Miracle-dz/UniFAR-FTUs huggingface dataset:
OralCam: Enabling Self-Examination and Awareness of Oral Health Using a Smartphone Camera

Due to a lack of medical resources or oral health awareness, oral diseases are often left unexamined and untreated, affecting a large population worldwide. With the advent of low-cost, sensor-equipped smartphones, mobile apps offer a promising possibility for promoting oral health. However, to the best of our knowledge, no mobile health (mHealth) solutions can directly support a user to self-examine their oral health condition. This paper presents OralCam, the first interactive app that enables end-users' self-examination of five common oral conditions (diseases or early disease signals) by taking smartphone photos of one's oral cavity. OralCam allows a user to annotate additional information (e.g. living habits, pain, and bleeding) to augment the input image, and presents the output hierarchically, probabilistically and with visual explanations to help a laymen user understand examination results. Developed on our in-house dataset that consists of 3,182 oral photos annotated by dental experts, our deep learning based framework achieved an average detection sensitivity of 0.787 over five conditions with high localization accuracy. In a week-long in-the-wild user study (N=18), most participants had no trouble using OralCam and interpreting the examination results. Two expert interviews further validate the feasibility of OralCam for promoting users' awareness of oral health.
Extent of Primary DNA Damage Measured by the Comet Assay in Health Professionals Exposed to Antineoplastic Drugs: A Systematic Review and Meta-Analysis

Background: Antineoplastic drugs (ANDs) are a broad group of chemicals showing, at the same time, carcinogenic effects. The potential, albeit true, risk of side effects cannot be accepted, especially if resulting from occupational exposure. The aim of this study was to evaluate the association between occupational exposure to ANDs and the extent of primary DNA damage in health professionals. Methods: A systematic review and meta-analysis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed/Medline, Web of Science, and Scopus were used to perform the literature search. The databases were examined in July 2019. Sub-group, moderator, and cumulative analyses were conducted. The trim and fill method was used in the case of potential publication bias. Results: Twenty studies were included in the qualitative analysis, and 19 in quantitative evaluation. The pooled effect size was 1.27 [(95% confidence interval (CI) = 0.66–1.88), p = 0.000] based on 1569 subjects. The moderator analysis by duration of exposure showed a positive association between duration of exposure and primary DNA damage. Conclusions: This systematic review clearly shows a significant association between occupational exposure to ANDs and the extent of primary DNA damage in health professionals. Considering these results, health professionals should be warned against this potential occupational risk.
Preoperative elevated plasma fibrinogen level predicts tumor recurrence and poor prognosis in patients with hepatocellular carcinoma.

Background Elevated plasma fibrinogen has been reported to be associated with poor prognosis in several cancers. The aim of present study was to investigate the prognostic value of preoperative plasma fibrinogen in hepatocellular carcinoma (HCC) patients. Methods Data were collected retrospectively from 302 HCC patients who received hepatectomy. The association between fibrinogen and clinicopathological characteristics was evaluated. Both univariate and multivariate analyses were performed to identify prognostic factors for disease-free survival (DFS) and overall survival (OS). And accordingly, the nomograms were constructed. Results Elevated plasma fibrinogen (>4 g/L) was correlated with larger tumor diameter, the presence of vascular invasion, lower MELD score, higher NLR, advanced Barcelona Clinic Liver Cancer stage and poor-moderate pathological differentiation. On multivariate analysis, the elevated plasma fibrinogen was found independently associated with poor DFS (HR =1.575, P=0.024) and OS (HR =2.051, P=0.025). And the nomograms including fibrinogen were constructed to predict DFS and OS for HCC patients. Both DFS and OS in patients with plasma fibrinogen >4 g/L were significantly lower than those with fibrinogen ≤4 g/L (1-, 3-, 5-year DFS: 34.2%, 19.5% and 0.0% vs. 60.4%, 34.2% and 30.2%; 1-, 3-, 5-year OS: 83.4%, 62.7% and 48.8% vs. 95.4%, 84.3% and 75.8%, both P<0.001). Besides, subgroup analyses also showed the prognostic values of fibrinogen in HCC patients with/without cirrhosis or high AFP levels, and in those with single tumor and BCLC 0-A stage. Conclusions Preoperative elevated plasma fibrinogen was an independent prognostic factor associated with poor prognosis in HCC patients receiving liver resection.
A Supervised Learning Algorithm for Multilayer Spiking Neural Networks Based on Temporal Coding Toward Energy-Efficient VLSI Processor Design

