Miracle-dz/UniFAR-FTUs huggingface dataset:
Wall Polysaccharide Antigen in Plaque Formation

The mechanism of adherence of Streptococcus mutans to smooth glass surfaces has been studied. The results with both viable and heat-killed cells showed that the process required (i) the synthesis of a water-insoluble dextran-levan polymer by cell-bound enzymes and (ii) the participation of a binding site on the surface of the S. mutans cell. Synthesis of the polymer from sucrose in the presence of the cells was required for adherence, and indicates that an "active" form of the polymer was required. Polymer synthesized by cell-free S. mutans enzymes when added to S. mutans cells did not produce adherence. Purified antibody globulin, specific for the a-d site in the polysaccharide S. mutans group a antigen, completely inhibited adherence. Antibody to the second antigen present in the polysaccharide molecule, the a antigen, did not inhibit adherence. The evidence indicates that adherence did not require an antigenic binding site which might be common to all S. mutans strains. The orientation of the synthetase enzyme(s), antigenic binding site, and dextran-levan polymer on the cell surface is under study.
Influence of Plasmodium falciparum Parasitaemia on White Blood Cell among children (6-59 Months). A Case Study of Comprehensive Health Centre Bulumkutu, Maiduguri, Borno, State – Nigeria

This study was conducted to assess the influence of Plasmodium falciparum parasitaemia on Platelate (children) (659), at Bulumkutu Comprehensive Health Centre Maiduguri, Borno State, between August to December, 2018. A total of 210 children were enrolled in the study which consisted of 88 (41.9%) patients with positive P. falciparum malaria and 122 (58.10%) negative malaria. Hematological parameters were analyzed using sysmex haematology auto-analyser (2011), while the Giemsa stained slides thick and thin blood films were prepared from the stock solution, and tested for Plasmodium falciparum malaria and count of malaria parasite density. A positive correlation was observed between the parasite density and white blood cell of (children tested positive) as index of leucocytosis (r2 = 0.831, P = 0.001) and positive correlation was observed between parasite and white cells of males females subjects respectively, where (r = 0.735, p = 0.001) for males and (r2 = 0.710, p = 0.001) for females.
Infrared laser velocity modulation detection of HCl

Using velocity modulation detection, infrared diode laser absorption spectra of seventeen transitions of the fundamental band of HCl+ have been measured covering both 2Π1/2 and 2Π3/2 states of H 35Cl+ and H 37Cl+. The transitions were assigned on the basis of their absolute positions and Λ doublet splittings predicted by earlier optical spectra. Some of the additional lines detected, but not yet accurately measured, are tentatively assigned to hot band transitions in HCl+.
Forest Changes by Precipitation Zones in Northern China after the Three-North Shelterbelt Forest Program in China

China launched the Three-North Shelterbelt Forest Program (TNSFP) in 1978 in northern China to combat desertification and dust storms, but it is still controversial in ecologically fragile arid and semi-arid areas, which is partly due to the uncertainties of monitoring of the spatial-temporal changes of forest distribution. In this study, we aim to provide an overall retrospect of the forest changes (i.e., forest gain and forest loss) in northern China during 2007–2017, and to analyze the forest changes in different precipitation zones. We first generated annual forest maps at 30 m spatial resolution during 2007–2017 in northern China through integrating Landsat and PALSAR/PALSAR-2 data. We found the PALSAR/Landsat-based forest maps outperform other four existing products (i.e., JAXA F/NF, FROM-GLC, GlobeLand30, and NLCD-China) from either PALSAR or Landsat data, with a higher overall accuracy 96% ± 1%. The spatial-temporal analyses of forests showed a substantial forest expansion from 316,898 ± 34,537 km2 in 2007 to 384,568 ± 35,855 km2 in 2017 in the central and eastern areas. We found a higher forest loss rate (i.e., 35%) in the precipitation zones with the annual mean precipitation less than 400 mm (i.e., the arid and semi-arid areas) comparing to that (i.e., 25%) in the zones with more than 400 mm (i.e., the humid areas), which suggests the lower surviving rate in the drylands. This study provides satellite-based evidence for the forest changes in different precipitation zones, and suggests that the likely impacts of precipitation on afforestation effectiveness should be considered in future implementation of ecological restoration projects like TNSFP.
Relationship between common interleukin 1-beta gene polymorphisms and the risk of gestational disorders: An updated meta-analysis

