Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time Dense Reconstruction of Tissue Surface From Stereo Optical Video

We propose an approach to reconstruct dense three-dimensional (3D) model of tissue surface from stereo optical videos in real-time, the basic idea of which is to first extract 3D information from video frames by using stereo matching, and then to mosaic the reconstructed 3D models. To handle the common low-texture regions on tissue surfaces, we propose effective post-processing steps for the local stereo matching method to enlarge the radius of constraint, which include outliers removal, hole filling, and smoothing. Since the tissue models obtained by stereo matching are limited to the field of view of the imaging modality, we propose a model mosaicking method by using a novel feature-based simultaneously localization and mapping (SLAM) method to align the models. Low-texture regions and the varying illumination condition may lead to a large percentage of feature matching outliers. To solve this problem, we propose several algorithms to improve the robustness of the SLAM, which mainly include 1) a histogram voting-based method to roughly select possible inliers from the feature matching results; 2) a novel 1-point RANSAC-based <inline-formula> <tex-math notation="LaTeX">$\text{P}{n}\text{P}$ </tex-math></inline-formula> algorithm called as DynamicR1PP<inline-formula> <tex-math notation="LaTeX">${n}\text{P}$ </tex-math></inline-formula> to track the camera motion; and 3) a GPU-based iterative closest points (ICP) and bundle adjustment (BA) method to refine the camera motion estimation results. Experimental results on <italic>ex</italic>- and <italic>in vivo</italic> data showed that the reconstructed 3D models have high-resolution texture with an accuracy error of less than 2 mm. Most algorithms are highly parallelized for GPU computation, and the average runtime for processing one key frame is 76.3 ms on stereo images with <inline-formula> <tex-math notation="LaTeX">${960}\times {540}$ </tex-math></inline-formula> resolution.
Structural determinants for substrate selectivity in guanine deaminase enzymes of the amidohydrolase superfamily.

Guanine deaminase is a metabolic enzyme, found in all forms of life, which catalyzes the conversion of guanine to xanthine. Despite the availability of several crystal structures, the molecular determinants of substrate orientation and mechanism remains to be elucidated for the amidohydrolase family of guanine deaminase enzymes. Here, we report the crystal structures of E. coli and S. cerevisiae guanine deaminase enzymes (EcGuaD and Gud1, respectively), both members of the amidohydrolase superfamily. EcGuaD and Gud1 retain the overall TIM barrel tertiary structure conserved among amidohydrolase enzymes. Both proteins also possess a single zinc cation with trigonal bipyrimidal coordination geometry within their active sites. We also determined a liganded structure of Gud1 bound to the product, xanthine. Analysis of this structure, along with kinetic data of native and site-directed mutants of EcGuaD, identify several key residues responsible for substrate recognition and catalysis. In addition, after screening a small library of compounds, two guanine derivatives, 8-azaguanine and 1-methylguanine, were identified as EcGuaD substrates. Interestingly, both EcGuaD and Gud1 also exhibit secondary ammeline deaminase activity. Overall, this work details key structural features of substrate recognition and catalysis of the amidohydrolase family of guanine deaminase enzymes in support of our long term goal to engineer these enzymes with altered activity and substrate specificity.
FNDC5/Irisin counteracts lipotoxic-induced apoptosis in hypoxic H9c2 cells.

Irisin is a newly identified adipokine critical to modulate body metabolism, fatty acid metabolism and oxidative stress; recent evidence suggests a cardioprotective role in ischemic injury. Loss of cardiomyocytes during acute myocardial infarction is strongly associated with energetic changes and lipotoxic-induced apoptosis. Our aim was to study FNDC5/irisin potential protective role against hypoxia and lipotoxicity, both related with myocardial infarction environment. H9c2 cells were treated with palmitate and/or irisin in normoxic/hypoxic conditions. Cell viability and apoptosis were assessed by MTT assay and annexin V/PI staining. Immunoblotting was used to confirm apoptotic cascade regulation. Irisin counteracts lipotoxic-induced apoptosis in hypoxic cardiomyoblasts by activating Akt signalling pathway suggesting the potential therapeutic role of irisin in ischemic heart disease.
Marine ice sheet instability amplifies and skews uncertainty in projections of future sea-level rise