Spiking neural networks (SNNs) are brain-inspired mathematical models with the ability to process information in the form of spikes. SNNs are expected to provide not only new machine-learning algorithms but also energy-efficient computational models when implemented in very-large-scale integration (VLSI) circuits. In this article, we propose a novel supervised learning algorithm for SNNs based on temporal coding. A spiking neuron in this algorithm is designed to facilitate analog VLSI implementations with analog resistive memory, by which ultrahigh energy efficiency can be achieved. We also propose several techniques to improve the performance on recognition tasks and show that the classification accuracy of the proposed algorithm is as high as that of the state-of-the-art temporal coding SNN algorithms on the MNIST and Fashion-MNIST datasets. Finally, we discuss the robustness of the proposed SNNs against variations that arise from the device manufacturing process and are unavoidable in analog VLSI implementation. We also propose a technique to suppress the effects of variations in the manufacturing process on the recognition performance.
Everybody’s Talkin’: Let Me Talk as You Want

We present a method to edit a target portrait footage by taking a sequence of audio as input to synthesize a photo-realistic video. This method is unique because it is highly dynamic. It does not assume a person-specific rendering network yet capable of translating one source audio into one random chosen video output within a set of speech videos. Instead of learning a highly heterogeneous and nonlinear mapping from audio to the video directly, we first factorize each target video frame into orthogonal parameter spaces, i.e., expression, geometry, and pose, via monocular 3D face reconstruction. Next, a recurrent network is introduced to translate source audio into expression parameters that are primarily related to the audio content. The audio-translated expression parameters are then used to synthesize a photo-realistic human subject in each video frame, with the movement of the mouth regions precisely mapped to the source audio. The geometry and pose parameters of the target human portrait are retained, therefore preserving the context of the original video footage. Finally, we introduce a novel video rendering network and a dynamic programming method to construct a temporally coherent and photo-realistic video. Extensive experiments demonstrate the superiority of our method over existing approaches. Our method is end-to-end learnable and robust to voice variations in the source audio.
Anti-PD-1 and Novel Combinations in the Treatment of Melanoma—An Update

Until recently, distant metastatic melanoma was considered refractory to systemic therapy. A better understanding of the interactions between tumors and the immune system and the mechanisms of regulation of T-cells led to the development of immune checkpoint inhibitors. This review summarizes the current novel data on the treatment of metastatic melanoma with anti-programmed cell death protein 1 (PD-1) antibodies and anti-PD-1-based combination regimens, including clinical trials presented at major conference meetings. Immune checkpoint inhibitors, in particular anti-PD-1 antibodies such as pembrolizumab and nivolumab and the combination of nivolumab with the anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) antibody ipilimumab can achieve long-term survival for patients with metastatic melanoma. The anti-PD-1 antibodies nivolumab and pembrolizumab were also approved for adjuvant treatment of patients with resected metastatic melanoma. Anti-PD-1 antibodies appear to be well tolerated, and toxicity is manageable. Nivolumab combined with ipilimumab achieves a 5 year survival rate of more than 50% but at a cost of high toxicity. Ongoing clinical trials investigate novel immunotherapy combinations and strategies (e.g., Talimogene laherparepvec (T-VEC), Bempegaldesleukin (BEMPEG), incorporation or sequencing of targeted therapy, incorporation or sequencing of radiotherapy), and focus on poor prognosis groups (e.g., high tumor burden/LDH levels, anti-PD-1 refractory melanoma, and brain metastases).
Improved Faraday Rotation Estimation in Spaceborne PolSAR Data Using Total Variation Denoising

