Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of gene expression models for laryngeal squamous cell carcinoma using co-expression network analysis

Abstract One of the most common head and neck cancers is laryngeal squamous cell carcinoma (LSCC). LSCC exhibits high mortality rates and has a poor prognosis. The molecular mechanisms leading to the development and progression of LSCC are not entirely clear despite genetic and therapeutic advances and increased survival rates. In this study, a total of 116 differentially expressed genes (DEGs), including 11 upregulated genes and 105 downregulated genes, were screened from LSCC samples and compared with adjacent noncancerous. Statistically significant differences (log 2-fold difference > 0.5 and adjusted P-value < .05) were found in this study in the expression between tumor and nontumor larynx tissue samples. Nine cancer hub genes were found to have a high predictive power to distinguish between tumor and nontumor larynx tissue samples. Interestingly, they also appear to contribute to the progression of LSCC and malignancy via the Jak-STAT signaling pathway and focal adhesion. The model could separate patients into high-risk and low-risk groups successfully when only using the expression level of mRNA signatures. A total of 4 modules (blue, gray, turquoise, and yellow) were screened for the DEGs in the weighted co-expression network. The blue model includes cancer-specific pathways such as pancreatic cancer, bladder cancer, nonsmall cell lung cancer, colorectal cancer, glioma, Hippo signaling pathway, melanoma, chronic myeloid leukemia, prostate cancer, and proteoglycans in cancer. Endocrine resistance (CCND1, RAF1, RB1, and SMAD2) and Hippo signaling pathway (CCND1, LATS1, SMAD2, and TP53BP2) could be of importance in LSCC, because they had high connectivity degrees in the blue module. Results from this study provide a powerful biomarker discovery platform to increase understanding of the progression of LSCC and to reveal potential therapeutic targets in the treatment of LSCC. Improved monitoring of LSCC and resulting improvement of treatment of LSCC might result from this information.
Outcome of left atrial appendage occlusion in high-risk patients

Objective Percutaneous left atrial appendage (LAA) occlusion can be an interventional alternative to oral anticoagulation for stroke prevention in patients with atrial fibrillation. Methods We delivered LAA occlusion therapy using a standardised approach to patient referral, multidisciplinary team assessment, implant criteria, imaging and follow-up. We analysed patient characteristics, efficacy and safety of the implant procedure, and 12-month outcomes. Results Of 143 referrals from October 2014 to December 2016, 83 patients (age 76±8years, 32.5% female, mean CHAD2S2-VASc score 4 ±1) were offered LAA occlusion. Eighty (95.3%) had previous major bleeding (intracranial in 59%). LAA occluder implantation with an Amulet device was successful in 82 (98.8%), with periprocedural major adverse events occurring in 5 (6.0%) patients (2 device embolisations including 1 death, 2 major bleeds). Cardiac imaging in 75 (94%) patients 2months following implant showed device-related thrombus in 1 case (1.3%) and minor (<5mm) device leaks in 13 (17.1%). Over a median 12-month follow-up, 3 (3.8%) ischaemic strokes, 2 (2.5%) haemorrhagic strokes and 5 (6.3%) major extracranial bleeds occurred. All-cause mortality was 10%, with most deaths (7, 87.5%) due to non-cardiovascular causes. Conclusions LAA occlusion may be a reasonable option for stroke prevention inhigh-risk patients with atrial fibrillation ineligible for anticoagulation. However, procedural complication rates are not insignificant, and patients remain at risk of serious adverse events and death even after successful implant.
A Robust Parallel Algorithm for Combinatorial Compressed Sensing

