Miracle-dz/UniFAR-FTUs huggingface dataset:
Pharmacotherapy for the prevention of malaria in pregnant women: currently available drugs and challenges

ABSTRACT Introduction: Malaria in pregnancy continues to be a significant public health burden globally, with over 100 million women at risk each year. Sulfadoxine-pyrimethamine (SP) is the only antimalarial recommended for intermittent preventive therapy in pregnancy (IPTp) but increasing parasite resistance threatens its viability. There are few other available antimalarial therapies that currently have sufficient evidence of tolerability, safety, and efficacy to replace SP. Areas covered: Novel antimalarial combinations are under investigation for potential use as chemoprophylaxis and in IPTp regimens. The present review summarizes currently available therapies, emerging candidate combination therapies, and the potential challenges to integrating these into mainstream policy. Expert opinion: Alternative drugs or combination therapies to SP for IPTp are desperately required. Dihydroartemisinin-piperaquine and azithromycin-based combinations are showing great promise as potential candidates for IPTp but pharmacokinetic data suggest that dose modification may be required to ensure adequate prophylactic efficacy. If a suitable candidate regimen is not identified in the near future, the success of chemopreventive strategies such as IPTp may be in jeopardy.
Prescription Opioid Type and the Likelihood of Prolonged Opioid Use After Orthopaedic Surgery

Introduction: A common belief is that some narcotic medications have a higher association with prolonged use. We assessed whether the initial opiate type prescribed to postoperative, opiate-naive orthopaedic trauma patients was associated with prolonged opioid use. Methods: We studied 17,961 adult, opiate-naive patients treated for a surgical musculoskeletal injury. Discharge prescription in morphine milligram equivalents (MMEs, a standardized dosing unit that allows for comparison across opioid types) was calculated. Opioid prescribing beyond 90 days after injury was defined as prolonged use. Results: Initial analysis demonstrated a higher likelihood of prolonged use for patients discharged on hydromorphone or morphine versus hydrocodone. However, when we adjusted for discharge MME, only opioid quantity was predictive of prolonged use (P < 0.001). In addition, discharge MME was associated with opioid type (P < 0.01). Discussion: Persistent opiate use was associated with discharge opioid quantity, not the opioid type. These results highlight the importance of calculating equivalence doses when selecting opioid types and considering amount of narcotics prescribed. Level of Evidence: Level III
Evaluation of Antimicrobial Efficacy of Nanosilver Solution, Sodium Hypochlorite and Normal Saline in Root Canal Irrigation of Primary Teeth

Background: Bacteria are the main etiological factors in the development of dentinal caries and its progression to pulpal and periapical disease. Enterococcus faecalis is the bacterial species most frequently recovered from the root-filled teeth. Aim: This study aimed to evaluate the antimicrobial effectiveness of nanosilver (NS) solution as an endodontic irrigation solution of primary teeth against E. faecalis. Settings and Design: Thirty-six canals of primary teeth were selected for this ex vivo study. Methods and Materials: Thirty-six canals of primary teeth were prepared up to the file #35 and all of the specimens were sterilized. Then, root canals were inoculated with a suspension containing E. faecalis bacteria. The teeth were then randomly divided into three groups. Antimicrobial effectiveness was evaluated immediately after dividing into groups by counting colony-forming units on brain heart infusion broth plates. Statistical Analysis: Data were analyzed using Kolmogorov–Smirnov, Welch, and Dunnett's T3 tests. Results: Sodium hypochlorite showed the highest antimicrobial effectiveness against E. faecalis and showed significant differences compared with normal saline and NS solution (P < 0.001). Conclusion: Based on the results of this study, solution of NS particles can be used as an alternative to other root canal irrigating solutions.
Promoting Students’ Conceptual Knowledge using Video Analysis on Tablet Computers

The common idea of video analysis in physics education so far is that students record the motion of an object using a video camera, transfer the video on a computer and analyze it with a suitable software. In our approach students perform video analysis time-efficiently using only tablet computers. We present the results of a study which examines learning effects of tablet PC-supported video analysis in high school physics courses in a pre-post-test design with treatment and control groups for two essential topics of mechanics, the uniform motion (N = 109 matched samples) and the accelerated motion (N = 70 matched samples). The results show that the treatment leads to significantly higher learning gain regarding conceptual knowledge in comparison to traditional teaching without video-based motion analysis, especially for the cognitively more demanding topic. We discuss the results in the context of learning theories and reveal implications for future research.
Organisational learning in hospitals: A concept analysis.

