Miracle-dz/UniFAR-FTUs huggingface dataset:
The Other Side of Malnutrition in Inflammatory Bowel Disease (IBD): Non-Alcoholic Fatty Liver Disease

Steatohepatitis and hepatobiliary manifestations constitute some of the most common extra-intestinal manifestations of Inflammatory Bowel Disease (IBD). On the other hand, non-alcoholic fatty liver disease (NAFLD) affects around 25% of the world’s population and is attracting ever more attention in liver transplant programs. To outline the specific pathways linking these two conditions is a pressing task for 21st-century researchers. We are accustomed to expecting the occurrence of fatty liver disease in obese people, but current evidence suggests that there are several different pathways also occurring in underweight patients. Genetic factors, inflammatory signals and microbiota are key players that could help in understanding the entire pathogenesis of NAFLD, with the aim of defining the multiple expressions of malnutrition. In the current review, we summarize the most recent literature regarding the epidemiology, pathogenesis and future directions for the management of NAFLD in patients affected by IBD.
Intensive Comprehensive Aphasia Programmes: a systematic scoping review and analysis using the TIDieR checklist for reporting interventions

Abstract Purpose Aphasia is an acquired language disorder that typically occurs as a result of a stroke. People with aphasia experience communication difficulties and risk secondary impacts, for example, affecting social and work life and mental health. Intensive Comprehensive Aphasia Programmes (ICAPs) aims to address the multiple consequences of aphasia using intensive intervention and a wide range of therapy approaches. Although basic parameters of ICAP intervention have been defined, a fuller characterisation is needed. This systematic scoping review aimed to determine what constitutes an ICAP. Methods Peer-reviewed and Grey databases were searched for articles on ICAPs using Joanna Brigg’s Institute methodology. Data was extracted following the Template for Intervention Description and Replication (TIDieR) checklist for reporting interventions and synthesised using a narrative synthesis. Results and conclusions 17 ICAPs were reported in 20 peer-reviewed literature sources (9 ICAPs supplemented by Grey literature sources). There were high degrees of variation in dose, professionals involved, and no qualitative data from participants. Of note, ICAP intervention was highly tailored to individual participants on the same ICAP, and intervention content varied between ICAPs. ICAPs appear to be rationalised as intensive impairment-based programmes with other components added for comprehensiveness. Stronger rationale and a logic model are required to justify the core components of ICAPs. The input of stakeholders into designing future ICAP interventions is recommended. IMPLICATIONS FOR REHABILITATION The ICAP model is in its infancy when it comes to mainstream clinical application as only the intensity component of the ICAP has clear theoretical underpinning as reported in the peer-reviewed literature. There have been clinical uptakes of the ICAP model which is likely to continue and is valid in the context of an under-researched area of aphasia therapy and on a background of a less than perfect relationship between evidence base and practice. Aspects of the ICAP model are valid for clinicians to implement, for example, intensive evidence-based aphasia therapy in combination with therapy which addresses some of the broader implications of aphasia, for example, social isolation. Clinicians can use the ICAP model to review their existing service provision and explore whether their service provides aphasia therapy that addresses the multiple aspects of aphasia (i.e., ensuring the focus is not only on impairment-based therapy).
Leveraging Genomics, Transcriptomics, and Epigenomics to Understand the Biology and Chemoresistance of Ovarian Cancer

Simple Summary Ovarian cancer is the leading cause of death by a gynecological tumor, mainly due to its common detection in advanced stages and to its high resistance to conventional chemotherapy. The study of the molecular mechanisms leading to ovarian tumor progression and chemoresistance are thus a priority in oncological research. In recent years, omics technologies have allowed the study of new aspects of ovarian cancer biology from a global point of view. In this review, we aim to summarize the main findings reached by recent studies in ovarian cancer using genomic, transcriptomic, and epigenomic techniques and how they have improved our understanding of ovarian cancer biology and chemoresistance. Abstract Ovarian cancer is a major cause of fatality due to a gynecological malignancy. This lethality is largely due to the unspecific clinical manifestations of ovarian cancer, which lead to late detection and to high resistance to conventional therapies based on platinum. In recent years, we have advanced our understanding of the mechanisms provoking tumor relapse, and the advent of so-called omics technologies has provided exceptional tools to evaluate molecular mechanisms leading to therapy resistance in ovarian cancer. Here, we review the contribution of genomics, transcriptomics, and epigenomics techniques to our knowledge about the biology and molecular features of ovarian cancers, with a focus on therapy resistance. The use of these technologies to identify molecular markers and mechanisms leading to chemoresistance in these tumors is discussed, as well as potential further applications.
Ultra-Low Energy CNFET-Based Ternary Combinational Circuits Designs