Faraday rotation (FR) is a serious problem for spaceborne polarization SAR (PolSAR) systems at L and P bands. One way to solve the problem is to estimate the FR from PolSAR data for further compensation. Therefore, precise estimation of FR from PolSAR data not only determines the compensation effect of polarimetric systems but also benefits the ionospheric sounding with high spatial resolution. Among the factors that affect the FR estimation, system noise is a non-neglectable factor. Although average filtering (AF) has been widely used in previous works for noise removing it depends on large window size, and therefore reduces the spatial resolution of FR estimation. In order to realize optimal noise suppression with minimized resolution loss, the total variation (TV) denoising method is applied in this paper. By testing the Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) full-pol datasets, TV and AF are compared and validated. Results using synthetic and real data show that, after TV denoising, the FR can be recovered with high spatial resolution and the noise level in estimated FR is reduced more effectively than that after AF.
PA‐CRM: A continuous reassessment method for pediatric phase I oncology trials with concurrent adult trials

Pediatric phase I trials are usually carried out after the adult trial testing the same agent has started, but not completed yet. As the pediatric trial progresses, in light of the accrued interim data from the concurrent adult trial, the pediatric protocol often is amended to modify the original pediatric dose escalation design. In practice, this is done frequently in an ad hoc way, interrupting patient accrual and slowing down the trial. We developed a pediatric‐continuous reassessment method (PA‐CRM) to streamline this process, providing a more efficient and rigorous method to find the maximum tolerated dose for pediatric phase I oncology trials. We use a discounted joint likelihood of the adult and pediatric data, with a discount parameter controlling information borrowing between pediatric and adult trials. According to the interim adult and pediatric data, the discount parameter is adaptively updated using the Bayesian model averaging method. Numerical study shows that the PA‐CRM improves the efficiency and accuracy of the pediatric trial and is robust to various model assumptions.
Risk Factors Analysis for Catheter-Associated Urinary Tract Infection in Medan, Indonesia

BACKGROUND: Catheter-associated urinary tract infection (CAUTI) is one of the most common infections in health care caused by several risk factors. AIM: This study aims at analysing the risky factors triggering CAUTI. METHODS: This research was designed by applying prospective study. It was conducted from July to November 2018 by involving 82 patients attached to the catheter and treated in the General Hospital of Medan as the sample. The study instrument used observational sheets by measuring the occurrence of urinary tract infection using urine culture analysis ≥ 105 CFU/ml. RESULTS: The results showed that there was a relationship (p < 0.05) amongs age (p = 0.01; RR = 0.51), diabetes mellitus (p = 0.00; RR = 7.61), duration of catheterization (p = 0.00; RR = 0.01), indications for catheter use (p = 0.00; RR = 0.34) with CAUTI, and there were not significant relationship (p > 0.05) amongs genre (p = 0.06; RR = 1.72), drainage system (p = 0.43; RR = 0.43) and catheter care (p = 0.08; RR = 0.50) with CAUTI. Diabetes mellitus (p = 0.00; OR = 8.92 95% CI = 1.02-11.83) and duration of catheterization (p = 0, 00; OR = 32.84 95% CI = 3.81-322.74) were the most significant factor related to CAUTI. CONCLUSION: CAUTI is influenced by various factors, and it can be controlled by understanding those factors so that the right interventions to prevent the infections can be taken and the quality of nursing care can be increased as well.
Crystal structure of botulinum neurotoxin subtype A3 cell binding domain in complex with GD1a co‐receptor ganglioside

Botulinum neurotoxins (BoNTs) are one of the most toxic proteins known to humans. Their molecular structure is comprised of three essential domains—a cell binding domain (HC), translocation domain and catalytic domain (light chain) . The HC domain facilitates the highly specific binding of BoNTs to the neuronal membrane via a dual‐receptor complex involving a protein receptor and a ganglioside. Variation in activity/toxicity across subtypes of serotype A has been attributed to changes in protein and ganglioside interactions, and their implications are important in the design of novel BoNT‐based therapeutics. Here, we present the structure of BoNT/A3 cell binding domain (HC/A3) in complex with the ganglioside GD1a at 1.75 Å resolution. The structure revealed that six residues interact with the three outermost monosaccharides of GD1a through several key hydrogen bonding interactions. A detailed comparison of structures of HC/A3 with HC/A1 revealed subtle conformational differences at the ganglioside binding site upon carbohydrate binding.
Remote Sensing Big Data Classification with High Performance Distributed Deep Learning

