Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel insights into habitat suitability for Amazonian freshwater mussels linked with hydraulic and landscape drivers

Abstract Novel insights into habitat suitability for two Unionida freshwater mussels, Castalia ambigua Lamarck, 1819 (Hyriidae) and Anodontites elongatus (Swainson, 1823) (Mycetopodidae), are presented on the basis of hydraulic variables linked with the riverbed in six 500‐m reaches in an eastern Amazonian river basin. Within the reaches, there was strong habitat heterogeneity in hydrodynamics and substrate composition. In addition, we investigated stressors based on landscape modification that are associated with declines in mussel density. We measured hydraulic variables for each 500‐m reach, and landscape stressors at two spatial scales (subcatchment and riparian buffer forest). We used the Random Forest algorithm, a tree‐based model, to predict the hydraulic variables linked with habitat suitability for mussels, and to predict which landscape stressors were most associated with mussel density declines. Both mussel species were linked with low substrate heterogeneity and greater riverbed stability (low Froude and Reynolds numbers), especially at high flow (low stream power). Different sediment grain size preferences were observed between mussel species: Castalia ambigua was associated with medium sand and Anodontites elongatus with medium and fine sand. Declines in mussel density were associated with modifications linked to urbanization at small scales (riparian buffer forest), especially with percent of and distance from rural settlements, distance to the nearest street, and road density. In summary, the high variance explained in both hydraulic and landscape models indicated high predictive power, suggesting that our findings may be extrapolated and used as a baseline to test hypotheses of habitat suitability in other Amazonian rivers for Castalia ambigua and Anodontites elongatus and also for other freshwater mussel species. Our results highlight the urgent need for aquatic habitat conservation to maintain sheltered habitats during high flow as well as mitigate the effects of landscape modifications at the riparian buffer scale, both of which are important for maintaining dense mussel populations and habitat quality.
The Neutrophil-to-Lymphocyte Ratio (NLR) Predicts the Prognosis of Unresectable Intermediate and Advanced Hepatocellular Carcinoma Treated with Apatinib

Purpose Patients with hepatocellular carcinoma (HCC) who might benefit most from anti-angiogenesis therapy remain unknown. In recent years, neutrophil-to-lymphocyte ratio (NLR), an indicator of inflammatory response, has received particular attention in HCC. Herein, we explored the prognostic value of pre-treatment NLR in individuals with unresectable intermediate and advanced hepatocellular carcinoma treated with apatinib, a second-line angiogenesis inhibitor. The findings of this study would assist in precision medicine and provide clinical decision support. Patients and Methods This is a retrospective study in which 171 HCC patients attending Tianjin Medical University Cancer Institute and Hospital and treated with apatinib between January 2016 and July 2018 were enrolled. The prognosis of the patients based on NLR signatures was then analyzed. Results Patients with a low pre-treatment NLR (NLR < 2.49) presented a significantly longer overall survival (OS) (P < 0.001) and progression-free survival (PFS) (P = 0.043). Furthermore, a low pre-treatment NLR level could be used to predict a longer OS in patients with non-macrovascular invasion (P < 0.001). Independent of serum alpha-fetoprotein (AFP) levels, a low NLR level in this cohort of patients is associated with a longer OS. Conclusion Pre-treatment NLR predicts the prognosis of patients with unresectable intermediate and advanced HCC treated with apatinib.
Morning Serum Cortisol as a Predictor for the HPA Axis Recovery in Cushing's Disease

