Miracle-dz/UniFAR-FTUs huggingface dataset:
ACESO: A 3D Agent-Based Simulation of the Interaction Between Immune and Tumor Cells

Immunotherapy consists of assisting or augmenting the immune system to prevent or treat diseases and has shown great promise in improving upon the conventional methods of treating cancer, namely radiation therapy, chemotherapy, and surgery [2]. However, conducting preclinical studies in this field can take months to years and often require costly equipment and training to execute. Researchers have used in-silico methods to reduce the time to conduct these studies, computationally exploring many different experimental configurations in a fraction of the time needed in a wet lab [1]. A common method used to describe the interaction of the immune system with tumor cells is through ordinary and partial differential equations of the evolution in the populations, but these top-down approaches often cannot capture the necessary detail required to understand the dynamics that operate at the cellular level [3]. Appropriately, studies that approach these systems from the bottom-up, particularly agent-based simulations, have shown success in capturing the dynamics of cell-to-cell interactions [4].
Verifying the unseen: interactive proofs for label-invariant distribution properties

Given i.i. d. samples from an unknown distribution over a large domain [N], approximating several basic quantities, including the distribution’s support size, its entropy, and its distance from the uniform distribution, requires Θ(N / logN) samples [Valiant and Valiant, STOC 2011]. Suppose, however, that we can interact with a powerful but untrusted prover, who knows the entire distribution (or a good approximation of it). Can we use such a prover to approximate (or rather, to approximately verify) such statistical quantities more efficiently? We show that this is indeed the case: the support size, the entropy, and the distance from the uniform distribution, can all be approximately verified via a 2-message interactive proof, where the communication complexity, the verifier’s running time, and the sample complexity are O(√N). For all these quantities, the sample complexity is tight up to polylog N factors (for any interactive proof, regardless of its communication complexity or verification time). More generally, we give a tolerant interactive proof system with the above sample and communication complexities for verifying a distribution’s proximity to any label-invariant property (any property that is invariant to re-labeling of the elements in the distribution’s support). The verifier’s running time in this more general protocol is also O(√N), under a mild assumption about the complexity of deciding, given a compact representation of a distribution, whether it is in the property or far from it.
Disengagement from Risk Management Practices in Software Development Projects: Framing Effects

Many software projects fail: they take more time than they were intended to, go over their budgets, and do not achieve the intended functionalities. Software project failures occur, in part, because software project managers (SPMs) often fail to manage project risks. Researchers have developed many risk management prescriptions to guide SPMs, including risk checklists, frameworks, practices, and risk response strategies. However, research has shown that SPMs do not use these prescriptions widely. This study addresses the research question: why do many SPMs not fully engage in formal risk management? The question will be answered using a case study approach. The findings will extend our understanding of software project risk management by demonstrating why SPMs sometimes act so differently from formal prescriptions.
RNA-Seq Analysis of Magnaporthe grisea Transcriptome Reveals the High Potential of ZnO Nanoparticles as a Nanofungicide

Magnaporthe grisea is one of the most destructive pathogen that encounters a challenge to rice production around the worldwide. The unique properties of ZnO nanoparticles (NPs), have high attractiveness as nanofungicide. In the present study, the response of fungi to ZnO NPs was evaluated using RNA sequencing (RNA-seq). Two different aligners (STAR and Hisat2) were used for aligning the clean reads, and the DEseq2 package was used to identify the differentially expressed genes (DEGs). In total, 1,438 and 761 fungal genes were significantly up- and down-regulated in response to ZnO NPs, respectively. The DEGs were subjected to functional enrichment analysis to identify significantly enriched biological pathways. Functional enrichment analysis revealed that “cell membrane components,” “ion (calcium) transmembrane transporter activity,” “steroid biosynthesis pathway” and “catalytic activity” were the contributed terms to fungal response mechanisms. The genes involved in aflatoxin efflux pumps and ribosome maturation were among the genes showing significant up- and down-regulation after ZnO NPs application. To confirm the obtained RNA-seq results, the expression of six randomly selected genes were evaluated using q-RT-PCR. Overall, the RNA-seq results suggest that ZnO NPs primarily act on the fungal cell membrane, but accumulation of ROS inside the cell induces oxidative stress, the fungal catalytic system is disrupted, resulting into the inhibition of ROS scavenging and eventually, to the death of fungal cells. Our findings provide novel insights into the effect of ZnO NPs as a promising nanofungicide for effective control of rice blast disease.
Next-Generation Anti-Angiogenic Therapies as a Future Prospect for Glioma Immunotherapy; From Bench to Bedside

