Miracle-dz/UniFAR-FTUs huggingface dataset:
Airway M Cells Arise in the Lower Airway Due to RANKL Signaling and Reside in the Bronchiolar Epithelium Associated With iBALT in Murine Models of Respiratory Disease

Microfold (M) cells residing in the follicle-associated epithelium of mucosa-associated lymphoid tissues are specialized for sampling luminal antigens to initiate mucosal immune responses. In the past decade, glycoprotein 2 (GP2) and Tnfaip2 were identified as reliable markers for M cells in the Peyer's patches of the intestine. Furthermore, RANKL–RANK signaling, as well as the canonical and non-canonical NFκB pathways downstream, is essential for M-cell differentiation from the intestinal stem cells. However, the molecular characterization and differentiation mechanisms of M cells in the lower respiratory tract, where organized lymphoid tissues exist rarely, remain to be fully elucidated. Therefore, this study aimed to explore M cells in the lower respiratory tract in terms of their specific molecular markers, differentiation mechanism, and functions. Immunofluorescence analysis revealed a small number of M cells expressing GP2, Tnfaip2, and RANK is present in the lower respiratory tract of healthy mice. The intraperitoneal administration of RANKL in mice effectively induced M cells, which have a high capacity to take up luminal substrates, in the lower respiratory epithelium. The airway M cells associated with lymphoid follicles were frequently detected in the pathologically induced bronchus-associated lymphoid tissue (iBALT) in the murine models of autoimmune disease as well as pulmonary emphysema. These findings demonstrate that RANKL is a common inducer of M cells in the airway and digestive tracts and that M cells are associated with the respiratory disease. We also established a two-dimensional culture method for airway M cells from the tracheal epithelium in the presence of RANKL successfully. This model may be useful for functional studies of M cells in the sampling of antigens at airway mucosal surfaces.
Single-Image Blind Deblurring Using Multi-Scale Latent Structure Prior

Blind image deblurring is a challenging problem in computer vision, which aims to restore both the blur kernel and the latent sharp image from only a blurry observation. Inspired by the prevalent self-example prior in image super-resolution, in this paper, we observe that a coarse enough image down-sampled from a blurry observation is approximately a low-resolution version of the latent sharp image. We prove this phenomenon theoretically and define the coarse enough image as a latent structure prior of the unknown sharp image. Starting from this prior, we propose to restore sharp images from the coarsest scale to the finest scale on a blurry image pyramid and progressively update the prior image using the newly restored sharp image. These coarse-to-fine priors are referred to as multi-scale latent structures (MSLSs). Leveraging the MSLS prior, our algorithm comprises two phases: 1) we first preliminarily restore sharp images in the coarse scales and 2) we then apply a refinement process in the finest scale to obtain the final deblurred image. In each scale, to achieve lower computational complexity, we alternately perform a sharp image reconstruction with fast local self-example matching, an accelerated kernel estimation with error compensation, and a fast non-blind image deblurring, instead of computing any computationally expensive non-convex priors. We further extend the proposed algorithm to solve more challenging non-uniform blind image deblurring problem. The extensive experiments demonstrate that our algorithm achieves the competitive results against the state-of-the-art methods with much faster running speed.
Making Emergency Calls More Accessible to Older Adults Through a Hands-free Speech Interface in the House

Wearable personable emergency response (PER) systems are the mainstream solution for allowing frail and isolated individuals to call for help in an emergency. However, these devices are not well adapted to all users and are often not worn all the time, meaning they are not available when needed. This article presents a Voice User Interface system for emergency-call recognition. The interface is designed to permit hands-free interaction using natural language. Crucially, this allows a call for help to be registered without necessitating physical proximity to the system. The system is based on an ASR engine and is tested on a corpus collected to simulate realistic situations. The corpus contains French speech from 4 older adults and 13 younger people wearing an old-age simulator to hamper their mobility, vision, and hearing. On-line evaluation of the preliminary system showed an emergency-call error rate of 27%. Subsequent off-line experimentation improved the results (call error rate 24%), demonstrating that emergency-call recognition in the home is achievable. Another contribution of this work is the corpus, which is made available for research with the hope that it will facilitate related research and quicker development of robust methods for automatic emergency-call recognition in the home.
Three Weeks of rTMS Treatment Maintains Clinical Improvement But Not Electrophysiological Changes in Patients With Depression: A 6-Week Follow-Up Pilot Study