Background: To quantitatively estimate the relationship between IL‐1β -511C>T, −31T>C, and +3954C>T polymorphisms and risk of gestational disorders. Methods: In this meta-analysis, eligible publications were searched in Web of Knowledge, MEDLINE, PubMed, Scopus, and Google Scholar databases (updated April 2020), using appropriate or relevant keywords. Case-control population-based reports were included if provided with genotypic frequencies of both studied groups. Statistical analyses were performed using the MetaGenyo web tool software, where a P value less than 0.05 indicated a significant association. For the assessment of between-study variations, heterogeneity analysis was applied with the I2 statistics. Results: A total of thirteen studies were included. We observed a significant association between IL‐1β−31T>C polymorphism and reduced risk of gestational disorders under codominant CT vs. CC [OR= 0.74, CI (0.59-0.92)], and dominant CT+TT vs. CC [OR= 0.74, CI (0.60-0.91)] contrasted genetic models. The stratified analysis considering the disease type showed that the 511C>T variant, under the recessive CC vs. CT+TT model, enhanced the risk of preterm birth by 1.29 fold. Conclusion: Our results failed to support an association between two IL‐1β polymorphisms, 511C>T and +3954C>T, with the overall risk of gestational disorders. In contrast, the 31T>C variant reduced the incidence of such diseases. Further studies are encouraged to get more precise estimates of effect sizes.
Decentralized Computational Caching for X-Reality Services in 5G and Beyond Networks

The Fifth Generation of mobile network (5G) has been introduced the current year which various advantages compared with the previous generations (4G, 3G, etc). The ultimate goals of 5G are providing high bandwidth, low latency, massive connectivity, network reliability, and energy efﬁciency. These advantages have completely changed the entire of the applications in networks such as a massive increase in quality of video up to 4 K, Virtual/Augmented/Mixed Reality (XR). However, the more proliferation of those applications the more computation capability required, and massive data trafﬁc in the networks posed a problem of network congestion and overutilization of Multi-access Edge Server (MEC). Therefore, we propose a network model in which computational results are partial or completely cached at either user’s devices or the MEC server. Our objective is saving the computational resources of MEC by caching the computational results of XR’s tasks and increase the spectrum utilization by adopting the device-to-device(D2D) communication technique to serve the demand of the other convincing devices. We formulate an optimization problem and apply the primal-dual decomposition approach to obtain near-optimal solution.
Development of an Ozone Monitoring Instrument (OMI) aerosol index (AI) data assimilation scheme for aerosol modeling over bright surfaces – a step toward direct radiance assimilation in the UV spectrum

Using the Vector LInearized Discrete Ordinate Radiative Transfer (VLIDORT) code as the main driver for forward model simulations, a first-of-its-kind data assimilation scheme has been developed for assimilating Ozone Monitoring Instrument (OMI) aerosol index (AI) measurements into the Naval Aerosol Analysis and Predictive System (NAAPS). This study suggests that both root mean square error (RMSE) and absolute errors can be significantly reduced in NAAPS analyses with the use of OMI AI data assimilation when compared to values from NAAPS natural runs. Improvements in model simulations demonstrate the utility of OMI AI data assimilation for aerosol model analysis over cloudy regions and bright surfaces. However, the OMI AI data assimilation alone does not outperform aerosol data assimilation that uses passive-based aerosol optical depth (AOD) products over cloud-free skies and dark surfaces. Further, as AI assimilation requires the deployment of a fully multiple-scatter-aware radiative transfer model in the forward simulations, computational burden is an issue. Nevertheless, the newly developed modeling system contains the necessary ingredients for assimilation of radiances in the ultraviolet (UV) spectrum, and our study shows the potential of direct radiance assimilation at both UV and visible spectrums, possibly coupled with AOD assimilation, for aerosol applications in the future. Additional data streams can be added, including data from the TROPOspheric Monitoring Instrument (TROPOMI), the Ozone Mapping and Profiler Suite (OMPS), and eventually the Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) mission.
Government Intervention and Bank Market Power: Lessons from the Global Financial Crisis for the COVID-19 Crisis

