Miracle-dz/UniFAR-FTUs huggingface dataset:
Creation of a computational pipeline to extract genes from quantitative trait loci for diabetes and obesity

CREATION OF A COMPUTATIONAL PIPELINE TO EXTRACT GENES FROM QUANTITATIVE TRAIT LOCI FOR DIABETES AND OBESITY Joseph F. Fox Marquette University, 2015 Type 2 Diabetes is a disease of relative insulin deficiency resulting from a combination of insulin resistance and decreased beta-cell function. Over the past several years, over 60 genes have been identified for Type 2 Diabetes in human genome-wide association studies (GWAS). It is important to understand the genetics involved with Type 2 diabetes in order to improve treatment and understand underlying molecular mechanisms. Heterogeneous stock (HS) rats are derived from 8 inbred founder strains and are powerful tools for genetic studies because they provide a basis for high resolution mapping of quantitative trait loci (QTL) in a relatively short time period. By measuring diabetic traits in 1090 HS male rats and genotyping 10K single nucleotide polymorphisms (SNPs) within these rats, Dr. Solberg Woods’ lab conducted genetic analysis to identify 85 QTL for diabetes and adiposity traits. To identify candidate genes within these QTL, we propose creation of a bioinformatics pipeline that combines general gene information, information from the rat genome database including disease portals and Variant Visualizer as well as the Attie Diabetes Expression Database. My project has involved writing code to pull data from these databases to determine which genes within each QTL are potential candidate genes. I have scripted the code to analyze genes within a single QTL or multiple QTL simultaneously. The resulting output is a single excel file for each QTL, listing all genes that are found in the disease portals, all genes that have a highly conserved nonsynonymous variant change and all genes that are differentially expressed in the Attie database. The program also highlights genes that are found in all three categories. After creating the pipeline, I ran the program for 85 QTL identified in my laboratory. The program identified 63 high priority candidate genes for future follow-up. This work has helped my laboratory rapidly identify candidate genes for type 2 diabetes and obesity. In the future, the code can be modified to identify candidate genes within QTL for any complex trait.
Oxide Thickness Profile Measurement by Dispersive White-Light Interferometry in CMP Process

Thickness profiles of CMPed oxide thin films are precisely measured by means of spectrally-resolved analysis based on dispersive white-light interferometry. This method allows not only the top surface profile but also the film thickness of an oxide film layer to be measured simultaneously with fine lateral and depth resolutions, leading to a complete 3-D tomographical reconstruction of the measured thin film. Besides, very thin films of less than 0.1 micrometer thickness can be accurately measured with precision in the nanometer range. No mechanical depth scanning is required during measurement, thus the proposed method is fast enough for the non- contact in-line inspection required for effective quality assurance of CMPed wafers. The measurement of the thickness of oxide thin films is essential in the fabrication of semiconductor integrated circuits. The International Technology Roadmap for Semiconductor (ITRS) (1) projects that the feature size will continue to shrink below 45 nm, whereas the wafer size will increase from the current standards of 200 and 300 mm up to 450 mm. In line with the global trend, many novel materials are required to be deposited in the form of thin films, whose thickness should be precisely controlled with high precision. For the purpose, suitable means of film thickness metrology is demanded which should be able to provide feedback necessary for effective control of the manufacturing process of semiconductor devices (2). Recently, much attention is being paid to the development of in-line measurement tools for the sake of advanced process control (APC), especially for the CMP (chemical mechanical polishing) process. Information on the film thickness is important not only for assessing the polishing performance real time, but also for appropriate detection of the end point of the polishing process (3). The chip makers have generally utilized various measurement methods, such as scanning electron microscope (SEM), transmission electron microscope (TEM), atomic force microscope (AFM), stylus, X-ray fluorescence (XRF), reflectometer, ellipsometer, and sheet resistance measurement for the CMP process. Although the wafer under inspection should be destroyed, the instruments such as SEM, TEM, AFM, and XRF can be used effectively for the process development in the initial stage and/or for the precise analysis of any problem encountered in the later stage of process control. It is because these methods are available for
Facile Synthesis of High Surface Area Molybdenum Carbide and Nitride: An Application in Support Materials for Oxygen Reduction Reaction