AIM To provide a clear definition and description of organisational learning in hospitals. BACKGROUND Organisational learning is a promising strategy nurse managers, and leaders can use to improve organisational performance. A clear definition and description of organisational learning is necessary to advance theory, research and practice in this field. METHODS Walker & Avant's method was used to conduct a concept analysis of organisational learning in hospitals. Data sources included 147 empirical studies, 16 review articles, three dictionary entries and three book chapters. RESULTS Organisational learning occurs when experiences are translated into positive changes in the organisation's collective knowledge, cognition and actions. Organisational context plays a key role in the learning process. Other manifestations of the concept are identified. CONCLUSIONS This concept analysis provides a clear definition of organisational learning and a description of its defining attributes, antecedents, empirical referents and consequences. IMPLICATIONS FOR NURSING MANAGEMENT Nurse managers and leaders can improve patient and organisational outcomes by creating an environment conducive to translating experiences into organisational learning. Further research is needed to continue advancing the science of organisational learning in hospitals.
Artificial Intelligence-Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists.

CONTEXT. — Nodal metastasis of a primary tumor influences therapy decisions for a variety of cancers. Histologic identification of tumor cells in lymph nodes can be laborious and error-prone, especially for small tumor foci. OBJECTIVE. — To evaluate the application and clinical implementation of a state-of-the-art deep learning-based artificial intelligence algorithm (LYmph Node Assistant or LYNA) for detection of metastatic breast cancer in sentinel lymph node biopsies. DESIGN. — Whole slide images were obtained from hematoxylin-eosin-stained lymph nodes from 399 patients (publicly available Camelyon16 challenge dataset). LYNA was developed by using 270 slides and evaluated on the remaining 129 slides. We compared the findings to those obtained from an independent laboratory (108 slides from 20 patients/86 blocks) using a different scanner to measure reproducibility. RESULTS. — LYNA achieved a slide-level area under the receiver operating characteristic (AUC) of 99% and a tumor-level sensitivity of 91% at 1 false positive per patient on the Camelyon16 evaluation dataset. We also identified 2 "normal" slides that contained micrometastases. When applied to our second dataset, LYNA achieved an AUC of 99.6%. LYNA was not affected by common histology artifacts such as overfixation, poor staining, and air bubbles. CONCLUSIONS. — Artificial intelligence algorithms can exhaustively evaluate every tissue patch on a slide, achieving higher tumor-level sensitivity than, and comparable slide-level performance to, pathologists. These techniques may improve the pathologist's productivity and reduce the number of false negatives associated with morphologic detection of tumor cells. We provide a framework to aid practicing pathologists in assessing such algorithms for adoption into their workflow (akin to how a pathologist assesses immunohistochemistry results).
Billing Models for Public Displays in Smart Cities

Pervasive applications support people in various situations. In smart cities additional infrastructure, like public displays, extends the functionality of pervasive applications. Even though, public display technology is available and is also adopted by users, they are not present in smart cities with few exceptions. We identified the lack of financial incentives and cost transparencies as the main issues to deploy displays in public places. In this paper, we present smart city displays, our vision to launch interactive public displays on a large scale. We discuss influence factors and derive requirements for our system design. Further, we develop a comprehensive billing model, which consists of multiple strategies and identify hybrid billing solutions for complex scenarios.
Bioenergetic Mechanisms of Seizure Control

Epilepsy is characterized by the regular occurrence of seizures, which follow a stereotypical sequence of alterations in the electroencephalogram. Seizures are typically a self limiting phenomenon, concluding finally in the cessation of hypersynchronous activity and followed by a state of decreased neuronal excitability which might underlie the cognitive and psychological symptoms the patients experience in the wake of seizures. Many efforts have been devoted to understand how seizures spontaneously stop in hope to exploit this knowledge in anticonvulsant or neuroprotective therapies. Besides the alterations in ion-channels, transmitters and neuromodulators, the successive build up of disturbances in energy metabolism have been suggested as a mechanism for seizure termination. Energy metabolism and substrate supply of the brain are tightly regulated by different mechanisms called neurometabolic and neurovascular coupling. Here we summarize the current knowledge whether these mechanisms are sufficient to cover the energy demand of hypersynchronous activity and whether a mismatch between energy need and supply could contribute to seizure control.
If It Wasn’t Rape, Was It Sexual Assault? Comparing Rape and Sexual Assault Acknowledgment in College Women Who Have Experienced Rape

