Miracle-dz/UniFAR-FTUs huggingface dataset:
Clustering Analysis of Risk Divergence of China Government's Debts

It is a difficult time for the world’s economics while the impact of COVID-19 is undergoing. A possible worldwide sovereign debt crisis could emerge, in short term, for supply chains blockage due to its slowing-down in many countries. China, having the second largest economy in the world, is crucial for the stability and sustainability of the economic recovery. China endures a long-term growth since 2000; nevertheless, a large amount of that growth is contributed by the government debt, which was spent on infrastructures. The accumulation of debts is a potential risk to the future growth of China. This research evaluates the central government and local government debts with a series of indicators. The weights of indicators are determined by objective methods of the CRITIC approach. Results confirm that the central government debt of China is on the edge of risk, while the risk of local governments debt is already in a concerning danger. The local government risk is 50% higher than the central government’s risk. Moreover, the K-means clustering algorithm performed on data, collected from various provinces, suggests that the local government debts of China follow a pattern of geographical distribution; that is, the closer to the coast, the lesser the risk, which is in accordance with the pattern of labor flowing. Labors are attracted by job opportunities which lie in the well-developed regions of China. This is confirmed by the crosscheck with the wage growth data. This indicates that the less developed areas of China rely more heavily on debt-investment stimulation that could be of a potential stagnation because the yield of investment follows diminishing marginal returns and the relative lacking labor weakens the potential economic growth.
Data Ecosystems for Scientific Experiments: Managing Combustion Experiments and Simulation Analyses in Chemical Engineering

The development of scientific predictive models has been of great interest over the decades. A scientific model is capable of forecasting domain outcomes without the necessity of performing expensive experiments. In particular, in combustion kinetics, the model can help improving the combustion facilities and the fuel efficiency reducing the pollutants. At the same time, the amount of available scientific data has increased and helped speeding up the continuous cycle of model improvement and validation. This has also opened new opportunities for leveraging a large amount of data to support knowledge extraction. However, experiments are affected by several data quality problems since they are a collection of information over several decades of research, each characterized by different representation formats and reasons of uncertainty. In this context, it is necessary to develop an automatic data ecosystem capable of integrating heterogeneous information sources while maintaining a quality repository. We present an innovative approach to data quality management from the chemical engineering domain, based on an available prototype of a scientific framework, SciExpeM, which has been significantly extended. We identified a new methodology from the model development research process that systematically extracts knowledge from the experimental data and the predictive model. In the paper, we show how our general framework could support the model development process, and save precious research time also in other experimental domains with similar characteristics, i.e., managing numerical data from experiments.
Contribution to HIV Prevention and Treatment by Antibody-Mediated Effector Function and Advances in Broadly Neutralizing Antibody Delivery by Vectored Immunoprophylaxis

HIV-1 broadly neutralizing antibodies (bNAbs) targeting the viral envelope have shown significant promise in both HIV prevention and viral clearance, including pivotal results against sensitive strains in the recent Antibody Mediated Prevention (AMP) trial. Studies of bNAb passive transfer in infected patients have demonstrated transient reduction of viral load at high concentrations that rebounds as bNAb is cleared from circulation. While neutralization is a crucial component of therapeutic efficacy, numerous studies have demonstrated that bNAbs can also mediate effector functions, such as antibody-dependent cellular cytotoxicity (ADCC), antibody-dependent cellular phagocytosis (ADCP), and antibody-dependent complement deposition (ADCD). These functions have been shown to contribute towards protection in several models of HIV acquisition and in viral clearance during chronic infection, however the role of target epitope in facilitating these functions, as well as the contribution of individual innate functions in protection and viral clearance remain areas of active investigation. Despite their potential, the transient nature of antibody passive transfer limits the widespread use of bNAbs. To overcome this, we and others have demonstrated vectored antibody delivery capable of yielding long-lasting expression of bNAbs in vivo. Two clinical trials have shown that adeno-associated virus (AAV) delivery of bNAbs is safe and capable of sustained bNAb expression for over 18 months following a single intramuscular administration. Here, we review key concepts of effector functions mediated by bNAbs against HIV infection and the potential for vectored immunoprophylaxis as a means of producing bNAbs in patients.
Classification of Electrocardiogram of Congenital Heart Disease Patients by Neural Network Algorithms