A method to synthesize high surface area α-MoC(1-x) without the temperature programmed reduction (TPR) of MoO3 is described. This method was then used, in conjunction with a novel in situ method to obtain highly dispersed PtMoz supported on αMoC(1-x), to explore the suitability of such materials as catalysts for oxygen reduction reaction (ORR) in a proton exchange membrane fuel cell (PEMFC). Mg2(Mo3O8) and Zn2(Mo3O8), alternative tetravalent Mo intermediates to MoO2, were obtained through the thermal decomposition of Mg(MoO4) and Zn(MoO4) in mixtures of CO and H2. A novel sacrificial support method was developed where MgO and ZnO were investigated sacrificial support materials formed in situ as decomposition products of the Mo precursor. Reaction conditions were explored: including feed composition, synthesis temperature, and synthesis duration. X-Ray powder diffraction (XRD) was used to determine the time dependence on conversion, where 30 to 140 minutes at a temperature of ~ 700 °C was required to reach full conversion of the precursors. Scanning electron microscopy (SEM) revealed an equiaxed microstructure with primary particle
Fast Single Image Rain Removal via a Deep Decomposition-Composition Network

Rain effect in images typically is annoying for many multimedia and computer vision tasks. For removing rain effect from a single image, deep leaning techniques have been attracting considerable attentions. This paper designs a novel multi-task leaning architecture in an end-to-end manner to reduce the mapping range from input to output and boost the performance. Concretely, a decomposition net is built to split rain images into clean background and rain layers. Different from previous architectures, our model consists of, besides a component representing the desired clean image, an extra component for the rain layer. During the training phase, we further employ a composition structure to reproduce the input by the separated clean image and rain information for improving the quality of decomposition. Experimental results on both synthetic and real images are conducted to reveal the high-quality recovery by our design, and show its superiority over other state-of-the-art methods. Furthermore, our design is also applicable to other layer decomposition tasks like dust removal. More importantly, our method only requires about 50ms, significantly faster than the competitors, to process a testing image in VGA resolution on a GTX 1080 GPU, making it attractive for practical use.
Associative Compression Networks for Representation Learning

This paper introduces Associative Compression Networks (ACNs), a new framework for variational autoencoding with neural networks. The system differs from existing variational autoencoders (VAEs) in that the prior distribution used to model each code is conditioned on a similar code from the dataset. In compression terms this equates to sequentially transmitting the dataset using an ordering determined by proximity in latent space. Since the prior need only account for local, rather than global variations in the latent space, the coding cost is greatly reduced, leading to rich, informative codes. Crucially, the codes remain informative when powerful, autoregressive decoders are used, which we argue is fundamentally difficult with normal VAEs. Experimental results on MNIST, CIFAR-10, ImageNet and CelebA show that ACNs discover high-level latent features such as object class, writing style, pose and facial expression, which can be used to cluster and classify the data, as well as to generate diverse and convincing samples. We conclude that ACNs are a promising new direction for representation learning: one that steps away from IID modelling, and towards learning a structured description of the dataset as a whole.
Effect of EGR on performance and emission characteristics of a dual fuel engine fuelled with CNG and JOME

