Miracle-dz/UniFAR-FTUs huggingface dataset:
Mass Treatment of Parasitic Disease: Implications for the Development and Spread of Anthelmintic Resistance

There has been a dramatic increase in the use of mass drug administration to reduce the morbidity associated with helminth infections of humans, raising the likelihood that anthelmintic resistance may become a public health concern of the future. After highlighting the scope and magnitude of the chemotherapy-based helminth control programs presently in place, this chapter emphasizes the mechanisms of action of the main anthelmintic drugs in use and how resistance may develop. To date, the most established population-based mass drug administration campaigns have been against the filarial parasites which cause human on chocerciasis and lymphatic filariasis. The molecular and parasitological evidence suggesting the presence of drug resistance in human filarial parasites is reviewed and factors influencing the spread of drug resistant parasites are discussed, taking examples from veterinary helminths and the use of mathematical models. In particular, the public health impact of the development of resistance by soil-transmitted helminths, such as hookworm, is a real concern. Implications of the development of anthelmintic resistance are discussed in relation to existing control programs, emphasizing how their monitoring and evaluation is essential to prevent it becoming a major public health concern of the future.
Learning to Recognise Exercises in the Self-Management of Low Back Pain

Globally, Low back pain (LBP) is one of the top three contributors to years lived with disability. Self-management with an active lifestyle is the cornerstone for preventing and managing LBP. Digital interventions are introduced in the recent past to improve and reinforce self-management where regular exercises are a core component and they rely on self-reporting to keep track of exercises performed. This data directly influences the recommendations made by the digital intervention where accurate and reliable reporting is fundamental to the success of the intervention. In addition, performing exercises with precision is important where current systems are unable to provide the guidance required. The main challenge to implementing an end-to-end solution is the lack of public sensor-rich datasets to implement Machine Learning algorithms to perform Exercise Recognition (ExR) and qualitative analysis. Accordingly we introduce the ExR benchmark dataset “MEx”, which we have shared publicly to encourage furthering research. In this paper we benchmark state-of-the art classification algorithms with deep and shallow architectures on each sensor and achieve performance up to 90.2%. We recognise the scope of each sensor in capturing exercise movements with confusion matrices and highlight the most suitable sensors for deployment considering performance vs. obtrusiveness.
Corporate Governance and Stock Price Synchronicity: Empirical Evidence from Vietnam

This research is conducted to investigate the impact of corporate governance on stock price synchronicity in the context of the Vietnamese market. The paper tests four hypotheses proposing the effect of four crucial components of corporate governance including board size, board independence, managerial ownership, and foreign ownership on stock price synchronicity. The study sample includes 247 non-financial listed companies on the Ho Chi Minh Stock Exchange (HOSE) in Vietnam over a period of five years from 2014 to 2018. The fixed effects model is employed to address econometric issues and to improve the accuracy of the regression coefficients. The research results show the positive impact of board size and foreign ownership but the negative impact of managerial ownership on stock price synchronicity. This study confirms the viewpoint that stocks in the market move more together when the firms’ corporate governance gets better. In other words, the research findings suggest that low synchronicity signifies the corporate intransparency and weak information environment and vice versa. From this, the paper provides a new insight to managers on how to improve stock price synchronicity with corporate governance.
Performance of Sickle SCAN® in the Screening of Sickle Cell Disease in Kisangani Pregnant Women and Attitude towards Results

In the Democratic Republic of the Congo, the sickle cell trait carriage is estimated at 25%. Routine neonatal screening is not a common practice, leading to a very late diagnosis. In this study, the screening of pregnant women was assessed as well as their attitudes. This is an analytical cross-sectional study conducted in 245 pregnant women, sampled by convenience in four hospitals in Kisangani city (Democratic Republic of Congo) and screened using the sickle SCAN® test, from February 1 to July 31, 2019. The sensitivity and specificity of the latter were determined using liquid chromatography coupled with mass spectrometry as the gold standard. The attitudes of 240 pregnant women without previous screening history were assessed upon the announcement of the results. The sensitivity of screening for hemoglobin (Hb) AA and Hb AS was 96.69% and 98.39%, respectively; while the specificities were 99.43% and 96.32%, respectively. The Kappa coefficient (κ) was excellent. Concerning attitudes, Hb SS pregnant women and 55.17% of AS pregnant women worried when the results relating to their hemoglobin status were announced. The sickle SCAN® test was found reliable for sickle cell disease screening in Kisangani. The announcement of the results, mainly positive, raises worry among pregnant woman. Therefore, we recommend the involvement of a clinician psychologist for pre-screening counselling and for results announcement, as well as early newborns and unmarried teenage girls screening.
Network video summarization based on key frame extraction via superpixel segmentation

