes in the moss sphingolipidome and in SG composition. Changes in membrane lipid composition may trigger growth defects by interfering with the localisation of membrane-associated proteins that are crucial for growth processes such as signalling receptors or callose-modifying enzymes. Loss of LCB-C4 hydroxylation substantially changes the P. patens sphingolipidome and reveals a key role for S4H during development of non-vascular plants. P. patens represents a valuable model for studying the diversification of plant sphingolipids. The simple anatomy of P. patens facilitates visualisation of physiological processes in biological membranes.
Mass spectrometry‐based top‐down and bottom‐up approaches for proteomic analysis of the Moroccan Buthus occitanus scorpion venom

Buthus occitanus (B. occitanus) is one of the most dangerous scorpions in the world. Despite the involvement of B. occitanus scorpion in severe cases of envenomation in Morocco, no study has focused yet on the proteomic composition of the Moroccan B. occitanus scorpion venom. Mass spectrometry‐based proteomic techniques are commonly used in the study of scorpion venoms. The implementation of top‐down and bottom‐up approaches for proteomic analyses facilitates screening by allowing a global view of the structural aspects of such complex matrices. Here, we provide a partial overview of the venom of B. occitanus scorpion, in order to explore the diversity of its toxins and hereafter understand their effects. To this end, a combination of top‐down and bottom‐up approaches was applied using nano‐high liquid chromatography coupled to nano‐electrospray tandem mass spectrometry (nano‐LC‐ESI MS/MS). The LC‐MS results showed that B. occitanus venom contains around 200 molecular masses ranging from 1868 to 16 720 Da, the most representative of which are those between 5000 and 8000 Da. Interestingly, combined top‐down and bottom‐up LC‐MS/MS results allowed the identification of several toxins, which were mainly those acting on ion channels, including those targeting sodium (NaScTxs), potassium (KScTxs), chloride (ClScTxs), and calcium channels (CaScTx), as well as antimicrobial peptides (AMPs), amphipathic peptides, myotropic neuropeptides, and hypothetical secreted proteins. This study reveals the molecular diversity of B. occitanus scorpion venom and identifies components that may have useful pharmacological activities.
Perceptions of Medical Providers on Morality and Decision-Making Capacity in Withholding and Withdrawing Life-Sustaining Treatment and Suicide

Abstract Background: This study attempts to understand if medical providers beliefs about the moral permissibility of honoring patient-directed refusals of life-sustaining treatment (LST) are tied to their beliefs about the patient’s decision-making capacity. The study aims to answer: 1) does concern about a patient’s treatment decision-making capacity relate to beliefs about whether it is morally acceptable to honor a refusal of LST, 2) are there differences between provider types in assessments of decision-making capacity and the moral permissibility to refuse LST, and 3) do provider demographics impact beliefs about decision-making capacity and the moral permissibility to refuse LST. Methods: A mixed-methods survey using Likert assessment and vignette-based questions was administered to medical providers within a single health system in the upper Midwest (N = 714) to assess their perspectives on the moral acceptance and decision-making capacity in cases of withholding and withdrawing treatment and suicide. Results: Behavioral health providers report accepting of the moral permissibility of suicide (91.2%) more than either medical providers (77.2%) or surgeons (74.4%) (n = 283). Decision-making capacity was questioned more in the vignettes of the patients refusing life-saving surgery (36%) and voluntarily starvation (40.8%) than in the vignette of the patient requesting to deactivate a pacemaker (13%) (n = 283). Behavioral health providers were more concerned about the capacity to refuse life-saving surgery (55.9%) than medical providers (33.8%) or surgeons (23.1%) (n = 283). Conclusions: Respondents endorse the moral permissibility of persons to withhold or withdraw from treatment regardless of motive. Clinical concerns about a patient’s treatment decision-making capacity do not strongly correlate to views about the moral permissibility of honoring refusals of LST. Different provider types appear to have different thresholds for when to question treatment decision-making capacity. Behavioral health providers tend to question treatment decision-making capacity to refuse LST more than non-behavioral health providers.
BMI status and associations between affect, physical activity and anxiety among U.S. children during COVID‐19

There is concern regarding how the COVID‐19 pandemic may impact the psychological and physical health of children, but to date, studies on mental health during the pandemic in children are limited. Furthermore, unprecedented lifestyle stressors associated with the pandemic may aggravate the childhood obesity epidemic, but the role of BMI on child activity levels and psychological outcomes during COVID‐19 is unknown.
Dosimetric properties of a newly developed thermoluminescent sheet‐type dosimeter for clinical proton beams

Abstract Purpose This study aimed to evaluate the dosimetric properties of a newly developed thermoluminescent sheet‐type dosimeter (TLD‐sheet) for clinical proton beams. Materials and Methods The TLD‐sheet is composed mainly of manganese doped lithium triborate, with a physical size and thickness of 150 mm × 150 mm and 0.15 mm respectively. It is flexible and can be cut freely for usage. The TLD‐sheet has an effective atomic number of 7.3 and tissue‐equivalent properties. We tested the reproducibility, fading effect, dose linearity, homogeneity, energy dependence, and water equivalent thickness (WET) of the TLD‐sheet for clinical proton beams. We conducted tests with both unmodulated and modulated proton beams at energies of 150 and 210 MeV. Results The measurement reproducibility was within 4%, which included the inhomogeneity of the TLD‐sheet. The fading rates were approximately 20% and 30% after 2 and 7 days respectively. The TLD‐sheet showed notable energy dependence in the Bragg peak and distal end of the spread‐out Bragg peak regions. However, the dose–response characteristics of the TLD‐sheet remained linear up to a physical dose of 10 Gy in this study. This linearity was highly superior to those of commonly used radiochromic film. The thin WET of the TLD‐sheet had little effect on the range. Conclusion Although notable energy dependences were observed in Bragg peak region, the response characteristics examined in this study, such as reproducibility, fading effects, dose linearity, dose homogeneity and WET, showed that the TLD‐sheet can be a useful and effective dosimetry tool. With its flexible and reusable characteristics, it may also be an excellent in vivo skin dosimetry tool for proton therapy.
Network Environment Design for Autonomous Cyberdefense

