Miracle-dz/UniFAR-FTUs huggingface dataset:
Physiological and pathological roles of FATP-mediated lipid droplets in Drosophila and mice retina

Increasing evidence suggests that dysregulation of lipid metabolism is associated with neurodegeneration in retinal diseases such as age-related macular degeneration and in brain disorders such as Alzheimer’s and Parkinson’s diseases. Lipid storage organelles (lipid droplets, LDs), accumulate in many cell types in response to stress, and it is now clear that LDs function not only as lipid stores but also as dynamic regulators of the stress response. However, whether these LDs are always protective or can also be deleterious to the cell is unknown. Here, we investigated the consequences of LD accumulation on retinal cell homeostasis under physiological and stress conditions in Drosophila and in mice. In wild-type Drosophila, we show that dFatp is required and sufficient for expansion of LD size in retinal pigment cells (RPCs) and that LDs in RPCs are required for photoreceptor survival during aging. Similarly, in mice, LD accumulation induced by RPC-specific expression of human FATP1 was non-toxic and promoted mitochondrial energy metabolism in RPCs and non-autonomously in photoreceptor cells. In contrast, the inhibition of LD accumulation by dFatp knockdown suppressed neurodegeneration in Aats-metFB Drosophila mutants, which carry elevated levels of reactive oxygen species (ROS). This suggests that abnormal turnover of LD may be toxic for photoreceptors cells of the retina under oxidative stress. Collectively, these findings indicate that FATP-mediated LD formation in RPCs promotes RPC and neuronal homeostasis under physiological conditions but could be deleterious for the photoreceptors under pathological conditions.
Kernel Sparse Representation with Hybrid Regularization for On-Road Traffic Sensor Data Imputation

The problem of missing values (MVs) in traffic sensor data analysis is universal in current intelligent transportation systems because of various reasons, such as sensor malfunction, transmission failure, etc. Accurate imputation of MVs is the foundation of subsequent data analysis tasks since most analysis algorithms need complete data as input. In this work, a novel MVs imputation approach termed as kernel sparse representation with elastic net regularization (KSR-EN) is developed for reconstructing MVs to facilitate analysis with traffic sensor data. The idea is to represent each sample as a linear combination of other samples due to inherent spatiotemporal correlation, as well as periodicity of daily traffic flow. To discover few yet correlated samples and make full use of the valuable information, a combination of l1-norm and l2-norm is employed to penalize the combination coefficients. Moreover, the linear representation among samples is extended to nonlinear representation by mapping input data space into high-dimensional feature space, which further enhances the recovery performance of our proposed approach. An efficient iterative algorithm is developed for solving KSR-EN model. The proposed method is verified on both an artificially simulated dataset and a public road network traffic sensor data. The results demonstrate the effectiveness of the proposed approach in terms of MVs imputation.
Kinesin-2 Controls the Motility of RAB5 Endosomes and Their Association with the Spindle in Mitosis

RAB5 is a small GTPase that belongs to the wide family of Rab proteins and localizes on early endosomes. In its active GTP-bound form, RAB5 recruits downstream effectors that, in turn, are responsible for distinct aspects of early endosome function, including their movement along microtubules. We previously reported that, at the onset of mitosis, RAB5positive vesicles cluster around the spindle poles and, during metaphase, move along spindle microtubules. RNAi-mediated depletion of the three RAB5 isoforms delays nuclear envelope breakdown at prophase and severely affects chromosome alignment and segregation. Here we show that depletion of the Kinesin-2 motor complex impairs long-range movement of RAB5 endosomes in interphase cells and prevents localization of these vesicles at the spindle during metaphase. Similarly to the effect caused by RAB5 depletion, functional ablation of Kinesin-2 delays nuclear envelope breakdown resulting in prolonged prophase. Altogether these findings suggest that endosomal transport at the onset of mitosis is required to control timing of nuclear envelope breakdown.
Landscape of the complete RNA chemical modifications in the human 80S ribosome