ABSTRACT The dual fuel engine shows significantly good results for brake thermal efficiency and brake specific energy consumption compared to the conventional diesel engine, at higher load. However, enhancements in NOx emissions were noticed. But it suffers with low thermal efficiency and high brake specific energy consumption, at low and intermediate loads. The application of exhaust gas recirculation (EGR) partially resolves the aforementioned problems of the dual fuel engine. In the present work, the dual fuel engine was operated with Jatropha oil methyl ester (JOME) as the pilot fuel for compressed natural gas (CNG). A comparative study has been done for various modes of operation: CNG-JOME dual fuel engine without EGR, CNG-JOME dual fuel engine with EGR, and conventional diesel engine at different operating loads. It was observed that the application of EGR improves the performance and emissions characteristics of the dual fuel engine compared to without EGR, particularly at low and intermediate loads. However, at higher loads, the use of EGR shows some adverse effect on performance and emissions characteristics, but decreased NOx emissions.
Multi-armed Bandit with Additional Observations

We study multi-armed bandit (MAB) problems with additional observations, where in each round, the decision maker selects an arm to play and can also observe rewards of additional arms (within a given budget) by paying certain costs. In the case of stochastic rewards, we develop a new algorithm KL-UCB-AO which is asymptotically optimal when the time horizon grows large, by smartly identifying the optimal set of the arms to be explored using the given budget of additional observations. In the case of adversarial rewards, we propose H-INF, an algorithm with order-optimal regret. H-INF exploits a two-layered structure where in each layer, we run a known optimal MAB algorithm. Such a hierarchical structure facilitates the regret analysis of the algorithm, and in turn, yields order-optimal regret. We apply the framework of MAB with additional observations to the design of rate adaptation schemes in 802.11-like wireless systems, and to that of online advertisement systems. In both cases, we demonstrate that our algorithms leverage additional observations to significantly improve the system performance. We believe the techniques developed in this paper are of independent interest for other MAB problems, e.g., contextual or graph-structured MAB.
SideRand: A Heuristic and Prototype of a Side-Channel-Based Cryptographically Secure Random Seeder Designed to Be Platform- and Architecture-Agnostic

Generating secure random numbers is vital to the security and privacy infrastructures we rely on today. Having a computer system generate a secure random number is not a trivial problem due to the deterministic nature of computer systems. Servers commonly deal with this problem through hardware-based random number generators, which can come in the form of expansion cards, dongles, or integrated into the CPU itself. With the explosion of network- and internet-connected devices, however, the problem of cryptography is no longer a server-centric problem; even small devices need a reliable source of randomness for cryptographic operations - for example, network devices and appliances like routers, switches and access points, as well as various Internet-of-Things (IoT) devices for security and remote management. This paper proposes a software solution based on side-channel measurements as a source of high-quality entropy (nicknamed "SideRand"), that can theoretically be applied to most platforms (large servers, appliances, even maker boards like RaspberryPi or Arduino), and generates a seed for a regular CSPRNG to enable proper cryptographic operations for security and privacy. This paper also proposes two criteria - openness and auditability - as essential requirements for confidence in any random generator for cryptographic use, and discusses how SideRand meets the two criteria (and how most hardware devices do not).
Continuous spinal anesthesia for lower limb surgery: a retrospective analysis of 1212 cases

Background Continuous spinal anesthesia is a very reliable and versatile technique for providing effective anesthesia and analgesia. However, the incidence of possible complications, including postdural puncture headache or neurological impairment, remains controversial. Therefore, the aim of the present retrospective study was to analyze a large number of patients for the incidence of adverse events after continuous spinal anesthesia with a microcatheter. Methods This retrospective study was conducted on 1212 patients who underwent surgery of the lower extremities with continuous spinal anesthesia, which was administered with 22-gauge Quincke spinal needles and 28-gauge microcatheters. Sociodemographic and clinical data were available from the patient records, and data on headaches and patient satisfaction were drawn from a brief postoperative patient questionnaire. Results The patient population included 825 females (68%) and 387 males; the median age was 61 (56–76). The types of operations performed were 843 hip prostheses, 264 knee prostheses, and 105 other leg operations. No major complications were observed in any of these patients. Tension headaches were experienced by 190 (15.7%) patients, but postdural puncture headaches were reported by only 18 (1.5%) patients. Nearly all patients (98.4%) were satisfied with continuous spinal anesthesia and confirmed that they would choose this kind of anesthesia again. Conclusion Based on the findings of this large data analysis, continuous spinal anesthesia using a 28-gauge microcatheter appears to be a safe and appropriate anesthetic technique in lower leg surgery for aged patients.
A Comparison of Recent Waveform Generation and Acoustic Modeling Methods for Neural-Network-Based Speech Synthesis

