Miracle-dz/UniFAR-FTUs huggingface dataset:
Atomic layer deposited high-k dielectric on graphene by functionalization through atmospheric plasma treatment

Atomic layer-deposited (ALD) dielectric films on graphene usually show noncontinuous and rough morphology owing to the inert surface of graphene. Here, we demonstrate the deposition of thin and uniform ALD ZrO2 films with no seed layer on chemical vapor-deposited graphene functionalized by atmospheric oxygen plasma treatment. Transmission electron microscopy showed that the ALD ZrO2 films were highly crystalline, despite a low ALD temperature of 150 °C. The ALD ZrO2 film served as an effective passivation layer for graphene, which was shown by negative shifts in the Dirac voltage and the enhanced air stability of graphene field-effect transistors after ALD of ZrO2. The ALD ZrO2 film on the functionalized graphene may find use in flexible graphene electronics and biosensors owing to its low process temperature and its capacity to improve device performance and stability.
Galectin-3 and Beclin1/Atg6 Genes In Human Cancers: Using cDNA Tissue Panel, qRT-PCR, and Logistic Regression Model to Identify Cancer Cell Biomarkers

Background Cancer biomarkers are sought to support cancer diagnosis, predict cancer patient response to treatment and survival. Identifying reliable biomarkers for predicting cancer treatment response needs understanding of all aspects of cancer cell death and survival. Galectin-3 and Beclin1 are involved in two coordinated pathways of programmed cell death, apoptosis and autophagy and are linked to necroptosis/necrosis. The aim of the study was to quantify galectin-3 and Beclin1 mRNA in human cancer tissue cDNA panels and determine their utility as biomarkers of cancer cell survival. Methods and Results A panel of 96 cDNAs from eight (8) different normal and cancer tissue types were used for quantitative real-time polymerase chain reaction (qRT-PCR) using ABI7900HT. Miner2.0, a web-based 4- and 3- parameter logistic regression software was used to derive individual well polymerase chain reaction efficiencies (E) and cycle threshold (Ct) values. Miner software derived formula was used to calculate mRNA levels and then fold changes. The ratios of cancer to normal tissue levels of galectin-3 and Beclin1 were calculated (using the mean for each tissue type). Relative mRNA expressions for galectin-3 were higher than for Beclin1 in all tissue (normal and cancer) types. In cancer tissues, breast, kidney, thyroid and prostate had the highest galectin-3 mRNA levels compared to normal tissues. High levels of Beclin1 mRNA levels were in liver and prostate cancers when compared to normal tissues. Breast, kidney and thyroid cancers had high galectin-3 levels and low Beclin1 levels. Conclusion Galectin-3 expression patterns in normal and cancer tissues support its reported roles in human cancer. Beclin1 expression pattern supports its roles in cancer cell survival and in treatment response. qRT-PCR analysis method used may enable high throughput studies to generate molecular biomarker sets for diagnosis and predicting cancer treatment response.
In the Beginning was the Genome: Genomics and the Bi-textuality of Human Existence

This paper addresses the cultural impact of genomics and the Human Genome Project (HGP) on human self-understanding. Notably, it addresses the claim made by Francis Collins (director of the HGP) that the genome is the language of God and the claim made by Max Delbrück (founding father of molecular life sciences research) that Aristotle must be credited with having predicted DNA as the soul that organises bio-matter. From a continental philosophical perspective I will argue that human existence results from a dialectical interaction between two types of texts: the language of molecular biology and the language of civilisation; the language of the genome and the language of our socio-cultural, symbolic ambiance. Whereas the former ultimately builds on the alphabets of genes and nucleotides, the latter is informed by primordial texts such as the Bible and the Quran. In applied bioethics deliberations on genomics, science is easily framed as liberating and progressive, religious world-views as conservative and restrictive (Zwart 1993). This paper focusses on the broader cultural ambiance of the debate to discern how the bi-textuality of human existence is currently undergoing a transition, as not only the physiological, but also the normative dimension is being reframed in biomolecular and terabyte terms.
Chapter 3 MAKING SENSE ABOUT SENSE

