Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating the added value of biomarkers compared with self-reported smoking in predicting future e-cigarette use: Evidence from a longitudinal UK cohort study

Biomarkers can be used to assess smoking behaviour more accurately and objectively than self-report. This study assessed the association between cotinine (a biomarker of smoke exposure) and later e-cigarette use among a population who were unexposed to e-cigarettes in youth. Young people in the Avon Longitudinal Study of Parents and Children took part in the study. We observed associations between cotinine at 15 years (measured between 2006 and 2008 before the wide availability of e-cigarettes) and self-reported ever use of e-cigarettes at 22 (measured between 2014 and 2015 when e-cigarettes were widely available) using logistic regression. A range of potential confounders were adjusted for (age, sex, body mass index, alcohol use and passive smoke exposure). Additionally, we adjusted for the young people’s self-reported smoking status/history to explore potential misreporting and measurement error. In a sample of N = 1,194 young people, cotinine levels consistent with active smoking at 15 years were associated with increased odds of e-cigarette ever use at 22 years (Odds Ratio [OR] = 7.24, 95% CI 3.29 to 15.93) even when self-reported active smoking status at age 16 (OR = 3.14, 95% CI 1.32 to 7.48) and latent classes of smoking behaviour from 14 to 16 (OR = 2.70, 95% CI 0.98 to 7.44) were included in the model. Cotinine levels consistent with smoking in adolescence were strongly associated with increased odds of later e-cigarette use, even after adjusting for reported smoking behaviour at age 16 and smoking transitions from 14 to 16.
Channel Capacity for MIMO Spectrally Precoded OFDM with Block Reflectors.

In contrast to the state-of-the-art technology, it is envisaged that 5G systems will accommodate a broad range of services and a variety of emerging applications. In fact, the advent of the Internet of Things (IoT) and its integration with conventional human-initiated transmissions yields the need for a fundamental system redesign. Hence, the ideal waveform for the upcoming 5G radio access technology should cope with several requirements, such as low computational complexity, good time and frequency localization and straightforward extension to \emph{multiple-input multiple-output} (MIMO) systems. In the light of reducing the algorithmic complexity of the spectrally precoded MIMO OFDM, we provide an in-depth analysis of the use of block reflectors. The channel capacity formula for MIMO spectrally precoded OFDM is then derived, and the proposed design techniques are compared to conventional MIMO OFDM from an information-theoretic perspective.
Prevention of thromboembolism: A personal journey

is is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-Share Alike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms. ©2019 Published by Scientific Scholar on behalf of Surgical Neurology International Editorial
Multi-Task Generalization and Adaptation between Noisy Digit Datasets: An Empirical Study

Transfer learning for adaptation to new tasks is usually performed by either finetuning all model parameters or parameters in the final layers. We show that good target performance can also be achieved on typical domain adaptation tasks by adapting only the normalization statistics and affine transformations of layers throughout the network. We apply this adaptation scheme to supervised domain adaptation on common digit datasets and study robustness properties under perturbation by noise. Our results indicate that (1) adaptation to noise exceeds the difficulty of widely used digit benchmarks in domain adaptation, (2) the similarity of the optimal adaptation parameters for different domains is strongly predictive of generalization performance, and (3) generalization performance is highest with training on a rich environment or high noise levels.
Integrating symbolic spatial information in robot navigation

Navigation cues – like labels, signs, maps, planners, spoken directions, and navigational gestures – provide humans with navigation capabilities that far surpass those of mobile robots. This thesis demonstrates how a mobile robot can use the symbolic spatial information embedded in navigation cues to proficiently navigate unseen built environments. The primary contributions of the thesis reside in the abstract map, a novel tool for grounding navigation symbols. The abstract map imagines abstract layouts and structures for unseen spaces through a mechanical analogy, then tethers the abstract spatial models with robot perceptions to inform navigation to symbolic goals in built environments.
Eclampsia at the university of Abuja teaching hospital: a ten-year review

