Miracle-dz/UniFAR-FTUs huggingface dataset:
Beyond HIV outbreaks: protocol, rationale and implementation of a prospective study quantifying the benefit of incorporating viral sequence clustering analysis into routine public health interventions

Introduction HIV continues to have great impact on millions of lives. Novel methods are needed to disrupt HIV transmission networks. In the USA, public health departments routinely conduct contact tracing and partner services and interview newly HIV-diagnosed index cases to obtain information on social networks and guide prevention interventions. Sequence clustering methods able to infer HIV networks have been used to investigate and halt outbreaks. Incorporation of such methods into routine, not only outbreak-driven, contact tracing and partner services holds promise for further disruption of HIV transmissions. Methods and analysis Building on a strong academic–public health collaboration in Rhode Island, we designed and have implemented a state-wide prospective study to evaluate an intervention that incorporates real-time HIV molecular clustering information with routine contact tracing and partner services. We present the rationale and study design of our approach to integrate sequence clustering methods into routine public health interventions as well as related important ethical considerations. This prospective study addresses key questions about the benefit of incorporating a clustering analysis triggered intervention into the routine workflow of public health departments, going beyond outbreak-only circumstances. By developing an intervention triggered by, and incorporating information from, viral sequence clustering analysis, and evaluating it with a novel design that avoids randomisation while allowing for methods comparison, we are confident that this study will inform how viral sequence clustering analysis can be routinely integrated into public health to support the ending of the HIV pandemic in the USA and beyond. Ethics and dissemination The study was approved by both the Lifespan and Rhode Island Department of Health Human Subjects Research Institutional Review Boards and study results will be published in peer-reviewed journals.
Dynamics of influenza‐like illness under urbanization procedure and COVID‐19 pandemic in the subcenter of Beijing during 2013–2021

Influenza‐like illness (ILI) varies in intensity year by year, generally keeping a stable pattern except for great changes of its epidemic pattern. Of the most impacting factors, urbanization has been suggested as shaping the intensity of influenza epidemics. Besides, growing evidence indicates the nonpharmaceutical interventions (NPIs) to severe acute respiratory syndrome coronavirus 2 offer great advantages in controlling infectious diseases. The present study aimed to evaluate the impact of urbanization and NPIs on the dynamic of ILI in Tongzhou, Beijing, during January 2013 to March 2021. ILI epidemiological surveillance data in Tongzhou district were obtained from Beijing Influenza Surveillance Network and separated into three periods of urbanization and four intervals of coronavirus disease 2019 pandemic. Standardized average incidence rates of ILI in each separate stages were calculated and compared by using Wilson method and time series model of seasonal ARIMA. Influenza seasonal outbreaks showed similar epidemic size and intensity before urbanization during 2013–2016. Increased ILI activity was found during the process of Tongzhou's urbanization during 2017–2019, with the rate difference of 2.48 (95% confidence interva [CI]: 2.44, 2.52) and the rate ratio of 1.75 (95% CI: 1.74, 1.76) of ILI incidence between preurbanization and urbanization periods. ILI activity abruptly decreased from the beginning of 2020 and kept at the bottom level almost in every epidemic interval. The top decrease in ILI activity by NPIs was shown in 5–14 years group in 2020–2021 influenza season, as 92.2% (95% CI: 78.3%, 95.2%). The results indicated that both urbanization and NPIs interrupted the epidemic pattern of ILI. We should pay more attention to public health when facing increasing population density, human contact, population mobility, and migration in the process of urbanization. NPIs and influenza vaccination should be implemented as necessary measures to protect people from common infectious diseases like ILI.
Exploring the Relationship Between Engagement in Enriching Early-Life Activities During Adolescence and Cognition in Later-Life: Results From the Health and Retirement Study

The cognitive benefits associated with mid- to late-life engagement have been demonstrated in several studies. However, the link between engagement in enriching early-life activities (EELAs) during adolescence and later-life cognition has been relatively unexplored in major epidemiological studies. We examined the EELA-cognition relationship in a nationally representative sample of adults aged 50+. A subset of Health and Retirement Study respondents (n=3482) completed cognitive tests and returned a retrospective early-life activity inventory. Linear regression models analyzed the EELA-cognition relationship, and multiple imputation addressed missingness. Each additional EELA was associated with a 0.36 point higher cognitive score (95% CI: 0.24, 0.47). This relationship remained significant after adjusting for potential confounders (B=0.16; 95% CI: [0.06, 0.26]). EELA engagement was associated with better later-life cognitive performance. This study is understood to be the first to examine the EELA-cognition relationship using a large, nationally representative dataset. The findings highlight the importance of early-life engagement during an important developmental period (e.g., adolescence).
Growth, biochemical, and antioxidant response of pot marigold (Calendula officinalis L.) grown in fly ash amended soil