We suggest that the standard fine-grained division of senses and (larger) homographs by a lexicographer for use by a human reader may not be an appropriate goal for the computational WSD task. We argue that the level of sense-discrimination that NLP needs corresponds roughly to homographs, though we discuss psycholinguistic evidence that there are broad sense divisions with some etymological derivation (i.e. non-homographic) that are as distinct for humans as homographic ones and they may be part of the broad class of sense-divisions we seek to identify here. We link this discussion to the observation that major NLP tasks like MT and IR seem not to need independent WSD modules of the sort produced in the research field, even though they are undoubtedly doing WSD by other means. Our conclusion is that WSD should continue to focus on these broad discriminations, at which it can do very well, thereby possibly offering the close-to-100% success that most NLP seemingly requires, with the possible exception of very fine questions of target word choice in MT. This proposal can be seen as reorienting WSD to what it can actually perform at the standard success levels, but we argue that this, rather than some more idealized vision of sense inherited from lexicography, is what humans and machines can reliably discriminate.
The Effects of Cupping Massage in Patients with Chronic Neck Pain - A Randomised Controlled Trial

Background: Chronic neck pain is a major public health burden with only limited evidence for the effectiveness of complementary therapies. This study aimed to test the efficacy of cupping massage in patients with neck pain. Patients and Methods: Patients with chronic non-specific neck pain were randomly assigned to cupping massage or a wait list control. The intervention group received 5 cupping massages on a twice-weekly basis while the control patients continued their usual treatments. The primary outcome measure was neck pain intensity (0-100 mm visual analogue scale (VAS)) after 3 weeks. Secondary outcomes included pain on movement, functional disability, health-related quality of life, mechanical detection and pain thresholds and adverse events. Results: 50 patients (52.6 ± 10.3 years, 92% female) were randomised to either cupping massage or a wait list (N = 25 each). Patients in the cupping group reported significantly less neck pain post intervention (difference per protocol -14.3 mm, 95% confidence interval (CI) -27.7 to -1.0, p = 0.037; difference intention-to-treat -10.8 mm, 95% CI -21.5 to -0.1, p = 0.047). Significant group differences in favour of the intervention were further found for pain on movement (p = 0.019) and functional disability (p < 0.001), the quality-of-life subscales pain (p = 0.002) and mental health (p = 0.003) and the mental component summary (p = 0.036). Changes were also found for pressure pain sensitivity at the site of maximal pain (p = 0.022). Five adverse events were reported. Conclusions: Cupping massage appears to be effective in reducing pain and increasing function and quality of life in patients with chronic non-specific neck pain. More rigorous studies are needed to confirm and extend these results.
Experiences with redu Problems

This paper presents practical studies of the TCP performance problems caused by the asymmetric nature of ADSL connections. Previously, it has been shown through simulation, wireless and satellite links that TCP throughput may be reduced by asymmetric links due to ack compression and that ack filtering and prioritizing acks can increase TCP throughput. The uplink capacity of ADSL products in general does not result in ack compression unless the uplink is congested. For a single user ADSL installation this is manageable but for larger networks connected to the Internet by ADSL the rise of peer-to-peer file sharing applications may result in a permanently congested uplink. We document the TCP throughput problem both through artificial and real world traffic from a network with approximately 200 users. We report on experiences with reducing this problem using the packet scheduler of a Linux based middle-box between the network and the ADSL link. We show how prioritizing acks improves throughput but hurts interactive traffic and that using hierarchical link-sharing and classifying the traffic into interactive, acknowledgments, well-known services and peer-to-peer traffic provides a reasonable solution. However, in order to reliably control latency, it is important to account for the variable ATM/AAL5 overhead when scheduling packets on the uplink. Finally, we show that the ack traffic on the uplink can be significant implying that ack filtering is interesting for ADSL. We are currently refining the ack filtering, implementing methods for detecting peer-to-peer traffic, and implementing more accurate overhead estimations in the packet schedulers. Keywords— Transmission Control Protocol (TCP), asymmetric links, ADSL, link-sharing
Effects of a peer education on cardiac self‐efficacy and readmissions in patients undergoing coronary artery bypass graft surgery: a randomized‐controlled trial

