Miracle-dz/UniFAR-FTUs huggingface dataset:
Transient fluctuation theorems for the currents and initial equilibrium ensembles

We prove a transient fluctuation theorem for the currents for continuous-time Markov jump processes with stationary rates, generalizing an asymptotic result by Andrieux and Gaspard (2007 J. Stat. Phys. 127 107) to finite times. The result is based on a graph-theoretical decomposition in cycle currents and an additional set of tidal currents that characterize the transient relaxation regime. The tidal term can then be removed by a preferred choice of a suitable initial equilibrium ensemble, a result that provides the general theory for the fluctuation theorem without ensemble quantities recently addressed in Bulnes-Cuetara et al (2014 Phys. Rev. E 89 052119). As an example we study the reaction network of a simple stochastic chemical engine, and finally we digress on general properties of fluctuation relations for more complex chemical reaction networks.
Elastin-like Polypeptide Hydrogels for Tunable, Sustained Local Chemotherapy in Malignant Glioma

Glioblastoma (GBM) is a primary brain tumor that carries a dismal prognosis, which is primarily attributed to tumor recurrence after surgery and resistance to chemotherapy. Since the tumor recurrence appears near the site of surgical resection, a concept of immediate and local application of chemotherapeutic after initial tumor removal could lead to improved treatment outcome. With the ultimate goal of developing a locally-applied, injectable drug delivery vehicle for GBM treatment, we created elastin-like polypeptide (ELP) hydrogels. The ELP hydrogels can be engineered to release anti-cancer drugs over an extended period. The purpose of this study was to evaluate the biomechanical properties of ELP hydrogels, to characterize their ability to release doxorubicin over time, and to investigate, in vitro, the anti-proliferative effect of Dox-laden ELP hydrogels on GBM. Here, we present microstructural differences, swelling ratio measurements, drug release characteristics, and in vitro effects of different ELP hydrogel compositions. We found that manipulation of the ELP–collagen ratio allows for tunable drug release, that the released drug is taken up by cells, and that incubation with a small volume of ELP-Dox hydrogel drastically reduced survival and proliferation of GBM cells in vitro. These results underscore the potential of ELP hydrogels as a local delivery strategy to improve prognosis for GBM patients after tumor resection.
A Parallel Cross Convolutional Recurrent Neural Network for Automatic Imbalanced ECG Arrhythmia Detection with Continuous Wavelet Transform

Automatic detection of arrhythmia using electrocardiogram (ECG) and deep learning (DL) is very important to reduce the global death rate from cardiovascular diseases (CVD). Previous studies on automatic arrhythmia detection relied largely on various ECG features and have achieved considerable classification accuracy using DL-based models. However, most previous research has ignored multi-class imbalanced problems in ECG arrhythmia detection. Therefore, it remains a challenge to improve the classification performance of the DL-based models. This paper proposes a novel parallel cross convolutional recurrent neural network in order to improve the arrhythmia detection performance of imbalanced ECG signals. The proposed model incorporates a recurrent neural network and a two-dimensional (2D) convolutional neural network (CNN) and can effectively learn temporal characteristics and rich spatial information of raw ECG signals. Continuous wavelet transform (CWT) is used to transform the ECG signals into a 2D scalogram composed of time–frequency components, and subsequently, the 2D-CNN can learn spatial information from the 2D scalogram. The proposed model is not only efficient in learning features with imbalanced samples but can also significantly improve model convergence with higher accuracy. The overall performance of our proposed model is evaluated based on the MIT-BIH arrhythmia dataset. Detailed analysis of evaluation metrics reveals that the proposed model is very effective in arrhythmia detection and significantly better than the existing hierarchical network models.
Additive Models for Symmetric Positive-Definite Matrices and Lie Groups