The embedded systems, IoT (Internet of Things) devices, and portable electronic devices spread very quickly recently. Most of them depend on batteries to operate. The target of this work is to decrease energy consumption by (1) using Multiple-valued logic (MVL) that shows notable enhancements regarding energy consumption over binary circuits and (2) using carbon nanotube field-effect transistors (CNFET) that show better performance than CMOS. This work proposes ternary combinational circuits using 32 nm CNFET: Ternary Half Adder (THA) with 36 transistors and Ternary Multiplier (TMUL) with 23 transistors. To reduce energy consumption by utilizing the unary operator of the ternary system and employing two voltage supplies (<inline-formula> <tex-math notation="LaTeX">$V_{dd}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$V_{dd}$ </tex-math></inline-formula>/2). The result of extensive HSPICE simulations regarding PVT (Process, Voltage, and Temperatures) variations and Noise Immunity Curve (NIC) show the improvements of the proposed designs up to 25% in transistors count and up to 98% in energy consumption reductions. Further, increasing the robustness of process variations and the noise tolerance compared to recent similar designs.
11C-Methionine PET for Identification of Pediatric High-Grade Glioma Recurrence

Visual Abstract Differentiating tumor recurrence or progression from pseudoprogression during surveillance of pediatric high-grade gliomas (PHGGs) using MRI, the primary imaging modality for evaluation of brain tumors, can be challenging. The aim of this study was to evaluate whether 11C-methionine PET, a molecular imaging technique that detects functionally active tumors, is useful for further evaluating MRI changes concerning for tumor recurrence during routine surveillance. Methods: Using 11C-methionine PET during follow-up visits, we evaluated 27 lesions in 26 patients with new or worsening MRI abnormalities for whom tumor recurrence was of concern. We performed quantitative and qualitative assessments of both 11C-methionine PET and MRI data to predict the presence of tumor recurrence. Further, to assess for an association with overall survival (OS), we plotted the time from development of the imaging changes against survival. Results: Qualitative evaluation of 11C-methionine PET achieved 100% sensitivity, 60% specificity, and 93% accuracy to correctly predict the presence of tumors in 27 new or worsening MRI abnormalities. Qualitative MRI evaluation achieved sensitivity ranging from 86% to 95%, specificity ranging from 40% to 60%, and accuracy ranging from 85% to 89%. The interobserver agreement for 11C-methionine PET assessment was 100%, whereas the interobserver agreement was only 50% for MRI (P < 0.01). Quantitative MRI and 11C-methionine PET evaluation using receiver-operating characteristics demonstrated higher specificity (80%) than did qualitative evaluations (40%–60%). Postcontrast enhancement volume, metabolic tumor volume, tumor-to-brain ratio, and presence of tumor as determined by consensus MRI assessment were inversely associated with OS. Conclusion: 11C-methionine PET has slightly higher sensitivity and accuracy for correctly predicting tumor recurrence, with excellent interobserver agreement, than does MRI. Quantitative 11C-methionine PET can also predict OS. These findings suggest that 11C-methionine PET can be useful for further evaluation of MRI changes during surveillance of previously treated PHGGs.
An Improved Method of Blood Vessel Enhancement Based on Hessian Matrix

The analysis of blood vessel images is of great significance to the diagnosis and treatment of vascular diseases. By performing blood vessel enhancement operations on blood vessel images, it can help clinicians perform diagnoses to reduce workload and time. In the blood vessel enhancement algorithm based on the Hessian matrix, to save time when calculating the Hessian matrix, usually use LoG operation for approximate replacement, resulting in the loss of some details. In this paper, we propose a new calculation method that uses successive small size convolution kernels to convolve the filtering results of the previous scale when calculating the Hessian matrix, thus avoiding excessive calculation time and not losing the details of blood vessel enhancement. We evaluate our method on real image data sets, and the results verify that our method has better performance.
Snapshot for IoT: Adaptive Measurement for Multidimensional QoS Resources