AIM This study aimed to investigate the effects of a peer education on cardiac self-efficacy (CSE) and readmission of the patients undergoing bypass surgery. BACKGROUND Self-efficacy is an antecedent vital factor in both initiating and maintaining healthy behaviours. It significantly improves after effects of heart attacks, anxiety and diminishes the possibility of readmission. DESIGN This study is a randomized-controlled trial. METHOD Sixty patients undergoing bypass surgery were chosen and assigned equally into the control and intervention groups. While routine education was presented to the patients in the control group, intervention group were taught using the peer education in two sessions. CSE of all the selected patients was assessed orderly in 5 days, 4 weeks and 8 months after surgery. Moreover, their readmission was investigated after 8 months from surgery. Data was collected using demographic and the CSE scale. Data were also analysed by using χ2 , Kolmogorov-Smirnov and repeated measures analysis of variance tests. RESULTS The mean score of CSE in the intervention group was significantly different from the corresponding number in the control group in all three stages of data collecting (p < 0·001). Compared to the control group, a smaller number of the patients in the intervention group were readmitted to the hospitals after 8 months (p = 0·011). CONCLUSIONS Implementation of peer education has positive effects on CSE in patients who have bypass surgery and reduces their hospital readmission. It can be beneficial to apply this method as an educative-supportive approach in cardiac surgery fields. RELEVANCE TO CLINICAL PRACTICE Implementation of peer education has positive effects on CSE in these patients and reduces their hospital readmission.
Control Flow-Based Malware VariantDetection

Static detection of malware variants plays an important role in system security and control flow has been shown as an effective characteristic that represents polymorphic malware. In our research, we propose a similarity search of malware to detect these variants using novel distance metrics. We describe a malware signature by the set of control flowgraphs the malware contains. We use a distance metric based on the distance between feature vectors of string-based signatures. The feature vector is a decomposition of the set of graphs into either fixed size k-subgraphs, or q-gram strings of the high-level source after decompilation. We use this distance metric to perform pre-filtering. We also propose a more effective but less computationally efficient distance metric based on the minimum matching distance. The minimum matching distance uses the string edit distances between programs' decompiled flowgraphs, and the linear sum assignment problem to construct a minimum sum weight matching between two sets of graphs. We implement the distance metrics in a complete malware variant detection system. The evaluation shows that our approach is highly effective in terms of a limited false positive rate and our system detects more malware variants when compared to the detection rates of other algorithms.
Sparse representation and convolutional neural networks for 3D human pose estimation

In the field of 3D Human Pose Estimation and Reconstruction based on body joints extracted from a 2D image; Exists challenges like self-occlusion and depth perception. These problems hinder approximate estimations. This article proposes a hybrid method that consists of semantic segmentation, sparse representation and 2D pose estimation for 3D estimation. Specifically, we train two fully Convolution neural networks (FCNs) to estimate 2D pose and semantic segmentation. Then, we narrow the estimation down using basic human body structure and results from the FCNs. Subsequently, utilizing the estimated 2D pose of the earlier step and a sparse representation model the 3D pose will estimate. Using a Convolution Neural Networks learning method for 2D human pose estimation and then by sparse representation will estimate. Results of the proposed method, show improvement relative to the previous works on 3D Human pose estimation. 3D Human Pose Estimation by this method demonstrates a significant mean error reduction compared to earlier studies.
An Information Theoretic Approach to Statistical Shape Modelling

Statistical shape models have been used widely as a basis for segmenting and interpreting images. A major drawback of the approach is the need to establish a set of dense correspondences across a training set of segmented shapes. By posing the problem as one of minimising the description length of the model, we develop an efficient method that automatically defines correspondences across a set of shapes. Results are given for several different training sets of shapes, showing that the automatic method constructs significantly better models than those built by hand - the current gold standard.
Nonlinear system identification: Finding structure in nonlinear black-box models

