Miracle-dz/UniFAR-FTUs huggingface dataset:
Augmented Ensemble Learning Model for Biomarkers Prioritization to Enhance Disease Identification Efficiency

Biomarkers assist in identification of various diseases present in biological beings. These markers include Lymphatic Vessel Endothelial Hyaluronan Receptor (LVYEI), Regenerating Family Member 1 Beta (REGIB), Trefoil Factor 1 (TFF1), Regenerating Family Member 1 Alpha (REGIA), etc. Each of these markers assists in estimation of presence of a particular protein type, which in turn assists in improving disease detection capabilities. Biomarkers are currently used to identify multiple diseases including Alzheimer’s disease, pancreatic cancer, posttraumatic stress disorder (PTSD), breast cancer, brain cancer, etc. Algorithms designed to evaluate the presence of these diseases work via prioritization of biomarkers, which assists in evaluating their genomic strength. The currently proposed models for biomarker prioritization work on single type of disease, which limits their scalability and performance when applied to real time clinical applications. Moreover, these models require large training & validation delays, which further reduces their applicability. In order to reduce the delay needed for biomarker prioritization and improve its scalability, this paper proposes design of an augmented ensemble learning model to enhance disease identification efficiency. The model is applied to multiple disease types including Alzheimer’s disease, pancreatic cancer, brain cancer and breast cancer. Results indicate that the proposed model has high accuracy, and reduced delay when compared with existing state-of-the-art approaches. This is mainly due to fusion of GoogLeNet and Inception net models, which assist in optimizing feature variance, thereby improving efficiency of prioritization. The proposed model is observed to have an average accuracy of 91%, with high precision & recall performance across multiple datasets; thereby it can be facilitating its use for real time applications.
Chronic alcohol consumption dysregulates innate immune response to SARS-CoV-2 in the lung

Alcohol consumption is widespread with over half of the individuals over 18 years of age in the U.S. reporting alcohol use in the last 30 days. Moreover, 9 million Americans engaged in binge or chronic heavy drinking (CHD) in 2019. CHD negatively impacts pathogen clearance and tissue repair, including in the respiratory tract, thereby increasing susceptibility to infection. Although, it has been hypothesized that chronic alcohol consumption negatively impacts COVID-19 outcomes; the interplay between chronic alcohol use and SARS-CoV-2 infection outcomes has yet to be elucidated. Therefore, in this study we investigated the impact of chronic alcohol consumption on SARS-CoV-2 anti-viral responses in bronchoalveolar lavage cell samples from humans with alcohol use disorder and rhesus macaques that engaged in chronic drinking. Our data show that in both humans and macaques, the induction of key antiviral cytokines and growth factors was decreased with chronic ethanol consumption. Moreover, in macaques fewer differentially expressed genes mapped to Gene Ontology terms associated with antiviral immunity following 6 month of ethanol consumption while TLR signaling pathways were upregulated. These data are indicative of aberrant inflammation and reduced antiviral responses in the lung with chronic alcohol drinking.
Gromov-Witten theory and spectral curve topological recursion

Gromov-Witten theory and spectral curve topological recursion are important parts of modern algebraic geometry and mathematical physics. In my thesis I study relations between these theories and some important new aspects and applications of them. In particular, a construction for a local spectral curve which produces the same invariants as a given Gromov-Witten theory is presented in the thesis, as well as constructions for quantum spectral curves for several important theories, and a new proof of the so-called ELSV formula.
Non-random panel attrition : comparison of two alternative estimations

Immigrants are a growing part of the population in western countries. In this sense, it is more relevant to study their conditions and their integrations. To do so, in Canada, the federal government has developed two datasets: Longitudinal Survey of Immigrants to Canada (LSIC) and the Longitudinal Immigration Database (IMDB). In immigrant panels the non-random attrition is likely higher than in native one. Thus, it is relevant to correct this bias. In this paper, we test two correction techniques. We compare statistical weighting and a three equations unweighted correction. In those two cases, we are correcting for participation in the labor market, but only the unweighted procedure corrects for the participation in the panel. We found that the unweighted has a better correction on the bias on the labor market participation, but no estimator has correctly corrected the wage rates.
Wavelet Support Vector Censored Regression

