Miracle-dz/UniFAR-FTUs huggingface dataset:
An integrated methylation and gene expression microarray analysis reveals significant prognostic biomarkers in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is a life-threatening disease with a poor prognosis. Although previous studies have reported that the methylation of certain genes is associated with the pathogenesis of OSCC, the methylation of genes that have relevance to OSCC progression is not clearly documented. The present study aimed to gain insights into the mechanisms underlying DNA methylation regulation associated with OSCC progression and to identify potential prognostic markers for OSCC treatment. DNA methylation dataset GSE41114 and gene expression dataset GSE74530 were downloaded from the Gene Expression Omnibus database. The global methylation status of OSCC tumor samples and normal control samples was determined, and differentially methylated genes (DMGs) in OSCC samples compared with control samples were identified. The mRNA expression data were then integrated to identify differentially expressed genes (DEGs) in OSCC samples compared with control samples. Overlapping genes between DEGs and DMGs were identified, and functional enrichment analysis was performed. In addition, survival analysis of the overlapping genes was performed to screen genes with prognostic significance in OSCC. A total of 40,115 differential methylation CpG sites spanning 3,360 DMGs were identified; CpG sites in the promoter, gene body and intergenic regions were generally highly hypermethylated or hypomethylated. Additionally, 508 DEGs in OSCC samples were identified, including 332 upregulated and 176 downregulated genes. A total of 82 overlapping genes between DEGs and DMGs were found, which were mainly involved in protein metabolism, regulation of the metabolic process and the immune system. Additionally, differential methylation or expression of several genes, including fibroblast activation protein α (FAP), interferon α inducible protein 27 (IFI27), laminin subunit γ2 (LAMC2), matrix metallopeptidase 1 (MMP1), serine peptidase inhibitor Kazal-type 5 (SPINK5) and zinc finger protein 662 (ZNF662), was significantly associated with the survival of OSCC patients, and their differential expression in OSCC patients was further confirmed by reverse transcription-quantitative polymerase chain reaction in OSCC and normal oral cell lines. Overall, FAP, IFI27, LAMC2, MMP1, SPINK5 and ZNF662 genes caused by epigenetic changes via DNA methylation may be associated with the development and progression of OSCC, and should be valuable OSCC therapeutic biomarkers.
Factorized Hidden Variability Learning for Adaptation of Short Duration Language Identification Models

Bidirectional long short term memory (BLSTM) recurrent neural networks (RNNs) have recently outperformed other state-of-the-art approaches, such as i-vector and deep neural networks (DNNs) in automatic language identification (LID), particularly when testing with very short utterances (‘3s). Mismatches conditions between training and test data, e.g. speaker, channel, duration and environmental noise, are a major source of performance degradation for LID. A factorized hidden variability subspace (FHVS) learning technique is proposed for the adaptation of BLSTM RNNs to compensate for these types of mismatches in recording conditions. In the proposed approach, condition dependent parameters are estimated to adapt the hidden layer weights of the BLSTM in the FHVS. We evaluate FHVS on the AP17-OLR data set. Experimental results show that the FHVS method outperforms the standard BLSTM approach, achieving 27% relative improvements with utterance-level adaptation over the standard BLSTM for 1 s duration utterances.
RF Transmit/Receive Quadrature Fed Body Birdcage Coil for a 1.5T MR System

Magnetic Resonance Imaging (MRI) is an imaging technique which is used to view the inner part of the human body in a non-invasive manner. This imaging technique is based on the principle of Nuclear Magnetic Resonance (NMR). The Radiofrequency coil in MRI can be of various types. Birdcage Coil is a type of volume coil which is selected for the implementation of the RF coil due to better field homogeneity and high signal to noise ratio which in turn guarantees a large field of view. This paper describes the implementation of a strip based birdcage coil for a Human Body operating in high-pass, low-pass and bandpass modes. Since its introduction, determining the resonant modes has been a difficult task. This paper attempts to determine the resonant modes for high pass and band pass structures for a 1.5T MR System. The resonant frequency for a 1.5T MR system is 63.87 MHz which is obtained by the Larmor equation and is known as the Larmor frequency. The dimensions of the coil are kept constant for all the designs. The theoretical ideas were further validated in a computer simulation for all the three designs.
Scriptnet: Icdar 2017 Competition On Baseline Detection In Archival Documents (Cbad)