Background: Worldwide, eclampsia remains one of the major causes of maternal and perinatal morbidity and mortality. Pre-eclampsia/eclampsia is a multisystem disease and despite extensive research, no definitive etiology has been identified. This disease therefore continues to pose a challenge in obstetric practice, especially in developing countries like ours. We therefore set out to determine the prevalence, associated factors, and outcome of eclampsia at the University of Abuja Teaching Hospital, Abuja, Nigeria.Methods: A descriptive retrospective review of cases of eclampsia managed at the University of Abuja Teaching Hospital, Abuja over a 10-year period. The case notes of these women were retrieved, and relevant data obtained included age, parity, booking status, type of eclampsia, gestational age at presentation, mode of delivery, maternal and perinatal outcome variables. Data was analysed using the statistical package for social sciences (SPSS Inc, Chicago) version 20 and outcome variables represented in simple percentages.Results: Of the 22,945 deliveries conducted during that period, 257 cases of eclampsia were managed, putting the prevalence of eclampsia at 1.12%. Of these, 90.5% were unbooked, 67.6% were primigravidae and 88.2% were below 30years of age. Antepartum eclampsia was the commonest form, occurring in 67.6% of the patients. Caesarean section was the mode of delivery in 82.2% of cases. Maternal and perinatal mortality were 4.6% and 11.1% respectively.Conclusions: Eclampsia is still a major cause of maternal and perinatal morbidity and mortality in our environment. One intervention to reduce its impact is education on the importance of antenatal care attendance. It’s also pertinent that this basic form of care be made more affordable and more easily accessible to those women who are socially disadvantaged.
A Dynamics-Agnostic State Estimator for Unmanned Aerial Vehicles Using Ultra-Wideband Radios

We present a state estimator for a UAV operating in an environment equipped with ultra-wideband radio beacons. The beacons allow the UAV to measure distances to known positions in the world. The estimator additionally uses the vehicle’s rate gyroscope and accelerometer, and crucially does not rely on any knowledge of the vehicle’s dynamic properties (e.g. mass, mass moment of inertia, aerodynamic properties). This makes the estimator especially useful in situations where the exact system parameters are unknown (e.g. due to unknown payloads), or where the environment is unpredictable (e.g. wind gusts). Experimental results demonstrate the approach’s efficacy, and demonstrate that the estimator can run on low-cost microcontrollers with typical sensors.
Multilevel Wavelet Decomposition Based Harris Corner Detection Algorithm for Remote-sensing Image

Because of the interference of the noise and the subtle texture, the Harris algorithm will generate much more false corners. An improved Harris algorithm based on multilevel wavelet decomposition is proposed. By multilevel wavelet decomposition, the high frequency components of image are extracted. These high frequency components with different scales are parsed, and the noises and subtle textures are suppressed while the original image edge features are kept. The final experiment shows that the improved corner detection algorithm can effectively reduce the number of false corners. Introduction The geometric feature extraction is a major task for remote-sensing data registration. The key to the extraction is the feature point matching between images. Harris algorithm is a widely used corner detection algorithm in feature extraction because of its simple calculation and high stability. However, in the process of extracting, corners are vulnerable to the interference of noise and subtle texture, which causes more false corners. In order to resolve this problem, an improved Harris algorithm based on multilevel wavelet decomposition is proposed in this paper. Though the decomposition, the subtle texture and noise are suppressed and corner detection results are optimized. It is verified by experiment that this improved corner detection algorithm can obviously improve the quality of corner detection and reduce the number of false corner points. This paper has five sections. The second section introduces the related research work of corner detection. In the third section, based on the analysis of Harris algorithm, an improved method based on multilevel wavelet decomposition is proposed. The fourth section is the experiment. The last section is the conclusion. Related work Feature-based image registration algorithms are widely studied [1],[2] . By comparing the feature sets with obvious gray scale changing, this kind of algorithm uses feature matching algorithm to find related feature points. And then the mapping relation is established according to the feature points to achieve the goal of image registration. According to the models of the feature points, feature-based image registration algorithms can be divided into three categories: edge point based [3] , corner based [4] and interest operator based [5] . Corner is a kind of feature points that reflects the local extremum of curvature on contour. Compared with other types of feature points, corner can represent the features of the ground surface much better [6],[7] . In 1981, Moravec proposed the idea of using the corners of local area on image for stereo matching. As the first widely used corner detection algorithm, this algorithm laid a foundation for theoretical research of corner detection [8] . In 1988, Harris improved Morvec's corner detection algorithm and enhanced the robustness of corners [9] .
Mechanisms of Ewing sarcoma development and therapeutic resistance