The COVID-19 pandemic could result in large government interventions in the banking industry. To shed light on the possible consequences on market power, we rely on the experience of the global financial crisis and exploit granular data on government interventions in more than 800 banks across 27 countries between 2007 and 2017. For identification, we use a multivariate matching method. We find that intervened banks experience a significant decline in market power with respect to matched non-intervened banks. This effect is more pronounced for larger and longer interventions and is driven by a rise in costs—mostly because of higher loan impairment charges—which is not followed by a similar increase in prices.
Defeating DNN-Based Traffic Analysis Systems in Real-Time With Blind Adversarial Perturbations

Deep neural networks (DNNs) are commonly used for various trafﬁc analysis problems, such as website ﬁngerprinting and ﬂow correlation, as they outperform traditional (e.g., statistical) techniques by large margins. However, deep neural networks are known to be vulnerable to adversarial examples: adversarial inputs to the model that get labeled incorrectly by the model due to small adversarial perturbations. In this paper, for the ﬁrst time, we show that an adversary can defeat DNN-based trafﬁc analysis techniques by applying adversarial perturbations on the patterns of live network trafﬁc. Applying adversarial perturbations (examples) on trafﬁc analysis classiﬁers faces two major challenges. First, the per-turbing party (i.e., the adversary) should be able to apply the adversarial network perturbations on live trafﬁc, with no need to buffering trafﬁc or having some prior knowledge about up-coming network packets. We design a systematic approach to create adversarial perturbations that are independent of their target network connections, and therefore can be applied in real-time on live trafﬁc. We therefore call such adversarial perturbations blind . Second, unlike image
Let's Stride Blindfolded in a Forest: Sublinear Multi-Client Decision Trees Evaluation

—Decision trees are popular machine-learning classi-ﬁcation models due to their simplicity and effectiveness. Tai et al. (ESORICS ’17) propose a privacy-preserving decision-tree evaluation protocol purely based on additive homomorphic encryption, without introducing dummy nodes for hiding the tree structure, but it runs a secure comparison for each decision node, resulting in linear complexity. Later protocols (DBSEC ’18, PETS ’19) achieve sublinear (client-side) complexity, yet the server-side path evaluation requires oblivious transfer among 2 d real and dummy nodes even for a sparse tree of depth d to hide the tree structure. This paper aims for the best of both worlds and hence the most lightweight protocol to date. Our complete-tree protocol can be easily extended to the sparse-tree setting and the reusable outsourcing setting: a model owner (resp. client) can outsource the decision tree (resp. attributes) to two non-colluding servers for classiﬁcations. The outsourced extension supports multi-client joint evaluation, which is the ﬁrst of its kind without using multi-key fully-homomorphic encryption (TDSC ’19). We also extend our protocol for achieving privacy against malicious adversaries. Our experiments compare in various network settings our of-ﬂine and online communication costs and the online computation time with the prior sublinear protocol of Tueno et al. (PETS ’19) and O (1) -round linear protocols of Kiss et al. (PETS ’19), which can be seen as garbled circuit variants of Tai et al. ’s. Our
Dense Dilated Inception Network for Medical Image Segmentation

In recent years, various encoder-decoder-based UNet architecture has shown remarkable performance in medical image segmentation. However, these encoder-decoder U-Net has a drawback in learning multi-scale features in complex segmentation tasks and weak ability to generalize to other tasks. This paper proposed a generalize encoder-decoder model called dense dilated inception network (DDI-Net) for medical image segmentation by modifying U-Net architecture. We utilize three steps; firstly, we propose a dense path to replace the skip connection in the middle of the encoder and decoder to make the model deeper. Secondly, we replace the U-Net's basic convolution blocks with a modified inception module called multi-scale dilated inception module (MDI) to make the model wider without gradient vanish and with fewer parameters. Thirdly, data augmentation and normalization are applied to the training data to improve the model generalization. We evaluated the proposed model on three subtasks of the medical segmentation decathlon challenge. The experiment results prove that DDI-Net achieves superior performance than the compared methods with a Dice score of 0.82, 0.68, and 0.79 in brain tumor segmentation for edema, non-enhancing, and enhancing tumor. For the hippocampus segmentation, the result achieves 0.92 and 0.90 for anterior and posterior, respectively. For the heart segmentation, the method achieves 0.95 for the left atrial. Keywords—Deep learning; Dense-Net; inception network; medical image segmentation; U-Net
Coupling lattice Boltzmann and atomistic models for fluids