The spread of insecure online video has been a serious social problem. The video summarization becomes one of key step for automatic filtering the expected video from the Internet. At present, the most existing video summarization methods are based on calculating the image similarity between video frames, so that the key frame can be selected properly. In this article, we introduce a superpixel segmentation based image similarity calculation, and then the metric is applied into video summarization. To identify the video key frames, we introduce superpixel segmentation to cluster the pixels locally by estimating the optical flow displacement field between successive frames, which can extract key frames and reduce video redundancy. On the VSUMM dataset and YouTube dataset, the experimental results demonstrate that the proposed method has clear advantages on both subjectively qualitative analysis and objectively quantitative evaluation comparing with the state of the art methods.
A Discriminative Distillation Network for Cross-Source Remote Sensing Image Retrieval

Nowadays, several remote sensing image capturing technologies are used ranging from unmanned aerial vehicles to satellites. Powerful learning-based discriminative features play an essential role in content-based remote sensing image retrieval (CBRSIR). Cross-source CBRSIR (CS-CBRSIR) is used to find relevant remote sensing images across different remote sensing sources (i.e., multispectral images and panchromatic images). But it is limited by large cross-source and intrasource variations caused by different semantic objects, spatial resolution, and spectral resolution. The main limitation of CS-CBRSIR is that it cannot address the inconsistency between different sources and exploit the intrinsic relation between them. This study proposes a discriminative distillation network for CS-CBRSIR to address this limitation. To enlarge the interclass variations and reduce the intraclass differences, the discriminative features from the first source are first extracted with a well-designed joint optimization configuration (JOC) on the basis of deep neural networks. Thereafter, the features extracted from the first source are used as a supervision signal for the second source; feature distribution in common feature space between the first and second sources are made significantly similar. The method proposed in this study simultaneously handles the cross-source and intersource variations, unlike the existing methods. Extensive experiments on the DSRSID dataset with Euclidean distance verify the effectiveness of our proposed method.
I walk, therefore I am: a multidimensional study on the influence of the locomotion method upon presence in virtual reality

A defining virtual reality (VR) metric is the sense of presence, a complex, multidimensional psychophysical construct that represents how intense is the sensation of actually being there, inside the virtual environment (VE), forgetting how technology mediates the experience. Our paper explores how locomotion influences presence, studying two different ways of artificial movement along the VE: walking-in-place (through head bobbing detection) and indirect walking (through touchpad). To evaluate that influence, a narrative-neutral maze was created, from where 41 participants (N=41) had to escape. Measuring presence is a controversial topic since there is not a single, objective measure but a wide range of metrics depending on the different theoretical basis. For this reason, we have used for the first time, representative metrics from all three traditional dimensions of presence: subjective presence (SP) (self-reported through questionnaires), behavioral presence (BP) (obtained from unconscious reactions while inside the VE), and physiological presence (PP) [usually measured using heart rate or electrodermal activity (EDA)]. SP was measured with the ITC-SOPI questionnaire, BP by collecting the participants’ reactions, and PP by using a bracelet that registered EDA. The results show two main findings: (i) There is no correlation between the different presence metrics. This opens the door to a simpler way of measuring presence in an objective, reliable way. (ii) There is no significant difference between the two locomotion techniques for any of the three metrics, which shows that the authenticity of VR does not rely on how you move within the VE.
Observer-Based Sampled-Data Model-Free Adaptive Control for Continuous-Time Nonlinear Nonaffine Systems With Input Rate Constraints

A sampled-data model-free adaptive control (SMFAC) strategy is proposed for continuous-time nonlinear nonaffine systems with input rate constraints. By using differential and integral mean value theorems as two basic mathematic tools, a sampled-data local dynamic linearization method is proposed at first to transform the continuous-time nonlinear nonaffine model into a sampled-data nonlinear affine I/O model, including a linear parametric term affined to the control input and a nonlinear uncertainty term. On this basis, we consequently propose an observer-based SMFAC (ObSMFAC) scheme, including a sampled-data parameter estimator to estimate the unknown partial derivatives and a sampled-data observer to estimate the residual nonlinear uncertainty, respectively. Note that the sampling period is incorporated explicitly in the proposed ObSMFAC which enhances the control performance by reducing its negative influence on the system stability. The constraint on the input rate is also considered in the control law as the transition condition of the input updating algorithms. The convergence of the proposed ObSMFAC is proved by using the contraction mapping principle. The simulation study demonstrates the theoretical results.
Clinical Microbiology: An Introduction for Healthcare Professionals

