Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk of community-acquired pneumonia and use of gastric acid-suppressive drugs.

CONTEXT Reduction of gastric acid secretion by acid-suppressive therapy allows pathogen colonization from the upper gastrointestinal tract. The bacteria and viruses in the contaminated stomach have been identified as species from the oral cavity. OBJECTIVE To examine the association between the use of acid-suppressive drugs and occurrence of community-acquired pneumonia. DESIGN, SETTING, AND PARTICIPANTS Incident acid-suppressive drug users with at least 1 year of valid database history were identified from the Integrated Primary Care Information database between January 1, 1995, and December 31, 2002. Incidence rates for pneumonia were calculated for unexposed and exposed individuals. To reduce confounding by indication, a case-control analysis was conducted nested in a cohort of incident users of acid-suppressive drugs. Cases were all individuals with incident pneumonia during or after stopping use of acid-suppressive drugs. Up to 10 controls were matched to each case for practice, year of birth, sex, and index date. Conditional logistic regression was used to compare the risk of community-acquired pneumonia between use of proton pump inhibitors (PPIs) and H2-receptor antagonists. MAIN OUTCOME MEASURE Community-acquired pneumonia defined as certain (proven by radiography or sputum culture) or probable (clinical symptoms consistent with pneumonia). RESULTS The study population comprised 364,683 individuals who developed 5551 first occurrences of pneumonia during follow-up. The incidence rates of pneumonia in non-acid-suppressive drug users and acid-suppressive drug users were 0.6 and 2.45 per 100 person-years, respectively. The adjusted relative risk for pneumonia among persons currently using PPIs compared with those who stopped using PPIs was 1.89 (95% confidence interval, 1.36-2.62). Current users of H2-receptor antagonists had a 1.63-fold increased risk of pneumonia (95% confidence interval, 1.07-2.48) compared with those who stopped use. For current PPI users, a significant positive dose-response relationship was observed. For H2-receptor antagonist users, the variation in dose was restricted. CONCLUSION Current use of gastric acid-suppressive therapy was associated with an increased risk of community-acquired pneumonia.
The role of the human globus pallidus in Huntington's disease

Huntington's disease (HD) is characterized by pronounced pathology of the basal ganglia, with numerous studies documenting the pattern of striatal neurodegeneration in the human brain. However, a principle target of striatal outflow, the globus pallidus (GP), has received limited attention in comparison, despite being a core component of the basal ganglia. The external segment (GPe) is a major output of the dorsal striatum, connecting widely to other basal ganglia nuclei via the indirect motor pathway. The internal segment (GPi) is a final output station of both the direct and indirect motor pathways of the basal ganglia. The ventral pallidum (VP), in contrast, is a primary output of the limbic ventral striatum. Currently, there is a lack of consensus in the literature regarding the extent of GPe and GPi neurodegeneration in HD, with a conflict between pallidal neurons being preserved, and pallidal neurons being lost. In addition, no current evidence considers the fate of the VP in HD, despite it being a key structure involved in reward and motivation. Understanding the involvement of these structures in HD will help to determine their involvement in basal ganglia pathway dysfunction in the disease. A clear understanding of the impact of striatal projection loss on the main neurons that receive striatal input, the pallidal neurons, will aid in the understanding of disease pathogenesis. In addition, a clearer picture of pallidal involvement in HD may contribute to providing a morphological basis to the considerable variability in the types of motor, behavioral, and cognitive symptoms in HD. This review aims to highlight the importance of the globus pallidus, a critical component of the cortical‐basal ganglia circuits, and its role in the pathogenesis of HD. This review also summarizes the current literature relating to human studies of the globus pallidus in HD.
Raman Imaging: Defining the Spatial Resolution of the Technology

Raman images constructed from hyperspectral cubes recorded under well-defined confocal conditions show high-quality chemical contrast. Careful studies are required to determine the optimum measurement conditions and limiting spatial resolution of the technique. Calculations and measurements of the laser spot size for the high numerical aperture (n.a.) objectives indicate that spatial resolution of a wavelength (< 0.5 mm) is achievable under good conditions. What does that mean in terms of the quality of a Raman image?
Backstepping sliding mode control with functional tuning based on an instantaneous power approach applied to an underwater vehicle

ABSTRACT In this paper, we present a modified backstepping sliding mode control to deal with Euler–Lagrange systems. The controller is applied in an underwater vehicle in order to show the effectiveness of the approach proposed. Instantaneous power data provided by the propulsion system are used to tune the controller in order to guarantee robust performance and energy saving. Thanks to the combination of an internal Proportional Integral and Derivative (PID) controller, it is possible implement high gains to deal with the influence of disturbances and uncertainties. A comparative study among this backstepping sliding mode controller and standard sliding mode controls is presented.
Comparing macroecological patterns across continents: evolution of climatic niche breadth in varanid lizards