Abstract During ribosome biogenesis, ribosomal RNAs acquire various chemical modifications that ensure the fidelity of translation, and dysregulation of the modification processes can cause proteome changes as observed in cancer and inherited human disorders. Here, we report the complete chemical modifications of all RNAs of the human 80S ribosome as determined with quantitative mass spectrometry. We assigned 228 sites with 14 different post-transcriptional modifications, most of which are located in functional regions of the ribosome. All modifications detected are typical of eukaryotic ribosomal RNAs, and no human-specific modifications were observed, in contrast to a recently reported cryo-electron microscopy analysis. While human ribosomal RNAs appeared to have little polymorphism regarding the post-transcriptional modifications, we found that pseudouridylation at two specific sites in 28S ribosomal RNA are significantly reduced in ribosomes of patients with familial dyskeratosis congenita, a genetic disease caused by a point mutation in the pseudouridine synthase gene DKC1. The landscape of the entire epitranscriptomic ribosomal RNA modifications provides a firm basis for understanding ribosome function and dysfunction associated with human disease.
DA-IICT/IIITV System for Low Resource Speech Recognition Challenge 2018

This paper presents an Automatic Speech Recognition (ASR) system, in the Gujarati language, developed for Low Resource Speech Recognition Challenge for Indian Languages in INTER-SPEECH 2018. For front-end, Amplitude Modulation (AM) features are extracted using the standard and data-driven auditory ﬁlterbanks. Recurrent Neural Network Language Models (RNNLM) are used for this task. There is a relative improvement of 36.18 % and 40.95 % in perplexity on the test and blind test sets, respectively, compared to 3-gram LM. Time-Delay Neural Network (TDNN) and TDNN-Long Short-Term Memory (LSTM) models are employed for acoustic modeling. The statistical signiﬁcance of proposed approaches is justiﬁed using a bootstrap-based % Probability of Improvement (POI) measure. RNNLM rescoring with 3-gram LM gave an absolute reduction of 0.69-1.29 % in Word Error Rate (WER) for various feature sets. AM features extracted using the gamma-tone ﬁlterbank (AM-GTFB) performed well on the blind test set compared to the FBANK baseline (POI > 70 %). The combination of ASR systems further increased the performance with an absolute reduction of 1.89 and 2.24 % in WER for test and blind test sets
Cascaded Deep Convolutional Neural Network for Robust Face Alignment

Face alignment is an essential preprocessing stage for most face-related visual tasks and has been widely studied. The performance of face alignment is boosted because of the rising and development of deep learning. While achieving a great success, existing face alignment methods overlooked a fact that different facial keypoints have different patterns and should be treated differently. For example, facial key-points that fall on facial contour are more likely to be misaligned than their peers that fall on five senses (eyes, nose and mouth). In this work, we propose a cascaded convolutional neural network (ConvNet) for robust face alignment. The network consists of multiple stages and refines predictions of the facial landmarks along the stages. Each stage of the network embeds Squeeze and Excitation (SE) units and residual attention unit into a deep ConvNet. The embedding of SE units and attention mechanism enables the network to have a global receptive field and locate different landmarks adaptively. We also improve the cascading way how stages are linked. Experiments reveal that, our method decreases the alignment error and achieves the state-of-the-art performance on 300 Faces In-the-Wild Challenging (300-W) dataset.
A Novel Parameter Estimation Method Based on a Tuneable Sigmoid in Alpha-Stable Distribution Noise Environments

In this paper, a novel method, that employs a fractional Fourier transform and a tuneable Sigmoid transform, is proposed, in order to estimate the Doppler stretch and time delay of wideband echoes for a linear frequency modulation (LFM) pulse radar in an alpha-stable distribution noise environment. Two novel functions, a tuneable Sigmoid fractional correlation function (TS-FC) and a tuneable Sigmoid fractional power spectrum density (TS-FPSD), are presented in this paper. The novel algorithm based on the TS-FPSD is then proposed to estimate the Doppler stretch and the time delay. Then, the derivation of unbiasedness and consistency is presented. Furthermore, the boundness of the TS-FPSD to the symmetric alpha stable (SαS) noise, the parameter selection of the TS-FPSD, and the feasibility analysis of the TS-FPSD, are presented to evaluate the performance of the proposed method. In addition, the Cramér–Rao bound for parameter estimation is derived and computed in closed form, which shows that better performance has been achieved. Simulation results and theoretical analysis are presented, to demonstrate the applicability of the forgoing method. It is shown that the proposed method can not only effectively suppress impulsive noise interference, but it also does not need a priori knowledge of the noise with higher estimation accuracy in alpha-stable distribution noise environments.
Robust Image Reconstruction for Block-Based Compressed Sensing Using a Binary Measurement Matrix