With the increasing and extensive use of intelligent Internet of things (IoT) devices, its operational aspect in the network has become a significant dependent data for network QoS management and scheduling. For the resilient intelligent IoT cluster with flexible increase and decrease of devices and heterogeneous operating systems, this paper proposes an adaptive measurement method MRAM, which can snapshot the multidimensional QoS resources view (MRV) of the IoT devices in cluster. MRAM uses the measurement offloading architecture based on extensible gateway platform and cloud computing to liberate the local resources of monitored IoT devices. Based on the improved LSTM algorithm, the MRV’s mutations detection method ELSTM is designed. Newly collected QoS resource can be judged whether mutations have occurred and adaptive measurement state machine is enabled by ELSTM. According to the state machine which ensures that the MRV is updated timely and reflected the current status of the cluster, MRAM adjusts the measurement granularity in real time. This method provides a high time efficiency global profile for the upper QoS services and reduces the impact of measurement on the IoT devices. A real environment is built to test the performance of this method. MRAM has high measurement accuracy and the precision of mutations detection is 98.29%. It converges the update MRV of second level under the condition of IoT devices’ low consumption of storage and CPU utilization.
Harnessing the Flexibility of District Heating System for Integrating Extensive Share of Renewable Energy Sources in Energy Systems

Lately, the European Union has reinforced the targets set to cut back carbon emissions. The energy generation sector and particularly, the district heating (DH) system, is still prevailed by combustion of fossil fuels that heavily contributes to such emissions. This paper presents a system-based approach to study the coupling between electricity and DH sector for effective mitigation of emissions. A mixed integer linear programming framework is proposed that aims to exploit the flexibility of electricity cogeneration together with partial electrification of the DH system by investing in renewable technologies. The objective is to simultaneously minimize the investment cost and emissions. Both the electricity and DH load profiles are segregated into critical and flexible types. Comprehensive demand response (DR) framework of thermostatically controlled loads and electric vehicles is considered while preserving the chronology. The framework is applied to the Finnish energy system considering the generation mix. Results prove that coordinating the electricity cogeneration with renewable generation combined with partly shifting from DH to electrified heating has a great potential in reducing the emissions. For an average weather scenario under DR, the least-cost solution guarantees an annual emission reduction of 12.04% relative to the total emissions of Finland against the total investment of €13.24Bn in wind and solar power generation.
Directional Navigability Analysis of Local Gravity Map Based on Selective Ensemble Learning

Aiming at the problems that human factors have great influence on navigability feature extraction and threshold setting of directional navigability analysis of local gravity maps, a directional navigability analysis method based on Log-Gabor filter banks and selective ensemble learning is proposed. First, directional navigability feature maps in five scales and eight typical directions are established by using the Log-Gabor filter banks. Second, a selective ensemble learning based on binary gravitation field algorithm and optimized voting scheme is used as directional navigability analysis model. Finally, simulation experiments are conducted to verify the proposed method. Results show that the proposed method can effectively avoid the blindness when artificially extracting directional navigability features and evaluation threshold setting, and the directional navigability analysis for local gravity map can be achieved automatically. Compared with random forest and selective ensemble learning based on BPSO, the accuracy of the proposed method is highest.
EC-Scheduler: A Load-Balanced Scheduler to Accelerate the Straggler Recovery for Erasure Coded Storage Systems

Erasure codes (EC) have become a typical technology for distributed storage systems in place of data replication, providing similar data availability but lower storage cost. However, a great number of data computations and migrations during the EC recovery process bring high I/O and network latency penalties. Although several EC recovery methods have been designed to compromise the recovery penalty with high parallelism, the performance of these schemes was usually bounded by the straggler problems due to the various (I/O) performance among different nodes in the storage system. Moreover, the variation of the access popularity from the upper layer application causes the dynamic load fluctuation and asymmetry upon different nodes, which makes the scheduling more difficult during the recovery. To address the above problem, we propose a dynamic load-balanced scheduling algorithm for straggler recovery called EC-Scheduler. EC-Scheduler adjusts the recovery schedule dynamically with the awareness of continuous load fluctuation on the nodes, guaranteeing high parallelism and load balance ability simultaneously. To demonstrate the effectiveness of EC-Scheduler, we conduct several experiments in a cluster. The results show that, compared to typical recovery schemes such as Fast-PR and EC-Store, EC-Scheduler could achieve a 1.3X speed-up in the recovery process and 10X improvement in recovery load imbalance factor.
Deep Learning Based Resource Availability Prediction for Local Mobile Crowd Computing

