Miracle-dz/UniFAR-FTUs huggingface dataset:
Advancements in Regenerative Strategies Through the Continuum of Burn Care

Burns are caused by several mechanisms including flame, scald, chemical, electrical, and ionizing and non-ionizing radiation. Approximately half a million burn cases are registered annually, of which 40 thousand patients are hospitalized and receive definitive treatment. Burn care is very resource intensive as the treatment regimens and length of hospitalization are substantial. Burn wounds are classified based on depth as superficial (first degree), partial-thickness (second degree), or full-thickness (third degree), which determines the treatment necessary for successful healing. The goal of burn wound care is to fully restore the barrier function of the tissue as quickly as possible while minimizing infection, scarring, and contracture. The aim of this review is to highlight how tissue engineering and regenerative medicine strategies are being used to address the unique challenges of burn wound healing and define the current gaps in care for both partial- and full-thickness burn injuries. This review will present the current standard of care (SOC) and provide information on various treatment options that have been tested pre-clinically or are currently in clinical trials. Due to the complexity of burn wound healing compared to other skin injuries, burn specific treatment regimens must be developed. Recently, tissue engineering and regenerative medicine strategies have been developed to improve skin regeneration that can restore normal skin physiology and limit adverse outcomes, such as infection, delayed re-epithelialization, and scarring. Our emphasis will be centered on how current clinical and pre-clinical research of pharmacological agents, biomaterials, and cellular-based therapies can be applied throughout the continuum of burn care by targeting the stages of wound healing: hemostasis, inflammation, cell proliferation, and matrix remodeling.
Efficient privacy-preserving motion detection for HEVC compressed video in cloud video surveillance

Privacy-preserving information utilization has attracted a lot of attention in the big data era where the data owners and data processers may be separate entities. Cloud video surveillance is a typical example that requires privacy-preserving data processing. Video surveillance can leverage the cloud for its storage, processing power, and accessibility from anywhere with any devices. In such a system, surveillance cameras simply stream video data to the cloud, which then stores the video data, performs motion detection and other analysis, and alerts users if needed. Cloud video surveillance presents a unique challenge: privacy protection of surveillance video since it will be transmitted over public networks, stored and possibly processed at a third-party cloud that may not be trusted. This paper addresses this challenge by proposing a method that allows a third party to perform motion detection directly on the encrypted and compressed surveillance video without decryption. Specifically, we propose the first compressed-domain privacy-preserving motion detection method that preserves the compression efficiency of HEVC compressed video. The proposed method can detect the coarse-grained shapes of moving objects and then estimate the motion trajectory. In addition to the preserved high compression efficiency of standard video coding, the proposed method also has the following desirable properties: 1) it only needs one cloud to participate; 2) authorized users can fully recover the original compressed video recorded by the camera; 3) low computational complexity.
Changes in oral microbiota due to orthodontic appliances: a systematic review

ABSTRACT Background: Oral microbiota has been at the center of cultural attention in recent years. In daily clinical practice, orthodontic appliances may be associated with an increased cariogenic risk and a worsening of preexisting periodontal diseases. Objective: The purpose of this review is to investigate the available evidence regarding the association between orthodontic appliances and changes in the quality and quantity of the oral microbiota. Design: The research included every article published up to October 2017 featuring the keywords ‘Orthodontic appliance* AND (microbiological colonization OR periodontal pathogen* OR Streptococcus mutans OR Lactobacillus spp. OR Candida OR Tannerella forsythia OR Treponema denticola OR Fusobacterium nucleatum OR Aggregatibacter actinomycetemcomitans OR Prevotella intermedia OR Prevotella nigrescens OR Porphyromonas gingivalis)’ and was conducted in the major medical databases. The methodological quality of selected papers was scored using the ‘Swedish Council on Technology Assessment in Health Care Criteria for Grading Assessed Studies’ (SBU) method. Results: Orthodontic appliances influence the oral microbiota with an increase in the counts of S. mutans and Lactobacillus spp. and in the percentage of potentially pathogenic gram-negative bacteria. Conclusions: There is moderate/high evidence regarding the association between orthodontic appliances and changes in the oral microbiota. PROSPERO registration number CRD42018091589.
Association of CpG-SNP and 3'UTR-SNP of WFS1 with the Risk of Type 2 Diabetes Mellitus in an Iranian Population