Our previous study demonstrated that 3 weeks of repetitive transcranial magnetic stimulation (rTMS) increases P200 amplitudes and improves the symptoms of depression and anxiety in depression patients. In the present study, we investigated whether 3 weeks of rTMS treatment maintained the P200 amplitude in patients with depression at 6 weeks of follow-up. We measured the 6-week maintenance effects of rTMS using clinical questionnaires and an auditory oddball paradigm. Twenty-one patients with medication-resistant major depression participated in this pilot study. All patients underwent rTMS treatment for 3 weeks; they completed clinical ratings and performed the auditory oddball task at the pre-treatment, post-treatment, and 6-week follow-up visit (3 weeks after finishing rTMS treatment). The results revealed an increase in P200 amplitudes as well as improvements in the symptoms of depression and anxiety by 3 weeks of rTMS treatment. Furthermore, the results demonstrated maintenance effects on clinical ratings at 6-week follow-up. Depression and anxiety scales showed improvements in post-treatment and maintenance effects at the 6-week follow-up. Although P200 amplitude showed a significant main effect for 3 time points (baseline, post-treatment, and 6-week follow-up visit), at 2 time point comparisons, P200 amplitudes significantly increased in post-treatment compared to those of the baseline condition but did not show the maintenance effects of long-term rTMS at the 6-week follow-up compared to those of the baseline condition ( p = .173, Bonferroni correction). Standardized low-resolution brain electromagnetic tomography (sLORETA) for P200 showed significant activation in the left middle frontal gyrus in post-treatment but no significant activation at the 6-week follow-up. Moreover, the amplitudes of overall topographic distribution were reduced at 6 weeks of follow-up. The 3 weeks of rTMS treatment induced the maintenance of the improvements in the symptoms of depression and anxiety. However, considering the results of the event-related potential (ERP) and sLORETA, 3 weeks of rTMS treatment may not be sufficient to maintain this improvement, implying that a treatment period of more than 3 weeks may be required to reveal the electrophysiological maintenance effect of rTMS.
Mental Flexibility Influences the Association Between Poor Balance and Falls in Older People – A Secondary Analysis

Impairments of balance predispose older people to falls. Some cognitive functions, especially executive functioning have been shown to affect balance and discriminate fallers from non-fallers. Mental flexibility is a component of the executive function and comprises multiple cognitive processes that work together to adjust the course of thoughts or actions according to the changing demands of a situation without the use of explicit instructions. However, the role of mental flexibility in balance in older people remains unclear. The study aim was to examine the relationship between mental flexibility and falls in a cohort of 212 older people (80.6 ± 4.9 years; 62% female). We hypothesized that: (i) participants with impaired balance would have worse mental flexibility compared to those with good balance; and (ii) poor mental flexibility would predict falls in the sub-group with impaired balance. Balance performance was assessed by measuring postural sway while standing on a medium density foam mat with eyes open for 30 s. Mental flexibility was assessed using a computerized short-form of the Wisconsin Card Sorting Test (WCST; 64 cards) with its sub-components comprising general performance, perseveration, failure-to-maintain set and conceptual ability. Falls were measured prospectively for 12-months using monthly calendars. MANCOVA revealed that WCST performance was associated with balance [Wilks’ Lambda = 0.883, F = 2.168; p = 0.013, partial eta squared (ηp2) = 0.061] due primarily to reduced concept formation ability [F(2,207) = 5.787, p = 0.004, ηp2 = 0.053]. Negative binomial regression analysis adjusting for age, education, contrast sensitivity, proprioception, inhibition, and inhibitory choice stepping reaction time (iCSRT) revealed that lower concept formation ability was predictive for falls [Incidence Rate Ratio 1.048 (95% confidence interval 1.016–1.081)]. Further, lower concept formation ability partly explained the association between balance and falls: i.e., fallers in the upper balance tertile had reduced concept formation performance whereas non-fallers had similar concept formation performance across the three balance tertiles. These findings suggest that poor mental flexibility affects the ability to maintain steady balance contributing to increased risk of falls in older people.
DC Shifts-fMRI: A Supplement to Event-Related fMRI

Event-related fMRI have been widely used in locating brain regions which respond to specific tasks. However, activities of brain regions which modulate or indirectly participate in the response to a specific task are not event-related. Event-related fMRI can't locate these regulatory regions, detrimental to the integrity of the result that event-related fMRI revealed. Direct-current EEG shifts (DC shifts) have been found linked to the inner brain activity, a fusion DC shifts-fMRI method may have the ability to reveal a more complete response of the brain. In this study, we used DC shifts-fMRI to verify that even when responding to a very simple task, (1) The response of the brain is more complicated than event-related fMRI generally revealed and (2) DC shifts-fMRI have the ability of revealing brain regions whose responses are not in event-related way. We used a classical and simple paradigm which is often used in auditory cortex tonotopic mapping. Data were recorded from 50 subjects (25 male, 25 female) who were presented with randomly presented pure tone sequences with six different frequencies (200, 400, 800, 1,600, 3,200, 6,400 Hz). Our traditional fMRI results are consistent with previous findings that the activations are concentrated on the auditory cortex. Our DC shifts-fMRI results showed that the cingulate-caudate-thalamus network which underpins sustained attention is positively activated while the dorsal attention network and the right middle frontal gyrus which underpin attention orientation are negatively activated. The regional-specific correlations between DC shifts and brain networks indicate the complexity of the response of the brain even to a simple task and that the DC shifts can effectively reflect these non-event-related inner brain activities.
Evolutionary Trigger Set Generation for DNN Black-Box Watermarking