Glioblastoma (grade IV glioma) is the most aggressive histopathological subtype of glial tumors with inordinate microvascular proliferation as one of its key pathological features. Extensive angiogenesis in the tumor microenvironment supplies oxygen and nutrients to tumoral cells; retains their survival under hypoxic conditions; and induces an immunosuppressive microenvironment. Anti-angiogenesis therapy for high-grade gliomas has long been studied as an adjuvant immunotherapy strategy to overcome tumor growth. In the current review, we discussed the underlying molecular mechanisms contributing to glioblastoma aberrant angiogenesis. Further, we discussed clinical applications of monoclonal antibodies, tyrosine kinase inhibitors, and aptamers as three major subgroups of anti-angiogenic immunotherapeutics and their limitations. Moreover, we reviewed clinical and preclinical applications of small interfering RNAs (siRNAs) as the next-generation anti-angiogenic therapeutics and summarized their potential advantages and limitations. siRNAs may serve as next-generation anti-angiogenic therapeutics for glioma. Additionally, application of nanoparticles as a delivery vehicle could increase their selectivity and lower their off-target effects.
What Facebook Messages Told Us About How We Handled Disaster Management during the COVID-19 Pandemic?

As COVID-19 continues, social media platforms such as Facebook have become an increasingly important tool for communication and information sharing for public and government agencies. The generic disaster management cycle (mitigation, preparedness, response, and recovery) provides systematic guidance to the public and government agencies to respond to the crisis and suggest appropriate measures for different disaster stages. This study examines various trending topics and themes during the COVID-19 outbreak. Using this generic disaster management cycle as our guiding framework, we examine news topics' evolution during the COVID-19 pandemic on Facebook during two of the four phases. Latent Dirichlet Allocation (LDA) is used for topic modeling to identify topics and themes.
Visual tag recognition for indoor positioning

In recent years, camera-equipped mobile devices have undergone significant evelopments due to increasing computational power as well as reduction of manufacturing unit costs. This has allowed for the development of new stateof- the-art applications, such as object analysis and detection. The focus of this thesis is the detection of visual markers which can be used in various applications such as positioning systems, object tracking and ‘Augmented Reality’. More specifically, the work has been motivated by ongoing research in indoor positioning, where global satellite navigation systems are not reliable enough. The goal is to develop a new visual tag detector, that is both simple and reliable. In the report the necessary background for understanding the development of the detector, as well as a detailed analysis of the current detectors, will be presented. Moreover, through an analysis of the current available image processing tools, we will present the proposed detector. Finally, in order to give a better overview of the system developed, complexity and performance results are presented, ending with some conclusions and suggested further lines of work.
Efficacy of Liraglutide in Non-Diabetic Obese Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Objective: We systematically assessed the efficacy of liraglutide in non-diabetic obese adults. Methods: Six databases were searched up to July 2021 for randomized controlled trials (RCTs) assessing liraglutide versus placebo in obese adults. Primary outcomes were body weight and body mass index (BMI). Secondary outcomes were treatment-emergent adverse events (TEAEs), hypoglycemic episodes, HbA1c, and blood pressure. Effect measures were risk ratio (RR) or mean difference (MD) with their confidence interval (95%CI). Random-effects models and inverse variance meta-analyses were used. Quality of evidence was assessed using GRADE. Results: Twelve RCTs (n = 8249) were included. In comparison to placebo, liraglutide reduced body weight (MD −3.35 kg; 95%CI −4.65 to −2.05; p < 0.0001), and BMI (MD −1.45 kg/m2; 95%CI −1.98 to −0.91; p < 0.0001). Liraglutide did not reduce TEAEs (RR 1.08; 95%CI 0.92 to 1.27; p = 0.25), and Hb1Ac (MD −0.76%; 95%CI −2.24 to 0.72; p = 0.31). Furthermore, it did not increase hypoglycemic episodes (RR 2.01; 95%CI 0.37 to 11.02; p = 0.28). Finally, liraglutide reduced systolic blood pressure (MD −3.07 mmHg; 95%CI −3.66 to −2.48; p < 0.0001) and diastolic blood pressure (MD −1.01 mmHg; 95%CI −1.55 to −0.47; p = 0.0003). Seven RCTs had a high risk of bias. Subgroup analyses by length of treatment and doses had effects similar to the overall analyses. Quality of evidence was low or very low for most outcomes. Conclusions: In non-diabetic obese adults, liraglutide reduced body weight, BMI and blood pressure in comparison to placebo. Adverse events, Hb1Ac levels and hypoglycemic episodes were not different than placebo.
Analysis of Magnetic Field Measurements for Indoor Positioning