The history of microbiology. Micro-organisms and their properties. The epidemiology of infection.The immune response. Antimicrobial chemotherapy. Cleaning, disinfection and sterilisation. The microbiology laboratory. A guide to pathogenic bacteria. A Guide to viruses. A guide to fungi, protozoa, helminths and ectoparasites. Infection associated with health care: the infection control programme.The principles of infection prevention and control. Important nosocomial infections: pathogenesis and prevention.The management of infectious patients (isolation nursing)
Comparison and Bias-Correction of Satellite-Derived Precipitation Datasets at Local Level in Northern Kenya

Understanding ongoing trends at local level is fundamental in research on climate change. However, in the Global South it is hampered by a lack of data. The scarcity of land-based observed data can be overcome through satellite-derived datasets, although performance varies according to the region. The purpose of this study is to compute the normal monthly values of precipitation for the eight main inhabited areas of North Horr Sub-County, in northern Kenya. The official decadal precipitation dataset from the Kenyan Meteorological Department (KMD), the Global Precipitation Climatology Centre (GPCC) monthly dataset and the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) monthly dataset are compared with the historical observed data by means of the most common statistical indices. The GPCC showed the best fit for the study area. The Quantile Mapping correction is applied to combine the high resolution of the KMD dataset with the high performance of the GPCC set. A new and more reliable bias-corrected monthly precipitation time series for 1983–2014 results for each location. This dataset allows a detailed description of the precipitation distribution through the year, which can be applied in the climate change adaptation and tailored territorial planning.
Deformation mechanics of sputtered copper layers during nanoindentation tests

The mechanical properties of the thin sputtered copper layer on the SiO2-coated silicon substrate is needed as part of the requirements in quantifying the reliability of the Through-Silicon Via (TSV) interconnects. In this respect, two different Cu coating layers, each from the different sputtering process, are examined. A series of nanoindentation tests are performed on the Cu coating layer samples with indenter speeds ranging from 80 to 400 nm/s, and the indentation depths of 320 nm. The properties of elastic modulus, hardness and the hardening behavior of the Cu coating layers have been quantified. Results show that the coating with higher contamination of C at 8.41 wt. % displays a significant hardening and a peak load level, as reflected in the measured nanoindentation load-displacement curves. However, insignificant effect of the applied probe displacement speeds up to 400 nm/s on the resulting properties of the coating is registered. The Johnson-Cook constitutive equation adequately describes the strain rate-dependent hardening behavior of the Cu coating layer.
Speaker Adaptation of a Multilingual Acoustic Model for Cross-Language Synthesis

Several studies have shown promising results in adapting DNN- based acoustic models as a mechanism to transfer characteristics from pre-trained models. One such example is speaker adaptation using a small amount of data, where fine-tuning has helped train models that extrapolate well to diverse linguistic contexts that are not present in the adaptation data. In the current work, our objective is to synthesize speech in different languages using the target speaker’s voice, regardless of the language of their data. To achieve this goal, we create a multilingual model using a corpus that consists of recordings from a large number of monolingual and a few bilingual speakers in multiple languages. The model is then adapted using the target speaker’s recordings in a language other than the target language. We also explore if additional adaptation data from a native speaker of the target language improves the performance. The subjective evaluation shows that the proposed approach of cross-language speaker adaptation is able to synthesize speech in the target language, in the target speaker's voice, without data spoken by the target speaker in that language. Also, extra data from a native speaker of the target language can improve model performance.
Ph, Electric Conductivity and Delayed Luminescence Changes in Human Sera of Subjects Undergoing the Relaxation Response: A Pilot Study

In our recent works we reported that physical and chemical characteristics of serum can vary in relation to the psychic activity of an individual depending on whether it is oriented to stress or relaxation. We wandered if these observations could be accompanied by an appreciable modification of the Ph, electric conductivity and Delayed Luminescence of the same serum samples. Our preliminary data may suggest that the serum pH could significantly increase during a Relaxation Response intervention while electric conductivity seems to decrease. Moreover, Delayed Luminescense could vary in the same subject according to the Relaxation Response practice. According to our proof of concept study, we postulate the appearance of a coherent system within the blood samples analyzed after the Relaxation Response. Further researches and some technical development are needed to support our preliminary findings.
Neural processing of chemosensory information from the locust legs