Learning methods in survival analysis have the ability to handle censored observations. The Cox model is a predictive prevalent statistical technique for survival analysis, but its use rests on the strong assumption of hazard proportionality, which can be challenging to verify, particularly when working with non-linearity and high-dimensional data. Therefore, it may be necessary to consider a more flexible and generalizable approach, such as support vector machines. This paper aims to propose a new method, namely wavelet support vector censored regression, and compare the Cox model with traditional support vector regression and traditional support vector regression for censored data models, survival models based on support vector machines. In addition, to evaluate the effectiveness of different kernel functions in the support vector censored regression approach to survival data, we conducted a series of simulations with varying number of observations and ratios of censored data. Based on the simulation results, we found that the wavelet support vector censored regression outperformed the other methods in terms of the C-index. The evaluation was performed on simulations, survival benchmarking datasets and in a biomedical real application.
Randomness in Geomagnetic Storms: Chaos or Noise?

Although geomagnetic forecasting is extremely important, geomagnetic predictions usually rely on forecasters' experience in distinguishing events expected to be geoeffective. Initial investigations with powerful forecasting techniques such as neural networks have recently been applied to geomagnetic prediction, but these are based on the assumption that the phenomenon to be forecasted has only several degrees of freedom. Thus a basic question related to geomagnetic forecasting is whether the randomness associated with geomagnetic evolution is produced by linear Gaussian noise or by nonlinear chaotic dynamics with only a few degrees of freedom. In order to try to answer this important question, we took a geomagnetic index time series and applied a recently developed technique which distinguishes nonlinear deterministic systems from linear ones.
Current Trends in Recombinant Human Bone Morphogenetic Protein 2 (rhBMP2) Usage for Spinal Fusion Surgery

(1) Background: Since first approved by the FDA, on-label and off-label usage of recombinant human bone morphogenetic protein 2 (rhBMP2) for spinal fusion surgeries has become widespread. While many studies have investigated the safety and efficacy of its use, as well as its economic impact, few have looked at the current trends in its on- and off-label use. The goal of this study is to evaluate the current trends of on- and off-label rhBMP2 use for spinal fusion surgery. (2) Methods: A deidentified survey was created and electronically distributed to members of two international spine societies. Surgeons were asked to report their demographic information, surgical experience, and current usage of rhBMP2. They were then presented with five spinal fusion procedures and asked to report if they use rhBMP2 for these indications in their current practice. Responses were stratified between rhBMP2 users vs. non-users and on-label vs. off-label use. Data were analyzed using chi-square with Fisher’s exact test for categorical data. (3) Results: A total of 146 respondents completed the survey with a response rate of 20.5%. There was no difference in overall rhBMP2 usage based on specialty, experience, or number of cases per year. Fellowship-trained surgeons and those who practice in the United States were more likely to use rhBMP2. Surgeons who were trained in the Southeast and Midwest regions reported the highest usage rates. rhBMP2 use was more common among fellowship-trained and US surgeons for ALIFs; non-US surgeons for multilevel anterior cervical discectomy and fusions; and fellowship-trained and orthopedic spine surgeons for lateral lumbar interbody fusions. Non-US surgeons were more likely to use rhBMP2 for off-label indications compared to surgeons from the US. (4) Conclusions: While various demographics of surgeons report different rates of rhBMP2 use, off-label use remains relatively commonplace amongst practicing spine surgeons.
Study of High Resolution Incompressible Flow Simulation Based on Cartesian Mesh

Three-dimensional large scale incompressible flow simulation was demonstrated in this paper by using Building-Cube Method that has advantages of equally-spaced Cartesian mesh method. The validations were performed by solving flow field around a sphere to compare drag coefficient at Reynolds number 100, 1000 and 5000, and Ahmed body to compare the velocity profile at Reynolds number 2.8 million. As an application, large scale parallel computation was conducted around a formula 1 racing car model to investigate the parallel efficiency by flat MPI or hybrid MPI parallelization
Systemic inflammatory response syndrome criteria (SIRS) and sepsis 3 criteria for assessing outcomes in sepsis: A prospective observational study

