Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing Upper and Lower Extremities Via Tissue Dielectric Constant: Suitability of Single Versus Multiple Measurements Averaged.

Background: Tissue dielectric constant (TDC) measurements as an index of local tissue water are useful in a range of applications most notably to characterize and assess lymphedema. Once a measuring device is applied to skin and a result is obtained in less than 10 seconds, but multiple sites may be required and use of the standard triplicate measurements may be time prohibitive. Thus, this study's goal was to provide data from which informed judgments could be made as to the impact of making a single measurement to reduce expended clinic time. Methods and Results: Sixty subjects (30 female) were recruited with an average age (mean ± standard deviation) of 30.6 ± 13.4 years. TDC was measured in triplicate bilaterally at forearm, hand palm, lateral calf, medial calf, and foot dorsum. The agreement in absolute TDC values and interside ratios was evaluated for assessments made using only the first TDC measurement, the average of duplicates and the standard triplicate. Results showed that differences between single and multiple measurement averages were anatomical site dependent with the smallest coefficient of variation (2.19%) at the forearm and the largest at the lateral calf (4.59%). Conclusions: Results suggest that when clinical time is of major concern, useful TDC data may be obtained in upper limbs using single TDC measurements per anatomical site whereas lower extremity skin assessments should be done using at least duplicate and preferably triplicate measurements. However, as with all such time reliability considerations, clinical judgment should be exercised and aided by the various findings of this study.
A Cross-Sectional Pilot Study to Examine the Criterion Validity of the Modified Shuttle Test-Paeds as a Measure of Cardiorespiratory Fitness in Children

With accumulating evidence that exercise capacity decreases all-cause mortality independent of adiposity, benefits may be gained by developing cardiorespiratory fitness measures that are specifically and sensitively designed for use with pediatric populations when cardiorespiratory fitness may be a contributing factor for obesity. This study aimed to examine the criterion validity of the Modified Shuttle Test-Paeds (MSTP) as a measure of cardiorespiratory fitness in children, against the gold-standard reference; VO2peak, compared to the commonly used field-test; 20-m Multi-Stage-Shuttle-Run-Test (20-m MSRT). A cross-sectional pilot study, with 25 school-aged children (age: 6–16 year; male/female: 19/5; BMI: 21 ± 9 kg/m2) was employed. Physical measures included: Bruininks-Oseretsky-Test-of-Motor-Proficiency-2nd Edition (BOT2), VO2peak, 20-m MSRT, MSTP, body composition/anthropometry. The mean cardiorespiratory fitness of participants was: VO2peak: 43.8 ± 11.2 (mL/kg/min); 20-m MSRT: 5.48 ± 2.96 (level); MSTP: 22.10 ± 3.05 (no.). A strong predictive relationship was found between the 20-m MSRT and VO2peak (r2 = 0.486, p < 0.001) whereas a very strong predictive relationship existed between the newly designed MSTP and VO2peak (r2 = 0.749, p < 0.001). Whilst further research with larger study cohorts is needed, this pilot study found the MSTP to have a very high predictive validity for estimating VO2peak in children, suggesting it may be a valid child-specific indicator of cardiorespiratory fitness requiring only a simple equation that is clinically relevant.
Silicon Photonic Biosensors Using Label-Free Detection

Thanks to advanced semiconductor microfabrication technology, chip-scale integration and miniaturization of lab-on-a-chip components, silicon-based optical biosensors have made significant progress for the purpose of point-of-care diagnosis. In this review, we provide an overview of the state-of-the-art in evanescent field biosensing technologies including interferometer, microcavity, photonic crystal, and Bragg grating waveguide-based sensors. Their sensing mechanisms and sensor performances, as well as real biomarkers for label-free detection, are exhibited and compared. We also review the development of chip-level integration for lab-on-a-chip photonic sensing platforms, which consist of the optical sensing device, flow delivery system, optical input and readout equipment. At last, some advanced system-level complementary metal-oxide semiconductor (CMOS) chip packaging examples are presented, indicating the commercialization potential for the low cost, high yield, portable biosensing platform leveraging CMOS processes.
Automatic classification of cervical cancer from cytological images by using convolutional neural network