Mobile crowd computing (MCC) that utilizes public-owned (crowd’s) smart mobile devices (SMDs) collectively can give adequate computing power without any additional financial and ecological cost. However, the major challenge is to cope with the mobility (or availability) issue of SMDs. User’s unpredicted mobility makes the SMDs really unstable resources. Selecting such erratic resources for job schedule would result in frequent job offloading and, in the worst case, job loss, which would affect the overall performance and the quality of service of MCC. In a Local MCC, generally, a set of users are available for a certain period regularly. Based on this information, the chances of a user being available for a certain duration from a given point of time can be predicted. In this paper, we provide an effective model to predict the availability of the users (i.e., their SMDs) in such an MCC environment. We argue that before submitting a job to an SMD, the stability of it is to be assessed for the duration of execution of the job to be assigned. If the predicted availability period is greater than the job size, then only the job should be assigned to the SMD. An accurate prediction will minimize the unnecessary job offloading or job loss due to the early departure of the designated SMD. We propose an advanced convolutional feature extraction mechanism that is applied to LSTM and GRU-based time-series prediction models for predicting SMD availability. To collect user mobility data, we considered a research lab scenario, where real mobility traces were recorded with respect to a Wi-Fi access point. We compared the prediction performances of convolutional LSTM and GRU with the basic LSTM and GRU and ARIMA in terms of MAE, RMSE, R2, accuracy, and perplexity. In all the measurements, the proposed convolutional LSTM exhibited considerably better prediction performance.
Repair Mechanisms of the Neurovascular Unit after Ischemic Stroke with a Focus on VEGF

The functional neural circuits are partially repaired after an ischemic stroke in the central nervous system (CNS). In the CNS, neurovascular units, including neurons, endothelial cells, astrocytes, pericytes, microglia, and oligodendrocytes maintain homeostasis; however, these cellular networks are damaged after an ischemic stroke. The present review discusses the repair potential of stem cells (i.e., mesenchymal stem cells, endothelial precursor cells, and neural stem cells) and gaseous molecules (i.e., nitric oxide and carbon monoxide) with respect to neuroprotection in the acute phase and regeneration in the late phase after an ischemic stroke. Commonly shared molecular mechanisms in the neurovascular unit are associated with the vascular endothelial growth factor (VEGF) and its related factors. Stem cells and gaseous molecules may exert therapeutic effects by diminishing VEGF-mediated vascular leakage and facilitating VEGF-mediated regenerative capacity. This review presents an in-depth discussion of the regeneration ability by which endogenous neural stem cells and endothelial cells produce neurons and vessels capable of replacing injured neurons and vessels in the CNS.
Possible Biochemical Processes Underlying the Positive Health Effects of Plant-Based Diets—A Narrative Review

Plant-based diets are becoming more popular for many reasons, and epidemiological as well as clinical data also suggest that a well-balanced vegan diet can be adopted for the prevention, and in some cases, in the treatment of many diseases. In this narrative review, we provide an overview of the relationships between these diets and various conditions and their potential biochemical background. As whole plant foods are very rich in food-derived antioxidants and other phytochemicals, they have many positive physiological effects on different aspects of health. In the background of the beneficial health effects, several biochemical processes could stand, including the reduced formation of trimethylamine oxide (TMAO) or decreased serum insulin-like growth factor 1 (IGF-1) levels and altered signaling pathways such as mechanistic target of rapamycin (mTOR). In addition, the composition of plant-based diets may play a role in preventing lipotoxicity, avoiding N-glycolylneuraminic acid (Neu5Gc), and reducing foodborne endotoxin intake. In this article, we attempt to draw attention to the growing knowledge about these diets and provide starting points for further research.
The evolution of Amazon Redshift

In 2013, Amazon Web Services revolutionized the data warehousing industry by launching Amazon Redshift [7], the first fully managed, petabyte-scale enterprise-grade cloud data warehouse. Amazon Redshift made it simple and cost-effective to efficiently analyze large volumes of data using existing business intelligence tools. This launch was a significant leap from the traditional on-premise data warehousing solutions, which were expensive, not elastic, and required significant expertise to tune and operate. Customers embraced Amazon Redshift and it became the fastest growing service in AWS. Today, tens of thousands of customers use Amazon Red-shift in AWS’s global infrastructure of 25 launched Regions and 81 Availability Zones (AZs), to process exabytes of data daily. The success of Amazon Redshift inspired a lot of innovation in the analytics segment, e.g. [1, 2, 4, 10], which in turn has benefited customers. In the last few years, the use cases for Amazon Redshift have evolved and in response, Amazon Redshift continues to deliver a series of innovations that delight customers. In this paper, we give an overview of Amazon Redshift’s system architecture. Amazon Redshift is a columnar MPP data warehouse [7]. As shown in Figure 1, an Amazon Redshift compute cluster consists of a coordinator node, called the leader node , and multiple compute nodes . Data is stored on Redshift Managed Storage , backed by Amazon S3, and cached in compute nodes on locally-attached SSDs in compressed columnar fashion. Tables are either replicated on every compute node or partitioned into multiple buckets that are distributed among all compute nodes. AQUA is a query acceleration layer that leverages FPGAs to improve performance. CaaS is a caching microservice of optimized generated code for the