The use of black-box models is wide-spread in signal processing and system identification applications. However, often such models possess a large number of parameters, and obfuscate their inner workings, as there are cross-connections between all inputs and all outputs (and possibly all internal states) of the model. Although black-box models have proven their success and wide applicability, there is a need to shed a light on what goes on inside the model. We have developed a tensor-based method that aims at pinpointing the nonlinearities of a given nonlinear model into a small number of univariate nonlinear mappings, with the advantageous side-effect of reducing the parametric complexity. In this contribution we will discuss how the method is conceived, and how it can be applied to the task of finding structure in blackbox models. We have found that the tensor-based decoupling method is able to reconstruct up to high accuracy a given blackbox nonlinear model, while reducing the parametric complexity and revealing some of the inner operation of the model. Due to their universal use, we will focus the presentation on the use of nonlinear state-space models, but the method is also suitable for other model structures. We validate the method on a case study in nonlinear system identification.
Variability in response to albuminuria‐lowering drugs: true or random?

AIMS Albuminuria-lowering drugs have shown different effect size in different individuals. Since urine albumin levels are known to vary considerably from day-to-day, we questioned whether the between-individual variability in albuminuria response after therapy initiation reflects a random variability or a true response variation to treatment. In addition, we questioned whether the response variability is drug dependent. METHODS To determine whether the response to treatment is random or a true drug response, we correlated in six clinical trials the change in albuminuria during placebo or active treatment (on-treatment) with the change in albuminuria during wash-out (off-treatment). If these responses correlate during active treatment, it suggests that at least part of the response variability can be attributed to drug response variability. We tested this for enalapril, losartan, aliskiren, atrasentan and paricalcitol. RESULTS No correlation between the on- and off-treatment albuminuria change was observed in the placebo arm of all clinical trials (R2 < 0.01). However, we observed significant associations between the on- and off-treatment response (R2 0.14 to 0.57; all P < 0.015) for different albuminuria lowering drugs. Additionally, the albuminuria responses strongly correlated when the same individual was re-exposed to the same drug at the same dose: lisinopril 10 mg day-1 (R2 = 53%; P < 0.01), losartan 50 mg day-1 (R2 = 63%; P < 0.01). CONCLUSION The degree of albuminuria lowering with antialbuminuric drugs varies between patients. This variability in response appears drug-class independent. Identifying which factors determine this initial short-term variation in drug response appears important since the degree of albuminuria lowering is related to subsequent long-term renoprotection.
Identifying Aspects and Analyzing Their Sentiments from Reviews

The popularity of internet along with the huge number of reviews posted daily via social media, blogs and review sites invokes the research challenges on topic or aspect based analysis. In the recent years, it also has become a challenging task to mine opinions with respect to the aspects from the available unstructured and noisy data. In this paper, we present a novel approach to identify the key terms and its sentiments from the reviews of Restaurants and Laptops with the help of different features and Conditional Random Field based machine learning algorithm. The supervised method achieves F-score of 0.7493380 and 0.6858054 for aspect term identification whereas 0.68982 and 0.6041 of accuracy for aspect based sentiment classification on Restaurant and Laptop reviews, respectively.
Channel-Recurrent Autoencoding for Image Modeling

Despite recent successes in synthesizing faces and bedrooms, existing generative models struggle to capture more complex image types (Figure 1), potentially due to the oversimplification of their latent space constructions. To tackle this issue, building on Variational Autoencoders (VAEs), we integrate recurrent connections across channels to both inference and generation steps, allowing the high-level features to be captured in global-to-local, coarse-to-fine manners. Combined with adversarial loss, our channelrecurrent VAE-GAN (crVAE-GAN) outperforms VAE-GAN in generating a diverse spectrum of high resolution images while maintaining the same level of computational efficacy. Our model produces interpretable and expressive latent representations to benefit downstream tasks such as image completion. Moreover, we propose two novel regularizations, namely the KL objective weighting scheme over time steps and mutual information maximization between transformed latent variables and the outputs, to enhance the training.
CC-401 Promotes &bgr;-Cell Replication via Pleiotropic Consequences of DYRK1A/B Inhibition

