Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of gene and protein expression and genetic polymorphism of CC chemokine ligand 4 in colorectal cancer

BACKGROUND Leukocytes, such as T cells and macrophages, play an important role in tumorigenesis. CC chemokine ligand (CCL) 4, which is produced by lymphocytes and macrophages, has been found to be expressed in the mucosa of the gastrointestinal tract and is a potent chemoattractant for various leukocytes. AIM To examine CCL4 expression and its genetic polymorphism rs10491121 in patients with colorectal cancer (CRC) and evaluate their prognostic significance. METHODS Luminex technology was used to determine CCL4 Levels in CRC tissue (n = 98), compared with paired normal tissue, and in plasma from patients with CRC (n = 103), compared with healthy controls (n = 97). Included patients had undergone surgical resection for primary colorectal adenocarcinomas between 1996 and 2019 at the Department of Surgery, Ryhov County Hospital, Jönköping, Sweden. Reverse transcription quantitative PCR was used to investigate the CCL4 gene expression in CRC tissue (n = 101). Paired normal tissue and TaqMan single nucleotide polymorphism assays were used for the CCL4 rs10491121 polymorphism in 610 CRC patients and 409 healthy controls. RESULTS The CCL4 protein and messenger RNA expression levels were higher in CRC tissue than in normal paired tissue (90%, P < 0.001 and 45%, P < 0.05, respectively). CRC tissue from patients with localized disease had 2.8-fold higher protein expression levels than that from patients with disseminated disease. Low CCL4 protein expression levels in CRC tissue were associated with a 30% lower cancer-specific survival rate in patients (P < 0.01). The level of plasma CCL4 was 11% higher in CRC patients than in healthy controls (P < 0.05) and was positively correlated (r = 0.56, P < 0.01) with the CCL4 protein level in CRC tissue. The analysis of CCL4 gene polymorphism rs10491121 showed a difference (P < 0.05) between localized disease and disseminated disease in the right colon, with a dominance of allele A in localized disease. Moreover, the rate of the A allele was higher among CRC patients with mucinous cancer than among those with non-mucinous cancer. CONCLUSION The present study indicates that the CRC tissue levels of CCL4 and CCL4 gene polymorphism rs10491121, particularly in the right colon, are associated with clinical outcome in CRC patients.
ZE AWESOME FRENCH AUDIO RESEARCHER (ZAFAR)

We present Zafar, Ze Awesome French Audio Researcher. The proposed researcher has a PhD in electrical engineering and computer science from Northwestern University, with a focus on audio signal analysis. He has about 40 publi-cations, including conference papers, journal articles, and patents, with more than 1,600 citations overall. He is actively involved within the research community, as a reviewer for numerous conferences and journals, a member of the IEEE audio and acoustic signal processing technical committee, and an organizer of networking meetups in the San Francisco Bay Area. He is currently a research scientist at Audible Magic, where he is working on audio recognition, among other projects.
Energy-Efficient Carbon Fiber Production with Concentrated Solar Power: Process Design and Techno-economic Analysis

Carbon fibers, if generated from anthropogenic CO2 via algae-based carbon fixation and subsequent energy and cost-efficient carbonization, would be a sustainable CO2 sink. They could replace materials that currently are major greenhouse gas (GHG) sources, hence contributing significantly to accomplishing targets of the 2015 Paris Agreement. For the first time, our study presents an integration of concentrated solar power (CSP) technology into a carbonization reactor (CR) for carbon fiber production combined with extensive energy recovery. Based on a mass and energy flow model of the corresponding CSP-CR concept, techno-economic analysis (TEA) was carried out, first with static base case values for broad variation analysis, and in a second step with a dynamic economic model, embedded in a Monte Carlo simulation, to quantify risks from market and modeling uncertainties. First results indicate significant reduction potential of energy consumption and cost. Economic viability perspectives are promising and ju.
The activation of cardiac Nmnat/NAD+/SIR2 pathways mediates endurance exercise resistance to lipotoxic cardiomyopathy in aging Drosophila.