The present study investigated (a) comparisons in rates of rape and sexual assault acknowledgment and (b) a comprehensive multivariate multinomial logistic model predicting rape and sexual assault acknowledgment in a sample of 174 college women who had experienced rape. Significantly more women acknowledged having experienced sexual assault than rape. Greater perceived perpetrator force was associated with increased likelihood of rape and sexual assault acknowledgment. Increased age and greater perceived emotional impact were associated with increased odds of rape acknowledgment. Implications for policy, education, and practice within university settings are discussed.
A Handheld Master Device for 3D Remote Micro-Manipulation

Micro-assembly has always been a substantial issue for automation: micro-objects are difficult to grasp due to micro-world physical laws and a lack of adapted sensors. Therefore, many tasks are teleoperated using a nonintuitive device as joystick or button. This paper proposes a fresh remote handling solution to fill that need. A new 1-DOF master device which mimics a tweezers is brought to the fore: it is an instrumented haptic tweezers, handheld, allowing a high intuitiveness for the user. This master device, coupled with a tracking system, controls a micro-positioner and a micro-gripper. Different coupling strategies using position or speed variables are demonstrated.
Integral-based event-triggered control systems with uniform quantization

This paper investigates the stability of linear event-triggered control systems subject to uniform quantization and observer-based output feedback. An integral-based triggering condition is introduced to schedule the transmissions of signals from observer node to controller node. By constructions, only the quantized output signal and the observer state are employed in the designed triggering conditions. It is proved that the system is input-to-state stable with respect to the quantization error and can achieve the ultimately bounded stability for any fixed uniform quantizers. Moreover, a positive estimate of the minimum inter-event time is provided to exclude Zeno behavior. Finally, a numerical example is provided to illustrate the efficiency and feasibility of the obtained results.
Road Extraction from High-Resolution Remote Sensing Imagery Using Deep Learning

The road network plays an important role in the modern traffic system; as development occurs, the road structure changes frequently. Owing to the advancements in the field of high-resolution remote sensing, and the success of semantic segmentation success using deep learning in computer version, extracting the road network from high-resolution remote sensing imagery is becoming increasingly popular, and has become a new tool to update the geospatial database. Considering that the training dataset of the deep convolutional neural network will be clipped to a fixed size, which lead to the roads run through each sample, and that different kinds of road types have different widths, this work provides a segmentation model that was designed based on densely connected convolutional networks (DenseNet) and introduces the local and global attention units. The aim of this work is to propose a novel road extraction method that can efficiently extract the road network from remote sensing imagery with local and global information. A dataset from Google Earth was used to validate the method, and experiments showed that the proposed deep convolutional neural network can extract the road network accurately and effectively. This method also achieves a harmonic mean of precision and recall higher than other machine learning and deep learning methods.
New Results on Deterministic Learning of Sampled-Data Nonlinear Systems

In this paper, our main concern is to establish new exponential stability-based identification results for a class of Euler nonlinear sampled-data systems using deterministic learning. At first, a new deterministic learning law is designed based on the Lyapunov function method. Rigorous analysis is provided to show that the resulting closed-loop linear time-varying (LTV) systems (containing tracking errors and parameter estimation errors) is exponentially stable. All the states of the closed-loop system converge to a small neighborhood around the origin exponentially. Thus, locally-accurate identification performance can be achieved under the new deterministic learning algorithm. Finally, simulation results on Duffing oscillator system are given to show the effectiveness of the proposed method.
Do women’s perspectives of quality of care during childbirth match with those of providers? A qualitative study in Uttar Pradesh, India

ABSTRACT Background: Persistently high maternal mortality levels are a concern in developing countries. In India, monetary incentive schemes have increased institutional delivery rates appreciably, but have not been equally successful in reducing maternal mortality. Maternal outcomes are affected by quality of obstetric care and socio-cultural norms. In this light there is need to examine the quality of care provided to women delivering in institutions. Objective: This study aimed to examine pregnant women’s expectations of high-quality care in public health facilities in Uttar Pradesh, India, and to contrast this with provider’s perceptions of the same, as well as the barriers that limit their ability to provide high-quality care. Methods: A qualitative descriptive analysis was conducted on data from two studies – focus group discussions with rural women in their last trimester of pregnancy (conducted in 2014) to understand women’s experience and satisfaction with maternal care services, and in-depth interviews with care providers (conducted in 2016–17) to understand provision of person-centred care. Provider perspectives were matched with themes of women’s perspectives on quality of childbirth care in facilities. Results: Major themes of care prioritised by women included availability of doctors at the facility; availability of medicines; food; ambulance services; maintenance of cleanliness and hygiene; privacy; good and safe delivery with no complications; client-provider interaction; financial cost of care. Many women also voiced no expectation of care, indicating disillusionment from the existing system. Providers concurred with women on all themes of care except availability of doctors, as they felt that trained nurses were proficient in conducting deliveries. Conclusions: This study shows that women have clear expectations of quality care from facilities where they go to deliver. Understanding their expectations and matching them with providers’ perspectives of care is critical for efforts to improve the quality of care and thereby impact maternal outcomes.
Just-in-time Customer Churn Prediction: With and Without Data Transformation