Pharmacologic expansion of endogenous β cells is a promising therapeutic strategy for diabetes. To elucidate the molecular pathways that control β-cell growth we screened ∼2400 bioactive compounds for rat β-cell replication-modulating activity. Numerous hit compounds impaired or promoted rat β-cell replication, including CC-401, an advanced clinical candidate previously characterized as a c-Jun N-terminal kinase inhibitor. Surprisingly, CC-401 induced rodent (in vitro and in vivo) and human (in vitro) β-cell replication via dual-specificity tyrosine phosphorylation-regulated kinase (DYRK) 1A and 1B inhibition. In contrast to rat β cells, which were broadly growth responsive to compound treatment, human β-cell replication was only consistently induced by DYRK1A/B inhibitors. This effect was enhanced by simultaneous glycogen synthase kinase-3β (GSK-3β) or activin A receptor type II-like kinase/transforming growth factor-β (ALK5/TGF-β) inhibition. Prior work emphasized DYRK1A/B inhibition-dependent activation of nuclear factor of activated T cells (NFAT) as the primary mechanism of human β-cell-replication induction. However, inhibition of NFAT activity had limited effect on CC-401-induced β-cell replication. Consequently, we investigated additional effects of CC-401-dependent DYRK1A/B inhibition. Indeed, CC-401 inhibited DYRK1A-dependent phosphorylation/stabilization of the β-cell-replication inhibitor p27Kip1. Additionally, CC-401 increased expression of numerous replication-promoting genes normally suppressed by the dimerization partner, RB-like, E2F and multivulval class B (DREAM) complex, which depends upon DYRK1A/B activity for integrity, including MYBL2 and FOXM1. In summary, we present a compendium of compounds as a valuable resource for manipulating the signaling pathways that control β-cell replication and leverage a DYRK1A/B inhibitor (CC-401) to expand our understanding of the molecular pathways that control β-cell growth.
Characterization of the complete mitochondrial genome of Bombyx huttoni (Lepidoptera: Bombycidae)

Abstract The complete mitochondrial genome of the wild silk moth Bombyx huttoni has been reconstructed from the whole-genome Illumina sequencing data. The circular genome was determined to be 15,638 bp in length, consisting of 13 protein-coding genes (PCGs), 22 transfer RNAs (tRNAs), two ribosomal RNAs (rRNAs), and one A+T-rich D-loop or control region. Twelve PCGs (COX2, COX3, CYTB, ND1, ND2, ND3, ND4, ND4L, ND5, ND6, ATP6, and ATP) are initiated with the typical ATN codons, whereas COX1 was tentatively determined to use the unusual CGA as its start codon. All PCGs are terminated with the TAA codon, except for ND1 and the other two PCGs (COX1 and COX2) with the TAG and the incomplete T stop codon, respectively. The nucleotide composition is highly asymmetric (43.8% A, 38.0% T, 7.1% G, and 11.1% C) with an overall A+T content of 81.8%.
MEF2C Enhances Dopaminergic Neuron Differentiation of Human Embryonic Stem Cells in a Parkinsonian Rat Model

Human embryonic stem cells (hESCs) can potentially differentiate into any cell type, including dopaminergic neurons to treat Parkinson's disease (PD), but hyperproliferation and tumor formation must be avoided. Accordingly, we use myocyte enhancer factor 2C (MEF2C) as a neurogenic and anti-apoptotic transcription factor to generate neurons from hESC-derived neural stem/progenitor cells (NPCs), thus avoiding hyperproliferation. Here, we report that forced expression of constitutively active MEF2C (MEF2CA) generates significantly greater numbers of neurons with dopaminergic properties in vitro. Conversely, RNAi knockdown of MEF2C in NPCs decreases neuronal differentiation and dendritic length. When we inject MEF2CA-programmed NPCs into 6-hydroxydopamine—lesioned Parkinsonian rats in vivo, the transplanted cells survive well, differentiate into tyrosine hydroxylase-positive neurons, and improve behavioral deficits to a significantly greater degree than non-programmed cells. The enriched generation of dopaminergic neuronal lineages from hESCs by forced expression of MEF2CA in the proper context may prove valuable in cell-based therapy for CNS disorders such as PD.