While dealing with fluid-structure interaction it was discovered that the macroscopic continuous boundary conditions at the interface fail at the microscopic scale [1]. A possible solution is the description of the interaction on atomistic level. Unfortunately, this approach is computationally expensive and only allows one to simulate small sub-domains while for the whole domain a coarse description is necessary. In this contribution, an approach for coupling the mesoscopic lattice Boltzmann method (LB) with the atomistic description for fluids (MD) is presented. The domain is split into two partially overlapping sub-domains. The overlapping is used to exchange the information between solutions which leads to the coupling between the models.
A Devil of a Time: How Vulnerable is NTP to Malicious Timeservers?

—The Network Time Protocol (NTP) synchronizes time across computer systems over the Internet and plays a crucial role in guaranteeing the correctness and security of many Internet applications. Unfortunately, NTP is vulnerable to so called time shifting attacks. This has motivated proposals and standardization efforts for authenticating NTP communications and for securing NTP clients . We observe, however, that, even with such solutions in place, NTP remains highly exposed to attacks by malicious timeservers . We explore the implications for time computation of two attack strategies: (1) compromising existing NTP timeservers, and (2) injecting new timeservers into the NTP timeserver pool. We ﬁrst show that by gaining control over fairly few existing timeservers, an opportunistic attacker can shift time at state-level or even continent-level scale. We then demonstrate that injecting new timeservers with disproportionate inﬂuence into the NTP timeserver pool is alarmingly simple, and can be leveraged for launching both large-scale opportunistic attacks, and strategic, targeted attacks. We discuss a promising approach for mitigating such attacks.
The voice of Physics in finance: A glance on the theoretical application of heat equation to stock price diffusions

Stock price volatility is considered the main matter of concern within the investment grounds. However, the diffusivity of these prices should as well be considered. As such, proper modelling should be done for investors to stay healthy-informed. This paper suggest to model stock price diffusions using the heat equation from physics. We hypothetically state that, our model captures and model the diffusion bubbles of stock prices with a better precision of reality. We compared our model with the standard geometric Brownian motion model which is the wide commonly used stochastic differential equation in asset valuation. Interestingly, the models proved to agree as evidenced by a bijective relation between the volatility coefficients of the Brownian motion model and the diffusion coefficients of our heat diffusion model as well as the corresponding drift components. Consequently, a short proof for the martingale of our model is done which happen to hold.
Total Knee Arthroplasty Improves Locomotive Syndrome in Patients with Knee Disease: A Prospective Cohort Study Focused on Total Clinical Decision Limits Stage 3

Shigeaki Miyazaki Rehabilitation Unit, University of Miyazaki Hospital Saori Yoshinaga School of Nursing, Faculty of Medicine, University of Miyazaki Kurumi Tsuruta School of Nursing, Faculty of Medicine, University of Miyazaki Amy Hombu Center for Language and Cultural Studies, Lecturer, University of Miyazaki Yoshinori Fujii Department of Mathematics Education, Faculty of Education, University of Miyazaki Hideki Arakawa Rehabilitation Unit, University of Miyazaki Hospital Etsuo Chosa (  chosa@med.miyazaki-u.ac.jp ) Rehabilitation Unit, University of Miyazaki Hospital
DERIVATIVE SPECTROPHOTOMETRIC METHOD FOR DETERMINATION OF LOSARTAN IN PHARMACEUTICAL FORMULATIONS