Nowadays, there are still difficulties in the implementation of Compressed Sensing (CS) sensors due to the nature of the measurement matrix. A binary measurement matrix can simplify the CS procedure significantly. However, due to the singularity of this class of measurement matrices, the convergence of some of existing CS reconstruction algorithms, such as the well-known block-based CS with smoothed-projected Landweber reconstruction (BCS-SPL) algorithm, is not guaranteed and can lead to an inaccurate recovery. In this paper we propose a simple, fast and efficient CS recovery algorithm that is able to recover the original image from compressed samples which are obtained using a binary measurement. Singular value decomposition (SVD) is coupled with the BCS-SPL algorithm in order to improve its recovery capability when a binary matrix is employed. The experimental results show that the proposed recovery algorithm has a better performance in terms of reconstruction quality when compared with existing reconstruction algorithm and yields images with quality that matches or exceeds those produced by the BCS-SPL algorithm. Additionally, the proposed algorithm is the most efficient in terms of recovery time, especially at high subrates.
Movement Classification in Video Using Kinematics-Driven Change Detection and Local Kinematics Shape Pattern

This paper studies the automatic classification of abnormal mutant and normal fishes by analyzing their movements recorded in the video. Motivated by the observation that mutant fishes have muscle disorders so that their bodies cannot bend sufficiently to swim normally, a kinematics-driven movement change detection approach is proposed to automatically segment the recorded video into different video segments. Furthermore, a new feature extraction method, called local kinematics shape pattern (LKSP), is proposed in this paper to provide discriminative spatiotemporal kinematics measurements of fish body movements. The histogram of the proposed LKSP features is incorporated into a motion classification approach to identify whether the fish is normal or a mutant. The experiments are conducted using the real-world recorded videos to demonstrate the superior performance of the proposed approach.
Integrating Multi-Level Convolutional Features for Correlation Filter Tracking

Discriminative correlation filters (DCFs) have drawn increasing interest in visual tracking. In particular, a few recent works treat DCFs as a special layer and adding it into a Siamese network for visual tracking. However, they adopt shallow networks to learn target representations, which lack robust semantic information in deeper layers and make these works fail to handle significant appearance changes. In this paper, we design a novel network to fuse multi-level convolutional features, each level of which characterize target from different perspectives. Then we integrate our network with the DCF layer to construct an end-to-end deep architecture for visual tracking. The overall architecture is trained end-to-end offline to adaptively learn target representations, which are not only enabled to encode high-level semantic features and low-level spatial detail features, but also closely related to correlation filters. Experiments show that our proposed tracker achieves superior performance against state-of the-art trackers.
Evaluation of preference of multimedia content using deep neural networks for electroencephalography

Evaluation of quality of experience (Qo $E$) based on electroencephalography (EEG) has received great attention due to its capability of real-time Qo $E$ monitoring of users. However, it still suffers from rather low recognition accuracy. In this paper, we propose a novel method using deep neural networks toward improved modeling of EEG and thereby improved recognition accuracy. In particular, we aim to model spatio-temporal characteristics relevant for QoE analysis within learning models. The results demonstrate the effectiveness of the proposed method.
An Improved Approach to the Cubic-Spline Interpolation

Cubic-spline interpolation (CSI) scheme is known to resample the discrete image data based on the least-square method with the cubic convolution interpolation (CCI) function. It is superior in performance to other interpolation functions for digital image processing. In this paper, an improved approach to the CSI scheme is proposed for the decimation and interpolation of image data. The proposed CSI scheme combines the least-square method with six-point CCI function to improve performance. Moreover, a novel low-complexity implementation algorithm is proposed to simplify the decimation and achieve improvement of the computational efficiency. Computer simulations indicate that the proposed CSI scheme can achieve better performance without increasing the computational complexity compared with the four-point CSI scheme.
Fast QTBT Partitioning Decision for Interframe Coding with Convolution Neural Network