Nicholas C. Gomez: Mechanisms of Ewing sarcoma development and therapeutic resistance. (Under the direction of Ian J. Davis.) Many large scale consortia have begun to use high-throughput sequencing technologies to identify key mutations and pathways involved in the etiology of cancers. Identification of specific mutations within tumors can also influence patient-directed therapeutic decisions. Recent evidence implicates the insulin-like growth factor pathway in development of Ewing Sarcoma, a highly malignant bone and soft tissue tumor that primarily affects children and young adults. Despite promising results from preclinical studies of therapies that target this pathway, early phase clinical trials have shown that a significant fraction of patients do not benefit, suggesting that cellular factors determine tumor sensitivity. All cases of Ewing sarcoma are characterized by a translocation between EWSR1 and an ETS-family transcription factor where 85% of the cases result in the EWSR1-FLI1 t(11;22)(q24:q12). This novel transcription factor is retargeted to a subset of repetitive elements in a cell-type specific manner by a previously unknown mechanism. In the chapters that follow, we describe the cellular consequences of PTEN loss in Ewing sarcoma as well as a mechanism for EWSR1FLI1 retargeting. We demonstrate that Ewing sarcoma cells with PTEN loss exhibited increased transformative properties, as well as reciprocal sensitivity to IGF-1R and mTOR inhibition, therapies currently undergoing testing in clinical trials. In addition, our studies also describe a novel chromatin environment of stem cells in which repetitive elements are enriched in accessible chromatin. Strikingly, a subset of accessible repetitive elements in stem cells is associated with Ewing Sarcoma development. Repetitive elements exhibiting the greatest FAIRE signal in stem cells, is associated
Selection of representative feature training sets with self-organized maps for optimized time series modeling and prediction: application to forecasting daily drought conditions with ARIMA and neural network models

While the simulation of stochastic time series is challenging due to their inherently complex nature, this is compounded by the arbitrary and widely accepted feature data usage methods frequently applied during the model development phase. A pertinent context where these practices are reflected is in the forecasting of drought events. This chapter considers optimization of feature data usage by sampling daily data sets via self-organizing maps to select representative training and testing subsets and accordingly, improve the performance of effective drought index (EDI) prediction models. The effect would be observed through a comparison of artificial neural network (ANN) and an autoregressive integrated moving average (ARIMA) models incorporating the SOM approach through an inspection of commonly used performance indices for the city of Brisbane. This study shows that SOM-ANN ensemble models demonstrate competitive predictive performance for EDI values to those produced by ARIMA models.
Research on Recommender System based on Social Trust

Recommender system has become an effective tool to solve information overload and helps users to make decisions. In order to better improve the quality of personalized recommendation, this paper studies the recommendation algorithms based on social information, such as trust and distrust, then analyses the differences and advantages of the recommender systems which are based on social trust and the traditional. Finally, the future development trend of recommender systems based on social trust is prospected.
Airport Detection on Remote Sensing Images Using Fater Region-based Convolutional Neural Network

An airport detection method on remote sensing images based on transfer learning and hard example mining is proposed. We use Faster region-based convolutional neural network framework with end-to-end advantage instead of the sliding windows and artificial features in the existing traditional methods. Transfer learning is used to solve this problem that the number of airport remote sensing data itself is limited. Hard example mining is used to make full use of hard examples. As a result, it can improve the ability to discriminate objects and make training more efficient. The experimental results show that the proposed method can detect different types of airports accurately under complex backgrounds. Compared with other airport detection methods, the proposed method has higher detection rate 92.6%, lower false-alarm rate 15% and shorter average processing time 0.2s.
Non-Steroidal Anti-Inflammatory Drug Use and Recovery after Achilles Tendon Rupture