Infrastructure-free magnetic fields are ubiquitous and have attracted tremendous interest in magnetic field-based indoor positioning. However, magnetic field-based indoor positioning applications face challenges such as low discernibility, heterogeneous devices, and interference from ferromagnetic materials. This paper first analyzes the statistical characteristics of magnetic field (MF) measurements from heterogeneous smartphones. It demonstrates that, in the absence of disturbances, the MF measurements in indoor environments follow a Gaussian distribution with temporal stability and spatial discernibility. It shows the fluctuations in magnetic field intensity caused by the rotation of a smartphone around the Z-axis. Secondly, it suggests that the RLOWESS method can be used to eliminate magnetic field anomalies, using magnetometer calibration to ensure consistent MF measurements in heterogeneous smartphones. Thirdly, it tests the magnetic field positioning performance of homogeneous and heterogeneous devices using different machine learning methods. Finally, it summarizes the feasibility/limitations of using only MF measurement for indoor positioning.
Sarcopenia Is an Independent Risk Factor for Severe Diabetic Nephropathy in Type 2 Diabetes: A Long-Term Follow-Up Propensity Score–Matched Diabetes Cohort Study

Simple Summary Diabetic nephropathy is a common cause of chronic kidney disease (CKD) and end-stage kidney disease (ESKD) worldwide and results in tremendous wastage of medical resources. Determining the indicators of diabetic nephropathy, such as sarcopenia, and implementing early interventions to prevent disease progression is crucial. The effect of sarcopenia on the risk of severe diabetic nephropathy in patients with type 2 diabetes (T2DM) remains unclear. This study, to date, has the largest sample size and the longest follow-up period among studies investigating this effect by comparing patients with T2DM with and without sarcopenia. This propensity score–matched, population-based cohort study demonstrated that patients with T2DM and sarcopenia may be at a higher risk of severe diabetic nephropathy than are those without sarcopenia. The patients with T2DM and sarcopenia were at a higher risk of severe diabetic nephropathy than were those without sarcopenia irrespective of age, sex, and diabetes severity. Our results may serve as a valuable reference for relevant government authorities in establishing health policies promoting early detection of sarcopenia and exercise to help patients with T2DM overcome sarcopenia. Abstract Background: Diabetic nephropathy is a common cause of chronic kidney disease (CKD) and end-stage kidney disease (ESKD) worldwide and results in tremendous wastage of medical resources. Determining the indicators of diabetic nephropathy, such as sarcopenia, and implementing early interventions to prevent disease progression is crucial. Purpose: The effect of sarcopenia on the risk of severe diabetic nephropathy in patients with type 2 diabetes (T2DM) remains unclear. Patients and Methods: We recruited patients with T2DM and categorized them into two groups, propensity score–matched at a ratio of 1:1, according to whether they had sarcopenia. We subsequently compared the groups’ risk of severe diabetic nephropathy. Results: The matching process yielded a final cohort of 105,166 patients with T2DM (52,583 and 52,583 in the sarcopenia and nonsarcopenia groups, respectively) who were eligible for inclusion in subsequent analyses. According to both the univariate and multivariate Cox regression analyses, the adjusted hazard ratio (aHR) (95% confidence interval) of severe diabetic nephropathy for the sarcopenia diabetes group compared with the control group was 1.10 (1.08–1.13; p < 0.001). Conclusion: The patients with T2DM and sarcopenia were at a higher risk of severe diabetic nephropathy than were those without sarcopenia. Our results may serve as a valuable reference for relevant government authorities in establishing health policies to promote early detection of sarcopenia and exercise to help patients with T2DM overcome sarcopenia.
Specular Microscopy of Human Corneas Stored in an Active Storage Machine