It was shown in previous work that a vector <inline-formula><tex-math notation="LaTeX">$\mathbf{x} \in \mathbb {R}^n$</tex-math></inline-formula> with at most <inline-formula><tex-math notation="LaTeX">$k < n$</tex-math> </inline-formula> nonzeros can be recovered from an expander sketch <inline-formula><tex-math notation="LaTeX"> $\mathbf{A}\mathbf{x}$</tex-math></inline-formula> in <inline-formula><tex-math notation="LaTeX"> $\mathcal{O}(\text{nnz}(\mathbf{A})\log k)$</tex-math></inline-formula> operations via the parallel-<inline-formula> <tex-math notation="LaTeX">$\ell _0$</tex-math></inline-formula> decoding algorithm, where <inline-formula> <tex-math notation="LaTeX">$\text{nnz}(\mathbf{A})$</tex-math></inline-formula> denotes the number of nonzero entries in <inline-formula><tex-math notation="LaTeX">$\mathbf{A} \in \mathbb {R}^{m \times n}$</tex-math></inline-formula>. In this paper, we present the robust-<inline-formula><tex-math notation="LaTeX">$\ell _0$</tex-math></inline-formula> decoding algorithm, which robustifies parallel-<inline-formula><tex-math notation="LaTeX">$\ell _0$</tex-math> </inline-formula> when the sketch <inline-formula><tex-math notation="LaTeX">$\mathbf{A}\mathbf{x}$</tex-math> </inline-formula> is corrupted by additive noise. This robustness is achieved by approximating the asymptotic posterior distribution of values in the sketch given its corrupted measurements. We provide analytic expressions that approximate these posteriors under the assumptions that the nonzero entries in the signal and the noise are drawn from continuous distributions. Numerical experiments presented show that robust-<inline-formula><tex-math notation="LaTeX"> $\ell _0$</tex-math></inline-formula> is superior to existing greedy and combinatorial compressed sensing algorithms in the presence of small to moderate signal-to-noise ratios in the setting of Gaussian signals and Gaussian additive noise.
Anthropometric changes over 5 years in elderly Canadians by age, gender, and cognitive status.

BACKGROUND Numerous changes in body composition occur with aging. This study reports on secondary analyses of data from a subsample of institutionalized and free-living elderly Canadians taking part in both phases of the Canadian Study of Health and Aging (CSHA-1 and CHSA-2; n = 10,263) to document and examine correlates of the evolution of anthropometric characteristics over a 5-year period. METHODS In CSHA-1, community-dwelling (n = 1464) and institutionalized (n = 963) participants' height and weight were measured in clinics. Surviving participants were remeasured in CSHA-2; valid data were available for 487 community-dwelling respondents (66.9% of those seen in clinics in CSHA-2) and 140 institutionalized participants (46.9% of those reassessed). Body mass index (BMI = weight [kg]/height [m(2)]) was calculated. Paired t tests were used to test changes over the interval, and repeated-measures multivariate analysis of variance was used to examine the extent of differences within and across categories. RESULTS The average weight loss between study phases in community-dwelling and institutionalized participants was approximately 2 kg (p <.001). In institutions, this was statistically significant in most stratification categories, as was the case in community-dwelling participants (by gender, age, dementia screening score, and cognitive diagnosis). Among those who were cognitively intact, the greatest weight losses occurred in participants under 90 years old and in those aged 70 to 79 years with a diagnosis of dementia (p <.01). Stature decreased more in institutionalized (2 cm) than in community-dwelling participants (1.4 cm). In institutions, this was significant among the oldest men (p <.005), while in the community there were no differences in the extent of height lost in all stratification categories. The average BMI was largely stable. CONCLUSIONS Body weight and stature declined with aging among elderly Canadian CSHA participants, particularly in the very old and those with dementia. Such longitudinal anthropometric data are needed along with information on dietary intakes, and medical, cognitive, and functional measures to plan interventions geared to maximizing nutritional and overall health in the elderly population, whatever their cognitive status.
Phospholipase C Isozymes Are Deregulated in Colorectal Cancer – Insights Gained from Gene Set Enrichment Analysis of the Transcriptome