Background The suppressed hypothalamic-pituitary-adrenal (HPA) axis after successful surgery for Cushing's disease (CD) will recover in almost all patients. We aimed to identify the predictive factors for HPA axis recovery in CD patients with postoperative remission. Design and Methods. This observational retrospective cross-sectional study enrolled 69 CD patients with postoperative remission in Huashan Hospital from 2015 to 2019. All subjects had a detailed clinical evaluation. The low-dose ACTH stimulation test (LDT) was conducted as the gold standard for assessing the HPA axis function. Results Peak cortisol in LDT was found only to be positively correlative with morning serum cortisol (MSC) (ρ=0.451, p < 0.001). The MSC was higher (p < 0.001), and the median postoperative course was significantly longer (p=0.025) in the patients with the recovered HPA axis function compared with unrecovered patients. The AUC value of MSC for predicting the recovery of the HPA axis was 0.701, and the optimal cutoff was 6.25 μg/dl (sensitivity 85.19% and specificity 47.62%). Other useful cutoff values were 10.74 μg/dl (specificity 100%) and 4.18 μg/dl (sensitivity 100%). Besides, combined with the postoperative course, the AUC values were higher than MSC alone (0.935 vs. 0.701, p < 0.001). Conclusions MSC is a viable first-step diagnostic predictor for HPA axis recovery in CD patients with postoperative remission. For the patients with cortisol levels between 4.18 and 10.74 μg/dl, a confirmatory test should be conducted. When the MSC level was 10.74 μg/dl or greater, the replacement therapy could be discontinued.
Sensor Adversarial Traits: Analyzing Robustness of 3D Object Detection Sensor Fusion Models

A critical aspect of autonomous vehicles (AVs) is the object detection stage, which is increasingly being performed with sensor fusion models: multimodal 3D object detection models which utilize both 2D RGB image data and 3D data from a LIDAR sensor as inputs. In this work, we perform the first study to analyze the robustness of a high-performance, open source sensor fusion model architecture towards adversarial attacks and challenge the popular belief that the use of additional sensors automatically mitigate the risk of adversarial attacks. We find that despite the use of a LIDAR sensor, the model is vulnerable to our purposefully crafted image-based adversarial attacks including disappearance, universal patch, and spoofing. After identifying the underlying reason, we explore some potential defenses and provide some recommendations for improved sensor fusion models.
Room-temperature ferromagnetic transitions and the temperature dependence of magnetic behaviors in FeCoNiCr-based high-entropy alloys

High-entropy alloys (HEAs) containing multiple principle alloying elements exhibit unique properties so they are currently receiving great attention for developing innovative alloy designs. In FeCoNi-based HEAs, magnetic behaviors strongly depend on the addition of alloying elements, usually accompanied by structural changes. In this work, the effect of non-magnetic components on the ferromagnetic transition and magnetic behaviors in equiatomic FeCoNiCrX (X=Al, Ga, Mn and Sn) HEAs was investigated. Alloy ingots of nominal compositions of HEAs were prepared by arc melting and the button ingots were cut into discs for magnetic measurements as functions of magnetic field and temperature. The HEAs of FeCoNiCrMn and FeCoNiCrSn show typical paramagnetic behaviors, composed of solid solution FCC matrix, while the additions of Ga and Al in FeCoNiCr exhibit ferromagnetic behaviors, along with the coexistence of FCC and BCC phases due to spinodal decomposition. The partial phase transition in both HEAs with the add.
Bootstrapping more QM systems

We test the bootstrap approach for determining the spectrum of one dimensional Hamiltonians. In this paper we focus on problems that have a two parameter search space in the bootstrap approach: the double well and a periodic potential associated with the Mathieu equation. For the double well, we compare the bootstrapped energies with contributions from perturbative and non-perturbative results, finding good agreement. For the periodic potentials, we notice that the bootstrap approach gives the band structure of the periodic potential, but it has trouble finding the quasi-momentum of the system. To make further progress on determining the dispersion relation of the bands, new techniques are needed.
Magnetic nickel ferrite nanoparticles as highly durable catalysts for catalytic transfer hydrogenation of bio-based aldehydes

Magnetic nickel ferrite (NiFe2O4) nanoparticles were exploited as stable and easily separable heterogeneous catalysts for catalytic transfer hydrogenation (CTH) of furfural to furfuryl alcohol with 2-propanol as both the hydrogen source and the solvent providing 94% product yield at 180 °C after 6 h of reaction. The magnetic properties of the catalysts provided facile recovery using an external magnet after reaction allowing it to be reused in five reaction cycles without loss of catalytic performance. Importantly, the NiFe2O4 nanoparticles were also applicable to CTH of other alkenyl/allyl/aromatic aldehydes affording over 94% selectivity towards the targeted alcohol products, thus being attractive as highly universal catalysts for CTH of aldehydes.
Leaf of Leaf Foliation and Beltrami Parametrization in $d>2$ dimensional Gravity