In the latest Joint Video Exploration Team (JVET) development, the quadtree plus binary tree (QTBT) block partition structure is proposed for more flexible block partitioning. Compared to the quadtree partitioning in HEVC, QTBT can achieve better compression performance at the expense of significantly increased encoding complexity. To address this issue, we propose a convolution neural network (CNN) oriented fast QTBT partitioning decision algorithm for inter coding. We analyze the QTBT in a statistical way, which effectively guides us to design the architecture of the CNN. Furthermore, the false prediction risk is controlled based on temporal correlation to improve the robustness of the scheme. Experimental results show that the proposed algorithm can speed up QTBT block partition structure by reducing 35% encoding time on average with only 0.55% increase in bit rate, which enables its applications in practical scenarios.
The Implementation Gap in Laparoscopic Simulation Training

Background and Aims: Simulation-based training in laparoscopy can improve patient safety and efficiency of care, but it depends on how it is used. Research in medical education has moved from demonstrating transferability of simulation training to the operating room to how to best implement it. This study aims to investigate how simulation-based training in laparoscopy has been implemented Scandinavia. Material and Methods: An online survey was sent out to medical doctors at surgical, gynecological, and urological departments at 138 hospitals in Denmark, Norway, and Sweden. The questionnaire included questions on respondents’ baseline characteristics, opinions, access, and actual use of simulation-based training in laparoscopy. Results: In total, 738 respondents completed the survey. Of these, 636 (86.2%) of respondents agreed or strongly agreed that simulation-based training in laparoscopy should be mandatory. A total of 602 (81.6%) had access to simulation-based training in laparoscopy. Of the total 738 respondents, 141 (19.1%) were offered structured training courses, 129 (17.5%) were required to reach a predefined level of competency, and 66 (8.9%) had mandatory courses in laparoscopy. In all, 72 (9.8%) had never used simulation-based training in laparoscopy. Conclusion: An implementation gap in laparoscopic simulation-based training still exists in Scandinavia. Simulation equipment is generally available, but there is a lack of structured simulation-based training.
Deep Convolutional Neural Network with Scalogram for Audio Scene Modeling

Deep learning has improved the performance of acoustic scene classiﬁcation recently. However, learning is usually based on short-time Fourier transform and hand-tailored ﬁlters. Learning directly from raw signals has remained a big challenge. In this paper, we proposed an approach to learning audio scene patterns from scalogram, which is extracted from raw signal with simple wavelet transforms. The experiments were conducted on DCASE2016 dataset. We compared scalogram with classical Mel energy, which showed that multi-scale feature led to an obvious accuracy increase. The convolutional neural network integrated with maximum-average downsampled scalo-gram achieved an accuracy of 90 . 5% in the evaluation step in DCASE2016.
A Fast Algorithm for Occlusion Detection and Removal

This paper describes a simple and fast algorithm for removing occlusions that may occur in multiple views of a scene. In contrast to many methods of the literature, no assumption is made on occlusion shapes, colors or motions. Instead, this new method assumes that the background can be re-warped using an homography and that the reflectivity is quasi-Lambertian. After geometric and photometric alignments, three methods are evaluated. A median based method, a novel algorithm based on maximal clique detection and a robust PCA method are compared on real and simulated image sequences. This comparison shows that the new clique-based method provides the best performance in terms of quality and reliability.
BRCA1/2 mutations, including large genomic rearrangements, among unselected ovarian cancer patients in Korea