Cervical cancer (CC) is one of the most common gynecologic malignancies in the world. The incidence and mortality keep high in some remote and poor medical condition regions in China. In order to improve the current situation and promote the pathologists’ diagnostic accuracy of CC in such regions, we tried to propose an intelligent and efficient classification model for CC based on convolutional neural network (CNN) with relatively simple architecture compared with others. The model was trained and tested by two groups of image datasets, respectively, which were original image group with a volume of 3012 datasets and augmented image group with a volume of 108432 datasets. Each group has a number of fixed-size RGB images (227*227) of keratinizing squamous, non-keratinizing squamous, and basaloid squamous. The method of three-folder cross-validation was applied to the model. And the classification accuracy of the models, overall, 93.33% for original image group and 89.48% for augmented image group. The improvement of 3.85% has been achieved by using augmented images as input data for the model. The results got from paired-samples ttest indicated that two models’ classification accuracy has a significant difference (P<0.05). The developed scheme we proposed was useful for classifying CCs from cytological images and the model can be served as a pathologist assistance to improve the doctor’s diagnostic level of CC, which has a great meaning and huge potential application in poor medical condition areas in China.
Genomic analysis of Lassa virus from the 2018 surge in Nigeria

&NA; During 2018, an unusual increase in Lassa fever cases occurred in Nigeria, raising concern among national and international public health agencies. We analyzed 220 Lassa virus genomes from infected patients, including 129 from the 2017–2018 transmission season, to understand the viral populations underpinning the increase. A total of 14 initial genomes from 2018 samples were generated at Redeemer's University in Nigeria, and the findings were shared with the Nigerian Center for Disease Control in real time. We found that the increase in cases was not attributable to a particular Lassa virus strain or sustained by human‐to‐human transmission. Instead, the data were consistent with ongoing cross‐species transmission from local rodent populations. Phylogenetic analysis also revealed extensive viral diversity that was structured according to geography, with major rivers appearing to act as barriers to migration of the rodent reservoir.
How generalizable is the inverse relationship between social class and emotion perception?

Compared to individuals in lower positions of power, higher-power individuals are theorized to be less motivated to attend to social cues. In support of this theory, previous research has consistently documented negative correlations between social class and emotion perception. Prior studies, however, were limited by the size and diversity of the participant samples as well as the systematicity with which social class and emotion perception were operationalized. Here, we examine the generalizability of prior research across 10,000+ total participants. In an initial modest sample, (n = 179), Study 1 partially replicated past results: emotion identification correlated negativity with subjective social class (β = -0.15, 95% CI = [-0.28,-0.02]) and one of two objective social class measures (participant education β = -0.15, 95% CI = [-0.03,-0.01]). Studies 2–4 followed up on Study 1’s mixed results for objective social class in three much larger samples. These results diverged from past literature. In Study 2, complex emotion identification correlated non-significantly with participant education (β = 0.02, p = 0.25; 95% CI = [-0.01, 0.05], n = 2,726), positively with childhood family income (β = 0.03, 95% CI = [0.01,0.06], n = 4,312), and positively with parental education (β = 0.06, 95% CI = [0.04,0.09], n = 4,225). In Study 3, basic emotion identification correlated positively with participant education (β = 0.05, 95% CI = [0.02, 0.09]), n = 2,564). In Study 4, basic emotion discrimination correlated positively with participant education (β = 0.09, 95% CI = [0.05,0.13], n = 2,079), positively with parental education (β = 0.06, 95% CI = [0.02,0.09], n = 3,225), and non-significantly with childhood family income (β = 0.2, 95% CI = [0.01,0.07], n = 3,272). Results remained similar when restricting analyses to U.S.-based participants. Taken together, these findings suggest that previously reported negative correlations between emotion perception and social class may generalize poorly past select samples and/or subjective measures of social class. Data from the three large web-based samples used in Studies 2–4 are available at osf.io/jf7r3 as normative datasets and to support future investigations of these and other research questions.
Charge-Controlled Surface Properties of Native and Fluorophore-Labeled Bovine Serum Albumin at the Air–Water Interface