Colorectal cancer (CRC) is one of the most common cancer types in developed countries. To identify molecular networks and biological processes that are deregulated in CRC compared to normal colonic mucosa, we applied Gene Set Enrichment Analysis to two independent transcriptome datasets, including a total of 137 CRC and ten normal colonic mucosa samples. Eighty-two gene sets as described by the Kyoto Encyclopedia of Genes and Genomes database had significantly altered gene expression in both datasets. These included networks associated with cell division, DNA maintenance, and metabolism. Among signaling pathways with known changes in key genes, the “Phosphatidylinositol signaling network”, comprising part of the PI3K pathway, was found deregulated. The downregulated genes in this pathway included several members of the Phospholipase C protein family, and the reduced expression of two of these, PLCD1 and PLCE1, were successfully validated in CRC biopsies (n = 70) and cell lines (n = 19) by quantitative analyses. The repression of both genes was found associated with KRAS mutations (P = 0.005 and 0.006, respectively), and we observed that microsatellite stable carcinomas with reduced PLCD1 expression more frequently had TP53 mutations (P = 0.002). Promoter methylation analyses of PLCD1 and PLCE1 performed in cell lines and tumor biopsies revealed that methylation of PLCD1 can contribute to reduced expression in 40% of the microsatellite instable carcinomas. In conclusion, we have identified significantly deregulated pathways in CRC, and validated repression of PLCD1 and PLCE1 expression. This illustrates that the GSEA approach may guide discovery of novel biomarkers in cancer.
Novel low cost fabrication of microneedle arrays for drug delivery applications

This paper reports a process used for the microfabrication of an array of hollow microneedles. The purpose of the array is for painless transdermal drug delivery. The fabrication process uses wet bulk silicon technology and copper electroplating technology. First, a microneedle array mold on <100>-oriented silicon was fabricated by wet anisotropic etching using KOH solution, then the silicon mold was electroplated with copper. After which, the hollow copper microneedle array was released by a lift-off process or by etching off the silicon mold in KOH solution. The hollow copper microneedle array has been mounted on a polycarbonate platform, which consist of laser ablated cavities and channel for external connection to drug source. In consideration of the contour of human’s skin and the geometry of the microneedle tip, which has walls of sloping gradient corresponding to the (111)-planes, the height of the microneedle array is 200 μm. Two arrays of hollow copper microneedle were fabricated. They have square base of dimensions 390 μm and 400 μm and square tips of size 100 μm and 120 μm with square holes of size 88 μm and 94 μm respectively. Both arrays have microneedle tips at 1900 μm apart from one another and consist of 10 × 10 microneedle tips.
Uncovering the structure and temporal dynamics of information propagation

Abstract Time plays an essential role in the diffusion of information, influence, and disease over networks. In many cases we can only observe when a node is activated by a contagion—when a node learns about a piece of information, makes a decision, adopts a new behavior, or becomes infected with a disease. However, the underlying network connectivity and transmission rates between nodes are unknown. Inferring the underlying diffusion dynamics is important because it leads to new insights and enables forecasting, as well as influencing or containing information propagation. In this paper we model diffusion as a continuous temporal process occurring at different rates over a latent, unobserved network that may change over time. Given information diffusion data, we infer the edges and dynamics of the underlying network. Our model naturally imposes sparse solutions and requires no parameter tuning. We develop an efficient inference algorithm that uses stochastic convex optimization to compute online estimates of the edges and transmission rates. We evaluate our method by tracking information diffusion among 3.3 million mainstream media sites and blogs, and experiment with more than 179 million different instances of information spreading over the network in a one-year period. We apply our network inference algorithm to the top 5,000 media sites and blogs and report several interesting observations. First, information pathways for general recurrent topics are more stable across time than for on-going news events. Second, clusters of news media sites and blogs often emerge and vanish in a matter of days for on-going news events. Finally, major events, for example, large scale civil unrest as in the Libyan civil war or Syrian uprising, increase the number of information pathways among blogs, and also increase the network centrality of blogs and social media sites.
Galerkin Finite Element Approximations for Stochastic Space-Time Fractional Wave Equations