This work shows the existence of a d > 2 dimensional covariant “Beltrami vielbein” that generalizes the d = 2 situation. Its definition relies on a sub-foliation Σ d−1 = Σd−3 × Σ2 of the Arnowitt–Deser– Misner leaves of d-dimensional Lorentzian manifolds Md. Σ2 stands for the sub-foliating randomly varying Riemann surfaces in Md. The “Beltrami d-bein” associated to any given generic vielbein of Md is systematically determined by a covariant gauge fixing of the Lorentz symmetry of the latter. It is parametrized by d(d+1) 2 independent fields belonging to different categories. Each one has a specific interpretation. The Weyl invariant field sector of the Beltrami d-bein selects the d(d−3) 2 physical local degrees of freedom of d > 2 dimensional gravity. The components of the Beltrami d-bein are in a one to one correspondence with those of the associated Beltrami d-dimensional metric. The Beltrami parametrization of the Spin connection and of the Einstein action delivers interesting expressions. Its use might easier the search of new Ricci flat solutions classified by the genus of the sub-manifold Σ2. A gravitational “physical gauge” choice is introduced that takes advantage of the geometrical specificities of the Beltrami parametrization. Further restrictions simplify the expression of the Beltrami vielbein when Md has a given spatial holonomy. This point is exemplified in the case of d = 8 Lorentzian spaces with G2 ⊂ SO(1, 7) holonomy. The Lorentzian results presented in this paper can be extended to the Euclidean case. ∗baulieu@lpthe.jussieu. fr 1
Biomarker Screening and Prognostic Significance Analysis for Renal Cell Carcinoma

Background Studies report that conventional treatment of clear cell renal cell carcinoma (ccRCC) is effective, but several advanced patients present with poor prognosis. The current study explored potential new tumor markers and therapeutic targets in advanced ccRCC. Methods Biomarker gene expression of ccRCC was retrieved from GEO database and the Cancer Genome Atlas Kidney Clear Cell Carcinoma (TCGA-KIRC) database. Gene ontology (GO) analysis and protein–protein interaction (PPI) networks of biomarker genes were constructed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) tool. Kaplan–Meier analysis and receiver operating characteristic curve (ROC) analysis were performed to explore the prognostic and diagnostic roles of these genes. Gene set enrichment analysis (GSEA) analysis was used to determine hallmark functions of the biomarker genes. qRT-PCR was used to verify the reliability of the analysis results in tumor tissues. Results A total of 21 upregulated genes were identified between advanced ccRCC and early ccRCC (grade III+IV vs grade I+II). Gene ontology analysis showed that the 21 upregulated genes were mainly implicated in biological processes including metabolic and lipid transport. The findings showed that 7 out of the 21 genes were significantly upregulated in 72-paired samples retrieved from the TCGA-KIRC. High expression of 5 genes indicated a poor prognosis of overall survival and disease-free survival in KIRC. Three genes effectively distinguished renal cancer tissue and adjacent renal tissues in a total of 533 ccRCC samples. GSEA showed that the 3 biomarkers were significantly enriched in epithelial–mesenchymal transition, G2M checkpoint, and angiogenesis. The results of qRT-PCR showed that STEAP3, IBSP, and AQP9 had a significant identification effect in ccRCC. Conclusion The findings showed that 3 biomarkers were significantly upregulated in advanced ccRCC and could be used for diagnosis, prediction, and potential novel therapeutic targets for progression of ccRCC.
Improving Test Case Generation for REST APIs Through Hierarchical Clustering