Purpose: Unlike corneas stored in cold storage (CS) which remain transparent and thin, corneas stored in organoculture (OC) cannot be assessed by specular microscopy (SM), because edema and posterior folds occur during storage and prevent from specular reflection. We previously developed an active storage machine (ASM) which restores the intraocular pressure while renewing the storage medium, thus preventing major stromal edema. Its transparent windows allow multimodal corneal imaging in a closed system. Aim: to present SM of corneas stored in this ASM. Methods: Ancillary study of two preclinical studies on corneas stored for one and three months in the ASM. A prototype non-contact SM was developed (CMOS camera, ×10 objective, collimated LED source, micrometric stage). Five non-overlapping fields (935 × 748 μm) were acquired in exactly the same areas at regular intervals. Image quality was graded according to defined categories (American Cornea Donor Study). The endothelial cell density (ECD) was measured with a center method. Finally, SMECD was also compared to Hoechst-stained cell nuclei count (HoechstECD). Results: The 62 corneas remained thin during storage, allowing SM at all time points without corneal deconditioning. Image quality varied depending on donors and days of control but, overall, in the 1100 images, we observed 55% of excellent and 30% of good quality images. SMECD did not differ from HoechstECD (p = 0.084). Conclusions: The ASM combines the advantages of CS (closed system) and OC (long-term storage). Specular microscopy is possible at any time in the ASM with a large field of view, making endothelial controls easy and safe.
Acute Coronary Syndrome in the COVID-19 Era—Differences and Dilemmas Compared to the Pre-COVID-19 Era

The COVID-19 pandemic has led to numerous negative implications for all aspects of society. Although COVID-19 is a predominant lung disease, in 10–30% of cases, it is associated with cardiovascular disease (CVD). The presence of myocardial injury in COVID-19 patients occurs with a frequency between 7–36%. There is growing evidence of the incidence of acute coronary syndrome (ACS) in COVID-19, both due to coronary artery thrombosis and insufficient oxygen supply to the myocardium in conditions of an increased need. The diagnosis and treatment of patients with COVID-19 and acute myocardial infarction (AMI) is a major challenge for physicians. Often the presence of mixed symptoms, due to the combined presence of COVID-19 and ACS, as well as possible other diseases, nonspecific changes in the electrocardiogram (ECG), and often elevated serum troponin (cTn), create dilemmas in diagnosing ACS in COVID-19. Given the often-high ischemic risk, as well as the risk of bleeding, in these patients and analyzing the benefit/risk ratio, the treatment of patients with AMI and COVID-19 is often associated with dilemmas and difficult decisions. Due to delays in the application of the therapeutic regimen, complications of AMI are more common, and the mortality rate is higher.
Numerical simulation of inviscid compressible flow by higher order numerical schemes

We deal with a numerical simulation of inviscid compressible flow by higher order methods. We present a semi-implicit scheme which is based on the discontinuous Galerkin method for the space discretization and the backward difference formula for the time discretization. Further, we discuss and compare a use of two types of nonreflecting boundary conditions at inflow/outflow parts of boundary. Finally, an example of nonstationary subsonic flow simulation demonstrating an efficiency of the scheme is presented.
Ispike: A Post-link Optimizer for the Intel®Itanium®Architecture

Ispike is a post-link optimizer developed for the Intel R Itanium Processor Family (IPF) processors. The IPF architecture poses both opportunities and challenges to post-link optimizations. IPF offers a rich set of performance counters to collect detailed profile information at a low cost, which is essential to post-link optimization being practical. At the same time, the predication and bundling features on IPF make post-link code transformation more challenging than on other architectures. In Ispike, we have implemented optimizations like code layout, instruction prefetching, data layout, and data prefetching that exploit the IPF advantages, and strategies that cope with the IPF-specific challenges. Using SPEC CINT2000 as benchmarks, we show that Ispike improves performance by as much as 40% on the Itanium R 2 processor, with average improvement of 8.5% and 9.9% over executables generated by the Intel R Electron compiler and by the Gcc compiler, respectively. We also demonstrate that statistical profiles collected via IPF performance counters and complete profiles collected via instrumentation produce equal performance benefit, but the profiling overhead is significantly lower for performance counters.
Explainable Vision Transformers and Radiomics for COVID-19 Detection in Chest X-rays