Telecom companies are facing a serious problem of customer churn due to exponential growth in the use of telecommunication based services and the fierce competition in the market. Customer churns are the customers who decide to quit or switch use of the service or even company and join another competitor. This problem can affect the revenues and reputation of the telecom company in the business market. Therefore, many Customer Churn Prediction (CCP) models have been developed; however these models, mostly study in the context of within company CCP. Therefore, these models are not suitable for a situation where the company is newly established or have recently adopted the use of advanced technology or have lost the historical data relating to the customers. In such scenarios, Just-In-Time (JIT) approach can be a more practical alternative for CCP approach to address this issue in cross-company instead of within company churn prediction. This paper has proposed a JIT approach for CCP. However, JIT approach also needs some historical data to train the classifier. To cover this gap in this study, we built JIT-CCP model using Cross-company concept (i.e., when one company (source) data is used as training set and another company (target) data is considered for testing purpose). To support JIT-CCP, the cross-company data must be carefully transformed before being applied for classification. The objective of this paper is to provide an empirical comparison and effect of with and without state-of-the-art data transformation methods on the proposed JIT-CCP model. We perform experiments on publicly available benchmark datasets and utilize Naive Bayes as an underlying classifier. The results demonstrated that the data transformation methods improve the performance of the JIT-CCP significantly. Moreover, when using well-known data transformation methods, the proposed model outperforms the model learned by using without data transformation methods.
AutoLoss: Learning Discrete Schedules for Alternate Optimization

Many machine learning problems involve iteratively and alternately optimizing different task objectives with respect to different sets of parameters. Appropriately scheduling the optimization of a task objective or a set of parameters is usually crucial to the quality of convergence. In this paper, we present AutoLoss, a meta-learning framework that automatically learns and determines the optimization schedule. AutoLoss provides a generic way to represent and learn the discrete optimization schedule from metadata, allows for a dynamic and data-driven schedule in ML problems that involve alternating updates of different parameters or from different loss objectives. We apply AutoLoss on four ML tasks: d-ary quadratic regression, classification using a multi-layer perceptron (MLP), image generation using GANs, and multi-task neural machine translation (NMT). We show that the AutoLoss controller is able to capture the distribution of better optimization schedules that result in higher quality of convergence on all four tasks. The trained AutoLoss controller is generalizable -- it can guide and improve the learning of a new task model with different specifications, or on different datasets.
Physical-layer encryption in massive MIMO systems with spatial modulation

Massive multiple input and multiple output (MIMO) is a key technology of the fifth generation (5G) wireless communication systems, which brings various advantages, such as high spectral efficiency and energy efficiency. In MIMO system, spatial modulation (SM) has recently emerged as a new transmission method. In this paper, in order to improve the security in SM-MIMO, a physical layer encryption approach named chaotic antenna-index three-dimensional modulation and constellation points rotated (CATMCPR) encryption scheme is proposed, which utilizes the chaotic theory and spatial modulation techniques. The conventional physical-layer encryption in SM-MIMO suffers from spectral efficiency (SE) performance degradation and usually needs a preshared key, prior channel state information (CSI) or excess jamming power. By contrast, we show that the CATMCPR scheme can not only achieve securely communication but also improve above drawbacks. We evaluate the performances of the proposed scheme by an analysis and computer simulations.
Ego-Surfing : Person Localization in First-Person Videos Using Ego-Motion Signatures

We envision a future time when wearable cameras are worn by the masses and recording first-person point-of-view videos of everyday life. While these cameras can enable new assistive technologies and novel research challenges, they also raise serious privacy concerns. For example, first-person videos passively recorded by wearable cameras will necessarily include anyone who comes into the view of a camera—with or without consent. Motivated by these benefits and risks, we developed a self-search technique tailored to first-person videos. The key observation of our work is that the egocentric head motion of a target person (i.e., the self) is observed both in the point-of-view video of the target and observer. The motion correlation between the target person’s video and the observer’s video can then be used to identify instances of the self uniquely. We incorporate this feature into the proposed approach that computes the motion correlation over densely-sampled trajectories to search for a target individual in observer videos. Our approach significantly improves self-search performance over several well-known face detectors and recognizers. Furthermore, we show how our approach can enable several practical applications such as privacy filtering, target video retrieval, and social group clustering.