Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic Association Study and Machine Learning to Investigate Differences in Platelet Reactivity in Patients with Acute Ischemic Stroke Treated with Aspirin

Aspirin resistance (AR) is a pressing problem in current ischemic stroke care. Although the role of genetic variations is widely considered, the data still remain controversial. Our aim was to investigate the contribution of genetic features to laboratory AR measured through platelet aggregation with arachidonic acid (AA) and adenosine diphosphate (ADP) in ischemic stroke patients. A total of 461 patients were enrolled. Platelet aggregation was measured via light transmission aggregometry. Eighteen single-nucleotide polymorphisms (SNPs) in ITGB3, GPIBA, TBXA2R, ITGA2, PLA2G7, HMOX1, PTGS1, PTGS2, ADRA2A, ABCB1 and PEAR1 genes and the intergenic 9p21.3 region were determined using low-density biochips. We found an association of rs1330344 in the PTGS1 gene with AR and AA-induced platelet aggregation. Rs4311994 in ADRA2A gene also affected AA-induced aggregation, and rs4523 in the TBXA2R gene and rs12041331 in the PEAR1 gene influenced ADP-induced aggregation. Furthermore, the effect of rs1062535 in the ITGA2 gene on NIHSS dynamics during 10 days of treatment was found. The best machine learning (ML) model for AR based on clinical and genetic factors was characterized by AUC = 0.665 and F1-score = 0.628. In conclusion, the association study showed that PTGS1, ADRA2A, TBXA2R and PEAR1 polymorphisms may affect laboratory AR. However, the ML model demonstrated the predominant influence of clinical features.
Growth, Pod Yield and Quality of Hot Pepper (Capsicum Annuum L.) as Affected by Variety and Rates of Nitrogen Fertilizer in Wolaita, Southern Ethiopia

- Hot pepper is one of the most important vegetables and spice crops cultivated in many parts of the country. Despite its economic, nutritional, and medicinal purposes, the research done so far on this crop is very limited. Therefore, the current research was conducted to identify best hot pepper variety for pod yield and quality and determine optimum rates of nitrogen (N) fertilizer for hot pepper production in Wolaita, Southern Ethiopia. The field experiment was laid out in a randomized complete block design (RCBD) with three replications. Four varieties (Melka Awaze, Melka Shote, Avpp0514, and Avpp0206 with four N fertilizer rates (0, 50, 100, and 150kg N ha -1 ) were assigned to the experimental plot with a total of 16 treatments. The result showed that interaction of variety and rates of N fertilizer significantly (P < 0.05) affected plant height, leaf area, leaf area index, total pod yield, marketable pod yield and significantly (P < 0.001) affected pod length, pod width, pod wall thickness and disease incidence of hot pepper. Abstract-Hot pepper is one of the most important vegetables and spice crops cultivated in many parts of the country. Despite its economic, nutritional, and medicinal purposes, the research done so far on this crop is very limited. Therefore, the current research was conducted to identify best hot pepper variety for pod yield and quality and determine optimum rates of nitrogen (N) fertilizer for hot pepper production in Wolaita, Southern Ethiopia. The field experiment was laid out in a randomized complete block design (RCBD) with three replications. Four varieties (Melka Awaze, Melka Shote, Avpp0514, and Avpp0206 with four N fertilizer rates (0, 50, 100, and 150kg N ha -1 ) were assigned to the experimental plot with a total of 16 treatments. The result showed that interaction of variety and rates of N fertilizer significantly (P < 0.05) affected plant height, leaf area, leaf area index, total pod yield, marketable pod yield and significantly (P < 0.001) affected pod length, pod width, pod wall thickness and disease incidence of hot pepper. The highest marketable pod yield (16.33 t ha -1 ) was achieved from variety Avpp0514 coupled with the rate of 100 kg N ha -1 followed by variety Avpp0514 at the rate of 50 kg N ha -1 (14.93 t ha -1 ) whereas the lowest pod yield was achieved from variety Melka Shote at 0kg N ha -1 . The highest oleoresin content (25.89%) was recorded by Avpp0514 at 50 kg N ha -1 . Based on the current investigation, it could be generalized that introduced varieties were more promising than local released ones in terms of growth, pod yield, quality, and disease resistance. Therefore, variety Avpp0514 at the rate of 50 kg N ha -1 could be used for the production of hot pepper in the Wolaita area.
The transition from School to Post-Secondary Education – What factors affect educational decisions?