Reinforcement learning (RL) has been demonstrated suitable to develop agents that play complex games with human-level performance. However, it is not understood how to effectively use RL to perform cybersecurity tasks. To develop such understanding, it is necessary to develop RL agents using simulation and emulation systems allowing researchers to model a broad class of realistic threats and network conditions. Demonstrating that a specific RL algorithm can be effective for defending a network under certain conditions may not necessarily give insight about the performance of the algorithm when the threats, network conditions, and security goals change. This paper introduces a novel approach for network environment design and a software framework to address the fundamental problem that network defense cannot be defined as a single game with a simple set of fixed rules. We show how our approach is necessary to facilitate the development of RL network defenders that are robust against attacks aimed at the agent's learning. Our framework enables the development and simulation of adversaries with sophisticated behavior that includes poisoning and evasion attacks on RL network defenders.
Degenerate processes killed at the boundary of a domain

We investigate certain properties of degenerate Feller processes that are killed when exiting a relatively compact set. Our main result provides general conditions ensuring that such a process possesses a (possibly non unique) quasi stationary distribution. Conditions ensuring uniqueness and exponential convergence are discussed. The results are applied to nonelliptic and hypoelliptic stochastic differential equations.
Medium-term Predictions of F10.7 and F30 cm Solar Radio Flux with the Adaptive Kalman Filter

The solar radio flux at F10.7 and F30 cm is required by most models characterizing the state of the Earth’s upper atmosphere, such as the thermosphere and ionosphere, to specify satellite orbits, re-entry services, collision avoidance maneuvers, and modeling of the evolution of space debris. We develop a method called RESONANCE (Radio Emissions from the Sun: ONline ANalytical Computer-aided Estimator) for the prediction of the 13-month smoothed monthly mean F10.7 and F30 indices 1–24 months ahead. The prediction algorithm has three steps. First, we apply a 13-month optimized running mean technique to effectively reduce the noise in the radio flux data. Second, we provide initial predictions of the F10.7 and F30 indices using the McNish–Lincoln method. Finally, we improve these initial predictions by developing an adaptive Kalman filter with identification of the error statistics. The rms error of predictions with lead times from 1 to 24 months is 5–27 solar flux units (sfu) for the F10.7 index and 3–16 sfu for F30, which statistically outperforms current algorithms in use. The proposed approach based on the Kalman filter is universal and can be applied to improve the initial predictions of a process under study provided by any other forecasting method. Furthermore, we present a systematic evaluation of re-entry forecast as an application to test the performance of F10.7 predictions on past ESA re-entry campaigns for payloads, rocket bodies, and space debris that re-entered from 2006 to 2019 June. The test results demonstrate that the predictions obtained by RESONANCE in general also lead to improvements in the forecasts of re-entry epochs.
Double Articulation Analyzer With Prosody for Unsupervised Word and Phone Discovery

Word and phone discovery are important tasks in the language development of human infants. Infants acquire words and phones from unsegmented speech signals using segmentation cues, such as distributional, prosodic, and co-occurrence information. Many pre-existing computational models designed to represent this process tend to focus on distributional or prosodic cues. In this study, we propose a nonparametric Bayesian probabilistic generative model called the prosodic hierarchical Dirichlet process-hidden language model (prosodic HDP-HLM) designed to perform simultaneous phone and word discovery from continuous speech signals encoded as time-series data that may exhibit a double articulation structure. Prosodic HDP-HLM, as an extension of HDP-HLM, considers both prosodic and distributional cues within a single integrative generative model. We further propose a prosodic double articulation analyzer (Prosodic DAA) based on an inference procedure derived for prosodic HDP-HLM. We conducted three experiments on different types of data sets, including, Japanese vowel sequence, utterances for teaching object names and features, and utterances following Zipf’s law, and the results demonstrated the validity of the proposed method. The results show that the Prosodic DAA successfully used prosodic cues and was able to discover words directly from continuous human speech using distributional and prosodic information in an unsupervised manner, outperforming a method that solely used distributional cues. In contrast, the phone discovery performance did not improve. We also show that prosodic cues contributed to word discovery performance more when the word frequency was distributed more naturally, i.e., following Zipf’s law.
VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments

Motivated by the rising abundance of observational data with continuous treatments, we investigate the problem of estimating the average dose-response curve (ADRF). Available parametric methods are limited in their model space, and previous attempts in leveraging neural network to enhance model expressiveness relied on partitioning continuous treatment into blocks and using separate heads for each block; this however produces in practice discontinuous ADRFs. Therefore, the question of how to adapt the structure and training of neural network to estimate ADRFs remains open. This paper makes two important contributions. First, we propose a novel varying coefficient neural network (VCNet) that improves model expressiveness while preserving continuity of the estimated ADRF. Second, to improve finite sample performance, we generalize targeted regularization to obtain a doubly robust estimator of the whole ADRF curve.