Proteins at interfaces are important for protein formulations and in soft materials such as foam. Here, interfacial stability and physicochemical properties are key elements, which drive macroscopic foam properties through structure–property relations. Native and fluorescein isothiocyanate-labeled bovine serum albumin (BSA) were used to modify air–water interfaces as a function of pH. Characterizations were performed with tensiometry and sum-frequency generation (SFG). SFG spectra of O–H stretching vibrations reveal a phase reversal and a pronounced minimum in O–H intensity at pH values of 5.3 and 4.7 for native and labeled BSA, respectively. This minimum is attributed to the interfacial isoelectric point (IEP) and is accompanied by a minimum in surface tension and negligible ζ-potentials in the bulk. Interfacial proteins at pH values close to the IEP can promote macroscopic foam stability and are predominately located in the lamellae between individual gas bubbles as evidenced by confocal fluorescence microscopy. Different from the classical stabilization mechanisms, for example, via the electrostatic disjoining pressure, we propose that the presence of more close-packed BSA, because of negligible net charges, inside the foam lamellae is more effective in reducing foam drainage as compared to a situation with strong repulsive electrostatic interactions.
The PInSoRo dataset: Supporting the data-driven study of child-child and child-robot social dynamics

The study of the fine-grained social dynamics between children is a methodological challenge, yet a good understanding of how social interaction between children unfolds is important not only to Developmental and Social Psychology, but recently has become relevant to the neighbouring field of Human-Robot Interaction (HRI). Indeed, child-robot interactions are increasingly being explored in domains which require longer-term interactions, such as healthcare and education. For a robot to behave in an appropriate manner over longer time scales, its behaviours have to be contingent and meaningful to the unfolding relationship. Recognising, interpreting and generating sustained and engaging social behaviours is as such an important—and essentially, open—research question. We believe that the recent progress of machine learning opens new opportunities in terms of both analysis and synthesis of complex social dynamics. To support these approaches, we introduce in this article a novel, open dataset of child social interactions, designed with data-driven research methodologies in mind. Our data acquisition methodology relies on an engaging, methodologically sound, but purposefully underspecified free-play interaction. By doing so, we capture a rich set of behavioural patterns occurring in natural social interactions between children. The resulting dataset, called the PInSoRo dataset, comprises 45+ hours of hand-coded recordings of social interactions between 45 child-child pairs and 30 child-robot pairs. In addition to annotations of social constructs, the dataset includes fully calibrated video recordings, 3D recordings of the faces, skeletal informations, full audio recordings, as well as game interactions.
Measurement of Free-Form Curved Surfaces Using Laser Triangulation

Laser triangulation (LT) is widely used in many fields due to its good stability, high resolution and fast speed. However, the accuracy in these applications suffers from severe constraints on the data acquisition accuracy of LT. To solve this problem, the optical triangulation principle, the object equation of the optical path relationship and the deviation of the laser spot centroid are applied to deduce a mathematical model. Therefore, the image sensor inclination errors can be quantitatively calculated, and the collected data are compensated in real time. Further, a threshold sub-pixel gray-gravity (GG) extraction algorithm is proposed; the gradient function and Gaussian fit algorithm are used to set thresholds to remove the impact of the spot edge noise area on the center location; and polynomial interpolation is employed to enhance the data density of the traditional GG method, thus improving the data acquisition accuracy of LT. Finally, the above methods are applied to on-machine measurement of the American Petroleum Institute (API) thread and the screw rotor, respectively. The experimental results prove that the proposed method can significantly improve the measurement accuracy of free-form curved surfaces using LT and that the improved laser spot center extraction algorithm is more suitable for free-form curved surfaces with smaller curvature and more uniform curvature changes.
Computerized Medical Imaging and Graphics.

