Miracle-dz/UniFAR-FTUs huggingface dataset:
Spousal order of migration, gender, and hospitalization among immigrants in Denmark

Background: Certain migration contexts that may help clarify immigrants’ health needs are understudied, including the order in which married individuals migrate. Research shows that men, who are healthier than women across most populations, often migrate to a host country before women. Using Danish register data, we investigate descriptive patterns in the order that married men and women arrive in Denmark, as well as whether migration order is related to overnight hospitalizations. Methods: The study base includes married immigrants who lived in Denmark between January 1, 1980 and December 31, 2014 (N = 13,680). We use event history models to examine the influence of spousal migration order on hospitalizations. Results: The order that married individuals arrive in Denmark is indeed highly gendered, with men tending to arrive first, and varies by country of origin. Risk of hospitalization after age 50 does not depend on whether an individual migrated before, after, or at the same time as their spouse among either men or women. However, among those aged 18+, men migrating before their wives are more likely to experience hospitalizations within the first 5 years of arrival. Conclusions: These findings provide the first key insights about gendered migration patterns in Denmark. Although spousal order of migration is not related to overnight hospitalization among women, our findings provide preliminary evidence that men age 18+ who are first to arrive experience more hospitalization events in the following 5 years. Future research should explore additional outcomes and whether other gendered migration contexts are related to immigrants’ health.
Time required to complete transvaginal cervical length in women receiving universal cervical length screening for preterm birth prevention

Abstract Background Preterm birth contributes to over one-third of infant deaths, and although there are several risk factors for preterm birth few tests predict this obstetric complication. Midtrimester transvaginal cervical length (TVCL) screening is currently the best clinical predictor of spontaneous preterm birth and has been suggested to be applied universally. Although several studies have examined the cost-efficacy of universal screening, the time consumption and resultant time management has not been examined. Objective We aim to quantitate the time requirement for transvaginal cervical length (TVCL) screening with the implementation of universal TVCL screening. Study design : This is a retrospective cohort study of women undergoing cervical length screening after implementation of universal Transvaginal Cervical Length (TVCL) screening at a university setting over a 1-year period. The primary outcome was time in minutes for TVCL screening from completion of transabdominal to completion of transvaginal ultrasound. Results This study included 2803 ultrasounds, 1673 of which involved completion of a TVCL after completion of abdominal imaging between 16 0/7 and 23 6/7 weeks of gestation. The mean duration of cervical length screening was 9.8 min (standard deviation [SD], 4.0 min). The time range was less than 1 min to 48 min. For women obtaining cervical lengths, 4.2% required less than 5 additional minutes to perform cervical length screening, 52.1% (872), required less than 10 additional minutes. The majority, 91.8%, of ultrasounds required less than 15 min to complete. Ultrasounds performed in private practice sites required less time compared to those at teaching sites (8.2 ± 3.1 min versus 10.2 ± 4.1 min, p < .001). No significant time improvement occurred after 6 months of universal TVCL screening, with the first 6 months average of 10.1 min versus 9.6 min in the later half, p = . 61. Conclusion Implementation of a universal TVCL program adds an average of 10 min to each ultrasound exam. This additional time does diminish with increasing experience with TVCL, but not to a significant degree.
Do Tangential Finger Forces Utilize Mechanical Advantage During Moment of Force production?

Abstract This study investigated the beneficial effects of the utilization of mechanical advantage (MA) of finger tangential forces during the moment production. Subjects produced the resistive moment of force against the external torque while the moment arms of the tangential forces were systematically changed. We observed a relatively large contribution to the net moment by the tangential forces with the increased moment arms, whereas the vector sum of normal and tangential forces decreased. The indices of multi-finger coordination for the stabilization of the moment of forces and force direction increased with the moment arms. The current results provide evidence that the utilization of MA is associated with both the efficiency of force production and the stabilization of performance variables.
Transcriptomic, Morphological, and Developmental Comparison of Adult Honey Bee Queens (Apis mellifera) Reared from Eggs or Worker Larvae of Differing Ages

Abstract Queens and workers are very distinct phenotypes that develop from the same genome. Larvae from worker cells up to 3.5 d old can be transferred to larger queen cells and will subsequently be reared as queens and develop into functional queens. This has become a very popular queen rearing practice in contemporary apiculture. Here we used RNA-Seq to study the consequences of rearing queens from transplanted worker larvae on the transcriptome of the adult queens. We found that queens reared from transferred older larvae developed slower, weighted less, and had fewer ovarioles than queens reared from transferred eggs, indicating queens were cryptically intercaste. RNA-Seq analysis revealed differentially expressed genes between queens reared from transferred larvae compared with queens reared from transferred eggs: the older the larvae transferred, the greater the number of differentially expressed genes. Many of the differentially expressed genes had functions related to reproduction, longevity, immunity, or metabolism, suggesting that the health and long-term viability of queens was compromised. Our finds verify the previous studies that adult queens reared from older transferred larvae were of lower quality than queens reared from transferred eggs or younger larvae. Graphical Abstract
Quantitative Study of Fuzzy Logics