The study intended to explore the effect of different neural network algorithms in the electrocardiogram (ECG) classification of patients with congenital heart disease (CHD). Based on the single convolutional neural network (CNN) ECG algorithm and the recurrent neural network (RNN) ECG algorithm, a multimodal neural network (MNN) ECG algorithm was constructed utilizing the MIT-BIH database as training set and test set. Furthermore, the MNN ECG algorithm was optimized to establish an improved MNN (IMNN) algorithm, which was applied to the diagnosis of CHD patients. The CHD patients admitted between August 2016 and August 2019 were selected for analysis to compare the classification effect and accuracy rate of IMNN, MNN, CNN ECG, and RNN ECG algorithms. It was found that the RNN ECG algorithm had higher classification sensitivity and true positive rate in terms of normal or bundle (NB) branch block beat, supraventricular abnormal (SA) rhythm, abnormal ventricular (AV) beat, and fusion beat (FB) than the CNN ECG algorithm ( P < 0.05 ), and the classification sensitivity and true positive rate of IMNN algorithm in the four aspects were significantly higher than those of MNN algorithm ( P < 0.05 ). The classification accuracy of CNN ECG algorithm and RNN ECG algorithm was above 98%, while that of MNN algorithm and IMNN algorithm was better than that of CNN ECG algorithm and RNN ECG algorithm, and the accuracy rate can reach 98.5% or more. Moreover, the accuracy rate of the IMNN algorithm can reach more than 98%. In conclusion, IMNN not only has a good classification ability in the simulated environment but also performs well in the actual environment, which is worthy of clinical promotion.
Maternal and fetal outcomes in pregnancies with obstructive sleep apnea

Abstract Objectives Obstructive sleep apnea (OSA) is linked to many health comorbidities. We aimed to ascertain if OSA correlates with a rise in poor obstetrical outcomes. Methods Employing the United States’ Healthcare Cost and Utilization Project – National Inpatient Sample, we performed our retrospective cohort study including all women who delivered between 2006 and 2015. ICD-9 codes were used to characterize women as having a diagnosis of OSA. Temporal trends in pregnancies with OSA were studied, baseline features were evaluated among gravidities in the presence and absence of OSA, and multivariate logistic regression analysis was utilized in assessing consequences of OSA on patient and newborn outcomes. Results Of a total 7,907,139 deliveries, 3,115 belonged to patients suffering from OSA, resulting in a prevalence of 39 per 100,000 deliveries. Rates rose from 10.14 to 78.12 per 100,000 deliveries during the study interval (p<0.0001). Patients diagnosed with OSA were at higher risk of having pregnancies with preeclampsia, OR 2.2 (95% CI 2.0–2.4), eclampsia, 4.1 (2.4–7.0), chorioamnionitis, 1.4 (1.2–1.8), postpartum hemorrhage, 1.4 (1.2–1.7), venous thromboembolisms, 2.7 (2.1–3.4), and to deliver by caesarean section, 2.1 (1.9–2.3). Cardiovascular and respiratory complications were also more common among these women, as was maternal death, 4.2 (2.2–8.0). Newborns of OSA patients were at elevated risk of being premature, 1.3 (1.2–1.5) and having congenital abnormalities, 2.3 (1.7–3.0). Conclusions Pregnancies with OSA were linked to an elevated risk of poor maternal and neonatal outcomes. During pregnancy, OSA patients should receive attentive follow-up care in a tertiary hospital.
Deconvolving breath alcohol concentration from biosensor measured transdermal alcohol level under uncertainty: a Bayesian approach