The commercialization of deep learning creates a compelling need for intellectual property (IP) protection. Deep neural network (DNN) watermarking has been proposed as a promising tool to help model owners prove ownership and fight piracy. A popular approach of watermarking is to train a DNN to recognize images with certain \textit{trigger} patterns. In this paper, we propose a novel evolutionary algorithm-based method to generate and optimize trigger patterns. Our method brings a siginificant reduction in false positive rates, leading to compelling proof of ownership. At the same time, it maintains the robustness of the watermark against attacks. We compare our method with the prior art and demonstrate its effectiveness on popular models and datasets.
Learning Selection Masks for Deep Neural Networks

Data have often to be moved between servers and clients during the inference phase. For instance, modern virtual assistants collect data on mobile devices and the data are sent to remote servers for the analysis. A related scenario is that clients have to access and download large amounts of data stored on servers in order to apply machine learning models. Depending on the available bandwidth, this data transfer can be a serious bottleneck, which can significantly limit the application machine learning models. In this work, we propose a simple yet effective framework that allows to select certain parts of the input data needed for the subsequent application of a given neural network. Both the masks as well as the neural network are trained simultaneously such that a good model performance is achieved while, at the same time, only a minimal amount of data is selected by the masks. During the inference phase, only the parts selected by the masks have to be transferred between the server and the client. Our experimental evaluation indicates that it is, for certain learning tasks, possible to significantly reduce the amount of data needed to be transferred without affecting the model performance much.
Conservation of Thermospermine Synthase Activity in Vascular and Non-vascular Plants

In plants, the only confirmed function for thermospermine is regulating xylem cells maturation. However, genes putatively encoding thermospermine synthases have been identified in the genomes of both vascular and non-vascular plants. Here, we verify the activity of the thermospermine synthase genes and the presence of thermospermine in vascular and non-vascular land plants as well as in the aquatic plant Chlamydomonas reinhardtii. In addition, we provide information about differential content of thermospermine in diverse organs at different developmental stages in some vascular species that suggest that, although the major role of thermospermine in vascular plants is likely to be xylem development, other potential roles in development and/or responses to stress conditions could be associated to such polyamine. In summary, our results in vascular and non-vascular species indicate that the capacity to synthesize thermospermine is conserved throughout the entire plant kingdom.
Pubertal Maturation and Trajectories of Depression During Early Adolescence

Beginning at puberty, prevalence of depression in females rises dramatically. The physical changes of puberty coincide with a period of social flux, during which relationships become less stable and more prone to conflict. While this social upheaval is normatively distressing for girls, it may be especially so for girls with cognitive styles that leave them more susceptible to depression. The present study investigated depressive symptoms at two time points during early pubertal maturation. N = 110 girls (Mage = 11.57, SD = 0.98) reported on depressive symptomology, pubertal maturation, ruminative coping style, frequency of peer conflict, and rejection sensitivity. Multivariate analyses suggest more advanced pubertal development and greater rejection sensitivity at Time 1 predicted higher levels of depressive symptoms at Time 2, after accounting for baseline levels of depressive symptoms and all other social and cognitive correlates of depression. This effect was also found in early maturing girls. Menarche status was not significant. Since menarche occurs toward the end of puberty, results suggest that risk for worsening depression is not associated with completing puberty, or with menstruation itself. Rather, increases in depressive symptoms seem to be associated with physical changes that emerge early in the pubertal transition, especially for early maturing girls, paired with anticipatory concerns about social rejection.
Deep learning analysis of cardiac CT angiography for detection of coronary arteries with functionally significant stenosis

In patients with obstructive coronary artery disease, the functional significance of a coronary artery stenosis needs to be determined to guide treatment. This is typically established through fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA). We present a method for automatic and non-invasive detection of functionally significant coronary artery stenosis, employing deep unsupervised analysis of complete coronary arteries in cardiac CT angiography (CCTA) images. We retrospectively collected CCTA scans of 187 patients, 137 of them underwent invasive FFR measurement in 192 different coronary arteries. These FFR measurements served as a reference standard for the functional significance of the coronary stenosis. The centerlines of the coronary arteries were extracted and used to reconstruct straightened multi-planar reformatted (MPR) volumes. To automatically identify arteries with functionally significant stenosis, each MPR volume was encoded into a fixed number of encodings using two disjoint 3D and 1D convolutional autoencoders performing spatial and sequential encodings, respectively. Thereafter, these encodings were employed to classify arteries according to the presence of functionally significant stenosis using a support vector machine classifier. The detection of functionally significant stenosis, evaluated using repeated cross-validation experiments, resulted in an area under the receiver operating characteristic curve of $0.81 \pm 0.02$ on the artery-level, and $0.87 \pm 0.02$ on the patient-level. The results demonstrate that automatic non-invasive detection of the functionally significant stenosis in coronary arteries, using characteristics of complete coronary arteries in CCTA images, is feasible. This could potentially reduce the number of patients that unnecessarily undergo ICA.
Chinese Embedding via Stroke and Glyph Information: A Dual-channel View