In this paper, we focus on two main 3-valued logics used by the fuzzy logic community. The Gödel-Dummett logic and the Łukasiewicz one. Both are based on the same language of implication and negation. In both, we consider fragments consisting of formulas formed with one variable. We investigate the proportion of the number of true (or satisfiable) formulas of a certain length n to the number of all formulas of such length. We are especially interested in the asymptotic behavior of this fraction when length n tends to infinity. If the limit exists it is represented by a real number between 0 and 1 which is called the density of truth or the density of SAT. Using the powerful theory of analytic combinatorics, we state several results comparing the density of truth and the density of satisfiable formulas for both Gödel-Dummett and Łukasiewicz logics.
Chemical Composition of Brazilian Propolis from Sao Paulo State

Abstract Two propolis samples from Sao Paulo State were investigated by GC/MS. 39 compounds were identified, 8 being new for propolis. Both samples showed some similarities in their qualitative composition. In one of them, coumaric acid and its prenylated derivatives predominated, while in the other one triterpenic alcolhols were the main constituents.
A Novel Approach to Pathogen Recognition for Diarrhoeagenic Enteroaggregative Escherichia coli

Enteroaggregative E. coli is known to cause diarrhoeal disease in developing and developed countries, but is also found in asymptomatic carriage and so the causal link between EAEC and disease is unknown. This study assessed bacterial load, the presence of co-infections and demographic data to assess if EAEC was independently associated with intestinal infectious disease in the United Kingdom. This study concluded that EAEC was independently capable of causing disease and accounts for ~1% of intestinal disease and therefore an important burden. A case control approach of analysis by multi locus sequence typing of 564 EAEC isolates from cases and controls in Bangladesh, Nigeria and the UK spanning the past 29 years, revealed multiple successful lineages of EAEC. The population structure of EAEC indicates some clusters are statistically associated with disease or carriage, further highlighting the heterogeneous nature of this group of organisms. Different clusters have evolved independently as a result of both mutational and recombination events; the EAEC phenotype is distributed throughout the population of E. coli. In vivo models looking at EAEC infection and virulence gene content of EAEC show that different complexes varied in their ability to cause disease further concluding that these complexes may have come from different ecological niches but that the EAEC phenotype enhanced survival and so these defined EAEC complexes have converged to stably retain the plasmid and phenotype. This study has identified several successful EAEC complexes associated with sporadic disease (ST 10, 38, 40, 295, 278, 394, 678 and 746 Cplx) with subset of complexes showing evidence of patho-adaptation to cause extra-intestinal infections (ST38 Cplx) and outbreaks (ST40, 278 and 678 Cplx) or form hybrid strains with Shiga toxin producing E. coli causing severe disease (ST40 and 678 Cplx).
General Sidelobe Cancellation for All Waveform of Pulsed Radars

This paper describes the design and implementation of a novel sidelobe cancellation filter (SCF)is introduced to the conventional matched filter (MF) to obtain a novel modified MF (Mod-MF). The proposed Mod-MF improves the detection capability of pulsed radars and can detect targets with a small radar cross-section (RCS). The proposed SCF implementation is based on the inverse filter technique, which has a general frequency response closed form that can be applied for all pulsed radar signals. The proposed Mod-MF keeps the values of mainlobes impulse response width and peak the same as the MF, without the use of adaptive techniques or iteration methods. In addition, performance analysis of the proposed Mod-MF is investigated for several pulsed radar signals with different values of signal to noise ratio and time-bandwidth product. The Doppler effect over the proposed Mod-MF is also investigated. The proposed Mod-MF is implemented by using a Peripheral component interconnect (PCI) eXtensions for Instrumentation (PXI) system.
Prediction of Patient Demographics using 3D Craniofacial Scans and Multi-view CNNs

3D data is becoming increasingly popular and accessible for computer vision tasks. A popular format for 3D data is the mesh format, which can depict a 3D surface accurately and cost-effectively by connecting points in the (x, y, z) plane, known as vertices, into triangles that can be combined to approximate geometrical surfaces. However, mesh objects are not suitable for standard deep learning techniques due to their non-euclidean structure. We present an algorithm which predicts the sex, age, and body mass index of a subject based on a 3D scan of their face and neck. This algorithm relies on an automatic pre-processing technique, which renders and captures the 3D scan from eight different angles around the x-axis in the form of 2D images and depth maps. Subsequently, the generated data is used to train three convolutional neural networks, each with a ResNet18 architecture, to learn a mapping between the set of 16 images per subject (eight 2D images and eight depth maps from different angles) and their demographics. For age and body mass index, we achieved a mean absolute error of 7.77 years and 4.04 kg/m2 on the respective test sets, while Pearson correlation coefficients of 0.76 and 0.80 were obtained, respectively. The prediction of sex yielded an accuracy of 93%. The developed framework serves as a proof of concept for prediction of more clinically relevant variables based on 3D craniofacial scans stored in mesh objects.
Kinematic Biomarkers of Chronic Neck Pain During Curvilinear Walking: A Data-driven Differential Diagnosis Approach*