We propose and investigate an additive regression model for symmetric positive-definite matrix valued responses and multiple scalar predictors. The model exploits the abelian group structure inherited from either of the log-Cholesky and log-Euclidean frameworks for symmetric positive-definite matrices and naturally extends to general abelian Lie groups. The proposed additive model is shown to connect to an additive model on a tangent space. This connection not only entails an efficient algorithm to estimate the component functions but also allows one to generalize the proposed additive model to general Riemannian manifolds. Optimal asymptotic convergence rates and normality of the estimated component functions are established and numerical studies show that the proposed model enjoys good numerical performance and is not subject to the curse of dimensionality when there are multiple predictors. The practical merits of the proposed model are demonstrated through an analysis of brain diffusion tensor imaging data.
The pathogenesis of superior mesenteric artery dissection: An in-depth study based on fluid-structure interaction and histology analysis

OBJECTIVE To explore the role of hemodynamic factors in the occurrence of superior mesenteric artery (SMA) dissection (SMAD) using a fluid-structure interaction (FSI) simulation method, and to identify histopathologic changes occurring in the wall of the SMA. METHODS A total of 122 consecutive patients diagnosed with SMAD and 122 controls were included in this study. Hemodynamic factors were calculated using a FSI simulation method. Additionally, SMA specimens obtained from 12 cadavers were stained for histological quantitative analysis. RESULTS The mean aortomesenteric angle (59.7° ± 21.4° vs 48.2° ± 16.8°; p < .001) and SMA maximum curvature (0.084 ± 0.078 mm-1 vs 0.032 ± 0.023 mm-1; p < .001) were higher in SMAD patients than the controls. Larger aortomesenteric angles and SMA curvatures were associated with higher and more concentrated wall shear stress at anterior wall of the SMA curve segment, co-located with the dissection origins. The mean thickness of media (325.18 ± 44.87 µm vs 556.92 ± 138.32 µm; p = .003) was thinner in the anterior wall of the SMA curve than in the posterior wall. The area fractions of elastin (17.96% ± 3.36% vs 27.06% ± 4.18%; p = .002) and collagen (45.43% ± 6.89% vs 55.57% ± 7.57%; p = .036) were lower in anterior wall of the SMA curve than in posterior wall. CONCLUSION Increased aortomesenteric angle and SMA curvature are risk factors for SMAD. Both of these factors can cause local hemodynamic abnormalities, which can lead to histopathologic changes in anterior wall of SMA.
An Improved Neural Network Model for Enhancing Rutting Depth Prediction

Rutting is the main distress form of asphalt pavement, and its prediction accuracy is directly related to the reliability of the designed road. This research developed a neural network model to improve the prediction ability about the rutting of a pavement performance criterion and compared it with the multiple linear regression model and the existing neural network model. The neural network model is developed using the Keras module from the TensorFlow package in Python. Two reports generated by the National Cooperative Highway Research Program project 01-37A and the Long-Term Pavement Performance website records have been used as data sources for training the neural network model, which are reliable data preserved after years of monitoring. The input variables include the pavement thickness, service time, average annual daily traffic of trucks and the deformation of the asphalt concrete layer, granular base layer and subgrade layer. This experiment used 440 samples, of which 352 samples (80%) were used for model training and 88 samples (20%) for testing. The training results of the model reveal that the neural network model is significantly better than the multiple linear regression model, and the newly built neural network model performs better than another similar neural network in predictive performance. For the multiple linear regression model, the correlation coefficient R2 value between the measured and predicted in the testing set increased from 0.265 to 0.712. In contrast, it promotes from 0.867 to 0.902 for the neural network model.
Deriving Boltzmann equations from Kadanoff-Baym equations in curved space-time

In this paper we consider the transition from the Kadanoff-Baym equations to the quantum kinetic equations and then to the Boltzmann equation in curved space-time. As one expects from general considerations, the derived equations appear to be covariant generalizations of the corresponding equations in Minkowski space-time. The formalism will be applied to test approximations commonly made in the computation of the baryon asymmetry in the leptogenesis scenario.
General degeneracy in density functional perturbation theory

Degenerate perturbation theory from quantum mechanics is inadequate in density functional theory (DFT) because of nonlinearity in the Kohn-Sham potential. Herein, we develop the fully general perturbation theory for open-shell, degenerate systems in Kohn-Sham DFT, without assuming the presence of symmetry or equal occupation of degenerate orbitals. To demonstrate the resulting methodology, we apply it to the iron atom in the central field approximation, perturbed by an electric quadrupole. This system was chosen because it displays both symmetry required degeneracy, between the five 3\textit{d} orbitals, as well as accidental degeneracy, between the 3\textit{d} and 4\textit{s} orbitals. The quadrupole potential couples the degenerate 3\textit{d} and 4\textit{s} states, serving as an example of the most general perturbation.
κ-Carrageenan Hydrogel as a Matrix for Therapeutic Enzyme Immobilization