LEVEL OF EVIDENCE: 3 SUMMARY Background. The effect of non-steroidal anti-inflammatory drugs (NSAIDs) on recovery after Achilles tendon rupture is unknown. This is the first study examining the relationship between NSAID use and recovery after Achilles tendon rupture in humans. Methods. Adult patients presenting between 01/01/14 and 03/30/2018 with acute Achilles tendon rupture were asked to complete a survey in the Spring of 2018 to determine whether they took NSAIDs after rupture. Their recovery was quantified in three ways: 1) the Achilles Tendon Total Rupture Score (ATRS), 2) the duration of time to walk again normally, and 3) the incidence of tendon re-rupture. Recovery was compared across NSAID and non-NSAID users. Results. Fifty-three percent of 361 patients who completed the survey used NSAIDs following tendon rupture. NSAID users reported a significantly lower ATRS (69) than non-NSAID users (77; p < 0.005). There was no difference in time to walk again normally or incidence of re-rupture. After controlling for age, sex, operative versus non-operative treatment, and time from diagnosis to survey completion, NSAID use was still associated with a significantly lower ATRS (p = 0.003). Conclusion. NSAID use was associated with a lower ATRS after Achilles tendon rupture.
Epidemiological study of alcohol and tobacco consumption among adults in a rural population of Barabanki, Uttar Pradesh, India

Background: e increasing consumption of tobacco and alcohol is leading to a surge in prevalence of cardiovascular diseases as well as hepatic morbidities in India. Also, high consumption of alcohol and tobacco has harmful social and economic consequences. Objectives: e present cross-sectional study was therefore undertaken to estimate the prevalence of tobacco and alcohol consumption among adults in a rural population and ascertain the factors associated with it. Materials &Methods: A cross-sectional study was conducted in field practice area of Rural Health and Training Centre (RHTC) Satrik, Barabanki and a total 796 study subjects were enrolled via multistage sampling. Details about personal habits like smoking, consumption of smokeless tobacco and alcohol intake was collected using a pre-designed, pre-tested semi structured questionnaire. Data about basic sociodemographic information was collected and cross checked with the data base available at RHTC. Results: e proportion of individuals with smoking, tobacco chewing, alcohol consumption & combined use of smoking and alcohol were 6.7%, 22.8%, 4.02% and 13.5% respectively. e association between type of addiction with age-group, gender, religion, caste, type of family, marital status, educational status, type of occupation and socio-economic status was found to be statistically significant (p<0.05). Conclusion: Low prevalence of tobacco and alcohol was found in the present study. But in spite of that community based comprehensive strategies must be implemented so as to keep a check over it and prevent its transformation into an epidemic.
Prognostic and predictive significance of microsatellite instability in stage II colorectal carcinoma: An 8-year study from a tertiary center in South India

BACKGROUND: Microsatellite instability (MSI) accounts for 15–20% of colorectal cancer (CRC) and is considered to have favorable stage-adjusted prognosis compared to Microsatellite stable (MSS) CRCs. Determination of MSI in stage II CRC is important for management decisions regarding adjuvant chemotherapy administration. The aim of this study was to determine the prognostic and predictive significance of MSI in stage 2 CRC in the Indian scenario. MATERIALS AND METHODS: A total of 195 patients who underwent curative surgery for stage II CRC from 2010 to 2017 were included. MSI testing by immunohistochemistry (DNA MisMatch Repair proteins) was performed in all. Various clinicopathological factors and disease-free survival and overall survival were assessed between MSI and MSS groups. The effect of treatment in terms of survival benefits with adjuvant therapy in the MSI group was also assessed. RESULTS: 27.1% of the CRCs' showed MSI. Younger age (<50 years), family history of cancer, synchronous/metachronous malignancies, proximal (right sided) location, poor morphological tumour differentiation, mucin production, and presence of peritumoral (Crohn's-like) lymphocytic response showed statistically significant association with MSI. Majority (56%) of our patients showed combined loss of MLH1 and PMS2. Overall, survival among the MSI patients was significantly higher (76.6 ± 4.149 months) than the MSS patients (65.05 ± 3.555)P= 0.04. MSI patients did not show any differences in survival with or without treatment. CONCLUSION: This study highlights the distinct clinicopathological features of MSI-related CRC and the relevance of MSI testing of stage II CRC for management decisions and prognostication.
Long non-coding RNA-HOTAIR promotes the progression of sepsis by acting as a sponge of miR-211 to induce IL-6R expression