Abstract The present study was carried out to determine the impact of FA application on growth performance, biochemical parameters, and antioxidant defense activity of Calendula officinalis. The results revealed that under a low dose of FA (40%) amended soil, the plant growth performance and metal tolerance index (MTI) were increased compared to control plants and further decreased with increased FA application (60%, 80%, and 100% FA). In addition, the incorporation of 40% FA in soil not only improved the physicochemical properties of soil but also increased the biochemical parameters in the Calendula plant, however, these parameters declined under high FA applications. It was also observed that antioxidant enzyme activity (SOD, CAT, POD, and APX) in leaves of Calendula officinalis increased at high FA application (100% FA) to combat heavy metal stress from FA. The overall study suggests that 40% FA amended soil is the best suitable dose for growing Calendula officinalis and can be considered as metal tolerant species for phytoremediation of 40% FA amended soil. Novelty statement: Fly ash (FA) management is a major problem nowadays. The present study was carried out for FA utilization and to determine the impact of FA amended soil on growth performance, antioxidant properties, and biochemical attributes of Calendula officinalis. This is a sustainable approach in which waste (FA) utilization was done simultaneously with the enhancement in response of the medicinally potent Calendula species. The novelty of this study also suggests that Calendula has phytoremediation potential for remediation of heavy metal polluted soil. Further, the relationship between the growth, biochemical parameters, and antioxidant defense mechanism of Calendula grown on FA amended soil was studied which has not been studied so far. It was found that Calendula is a hyperaccumulator that can adapt to heavy metal stress from FA due to its ability to mitigate oxidative damage. Statistical analysis (ANOVA, Duncan’s multiple range test, and PCA) was done for the results obtained using SPSS (11.5) and Origin 8 Pro software. Graphical Abstract
Meta-analysis of the robustness of COVID-19 diagnostic kit performance during the early pandemic

Background Accurate detection of SARS-CoV-2 is necessary to mitigate the COVID-19 pandemic. However, the test reagents and assay platforms are varied and may not be sufficiently robust to diagnose COVID-19. Methods We reviewed 85 studies (21 530 patients), published from five regions of the world, to highlight issues involved in the diagnosis of COVID-19 in the early phase of the pandemic. All relevant articles, published up to 31 May 2020, in PubMed, BioRiXv, MedRiXv and Google Scholar, were included. We evaluated the qualitative (9749 patients) and quantitative (10 355 patients) performance of RT-PCR and serologic diagnostic tests for real-world samples, and assessed the concordance (5538 patients) between test performance in meta-analyses. Synthesis of results was done using random effects modelling and bias was evaluated according to QUADAS-2 guidelines. Results The RT-PCR tests exhibited heterogeneity in the primers and reagents used. Of 1957 positive RT-PCR COVID-19 participants, 1585 had positive serum antibody (IgM±IgG) tests (sensitivity 0.81, 95% CI 0.66 to 0.90). While 3509 of 3581 participants RT-PCR negative for COVID-19 were found negative by serology testing (specificity 0.98, 95% CI 0.94 to 0.99). The chemiluminescent immunoassay exhibited the highest sensitivity, followed by ELISA and lateral flow immunoassays. Serology tests had higher sensitivity and specificity for laboratory approval than for real-world reporting data. Discussion The robustness of the assays/platforms is influenced by variability in sampling and reagents. Serological testing complements and may minimise false negative RT-PCR results. Lack of standardised assay protocols in the early phase of pandemic might have contributed to the spread of COVID-19.
Impact of Input Pump Profile on the Gain Spectrum and the Saturation Behavior of One-Pump Fiber Optical Parametric Amplifiers

In this article, the impact of input pump profile on the gain spectrum as well as the saturation behavior of one-pump fiber optical parametric amplifiers (FOPAs) is investigated. Since in practical circumstances, pump sources used for FOPAs have LorentzGaussian profile instead of Gaussian, a more realistic case is considered for simulating FOPAs in this article. The results of simulations for the Gaussian and the Lorentz-Gaussian profiles show that a higher gain and a faster saturation are obtained for a pump with a Lorentz-Gaussian profile than a Gaussian pump. The results of this article provide a more realistic model for FOPAs.
New Approaches to Aberration Correction in Medical Ultrasound Imaging