Macroecological analyses often test hypotheses at the global scale, or among more closely related species in a single region (e.g. continent). Here, we test several hypotheses about climatic niche widths among relatively closely related species that occur across multiple continents, and compare patterns within and across continents to see if they differ. We focus on the lizard genus Varanus (monitor lizards), which occurs in diverse environments in Africa, Asia, and Australia. We address three main questions. 1) How do climatic niche breadths of species on a given niche axis change based on the position of species along that niche axis? (E.g. are species that occur in more extreme environments more narrowly specialized for those conditions? ) 2) Are there trade-offs in niche breadths on temperature and precipitation axes among species, or are niche widths on different axes positively related? 3) Is variation in niche breadths among species explained primarily by within-locality seasonal variation, or by differences in climatic conditions among localities across the species range? We generate a new time-calibrated phylogeny for Varanus and test these hypotheses within and between continents using climatic data and phylogenetic methods. Our results show that patterns on each continent often parallel each other and global patterns. However, in many other cases, the strength of relationships can change dramatically among closely related species on different continents. Overall, we found that: 1) species in warmer environments have narrower temperature niche breadths, but there is no relationship between precipitation niche breadth and niche position; 2) temperature and precipitation niche breadths tend to be positively related among species, rather than showing trade-offs; and 3) within-locality seasonal variation explains most variation in climatic niche breadths. Some of these results are concordant with previous studies (in amphibians and North American lizards), and might represent general macroecological patterns.
The Cleft Care UK study. Part 4: perceptual speech outcomes

Structured Abstract Objectives To describe the perceptual speech outcomes from the Cleft Care UK (CCUK) study and compare them to the 1998 Clinical Standards Advisory Group (CSAG) audit. Setting and sample population A cross-sectional study of 248 children born with complete unilateral cleft lip and palate, between 1 April 2005 and 31 March 2007 who underwent speech assessment. Materials and methods Centre-based specialist speech and language therapists (SLT) took speech audio–video recordings according to nationally agreed guidelines. Two independent listeners undertook the perceptual analysis using the CAPS-A Audit tool. Intra- and inter-rater reliability were tested. Results For each speech parameter of intelligibility/distinctiveness, hypernasality, palatal/palatalization, backed to velar/uvular, glottal, weak and nasalized consonants, and nasal realizations, there was strong evidence that speech outcomes were better in the CCUK children compared to CSAG children. The parameters which did not show improvement were nasal emission, nasal turbulence, hyponasality and lateral/lateralization. Conclusion These results suggest that centralization of cleft care into high volume centres has resulted in improvements in UK speech outcomes in five-year-olds with unilateral cleft lip and palate. This may be associated with the development of a specialized workforce. Nevertheless, there still remains a group of children with significant difficulties at school entry.
Periodic silicon nanocones arrays with controllable dimensions prepared by two-step etching using nanosphere lithography and NH 4 OH/H 2 O 2 solution

An electroless chemical etching technique using polystyrene nanospheres as self-assembled mask is developed to fabricate size-controllable, periodic silicon nanopillars (NPs) and subsequent nanocones (NCs) arrays. The Si NCs are obtained based on the NPs structure using cost-effective ammonia-related etching chemistry. The diameter, height, and periodicity of the NCs can be systematically controlled. This method is potentially beneficial to many device applications including super-capacitors, batteries, solar cell, etc.
On Higher-Order Fourier Analysis over Non-Prime Fields

The celebrated Weil bound for character sums says that for any low-degree polynomial P and any additive character chi, either chi(P) is a constant function or it is distributed close to uniform. The goal of higher-order Fourier analysis is to understand the connection between the algebraic and analytic properties of polynomials (and functions, generally) at a more detailed level. For instance, what is the tradeoff between the equidistribution of chi(P) and its "structure"? Previously, most of the work in this area was over fields of prime order. We extend the tools of higher-order Fourier analysis to analyze functions over general finite fields. Let K be a field extension of a prime finite field F_p. Our technical results are: 1. If P: K^n -> K is a polynomial of degree |K|^{-s} for some s > 0 and non-trivial additive character chi, then P is a function of O_{d, s}(1) many non-classical polynomials of weight degree < d. The definition of non-classical polynomials over non-prime fields is one of the contributions of this work. 2. Suppose K and F are of bounded order, and let H be an affine subspace of K^n. Then, if P: K^n -> K is a polynomial of degree d that is sufficiently regular, then (P(x): x in H) is distributed almost as uniformly as possible subject to constraints imposed by the degree of P. Such a theorem was previously known for H an affine subspace over a prime field. The tools of higher-order Fourier analysis have found use in different areas of computer science, including list decoding, algorithmic decomposition and testing. Using our new results, we revisit some of these areas. (i) For any fixed finite field K, we show that the list decoding radius of the generalized Reed Muller code over K equals the minimum distance of the code. (ii) For any fixed finite field K, we give a polynomial time algorithm to decide whether a given polynomial P: K^n -> K can be decomposed as a particular composition of lesser degree polynomials. (iii) For any fixed finite field K, we prove that all locally characterized affine-invariant properties of functions f: K^n -> K are testable with one-sided error.
A Galerkin discretisation-based identification for parameters in nonlinear mechanical systems