: This paper analyses the concrete post-school decision of school students whether to study or to enter the German VET system. It focuses on the investigation of individual risk preferences and the social background of individuals, and the effect on the ultimate decision to enrol in university or to start an apprenticeship, given the same level of qualification. For the empirical approach, the German SOEP is used, and information on individuals’ educational decisions between 2007 and 2013 is considered. The results indicate that (i) individual risk preferences do not have an overall effect on the real transition and are not conditional on the academic background of parents; (ii) privileged individuals are more likely to take up higher education; and (iii) even when parents without an academic background support their children during school, they are less likely to guide their children into tertiary education.
AUTOMATIC DETERMINATION OF STELLAR PARAMETERS VIA ASTEROSEISMOLOGY OF STOCHASTICALLY OSCILLATING STARS: COMPARISON WITH DIRECT MEASUREMENTS

Space-based projects are providing a wealth of high-quality asteroseismic data, including frequencies for a large number of stars showing solar-like oscillations. These data open the prospect for precise determinations of key stellar parameters, of particular value to the study of extra-solar planetary systems. Given the quantity of the available and expected data, it is important to develop efficient and reliable techniques for analyzing them, including the determination of stellar parameters from the observed frequencies. Here we present the SEEK package developed for the analysis of asteroseismic data from the Kepler mission. A central goal of the package is to obtain a fast and automatic determination of the stellar radius and other parameters in a form that is statistically well defined. The algorithms are tested by comparing the results of the analysis with independent measurements of stellar radius and mass for a sample of well-observed stars. We conclude that the SEEK package fixes stellar parameters with accuracy and precision.
Experimental, Numerical and Analytical Studies of the MHD-driven plasma jet, instabilities and waves

This thesis describes a series of experimental, numerical, and analytical studies involving the Caltech magnetohydrodynamically (MHD)-driven plasma jet experiment. The plasma jet is created via a capacitor discharge that powers a magnetized coaxial planar electrodes system. The jet is collimated and accelerated by the MHD forces. We present three-dimensional ideal MHD finite-volume simulations of the plasma jet experiment using an astrophysical magnetic tower as the baseline model. A compact magnetic energy/helicity injection is exploited in the simulation analogous to both the experiment and to astrophysical situations. Detailed analysis provides a comprehensive description of the interplay of magnetic force, pressure, and flow effects. We delineate both the jet structure and the transition process that converts the injected magnetic energy to other forms. When the experimental jet is sufficiently long, it undergoes a global kink instability and then a secondary local Rayleigh-Taylor instability caused by lateral acceleration of the kink instability. We present an MHD theory of the Rayleigh-Taylor instability on the cylindrical surface of a plasma flux rope in the presence of a lateral external gravity. The Rayleigh-Taylor instability is found to couple to the classic current-driven instability, resulting in a new type of hybrid instability. The coupled instability, produced by combination of helical magnetic field, curvature of the cylindrical geometry, and lateral gravity, is fundamentally different from the classic magnetic Rayleigh-Taylor instability occurring at a two-dimensional planar interface. In the experiment, this instability cascade from macro-scale to micro-scale eventually leads to the failure of MHD. When the Rayleigh-Taylor instability becomes nonlinear, it compresses and pinches the plasma jet to a scale smaller than the ion skin depth and triggers a fast magnetic reconnection. We built a specially designed high-speed 3D magnetic probe and successfully detected the high frequency magnetic fluctuations of broadband whistler waves associated with the fast reconnection. The magnetic fluctuations exhibit power-law spectra. The magnetic components of single-frequency whistler waves are found to be circularly polarized regardless of the angle between the wave propagation direction and the background magnetic field.
Spectral Analysis of Gravity Waves in the Martian Thermosphere during Low Solar Activity Based on MAVEN/NGIMS Observations