During the last few decades, polysaccharide hydrogels attract more and more attention as therapeutic protein delivery systems due to their biocompatibility and the simplicity of the biodegradation of natural polymers. The protein retention by and release from the polysaccharide gel network is regulated by geometry and physical interactions of protein with the matrix. In the present work, we studied the molecular details of interactions between κ-carrageenan and three lipases, namely the lipases from Candida rugosa, Mucor javanicus, and Rhizomucor miehei—which differ in their size and net charge—upon protein immobilization in microparticles of polysaccharide gel. The kinetics of protein release revealed the different capability of κ-carrageenan to retain lipases, which are generally negatively charged; that was shown to be in line with the energy of interactions between polysaccharides and positively charged epitopes on the protein surface. These data create a platform for the novel design of nanocarriers for biomedical probes of enzymatic origin.
The Conversion of Superoxide to Hydroperoxide on Cobalt(III) Depends on the Structural and Electronic Properties of Azole-Based Chelating Ligands

Conversion from superoxide (O2−) to hydroperoxide (OOH−) on the metal center of oxygenases and oxidases is recognized to be a key step to generating an active species for substrate oxidation. In this study, reactivity of cobalt(III)-superoxido complexes supported by facially-capping tridentate tris(3,5-dimethyl-4-X-pyrazolyl)hydroborate ([HB(pzMe2,X)3]−; TpMe2,X) and bidentate bis(1-methyl-imidazolyl)methylborate ([B(ImN-Me)2Me(Y)]−; LY) ligands toward H-atom donating reagent (2-hydroxy-2-azaadamantane; AZADOL) has been explored. The oxygenation of the cobalt(II) precursors give the corresponding cobalt(III)-superoxido complexes, and the following reaction with AZADOL yield the hydroperoxido species as has been characterized by spectroscopy (UV-vis, resonance Raman, EPR). The reaction of the cobalt(III)-superoxido species and a reducing reagent ([CoII(C5H5)2]; cobaltocene) with proton (trifluoroacetic acid; TFA) also yields the corresponding cobalt(III)-hydroperoxido species. Kinetic analyses of the formation rates of the cobalt(III)-hydroperoxido complexes reveal that second-order rate constants depend on the structural and electronic properties of the cobalt-supporting chelating ligands. An electron-withdrawing ligand opposite to the superoxide accelerates the hydrogen atom transfer (HAT) reaction from AZADOL due to an increase in the electrophilicity of the superoxide ligand. Shielding the cobalt center by the alkyl group on the boron center of bis(imidazolyl)borate ligands hinders the approaching of AZADOL to the superoxide, although the steric effect is insignificant.
OGLE-LMC-ECL-11893: THE DISCOVERY OF A LONG-PERIOD ECLIPSING BINARY WITH A CIRCUMSTELLAR DISK

We report the serendipitous discovery of a disk–eclipse system OGLE-LMC-ECL-11893. The eclipse occurs with a period of 468 days, a duration of about 15 days, and a deep (up to ΔmI ≈ 1.5), peculiar, and asymmetric profile. A possible origin of such an eclipse profile involves a circumstellar disk. The presence of the disk is confirmed by the H-α line profile from the follow-up spectroscopic observations, and the star is identified as Be/Ae type. Unlike the previously known disk–eclipse candidates, the eclipses of OGLE-LMC-ECL-11893 retain the same shape throughout the span of ∼17 yr (13 orbital periods), indicating no measurable orbital precession of the disk.
MASKED AREAS IN SHEAR PEAK STATISTICS: A FORWARD MODELING APPROACH