This dataset contains the training and test set for the ICDAR 2017 Competition on Baseline Detection in Archival Documents (cBAD). A newly created freely available real world dataset consisting of 2035 annotated document page images that are collected from 9 different archives and form the basis of cBAD. Two competition tracks test different characteristics of the methods submitted. Track A [Simple Documents] is published with annotated text regions and tests therefore a method's quality of text line segmentation. The more challenging Track B [Complex Documents] provides only the page area. Hence, baseline detection algorithms need to correctly locate text lines in the presence of marginalia, tables, and noise. The dataset comprises images with additional PAGE XMLs. The PAGE XMLs contain text regions and baseline annotations. Competition Website: https://scriptnet.iit. demokritos.gr/competitions/5/
Collaborative Route Plan for Parking Sites Selection in Bike-Sharing Systems

In order to alleviate the traffic congestion caused by the bike-sharing system, the bicycles should be parked in designated parking sites, particularly around the hot scenic spots. A proper route, which guides the cyclers to select a vacant place among the sites to park the bike, is required. In this paper, the Expected Travel Distance (ETD) and the Probability of Successful Parking (PSP) are formulated to evaluate the routes, which will guide the users to travel all the parking sites. We exploit the Poisson process to model the increment of the bicycle number in the parking site and construct the travel tree for the route plan problem. To provide a proper route, we propose the GOR algorithm and the F-M method based on the travel tree. Through extensive simulations, our algorithms are compared with TSP in terms of ETD, PSP and the execution time.
Enhanced in vitro biocompatibility and osteogenesis of titanium substrates immobilized with dopamine-assisted superparamagnetic Fe3O4 nanoparticles for hBMSCs

Titanium (Ti) is an ideal bone substitute due to its superior bio-compatibility and remarkable corrosion resistance. However, in order to improve the osteoconduction and osteoinduction capacities in clinical applications, different kinds of surface modifications are typically applied to Ti alloys. In this study, we fabricated a tightly attached polydopamine-assisted Fe3O4 nanoparticle coating on Ti with magnetic properties, aiming to improve the osteogenesis of the Ti substrates. The PDA-assisted Fe3O4 nanoparticle coatings were characterized by scanning electron microscopy, energy dispersive spectroscopy, atomic force microscopy and water contact angle measurements. The cell attachment and proliferation rate of the human bone mesenchymal stem cells (hBMSCs) on the Ti surface significantly improved with the Fe3O4/PDA coating when compared with the pure Ti without a coating. Furthermore, the results of in vitro alkaline phosphatase (ALP) activity at 7 and 14 days and alizarin red S staining at 14 days showed that the Fe3O4/PDA coating on Ti promoted the osteogenic differentiation of hBMSCs. Moreover, hBMSCs co-cultured with the Fe3O4/PDA-coated Ti for approximately 14 days also exhibited a significantly higher mRNA expression level of ALP, osteocalcin and runt-related transcription factor-2 (RUNX2). Our in vitro results revealed that the present PDA-assisted Fe3O4 nanoparticle surface coating is an innovative method for Ti surface modification and shows great potential for clinical applications.
Deep Learning for Predicting Image Memorability