With the ever-increasing use of web APIs in modern- day applications, it is becoming more important to test the system as a whole. In the last decade, tools and approaches have been proposed to automate the creation of system-level test cases for these APIs using evolutionary algorithms (EAs). One of the limiting factors of EAs is that the genetic operators (crossover and mutation) are fully randomized, potentially breaking promising patterns in the sequences of API requests discovered during the search. Breaking these patterns has a negative impact on the effectiveness of the test case generation process. To address this limitation, this paper proposes a new approach that uses Agglomerative Hierarchical Clustering (AHC) to infer a linkage tree model, which captures, replicates, and preserves these patterns in new test cases. We evaluate our approach, called LT-MOSA, by performing an empirical study on 7 real-world benchmark applications w.r. t. branch coverage and real-fault detection capability. We also compare LT-MOSA with the two existing state-of-the-art white-box techniques (MIO, MOSA) for REST API testing. Our results show that LT-MOSA achieves a statistically significant increase in test target coverage (i.e., lines and branches) compared to MIO and MOSA in 4 and 5 out of 7 applications, respectively. Furthermore, LT-MOSA discovers 27 and 18 unique real-faults that are left undetected by MIO and MOSA, respectively.
Quantum Krylov subspace algorithms for ground- and excited-state energy estimation

Quantum Krylov subspace diagonalization (QKSD) algorithms provide a low-cost alternative to the conventional quantum phase estimation algorithm for estimating the ground and excited-state energies of a quantum many-body system. While QKSD algorithms typically rely on using the Hadamard test for estimating Krylov subspace matrix elements of the form, $\langle \phi_i|e^{-i\hat{H}\tau}|\phi_j \rangle$, the associated quantum circuits require an ancilla qubit with controlled multi-qubit gates that can be quite costly for near-term quantum hardware. In this work, we show that a wide class of Hamiltonians relevant to condensed matter physics and quantum chemistry contain symmetries that can be exploited to avoid the use of the Hadamard test. We propose a multi-fidelity estimation protocol that can be used to compute such quantities showing that our approach, when combined with efficient single-fidelity estimation protocols, provides a substantial reduction in circuit depth. In addition, we develop a unified theory of quantum Krylov subspace algorithms and present three new quantum-classical algorithms for the ground and excited-state energy estimation problems, where each new algorithm provides various advantages and disadvantages in terms of total number of calls to the quantum computer, gate depth, classical complexity, and stability of the generalized eigenvalue problem within the Krylov subspace.
Molecules with ALMA at Planet-forming Scales (MAPS). X. Studying Deuteration at High Angular Resolution toward Protoplanetary Disks

Deuterium fractionation is dependent on various physical and chemical parameters. Thus, the formation location and thermal history of material in the solar system is often studied by measuring its D/H ratio. This requires knowledge about the deuteration processes operating during the planet formation era. We aim to study these processes by radially resolving the DCN/HCN (at 0.″3 resolution) and N2D+/N2H+ (∼0.″3–0.″9) column density ratios toward the five protoplanetary disks observed by the Molecules with ALMA at Planet-forming scales (MAPS) Large Program. DCN is detected in all five sources, with one newly reported detection. N2D+ is detected in four sources, two of which are newly reported detections. We derive column density profiles that allow us to study the spatial variation of the DCN/HCN and N2D+/N2H+ ratios at high resolution. DCN/HCN varies considerably for different parts of the disks, ranging from 10−3 to 10−1. In particular, the inner-disk regions generally show significantly lower HCN deuteration compared with the outer disk. In addition, our analysis confirms that two deuterium fractionation channels are active, which can alter the D/H ratio within the pool of organic molecules. N2D+ is found in the cold outer regions beyond ∼50 au, with N2D+/N2H+ ranging between 10−2 and 1 across the disk sample. This is consistent with the theoretical expectation that N2H+ deuteration proceeds via the low-temperature channel only. This paper is part of the MAPS special issue of the Astrophysical Journal Supplement.
Vaccination strategies and transmission of COVID-19: evidence across leading countries