Significance The potential for collapse of the Antarctic ice sheet remains the largest single source of uncertainty in projections of future sea-level rise. This uncertainty comes from an imperfect understanding of ice sheet processes and the internal variability of climate forcing of ice sheets. Using a mathematical technique from statistical physics and large ensembles of state-of-the-art ice sheet simulations, we show that collapse of ice sheets widens the range of possible scenarios for future sea-level rise. We also find that the collapse of marine ice sheets makes worst-case scenarios of rapid sea-level rise more likely in future projections. Sea-level rise may accelerate significantly if marine ice sheets become unstable. If such instability occurs, there would be considerable uncertainty in future sea-level rise projections due to imperfectly modeled ice sheet processes and unpredictable climate variability. In this study, we use mathematical and computational approaches to identify the ice sheet processes that drive uncertainty in sea-level projections. Using stochastic perturbation theory from statistical physics as a tool, we show mathematically that the marine ice sheet instability greatly amplifies and skews uncertainty in sea-level projections with worst-case scenarios of rapid sea-level rise being more likely than best-case scenarios of slower sea-level rise. We also perform large ensemble simulations with a state-of-the-art ice sheet model of Thwaites Glacier, a marine-terminating glacier in West Antarctica that is thought to be unstable. These ensemble simulations indicate that the uncertainty solely related to internal climate variability can be a large fraction of the total ice loss expected from Thwaites Glacier. We conclude that internal climate variability alone can be responsible for significant uncertainty in projections of sea-level rise and that large ensembles are a necessary tool for quantifying the upper bounds of this uncertainty.
Hydrogels for Liver Tissue Engineering

Bioengineered livers are promising in vitro models for drug testing, toxicological studies, and as disease models, and might in the future be an alternative for donor organs to treat end-stage liver diseases. Liver tissue engineering (LTE) aims to construct liver models that are physiologically relevant. To make bioengineered livers, the two most important ingredients are hepatic cells and supportive materials such as hydrogels. In the past decades, dozens of hydrogels have been developed to act as supportive materials, and some have been used for in vitro models and formed functional liver constructs. However, currently none of the used hydrogels are suitable for in vivo transplantation. Here, the histology of the human liver and its relationship with LTE is introduced. After that, significant characteristics of hydrogels are described focusing on LTE. Then, both natural and synthetic materials utilized in hydrogels for LTE are reviewed individually. Finally, a conclusion is drawn on a comparison of the different hydrogels and their characteristics and ideal hydrogels are proposed to promote LTE.
"Improving the Seventh Grade Students' Vocabulary Achievement at SMP Negeri 11 Jember in the 2014/2015 Academic Year"

This research was intended to improve the seventh grade students’ vocabulary achievement through English songs at SMP Negeri 11 Jember in the 2014/2015 academic year. A Classroom Action Research was used by the researcher in this research to overcome the problems faced by the students in learning English due to the lack of vocabulary. The subjects of the research were chosen by using purposive method. The students of class VII-A were taken because they had the lowest mean score of the previous English midterm test. The data collection methods of this research were vocabulary achievement test, observation in the form of checklist, documentation, and interview. This research was conducted in two cycles. Each cycle consisted of two meetings and a vocabulary test. The target minimum score of this research is based on the English minimum requirement score at the school, that is 72. The average results of observations in Cycle 1 showed that 65.8% of the students were categorized as active students during the teaching learning process of vocabulary through English songs and it increased to 82% in Cycle 2. Moreover, the result of vocabulary achievement test proved that there were 23 students of 38 students (60.5%) could achieve the target minimum score of this research in Cycle 1, and it increased to 81.6% or 31 students in Cycle 2. Based on the research finding above, it was found that the use of English songs could improve the seventh grade students' vocabulary achievement.
Abnormal reward system network in primary dysmenorrhea