Medical ultrasound imaging is in widespread use today due to its low cost, portability, lack of side effects, and unique ability to probe the mechanical properties of tissue. As transducer apertures and operating frequencies grow, however, ultrasound’s resolving power continues to be limited by variations in the speed of sound within tissue. Concepts borrowed from other imaging disciplines can provide new insights into the aberration problem in medical ultrasound; in particular, some of the same issues have been studied for many years to improve acoustic imaging of the nonhomogeneous Earth. The seismic imaging community has understood for some time that layered media may be approximated by constant-sound-speed media for beamforming purposes, leading to so-called time-migration algorithms. This raises the possibility of medical ultrasound applications—for example, brain imaging through the adult human skull. While our simulation results have been encouraging, experiments with animal skulls have been inconclusive due to the high attenuation of ultrasound in skull bone. Further research may validate the time-migration concept for brain imaging. Complete data sets are composed of the raw reflection data from every combination of one transmit element and one receive element in an array. The tremendous redundancy of a complete data set can be exploited for aberration correction by analyzing the time shifts on common-midpoint gathers. Until now, however, the wide-angle, randomscattering nature of medical ultrasound targets has limited the accuracy and robustness of this approach, particularly when estimating azimuth-dependent aberration profiles. Prefiltering the data with two-dimensional fan filters largely solves this problem and produces an aberration-correction algorithm (OFF) that outperforms the most popular existing algorithms in almost all cases. The concept of focusing-operator updating, recently popular in seismic imaging, provides insight into iterative aberration-correction algorithms using a transmit focus. We
Non-invasive techniques for musculoskeletal model calibration

Subject-specific musculoskeletal models are mandatory to conduct efficient analyses of muscle and joint forces involved in human motion. Thus, proper model calibration at geometrical, inertial, and muscular levels is critical. This article present a threefold approach for model calibration that can be easily deployed in any biomechanical lab equipped with classical motion analysis facilities. First, motion capture data is used to calibrate geometrical parameters of the model (bones lengths, joint centers, and joint orientations). The calibration minimizes the distance between real and reconstructed trajectories of markers. Second, motion capture and force platforms data are used to calibrate inertial parameters of the model. The calibration minimizes the residual forces arising from the model inertial inaccuracies in the dynamics of the system. Last, isokinetic ergometer data are used to calibrate muscular parameters. The calibration minimizes the distance between the experimental maximal isometric torque curve and the simulated one for a given joint. Examples are provided throughout the paper and results are discussed. A focus is made on the idea of using such methods as a tool in any motion analysis lab.
The DIMBI project – innovative approaches for teaching business informatics

The purpose of this article is to collect and analyze data on existing methods of teaching business informatics in leading Bulgarian universities and suggest areas for improvements. Based on a collected data guidelines for innovative teaching methods in the field of BI and DW are developed. Proposed methods are divided in several sections – lectures, exercises (groups’ size, tools used, software, hardware, teaching methods, and real life customers), students’ projects, control methods. The findings of conducted feasibility study show that the business, students and universities need an innovative methodology of teaching business informatics and properly implemented this methodology has a high probability of success. This paper is written within the Erasmus plus KA2 project “Developing the innovative methodology of teaching business informatics” (DIMBI), 2015-1-PL01-KA203-0016636.
Quantitative appraisal and limiting factors analysis of sustainable development

The method in quantitative appraisal of sustainable development was researched in this paper such as the building of index regime, the bolting of indicators, the determination of index weight and the setting of the degree decision model of sustainable development. First, it offered a new way to select and reject the indicators on the basis of the primary component analysis and to determine the index weight by the absolute value of the index coefficient in the first primary component. Second, it brought forth a new method to distinguish the limiting factors according to the index coefficient in the first and the second primary component. At last, The method was used to evaluate the degree of sustainable development quantitatively in Xiaoshan City.
Design and Control Studies for the VectoRotor Hybrid VTOL Heavy Lift Vehicle

There has been a resurgence of interest in both lighter than air vehicles and hybrid designs that combine buoyant lift with powered or rotorborne propulsion. While the potential of such vehicles to undertake short range heavy lift functions has been long understood, many concepts have been found to lack the control capability necessary for precision vertical flight or easily controllable forward flight. This paper describes initial design work on a novel concept – denoted VectoRotor – that bypasses these problems; VectoRotor combines buoyant and dynamic lift in a hybrid, rotary-wing aircraft that employs a unique joined-rotor design. The resulting configuration provides vertical thrust and direct lateral force control for precision hovering in a structurally efficient manner. Preliminary analysis suggests this concept offers reduced fuel burn, lower noise, and lower operating costs than heavy lift helicopters conducting similar missions. This paper will outline the design history of the concept and results of preliminary scaling analyses on sizing for practical flight vehicles. It will also describe the application of a comprehensive rotorcraft model to define key performance issues for hover and forward flight, in particular engine power and flight control requirements and estimated limits on trimmed forward flight.
Discussion of “Co-citation and Co-authorship Networks of Statisticians” by Pengsheng Ji, Jiashun Jin, Zheng Tracy Ke, and Wanshan Li