Vaccination has been perceived as a key to reaching "herd immunity" in the current COVID-19 pandemic. This paper examines effectiveness of different vaccination strategies. We investigate the effects of two key elements in mass vaccination, which are allocations and timing of first and second doses and types of vaccines, on the spread of COVID-19. Amid limited supply of approved vaccines and constrained medical resources, the choice of a vaccination strategy is fundamentally an economic problem. We employ standard time-series and panel data models commonly used in economic research with real world data to estimate the effects of progress in vaccination and types of vaccines on health outcomes. Potential confounders such as government responses and people's behavioral changes are also taken into account. Our findings suggest that the share of people vaccinated with at least one dose is significantly negatively associated with new infections and deaths. Conditioning on first dose progress, full vaccination offers no further reductions in new cases and deaths. For vaccines from China, however, we find weaker effects of vaccination progress on health outcomes. Our results support the extending interval between first and second dose policy adopted by Canada and the UK among others for mRNA-based vaccines. As vaccination progressed, people's mobility increased and it offset the direct effects of vaccination. Therefore, public health measures are still important to contain the transmission by refraining people from being more mobile after vaccinated.
Efficient Inference for Multilingual Neural Machine Translation

Multilingual NMT has become an attractive solution for MT deployment in production. But to match bilingual quality, it comes at the cost of larger and slower models. In this work, we consider several ways to make multilingual NMT faster at inference without degrading its quality. We experiment with several “light decoder” architectures in two 20-language multi-parallel settings: small-scale on TED Talks and large-scale on ParaCrawl. Our experiments demonstrate that combining a shallow decoder with vocabulary filtering leads to almost 2 times faster inference with no loss in translation quality. We validate our findings with BLEU and chrF (on 380 language pairs), robustness evaluation and human evaluation.
The Effects of Statin Dose, Lipophilicity, and Combination of Statins plus Ezetimibe on Circulating Oxidized Low-Density Lipoprotein Levels: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background Elevated plasma low-density lipoprotein cholesterol (LDL-C) is the main risk factor for atherosclerotic cardiovascular disease (ASCVD). Statins are the drugs of choice for decreasing LDL-C and are used for the prevention and management of ASCVD. Guidelines recommend that subjects with high and very high ASCVD risk should be treated with high-intensity statins or a combination of high-intensity statins and ezetimibe. The lipophilicity or hydrophilicity (solubility) of statins is considered to be important for at least some of their LDL-C lowering independent pleiotropic effects. Oxidative modification of LDL (ox-LDL) is considered to be the most important atherogenic modification of LDL and is supposed to play a crucial role in atherogenesis and ASCVD outcomes. Objective The aim of this systematic review and meta-analysis was to find out what are the effects of statin intensity, lipophilicity, and combination of statins plus ezetimibe on ox-LDL. Methods PubMed, Scopus, Embase, and Web of Science were searched from inception to February 5, 2021, for randomized controlled trials (RCTs). Two independent and blinded authors evaluated eligibility by screening the titles and abstracts of the studies. Risk of bias in the studies included in this meta-analysis was evaluated according to the Cochrane instructions. Meta-analysis was performed using Comprehensive Meta-Analysis (CMA) V2 software. Evaluation of funnel plot, Begg's rank correlation, and Egger's weighted regression tests were used to assess the presence of publication bias. Results Among the 1427 published studies identified by a systematic databases search, 20 RCTs were finally included in the systematic review and meta-analysis. A total of 1874 patients are included in this meta-analysis. This meta-analysis suggests that high-intensity statin treatment is associated with a significant decrease in circulating concentrations of ox-LDL when compared with low-to-moderate treatment (SMD: -0.675, 95% CI: -0.994, -0.357, p < 0.001; I2: 55.93%). There was no difference concerning ox-LDL concentration between treatments with hydrophilic and lipophilic statins (SMD: -0.129, 95% CI: -0.330, -0.071, p = 0.206; I2: 45.3%), but there was a significant reduction in circulating concentrations of ox-LDL associated with statin plus ezetimibe combination therapy when compared with statin monotherapy (SMD: -0.220, 95% CI: -0.369, -0.071, p = 0.004; I2: 0%). Conclusion High-dose statin or combination of statins with ezetmibe reduces plasma ox-LDL in comparison low-to-moderate intensity statin therapy alone. Statin lipophilicity is not associated with reduction in ox-LDL plasma concentrations.