The traditional wave equation models wave propagation in an ideal medium. To characterize wave propagation in inhomogeneous media with frequency-dependent power-law attenuation, the space-time fractional wave equation is needed; further incorporating the additive white Gaussian noise (external noise) coming from many natural sources leads to the stochastic space-time fractional wave equation. This paper discusses the Galerkin finite element approximations for the stochastic space-time fractional wave equation forced by an additive space-time white noise. We first discretize the space-time additive noise, which introduces a modeling error and results in a regularized stochastic space-time fractional wave equation; then the regularity of the regularized equation is analyzed. For the discretization in space, the finite element approximation is used and the definition of the discrete fractional Laplacian is introduced. We derive the mean-squared $L^2$-norm a priori estimates for the modeling error and for the.
Routes to diagnosis of heart failure: observational study using linked data in England

Objective Timely diagnosis and management of heart failure (HF) is critical, but identification of patients with suspected HF can be challenging, especially in primary care. We describe the journey of people with HF in primary care from presentation through to diagnosis and initial management. Methods We used the Clinical Practice Research Datalink (primary care consultations linked to hospital admissions data and national death registrations for patients registered with participating primary care practices in England) to describe investigation and referral pathways followed by patients from first presentation with relevant symptoms to HF diagnosis, particularly alignment with recommendations of the National Institute for Health and Care Excellence guideline for HF diagnosis. Results 36 748 patients had a diagnosis of HF recorded that met the inclusion criteria between 1 January 2010 and 31 March 2013. For 29 113 (79.2%) patients, this was first recorded in hospital. In the 5 years prior to diagnosis, 15 057 patients (41.0%) had a primary care consultation with one of three key HF symptoms recorded, 17 724 (48.2%) attended for another reason and 3967 (10.8%) did not see their general practitioner. Only 24% of those with recorded HF symptoms followed a pathway aligned with guidelines (echocardiogram and/or serum natriuretic peptide test and specialist referral), while 44% had no echocardiogram, natriuretic peptide test or referral. Conclusions Patients follow various pathways to the diagnosis of HF. However, few appear to follow a pathway supported by guidelines for investigation and referral. There are likely to be missed opportunities for earlier HF diagnosis in primary care.
A Computationally Inexpensive Radio Propagation Model for Vehicular Communication on Flyovers and Inside Underpasses

Vehicular Ad Hoc Networks (VANETs) utilize radio propagation models (RPMs) to predict path loss in vehicular environment. Modern urban vehicular environment contains road infrastructure units that include road tunnels, straight roads, curved roads flyovers and underpasses. Different RPMs were proposed in the past to predict path loss, but modern road infrastructure units especially flyovers and underpasses are neglected previously. Most of the existing RPMs are computationally complex and ignore some of the critical features such as impact of infrastructure units on the signal propagation and the effect of both static and moving radio obstacles on signal attenuation. Therefore, the existing RPMs are incapable of predicting path loss in flyovers and underpass accurately. This paper proposes an RPM to predict path loss for vehicular communication on flyovers and inside underpasses that considers both the static and moving radio obstacles while requiring only marginal overhead. The proposed RPM is validated based upon the field measurements in 5 ㎓ frequency band. A close agreement is found between the measured and predicted values of path loss.
A simultaneous multithreading heterogeneous object recognition processor with machine learning based dynamic resource management

A simultaneous multithreading multicore processor is proposed to accelerate object recognition for 720p HD video streams. The multithreading architecture with Q-learning based dynamic resource management enables concurrent processing of 8 region-of-interests with 5-stage fine grained recognition pipeline outperforming previous object recognition processors with 342GOPS computing power. In addition, the dynamic resource management contributes to increase of energy efficiency by applying the on-line learning DVFS and dynamic tile allocation based on task variance and hardware utilization to achieve 9.6mJ/frame with 1280×720 pixel image. It achieves 2.72× throughput and 3.7× energy efficiency compared to previous recognition processors.
Dynamic Resource Routing using Real-Time Information