The statistics of shear peaks have been shown to provide valuable cosmological information beyond the power spectrum, and will be an important constraint of models of cosmology in forthcoming astronomical surveys. Surveys include masked areas due to bright stars, bad pixels etc., which must be accounted for in producing constraints on cosmology from shear maps. We advocate a forward-modeling approach, where the impacts of masking and other survey artifacts are accounted for in the theoretical prediction of cosmological parameters, rather than correcting survey data to remove them. We use masks based on the Deep Lens Survey, and explore the impact of up to 37% of the survey area being masked on LSST and DES-scale surveys. By reconstructing maps of aperture mass the masking effect is smoothed out, resulting in up to 14% smaller statistical uncertainties compared to simply reducing the survey area by the masked area. We show that, even in the presence of large survey masks, the bias in cosmological parameter estimation produced in the forward-modeling process is ≈1%, dominated by bias caused by limited simulation volume. We also explore how this potential bias scales with survey area and evaluate how much small survey areas are impacted by the differences in cosmological structure in the data and simulated volumes, due to cosmic variance.
A second course in stochastic processes

Preface. Preface to A First Course. Preface to First Edition. Contents of A First Course. Algebraic Methods in Markov Chains. Ratio Theorems of Transition Probabilities and Applications. Sums of Independent Random Variables as a Markov Chain. Order Statistics, Poisson Processes, and Applications. Continuous Time Markov Chains. Diffusion Processes. Compounding Stochastic Processes. Fluctuation Theory of Partial Sums of Independent Identically Distributed Random Variables. Queueing Processes. Miscellaneous Problems. Index.
Baryogenesis via mesino oscillations

We propose a new mechanism for baryogenesis at the 1\char21{}200 MeV scale. Enhancement of charge parity ($CP$) violation takes place via interference between oscillations and decays of mesinos\char22{}bound states of a scalar quark and antiquark and their $CP$ conjugates. We present the mechanism in a simplified model with four new fundamental particles, with masses between 300 GeV and 10 TeV, and show that some of the experimentally allowed parameter space can give the observed baryon-to-entropy ratio.
Sublingual Dexmedetomidine for the Treatment of Acute Agitation in Adults With Schizophrenia or Schizoaffective Disorder: A Randomized Placebo-Controlled Trial.

Objective: Determine if sublingual dexmedetomidine, a selective α2 adrenergic receptor agonist, reduces symptoms of acute agitation associated with schizophrenia or schizoaffective disorder. Methods: This phase 3, randomized, double-blind, placebo-controlled study was conducted in adults diagnosed with schizophrenia or schizoaffective disorder per the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria. The study was conducted at 15 US sites between January 23, 2020, and May 8, 2020. Participants were randomized to sublingual dexmedetomidine 180 μg, 120 μg, or matching placebo. The primary efficacy endpoint was mean change from baseline in the Positive and Negative Syndrome Scale-Excited Component (PEC) total score at 2 hours postdose. Results: Altogether, 380 participants (mean age 45.6 years, 63.4% identifying as male, 77.9% identifying as Black or African American) were randomized; 380 (100%) self-administered study medication, and 372 (97.9%) completed the study. The mean PEC total score at baseline (17.6) indicated mild to moderate agitation. At 2 hours postdose, the least squares mean changes (SE) from baseline were -10.3 (0.4) for sublingual dexmedetomidine 180 μg, -8.5 (0.4) for 120 μg, and -4.8 (0.4) for placebo. Least squares mean differences (97.5% confidence intervals) in the sublingual dexmedetomidine groups were -5.5 (-6.7 to -4.3) for 180 μg and -3.7 (-4.9 to -2.5) for 120 μg (both P < .001 vs placebo). The most commonly encountered adverse events with dexmedetomidine (incidence ≥ 5% and ≥ 2× rate observed with placebo) were somnolence, dry mouth, and hypotension for the 120 μg dose, and somnolence, dizziness, orthostatic hypotension, and oral hypoesthesia for the 180 μg dose. Conclusions: Treatment with sublingual dexmedetomidine 180 μg or 120 μg was more efficacious than placebo in reducing acute agitation associated with schizophrenia as measured by PEC scores at 2 hours postdose. Trial Registration: ClinicalTrials.gov identifier: NCT04268303.
Application of Spectral Entropy in Haul Truck Joint Damage Detection