Background: Sepsis is a major cause of death in hospitalised patients worldwide. Most studies for assessing outcomes in sepsis are from the western literature. Sparse data from Indian settings are available comparing the systemic inflammatory response syndrome (SIRS), Sequential Organ Failure Assessment (SOFA) and quick SOFA (qSOFA) (sepsis 3 criteria) for assessing outcomes in sepsis. In this study, we aimed to compare the SIRS criteria and sepsis 3 criteria to assess disease outcome at day 28 (recovery/mortality) in a North Indian tertiary care teaching hospital. Methods: A prospective observational study was performed in the Department of Medicine from 2019 to early 2020. Patients admitted to the medical emergency with clinical suspicion of sepsis were included. Systemic inflammatory response syndrome, qSOFA and SOFA scores were calculated at the time of presentation to the hospital. Patients were followed through the course of their hospital stay. Results: Out of 149 patients, 139 were included in the analysis. Patients who died had significantly higher mean SOFA, qSOFA scores and mean change in SOFA score than patients who survived (P value <0.01). There was no statistical difference between recovery and deaths at similar SIRS scores. A 40.30% fatality rate was recorded. Systemic inflammatory response syndrome had low Area Under Curve (AUC) (0.47) with low sensitivity (76.8) and specificity (21.7). SOFA had the maximum AUC (0.68) compared to qSOFA (0.63) and SIRS (0.47). SOFA also had the maximum sensitivity (98.1) while the qSOFA score had the maximum specificity (84.3). Conclusion: SOFA and qSOFA scores had superior predictive ability as compared to the SIRS score in assessing mortality in sepsis patients.
New Tricholidic Acid Triterpenoids from the Mushroom Tricholoma ustaloides Collected in an Italian Beech Wood

The secondary metabolites produced by Tricholoma ustaloides Romagn. , a mushroom species belonging to the large Tricholoma genus (Basidiomycota, Tricholomataceae), are unknown. Therefore, encouraged by the interesting results obtained in our previous chemical analyses of a few Tricholoma species collected in Italian woods, we aimed to investigate the secondary metabolites of Tricholoma ustaloides. The chemical analysis involved the isolation and characterization of secondary metabolites through an extensive chromatographic study. The structures of isolated metabolites, including the absolute configuration, were established based on a detailed analysis of MS, NMR spectroscopic, optical rotation, and circular dicroism data, and on comparison with those of related compounds reported in the literature. Two novel lanostane triterpenoids, named tricholidic acids B and C, together with triglycerides, a mixture of free fatty acids, five unidentified metabolites, and the known rare saponaceolides F and J, tricholidic acid, and tricholomenyn C, were isolated from an EtOAc extract of fruiting bodies of Tricholoma ustaloides that were collected in an Italian beech wood. This is the second example of isolation of tricholidic acid derivatives from a natural source. Saponaceolides F and J exhibited high cytotoxicity (IC50 values ≤ 10 μM) against a panel of five human cancer cell lines. The toxicity against myeloid leukemia (HL-60), lung cancer (A-549), hepatocellular cancer (HepG2), renal cancer (Caki-1), and breast cancer (MCF-7) cells was higher than that shown by the very well-known cytotoxic drug cisplatin.
On the Physical Model of the Phenomena Registered in the Experiments by Shnoll's Group and Smirnov's Group

The study of experimental data leads to the conclusion about the existence of the fields of the Earth as not being of clear physical nature. The structure and properties of these fields on the Earth’s surface are studied. These fields turn out to be related to the motions of matter and, in particular, to the internal motions of the Earth itself. Therefore, the fields may include precursors to earthquakes that conform to experiments. The disclosed statistical relations of seismicity with the planet configurations, sunrises and sunsets, and with the pulsar impact becomes logical. Other planets, the Sun and the Moon must possess the same fields.
Similarity, Data Compression and a Dead Composer