Memorability of media content such as images and videos has recently become an important research subject in computer vision. This paper presents our computation model for predicting image memorability, which is based on a deep learning architecture designed for a classification task. We exploit the use of both convolutional neural network (CNN) - based visual features and semantic features related to image captioning for the task. We train and test our model on the large-scale benchmarking memorability dataset: LaMem. Experiment result shows that the proposed computational model obtains better prediction performance than the state of the art, and even outperforms human consistency. We further investigate the genericity of our model on other memorability datasets. Finally, by validating the model on interestingness datasets, we reconfirm the uncorrelation between memorability and interestingness of images.
A Triplet-Loss Embedded Deep Regressor Network for Estimating Blood Pressure Changes Using Prosodic Features

Studies have shown that measures of personal physiology, e.g., blood pressure (BP) variation and heart rate variability (HRV), is closely related to a subject's psychological states and are being used regularly to track patients' health conditions in medical settings. The conventional method of monitoring physiology requires wearing specialized sensors or utilizing medical instruments, which hinders the ability of scalable and just-in-time monitoring of patients. In this study, we propose a triplet-loss embedded deep regressor network to predict changes of BP using expressive prosodic features for on-boarding emergency room patients between pre- and post-triage sessions. The framework achieves correlations of 0.419 and 0.386 in predicting changes in SBP (systolic blood pressure) and DBP (diastolic blood pressure) respectively, which is 26.1% and 17.3% relative improvement compared to DNN-regressors without triplet-loss embedding. Further correlation analyses on the relationship between prosodic features and BP changes are presented.
Mobile robot navigation based on Fuzzy Cognitive Map optimized with Grey Wolf Optimization Algorithm used in Augmented Reality

this work presents a control technique for Mobile Robot Navigation using augmented reality (AR). This navigation technique is based on optimized Fuzzy Cognitive Map (FCM) and AR's Glyphs. AR's symbols are provided by the overhead camera. The patterns are made up of glyphs and a clear path. Six practical test are manipulated to examine the strength of optimizing FCM by a mobile robot for navigation with AR's symbols. The experiment examined the effectiveness of a Grey Wolf Optimization Algorithm (GWOA) in optimizing the FCM. Two practical experiments confirm that AR's Glyphs are an effective symbol for a robot to navigation in an unknown environment. A practical experiment reveals that a robot can use AR to manage its intended movement. Augmented reality, such as the Glyphs and a simplified map, are an effective tool for mobile robots to use in navigation in unknown environments. A prototype system is made to navigate the mobile robot by using AR and FCM.
A Flexible Dirty Model Dictionary Learning Approach for Classification

Various dictionary learning methods have gained tremendous success for signal classification. However, traditional dictionary learning methods for classification assume there is no outlier in the training data, which may not be the case in practical applications. In this paper, we propose a new discriminative dictionary learning framework for classification, which simultaneously learns a discriminative dictionary and detects outliers in the data. We formulate the dictionary learning framework into an optimization problem with designed regularizers to promote both the discrimination and outlier-detection capability. An efficient and effective iterative algorithm based on the alternating direction method of multipliers (ADMM) is provided to solve the proposed optimization problem. We demonstrate the superior performance of the proposed approach in comparison with state-of-the-art methods on some image classification tasks.
MAPK and GSK3/ß-TRCP-mediated degradation of the maternal Ets domain transcriptional repressor Yan/Tel controls the spatial expression of nodal in the sea urchin embryo