We congratulate the authors on an interesting paper and on making an important contribution to the network analysis community through compiling a large new dataset which will spur further work on multilayer, dynamic and other complex network settings. This discussion focuses on the paper’s particular methods and applications in dynamic network analysis. Complexity of dynamic network data leads to many necessary analyst choices in both data processing and network modeling. Where possible, we will compare the choices made in this paper with other possibilities from recent literature on dynamic network analysis. One of the important points of the paper is that much of our network data has always been dynamic. For instance, communication networks consisting of sent and received E-mails come with time stamps, whether we choose to incorporate them or not. Developing statistical methods that take advantage of this time varying structure will lead to greater efficiency, novel insights, and generally allow us to take full advantage of rich modern datasets like the one featured in this paper.
Presence of selected organophosphorus pesticide residues in raw cucumber and tomato samples in Taman Universiti, Johor

Consumption of raw vegetables is common among Malaysians due to its nutritional values as well as anecdotal evidence supporting its medicinal properties. Although periodical analyses of pesticide residues on imported vegetables have been performed by the relevant authority, the same has not been amply emphasized on the locally produced vegetables and those that are available at food outlets, rendering the possible threat towards human health. In this research, the possible presence of diazinon, chlorpyrifos and dimethoate residues in two commonly eaten raw vegetables viz. cucumber and tomato conveniently sampled from four food outlets within Taman Universiti was investigated using gas chromatography with electron capture detector (GC-ECD) preceded by the Jabatan Kimia Malaysia-validated liquidliquid extraction method (JKM F0702). Samples of organic cucumber and tomato were used as controls. Once detected, the confirmation was done using gas chromatography with mass spectrometer (GC-MS). Results of this research revealed that presence of diazinon, chlorpyrifos and dimethoate residues were not detected in all the samples of cucumber and tomato sampled from the selected permanent food outlets. The fact that two unknown compounds were found in both of the organic cucumber and tomato samples eluted at 2.25 minutes and 3.58 minutes, respectively, while an unknown compound eluted at 3.47 minutes was evident in one of the tomato samples included in this research, expanding the spectrum of pesticide residues in these vegetables for future studies deserves consideration. Although the concentrations of diazinon, chlorpyrifos and dimethoate found in those vegetables appear to be within the acceptable levels for human consumption, absence of possible health threats for human may not be construed, considering the limited samples and organophosphorus pesticide residues included in this research.
A Survey on Cyber Situation-awareness Systems: Framework, Techniques, and Insights

Cyberspace is full of uncertainty in terms of advanced and sophisticated cyber threats that are equipped with novel approaches to learn the system and propagate themselves, such as AI-powered threats. To debilitate these types of threats, a modern and intelligent Cyber Situation Awareness (SA) system needs to be developed that has the ability of monitoring and capturing various types of threats, analyzing, and devising a plan to avoid further attacks. This article provides a comprehensive study on the current state-of-the-art in the cyber SA to discuss the following aspects of SA: key design principles, framework, classifications, data collection, analysis of the techniques, and evaluation methods. Last, we highlight misconceptions, insights, and limitations of this study and suggest some future work directions to address the limitations.
A Low-power Programmable Machine Learning Hardware Accelerator Design for Intelligent Edge Devices

With the advent of the machine learning and IoT, many low-power edge devices, such as wearable devices with various sensors, are used for machine learning–based intelligent applications, such as healthcare or motion recognition. While these applications are becoming more complex to provide high-quality services, the performance of conventional low-power edge devices with extremely limited hardware resources is insufficient to support the emerging intelligent applications. We designed a hardware accelerator, called an Intelligence Boost Engine (IBE), for low-power smart edge devices to enable the real-time processing of emerging intelligent applications with energy efficiency and limited programmability. The measurement results confirm that the proposed IBE can reduce the power consumption of the edge node device by 75% and achieve performance improvement in processing the kernel operations of applications such as motion recognition by 69.9 times.
Data Mining on Smartphones: An Introduction and Survey

Data mining is the science of extracting information or “knowledge” from data. It is a task commonly executed on cloud computing resources, personal computers and laptops. However, what about smartphones? Despite the fact that these ubiquitous mobile devices now offer levels of hardware and performance approaching that of laptops, locally executed model-training using data mining methods on smartphones is still notably rare. On-device model-training offers a number of advantages. It largely mitigates issues of data security and privacy, since no data is required to leave the device. It also ensures a self-contained, fully portable data mining solution requiring no cloud computing or network resources and able to operate in any location. In this article, we focus on the intersection of smartphones and data mining. We investigate the growth in smartphone performance, survey smartphone usage models in previous research, and look at recent developments in locally executed data mining on smartphones.