High-Performance Computing (HPC) has recently been attracting more attention in remote sensing applications due to the challenges posed by the increased amount of open data that are produced daily by Earth Observation (EO) programs. The unique parallel computing environments and programming techniques that are integrated in HPC systems are able to solve large-scale problems such as the training of classification algorithms with large amounts of Remote Sensing (RS) data. This paper shows that the training of state-of-the-art deep Convolutional Neural Networks (CNNs) can be efficiently performed in distributed fashion using parallel implementation techniques on HPC machines containing a large number of Graphics Processing Units (GPUs). The experimental results confirm that distributed training can drastically reduce the amount of time needed to perform full training, resulting in near linear scaling without loss of test accuracy.
DeepFactors: Real-Time Probabilistic Dense Monocular SLAM

The ability to estimate rich geometry and camera motion from monocular imagery is fundamental to future interactive robotics and augmented reality applications. Different approaches have been proposed that vary in scene geometry representation (sparse landmarks, dense maps), the consistency metric used for optimising the multi-view problem, and the use of learned priors. We present a SLAM system that unifies these methods in a probabilistic framework while still maintaining real-time performance. This is achieved through the use of a learned compact depth map representation and reformulating three different types of errors: photometric, reprojection and geometric, which we make use of within standard factor graph software. We evaluate our system on trajectory estimation and depth reconstruction on real-world sequences and present various examples of estimated dense geometry.
Astrocyte senescence promotes glutamate toxicity in cortical neurons

Neurodegeneration is a major age-related pathology. Cognitive decline is characteristic of patients with Alzheimer’s and related dementias and cancer patients after chemo- or radio-therapies. A recently emerged driver of these and other age-related pathologies is cellular senescence, a cell fate that entails a permanent cell cycle arrest and pro-inflammatory senescence-associated secretory phenotype (SASP). Although there is a link between inflammation and neurodegenerative diseases, there are many open questions regarding how cellular senescence affects neurodegenerative pathologies. Among the various cell types in the brain, astrocytes are the most abundant. Astrocytes have proliferative capacity and are essential for neuron survival. Here, we investigated the phenotype of primary human astrocytes made senescent by X-irradiation, and identified genes encoding glutamate and potassium transporters as specifically downregulated upon senescence. This down regulation led to neuronal cell death in co-culture assays. Unbiased RNA sequencing of transcripts expressed by non-senescent and senescent astrocytes confirmed that glutamate homeostasis pathway declines upon senescence. Our results suggest a key role for cellular senescence, particularly in astrocytes, in excitotoxicity, which may lead to neurodegeneration including Alzheimer’s disease and related dementias.
Comparison of Forceps and Vacuum-Assisted Vaginal Deliveries in Terms of Levator Ani Muscle Injury

INTRODUCTION: This study compared the relative odds of levator ani muscle injury using three-dimensional ultrasonography after forceps compared with vacuum delivery. METHODS: This was a retrospective cohort study. Women who had undergone at least one forceps delivery were compared with women who had undergone at least one vacuum birth. Participants were 5–10 years from first delivery. Three-dimensional transperineal ultrasound volumes were captured as cine loops at rest, with Valsalva and with squeeze. The primary outcome was levator ani muscle avulsion. Secondary outcomes included levator–urethral gap, hiatal diameter, and area. RESULTS: Among 89 participants, there were no differences in maternal age at first delivery, parity, body mass index, birth weight, episiotomy, any perineal laceration, or duration of the second stage of labor among women in the two vaginal delivery groups. However, anal sphincter lacerations were more common in the forceps vaginal delivery group. The odd of levator ani muscle avulsion was four times higher for women in forceps vaginal delivery group compared to vacuum vaginal delivery group (odds ratio 4.15, 95% confidence interval 1.32–14.42). Women in the forceps vaginal delivery group also had wider levator–urethral gap, larger hiatal diameter, and widened hiatal area at Valsalva and squeeze. Among women who had forceps vaginal delivery, levator avulsions were associated with older age and lower parity. CONCLUSIONS: Levator avulsion is more common among women with a history of forceps vaginal delivery than vacuum-assisted vaginal delivery. This difference may contribute to the increased prevalence of pelvic floor disorders after forceps compared with vacuum delivery.
Spatial Downscaling of GRACE TWSA Data to Identify Spatiotemporal Groundwater Level Trends in the Upper Floridan Aquifer, Georgia, USA