Chronic Neck Pain (CNP) can be associated with biomechanical changes. This paper investigates the changes in patterns of walking kinematics along a curvilinear trajectory and uses a specially designed feature space, coupled with a machine learning framework to conduct a data-driven differential diagnosis, between asymptomatic individuals and those with CNP. For this, 126 kinematic features were collected from seven body segments of 40 participants (20 asymptomatic, 20 individuals with CNP). The features space was processed through a Neighbourhood Component Analysis (NCA) algorithm to systematically select the most significant features which have the maximum discriminative power for conducting the differential diagnosis. The selected features were then processed by a K-Nearest Neighbors (K-NN) classifier to conduct the task. Our results show that, through a systematic selection of feature space, we can significantly increase the classification accuracy. In this regard, a 35% increase is reported after applying the NCA. Thus, we have shown that using only 13 features (of which 61% belong to kinematic features and 39% to statistical features) from five body segments (Head, Trunk, Pelvic, Hip and Knee) we can achieve an accuracy, sensitivity and specificity of 82.50%, 80.95% and 84.21% respectively. This promising result highlights the importance of curvilinear kinematic features through the proposed information processing pipeline for conducting differential diagnosis and could be tested in future studies to predict the likelihood of people developing recurrent neck pain.
Exploring Convolution Neural Network for Branch Prediction

Recently, there have been significant advances in deep neural networks (DNNs) and they have shown distinctive performance in speech recognition, natural language processing, and image recognition. In this paper, we explore DNNs to push the limit for branch prediction. We treat branch prediction as a classification problem and employ both deep convolutional neural networks (CNNs), ranging from LeNet to ResNet-50, and deep belief network (DBN) for branch prediction. We compare the effectiveness of DNNs with the state-of-the-art branch predictors, including the perceptron, our prior work, Multi-poTAGE+SC, and MTAGE+SC branch predictors. The last two are the most recent winners of championship branch prediction (CBP) contests. Several interesting observations emerged from our study. First, for branch prediction, the DNNs outperform the perceptron model as high as 60–80%. Second, we analyze the impact of the depth of CNNs (i.e., number of convolutional layers and pooling layers) on the misprediction rates. The results confirm that deeper CNN structures can lead to lower misprediction rates. Third, the DBN could outperform our prior work, but not outperform the state-of-the-art TAGE-like branch predictor; the ResNet-50 could not only outperform our prior work, but also the Multi-poTAGE+SC and MTAGE+SC.
Improved Achievable Regions in Networked Scalable Coding Problems

In this paper, we present new results on the achievable rate-distortion regions in networked scalable compression problems, based on a flexible codebook generation and binning method. First, we consider the problem of scalable coding in the presence of decoder side information, for which the prior work analyzed the two important cases the degraded side information where source X and the side information variables (Y1, Y2) form a Markov chain in the order of either X − Y1 −Y2 or X − Y2 − Y1. First, we present an example non-Markov side information scenario where the proposed coding strategy achieves a strictly larger rate-distortion region compared to prior work. We then consider the problem of multi-user successive refinement where different users that are connected to a central server via links with different noiseless capacities strive to reconstruct the source in a progressive fashion. It is shown that a prior rate-distortion region is suboptimal in general, albeit its optimality for a Gaussian source with MSE distortion, and the proposed coding scheme achieves points beyond the achievable region of prior work.
Classifying Mild Cognitive Impairment from Behavioral Responses in Emotional Arousal and Valence Evaluation Task – AI Approach for Early Dementia Biomarker in Aging Societies –