Domenico Scarlatti (1685-1759) is well-known for his 555 keyboard sonatas. Although his work is greatly revered by many professional musicians, some claim that it does not show any compository development. In this paper, his sonatas are clustered by normalized compression distance (NCD), an algorithmical similarity metric with no musical background knowledge. NCD is rooted in Kolmogorov Complexity (KC), a measure that captures the similarity between any two sonatas in a single number. The results show clusters of similar sonatas and suggest Scarlatti’s work does show compository development, even ‘milestone sonatas’ marking changes in artistic style during his lifetime.
Cytotoxicity Evaluation of Photosensitizer-Conjugated Hexagonal Upconverting Nanoparticles

In this report, we synthesized hexagonal NaYF4:Yb,Er upconverting nanoparticles (UCNPs) of 171 nm in size with a narrow particle size distribution. To address their colloidal stabi-lity in aqueous media and to incorporate a photosensitizer that can produce reactive singlet oxygen (1O2) to kill tumor cells, UCNPs were conjugated with 6-bromohexanoic acid-functionalized Rose Bengal (RB) and coated with PEG-alendronate (PEG-Ale). The particles were thoroughly characterized by transmission electron microscopy, dynamic light scattering, ATR FTIR, X-ray photoelectron spectroscopy, thermogravimetric analysis, and spectrofluorometry, and 1O2 formation was detected using a 9,10-diphenylanthracene spectrophotometric probe. Cytotoxicity determination on rat mesenchymal stem cells by using the MTT assay showed that neutralization of the large positive surface charge of neat UCNPs with PEG-Ale and the bound RB sensitizer significantly reduced the concentration-dependent cytotoxicity. The presented strategy shows great potential for the use of these particles as a novel agent for the photodynamic therapy of tumors.
Exploring Whether Managed Lanes Contribute to the Wide Adoption of the Connected and Autonomous Vehicles

The rapid development of connected and autonomous vehicle (CAV) technology and the outperformance of the managed lane have drawn increasing attention with respect to wonder whether the CAV operating in the managed lane is helpful. Therefore, this study focuses on the methodology applied to explore whether the managed lane is beneficial in the gradual application of CAVs. Three managed lanes are considered, namely a bus lane, CAV dedicated lane, and high-occupancy vehicle lane. Given the unfamiliarity with the travel mode related to CAVs, the attitudinal variables for CAVs and their influencing relationship are designed and quantified by a structural equation model based on the extended technology acceptance model. A two-level nested logit model with different types of managed lanes as explanatory variables is further developed to analyze the travel choice of traditional modes and CAV modes. The difference in probabilities of travel choices under managed lanes is also explored. The results indicate that the three managed lanes have a positive influence on promoting the adoption of the CAV modes, with an increase of at least 13.5% shifted from traditional modes in the conventional lane. Moreover, the influences of managed lanes differ between transit CAVs and non-transit CAVs. The probabilities of choosing transit CAVs under three managed lanes increase in the range of 12.9%–27.2%, while those of non-transit CAVs only show a maximum 6.5% increase in the CAV dedicated lane. The study is expected to assist in designing differentiated development of CAVs with the deployment of managed lanes.
Alfven Wave Propagation in Inhomogeneous Plasmas

Damping of Alfvén waves is one of the most likely mechanisms for ion heating in the solar corona. Density gradients have significant but poorlyunderstood effects on energy transfer and Alfvén wave propagation in partially ionized plasmas, such as those found in the solar chromosphere. Reflection of Alfvén waves at density and magnetic field gradients can give rise to turbulence which sustains particle heating. The density profile in the Hot hELIcon eXperiment (HELIX) varies strongly with radius, giving access to a wide range of Alfvén dynamics across the plasma column and providing an ideal environment to observe Alfvén wave-driven particle heating. A new internal wave-launching antenna, situated at the edge of the highdensity core and the density-gradient region of HELIX has been used to excite low-frequency waves in argon plasma. The propagation behavior of the launched waves was measured with a small-scale (smaller than the ion gyroradius) magnetic sense coil at multiple radial locations across the plasma column (from the high-density core through the density gradient region). Time-resolved laser induced fluorescence (LIF) and Langmuir probe measurements also yield insight into the plasma response to the perturbation. This dissertation presents cross-spectral and wavelet analysis of low-frequency waves in a helicon plasma with a strong density gradient. Building on the work of Houshmandyar, shear Alfvén waves were launched in a helicon plasma source with a strong density gradient. Alfvén wave turbulence is suggested from phase angle and wavelet analysis of magnetic sense coil probe measurements. The perturbation wavelength derived from phase angle measurements is consistent with predictions from the full Alfvén wave dispersion relation (taking electron Landua damping, electron-ion collisions, and finite frequency effects into account). Time-resolved LIF measurements across the plasma column suggest ion heating where the turbulence is strongest. Timeresolved Langmuir probe measurements show electron heating in response to the launched Alfvén waves. This work is dedicated to the spirit of Shakespeare’s sister and every woman who has ever worked to build a room of her own.
On the Expressiveness of