Neuroimaging studies have demonstrated that reward system is associated with chronic pain diseases. In addition, previous studies have also demonstrated abnormal functional and structural brain regions in primary dysmenorrhea. However, the relation of reward system and primary dysmenorrhea is still unknown. Using the resting state functional magnetic resonance imaging, we aimed to investigate the functional connectivity changes of reward system during periovulatory phase in primary dysmenorrhea. Forty-one primary dysmenorrhea patients and 39 matched female healthy controls participated in this study. Compared to healthy controls, primary dysmenorrhea patients showed decreased connectivity of left nucleus accumbens with the bilateral anterior insula and the left amygdala and decreased connectivity of right nucleus accumbens with ventral tegmental area, the left hippocampus, the right orbital frontal cortex, and the right anterior insula. In addition, the decreased functional connectivity between the right nucleus accumbens-ventral tegmental area negatively correlated with the level of prostaglandin F2 alpha. Our findings provide neuroimaging evidence in support of the abnormal reward system connectivity in primary dysmenorrhea patients, which might contribute to a better understanding of the cerebral pathophysiology of primary dysmenorrhea.
Signal Processing Methods for Network Anomaly Detection

3 Introduction and Background • DoS is popular now, with possible catastrophe, by consuming finite resource • Detection of and response to DoS is essential for network • Network Traffic itself is hard to be analyze – Non-Gaussian, Non-Stationary, Long Range Dependence, Heavy tailed • Attackers will try to make the attack traffic hard to be distinguished from normal traffic • Not sure which measurements best fit for anomalies detection • Not sure which method is best
Co-expression of key gene modules and pathways of human breast cancer cell lines

Breast cancer (BC) is the most common leading cause of cancer-related death in women worldwide. Gene expression profiling analysis for human BCs has been studied previously. However, co-expression analysis for BC cell lines is still devoid to date. The aim of the study was to identify key pathways and hub genes that may serve as a biomarker for BC and uncover potential molecular mechanism using weighted correlation network analysis. We analyzed microarray data of BC cell lines (GSE 48213) listed in the Gene Expression Omnibus database. Gene co-expression networks were used to construct and explore the biological function in hub modules using the weighted correlation network analysis algorithm method. Meanwhile, Gene ontology and KEGG pathway analysis were performed using Cytoscape plug-in ClueGo. The network of the key module was also constructed using Cytoscape. A total of 5000 genes were selected, 28 modules of co-expressed genes were identified from the gene co–expression network, one of which was found to be significantly associated with a subtype of BC lines. Functional enrichment analysis revealed that the brown module was mainly involved in the pathway of the autophagy, spliceosome, and mitophagy, the black module was mainly enriched in the pathway of colorectal cancer and pancreatic cancer, and genes in midnightblue module played critical roles in ribosome and regulation of lipolysis in adipocytes pathway. Three hub genes CBR3, SF3B6, and RHPN1 may play an important role in the development and malignancy of the disease. The findings of the present study could improve our understanding of the molecular pathogenesis of breast cancer.
Management of Thyroid Nodules Seen on US Images: Deep Learning May Match Performance of Radiologists.