Endurance exercise is an important way to resist and treat a high-fat-diet(HFD)-induced lipotoxic cardiomyopathy, but the underlying molecular mechanisms are poorly understood. Here, we used Drosophila to identify whether cardiac Nmnat/NAD+/SIR2 pathways activation could mediate endurance exercise resistance to lipotoxic cardiomyopathy. The results showed that endurance exercise activated the cardiac Nmnat/NAD+/SIR2/FOXO pathway and Nmnat/NAD+/SIR2/PGC-1α pathway, including up-regulating cardiac Nmnat, SIR2, FOXO, PGC-1α expression, SOD activity, and NAD+ level, and it prevented HFD-induced or cardiac Nmnat knock-down-induced cardiac lipid accumulation, MDA content and fibrillation increase, and fractional shortening decrease. Cardiac Nmnat overexpression also activated heart Nmnat/NAD+/SIR2 pathways and resisted HFD-induced cardiac malfunction, but it could not protect against HFD-induced lifespan reduction and locomotor impairment. Exercise improved lifespan and mobility in cardiac Nmnat knock-down flies. Therefore, current results confirmed that cardiac Nmnat/NAD+/SIR2 pathways were important antagonists of HFD-induced lipotoxic cardiomyopathy. The cardiac Nmnat/NAD+/SIR2 pathways activation was the important underlying molecular mechanism of endurance exercise and cardiac Nmnat overexpression against lipotoxic cardiomyopathy in Drosophila.
Internet addiction in Gulf countries: A systematic review and meta-analysis

Abstract Background and aims The prevalence of internet addiction (IA) varies widely in the Gulf Cooperation Council (GCC) countries (4%–82.6%). We aimed to assess the quality of IA studies from the GCC and pool their data to get an accurate estimate of the problem of IA in the region. Methods A systematic review of available studies was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. PubMed, Embase, and Cochrane Controlled Register of Trials were systematically searched; studies conducted in GCC countries (i.e., Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates) with a validated instrument for internet addiction assessment were eligible. Ten studies were eligible for the systematic review, all of which were included in the meta-analysis. The Newcastle Ottawa Scale was used for quality assessment. Results Nine out of ten of the included studies had either adolescent and/or young adult participants (age < 25). Two studies were of ‘good’ quality, six were of ‘satisfactory’ quality, and two were of ‘unsatisfactory’ quality. The pooled internet addiction prevalence was 33%; it was significantly higher among females than males (male = 24%, female = 48%, P = 0.05) and has significantly increased over time (P < 0.05). Discussion and conclusions One in every three individuals in GCC countries was deemed to be addicted to the internet, according to Young's Internet Addiction Test. A root cause analysis focusing on family structure, environment, and religious practices is needed to identify modifiable risk factors.
Desiderata for Representation Learning: A Causal Perspective

Representation learning constructs low-dimensional representations to summarize essential features of high-dimensional data. This learning problem is often approached by describing various desiderata associated with learned representations; e.g., that they be non-spurious, e cient, or disentangled. It can be challenging, however, to turn these intuitive desiderata into formal criteria that can be measured and enhanced based on observed data. In this paper, we take a causal perspective on representation learning, formalizing non-spuriousness and e ciency (in supervised representation learning) and disentanglement (in unsupervised representation learning) using counterfactual quantities and observable consequences of causal assertions. This yields computable metrics that can be used to assess the degree to which representations satisfy the desiderata of interest and learn non-spurious and disentangled representations from single observational datasets.
Egocentric View Hand Action Recognition by Leveraging Hand Surface and Hand Grasp Type

We introduce a multi-stage framework that uses mean curvature on a hand surface and focuses on learning interaction between hand and object by analyzing hand grasp type for hand action recognition in egocentric videos. The proposed method does not require 3D information of objects including 6D object poses which are difficult to annotate for learning an object's behavior while it interacts with hands. Instead, the framework synthesizes the mean curvature of the hand mesh model to encode the hand surface geometry in 3D space. Additionally, our method learns the hand grasp type which is highly correlated with the hand action. From our experiment, we notice that using hand grasp type and mean curvature of hand increases the performance of the hand action recognition.
Elastic Significant Bit Quantization and Acceleration for Deep Neural Networks