The posterior distribution (PD) of random parameters in a distributed parameter-based population model for biosensor measured transdermal alcohol is estimated. The output of the model is transdermal alcohol concentration (TAC), which, via linear semigroup theory can be expressed as the convolution of blood or breath alcohol concentration (BAC or BrAC) with a filter that depends on the individual participant or subject, the biosensor hardware itself, and environmental conditions, all of which can be considered to be random under the presented framework. The distribution of the input to the model, the BAC or BrAC, is also sequentially estimated. A Bayesian approach is used to estimate the PD of the parameters conditioned on the population sample’s measured BrAC and TAC. We then use the PD for the parameters together with a weak form of the forward random diffusion model to deconvolve an individual subject’s BrAC conditioned on their measured TAC. Priors for the model are obtained from simultaneous temporal population observations of BrAC and TAC via deterministic or statistical methods. The requisite computations require finite dimensional approximation of the underlying state equation, which is achieved through standard finite element (i.e., Galerkin) techniques. The posteriors yield credible regions, which remove the need to calibrate the model to every individual, every sensor, and various environmental conditions. Consistency of the Bayesian estimators and convergence in distribution of the PDs computed based on the finite element model to those based on the underlying infinite dimensional model are established. Results of human subject data-based numerical studies demonstrating the efficacy of the approach are presented and discussed.
Statistical limits of dictionary learning: random matrix theory and the spectral replica method

We consider increasingly complex models of matrix denoising and dictionary learning in the Bayes-optimal setting, in the challenging regime where the matrices to infer have a rank growing linearly with the system size. This is in contrast with most existing literature concerned with the low-rank (i.e., constant-rank) regime. We first consider a class of rotationally invariant matrix denoising problems whose mutual information and minimum mean-square error are computable using techniques from random matrix theory. Next, we analyze the more challenging models of dictionary learning. To do so we introduce a combination of the replica method from statistical mechanics together with random matrix theory, coined spectral replica method. This allows us to derive variational formulas for the mutual information between hidden representations and the noisy data of the dictionary learning problem, as well as for the overlaps quantifying the optimal reconstruction error. The proposed method reduces the number of degrees of freedom from Θ(N^{2}) matrix entries to Θ(N) eigenvalues (or singular values), and yields Coulomb gas representations of the mutual information which are reminiscent of matrix models in physics. The main ingredients are a combination of large deviation results for random matrices together with a replica symmetric decoupling ansatz at the level of the probability distributions of eigenvalues (or singular values) of certain overlap matrices and the use of Harish-Chandra-Itzykson-Zuber spherical integrals.
Design and evaluation of glutathione responsive glycosylated camptothecin nanosupramolecular prodrug

Abstract A novel tumor-targeted glutathione responsive Glycosylated-Camptothecin nanosupramolecular prodrug (CPT-GL NSp) was designed and fabricated via a disulfide bond. The effects of glycoligand with different polarities on solubility, self-assembly, stability, cellular uptake, and glutathione responsive cleaving were explored, and an optimal glycosylated ligand was selected for nanosupramolecular prodrug. It has been found that CPT-GL NSp exhibited higher drug loading than traditional nanoparticles. Among of which maltose modified NSp had the strongest anti-tumor effects than that of glucose and maltotriose. CPT-SS-Maltose had a similar anti-tumor ability to Irinotecan (IR), but the superior performance in solubility, hemolysis, and uptake of HepG2 cells.
Molecular Diversity of Lactic Acid Bacteria on Ileum and Coecum Broiler Chicken Fed by Chrysonilia crassa Fermentation