ABSTRACT In the paper, a new parameter identification method is proposed for mechanical systems. Based on the idea of Galerkin finite-element method, the displacement over time history is approximated by piecewise linear functions, and the second-order terms in model equation are eliminated by integrating by parts. In this way, the lost function of integration form is derived. Being different with the existing methods, the lost function actually is a quadratic sum of integration over the whole time history. Then for linear or nonlinear systems, the optimisation of the lost function can be applied with traditional least-squares algorithm or the iterative one, respectively. Such method could be used to effectively identify parameters in linear and arbitrary nonlinear mechanical systems. Simulation results show that even under the condition of sparse data or low sampling frequency, this method could still guarantee high accuracy in identifying linear and nonlinear parameters.
MICROFLUIDIC SUPERPARAMAGNETIC BEAD-BASED MULTIPLEX DETECTION SYSTEM

We introduce the design and fabrication of a microfluidic device for the parallel detection / trapping of different sized magnetic beads on the same chip. Dielectrophoretic and Magnetophoretic forces were used to manipulate beads by sorting and trapping respectively. Dielectrophoretic force-frequency spectra for 1 µm, 2.8 µm and 4.5 μm magnetic beads were deduced using our microfluidic device. Magnetic field force exerted by the system on these beads was also studied. This detection system enables fast separation and analysis of two different magnetic beads from single sample solution.
Dairy products and bone health: how strong is the scientific evidence?

Abstract The relevance of dairy produce for the diminishment of osteoporotic risk is still a matter of scientific debate due to the outcome of a few single observational studies. This review will address the most robust point estimate on the role of dairy products, as reported in systematic reviews and meta-analyses on randomised controlled trials in the case of bone mineralisation or prospective studies in the case of fracture risk. Plain dairy products or those fortified with Ca and/or vitamin D improve total body bone mineral content (BMC) by 45–50 g over 1 year when the daily baseline Ca intake is lower than 750 mg in Caucasians and Chinese girls. In Caucasian and Chinese women, Ca from (fortified) dairy products increases bone mineral density (BMD) by 0·7–1·8 % over 2 years dependent on the site of measurement. Despite the results on BMC, there are currently no studies that have investigated the potential of dairy products to reduce fracture risk in children. In adult Caucasian women, daily intake of 200–250 ml of milk is associated with a reduction in fracture risk of 5 % or higher. In conclusion, the role of dairy products for BMC or BMD has been sufficiently established in Chinese and Caucasian girls and women. In Caucasian women, drinking milk also reduces fracture risk. More research on the role of dairy products within the context of bone health-promoting diets is needed in specific ethnicities, other than Chinese and Caucasians, and in men.
Jeffrey's divergence between complex-valued sinusoidal processes

Like other divergences, Jeffrey's divergence (JD) is used for change detection, for model comparison, etc. Recently, a great deal of interest has been paid to this symmetric version of the Kullback-Leibler (KL) divergence. This led to analytical expressions of the JD between autoregressive (AR) processes, moving-average (MA) processes, either noise-free or disturbed by additive white noises, as well as ARMA processes. In this paper, we propose to study the JD between processes that are defined as sums of complex-valued sinusoidal processes disturbed by additive white noises. We show that the JD tends to a stationary behavior when the number of variates becomes large. The derivative of the JD becomes a constant that depends on the parameters defining the processes. The convergence speed towards this stationary regime depends on the differences between the normalized angular frequencies. The smaller the difference, the slower the convergence. This result can be obtained by interpreting some steps to compute the JD as orthogonal projections. Some examples illustrate the theoretical analysis.
ZIGFI: Harnessing Channel State Information for Cross-Technology Communication

Cross-technology communication (CTC) is a technique that enables direct communication among different wireless technologies. Recent works in this area have made positive progress, but high-throughput CTC from ZigBee to WiFi remains an open problem. In this paper, we propose ZigFi, a novel CTC framework that enables direct communication from ZigBee to WiFi. Without impacting the ongoing WiFi transmissions, ZigFi carefully overlaps ZigBee packets with WiFi packets. Through experiments we show that Channel State Information (CSI) of the overlapped packets can be used to convey data from ZigBee to WiFi. Based on this finding, we propose a receiver-initiated protocol and translate the decoding problem into a problem of CSI classification with Support Vector Machine. We further build a generic model through experiments, which describes the relationship between the Signal to Interference and Noise Ratio (SINR) and the symbol error rate (SER). We implement ZigFi on commercial-off-the-shelf WiFi and ZigBee devices. We evaluate the performance of ZigFi under different experimental settings. The results demonstrate that ZigFi achieves a throughput of 215.9bps, which is 18X faster than the state-of-the-art.
Intestinal Pathogenic Escherichia coli: Insights for Vaccine Development