Quantization has been proven to be a vital method for improving the inference efficiency of deep neural networks (DNNs). However, it is still challenging to strike a good balance between accuracy and efficiency while quantizing DNN weights or activation values from high-precision formats to their quantized counterparts. We propose a new method called elastic significant bit quantization(ESB) that controls the number of significant bits of quantized values to obtain better inference accuracy with fewer resources. We design a unified mathematical formula to constrain the quantized values of the ESB with a flexible number of significant bits. We also introduce a distribution difference aligner (DDA) to quantitatively align the distributions between the full-precision weight or activation values and quantized values. Consequently, ESB is suitable for various bell-shaped distributions of weights and activation of DNNs, thus maintaining a high inference accuracy. Benefitting from fewer significant bits of quantized values, ESB can reduce the multiplication complexity. We implement ESB as an accelerator and quantitatively evaluate its efficiency on FPGAs. Extensive experimental results illustrate that ESB quantization consistently outperforms state-of-the-art methods and achieves average accuracy improvements of 4.78%, 1.92%, and 3.56% over AlexNet, ResNet18, and MobileNetV2, respectively. Furthermore, ESB as an accelerator can achieve 10.95 GOPS peak performance of 1k LUTs without DSPs on the Xilinx ZCU102 FPGA platform. Compared with CPU, GPU, and state-of-the-art accelerators on FPGAs, the ESB accelerator can improve the energy efficiency by up to 65, 11, and 26, respectively.
Functionalization of multi-walled carbon nanotubes and its application in preparing the 3D graphene/carbon nanotubes hybrid architectures

ABSTRACT A facile strategy for the preparation of water-dispersible multi-walled carbon nanotubes (MWCNTs) in aqueous solution for the preparation of the three-dimensional (3D) graphene/carbon nanotube (G/CNT) hybrid architectures is proposed, where MWCNTs were functionalized by simultaneous radiation-induced graft polymerization of acrylic acid under the γ-ray (denoted as MWCNT-g-PAA) for improving its dispersibility. The stability of the aqueous solution of MWCNT-g-PAA in water is highly improved. We also use the MWCNT-g-PAA fabricating three-dimensional cylindrical graphene/carbon nanotube (G/CNT) hybrid architectures by a simple one-step hydrothermal process. We found that the as-prepared MWCNTs-g-PAA showed a very good dispersibility in GO solution with different concentration ratio and a promising precursor for preparing the graphene/CNT hybrid materials.
Nitrogen evolution in the halo, thick disc, thin disc, and bulge of the Galaxy

We study the evolution of nitrogen (N) in the Galactic halo, thick disc, thin disc, and bulge by comparing detailed chemical evolution models with recent observations. The models used in this work have already been constrained to explain the abundance patterns of α-elements and the metallicity distribution functions of halo, disc, and bulge stars; here, we adopt them to investigate the origin and evolution of N in the different Galactic components. First, we consider different sets of yields and study the importance of the various channels proposed for N production. Secondly, we apply the reference models to study the evolution of both the Galactic discs and bulge. We conclude that: i) primary N produced by rotating massive stars is required to reproduce the plateau in log(N/O) and [N/Fe] ratios at low metallicity, as well as the secondary and primary production from low- and intermediate-mass stars to reproduce the data of the thin disc; ii) the parallel model can provide a good explanation of the evolution of N abundance in the thick and thin discs, and we confirm that the thick disc has evolved much faster than the thin disc, in agreement with the results from the abundance patterns of other chemical elements; and iii) finally, we present new model predictions for N evolution in the Galactic bulge, and we show that the observations in bulge stars can be explained if massive stars rotate fast during the earliest phases of Galactic evolution, in agreement with findings from the abundance pattern of carbon.
Fog Computing, Applications, Security and Challenges: - A Survey

The internet of things originates a world where on daily basis objects can join the internet and interchange information and in addition process, store, gather them from the nearby environment, and effectively mediate on it. A remarkable number of services might be imagined by abusing the internet of things. Fog computing which is otherwise called edge computing was introduced in 2012 as a considered is a prioritized choice for the internet of things applications. As fog computing extend services of cloud near to the edge of the network and make possible computations, communications, and storage services in proximity to the end user. Fog computing cannot only provide low latency, location awareness but also enhance real-time applications, quality of services, mobility, security and privacy in the internet of things applications scenarios. In this paper, we will summarize and overview fog computing model architecture, characteristic, similar paradigm and various applications in real-time scenarios such as smart grid, traffic control system and augmented reality. Finally, security challenges are presented.
Continuous Entailment Patterns for Lexical Inference in Context

