Miracle-dz/UniFAR-FTUs huggingface dataset:
Microbiological analysis of contact lens cases: impact of the hospital environment.

PURPOSE The aim of this study was to analyze the bacterial and fungal microbiota found in contact lens cases among two groups of patients to correlate the data on the habits of contact lens users and to evaluate whether there is a difference in the culture results between users of ridged and nonridged contact lens cases. METHODS Two groups (35 patients per group) were included, consisting of hospital workers and those who had not visited a hospital in the past 30 days, and a questionnaire regarding epidemiological data and habits related to contact lens and lens case use was administered. In addition, 140 samples collected from the right and left compartments of each lens case by swabbing the bottom of the wells were tested using bacterioscopy as well as fungal and bacterial cultures via computerized identification of the species. RESULTS No fungal growth was identified in any of the 70 contact lens cases; however, bacteria were found in 39 cases, and there was no statistical difference between the groups. Most bacteria (>85%) were gram-negative bacilli. Contamination inone compartment of the contact lens case elevated the risk of contamination of the other side (>80%). Moreover, contamination was statistically higher in the ridged cases than in nonridged cases (p=0.0149). CONCLUSION The types of bacteria contaminating the cases are generally not seen in eye diseases associated with contact lens use, suggesting that other decisive variables are involved in eye infection from a contaminated lens or case. Fungal contamination of contact lens cases appears to be an exception. Ridged cases are commonly used by contact lens wearers and present a potential risk to eye health. In addition, the results of bacterial tests between hospital workers and those who did not visit a hospital were not significantly different.
Security and Privacy in Smart Cities: Challenges and Opportunities

Smart cities are expected to improve the quality of daily life, promote sustainable development, and improve the functionality of urban systems. Now that many smart systems have been implemented, security and privacy issues have become a major challenge that requires effective countermeasures. However, traditional cybersecurity protection strategies cannot be applied directly to these intelligent applications because of the heterogeneity, scalability, and dynamic characteristics of smart cities. Furthermore, it is necessary to be aware of security and privacy threats when designing and implementing new mechanisms or systems. Motivated by these factors, we survey the current situations of smart cities with respect to security and privacy to provide an overview of both the academic and industrial fields and to pave the way for further exploration. Specifically, this survey begins with an overview of smart cities to provide an integrated context for readers. Then, we discuss the privacy and security issues in current smart applications along with the corresponding requirements for building a stable and secure smart city. In the next step, we summarize the existing protection technologies. Finally, we present open research challenges and identify some future research directions.
"I still like Google": University student perceptions of searching OPACs and the web

This paper reports on an exploratory study of how university students perceive and interact with Web search engines compared to Web-based OPACs. A qualitative study was conducted involving sixteen students, eight of whom were first-year undergraduates and eight of whom were graduate students in Library and Information Science. The participants performed searches on Google and on a university OPAC. The interviews and think-afters revealed that while students were aware of the problems inherent in Web searching and of the many ways in which OPACS are more organized, they generally preferred Web searching. The coding of the data suggests that the reason for this preference lies in psychological factors associated with the comparative ease with which search engines can be used, and system and interface factors which made searching the Web much easier and less confusing. As a result of these factors, students were able to approach even the drawbacks of the Web—its clutter of irrelevant pages and the dubious authority of the results—in an enthusiastic and proactive manner, very different from the passive and ineffectual admiration they expressed for the OPAC. The findings suggest that requirements of good OPAC interface design must be aggressively redefined in the face of new, Web-based standards of usability.
The Wisdom of MaSSeS: Majority, Subjectivity, and Semantic Similarity in the Evaluation of VQA

We introduce MASSES, a simple evaluation metric for the task of Visual Question Answering (VQA). In its standard form, the VQA task is operationalized as follows: Given an image and an open-ended question in natural language, systems are required to provide a suitable answer. Currently, model performance is evaluated by means of a somehow simplistic metric: If the predicted answer is chosen by at least 3 human annotators out of 10, then it is 100% correct. Though intuitively valuable, this metric has some important limitations. First, it ignores whether the predicted answer is the one selected by the Majority (MA) of annotators. Second, it does not account for the quantitative Subjectivity (S) of the answers in the sample (and dataset). Third, information about the Semantic Similarity (SES) of the responses is completely neglected. Based on such limitations, we propose a multi-component metric that accounts for all these issues. We show that our metric is effective in providing a more fine-grained evaluation both on the quantitative and qualitative level.
The Anatomy of a Neural Network

