Miracle-dz/UniFAR-FTUs huggingface dataset:
Engineering Multi‐Cellular Spheroids for Tissue Engineering and Regenerative Medicine

Multi‐cellular spheroids are formed as a 3D structure with dense cell–cell/cell–extracellular matrix interactions, and thus, have been widely utilized as implantable therapeutics and various ex vivo tissue models in tissue engineering. In principle, spheroid culture methods maximize cell–cell cohesion and induce spontaneous cellular assembly while minimizing cellular interactions with substrates by using physical forces such as gravitational or centrifugal forces, protein‐repellant biomaterials, and micro‐structured surfaces. In addition, biofunctional materials including magnetic nanoparticles, polymer microspheres, and nanofiber particles are combined with cells to harvest composite spheroids, to accelerate spheroid formation, to increase the mechanical properties and viability of spheroids, and to direct differentiation of stem cells into desirable cell types. Biocompatible hydrogels are developed to produce microgels for the fabrication of size‐controlled spheroids with high efficiency. Recently, spheroids have been further engineered to fabricate structurally and functionally reliable in vitro artificial 3D tissues of the desired shape with enhanced specific biological functions. This paper reviews the overall characteristics of spheroids and general/advanced spheroid culture techniques. Significant roles of functional biomaterials in advanced spheroid engineering with emphasis on the use of spheroids in the reconstruction of artificial 3D tissue for tissue engineering are also thoroughly discussed.
Investigation on Deep Learning Methods for Privacy and Security Challenges of Cognitive IoV

Internet of Vehicle (IoV) has the ability to achieve an integrated intelligent transportation system in order to control and impact traffics, road, safety, accidents, and driving experiences through technology by data sharing and exploration between vehicles, things, and human. Accordingly, as the number of Intelligent and Connected Vehicles (ICV) keeps increasing, a new generation of IoV solutions based on intelligent reasoning known as Cognitive IoV (CIoV) faces various types of attacks related to security and privacy risks and vulnerabilities. Hence, the improvement of existing security methods in the IoV ecosystem is required. Therefore, recent advanced Deep Learning (DL) methods of data exploration are used for detecting and learning normal or abnormal behavior among the CIoV and make intelligent decisions on its own against attacks and threats. In this context, we aim to review the advances in issues of security and privacy in cognition and computing intelligence associated with you. In addition, we discuss the opportunities and challenges involved in applying DL methods to CIoV security. Furthermore, we highlight possible threats and attacks to develop enhanced security methods for IoV systems.
PoSeiDon: a Nextflow pipeline for the detection of evolutionary recombination events and positive selection

Summary PoSeiDon is an easy-to-use pipeline that helps researchers to find recombination events and sites under positive selection in protein-coding sequences. By entering homologous sequences, PoSeiDon builds an alignment, estimates a best-fitting substitution model, and performs a recombination analysis followed by the construction of all corresponding phylogenies. Finally, significantly positive selected sites are detected according to different models for the full alignment and possible recombination fragments. The results of PoSeiDon are summarized in a user-friendly HTML page providing all intermediate results and the graphical representation of recombination events and positively selected sites. Availability and implementation PoSeiDon is freely available at https://github.com/hoelzer/poseidon. The pipeline is implemented in Nextflow with Docker support and processes the output of various tools. Contact hoelzer.martin@gmail.com
(Special Topic Submission) Enabling Domain-Specific Architectures with an Open-Source Soft-Core GPGPU

Graphics Processing Units (GPUs) have found application in a broad range of domains due to their ability to massively exploit data parallelism and thereby achieve high energy efficiency, being now referred to as General-Purpose GPUs (GPGPUs). However, with a market dominated by two or three industrial players and a lack of open-source hardware realizations and standardized GPU Instruction-Set Architectures (ISAs), the full potential of these architectures for academic research and use by small hardware development enterprises remains far from being unlocked. In this paper, we start by discussing a few exciting research directions in GPGPU architectures and the benefits of open-source Hardware realizations. Furthermore, we show how our open-source FGPU core can be used to enable overcoming these challenges and contribute to the agile development of Domain-Specific Architectures (DSAs) featuring System-on-Chip (SoC) platforms extended with adapted GPGPU cores. FGPU is described in VHDL, can be programmed in OpenCL by use of our LLVM-based compiler, and has been validated against an ARM Cortex A9 with NEON support, achieving 4 performance improvement in fixed and floating-point benchmarks.
The conventional isoproterenol-induced heart failure model does not consistently mimic the diaphragmatic dysfunction observed in patients