Tarsal chemoreceptors in phytophagous insects can provide information on the chemical nature of the leaf surface, and are thus potentially important in host plant discrimination. Ovipositing Pieris brassicae, for example, drum the surface of leaves with their tarsi and tarsal chemoreceptors have been shown to respond to host-specific glucosinolates (Ma and Schoonhoven,1973). The classic work of Dethier on the blowfly, showed that chemical stimulation of the tarsi of Phormia regina led to proboscis extension, which was necessary to allow subsequent stimulation of additional chemoreceptors on the labellum and oral papillae, which finally led to drinking. This work was conducted in order to investigate the reflex of the pro and mesothoracic legs in Locusta migratoria L under control of their chemoreceptors in responses to be stimulating media. The study was conducted utilising different stimulating media namely : salt (10mM and 100mM NaCl), sugar (50mM sucrose and 50mM glucose), acid (25mM and100mMcitric acid) and alkaloid (50mM NHT) solutions, in addition to a repellent agent (10mM and 100mM quinine hydrochloride). The central projection of basiconic sensilla, canal sensilla and meshanoreceptors sensillum were studied by using neurobiotin backfilling and single cell recording. Results indicated that sensitivity of the prothoracic leg and its reflex are faster in comparison to the mesothoracic leg which could be explained by the dense spiking of the sensory neurones of the basiconic sensilla in the prothorecic ganglia controlling the prothoracic leg as compared to the spiking of the sensory neurones of the mesothoracic leg. Denser spiking of the sensory neurones of both canal and meshano sensilla in the prothoracic ganglia were also recorded in comparison to less spiking in mesothoracic ganglia . The basiconic sensilla shows responses to one or more of the tested stimulators indicating a partial specificity of basiconic sensilla . Results on the responses of motor neurones and their effect on the basiconic sensilla in relation to tested stimulus indicated that not all the motor neurones controlling the depressor muscles show similar responses. Some responded only to salt solution others only to sugars solutions, however, some responded to both stimuli at the same time. Local interneurones were also found to responded differently to stimulating media. One Local interneurones was identified for both salt(NaCl) and sugar(Glucose) solutions and a second was identified for the repellent agent (quinine hydrochloride). Based on the pervious result it could be concluded that chemoreceptors and specially basiconic sensilla on the tarsus, pro and mesothoracic legs play an essential role in identifying the nature of surrounding media which is intern translated as reflexes by the pro and mesothoracic legs.
A non-randomized pilot study to test the feasibility of treating chronic pain and opioid prescription use in rural areas with acceptance and commitment therapy (T-PACT)

Abstract Chronic non-cancer pain (CNCP) involves one-third of the US population, and prescription opioids contribute to the opioid epidemic. The Centers for Disease Control and Prevention emphasizes maximizing non-opioid treatment, but many rural populations cannot access alternative therapies. Clinical and Translational Science Award hubs across four rural states performed a multi-site, single-arm intervention feasibility study testing methods and procedures of implementing a behavioral intervention, acceptance and commitment therapy, in primary care CNCP patients on chronic opioids. Using the CONSORT extension for feasibility studies, we describe lessons learned in recruiting/retaining participants, intervention implementation, data measurement, and multi-site procedures. Results inform a future definitive trial and potentially others conducting rural trials.
Comparative Analysis of Low-Dimensional Features and Tree-Based Ensembles for Malware Detection Systems

Advances in machine learning algorithms have improved the performance of malware detection systems for the last decade. However, there are still some challenges such as processing a large amount of malware, learning high-dimensional vectors, high storage usage, and low scalability in learning. This paper proposes low-dimensional but effective features for a malware detection system and analyzes them with tree-base ensemble models. Expert knowledge and frequency analysis are adapted for relevant feature selection from the collected data set, which contributes to fast low-dimensional feature preparation, low storage usage, and fast learning. We extract the five types of malware features represented from binary or disassembly files. Specifically, the novel WEM (Window Entropy Map) image is designed to represent malware with variable length, and the set of frequently used APIs is analyzed to shorten the processing time. To validate the effectiveness of the selected features, we compare the performance of tree-based ensemble models such as AdaBoost, XGBoost, random forest, extra trees, and rotation trees. The proposed feature can reduce the original feature dimensionality by several tens to hundreds of times and decrease the training time of ensemble models without degrading the malware detection rate when compared to the performance of the whole set of malware features. In accuracy and AUC-PRC evaluation, XGBoost is the highest in rank.