It is true there have been great improvements with the effectiveness of utilizing Neural Networks. However, these improvements are, for the most part, relegated to improved clock speeds, leveraging increase in memory, and GPU enabled parallelization of up-front processing. However, what has been seemingly forgotten over the last twenty or so years is the understanding of how the internal layers are reacting with respect to convergence in training, and information transformation across layers during test, which in turn may account for a common perception that the internal neural layers are opaque black boxes. This paper will show in two parts that in fact, this is not true. Part one will demonstrate, through matrix visualization, the feed-forward processing throughout a multi-layer convolutional neural network. Part 2 will discuss our unique derivative application of Kohonen's and Kosko's correlation matrix memory methods to the consecutive pairs of layers within the network in order to form stabilized and compressible associative memory matrices. The subtlety of Part 2 is that our stabilized matrices can be simply multiplied together, thus forming a single layer, and therefore realizing The Universal Approximation Theorem of Cybenko and Hornik. In effect, the anatomy of the neural network will reveal how to open up the black box and take advantage of its inner workings.
Weakly Supervised Learning of Metric Aggregations for Deformable Image Registration

Deformable registration has been one of the pillars of biomedical image computing. Conventional approaches refer to the definition of a similarity criterion that, once endowed with a deformation model and a smoothness constraint, determines the optimal transformation to align two given images. The definition of this metric function is among the most critical aspects of the registration process. We argue that incorporating semantic information (in the form of anatomical segmentation maps) into the registration process will further improve the accuracy of the results. In this paper, we propose a novel weakly supervised approach to learn domain-specific aggregations of conventional metrics using anatomical segmentations. This combination is learned using latent structured support vector machines. The learned matching criterion is integrated within a metric-free optimization framework based on graphical models, resulting in a multi-metric algorithm endowed with a spatially varying similarity metric function conditioned on the anatomical structures. We provide extensive evaluation on three different datasets of CT and MRI images, showing that learned multi-metric registration outperforms single-metric approaches based on conventional similarity measures.
How Does a Robot Develop Its Reaching Ability Like Human Infants Do?

In this paper, we address the issue robots developing a reaching ability, as human infants do. Specifically, a novel infant-inspired framework is proposed based on recent findings that the emergence of reaching is the product of a deeply embodied process. The methodology behind our framework is to develop proprioception, fixation, and internal models (including both the forward and inverse models) in three sequential phases, with the whole developing process being driven by self-produced motor babbling. In the first phase, an autoencoder-based proprioception model is proposed. In the second phase, a simplified strategy for imitating the function of fixation is developed. In the third phase, a new forward model and two new inverse models are further proposed. We evaluated our framework and associated models with the PKU-HR6.0II physical robot and two simulated robots. Experiments confirm that the PKU-HR6.0II could successfully develop its reaching ability in a manner similar to that of human infants through our proposed framework. Better performance and adaptability are also demonstrated in comparison to existing benchmarks. It is also shown that the proposed framework has the potential to achieve other manipulation abilities such as grasping and placing.
Universal Approximation Capability of Broad Learning System and Its Structural Variations

After a very fast and efficient discriminative broad learning system (BLS) that takes advantage of flatted structure and incremental learning has been developed, here, a mathematical proof of the universal approximation property of BLS is provided. In addition, the framework of several BLS variants with their mathematical modeling is given. The variations include cascade, recurrent, and broad–deep combination structures. From the experimental results, the BLS and its variations outperform several exist learning algorithms on regression performance over function approximation, time series prediction, and face recognition databases. In addition, experiments on the extremely challenging data set, such as MS-Celeb-1M, are given. Compared with other convolutional networks, the effectiveness and efficiency of the variants of BLS are demonstrated.
The influence of cultural competence of nurses on patient satisfaction and the mediating effect of patient trust ASP PDF

AIMS To test a path model evaluating the influence of cultural competence of nurses on patient satisfaction and the mediating role of patient trust. BACKGROUND In China, there has been an increase in medical disputes, which at times has resulted in physical assaults on healthcare providers. Enhanced patient satisfaction may reduce disputes and can perhaps be bolstered by the improved cultural competence of providers. DESIGN A cross-sectional path analytic research design was used. METHODS A random sample of 583 hospitalized patients recruited from six tertiary comprehensive hospitals in Changsha, China in 2016 completed a face-to-face structured questionnaire. Measures included the Consumer Assessment of Healthcare Providers and Systems Cultural Competency Item Set, the Wake Forest Physician Trust Scale, the Patient Satisfaction with Primary Nurses Scale and the Patient Overall Satisfaction with the Hospital Scale. RESULTS Significant associations among cultural competence of nurses, patient satisfaction and patient trust were confirmed, and path analyses supported the proposed mediating role of patient trust. Furthermore, the cultural competence subscales of communication-positive behaviours, trust-building behaviours and shared decision-making were positively and significantly correlated with patient satisfaction. CONCLUSION Findings suggest interventions should be designed to improve nurse's cultural competence, especially in the domains of communication-positive behaviours, trust-building behaviours and shared decision-making, and trust, to improve patient satisfaction.
Lorikeet: A Model-Driven Engineering Tool for Blockchain-Based Business Process Execution and Asset Management