Alzheimer’s disease (AD) is an irreversible neurodegenerative disorder with progressive impairment of memory and cognitive functions. Structural magnetic resonance images (MRI) play important role to evaluate the brain anatomical changes for AD Diagnosis. Machine learning technologies have been widely studied on MRI computation and analysis for quantitative evaluation and computer-aided-diagnosis of AD. Most existing methods extract the hand-craft features after image processing such as registration and segmentation, and then train a classiﬁer to distinguish AD subjects from other groups. Motivated by the success of deep learning in image classiﬁcation, this paper proposes a classiﬁcation method based on multiple cluster dense convolutional neural networks (DenseNets) to learn the various local features of MR brain images, which are combined for AD classiﬁcation. First, we partition the whole brain image into different local regions and extract a number of 3D patches from each region. Second, the patches from each region are grouped into different clusters with the K-Means clustering method. Third, we construct a DenseNet to learn the patch features for each cluster and the features learned from the discriminative clusters of each region are ensembled for classiﬁcation. Finally, the classiﬁcation results from different local regions are combined to enhance ﬁnal image classiﬁcation. The proposed method can gradually learn the MRI features from the local patches to global image level for the classiﬁcation task. There are no rigid registration and segmentation required for preprocessing MRI images. Our method is evaluated using T1-weighted MRIs of 831 subjects including 199 AD patients, 403 mild cognitive impairment (MCI) and 229 normal control (NC) subjects from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an accuracy of 89.5% and an AUC (area under the ROC curve) of 92.4% for AD vs. NC classiﬁcation, and an accuracy of 73.8% and an AUC of 77.5% for MCI vs. NC classiﬁcation, demonstrating the promising classiﬁcation performances. © 2018 Elsevier Ltd. All rights reserved.
Genetic Analysis of Small Well-differentiated Pancreatic Neuroendocrine Tumors Identifies Subgroups With Differing Risks of Liver Metastases.

OBJECTIVE: The aim of this study was to investigate the key molecular alterations in small primary pancreatic neuroendocrine tumors (PanNETs) associated with the development of liver metastases. BACKGROUND Well-differentiated PanNETs with small size are typically indolent; however, a limited subset metastasize to the liver. METHODS A total of 87 small primary PanNETs (<3 cm), including 32 metastatic cases and 55 nonmetastatic cases after a 5-year follow-up, were immunolabeled for DAXX/ATRX and analyzed for alternative lengthening of telomeres (ALT) by Fluorescence In Situ Hybridization. A subset of these cases, 24 that metastasized and 24 that did not metastasize, were assessed by targeted next-generation sequencing and whole-genome copy number variation. RESULTS In the entire cohort, high Ki-67 (OR 1.369; 95% CI 1.121-1.673; P = 0.002), N-stage (OR 4.568; 95% CI 1.458-14.312; P = 0.009), and ALT-positivity (OR 3.486; 95% CI 1.093-11.115; P = 0.035) were independently associated with liver metastases. In the subset assessed by next-generation sequencing and copy number variation analysis, 3 molecular subtypes with differing risks of liver metastases were identified. Group 1 (n = 15; 73% metastasized) was characterized by recurrent chromosomal gains, CN-LOH, DAXX mutations, and ALT-positivity. Group 2 (n = 19; 42% metastasized, including 5 G1 tumors) was characterized by limited copy number alterations and mutations. Group 3 (n = 14; 35% metastasized) were defined by chromosome 11 loss. CONCLUSIONS We identified genomic patterns of small PanNETs associated with a different risk for liver metastases. Molecular alterations, such as DAXX mutations, chromosomal gains, and ALT, are associated with an increased risk of metastasis in small PanNETs. Therefore, targeted sequencing and/or ALT analysis may help in the clinical decisions for these small PanNETs.
Congenital Cytomegalovirus Infection Alters Olfaction Before Hearing Deterioration In Mice