Heart failure (HF) impairs diaphragm function. Animal models realistically mimicking HF should feature both the cardiac alterations and the diaphragmatic dysfunction characterizing this disease. The isoproterenol-induced HF model is widely used, but whether it presents diaphragmatic dysfunction is unknown. However, indirect data from research in other fields suggest that isoproterenol could increase diaphragm function. The aim of this study was to test the hypothesis that the widespread rodent model of isoproterenol-induced HF results in increased diaphragmatic contractility. Forty C57BL/6J male mice were randomized into 2 groups: HF and healthy controls. After 30 days of isoproterenol infusion to establish HF, in vivo diaphragmatic excursion and ex vivo isolated diaphragm contractibility were measured. As compared with healthy controls, mice with isoproterenol-induced HF showed the expected changes in structural and functional echocardiographic parameters and lung edema. isoproterenol-induced HF increased in vivo diaphragm excursion (by ≈30%, p<0.01) and increased by ≈50% both ex vivo peak specific force (p<0.05) and tetanic force (p<0.05) at almost all 10–100 Hz frequencies (p<0.05), with reduced fatigue resistance (p<0.01) when compared with healthy controls. Expression of myosin genes encoding the main muscle fiber types revealed that Myh4 was higher in isoproterenol-induced HF than in healthy controls (p<0.05), suggesting greater distribution of type IIb fibers. These results show that the conventional isoproterenol-induced HF model increases diaphragm contraction, a finding contrary to what is observed in patients with HF. Therefore, this specific model seems limited for translational an integrative HF research, especially when cardio-respiratory interactions are investigated.
Design of Locality-aware MPI-IO for Scalable Shared File Write Performance

Difficult and challenging I/O access pattern among applications is N-1 access pattern such that multiple N processes share and access a single file. This I/O pattern is commonly and widely used because it can reduce the number of output files, while there is a problem of I/O performance degradation due to lock conflicts in a parallel file system among processes. In this study, we propose a locality-aware MPI-IO to improve N-1 write performance by using node-local storage. Our method achieved a significantly scalable performance with N-1 noncollective write performance reaching 1062.36 GiB/s for read and 268.08 GiB/s for write using 256 nodes. The parallel efficiency of N-1 noncollective write was 58% using 256 nodes and that of N-1 collective write was 63% using 72 nodes. The scalability of the proposed method outperforms the Lustre and BeeOND. It also achieves a scalable performance for real application benchmarks such as S3D-IO, LES-IO and VPIC-IO, which shows that it is promising for Exascale systems.
Genomic Organization and Molecular Characterization of Clostridium difficile Bacteriophage ΦCD119

ABSTRACT In this study, we have isolated a temperate phage (ΦCD119) from a pathogenic Clostridium difficile strain and sequenced and annotated its genome. This virus has an icosahedral capsid and a contractile tail covered by a sheath and contains a double-stranded DNA genome. It belongs to the Myoviridae family of the tailed phages and the order Caudovirales. The genome was circularly permuted, with no physical ends detected by sequencing or restriction enzyme digestion analysis, and lacked a cos site. The DNA sequence of this phage consists of 53,325 bp, which carries 79 putative open reading frames (ORFs). A function could be assigned to 23 putative gene products, based upon bioinformatic analyses. The ΦCD119 genome is organized in a modular format, which includes modules for lysogeny, DNA replication, DNA packaging, structural proteins, and host cell lysis. The ΦCD119 attachment site attP lies in a noncoding region close to the putative integrase (int) gene. We have identified the phage integration site on the C. difficile chromosome (attB) located in a noncoding region just upstream of gene gltP, which encodes a carrier protein for glutamate and aspartate. This genetic analysis represents the first complete DNA sequence and annotation of a C. difficile phage.
Empirical Study on Anti-Virus Architecture for Container Platforms

Container platforms provide many functions for diverse applications and are used to build and operate various information services. They have been extended not only to Linux and Unix-based servers but also to Windows and macOS-based desktops and laptops. Many systems use anti-virus software to minimize damage caused by malware. Most anti-virus software provide real-time malware detection functions and block the execution of malware by enforcing access denial functions for malware that cannot be deleted or for original files that cannot be restored. However, current anti-virus technologies are not designed for container platforms. Therefore, they cannot detect malware in containers in real time; nor can they block malware execution or user access to malware owing to the isolation feature provided by container platforms. To resolve these issues, we propose a functionally-isolated anti-virus architecture for container platforms. The proposed anti-virus architecture separates the functions of a legacy anti-virus engine to ensure compatibility with the isolation features of a container platform. By implementation, it was confirmed that the proposed anti-virus architecture can detect in real-time the entry of malware in a container platform and block the execution of, and user access to unrecoverable malware-infected files. The performance of the proposed functionally-isolated anti-virus architecture is similar to that of legacy anti-virus technology and was verified to be sufficiently effective.
Estimation of Dynamically Varying Support of Sparse Signals via Sequential Monte-Carlo Method