Searching the nearest available resource is an important query with many applications in spatial database systems. Examples are searching free parking spots or charging stations in a road network. Information about the availability is usually approximated as long-term statistics. Recently, online systems and sensor networks become more and more common providing access to real-time information about resource availability. In this paper, we consider searching the next available resource in a road network considering real-time information about all resources in a target area. To make the best use of this information, it is not enough to predict a static result route. Instead we propose to model the problem as a Markov decision process (MDP) and compute a routing policy which allows flexible reaction to newly observed developments. In our experiments, we demonstrate that our new approach is capable to exploit the additional information and outperforms previous approaches built on long-term distributions on a real-world parking data set.
Polynomial Collocation for Handling an Inaccurately Known Measurement Configuration in Electrical Impedance Tomography

The objective of electrical impedance tomography is to reconstruct the internal conductivity of a physical body based on measurements of current and potential at a finite number of electrodes attached to its boundary. Although the conductivity is the quantity of main interest in impedance tomography, a real-world measurement configuration includes other unknown parameters as well: The information on the contact resistances, electrode positions, and body shape is almost always incomplete. In this work, the dependence of the electrode measurements on all aforementioned model properties is parametrized via polynomial collocation. The availability of such a parametrization enables efficient simultaneous reconstruction of the conductivity and other unknowns by a Newton-type output least squares algorithm, which is demonstrated by two-dimensional numerical experiments based on both noisy simulated data and experimental data from two water tanks.
No Effects of Psychosocial Stress on Intertemporal Choice

Intertemporal choices - involving decisions which trade off instant and delayed outcomes - are often made under stress. It remains unknown, however, whether and how stress affects intertemporal choice. We subjected 142 healthy male subjects to a laboratory stress or control protocol, and asked them to make a series of intertemporal choices either directly after stress, or 20 minutes later (resulting in four experimental groups). Based on theory and evidence from behavioral economics and cellular neuroscience, we predicted a bidirectional effect of stress on intertemporal choice, with increases in impatience or present bias immediately after stress, but decreases in present bias or impatience when subjects are tested 20 minutes later. However, our results show no effects of stress on intertemporal choice at either time point, and individual differences in stress reactivity (changes in stress hormone levels over time) are not related to individual differences in intertemporal choice. Together, we did not find support for the hypothesis that psychosocial laboratory stressors affect intertemporal choice.
Android Inter-App Communication Threats, Solutions, and Challenges

Researchers and commercial companies have made a lot of efforts on detecting malware in Android platform. However, a recent malware threat, App collusion, makes malware detection challenging. In App collusion, two or more Apps collaborate to perform malicious actions by communicating with each other, which makes single App analysis insufficient. In this paper, we first introduce Android security mechanism and communication channels used by android Applications. Then we summarize the security vulnerabilities and potential threats introduced by App communication. Finally, we discuss state of art researches and challenges on App collusion detection.
Influence of Genetically Modified Human Umbilical Cord Blood Mononuclear Cells on the Expression of Schwann Cell Molecular Determinants in Spinal Cord Injury

Spinal cord injury (SCI) unavoidably results in death of not only neurons but also glial cells. In particular, the death of oligodendrocytes leads to impaired nerve impulse conduction in intact axons. However, after SCI, the Schwann cells (SCs) are capable of migrating towards an area of injury and participating in the formation of functional myelin. In addition to SCI, cell-based therapy can influence the migration of SCs and the expression of their molecular determinants. In a number of cases, it can be explained by the ability of implanted cells to secrete neurotrophic factors (NTFs). Genetically modified stem and progenitor cells overexpressing NTFs have recently attracted special attention of researchers and are most promising for the purposes of regenerative medicine. Therefore, we have studied the effect of genetically modified human umbilical cord blood mononuclear cells on the expression of SC molecular determinants in SCI.