Type 2 diabetes mellitus (T2DM) is one of the most common multifactorial disorders in Iran. Recent genome wide association studies (GWASs) and functional studies have suggested that WFS1 may predispose individuals to T2DM. However, to date, the possible association of such variants with T2DM in Iranians remained unknown. Here, we investigated the association of the two polymorphisms of WFS1 (rs1801214 a CpG-SNP, and rs1046320 a 3’UTR-SNP) with T2DM in an Iranian population. The study population comprised 432 unrelated Iranian individuals including 220 patients with T2DM, and 211 unrelated healthy control subjects. Genotyping was performed using PCR-RFLP, and confirmed with sequencing. In a logistic regression analysis, the rs1801214-T allele was associated with a significantly lower risk of T2DM assuming the log-additive model (OR: 0.68, 95% CI: 0.52-0.91, P= 0.007539). Moreover, the G allele of rs1046320 was associated with a lower risk of T2DM assuming the log-additive model (OR: 0.68, 95% CI: 0.50- 0.91, P= 0.008313). Haplotype analysis revealed that haplotypes that carry at least one protective allele are associated with a lower risk of T2DM. This is a first evidence for the association of WFS1 rs1801214, and rs1046320 with T2DM in an Iranian population.
Research of image deblurring based on the deep neural network

Image deblurring has a lot of applications which include but not limit recovery a high-quality Satellite imagery from a blurry one, extract more detail information from low resolution monitoring images, image compression and decompression. However, image deblurring has always been a difficult problem in the field of image processing. The traditional methods can effectively improve the image quality by image denoising technology. But this kind of methods has limit ability in recovery clear image from very vague images. Instead of previous methods, we focus on more challenger task that recovery high-resolution image from very vague images. We proposed to use the deep learning way to solve the problem and designed an end-to-end deep neural network for this task. To improve the quality of vague and ensure the accuracy of recovery images, we train our deep neural work by the adversarial training way and design our network based on encoder-decoder network using the convolution layer as network layers. The dataset we used to train our model is Celeba dataset. The results of our methods is promising compared with the traditional methods.
Fast and accurate classifier-based brain-computer interface system using single channel EEG data

In brain-computer interface system (BCIs), direct communication between humans and computers is performed by analyzing neural signals and transforming them into digital signals. In the continuation of our previous works, in this paper, we advanced the proposed BCI system that was based on the gaze on rotating vanes. The speed of communication and convenience of the user are very important factors in BCI systems. Therefore, in this paper, for the convenience of the user, a single EEG channel was used. Also, for to increase the speed of the system, we tried to the classification of 0.5 sec epochs with a partial least squares regression (PLSR) as a fast classifier. In addition, we computed the information transfer rate (ITR) that has proved our proposed BCI system is fast and accurate. This system could be used in real-time implementations due to having high classification rate, speed and convenience of the user.
A user-centered approach for integrating social actors into communities of interest

Detecting communities of interest is a complex problem that has been addressed from different perspectives. In this work, we propose a user-centered approach incorporating social user profiles in community detection for online Social Networks. In our approach, we first compute explicit knowledge acquisition. By exploring the egocentric networks of users, we can infer implicit similarities of interest. The similarity is estimated with reference to homophily and social influence. The latter is leveraged to enhance Sentiment Analysis within communities. Finally, we conduct experiments on datasets extracted from real-world Social Networks.
Unique maternal and environmental effects on the body morphology of the Least Killifish, Heterandria formosa

Abstract An important step in diagnosing local adaptation is the demonstration that phenotypic variation among populations is at least in part genetically based. To do this, many methods experimentally minimize the environmental effect on the phenotype to elucidate the genetic effect. Minimizing the environmental effect often includes reducing possible environmental maternal effects. However, maternal effects can be an important factor in patterns of local adaptation as well as adaptive plasticity. Here, we report the results of an experiment with males from two populations of the poeciliid fish, Heterandria formosa, designed to examine the relative influence of environmental maternal effects and environmental effects experienced during growth and development on body morphology, and, in addition, whether the balance among those effects is unique to each population. We used a factorial design that varied thermal environment and water chemistry experienced by mothers and thermal environment and water chemistry experienced by offspring. We found substantial differences between the two populations in their maternal and offspring norms of reaction of male body morphology to differences in thermal environment and water chemistry. We also found that the balance between maternal effects and postparturition environmental effects differed from one thermal regime to another and among traits. These results indicate that environmental maternal effects can be decidedly population‐specific and, as a result, might either contribute to the appearance of or blur evidence for local adaptation. These results also suggest that local adaptation might also occur through the evolution of maternal norms of reaction to important, and varying, environmental factors.
Challenges faced and lessons learned A multi-component prospective memory training program for Malaysian older adults