Sepsis remains the primary cause of death in intensive care units and multiple long non-coding RNAs (lncRNAs) have been demonstrated to be dysregulated in samples of patients with sepsis. However, whether lncRNA-HOTAIR is involved in the etiology of sepsis remains unclear. The aim of the present study was to investigate the role of HOTAIR in sepsis and to reveal the associated mechanisms. A bioinformatics analysis and dual-luciferase reporter assay was performed to evaluate the interaction between HOTAIR and miR-211, as well as miR-211 and IL-6R. An animal model of sepsis was established in mice via cecal ligation and puncture. Interferon (IFN)-γ, interleukin (IL)-6, IL-17, tumor necrosis factor (TNF)-α, IL-1β, IL-6 receptor (R), microRNA (miR)-211 and HOTAIR expression was measured using reverse transcription-quantitative PCR. Cellular proliferation and apoptosis of monocytes were assessed using cell counting kit-8 assay and flow cytometry, respectively. miR-211 was revealed to be targeted by HOTAIR and IL-6R. The expression of IFN-γ, IL-6, IL-17, TNF-α, IL-1β, IL-6R and HOTAIR was significantly upregulated in the septic mice, whereas miR-211 expression was downregulated. The overexpression of hox transcript antisense RNA (HOTAIR) and knockdown of miR-211 were associated with an increased expression of IFN-γ, IL-6, IL-17, TNF-α, IL-1β and IL-6R in monocytes, while the overexpression of miR-211 exhibited the opposite effect. HOTAIR overexpression and miR-211 knockdown significantly inhibited cellular proliferation and promoted monocyte apoptosis, whereas the overexpression of miR-211 exhibited the opposite effects in monocytes. Therefore, HOTAIR may promote the progression of sepsis by indirectly regulating the expression of IL-6R via miR-211.
Anxiety and Depression among nurses working in a tertiary care hospital in South India

Background: Nursing binds human society with a bond of care and affection. Nurses are often the first healthcare professional that patients meet. Quality of care for patients is strongly linked to the performance of the nursing staff. If the mental health status of these nurses is troubled, then they will not be able to give their full attention to this demanding task. Nowadays, all efforts to fight health workers’ illnesses are extremely important. This descriptive cross-sectional study will give us knowledge regarding the anxiety and depression status among nurses and give the nursing staff an opportunity to express their difficulty in professional life working in a tertiary care hospital in South India. Objective of this study was conducted to assess the prevalence of anxiety and depression among nurses working in a tertiary care hospital and to assess the correlation between various factors influencing the level of anxiety and depression among nurses.Methods: Nurses were selected by simple random sampling till the sample size was obtained. A standardised questionnaire was handed over to the participants to collect relevant data and was assessed by Hospital Anxiety and Depression Score (HADS) and analysed using SPSS software.Results: The participants consist of 120 nurses. The study gives frequencies of nurses with anxiety as 48(40%) and depression as 43(35.8%). There does not exist any significant correlation of anxiety and depression with various factors influencing them.Conclusions: Anxiety and depression levels are increased in the younger and less experienced nurses. Thereafter, interventions can be planned to improve the quality of work-life of nurses that can improve patient care and in turn benefit the society.