Accurate assessments of groundwater resources in major aquifers across the globe are crucial for sustainable management of freshwater reservoirs. Observations from the Gravity Recovery and Climate Experiment (GRACE) satellite have become invaluable as a means to identify regions groundwater change. While there is a large body of research that focuses on downscaling coarse (1°) GRACE products, few studies have attempted to spatially downscale GRACE to produce fine resolution (5 km) maps that are more useful to resource managers. This study trained a boosted regression tree model to statistically downscale GRACE total water storage anomaly to monthly 5 km groundwater level anomaly maps in the karstic upper Floridan aquifer (UFA) using multiple hydrologic datasets. Evaluation of spatial predictions with existing groundwater wells indicated satisfactory performance (R = 0.79, NSE = 0.61). Results demonstrate that groundwater levels were stable between 2002–2016 but varied seasonally. The data also highlights areas where groundwater pumping is exacerbating UFA water-level declines. While results demonstrate the applicability of machine learning based methods for spatial downscaling of GRACE data, future studies should account for preferential flowpaths (i.e., conduits, lineaments) in karstic systems.
Predictions of Cu, Zn, and Cd Concentrations in Soil Using Portable X-Ray Fluorescence Measurements

Portable X-ray fluorescence (PXRF) measurements on 1520 soil samples were used to create national prediction models for copper (Cu), zinc (Zn), and cadmium (Cd) concentrations in agricultural soil. The models were validated at both national and farm scales. Multiple linear regression (MLR), random forest (RF), and multivariate adaptive regression spline (MARS) models were created and compared. National scale cross-validation of the models gave the following R2 values for predictions of Cu (R2 = 0.63), Zn (R2 = 0.92), and Cd (R2 = 0.70) concentrations. Independent validation at the farm scale revealed that Zn predictions were relatively successful regardless of the model used (R2 > 0.90), showing that a simple MLR model can be sufficient for certain predictions. However, predictions at the farm scale revealed that the non-linear models, especially MARS, were more accurate than MLR for Cu (R2 = 0.94) and Cd (R2 = 0.80). These results show that multivariate modelling can compensate for some of the shortcomings of the PXRF device (e.g., high limits of detection for certain elements and some elements not being directly measurable), making PXRF sensors capable of predicting elemental concentrations in soil at comparable levels of accuracy to conventional laboratory analyses.
The Positive Impact of Task Familiarity, Risk Propensity, and Need For Cognition on Observed Timing Decisions in a Security Game

This paper addresses the role of personality characteristics in decisions on the timing of an action, such as in the context of security and safety choices. Examples of such decisions include when to check log files for intruders and when to monitor financial accounts for fraud or errors. Two behavioral studies (n = 461) are conducted. Individual risk propensity and need for cognition are obtained via scales. The task is a game against an opaque computer opponent in which participants make decisions about the timing of actions in response to an unknown external risk factor. The task is not payoff-neutral w.r. t. risk. Difficulty is varied through the availability of explicitly given or decision-critical information, which is observable visually (Study 1) or in temporal memory (Study 2). Across this problem space, we find that risk propensity is not generally a hindrance in timing tasks. Participants of average risk propensity generally benefit from a high need for cognition, particularly when externalized memory is available, as in Study 1. In the more difficult temporal-estimation task, need for cognition was associated with increased payoffs from task experience. In both tasks, higher risk propensity in participants was associated with increased improvements in payoffs from task experience.