Early detection of machine failures is often beneficial, both financially and in terms of worker safety. The article presents the problem of frequently damaged joints in haul trucks, which are a real threat to the health and life of drivers. It was decided to investigate the problem in terms of dynamic overloads using two NGIMU inertial sensors and placing them in two places on the machine in close proximity to a joint. The data were captured during the standard operation of various machines in several mining departments, which allowed for the detection of a variety of factors influencing vibration. A hypothesis was developed that any changes in the joint would cause a change in the characteristics of vibrations, which were measured using the spectral entropy of vertical vibrations. Analyses have shown that there is a relationship between the change in spectral entropy difference (between the front and back of the vehicle) and joint events: nut tightening, nut replacement, and even joint fracture and replacement. The presented results offer the potential to create a tool for joint diagnostics and the early detection of damage or backlash.
Importance-Aware Information Bottleneck Learning Paradigm for Lip Reading

Lip reading is the task of decoding text from speakers' mouth movements. Numerous deep learning-based methods have been proposed to address this task. However, these existing deep lip reading models suffer from poor generalization due to overfitting the training data. To resolve this issue, we present a novel learning paradigm that aims to improve the interpretability and generalization of lip reading models. In specific, a Variational Temporal Mask (VTM) module is customized to automatically analyze the importance of frame-level features. Furthermore, the prediction consistency constraints of global information and local temporal important features are introduced to strengthen the model generalization. We evaluate the novel learning paradigm with multiple lip reading baseline models on the LRW and LRW-1000 datasets. Experiments show that the proposed framework significantly improves the generalization performance and interpretability of lip reading models.
A Novel Beta-Glucosidase Gene for Plant Type Was Identified by Genome-Wide Association Study and Gene Co-Expression Analysis in Widespread Bermudagrass

Bermudagrass (Cynodon spp.) is one of the most widely distributed warm-season grasses globally. The growth habits and plant type of bermudagrass are strongly associated with the applied purpose of the landscape, livestock, and eco-remediation. Therefore, persistent efforts are made to investigate the genetic basis of plant type and growth habits of bermudagrass. Here, we dissect the genetic diversity of 91 wild bermudagrass resources by genome-wide association studies (GWAS) combined with weighted gene co-expression analysis (WGCNA). This work is based on the RNA-seq data and the genome of African bermudagrass (Cynodon transvaalensis Burtt Davy). Sixteen reliable single-nucleotide polymorphisms (SNPs) in transcribed regions were identified to be associated with the plant height and IAA content in diverse bermudagrass by GWAS. The integration of the results from WGCNA indicates that beta-glucosidase 31 (CdBGLU31) is a candidate gene underlying a G/A SNP signal. Furthermore, both qRT-PCR and correlation coefficient analyses indicate that CdBGLU31 might play a comprehensive role in plant height and IAA biosynthesis and signal. In addition, we observe lower plant height in Arabidopsis bglu11 mutants (homologs of CdBGLU31). It uncovers the breeding selection history of different plant types from diverse bermudagrass and provides new insights into the molecular function of CdBGLU31 both in plant types and in IAA biosynthetic pathways.
Harnessing a direct scan-to-modeling approach for structural mechanics

To ensure the safety and reliability operation of infrastructures, accurate and rapid condition assessment of in-service infrastructures is extremely imperative. Image technologies and techniques, as a non-destructive evaluation (NDE) tool, have created new opportunities to achieve this. It is able to resolve sub-millimeter details including surface flaws and defects, providing a foundation for quantitatively linking observable features to operational performance. This linkage is possible when the quantified feature is directly integrated into tools suitable for describing mechanical response, such as the finite element method. However, the workflow associated with this integration is indirect and presents challenges. This work creates a pathway for a direct scan-to-model strategy suitable for translating condition data derived from a 3D laser scanning system into a computation model capable of describing the mechanical response of the component. The geometric characterization capabilities of the proposed approach and the influence of scan resolution on the geometric characterization are explored. Results of this study provide the foundation of a computational framework for establishing the fundamental link between visually observable geometric changes and the numerical models that engineers use to understand the performance of engineered systems.