Objective We performed small-scale mutation and large genomic rearrangement (LGR) analysis of BRCA1/2 in ovarian cancer patients to determine the prevalence and the characteristics of the mutations. Methods All ovarian cancer patients who visited a single institution between September 2015 and April 2017 were included. Sanger sequencing, multiplex ligation-dependent probe amplification (MLPA), and long-range polymerase chain reaction (PCR) were performed to comprehensively study BRCA1/2. The genetic risk models BRCAPRO, Myriad, and BOADICEA were used to evaluate the mutation analysis. Results In total, 131 patients were enrolled. Of the 131 patients, Sanger sequencing identified 16 different BRCA1/2 small-scale mutations in 20 patients (15.3%). Two novel nonsense mutations were detected in 2 patients with a serous borderline tumor and a large-cell neuroendocrine carcinoma. MLPA analysis of BRCA1/2 in Sanger-negative patients revealed 2 LGRs. The LGRs accounted for 14.3% of all identified BRCA1 mutations, and the prevalence of LGRs identified in this study was 1.8% in 111 Sanger-negative patients. The genetic risk models showed statistically significant differences between mutation carriers and non-carriers. The 2 patients with LGRs had at least one blood relative with breast or ovarian cancer. Conclusion Twenty-two (16.8%) of the unselected ovarian cancer patients had BRCA1/2 mutations that were detected through comprehensive BRCA1/2 genetic testing. Ovarian cancer patients with Sanger-negative results should be considered for LGR detection if they have one blood relative with breast or ovarian cancer. The detection of more BRCA1/2 mutations in patients is important for efforts to provide targeted therapy to ovarian cancer patients.
Whole Exome Sequencing Uncovers Germline Variants of Cancer-Related Genes in Sporadic Pheochromocytoma

Background Pheochromocytomas (PCCs) show the highest degree of heritability in human neoplasms. However, despite the wide number of alterations until now reported in PCCs, it is likely that other susceptibility genes remain still unknown, especially for those PCCs not clearly syndromic. Methods Whole exome sequencing of tumor DNA was performed on a set of twelve PCCs clinically defined as sporadic. Results About 50% of PCCs examined had somatic mutations on the known susceptibility VHL, NF1, and RET genes. In addition to these driver events, mutations on SYNE1, ABCC10, and RAD54B genes were also detected. Moreover, extremely rare germline variants were present in half of the sporadic PCC samples analyzed, in particular variants of MAX and SAMD9L were detected in the germline of cases wild-type for mutations in the known susceptibility genes. Conclusions Additional somatic passenger mutations can be associated with known susceptibility VHL, NF1, and RET genes in PCCs, and a wide number of germline variants with still unknown clinical significance can be detected in these patients. Therefore, many efforts should be aimed to better define the pathogenetic role of all these germline variants for discovering novel potential therapeutic targets for this disease still orphan of effective treatments.
The Relationship between Bone Remodeling and the Clockwise Rotation of the Facial Skeleton: A Computed Tomographic Imaging–Based Evaluation

Background: Information on the onset and gender differences of midfacial skeletal changes, including the complete understanding of the theory behind the clockwise rotational theory, remains elusive. Methods: One hundred fifty-seven Caucasian individuals (10 men and 10 women aged 20 to 29 years, 30 to 39 years, 40 to 49 years, 50 to 59 years, 60 to 69 years, 70 to 79 years, and 80 to 89 years, and eight men and nine women aged 90 to 98 years) were investigated. Multiplanar computed tomographic scans with standardized angle and distance measurements in all three anatomical axes and in alignment to the sella-nasion (horizontal) line were conducted. Results: Both men and women displayed an increase in orbital floor angle (p < 0.001, maximum at 60 to 69 years), decrease in maxillary angle (p = 0.035, 40 to 49 years), increase in palate angle (p < 0.001, 50 to 59 years), increase in vomer angle (p = 0.022, 30 to 39 years), but a decrease in the pterygoid angle (p = 0.002, 80 to 89 years). Orbital width decreased (p < 0.001, 60 to 69 years), pyriform aperture width increased (p = 0.015, 60 to 69 years), and midfacial height decreased with aging (p < 0.001, 60 to 69 years). Conclusions: Age-related changes of the midfacial skeleton occurred independently of gender, but at various time points in different locations. The observed changes seem to be driven by a bone resorption center located in the posterior maxilla, rather than by a rotational movement of the facial skeleton.