Recent advances in speech synthesis suggest that limitations such as the lossy nature of the amplitude spectrum with minimum phase approximation and the over-smoothing effect in acoustic modeling can be overcome by using advanced machine learning approaches. In this paper, we build a framework in which we can fairly compare new vocoding and acoustic modeling techniques with conventional approaches by means of a large scale crowdsourced evaluation. Results on acoustic models showed that generative adversarial networks and an autoregressive (AR) model performed better than a normal recurrent network and the AR model performed best. Evaluation on vocoders by using the same AR acoustic model demonstrated that a Wavenet vocoder outperformed classical source-filter-based vocoders. Particularly, generated speech waveforms from the combination of AR acoustic model and Wavenet vocoder achieved a similar score of speech quality to vocoded speech.
Inhibition of endothelial nitric oxide synthase in cholangiocarcinoma cell lines – a new strategy for therapy

The isoform of nitric oxide synthase (NOS) found in endothelial cells (eNOS) plays a crucial role in vasodilation. We recently reported the activation of eNOS in cholangiocarcinoma (CCA) tissues and cell lines. Moreover, we also reported that the abundance of eNOS and phosphorylated eNOS (p‐eNOS), as well as its upstream regulator proteins, is significantly associated with the metastatic status of CCA patients. However, the function of eNOS in CCA progression has not been addressed. Therefore, the present study aimed to investigate the function of eNOS involved in the migration and invasion ability of CCA cell lines. The results reveal that eNOS activation significantly increases migration and invasion ability of CCA cells via the up‐regulation of phosphorylated vasodilator‐stimulated protein (p‐VASP). A combination treatment with recombinant human vascular endothelial growth factor C and eNOS inhibitor (Nω‐nitro‐l‐arginine methyl ester hydrochloride) resulted in the down‐regulation of p‐VASP, as well as a decreased migration and invasion ability of the CCA cell line. Thus, this work suggests that eNOS can serve as an attractive target to inhibit the progression of CCA.
A Control Scheme That Uses Dynamic Postural Synergies to Coordinate a Hybrid Walking Neuroprosthesis: Theory and Experiments

A hybrid walking neuroprosthesis that combines functional electrical stimulation (FES) with a powered lower limb exoskeleton can be used to restore walking in persons with paraplegia. It provides therapeutic benefits of FES and torque reliability of the powered exoskeleton. Moreover, by harnessing metabolic power of muscles via FES, the hybrid combination has a potential to lower power consumption and reduce actuator size in the powered exoskeleton. Its control design, however, must overcome the challenges of actuator redundancy due to the combined use of FES and electric motor. Further, dynamic disturbances such as electromechanical delay (EMD) and muscle fatigue must be considered during the control design process. This ensures stability and control performance despite disparate dynamics of FES and electric motor. In this paper, a general framework to coordinate FES of multiple gait-governing muscles with electric motors is presented. A muscle synergy-inspired control framework is used to derive the controller and is motivated mainly to address the actuator redundancy issue. Dynamic postural synergies between FES of the muscles and the electric motors were artificially generated through optimizations and result in key dynamic postures when activated. These synergies were used in the feedforward path of the control system. A dynamic surface control technique, modified with a delay compensation term, is used as the feedback controller to address model uncertainty, the cascaded muscle activation dynamics, and EMD. To address muscle fatigue, the stimulation levels in the feedforward path were gradually increased based on a model-based fatigue estimate. A Lyapunov-based stability approach was used to derive the controller and guarantee its stability. The synergy-based controller was demonstrated experimentally on an able-bodied subject and person with an incomplete spinal cord injury.
TCP Decoupling for Next Generation Communication System