Diarrheal diseases are one of the major causes of mortality among children under five years old and intestinal pathogenic Escherichia coli (InPEC) plays a role as one of the large causative groups of these infections worldwide. InPECs contribute significantly to the burden of intestinal diseases, which are a critical issue in low- and middle-income countries (Asia, Africa and Latin America). Intestinal pathotypes such as enteropathogenic E. coli (EPEC) and enterotoxigenic E. coli (ETEC) are mainly endemic in developing countries, while ETEC strains are the major cause of diarrhea in travelers to these countries. On the other hand, enterohemorrhagic E. coli (EHEC) are the cause of large outbreaks around the world, mainly affecting developed countries and responsible for not only diarrheal disease but also severe clinical complications like hemorrhagic colitis and hemolytic uremic syndrome (HUS). Overall, the emergence of antibiotic resistant strains, the annual cost increase in the health care system, the high incidence of traveler diarrhea and the increased number of HUS episodes have raised the need for effective preventive treatments. Although the use of antibiotics is still important in treating such infections, non-antibiotic strategies are either a crucial option to limit the increase in antibiotic resistant strains or absolutely necessary for diseases such as those caused by EHEC infections, for which antibiotic therapies are not recommended. Among non-antibiotic therapies, vaccine development is a strategy of choice but, to date, there is no effective licensed vaccine against InPEC infections. For several years, there has been a sustained effort to identify efficacious vaccine candidates able to reduce the burden of diarrheal disease. The aim of this review is to summarize recent milestones and insights in vaccine development against InPECs.
Deep RNN learning for EEG based functional brain state inference

Use of deep learning for sequence dependent task classification has shown considerable improvement in recent past. This paper is on task identification from the associated electroencephalograph (EEG) signal. This paper deals with extraction of EEG sub-band features by using wavelet transform followed through classification by means of recurrent neural network (RNN) and deep learning. The methodology is based on comparison of correlation among various EEG spectral components by using a holdup RNN architecture and winner-takes-all logistic regression approach to identify the associated task. The bottleneck of current EEG processing approaches is signal power getting biased due to noisy trials which deteriorates the analysis in the manifold space. This study uses a similarity or coherence analysis between sub-band spectrum of individual trial which invalidates the effect of noise. To assess the proposed method, a dataset, containing 5 subjects performing four mental tasks, is utilized. The results show that in the multiclass scenario the proposed algorithm performs at par with state-of-art results, whereas in two class scenario, the proposed algorithm outperforms most of the reported results.
Machine Learning Methods to Predict Diabetes Complications

One of the areas where Artificial Intelligence is having more impact is machine learning, which develops algorithms able to learn patterns and decision rules from data. Machine learning algorithms have been embedded into data mining pipelines, which can combine them with classical statistical strategies, to extract knowledge from data. Within the EU-funded MOSAIC project, a data mining pipeline has been used to derive a set of predictive models of type 2 diabetes mellitus (T2DM) complications based on electronic health record data of nearly one thousand patients. Such pipeline comprises clinical center profiling, predictive model targeting, predictive model construction and model validation. After having dealt with missing data by means of random forest (RF) and having applied suitable strategies to handle class imbalance, we have used Logistic Regression with stepwise feature selection to predict the onset of retinopathy, neuropathy, or nephropathy, at different time scenarios, at 3, 5, and 7 years from the first visit at the Hospital Center for Diabetes (not from the diagnosis). Considered variables are gender, age, time from diagnosis, body mass index (BMI), glycated hemoglobin (HbA1c), hypertension, and smoking habit. Final models, tailored in accordance with the complications, provided an accuracy up to 0.838. Different variables were selected for each complication and time scenario, leading to specialized models easy to translate to the clinical practice.
The Dynamic Façade Pattern Grammar

This paper presents a generative grammar producing a language of patterns for the south façade of a prototype sustainable house. The patterns are produced through the activation of the electrochromic material that is applied on the windowpanes of the façade. The class of the performatively effective configurations of the façade is approached as a visual language and the productive (generation), combinatorial (enumeration) and performative (verification) attributes of this language are examined. Random, performance driven, patterns could supply sufficient interior daylight without acknowledging the visual potential of façade pattern generation. The uniqueness of the chosen approach is that the shape grammar encodes the performative constraints pertaining to the generation of façade patterns in a visual manner by associating principles of two-dimensional pattern generation to levels of illuminance.