Gravity waves (GWs) are important for vertical coupling between the lower and upper atmosphere on Mars. Saturated GWs will promote the acceleration of mean flow and turbulence generation, causing diffuse transport of energy and momentum. We analyze the spectral characteristics of GWs in the thermosphere on Mars during the low solar activity of Martian Year (MY) 35, based on CO2 density measured by the Neutral Gas and Ion Mass Spectrometer (NGIMS) on board Mars Atmosphere and Volatile EvolutioN (MAVEN). The results are compared with those of MY33 at the higher solar activity. The vertical wavenumber spectral density obtained in the Martian thermosphere is similar to the semi-empirically predicted saturation spectrum in the Earth’s atmosphere, with a logarithmic spectral slope around −3. The average spectral density of GWs shows saturation between 160 and 200 km and decreases with increasing altitude. Compared to MY33, GW activity is larger and less attenuated in MY35 as it propagates upward, implying that waves experience more favorable propagation conditions during low solar activity. Also, the dependence of GW activity on local time during this period is found to be different from previous studies, with the strongest GWs in the spring of MY35 occurring within 6–12 hr on the dayside, suggesting that the amplitude of the GWs in the Martian thermosphere during low solar activity is less controlled by the background temperature and is subject to dissipative effects of other factors, such as molecular viscosity and thermal conduction.
Lagrange versus Lyapunov Stability of Hierarchical Triple Systems: Dependence on the Mutual Inclination between Inner and Outer Orbits

While there have been many studies examining the stability of hierarchical triple systems, the meaning of “stability” is somewhat vague and has been interpreted differently in previous literatures. The present paper focuses on “Lagrange stability,” which roughly refers to the stability against the escape of a body from the system, or “disruption” of the triple system, in contrast to “Lyapunov-like stability,” which is related to the chaotic nature of the system dynamics. We compute the evolution of triple systems using direct N-body simulations up to 107 P out, which is significantly longer than previous studies (with P out being the initial orbital period of the outer body). We obtain the resulting disruption timescale T d as a function of the triple orbital parameters with particular attention to the dependence on the mutual inclination between the inner and outer orbits, i mut. By doing so, we have clarified explicitly the difference between Lagrange and Lyapunov stabilities in astronomical triples. Furthermore, we find that the von Zeipel–Kozai–Lidov oscillations significantly destabilize inclined triples (roughly with 60° < i mut < 150°) relative to those with i mut = 0°. On the other hand, retrograde triples with i mut > 160° become strongly stabilized with much longer disruption timescales. We show the sensitivity of the normalized disruption timescale T d/P out to the orbital parameters of triple system. The resulting T d/P out distribution is practically more useful in a broad range of astronomical applications than the stability criterion based on the Lyapunov divergence.
QUANTITATIVE CONTROL OF SOLUTIONS TO AXISYMMETRIC NAVIER-STOKES EQUATIONS IN TERMS OF THE WEAK L NORM

We are concerned with strong axisymmetric solutions to the 3D incompressible Navier-Stokes equations. We show that if the weak L 3 norm of a strong solution u on time interval [0 , T ] is bounded by A ≫ 1 then for each k ≥ 0 there exists C k > 1 such that k D k u ( t ) k L ∞ ( R 3 ) ≤ t − (1+ k ) / 2 exp exp A C k for all t ∈ (0 , T ].
Impact of updated Multipole Love and f-Love Universal Relations in context of Binary Neutron Stars