In traditional networks, interfaces of network nodes are duplex. But, emerging communication technologies such as visible light communication, millimeter-wave communications, can only provide a unidirectional interface when cost is limited. It's urgent to find effective solutions to utilize such new unidirectional communication skills. Decoupling implies separating one single resource to two independent resources. This idea can be applied at physical layer, link layer, network layer, even transport layer. TCP decoupling is an end to end solution provided at transport layer. With decoupled TCP, two distinct unidirectional path can be created to meet the requirements of reliable information transfer. However, it is not an easy task to decouple a bidirectional logical path at transport layer. In this paper, we dwell on the idea of TCP decoupling. Advantages of decoupling at transport layer are analyzed also. In addition, an experiment is carried out to figure out how to implement a decouple TCP. Our results show decoupling at transport layer is possible and the modified protocol is available.
Cloud architecture continuity: Change models and change rules for sustainable cloud software architectures

Cloud systems provide elastic execution environments of resources that link application and infrastructure/platform components, which are both exposed to uncertainties and change. Change appears in 2 forms: the evolution of architectural components under changing requirements and the adaptation of the infrastructure running applications. Cloud architecture continuity refers to the ability of a cloud system to change its architecture and maintain the validity of the goals that determine the architecture. Goal validity implies the satisfaction of goals in adapting or evolving systems. Architecture continuity aids technical sustainability, that is, the longevity of information, systems, and infrastructure and their adequate evolution with changing conditions. In a cloud setting that requires both steady alignment with technological evolution and availability, architecture continuity directly impacts economic sustainability. We investigate change models and change rules for managing change to support cloud architecture continuity. These models and rules define transformations of architectures to maintain system goals: Evolution is about unanticipated change of structural aspects of architectures, and adaptation is about anticipated change of architecture configurations. Both are driven by quality and cost, and both represent multidimensional decision problems under uncertainty. We have applied the models and rules for adaptation and evolution in research and industry consultancy projects.
Epstein–Barr Virus Gene BARF1 Expression is Regulated by the Epithelial Differentiation Factor ΔNp63α in Undifferentiated Nasopharyngeal Carcinoma

Epstein–Barr Virus (EBV) BamHI-A rightward frame 1 (BARF1) protein is considered a viral oncogene in epithelial cells and has immune-modulating properties. During viral lytic replication BARF1 is expressed as an early gene, regulated by the immediate early EBV protein R. However, in viral latency BARF1 is exclusively expressed in epithelial tumors such as nasopharyngeal (NPC) and gastric carcinoma (GC) but not in lymphomas, indicating that activation of the BARF1 promoter is cell type specific. Undifferentiated NPC is characterized by high expression of ΔNp63 isoforms of the epithelial differentiation marker p63, a member of the p53 family of transcription factors. Transcription factor binding site analysis indicated potential p53 family binding sites within the BARF1 promoter region. This study investigated ability of various p53 family members to transactivate the BARF1 promoter. Using BARF1 promoter luciferase reporter constructs we demonstrate that only p63 isoform ΔNp63α is capable of transactivating the BARF1 promoter, but not the TAp63 isoforms, p53 or p73. Direct promoter binding of ΔNp63α was confirmed by Chromatin Immune Precipitation (ChIP) analysis. Deletion mutants of the BARF1 promoter revealed multiple ΔNp63 response elements to be responsible for BARF1 promoter transactivation. However, ΔNp63α alone was not sufficient to induce BARF1 in tumor cells harboring full EBV genomes, indicating that additional cofactors might be required for full BARF1 regulation. In conclusion, in EBV positive NPC and GC, BARF1 expression might be induced by the epithelial differentiation marker ΔNp63α, explaining BARF1 expression in the absence of lytic reactivation.