Combining a pretrained language model (PLM) with textual patterns has been shown to help in both zero- and few-shot settings. For zero-shot performance, it makes sense to design patterns that closely resemble the text seen during self-supervised pretraining because the model has never seen anything else. Supervised training allows for more flexibility. If we allow for tokens outside the PLM’s vocabulary, patterns can be adapted more flexibly to a PLM’s idiosyncrasies. Contrasting patterns where a “token” can be any continuous vector from those where a discrete choice between vocabulary elements has to be made, we call our method CONtinous pAtterNs (CONAN). We evaluate CONAN on two established benchmarks for lexical inference in context (LIiC) a.k. a. predicate entailment, a challenging natural language understanding task with relatively small training data. In a direct comparison with discrete patterns, CONAN consistently leads to improved performance, setting a new state of the art. Our experiments give valuable insights on the kind of pattern that enhances a PLM’s performance on LIiC and raise important questions regarding our understanding of PLMs using text patterns.
Exploring potential serum levels of Homocysteine, interleukin‐1 beta, and apolipoprotein B 48 as new biomarkers for patients with ischemic stroke

Abstract Background Stroke is the second leading cause of death worldwide with heterogeneous characteristics. The subtypes of stroke are due to different pathophysiological regulations and causes. This study aimed to investigate the correlation of serum levels of apolipoprotein B 48, interleukin‐1β and Homocysteine with BMI in patients with ischemic stroke (IS). Methods Over one hundred controls (120) and an equal number of IS patients, including 31 women and 89 men, were recruited to participate in the case‐control study conducted at Imam Reza Hospital (Tabriz, Iran) from February 2019 to March 2020. We measured serum levels of apolipoprotein B 48, interleukin‐1β, and Homocysteine. Receiver operating characteristic analysis (ROC) was performed to evaluate the diagnostic value of these indices in patients and control groups. Results The mean serum levels of apolipoprotein B 48, interleukin‐1β, and Homocysteine, were significantly increased in the experimental group compared to the control group with a p‐value of 0.001. The ROC curve analysis showed that the area under the curve for apo B48, IL −1β, hs‐CRP, and Homocysteine serum levels were 0.94, 0.98, 0.99, and 1, respectively. Conclusions The results of our current study show that the determination of serum levels of apolipoprotein B 48, interleukin‐1β, and Homocysteine can potentially be used to monitor and diagnose IS patients. However, there was no statistically significant correlation between serum levels of apolipoprotein B 48, interleukin 1β and Homocysteine and BMI in the patient group. However, there was a statistically significant inverse correlation between serum levels of high‐sensitivity C‐reactive protein (hs‐CRP) and BMI in the patient group.
Pretransplantation EASIX predicts intensive care unit admission in allogeneic hematopoietic cell transplantation.

The Endothelial Activation and Stress Index (EASIX) is a laboratory-based prognosis index defined as creatinine × lactate dehydrogenase/platelets. When measured at pretransplantation evaluation (EASIX-PRE), it predicts allogeneic hematopoietic cell transplantation (alloHCT) mortality. This study explores its ability to predict intensive care unit (ICU) admission and validates EASIX-PRE predictive power for overall survival (OS) and nonrelapse mortality (NRM) in 167 consecutive patients undergoing alloHCT. EASIX-PRE was calculated retrospectively in all patients and transformed into log2 values (log2-EASIX-PRE). Log2-EASIX-PRE predicted ICU admission (hazard ratio [HR], 1.41; P < .001), OS (HR, 1.19; P = .011), and NRM (HR, 1.28; P = .004). The most discriminating EASIX-PRE cutoff value for risk of ICU admission was the 75th percentile (2.795); for OS and NRM, it was the median value (1.703). Patients with EASIX-PRE >2.795 had higher incidence of ICU admission in comparison with patients with lower EASIX-PRE values (day +180, 35.8% vs 12.8%; HR, 2.28; P = .010). Additionally, patients with EASIX-PRE >1.073 had lower OS (2 years, 57.7% vs 68.7%; HR, 1.98; P = .006) and higher NRM (2 years, 38.7% vs 18.5%; HR, 2.92; P = .001) than patients with lower EASIX-PRE results. Log2-EASIX-PRE was not associated with incidence of transplantation-associated microangiopathy, sinusoidal obstruction syndrome, or acute graft-versus-host disease. This study proposes EASIX-PRE as a prognostic tool to identify patients undergoing alloHCT at increased risk of severe organ dysfunction and who would therefore require ICU admission. Early identification of patients at high risk of severe events could contribute to personalized intervention design. Additionally, it validates the association between EASIX-PRE and OS and NRM in those undergoing alloHCT.
Paulihedral: a generalized block-wise compiler optimization framework for Quantum simulation kernels