The Lactid Acid Bakteria (LAB) are microflora in the digestive tract which has positive roles in poultry’s health. One of the factors diversity of LAB in the gatrointestinal tract are influenced by feeding factor. The purpose of this study was to analyze the LAB diversity in ileum and coecum after being fed on fermented Chrysonilia crassa molecularly. LAB species diversity was analysed to provide a baseline profile of the microbial community database on the ileum and coecum digestive tract of broiler chicken of control (commercial feed) and treatment (feed with Chrysonilia crassa fermentation) by the method of Terminal Restriction Fragment Lenght Polymorphism The calculated values werethe number of phylotypes, relative abundace, Shannon-Wiener diversity index (H’), evennes index (E’), and similarity. Group of LAB detected in the control group were Lactobacillus delbrueckii (180 bp), Lactobacillus sp. (187 bp), Lactobacillus plantarum (572 bp), uncultured bacterium (87 bp) and unidentified (50 bp, 582bp). The result of this study showed that by feeding on the fermented Chrysonilia crassa feed had resulted in the decreasing of LAB diversity, i.e. ileum (0.66), coecum (0.48) compared with commercial feed (control) that was ileum (0.84), coecum (1.05).
A flexible anatomical set of mechanical models for the organ of Corti

We build a flexible platform to study the mechanical operation of the organ of Corti (OoC) in the transduction of basilar membrane (BM) vibrations to oscillations of an inner hair cell bundle (IHB). The anatomical components that we consider are the outer hair cells (OHCs), the outer hair cell bundles, Deiters cells, Hensen cells, the IHB and various sections of the reticular lamina. In each of the components we apply Newton’s equations of motion. The components are coupled to each other and are further coupled to the endolymph fluid motion in the subtectorial gap. This allows us to obtain the forces acting on the IHB, and thus study its motion as a function of the parameters of the different components. Some of the components include a nonlinear mechanical response. We find that slight bending of the apical ends of the OHCs can have a significant impact on the passage of motion from the BM to the IHB, including critical oscillator behaviour. In particular, our model implies that the components of the OoC could cooperate to enhance frequency selectivity, amplitude compression and signal to noise ratio in the passage from the BM to the IHB. Since the model is modular, it is easy to modify the assumptions and parameters for each component.
Traffic classification using deep learning: being highly accurate is not enough

As Deep Learning (DL) algorithms have rapidly become a methodology of choice in various domains, they have recently entered also the field of the Internet traffic classification, successfully demonstrating impressive results. Most of the research work up to this point has focused on improving the accuracy of classification systems, yet there has been little attempt to provide (i) systematic comparison of the various DL algorithms used and (ii) analysis on where the higher accuracy come from, particularly when comparing with the traditional machine learning algorithms like C4.5. To fill this gap, we conduct experiments with four DL algorithms proposed for traffic classification, including CNN, LSTM, Stacked Auto-Encoder (SAE), and Hierarchical Attention Networks (HAN). Further, we propose to leverage and visualize hierarchical attention layers to highlight which parts of the traffic packet traces were most informative for accurate classification, which provides hints about why (and how) DL algorithms achieve the state-of-the-art level high accuracy. We view this paper as the first step towards answering the aforementioned "why" question, which is critical in understanding the real benefit and contribution of deep learning to the field of the Internet traffic classification, and advancing its state-of-the-art.
Association of ANGPTL8 and Resistin With Diabetic Nephropathy in Type 2 Diabetes Mellitus