Abstract The surrounding circumstances and environments of Malaysian older adults could make conducting interventions (mainly in terms of clinical or randomized controlled trials) a challenge. Working with older adults and facing cultural issues could be challenging. Objective: This paper illustrates a significant perspective of some of the challenges faced while conducting a randomized controlled trial exploring the impact of a multi-component intervention that included strategy- and process-based prospective memory (PM) training among Malaysian older adults. Methods: The current study was a randomized controlled trial (RCT) and therefore the challenges were presented in accordance with the CONSORT statement style. Results: A discussion on how these issues were addressed is provided. Conclusion: Some suggestions were presented to help researchers plan and create interventions for similar studies and to support a practical method of addressing all related challenges.
Component-based combination of online-diagnosis methods using diagnostic directed acyclic graphs

In safety-critical application domains online fault-diagnosis contributes to a significantly increased system reliability and safety by detecting and diagnosing occurred faults and, if applicable, making the system recover from faults. In order to enable fault-specific recovery actions, e.g., a reconfiguration of the system, cause-based fault identification is needed. This typically requires a large amount of data to be analyzed and evaluated during a diagnostic process. For sound decisions on occurred faults within complex systems, it is often beneficial to combine several online-diagnosis methods. In this work we present a component-based diagnostic framework based on a diagnostic dependency graph. Herein, multiple online-diagnosis methods are combined in the form of encapsulated tasks. The dependencies as well as the tasks adapt to the system at run time. Our diagnostic framework is demonstrated by means of an automotive use-case.
The “Sticky Choice” Bias in Sequential Decision-Making

Consumers often encounter product information in stages. Sometimes new information favors an option that was not favored by the initial information set. Because choices are based on available information, new information may challenge an earlier choice. In response, consumers might be expected to switch to an option favored by new information. However, we show that expressing an initial decision produces a “sticky choice” bias; consumers who make sequential choices are less likely than those who make a single choice to ultimately choose an option favored by new information. In this research, we aim to show that “sticky choice” bias is caused by consistency-seeking across repeated decisions.
Credit risk analysis based on hybrid classification: Case studies on German and Turkish credit datasets

In finance sector, credit risk analysis plays a major role in decision process. Banks and finance institutions gather large amounts of raw data from their customers. Data mining techniques can be employed to obtain useful information from this raw data. Several data mining techniques, such as support-vector machines (SVM), neural networks, naive-bayes, have already been used to classify customers. In this paper, we propose hybrid classification approaches, which try to combine several classifiers and ensemble learners to boost accuracy on classification results. Furthermore, we compare these approaches' performance with respect to their classification accuracy. We work with two diverse datasets; namely, German credit dataset and Turkish bank dataset. The goal of using such diverse dataset is to show generalization capabality of our approaches. Experimental results provide three important consequences. First, feature selection stage has a major role both on result accuracy and calculation complexity. Second, hybrid approaches have better generalability over single classifiers. Third, using SVM-Radial Basis Function (RBF) as the base classifier and a hybrid model member gives the best accuracy and type-1 accuracy results among others.
Reservoir Computing Universality With Stochastic Inputs

The universal approximation properties with respect to <inline-formula> <tex-math notation="LaTeX">$L ^{p} $ </tex-math></inline-formula>-type criteria of three important families of reservoir computers with stochastic discrete-time semi-infinite inputs are shown. First, it is proven that linear reservoir systems with either polynomial or neural network readout maps are universal. More importantly, it is proven that the same property holds for two families with linear readouts, namely, trigonometric state-affine systems and echo state networks, which are the most widely used reservoir systems in applications. The linearity in the readouts is a key feature in supervised machine learning applications. It guarantees that these systems can be used in high-dimensional situations and in the presence of large data sets. The <inline-formula> <tex-math notation="LaTeX">$L ^{p} $ </tex-math></inline-formula> criteria used in this paper allow the formulation of universality results that do not necessarily impose almost sure uniform boundedness in the inputs or the fading memory property in the filter that needs to be approximated.
Lane keeping control on Mecanum wheeled omnidirectional vehicles using laser scanner

In recent years, there is a strong demand for automated guided vehicles in many fields such as manufacturing or studying and a variety of omnidirectional vehicle systems has been developed for these purposes, which includes the Mecanum-wheel vehicles. In the meantime, identifying the environment by laser scanner has increasingly become an efficient solution for robotic recognition. In order to implement an intelligent vehicle for industrial environment, a lane keeping method for Mecanum wheeled Omni-directional vehicles is proposed in this paper. We first design a LQR controller for the omnidirectional vehicle motion control. After that, a laser scanner is used to sense the lane for the vehicle. In the aspect of algorithm, a set of waypoints is used to determine the heading direction of the vehicle when cornering and a PID controller is designed to keep the vehicle in the detected lane when it is passing a relatively straight lane. The simulation experiment shows that the omnidirectional platform tracking system has a good response by using PID control. Finally, experimental result shows that the real vehicle can track the desired trajectory rapidly and smoothly, and the vehicle meanwhile is constrained in the lane in the whole process.
Object tracking by deep object detectors and particle filtering