The quantum simulation kernel is an important subroutine appearing as a very long gate sequence in many quantum programs. In this paper, we propose Paulihedral, a block-wise compiler framework that can deeply optimize this subroutine by exploiting high-level program structure and optimization opportunities. Paulihedral first employs a new Pauli intermediate representation that can maintain the high-level semantics and constraints in quantum simulation kernels. This naturally enables new large-scale optimizations that are hard to implement at the low gate-level. In particular, we propose two technology-independent instruction scheduling passes, and two technology-dependent code optimization passes which reconcile the circuit synthesis, gate cancellation, and qubit mapping stages of the compiler. Experimental results show that Paulihedral can outperform state-of-the-art compiler infrastructures in a wide-range of applications on both near-term superconducting quantum processors and future fault-tolerant quantum computers.
Comparing Perimenstrual and Nonperimenstrual Migraine Attacks Using an e-Diary

Background and Objectives Endogenous and exogenous female sex hormones are considered important contributors to migraine pathophysiology. Previous studies have cautiously suggested that perimenstrual migraine attacks have a longer duration and are associated with higher disability compared to nonperimenstrual attacks, but they showed conflicting results on acute therapy efficacy, pain intensity, and associated symptoms. We compared perimenstrual and nonperimenstrual migraine attack characteristics and assessed premenstrual syndrome (PMS) in women with migraine. Methods Women with migraine were invited to complete a headache e-diary. Characteristics of perimenstrual attacks and nonperimenstrual attacks were compared. The primary outcome was attack duration. Secondary outcomes were headache intensity, accompanying symptoms, acute medication intake, and pain coping. Mixed effects models were used to account for multiple attacks within patients. PMS was assessed in patients without hormonal contraceptives. Subgroup analyses were performed for women with menstrually related migraine (MRM) and nonmenstrually related migraine (non-MRM) and women with a natural menstrual cycle and women using hormonal contraceptives. Results A representative group of 500 participants completed the e-diary for at least 1 month. Perimenstrual migraine attacks (n = 998) compared with nonperimenstrual attacks (n = 4097) were associated with longer duration (20.0 vs 16.1 hours, 95% confidence interval 0.2–0.4), higher recurrence risk (odds ratio [OR] 2.4 [2.0–2.9]), increased triptan intake (OR 1.2 [1.1–1.4]), higher headache intensity (OR 1.4 [1.2–1.7]), less pain coping (mean difference −0.2 [−0.3 to −0.1]), more pronounced photophobia (OR 1.3 [1.2–1.4]) and phonophobia (OR 1.2 [1.1–1.4]), and less aura (OR 0.8 [0.6–1.0]). In total, 396/500 women completed the diary for ≥3 consecutive menstrual cycles, of whom 56% (221/396) fulfilled MRM criteria. Differences in attack characteristics became more pronounced when focusing on women with MRM and women using hormonal contraceptives. Prevalence of PMS was not different for women with MRM compared to non-MRM (11% vs 15%). Discussion The longer duration of perimenstrual migraine attacks in women (with MRM) is associated with higher recurrence risk and increased triptan use. This may increase the risk of medication overuse and emphasizes the need to develop female-specific prophylactic treatment.