Blockchain has attracted broad interest as a distributed ledger technology for building the next generation of applications to address lack-of-trust issues in business. Business processes that manage assets (e.g. transferring car/grain/land titles) are a promising domain for applying blockchain technology: secure asset management (including tokens and crypto-currency) is a major application area of blockchain. Solutions for non-fungible assets (like cars and houses, but also supply chain goods) are recurrently addressed case by case and traditionally rely on a centralised trusted authority. This can cause trust issues and introduce inefficiencies or counterparty risks. However, developing blockchain applications is far from easy, and mistakes may not be fixable. Thus, in this paper, we present a model-driven engineering (MDE) tool named Lorikeet for the implementation of business processes on blockchain, which can manage assets. Lorikeet can automatically create well-tested smart contract code from specifications that are encoded in the business process and data registry models based on the implemented model transformations. We demonstrate the tool with an industrial use case.
Glycosaminoglycan-Protein Interactions by Nuclear Magnetic Resonance (NMR) Spectroscopy

Nuclear magnetic resonance (NMR) spectroscopy is one of the most utilized and informative analytical techniques for investigating glycosaminoglycan (GAG)-protein complexes. NMR methods that are commonly applied to GAG-protein systems include chemical shift perturbation, saturation transfer difference, and transferred nuclear Overhauser effect. Although these NMR methods have revealed valuable insight into the protein-GAG complexes, elucidating high-resolution structural and dynamic information of these often transient interactions remains challenging. In addition, preparation of structurally homogeneous and isotopically enriched GAG ligands for structural investigations continues to be laborious. As a result, understanding of the structure-activity relationship of GAGs is still primitive. To overcome these deficiencies, several innovative NMR techniques have been developed lately. Here, we review some of the commonly used techniques along with more novel methods such as waterLOGSY and experiments to examine structure and dynamic of lysine and arginine side chains to identify GAG-binding sites. We will also present the latest technology that is used to produce isotopically enriched as well as paramagnetically tagged GAG ligands. Recent results that were obtained from solid-state NMR of amyloid’s interaction with GAG are also presented together with a brief discussion on computer assisted modeling of GAG-protein complexes using sparse experimental data.
A Shifted Delta Coefficient Objective for Monaural Speech Separation Using Multi-task Learning

This paper addresses the problem of monaural speech separation for simultaneous speakers. Recent studies such as uPIT, cuPIT-Grid LSTM and their variants have advanced the state-of-the-art separation models. Delta and acceleration coefﬁcients are typically used in the objective function to capture short time dynamics. We consider that such coefﬁcients don’t beneﬁt from the temporal information over a long range such as phoneme and syllable. In this paper, we propose a shifted delta coefﬁ-cient (SDC) objective to explore the temporal information over a long range of the spectral dynamics. The SDC ensures the temporal continuity of output frames within the same speaker. In addition, we propose a novel multi-task learning framework, that we call SDC-MTL, by extending the SDC objective with a subtask of predicting the time-frequency labels ( { silence, single, overlapped } ) of the mixture. The experimental results show 11.7% and 3.9% relative improvements on WSJ0-2mix dataset under open conditions over the uPIT and cuPIT-Grid LSTM baselines. A further analysis shows 17.8% and 6.2% relative improvements with speakers of same gender.
Management of prolonged second stage of labor

Second stage of labour has been often neglected leading to increased maternal and perinatal morbidity. Recognising and managing prolonged second stage is a challenge even to an experienced obstetrician. This article reviews the changes in the definition of prolonged second stage over the years. It discusses the causes, recognition and management options along with the difficulties in delivering a patient with prolonged second stage of labour. Though adding an extra hour to the definition of second stage in patients may reduce the rate of caesarean section but some studies also show a rise in maternal and perinatal morbidity. Obstetricians are familiarized to the different techniques of delivery of the impacted head. Once diagnosis of prolonged second stage is confirmed, causes should be identified and addressed, and treatment should be individualised, and timing and mode of intervention planned.
NKG2C Natural Killer Cells in Bronchoalveolar Lavage Are Associated With Cytomegalovirus Viremia and Poor Outcomes in Lung Allograft Recipients