Developing object tracking techniques robust to blur, scale changes, occlusion and illumination changes is a challenging problem for several applications. Recently many algorithms using deep learning for visual object tracking are proposed. These algorithms mostly perform object detection without using the temporal information for video object tracking. Nevertheless, they provide high object detection accuracy as a result of an extended training scheme. However, particle filtering enables us to track the objects with a lower complexity without requiring any training, when the state transition and observation models are formulated appropriately. In this paper, tracking performance of two visual object trackers (Faster R-CNN, Mask R-CNN) and the variable rate color based particle filtering are tested on OTB-50, VOT 2016 and 2017 datasets. Benefits and deficits of both these approaches are examined. It is concluded that the deep learning methods outperform particle filtering under occlusion and scale changes, whereas particle filtering is more robust to illumination changes and blur. Integration of both approaches improves object tracking accuracy.
Detecting the use of unsafe data in software of embedded systems by means of static analysis methodology

The article considers the approach to identifying potentially unsafe data in program code of embedded systems which can lead to errors and fails in the functioning of equipment. The sources of invalid data are revealed and the process of changing the status of this data in process of static code analysis is shown. The mechanism for annotating functions that operate on unsafe data is described, which allows to control the entire process of using them and thus it will improve the quality of the output code.
Towards analog filter-free all-digital transmitters through hybrid estimation and cancellation of ASM's quantization noise

This paper discusses one first approach towards the design of analog filter-free All-Digital RF Transmitters suitable for multi-band scenarios. In particular, the implementation of a feedforward technique will be targeted with the overall aim of analog canceling the digitally estimated out-of-band quantization noise. Simulated results are presented and evaluated in terms of Normalized Mean Square Error (NMSE), Adjacent Channel Power Ratio (ACPR) and obtained spectra.
Debromination of Hexabromocyclododecane by Anaerobic Consortium and Characterization of Functional Bacteria

A microbial consortium which can efficiently remove hexabromocyclododecane (HBCD) under anaerobic condition have been successfully enriched over 300 days. Under the optimal conditions, the degradation efficiency was 92.4% removal after treatment of 12 days with original addition of 500 μg/L HBCD, yielding 321.7 μg/L bromide in total as well. A typical debromination product, dibromocyclododecadiene (DBCD), was detected during the degradation process. The debromination profiles of three main HBCD diastereomers fitted well with first-order model (R2: 0.96–0.99), with the rate constants ranging from 1.3 × 10-1 to 1.9 × 10-1. The microbial community analysis by high throughput sequencing showed that the composition of the microbial communities varied dynamically with time and the population of functional bacteria increase sharply after enrichment. The population of Bacteroidetes increased from 5 to 47%. And some bacteria which are relatively minority in population at the beginning, such as Azospira oryzae (OTU2), Microbacterium (OTU13), and Achromobacter insolitus (OTU39) increased more than 22 times after enrichment (from 0.5 to 13%, 12%, and 11%, respectively). However, no reported dehalogenating bacteria were found after enrichment. And the contribution for debromination may come from new dehalogenating bacteria. All in all, the present study provided in-depth information on anaerobic microbial communities for HBCD removal by debromination.
Interpretability analysis for Turkish word embeddings

Due to the performance improvements they provided in natural language processing (NLP) applications, word embeddings are commonly studied and used. The algorithms that generate word embeddings, learn low dimensional, dense vector spaces that encode semantic relations among words in an unsupervised manner from large unannotated corpora. However, these vector spaces usually do not have interpretable dimensions making their semantic structure more challenging to be comprehended by the researchers. To have a better understanding of the inner structures of the word embeddings and further improve their utility, learning new, interpretable word embeddings is an active research area. In this study, a semantic category dataset (ANKAT) that contains more than 4000 unique Turkish words grouped under 62 different categories is composed to quantitatively evaluate the interpretability of the word embeddings. An interpretability analysis method based on this dataset is proposed and tested on five different embedding spaces.
Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning

This paper presents KeypointNet, an end-to-end geometric reasoning framework to learn an optimal set of category-specific 3D keypoints, along with their detectors. Given a single image, KeypointNet extracts 3D keypoints that are optimized for a downstream task. We demonstrate this framework on 3D pose estimation by proposing a differentiable objective that seeks the optimal set of keypoints for recovering the relative pose between two views of an object. Our model discovers geometrically and semantically consistent keypoints across viewing angles and instances of an object category. Importantly, we find that our end-to-end framework using no ground-truth keypoint annotations outperforms a fully supervised baseline using the same neural network architecture on the task of pose estimation. The discovered 3D keypoints on the car, chair, and plane categories of ShapeNet [6] are visualized at keypointnet.github. io.