Background Management of thyroid nodules may be inconsistent between different observers and time consuming for radiologists. An artificial intelligence system that uses deep learning may improve radiology workflow for management of thyroid nodules. Purpose To develop a deep learning algorithm that uses thyroid US images to decide whether a thyroid nodule should undergo a biopsy and to compare the performance of the algorithm with the performance of radiologists who adhere to American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS). Materials and Methods In this retrospective analysis, studies in patients referred for US with subsequent fine-needle aspiration or with surgical histologic analysis used as the standard were evaluated. The study period was from August 2006 to May 2010. A multitask deep convolutional neural network was trained to provide biopsy recommendations for thyroid nodules on the basis of two orthogonal US images as the input. In the training phase, the deep learning algorithm was first evaluated by using 10-fold cross-validation. Internal validation was then performed on an independent set of 99 consecutive nodules. The sensitivity and specificity of the algorithm were compared with a consensus of three ACR TI-RADS committee experts and nine other radiologists, all of whom interpreted thyroid US images in clinical practice. Results Included were 1377 thyroid nodules in 1230 patients with complete imaging data and conclusive cytologic or histologic diagnoses. For the 99 test nodules, the proposed deep learning algorithm achieved 13 of 15 (87%: 95% confidence interval [CI]: 67%, 100%) sensitivity, the same as expert consensus (P > .99) and higher than five of nine radiologists. The specificity of the deep learning algorithm was 44 of 84 (52%; 95% CI: 42%, 62%), which was similar to expert consensus (43 of 84; 51%; 95% CI: 41%, 62%; P = .91) and higher than seven of nine other radiologists. The mean sensitivity and specificity for the nine radiologists was 83% (95% CI: 64%, 98%) and 48% (95% CI: 37%, 59%), respectively. Conclusion Sensitivity and specificity of a deep learning algorithm for thyroid nodule biopsy recommendations was similar to that of expert radiologists who used American College of Radiology Thyroid Imaging and Reporting Data System guidelines. © RSNA, 2019 Online supplemental material is available for this article.
Validation of the Zung self-rating depression scale (SDS) in older adults

Abstract Objective: The main objective of this study was to investigate the psychometric properties of the Zung Self-Rating Depression Scale (SDS) and evaluate screening parameters capability of the SDS with the Beck Depression Inventory (BDI-21) among the elderly population. Design: A population-based study Setting: Community Subjects: 520 adults, aged 72–73 years, living in the city of Oulu, Finland. Main outcome measures: The screening parameters of the SDS questions and BDI-21 for detecting severity of depression. The Mini Neuropsychiatric Interview for diagnosing major depression. Results: The optimal cut-off point for the SDS was 39. The sensitivity and specificity parameters for this cut-off point were 79.2% (95% CI 57.8–92.9) and 72.2% (95% CI 67.9–76.1), respectively. Positive and negative predictive values were 12.5% (95% CI 7.7–18.8) and 98.6% (95% CI 96.7–99.5), respectively. Moreover, there was no statistically significant difference in diagnostic accuracy indices of the cut-off points 39 and 40. In a receiver operating characteristic analysis, the area under the curve was 0.85 (95%CI 0.77–0.92) for the SDS total score and 0.89 (95% CI 0.83-0.96) for the BDI-21 (p = 0.137). Conclusion: Using the traditional cut-off point, the SDS was convenient for identifying clinically meaningful depressive symptoms in an elderly Finnish population when compared with the BDI-21 which is one of the most commonly used depression screening scales. The sensitivity and specificity of these two screening tools are comparable. Based on our study, the SDS is convenient for identifying clinically meaningful depressive symptoms among older adults at the community level. Key points The widely used Zung Self-Rating Depression Scale (SDS) has not previously been validated among elderly people at the community level. The sensitivity and specificity of SDS (cut-off point 39) were 79.2% and 72.2%. The positive and negative predictive values for SDS were 12.5% and 98.6%. SDS is convenient for identifying major depression in an elderly population and regarding sensitivity and specificity comparable to BDI-21.
Plasmon Free Surface Enhanced Raman Spectroscopy Using Metallic 2D Materials.