Background. Cytomegalovirus (CMV) infection is a risk factor for chronic lung allograft dysfunction (CLAD), which limits survival in lung allograft recipients. Natural killer (NK) cells that express the NKG2C receptor mediate CMV-specific immune responses. We hypothesized that NKG2C+ NK cells responding to CMV in the lung allograft would reduce CMV-related inflammation and would improve CLAD-free survival. Methods. We prospectively followed 130 subjects who underwent lung transplantation from 2012 to 2016. Bronchoalveolar lavage (BAL) NK cells were immunophenotyped for NKG2C, maturation, and proliferation markers. CMV viral load, serologies, serial spirometry, and mortality were recorded from medical records. Natural killer cell subset association with CMV endpoints were made using generalized estimating equation-adjusted linear models. BAL NKG2C+ NK cell association with CLAD-free survival was assessed by Cox proportional hazards modeling. Results. NKG2C+ NK cells were more mature and proliferative than NKG2C− NK cells and represented a median of 7.8% of BAL NK cells. The NKG2C+ NK cell proportion increased prior to the first detection of viremia and was nearly tripled in subjects with high level viremia (>1000 copies/mL) compared with no detected viremia. Subjects with increased BAL NKG2C+ NK cells, relative to the median, had a significantly increased risk for CLAD or death (hazard ratio, 4.2; 95% confidence interval, 1.2–13.3). Conclusions. The BAL NKG2C+ NK cell proportion may be a relevant biomarker for assessing risk of CMV viremia and quantifying potential CMV-related graft injury that can lead to CLAD or death.
Non-Orthogonal Multiple Access With Alamouti Space–Time Block Coding

In this letter, we investigate the combination of non-orthogonal multiple access (NOMA) with the conventional Alamouti space–time lock coding scheme over Nakagami-m fading environments. In the network, one base station serves multiple users simultaneously by using Alamouti scheme. The exact outage probability (OP) expression is obtained in closed-form. Moreover, asymptotic analysis is also conducted to provide more insights into the OP through diversity order and array gain. The theoretical results verified by the Monte Carlo simulations show that Alamouti scheme can double the diversity order of NOMA network, which also depends on Nakagami channel parameter m and user’s ordinal number.
Genetic analysis of a major international collection of cultivated apple varieties reveals previously unknown historic heteroploid and inbred relationships

Domesticated apple (Malus x domestica Borkh.) is a major global crop and the genetic diversity held within the pool of cultivated varieties is important for the development of future cultivars. The aim of this study was to investigate the diversity held within the domesticated form, through the analysis of a major international germplasm collection of cultivated varieties, the UK National Fruit Collection, consisting of over 2,000 selections of named cultivars and seedling varieties. We utilised Diversity Array Technology (DArT) markers to assess the genetic diversity within the collection. Clustering attempts, using the software STRUCTURE revealed that the accessions formed a complex and historically admixed group for which clear clustering was challenging. Comparison of accessions using the Jaccard similarity coefficient allowed us to identify clonal and duplicate material as well as revealing pairs and groups that appeared more closely related than a standard parent-offspring or full-sibling relations. From further investigation, we were able to propose a number of new pedigrees, which revealed that some historically important cultivars were more closely related than previously documented and that some of them were partially inbred. We were also able to elucidate a number of parent-offspring relationships that had resulted in a number of important polyploid cultivars. This included reuniting polyploid cultivars that in some cases dated as far back as the 18th century, with diploid parents that potentially date back as far as the 13th century.
Continual Trials Spontaneous Recognition Tasks in Mice: Reducing Animal Numbers and Improving Our Understanding of the Mechanisms Underlying Memory

Spontaneous recognition tasks are widely used as a laboratory measure of memory in animals but give rise to high levels of behavioral noise leading to a lack of reliability. Previous work has shown that a modification of the procedure to allow continual trials testing (in which many trials are run concurrently in a single session) decreases behavioral noise and thus significantly reduces the numbers of rats required to retain statistical power. Here, we demonstrate for the first time that this improved method of testing extends to mice, increasing the overall power of the approach. Moreover, our results show that the new continual trials approach provides the additional benefits of heightened sensitivity and thus provides greater insight into the mechanisms at play. Standard (c57) and transgenic Alzheimer model (TASTPM) mice were tested both at 7 and 10 months of age in both object recognition (OR) and object-location (OL) spontaneous recognition tasks using the continual trials methodology. Both c57 and TASTPM mice showed age-dependent changes in performance in OR. While c57 mice also showed age-related changes in performance of OL, TASTPM mice were unable to perform OL at either age. Significantly, we demonstrate that differences in OL performance in c57s and TASTPM animals is a result of proactive interference rather than an absolute inability to recognize OL combinations. We argue that these continual trials approaches provide overall improved reliability and better interpretation of the memory ability of mice, as well as providing a significant reduction in overall animal use.