The presented paper discusses a practical application of machine learning (ML) in the so-called ‘AI for social good’ domain and in particular concerning the problem of a potential elderly adult dementia onset prediction. An increase in dementia cases is producing a significant medical and economic weight in many countries. Approximately 47 million older adults live with a dementia spectrum of neurocognitive disorders, according to an up-to-date statement of the World Health Organization (WHO), and this amount will triple within the next thirty years. This growing problem calls for possible application of AI-based technologies to support early diagnostics for cognitive interventions and a subsequent mental wellbeing monitoring as well as maintenance with so-called ’digital-pharma’ or ’beyond a pill’ therapeutical strategies. The paper explains our attempt and encouraging preliminary study results of behavioral responses analysis in a facial emotion implicit-short-term-memory learning and evaluation experiment. We present results of various shallow and deep learning machine learning models for digital biomarkers of dementia progress detection and monitoring. The discussed machine-learning models result in median accuracies right below a 90% benchmark using classical shallow and deep learning approaches for automatic discrimination of normal cognition versus a mild cognitive impairment (MCI). The classifier input features consist of an older adult emotional valence and arousal recognition responses, together with reaction times, as well as with self-reported university-level degree education and age, as obtained from a group of 35 older adults participating voluntarily in the reported dementia biomarker development project. The presented results showcase the inherent social benefits of artificial intelligence (AI) utilization for the elderly and establish a step forward to advance machine learning (ML) approaches for the subsequent employment of simple behavioral examination for MCI and dementia onset diagnostics.Clinical relevance— This manuscript establishes a behavioral and cognitive biomarker candidate potentially substituting a Montreal Cognitive Assessment (MoCA) evaluation without a paper and pencil test.
A Point Process Framework for the Characterization of Fetal Sleep States

In the fetal period, the progressive coordination among several subsystems promotes the emergence of sleep states. For this reason, the characterization of fetal behavioral states plays a crucial role in assessing fetal wellbeing. Nevertheless, current methodologies aimed at assessing fetal sleep states over limited time intervals require visual observation of the traces. In this work, we validate a point process approach for a continuous in time characterization of fetal behavioral states. We compare traditional heart rate variability (HRV) parameters and the corresponding point process-extracted sets of time and frequency measures in a population of 39 fetuses whose fetal ECG was recorded overnight during the third trimester of gestation.Clinical Relevance— Our results provide evidence for the proposed point process framework to capture fetal HRV dynamics with a high degree of reliability, suggesting its potential application for instantaneous estimates of fetal sleep states.
SEASONAL VARIATION IN EGG PRODUCTION AND MORTALITY OF MUSCOVY DUCKS (CAIRINA MOSCHATA)

Seasonal variation is one of the principal non-genetic factors influencing performance of poultry in tropical environment. This study was conducted to investigate influence of seasonal variation on egg production and incidence of mortality in intensively-reared non-descript Muscovy ducks in Nigeria. Egg production and incidence of mortality in sixty two (62) female Muscovy ducks was studied in a 12-month trial divided into two major seasons: wet (April - September) and dry (October - March) and four sub-seasons: early rainy season (April - June), late rainy season (July - September), early dry season (October - December) and late dry season (January - March). Student’s t-test and Completely Randomized Design was used to analyse seasonal and sub-seasonal effect on performance, respectively. Season and sub-season significantly (P < 0.05) affected egg production; higher egg production was recorded in wet season compared with dry season (16.18% vs. 1.32%). Among sub-seasons, highest egg production was recorded in late rainy season (20.92%) while the least (0.00%) was obtained in late dry season. Conversely, there was no significant (P > 0.05) effect of season and sub-season on mortality. It is evident that seasonal variation had no effect on incidence of mortality but significantly affected egg production of Muscovy duck and the adverse effect is more pronounced in dry season most especially in late dry season.
Detection of information flow between cortical laminae activities and auditory evoked potentials in rats by means of Granger causality

We seek to understand the relation between invasive high-resolution data and non-invasive measurement in an animal model in an auditory sensory adaptation experimental setting. In a previous study, we estimated the mutual information between the phase of auditory evoked responses (AER) with the phase of local field potentials (LFP) of auditory cortices at different frequency ranges. The results showed a consistently high level of information sharing between the AER activities as well as the responses from the granular layer, which was known as the main thalamo-recipient layer. However, mutual information was fundamentally an undirected measure of information flow. In this study we investigated how well we could characterize direction of information flow, by using Granger causality (GC), between different cortical laminae and functional projections on to the AER activities. We obtained that based on the GC coefficients, we are able to extract the connectivity between different cortical laminae to some extend and also a strong connection between the AER and granular layer. In our future study, we would like to construct a reliable picture of network connectivity, both functionally and anatomically, between different layers at more specified frequencies and much finer temporal resolutions.
Simplified Successive Cancellation Decoding of Polar Codes Has Sublinear Latency

This work analyzes the latency of the simplified successive cancellation (SSC) decoding scheme for polar codes proposed by Alamdar-Yazdi and Kschischang. It is shown that, unlike conventional successive cancellation decoding, where latency is linear in the block length, the latency of SSC decoding is sublinear. More specifically, the latency of SSC decoding is O(N1−1/µ), where N is the block length and µ is the scaling exponent of the channel, which captures the speed of convergence of the rate to capacity. Numerical results demonstrate the tightness of the bound and show that most of the latency reduction arises from the parallel decoding of subcodes of rate 0 and 1.