In the sea urchin embryo, specification of the dorsal-ventral axis critically relies on the spatially restricted expression of nodal in the presumptive ventral ectoderm. The ventral restriction of nodal expression requires the activity of the maternal TGF-β ligand Panda but the mechanism by which Panda restricts nodal expression is unknown. Similarly, what initiates expression of nodal in the ectoderm and what are the mechanisms that link patterning along the primary and secondary axes is not well understood. We report that in Paracentrotus lividus, the activity of the maternally expressed ETS-domain transcription factor Yan/Tel is essential for the spatial restriction of nodal. Inhibiting translation of maternal yan/tel mRNA disrupted dorsal-ventral patterning in all germ layers by causing a massive ectopic expression of nodal starting from cleavage stages, mimicking the phenotype caused by inactivation of the maternal Nodal antagonist Panda. We show that like in the fly or in vertebrates, the activity of sea urchin Yan/Tel is regulated by phosphorylation by MAP kinases. However, unlike in the fly or in vertebrates, phosphorylation by GSK3 plays a central role in the regulation Yan/Tel stability in the sea urchin. We show that GSK3 phosphorylates Yan/Tel in vitro at two different sites including a β-TRCP ubiquitin ligase degradation motif and a C-terminal Ser/Thr rich cluster and that phosphorylation of Yan/Tel by GSK3 triggers its degradation by a β-TRCP/proteasome pathway. Finally, we show that, Yan is epistatic to Panda and that the activity of Yan/Tel is required downstream of Panda to restrict nodal expression. Our results identify Yan/Tel as a central regulator of the spatial expression of nodal in Paracentrotus lividus and uncover a key interaction between the gene regulatory networks responsible for patterning the embryo along the dorsal-ventral and animal-vegetal axes.
Faster R-CNN with Classifier Fusion for Small Fruit Detection

The-state-of-the-art of fruit detection with Faster R-CNN shows lack of detection advantage on small fruits. One of reasons is only single level features is used for localization of proposal candidates. In this paper, we propose to incorporate a multiple classifier fusion strategy into a Faster R-CNN network for small fruit detection. We utilize features from three different levels to learn three classifiers for objectness classification in the stage of proposal localization. Probabilities from classifiers are combined by a simple convolutional layer to generate final objectness classification for proposal candidates. In order to keep diversity of multiple classifiers, a novel loss term of classifier correlation is introduced into original loss function. Experimental results show that our model is feasible for detecting small fruits.
A dwarf walrus from the Miocene of Baja California Sur, Mexico

Here, we describe the odobenid Nanodobenus arandai gen. et sp. nov., based on a nearly complete left mandible from the mid to late Miocene Tortugas Formation in Baja California Sur. Nanodobenus is distinguished among odobenids by displaying a unique combination of plesiomorphic and derived characters, such as narrow mandibular symphysis, well-developed genial tuberosity, bilobed canine and p2 roots, bulbous post-canine teeth with the paraconid, protoconid and hypoconid, and smooth lingual cingula. Moreover, it is characterized by its small adult body length, which is estimated at about 1.65 m. Throughout the Miocene–Pliocene odobenids are characterized by an increase in body size, especially after the extinction of desmatophocids in the late Miocene. The small size of Nanodobenus departs from this trend, demonstrating that there was greater size disparity among odobenids in the mid–late Miocene than previously thought. It is hypothesized that Nanodobenus occupied a niche that was later on occupied by similar-sized otariids, such as Thalassoleon mexicanus, which occurs sympatrically with large odobenids in the overlying Almejas Formation.
Differentiation of human iPSCs into functional podocytes

Podocytes play a critical role in glomerular barrier function, both in health and disease. However, in vivo terminally differentiated podocytes are difficult to be maintained in in vitro culture. Induced pluripotent stem cells (iPSCs) offer the unique possibility for directed differentiation into mature podocytes. The current differentiation protocol to generate iPSC-derived podocyte-like cells provides a robust and reproducible method to obtain podocyte-like cells after 10 days that can be employed in in vitro research and biomedical engineering. Previous published protocols were improved by testing varying differentiation media, growth factors, seeding densities, and time course conditions. Modifications were made to optimize and simplify the one-step differentiation procedure. In contrast to earlier protocols, adherent cells for differentiation were used, the use of fetal bovine serum (FBS) was reduced to a minimum, and thus ß-mercaptoethanol could be omitted. The plating densities of iPSC stocks as well as the seeding densities for differentiation cultures turned out to be a crucial parameter for differentiation results. Conditionally immortalized human podocytes served as reference controls. iPSC-derived podocyte-like cells showed a typical podocyte-specific morphology and distinct expression of podocyte markers synaptopodin, podocin, nephrin and WT-1 after 10 days of differentiation as assessed by immunofluorescence staining or Western blot analysis. qPCR results showed a downregulation of pluripotency markers Oct4 and Sox-2 and a 9-fold upregulation of the podocyte marker synaptopodin during the time course of differentiation. Cultured podocytes exhibited endocytotic uptake of albumin. In toxicological assays, matured podocytes clearly responded to doxorubicin (Adriamycin™) with morphological alterations and a reduction in cell viability after 48 h of incubation.
Robust Spoken Language Understanding with Unsupervised ASR-Error Adaptation