Two dimensional (2D) materials-based plasmon free surface enhanced Raman scattering (SERS) is an emerging field in non-destructive analysis. However, impeded by the low density of states (DOS), an inferior detection sensitivity is frequently encountered due to the low enhancement factor of most 2D materials. Metallic transition metal dichalcogenides (TMDs) could be ideal plasmon free SERS substrates because of their abundant DOS near the Fermi level. However, the absence of controllable synthesis of metallic 2D TMDs has hindered their study as SERS substrates. Here, we realize controllable synthesis of ultra-thin metallic 2D niobium disulfide (NbS2) (<2.5 nm) with large domain size (>160 μm). We have explored the SERS performance of as-obtained NbS2, which shows a detection limit down to 10^-14 mol•L-1. The enhancement mechanism was studied in-depth by density functional theory, which suggested a strong correlation between the SERS performance and DOS near the Fermi level. NbS2 features most abundant DOS and strongest binding energy with probe molecules as compared with other 2D materials such as graphene, 1T phase MoS2 and 2H phase MoS2. The large DOS increases the intermolecular charge transfer probability and thus induces prominent Raman enhancement. To extend the results to practical applications, the resulting NbS2-based plasmon free SERS substrates were applied for distinguishing different types of red wines.
Effect of Freeze–Thaw Cycling on the Failure of Fibre-Cement Boards, Assessed Using Acoustic Emission Method and Artificial Neural Network

This paper presents the results of investigations into the effect of freeze–thaw cycling on the failure of fibre-cement boards and on the changes taking place in their structure. Fibre-cement board specimens were subjected to one and ten freeze–thaw cycles and then investigated under three-point bending by means of the acoustic emission method. An artificial neural network was employed to analyse the results yielded by the acoustic emission method. The investigations conclusively proved that freeze–thaw cycling had an effect on the failure of fibre-cement boards, as indicated mainly by the fall in the number of acoustic emission (AE) events recognized as accompanying the breaking of fibres during the three-point bending of the specimens. SEM examinations were carried out to gain better insight into the changes taking place in the structure of the tested boards. Interesting results with significance for building practice were obtained.
Acceleration of a Stereo Navigation Application for Autonomous Vehicles

Throughout the years, embedded processor systems are becoming more and more used for applications in the most diverse fields. There are however some applications that are too time demanding to be executed solely by a standalone embedded processor. To relieve some of the processing load, dedicated hardware blocks can be coupled to the processor aiding it by processing the parts of the application that are, computationally speaking, more intensive. The main objective of this thesis was to hardware accelerate a third party C-language application that was aimed to execute in a general purpose processor. The application to be accelerated is aimed for a stereo navigation system where, through image processing of data coming from two cameras, an autonomous vehicle could infer its relative position, rotation and translation. The objective to accelerate this application is inserted into the European REFLECT project. In this thesis, the three functions that consumed most of the application’s time were identified when executed in an PowerPC 440 processor at 400MHz. Using the high level synthesis tool Catapult C, the corresponding modules that would replace this three functions were created. These modules were synthesized in a Xilinx Virtex-5 FPGA and executed at 100MHz. It was created an hardware/software hybrid where the most computational intensive functions were carried by the hardware blocks, being the rest left to be carried by the processor. Using these hardware modules to aid the application that was executed by the PowerPC 440 processor, a speed-up of 1,5 times was achieved when compared to the same application being carried solely as software by the processor.
The Foundation Fighting Blindness Plays an Essential and Expansive Role in Driving Genetic Research for Inherited Retinal Diseases

The Foundation Fighting Blindness leads a collaborative effort among patients and families, scientists, and the commercial sector to drive the development of preventions, treatments, and cures for inherited retinal diseases (IRDs). When the nonprofit was established in 1971, it sought the knowledge and insights of leaders in the retinal research field to guide its research funding decisions. While the Foundation’s early investments focused on gaining a better understanding of the genetic causes of IRDs, its portfolio of projects would come to include some of the most innovative approaches to saving and restoring vision, including gene replacement/augmentation therapies, gene editing, RNA modulation, optogenetics, and gene-based neuroprotection. In recent years, the Foundation invested in resources such as its patient registry, natural history studies, and genetic testing program to bolster clinical development and trials for emerging genetic therapies. Though the number of clinical trials for such therapies has surged over the last decade, the Foundation remains steadfast in its commitment to funding the initiatives that hold the most potential for eradicating the entire spectrum of IRDs.