In this paper, we address the problem of tracking time-varying support of a sparse signal given a sequence of observation vectors. We model the dynamic variation of the support set using the discrete-state Markov process and employ the Rao-Blackwellized sequential Monte Carlo method, which allows for separate tracking of the support set and the amplitude of the unknown signals. Specifically, the samples for the support variables are drawn from their posteriori joint distributions using a Gibbs sampler while the continuous amplitude variables are separately estimated using the Kalman filter. Our numerical evaluation shows that the proposed method achieves significant performance gain over the existing sparse estimation methods.
Systematic Evaluation of the Quality Benefits of Spatiotemporal Sample Reprojection in Real-Time Stereoscopic Path Tracing

Path tracing is a commonly used but computationally highly expensive stochastic ray tracing method for rendering photorealistic visual content. Combined with a real-time constraint, for example in stereoscopic virtual/augmented reality applications, it typically limits us to rendering at most a few samples per pixel, yielding very noisy results. However, the spatial and temporal redundancies are commonly utilized by reprojecting existing samples between different viewpoints and frames, thus cheaply improving the quality. We provide new insights to the quality benefits of reprojection by systematically evaluating the effective quality of spatiotemporally reprojected stereoscopic path traced data. We show that spatiotemporal reprojection increases the quality of 1 sample per pixel (spp) data by almost a factor of 25 on average, in terms of the effective spp count of the result. Since we are able to reproject 94–98% of the samples, only the remaining 2–6% of the samples in the target frame need to be path traced. We also evaluate how the quality improvement gained through spatiotemporal reprojection scales as the number of input samples per pixel increases, showing that the highest gains are achieved at the lowest input spp counts. Finally, we show how blending existing path traced data and stereoscopically reprojected data further improves the quality of spatiotemporal reprojection, on average yielding a 47% higher effective spp than without blending.
Dynamic Network Slice Scaling Assisted by Prediction in 5G Network

Network slicing is a key technology in fifth-generation (5G) mobile networks. Slicing divides a physical network into multiple dedicated logical networks to meet the requirements of diverse use cases. Efficient slice deployment algorithms are critical in reducing network operators’ costs and energy consumption and in providing users better service. Many researchers have focused on static deployment when investigating network slices, effectively ignoring network operators’ requirements for the dynamic deployment and expansion of such slices. In this paper, we first construct a joint optimization problem of cost and energy consumption. Then, we propose a prediction-assisted adaptive network slice expansion algorithm to deploy network slices dynamically. The proposed algorithm consists of three parts. First, we devise a Holt-Winters (HW) prediction algorithm to determine traffic demand for network slices. This method is intended to avoid frequent changes in network topology. Second, we propose a virtual network function (VNF) adaptive scaling strategy to reasonably determine the number of VNFs and resources required for network slices to avoid resource wastage. Finally, we develop a proactive online algorithm to deploy network slices. This method deploys network slices reasonably via the VNF deployment algorithm and link-routing algorithm to ensure slices’ service requirements. Resource capacity and delay requirements are also considered in our evaluation to ensure that network costs and energy consumption are minimized. We then perform a series of simulation experiments to compare the proposed method’s performance to state-of-the-art dynamic network slicing technologies. Ultimately, our solution is deemed a suitable candidate for dynamic deployment of 5G network slices; the solution demonstrates advantages of high resource utilization, low deployment costs, and low energy consumption.
Markers of protein-energy wasting and physical performance in haemodialysis patients: A cross-sectional study

Background Physical impairments are common in uraemia, as reflected by the high risk of falls of haemodialysis (HD) patients. Furthermore, these patients often suffer from malnutrition. Objective Up to now, it is unknown which aspects of physical performance are predominantly driven by malnutrition in HD patients. As this answer could steer different interventions, the aim of this study was to evaluate the cross-sectional relationship between nutritional status, muscle strength, exercise capacity and the risk of falls. Methods This study recruited HD patients between December 2016 and March 2018 from two hospital-based and five satellite dialysis units (registration number on clinicaltrial.gov: NCT03910426). The mini-nutritional assessment scale as well as objective measures of protein-energy wasting were obtained (total iron-binding capacity, total protein levels, and CRP). Physical assessment included muscle strength (quadriceps, handgrip force, and sit-to-stand test), exercise capacity (six-minute walking test) and the risk of falls (Tinetti, FICSIT, and dialysis fall index). Their interrelationship was analysed by ridge regression models. Results Out of 113 HD patients (mean age 67 years ± 16.1, 57.5% male) 36.3% were malnourished according to the mini-nutritional assessment scale and a majority had impaired quadriceps force (86.7%), six-minute walking test (92%), and an increased risk of falls (73.5%). Total protein and CRP levels were identified as relevant nutritional factors in the association with physical performance. Nutritional parameters explained 9.2% of the variance in the risk of falls and 7.6% of the variance in exercise capacity. No conclusive association was found between nutritional status and muscle strength. Conclusion Protein-energy wasting is a determinant of the risk of falls and exercise capacity in patients on HD. The association between malnutrition and muscle weakness remains inconclusive.
Predicted Hotspot Residues Involved in Allosteric Signal Transmission in Pro-Apoptotic Peptide—Mcl1 Complexes