Robustness to errors produced by automatic speech recognition (ASR) is essential for Spoken Language Understanding (SLU). Traditional robust SLU typically needs ASR hypotheses with semantic annotations for training. However, semantic annotation is very expensive, and the corresponding ASR system may change frequently. Here, we propose a novel unsupervised ASR-error adaptation method, obviating the need of annotated ASR hypotheses. It only requires semantically annotated transcripts for the slot-tagging task and the transcripts paired with hypotheses for an input sentence reconstruction task. In this method, feature encoders which share part of the parameters are exploited to enforce the tasks in a similar feature space. Therefore, the transcript side slot-tagging model can be transferred to ASR hypotheses side easily. Experiments show that the proposed approach can yield significant improvement over strong baselines, and achieve performance very close to the oracle system.
Deep Facial Attribute Detection in the Wild: From General to Specific

Accurate facial attribute interpretation is a challenging task in real life due to large head poses, occlusion and illumination variations. This work proposes a general-to-speciﬁc deep convolutional network architecture for predicting multiple attributes from a single image in the wild. First, we model the interdependencies of local facial regions by joint learning of all the attributes. Second, task-aware learning is established to explore the disparity regarding each attribute. Finally, an attribute-aware face cropping scheme is proposed to extract more discriminative features from where a certain attribute naturally shows up. The proposed general-to-speciﬁc learning strategy ensures both robustness and performance of our model. Extensive experiments on the CelebA and LFWA datasets demonstrate the effectiveness of our architecture and the superiority to state-of-the-art alternatives.
A Robust Robot Design for Item Picking

In order to build a stable and reliable system for the Amazon Robotics Challenge we went through a detailed study of the performance and system requirements based on the rules and our past experience of the challenge. The challenge was to build a robot that integrates grasping, vision, motion planning, among others, to be able to pick items from a shelf to specific order boxes. This paper presents the development process including component selection, module designs, and deployment. The resulting robot system has dual 6 degrees of freedom industrial arms mounted on fixed bases, which in turn are mounted on a calibrated table. The robot works with a custom-designed top-open extendable shelf. The vision system uses multiple stereo cameras mounted on a fixed calibrated frame. Feature-based comparison and machine-learning based matching are used to identify and determine item pose. The gripper system uses suction cup and the grasping strategy is pick from the top. Error recovery strategies were also implemented to ensure robust performance. During the competition, the robot was able to pick all target items with the shortest amount of time.
A Virtual Reality Assisted Rehabilitation System for Physical Therapy

Virtual Assisted Rehabilitation System (VARS) provides rehabilitation for patients suffering from lower part injuries, such as muscular dystrophy, at the lowest cost possible. VARS is a combination of software (Processing, ElectricGuru and PLX software) and hardware (pressure sensors, electromyography, accelerometers, platform and virtual reality eyeglasses). The collected data, saved on host PC, is used by the physiotherapist for status estimation. The latter then identifies specific gaming profile (type of game and time needed for treatment) for the patient depending on his status. VARS had been tested on 22 candidates (12 abnormal and 10 normal). The result showed that gaming techniques in this system simulates the muscle activity over time by simulating the action-perception. Moreover, the foot pressure distribution (FPD) profiles vary obviously between normal and abnormal patients where specific disease is determined (e.g. Rheumatism).