Recent studies have consistently given positive hints that morphology is helpful in enriching word embeddings. In this paper, we argue that Chinese word embeddings can be substantially enriched by the morphological information hidden in characters which is reflected not only in strokes order sequentially, but also in character glyphs spatially. Then, we propose a novel Dual-channel Word Embedding (DWE) model to realize the joint learning of sequential and spatial information of characters. Through the evaluation on both word similarity and word analogy tasks, our model shows its rationality and superiority in modelling the morphology of Chinese.
Intracortical Inhibition and Surround Inhibition in the Motor Cortex: A TMS-EEG Study

Background Short-latency intracortical inhibition (SICI) and motor surround inhibition (mSI) are cortical phenomena that have been investigated with transcranial magnetic stimulation (TMS). mSI is believed to be necessary for the execution of fine finger movements, SICI may participate in mSI genesis, and however, the mechanisms underlying both mSI and SICI are not entirely clear. Objective We explored the cortical physiology of SICI and mSI in healthy subjects by TMS-evoked cortical potentials (TEPs). Methods: Single (sp) and paired-pulse (pp) TMS were delivered on the ADM muscle cortical hotspot while recording EEG and EMG. Three conditions were tested: spTMS and ppTMS at rest, and spTMS at the onset of an index finger movement. SICI and mSI were calculated on the ADM motor evoked potential (MEP) and two groups were defined based on the presence of mSI. Average TEPs were calculated for each condition and for five regions of interest. Results At movement onset we observed a widespread reduction of the inhibitory late component N100 suggesting cortical facilitation associated with motor performance. At motor cortex level, SICI and mSI are associated with similar modulation of TEPs consisting in a reduction of P30 and an increase of N45 amplitude. Conclusion Our findings suggest that SICI and mSI modulate cortical excitability with shared inhibitory mechanisms.
Estimation of the Seroconversion Duration of HIV-1 Antibodies in Individuals With Recent Infection in China

The identification of recent HIV-1 infection is clinically important for the effective treatment and prevention of transmission. However, the window period for seroconversion with respect to various HIV-1 antibodies is not well characterized. In addition, the routine HIV testing algorithms are not particularly appropriate for the identification of recent HIV-1 infection. In this study, we enrolled individuals who showed seroconversion from negative Western blot (WB) or indeterminate WB results and analyzed the window periods for appearance of HIV-1 antibodies. A total of 10,934 individuals with suspected HIV infection were tested by Wuhan CDC between 2012 and 2017; of these, 40 individuals with initial negative WB and 102 individuals with initial indeterminate WB who showed positive WB results within 100 days were included in the analysis. The mean time for seroconversion was 43.90 (95% confidence interval [CI]: 37.30–50.50) days and 42.15 (95% CI: 37.99–46.30) days, respectively. The time duration for p31 seroconversion among people with negative WB and indeterminate WB was 58.11 (95% CI, 44.30–71.92) days and 51.91 (95% CI, 44.55–59.28) days, respectively, both of which were significantly longer (p = 0.0169) than those in people without p31 seroconversion. A similar difference was observed with respect to p66 seroconversion, with a window time of 53.53 (95% CI, 43.54–63.52) days and 47.87 (95% CI, 43.16–52.57) days among people with negative WB and indeterminate WB, respectively. These data suggest that HIV-1 antibody p66, like p31, may serve as a potential serological marker for distinguishing Fiebig stage V and stage VI at day 70 post-infection.
Rethinking Person Re-Identification with Confidence

A common challenge in person re-identification systems is to differentiate people with very similar appearances. The current learning frameworks based on cross-entropy minimization are not suited for this challenge. To tackle this issue, we propose to modify the cross-entropy loss and model confidence in the representation learning framework using three methods: label smoothing, confidence penalty, and deep variational information bottleneck. A key property of our approach is the fact that we do not make use of any hand-crafted human characteristics but rather focus our attention on the learning supervision. Although methods modeling confidence did not show significant improvements on other computer vision tasks such as object classification, we are able to show their notable effect on the task of re-identifying people outperforming state-of-the-art methods on 3 publicly available datasets. Our analysis and experiments not only offer insights into the problems that person re-id suffers from, but also provide a simple and straightforward recipe to tackle this issue.