Mcl1 is a primary member of the Bcl–2 family—anti–apoptotic proteins (AAP)—that is overexpressed in several cancer pathologies. The apoptotic regulation is mediated through the binding of pro-apoptotic peptides (PAPs) (e.g., Bak and Bid) at the canonical hydrophobic binding groove (CBG) of Mcl1. Although all PAPs form amphipathic α-helices, their amino acid sequences vary to different degree. This sequence variation exhibits a central role in the binding partner selectivity towards different AAPs. Thus, constructing a novel peptide or small organic molecule with the ability to mimic the natural regulatory process of PAP is essential to inhibit various AAPs. Previously reported experimental binding free energies (BFEs) were utilized in the current investigation aimed to understand the mechanistic basis of different PAPs targeted to mMcl1. Molecular dynamics (MD) simulations used to estimate BFEs between mMcl1—PAP complexes using Molecular Mechanics-Generalized Born Solvent Accessible (MMGBSA) approach with multiple parameters. Predicted BFE values showed an excellent agreement with the experiment (R2 = 0.92). The van–der Waals (ΔGvdw) and electrostatic (ΔGele) energy terms found to be the main energy components that drive heterodimerization of mMcl1—PAP complexes. Finally, the dynamic network analysis predicted the allosteric signal transmission pathway involves more favorable energy contributing residues. In total, the results obtained from the current investigation may provide valuable insights for the synthesis of a novel peptide or small organic inhibitor targeting Mcl1.
An Assessment of Algorithms to Estimate Respiratory Rate from the Remote Photoplethysmogram

The respiratory rate is important information in the healthcare environment. Consequently, research is done to develop a device that could measure the respiratory rate continuously with non-contact devices. Various methods were tried, such as radio-based, thermal imaging or remote photoplethysmography (rPPG). The rPPG method uses a video recording of the skin in ambient light conditions. It measures the small variations of light reflection induced by the amount of blood in vessels. This method allows the extraction of physiological parameters such as the heart rate or respiratory rate without any contact with the skin. The main issue with the rPPG technique is the lower signal quality compared with contact-based methods. In this paper, we assess the performance of the respiratory rate estimation algorithms with rPPG signals. The tested algorithms were designed for contact-PPG signals input. The use of the algorithms designed for contact PPG on remote PPG signals can lead to respiratory rate estimations with a mean absolute error below 3 breaths-per-minute. We benchmark our results using this standard and some other metrics to interpret the quality of the assessment.
On the Effects of PA Nonlinearities for Hybrid Beamforming Based Wideband Massive MIMO Systems

In this paper, we present a general framework for the investigation of the effects of power amplifier (PA) non-linearities on the hybrid beamforming based massive multiple-input multiple-output (MIMO) systems. In literature, much of the attention is devoted to phase shifters as the analog beamformer stage; however, in this work, a more comprehensive scenario, based on fully connected analog beamformer architecture, is considered. Firstly, out-of-band (OOB) radiation pattern is evaluated to investigate the spatial characteristics of the undesired radiation. It is observed that OOB radiation due to nonlinear distortion is concentrated on the beamforming directions. In addition, a framework is proposed to design digital predistorters (DPD) and a conventional DPD architecture is adopted to the proposed design framework. It is shown that conventional DPD can solve the OOB problem for a single user environment; however, its performance degrades in the multi-user case. Even though, there are many studies on the effects of nonlinear PA distortions, a few of them consider the achievable information rate (AIR). In this work, a framework is adopted to study the capacity of the considered system under PA nonlinearities.
PHRYCTORIA: A Messaging System for Transprecision OpenCAPI-attached FPGA Accelerators

Messaging frameworks are prevalent in distributed software systems as they simplify the exchange of data between applications running on different platforms. Messages are serialized and deserialized to transform user data structures into binary data for sending across the network. In a similar spirit, modern heterogenous computing platorms typically containing FPGAs can also profit from such messaging frameworks. Efficient compact serialization of data types is needed to take full advantage of the high throughput communication links. Transprecision extensions of FPGA replace native data types with novel low-precision data types. Transprecision extensions are shown to result in less-compact serializations. In this paper, we present a messaging framework for transprecision hardware accelerators which supports messages expressed in Protobuf and automatically generates serialization and deserialization functions. Our messageing framework leverages the varint encoding of Protocol buffers for efficient serialization and deserialization of transprecision data types. Evaluation results show that it can increase the effective throughput of OpenCAPI by up to $7.4 \times $ for various transprecision data-types.