The rapid spread of COVID-19 across the globe since its emergence has pushed many countries’ healthcare systems to the verge of collapse. To restrict the spread of the disease and lessen the ongoing cost on the healthcare system, it is critical to appropriately identify COVID-19-positive individuals and isolate them as soon as possible. The primary COVID-19 screening test, RT-PCR, although accurate and reliable, has a long turn-around time. More recently, various researchers have demonstrated the use of deep learning approaches on chest X-ray (CXR) for COVID-19 detection. However, existing Deep Convolutional Neural Network (CNN) methods fail to capture the global context due to their inherent image-specific inductive bias. In this article, we investigated the use of vision transformers (ViT) for detecting COVID-19 in Chest X-ray (CXR) images. Several ViT models were fine-tuned for the multiclass classification problem (COVID-19, Pneumonia and Normal cases). A dataset consisting of 7598 COVID-19 CXR images, 8552 CXR for healthy patients and 5674 for Pneumonia CXR were used. The obtained results achieved high performance with an Area Under Curve (AUC) of 0.99 for multi-class classification (COVID-19 vs. Other Pneumonia vs. normal). The sensitivity of the COVID-19 class achieved 0.99. We demonstrated that the obtained results outperformed comparable state-of-the-art models for detecting COVID-19 on CXR images using CNN architectures. The attention map for the proposed model showed that our model is able to efficiently identify the signs of COVID-19.
Optimized Heterologous Expression and Efficient Purification of a New TRAIL-Based Antitumor Fusion Protein SRH–DR5-B with Dual VEGFR2 and DR5 Receptor Specificity

In the last two decades, bifunctional proteins have been created by genetic and protein engineering methods to increase therapeutic effects in various diseases, including cancer. Unlike conventional small molecule or monotargeted drugs, bifunctional proteins have increased biological activity while maintaining low systemic toxicity. The recombinant anti-cancer cytokine TRAIL has shown a limited therapeutic effect in clinical trials. To enhance the efficacy of TRAIL, we designed the HRH–DR5-B fusion protein based on the DR5-selective mutant variant of TRAIL fused to the anti-angiogenic synthetic peptide HRHTKQRHTALH. Initially low expression of HRH–DR5-B was enhanced by the substitution of E. coli-optimized codons with AT-rich codons in the DNA sequence encoding the first 7 amino acid residues of the HRH peptide. However, the HRH–DR5-B degraded during purification to form two adjacent protein bands on the SDS-PAGE gel. The replacement of His by Ser at position P2 immediately after the initiator Met dramatically minimized degradation, allowing more than 20 mg of protein to be obtained from 200 mL of cell culture. The resulting SRH–DR5-B fusion bound the VEGFR2 and DR5 receptors with high affinity and showed increased cytotoxic activity in 3D multicellular tumor spheroids. SRH–DR5-B can be considered as a promising candidate for therapeutic applications.
A Lightweight Hybrid Detection Method for Botnet

Botnet is a serious threat for the Internet and it has created great damage to the Internet. How to detect botnet has become an ongoing endeavor research. Series of methods have been discussed in recent research. However, one of the remaining challenges is that the high computational overhead. In this paper, a lightweight hybrid botnet detection method is proposed. Considering the features in the botnet data packets and the characteristic of employing DGA (Domain Generation Algorithm) domain names to connect to the botnet, two sensors are designed and deployed individually and parallelly. Signature detection is used on the gateway sensor to dig out known bot software and deep learning based techniques are used on the DNS (Domain Name Server) server sensor to find DGA domain names. With this method, the computational overhead would be shared by the two sensors and experiments are conducted and the results indicate that the method is effective in detecting botnet