Neutron star (NS) equation of state (EoS) insensitive relations or universal relations (UR) involving neutron star bulk properties play a crucial role in gravitational-wave astronomy. Considering a wide range of equations of state originating from (i) phenomenological relativistic mean ﬁeld models, (ii) realistic EoS models based on diﬀerent physical motivations, and (iii) polytropic EoSs described by spectral decomposition method, we update the EoS-insensitive relations involving NS tidal deformability (Multipole Love relation) and the UR between f-mode frequency and tidal deformability (f-Love relation). We analyze the binary neutron star (BNS) event GW170817 using the frequency domain TaylorF2 waveform model with updated universal relations and ﬁnd that the additional contribution of the octupolar electric tidal parameter and quadrupolar magnetic tidal parameter or the change of multipole Love relation has no signiﬁcant impact on the inferred NS properties. However, adding the f-mode dynamical phase lowers the 90% upper bound on ˜Λ by 16-20% as well as lowers the upper bound of NSs radii by ∼ 500m. The combined URs (multipole Love and f-Love) developed in this work predict a higher median (also a higher 90% upper bound) for ˜Λ by 6% and also predict higher radii for the binary components of GW170817 by 200-300m compared to the URs used previously in the literature. We further perform injection and recovery studies on simulated events with diﬀerent EoSs in A+ detector conﬁguration as well as with third generation (3G) Einstein telescope. In agreement with the literature, we ﬁnd that neglecting f-mode dynamical tides can signiﬁcantly bias the inferred NS properties, especially for low mass NSs. However, we also ﬁnd that the impact of the URs is within statistical errors.
Integrative Pan-Cancer Analysis of RNMT: a Potential Prognostic and Immunological Biomarker

Background: RNA guanine-7 methyltransferase (RNMT) is one of the main regulators of N7-methylguanosine, and the deregulation of RNMT correlated with tumor development and immune metabolism. However, the specific function of RNMT in pan-cancer remains unclear. Methods: RNMT expression in different cancers was analyzed using multiple databases, including Cancer Cell Line Encyclopedia (CCLE), Genotype-Tissue Expression Project (GTEx), and The Cancer Genome Atlas (TCGA). Cox regression analysis and Kaplan-Meier analysis were used to estimate the correlation of RNMT expression to prognosis. The data was also used to research the relationship between RNMT expression and common immunoregulators, tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR), and DNA methyltransferase (DNMT). Additionally, the cBioPortal website was used to evaluate the characteristics of RNMT alteration. The TISDB database was used to obtain the expression of different subtypes. The Tumor Immune Estimation Resource (TIMER) database was used to analyze the association between RNMT and tumor immune infiltration. Gene set enrichment analysis (GSEA) was used to identify the relevant pathways. Results: RNMT was ubiquitously highly expressed across cancers and survival analysis revealed that its expression was highly associated with the clinical prognosis of various cancer types. Remarkably, RNMT participates in immune regulation and plays a crucial part in the tumor microenvironment. A positive association was found between RNMT expression and six immune cell types expression in colon adenocarcinoma, kidney renal clear cell carcinoma, and liver hepatocellular carcinoma. Moreover, RNMT expression was highly associated with immunoregulators in most cancer types, and correlated to TMB, MSI, MMR, and DNMT. Finally, GSEA indicated that RNMT may correlate with tumor immunity.
Decoupling Features in Hierarchical Propagation for Video Object Segmentation

This paper focuses on developing a more effective method of hierarchical propagation for semi-supervised Video Object Segmentation (VOS). Based on vision transformers, the recently-developed Associating Objects with Transformers (AOT) approach introduces hierarchical propagation into VOS and has shown promising results. The hierarchical propagation can gradually propagate information from past frames to the current frame and transfer the current frame feature from object-agnostic to object-specific. However, the increase of object-specific information will inevitably lead to the loss of object-agnostic visual information in deep propagation layers. To solve such a problem and further facilitate the learning of visual embeddings, this paper proposes a Decoupling Features in Hierarchical Propagation (DeAOT) approach. Firstly, DeAOT decouples the hierarchical propagation of object-agnostic and object-specific embeddings by handling them in two independent branches. Secondly, to compensate for the additional computation from dual-branch propagation, we propose an efficient module for constructing hierarchical propagation, i.e., Gated Propagation Module, which is carefully designed with single-head attention. Extensive experiments show that DeAOT significantly outperforms AOT in both accuracy and efficiency. On YouTube-VOS, DeAOT can achieve 86.0% at 22.4fps and 82.0% at 53.4fps. Without test-time augmentations, we achieve new state-of-the-art performance on four benchmarks, i.e., YouTube-VOS (86.2%), DAVIS 2017 (86.2%), DAVIS 2016 (92.9%), and VOT 2020 (0.622). Project page: https://github.com/z-x-yang/AOT.
SA-MLP: Distilling Graph Knowledge from GNNs into Structure-Aware MLP