In developed countries, cytomegalovirus (CMV)-infected newborns are at high risk of developing sensorineural handicaps such as hearing loss, requiring extensive follow-up. However, early prognostic tools for auditory damage in children are not yet available. In the fetus, CMV infection leads to early olfactory bulb (OB) damage, suggesting that olfaction might represent a valuable prognosis for neurological outcome of this viral infection. Here, we demonstrate that in utero CMV inoculation causes fetal infection and growth retardation in mice of both sexes. It disrupts OB normal development, leading to disproportionate OB cell layers and rapid major olfactory deficits. Olfaction is impaired as early as day 6 after birth in both sexes, long before the emergence of auditory deficits. Olfactometry in males reveals a long-lasting alteration in olfactory perception and discrimination, particularly in binary mixtures of monomolecular odorants. Although sensory inputs to the OB remain unchanged, hallmarks of autophagy are increased in the OB of 3-postnatal week-old mice, leading to local neuroinflammation and loss of neurons expressing tyrosine hydroxylase and calbindin. At the cellular level, we found CMV-infected cells and an increased number of apoptotic cells scattered throughout the OB layers, whereas cell proliferation in the neurogenic subventricular zone was decreased. These cellular observations were long-lasting, persisting up to 16 weeks after birth in both males and females and thus providing a mechanism supporting olfactory loss. Despite obvious differences in neurogenesis between human and mouse, these findings offer new strategies aimed at early detection of neurological dysfunctions caused by congenital infections. SIGNIFICANCE STATEMENT In developed countries, congenital cytomegalovirus (CMV)-infected newborns are at high risk of developing sensory handicaps such as hearing loss, thus requiring prolonged follow-up. In this study, we describe for the first time the functional impact of congenital CMV infection on the olfactory system and its associated sense of smell. We demonstrate that a mouse model of congenital CMV infection shows defects in olfactory bulb (OB) normal development and pronounced olfactory deficits affecting acuity and discrimination of odorants. These major olfactory deficits occur long before the emergence of auditory deficits through the upregulation of OB autophagy inducing local neuroinflammation and altered neuron content. Our findings provide new opportunities for designing olfactory means to monitor the possible neurological outcome during congenital CMV infection.
Colorectal cancer invasiveness in vitro: Predominant contribution of neonatal Nav1.5 under normoxia and hypoxia

Functional expression of voltage‐gated Na+ channels (VGSCs) occurs in human carcinomas and promotes invasiveness in vitro and metastasis in vivo. Both neonatal and adult forms of Nav1.5 (nNav1.5 and aNav1.5, respectively) have been reported to be expressed at messenger RNA (mRNA) level in colorectal cancer (CRCa) cells. Here, three CRCa cell lines (HT29, HCT116 and SW620) were studied and found to express nNav1.5 mRNA and protein. In SW620 cells, adopted as a model, effects of gene silencing (by several small interfering RNAs [siRNAs]) selectively targeting nNav1.5 or aNav1.5 were determined on (a) channel activity and (b) invasiveness in vitro. Silencing nNav1.5 made the currents more “adult‐like” and suppressed invasion by up to 73%. Importantly, subsequent application of the highly specific, general VGSC blocker, tetrodotoxin (TTX), had no further effect. Conversely, silencing aNav1.5 made the currents more “neonatal‐like” but suppressed invasion by only 17% and TTX still induced a significant effect. Hypoxia increased invasiveness and this was also blocked completely by siRNA targeting nNav1.5. The effect of hypoxia was suppressed dose dependently by ranolazine, but its effect was lost in cells pretreated with nNav1.5‐siRNA. We conclude that (a) functional nNav1.5 expression is common to human CRCa cells, (b) hypoxia increases the invasiveness of SW620 cells, (c) the VGSC‐dependent invasiveness is driven predominantly by nNav1.5 under both normoxic and hypoxic conditions and (d) the hypoxia‐induced increase in invasiveness is likely to be mediated by the persistent current component of nNav1.5.