In this paper I compare the expressive power of several models of concurrency based on their ability to represent causal dependence. To this end, I translate these models, in behaviour preserving ways, into the model of higher dimensional automata, which is the most expressive model under investigation. In particular, I propose four dieren t translations of Petri nets, corresponding to the four dieren t computational interpretations of nets found in the literature. I also extend various equivalence relations for concurrent systems to higher dimensional automata. These include the history preserving bisimulation, which is the coarsest equivalence that fully respects branching time, causality and their interplay, as well as the ST-bisimulation, a branching time respecting equivalence that takes causality into account to the extent that it is expressible by actions overlapping in time. Through their embeddings in higher dimensional automata, it is now well-dened whether members of dieren t models of concurrency are equivalent.
Synergies and Trade-Offs among Different Ecosystem Services through the Analyses of Spatio-Temporal Changes in Beijing, China

Increasing global urbanization has not only resulted in economic development but it has also caused a number of ecological issues, such as haze, global warming, and storm surges, which can end up hindering the development of human society in the long term. One method of maintaining the long-term growth of human ecosystems is by considering ecosystem services (ES) when making decisions over land use. This study provides information to aid with decision making in the maintenance of Beijing’s ES provision in the long term. Firstly, three key ES, namely, carbon storage (CS), habitat quality (HQ), and water yield (WY), were evaluated by the InVEST model. Then, the spatial patterns of synergies and trade-offs among three ES at the city and grid scales were explored through the correlation coefficients analysis and geographically weighted regression (GWR). Finally, the strength of trade-offs among ES was calculated based on root mean squared error (RMSE), and the potential ecological risk areas are recognized. We discovered that (1) the total carbon storage decreases from 3.74 million tons in 2000 to 3.66 million tons in 2020, and HQ has the same trend, with its average value decreasing from 0.72 to 0.67; in contrast, water yield is more stable, increasing slightly from 8.22 × 1010 m3 in 2000 to 8.23 × 1010 m3 in 2020. (2) The synergies and trade-offs of ES are spatially heterogeneous. Among them, the correlation coefficients at the city-level indicated synergistic relationships among the three ES, but CS-WY and WY-HQ always have trade-off relationships at the grid level, where 37.88% of WY-HQ and 14.59% of CS-WY were trade-offs in 2020. (3) At the urban-rural interface, the trade-offs among ES are stronger than those in other regions. In rural-urban areas, the RMSE in CS-HQ, CS-WY and WY-HQ always had high values (>0.5), accounting for 16.72%, 9.33%, and 26.94% of the entire area, respectively; these areas are identified as potential ecological risk areas, which will be the focus area for future ES regulation. These findings provide opportunities for clear trade-offs among ES and promote positive synergies. In addition, land-use management may use the results to guide ecosystem service use, identify critical areas, and ensure regional sustainability in urban development.
KEKB B-Factory, ˜) The Luminosity Frontier

The experiment at the KEKB B-Factory [ KEKB B-Factory Design Report, National Laboratory for High Energy Physics, KEK Report 95–7 (1995)], as well as PEP-II, brought the ﬁnal blow on the 2008 Nobel Prize in Physics for the Kobayashi-Maskawa theory. A few key issues will be described on the design and performance of KEKB to make the world’s highest luminosity possible.