The message-passing mechanism helps Graph Neural Networks (GNNs) achieve remarkable results on various node classification tasks. Nevertheless, the recursive nodes fetching and aggregation in message-passing cause inference latency when deploying GNNs to large-scale graphs. One promising inference acceleration direction is to distill the GNNs into message-passing-free student multi-layer perceptrons (MLPs). However, the MLP student cannot fully learn the structure knowledge due to the lack of structure inputs, which causes inferior performance in the heterophily and inductive scenarios. To address this, we intend to inject structure information into MLP-like students in low-latency and interpretable ways. Specifically, we first design a Structure-Aware MLP (SA-MLP) student that encodes both features and structures without message-passing. Then, we introduce a novel structure-mixing knowledge distillation strategy to enhance the learning ability of MLPs for structure information. Furthermore, we design a latent structure embedding approximation technique with two-stage distillation for inductive scenarios. Extensive experiments on eight benchmark datasets under both transductive and inductive settings show that our SA-MLP can consistently outperform the teacher GNNs, while maintaining faster inference as MLPs. The source code of our work can be found in https://github.com/JC-202/SA-MLP.
Transfer learning with weak labels from radiology reports: application to glioma change detection

— Creating large annotated datasets represents a major bottleneck for the development of deep learning models in radiology. To overcome this, we propose a combined use of weak labels (imprecise, but fast-to-create annotations) and Transfer Learning (TL). Specifically, we explore inductive TL, where source and target domains are identical, but tasks are different due to a label shift: our target labels are created manually by three radiologists, whereas the source weak labels are generated automatically from textual radiology reports. We frame knowledge transfer as hyperparameter optimization, thus avoiding heuristic choices that are frequent in related works. We investigate the relationship between model size and TL, comparing a low-capacity VGG with a higher-capacity SEResNeXt. The task that we address is change detection in follow-up glioma imaging: we extracted 1693 T2-weighted magnetic resonance imaging difference maps from 183 patients, and classified them into stable or unstable according to tumor evolution. Weak labeling allowed us to increase dataset size more than 3-fold, and improve VGG classification results from 75% to 82% Area Under the ROC Curve (AUC) (p=0.04). Mixed training from scratch led to higher performance than fine-tuning or feature extraction. To assess generalizability, we also ran inference on an open dataset (BraTS-2015: 15 patients, 51 difference maps), reaching up to 76% AUC. Overall, results suggest that medical imaging problems may benefit from smaller models and different TL strategies with respect to computer vision problems, and that report-generated weak labels are effective in improving model performances. Code, in-house dataset and BraTS labels are released.
Learning to simulate realistic limit order book markets from data as a World Agent

Multi-agent market simulators usually require careful calibration to emulate real markets, which includes the number and the type of agents. Poorly calibrated simulators can lead to misleading conclusions, potentially causing severe loss when employed by investment banks, hedge funds, and traders to study and evaluate trading strategies. In this paper, we propose a world model simulator that accurately emulates a limit order book market – it requires no agent calibration but rather learns the simulated market behavior directly from historical data. Traditional approaches fail short to learn and calibrate trader population, as historical labeled data with details on each individual trader strategy is not publicly available. Our approach proposes to learn a unique "world" agent from historical data. It is intended to emulate the overall trader population, without the need of making assumptions about individual market agent strategies. We implement our world agent simulator models as a Conditional Generative Adversarial Network (CGAN), as well as a mixture of parametric distributions, and we compare our models against previous work. Qualitatively and quantitatively, we show that the proposed approaches consistently outperform previous work, providing more realism and responsiveness.