Losartan, a highly effective blood pressure lowering agent, has been widely used for the treatment of hypertension. A fast and reliable method for the determination of losartan was highly desirable to support formulation screening and quality control. A first derivative UV spectroscopic method was developed for determination of losartan in the tablet dosage form. The first derivative spectrum recorded between 220 and 320 nm, and a zero-crossing technique for first derivative measurement at 232.5 nm was selected. It is found that the selectivity and sensitivity of method to be in desirable range. In comparison with the direct UV method, the first derivative UV spectroscopy has a definite through without any interference from UV absorbing excipients. This method is also fast and economical in comparison to the more timeconsuming HPLC method regularly used for formulation screening and quality control and can be used routinely by any laboratory possessing a spectrophotometer with a derivative accessory. The linear concentration ranges were 2-50 µg/mL, (Dl = -0.0159C - 0.0056, r =0.9994, n=6). Between days CV% ≤ 2.9, within day CV% ≤ 2.1, analytical recovery close to 98.1 % shows the suitability of the method for determination in quality control.
Dissecting the Impact of Import Competition on U.S. Earnings Inequality

This paper studies the impact of globalization on U.S. earnings inequality in the context of rapidly growing import competition from China. The increase in U.S. inequality during 2000-2007 has been driven entirely by changes within regions}. While the existing literature has established differences in wage growth across regions as a consequence of import competition, understanding the impact of globalization on rising U.S. inequality requires then focusing on its impact on inequality within regions. Exploiting variation in exposure to this unprecedented trade shock across local labor markets I find that import competition causes an increase in earnings inequality. This impact occurs primarily on the lower tail of the earnings distribution. I decompose the variation in regional inequality into changes occuring within and between industries, occupations, earnings deciles, and skill categories. While the relative share of the impact on betweenand within-group inequality varies across these various dimensions, in each case the impact of trade on within-group inequality represents a relevant share of the overall impact. 1 Department of Economics, Gatton College of Business and Economics, University of Kentucky. Email: fbe225@uky.edu. Website: felipebenguria.weebly. com
Arbitrary Power-Division Branch-Line Hybrids for High-Performance, Wideband, and Selective Harmonic Suppressions From <inline-formula> <tex-math notation="LaTeX">$2f_{0}$ </tex-math></inline-formula>

A branch-line hybrid (BH) topology is suggested, consisting of four equal lengths of transmission-line sections (TLs) with two different characteristic impedances and four identical L-sections with each a TL and an open stub. The topology can provide three distinct properties; lower characteristic impedances of TLs, longer electrical paths for the scattering parameters, and additional transmission zeros, enabling high performance of harmonic suppressions from 2 f0, where f0 is a design frequency, wideband and selective harmonic suppressions. A compact equivalent circuit of a TL is suggested for harmonic suppression BH which can be applied for all possible power-division ratios. As a proof-of-concept demonstration, two prototypes for the powerdivision ratios of 0 and 6 dB are tested and measured. The measured frequency responses are in a good agreement with the predicted ones, given fabrication errors.
Impact of Knowledge Management, Financial Resource, Innovation, and Foreign Direct Investment on Entrepreneurship

The recent “Black Swan” event, Covid-19 is dramatically transforming the landscape of industry and business that will further damper the survival of necessity-driven entrepreneurs. The objective of the present study is to examine the roles of knowledge management, financial resource, innovation, and foreign direct investment on necessity-driven entrepreneurship. By using a sample from 37 countries spanning from the year 2010 to 2016, the panel regression random effect model finds that financial resource is the most critical factor as financial capital supports necessity-driven entrepreneurship in the venture creation process. The results show that knowledge management, innovation, and foreign direct investment are negative and significantly related to necessity-driven entrepreneurship. We also attempt to investigate the interaction between financial resource and knowledge management when analysing the relationship between knowledge management and entrepreneurial outcomes. The impact of knowledge management on necessity-driven entrepreneurship is contingent on the financial resource. The implications of the study reveal that: (1) financial credit from the banks cannot effectively enhance the survivability of the necessity-driven entrepreneurship, which requires governmentbacked financial and support. (2) the stimulation on innovation and foreign direct investment may not benefit necessity-driven entrepreneurship as it is pushing them towards marginal market niches. (3) In the absence of adequate financial capital, necessitydriven entrepreneurship unable to reap the benefit from the cultivation of knowledge management. Hence, policymakers should ensure commensurate amounts of financial support and knowledge spillovers to reduce bankruptcy risk among necessity-driven entrepreneurs.