Background Previous studies showed altered angiopoietin-like protein-8 (ANGPTL-8) and resistin circulating levels in type 2 diabetes mellitus (T2DM). Whether or not the alteration in ANGPTL-8 and resistin level can be a predictive maker for increased diabetic nephropathy risk remains unclear. Aim To Investigate the possible association of ANGPTL-8 and resistin with DN, and whether this association is affected by NAFLD status. Methods A total of 278 T2DM patients were enrolled. Serum levels of ANGPTL8, resistin, BMI, blood pressure, duration of diabetes, glycosylated hemoglobin (HbA1c), fasting blood glucose (FPG), hypersensitive C-reactive protein (hs-CRP), lipid profile, liver, and kidney function tests were assessed. The relationship between DN with ANGPTL8 and resistin was analyzed in the unadjusted and multiple-adjusted regression models. Results Serum levels of ANGPTL8 and resistin were significantly higher in DN compared with T2DM subjects without DN (respectively; P <0.001), especially in non-NAFLD populations. ANGPTL8 and resistin showed positive correlation with hs-CRP (respectively; P<0.01), and negative correlation with estimated GFR (eGFR) (respectively; P=<0.001) but no significant correlation to HOMA-IR(respectively; P>0.05). Analysis showed ANGPTL8 levels were positively associated with resistin but only in T2DM patients with DN(r=0.1867; P<0.05), and this significant correlation disappeared in T2DM patients without DN. After adjusting for confounding factors, both ANGPTL8(OR=2.095, 95%CI 1.253-3.502 P=0.005) and resistin (OR=2.499, 95%CI 1.484-4.208 P=0.001) were risk factors for DN. Data in non-NAFLD population increased the relationship between ANGPTL8 (OR=2.713, 95% CI 1.494-4.926 P=0.001), resistin (OR=4.248, 95% CI 2.260-7.987 P<0.001)and DN. The area under the curve (AUC) on receiver operating characteristic (ROC) analysis of the combination of ANGPTL8 and resistin was 0.703, and the specificity was 70.4%. These data were also increased in non-NAFLD population, as the AUC (95%CI) was 0.756, and the specificity was 91.2%. Conclusion This study highlights a close association between ANGPTL8, resistin and DN, especially in non-NAFLD populations. These results suggest that ANGPTL-8 and resistin may be risk predictors of DN.
Estimating the reproducibility of social learning research published between 1955 and 2018

Reproducibility is integral to science, but difficult to achieve. Previous research has quantified low rates of data availability and results reproducibility across the biological and behavioural sciences. Here, we surveyed 560 empirical publications, published between 1955 and 2018 in the social learning literature, a research topic that spans animal behaviour, behavioural ecology, cultural evolution and evolutionary psychology. Data were recoverable online or through direct data requests for 30% of this sample. Data recovery declines exponentially with time since publication, halving every 6 years, and up to every 9 years for human experimental data. When data for a publication can be recovered, we estimate a high probability of subsequent data usability (87%), analytical clarity (97%) and agreement of published results with reproduced findings (96%). This corresponds to an overall rate of recovering data and reproducing results of 23%, largely driven by the unavailability or incompleteness of data. We thus outline clear measures to improve the reproducibility of research on the ecology and evolution of social behaviour.
Seeing in the swamp: hydrogen sulfide inhibits eye metabolism and visual acuity in a sulfide-tolerant fish

In fish, vision may be impaired when eye tissue is in direct contact with environmental conditions that limit aerobic ATP production. We hypothesized that the visual acuity of fishes exposed to hydrogen sulfide (H2S)-rich water would be altered owing to changes in cytochrome c oxidase (COX) activity. Using the H2S-tolerant mangrove rivulus (Kryptolebias marmoratus), we showed that a 10 min exposure to greater than or equal to 200 µM of H2S impaired visual acuity and COX activity in the eye. Visual acuity and COX activity were restored in fish allowed to recover in H2S-free water for up to 1 h. Since K. marmoratus are found in mangrove pools with H2S concentrations exceeding 1000 µM, visual impairment may impact predator avoidance, navigation and foraging behaviour in the wild.
DIVERSIFICATION IN CATASTROPHE INSURANCE MARKETS

Abstract Catastrophe insurance markets fail to provide sufficient protections against natural catastrophes, whereas they have the capacity to absorb the losses. In this paper, we assume the catastrophic risks are dependent and extremely heavy-tailed, and insurers have limited liability to cover losses up to a certain amount. We provide a comprehensive study to show that the diversification in the catastrophe insurance markets can be transited from suboptimal to preferred by increasing the number of insurers in the market. This highlights the importance of coordination among insurers and the government intervention in encouraging insurers to participate in the catastrophe insurance market to exploit risk sharing. Simulation studies are provided to illuminate the key findings of our results.