Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting chemotherapy response using a variational autoencoder approach

Multiple studies have shown the utility of transcriptome-wide RNA-seq profiles as features for machine learning-based prediction of response to chemotherapy in cancer. While tumor transcriptome profiles are publicly available for thousands of tumors for many cancer types, a relatively modest number of tumor profiles are clinically annotated for response to chemotherapy. The paucity of labeled examples and the high dimension of the feature data limit performance for predicting therapeutic response using fully-supervised classification methods. Recently, multiple studies have established the utility of a deep neural network approach, the variational autoencoder (VAE), for generating meaningful latent features from original data. Here, we report the first study of a semi-supervised approach using VAE-encoded tumor transcriptome features and regularized gradient boosted decision trees (XGBoost) to predict chemotherapy drug response for five cancer types: colon, pancreatic, bladder, breast, and sarcoma. We found: (1) VAE-encoding of the tumor transcriptome preserves the cancer type identity of the tumor, suggesting preservation of biologically relevant information; and (2) as a feature-set for supervised classification to predict response-to-chemotherapy, the unsupervised VAE encoding of the tumor’s gene expression profile leads to better area under the receiver operating characteristic curve and area under the precision-recall curve classification performance than the original gene expression profile or the PCA principal components or the ICA components of the gene expression profile, in four out of five cancer types that we tested. Given high-dimensional “omics” data, the VAE is a powerful tool for obtaining a nonlinear low-dimensional embedding; it yields features that retain biological patterns that distinguish between different types of cancer and that enable more accurate tumor transcriptome-based prediction of response to chemotherapy than would be possible using the original data or their principal components.
Serial passage of the human probiotic E. coli Nissle 1917 in an insect host leads to changed bacterial phenotypes

Probiotics are living microorganisms that are increasingly and successfully used for the therapy of various diseases. The most common use of probiotics is the therapeutic and preventive application for gastrointestinal disorders. The probiotic Escherichia coli strain Nissle 1917 (EcN) has been proven to effectively prevent and alleviate intestinal diseases, including various types of inflammatory bowel disease. Despite the widespread medical application of EcN, the underlying mechanisms of its protective effect remain elusive. The present work aimed to establish an insect model system to enable further research on the modes of action of EcN and the dynamics of adaptation to a novel host organism. Using a long-term serial passage approach, we orally introduced EcN to the host, the red flour beetle Tribolium castaneum. After multiple cycles of intestinal colonization in beetle larvae, several attributes of the passaged replicate lines were assessed. We observed phenotypic changes in growth and motility but no genetic changes in the lines after passaging through the host and its flour environment. One of the EcN lines exposed to the host displayed peculiar morphological and physiological characteristics showing that serial passage of EcN can generate differential phenotypes.
Synchronous Brain Dynamics Establish Brief States of Communality in Distant Neuronal Populations

Abstract Intrinsic brain dynamics co-fluctuate between distant regions in an organized manner during rest, establishing large-scale functional networks. We investigate these brain dynamics on a millisecond time scale by focusing on electroencephalographic (EEG) source analyses. While synchrony is thought of as a neuronal mechanism grouping distant neuronal populations into assemblies, the relevance of simultaneous zero-lag synchronization between brain areas in humans remains largely unexplored. This negligence is because of the confound of volume conduction, leading inherently to temporal dependencies of source estimates derived from scalp EEG [and magnetoencephalography (MEG)], referred to as spatial leakage. Here, we focus on the analyses of simultaneous, i.e., quasi zero-lag related, synchronization that cannot be explained by spatial leakage phenomenon. In eighteen subjects during rest with eyes closed, we provide evidence that first, simultaneous synchronization is present between distant brain areas and second, that this long-range synchronization is occurring in brief epochs, i.e., 54–80 ms. Simultaneous synchronization might signify the functional convergence of remote neuronal populations. Given the simultaneity of distant regions, these synchronization patterns might relate to the representation and maintenance, rather than processing of information. This long-range synchronization is briefly stable, not persistently, indicating flexible spatial reconfiguration pertaining to the establishment of particular, re-occurring states. Taken together, we suggest that the balance between temporal stability and spatial flexibility of long-range, simultaneous synchronization patterns is characteristic of the dynamic coordination of large-scale functional brain networks. As such, quasi zero-phase related EEG source fluctuations are physiologically meaningful if spatial leakage is considered appropriately.
About the Framework of Quality Evaluation Using SQuaRE

—Of the five core divisions of the SQuaRE series, the revision study of the quality evaluation division has started. As systems and software have become more complicated and diversified in recent years, it is important to have a mechanism to ensure the quality evaluation of them by a whole society. In order to do so, this revision aims to provide a framework for systematic quality evaluation using quality models and quality measures in the SQuaRE series. Furthermore, the framework will expand its scope of application to include four purposes: comparison, qualification to standard, conformity checking to requirements, and suitability evaluation in the market. This paper organizes and reports on the idea.
Decomposition and sequential-AND analysis of known cyber-attacks on critical infrastructure control systems

We perform a detailed survey and analysis of the most significant attacks, which have targeted industrial control systems over the past decade, based on detailed incident reports from scientific and non-traditional resources. This work is the first that considers together a comprehensive set of real-world cyber-attacks with the purpose of deriving a set of common features focusing particularly on the process control network. Each attack is decomposed to provide a comprehensive overview followed by a discussion of the commonalities identified across attacks. To achieve this, each attack is modelled using Attack Trees with Sequential AND, and mapped to the industrial control system Cyber Kill Chain. We focus on the methods of intrusion rather than the identification of actors. This article can be read in two parts: first, an analysis of each attack, and secondly a discussion of the derived commonalities. The resulting commonalities can be used to develop improved detection strategies to detect modern adversarial techniques and tactics.
Performance of Cell-Free Systems in Generalized Fading Channels

Cell-free systems are those in which the users are served by a large number of low cost and low power distributed antennas (Access Points – AP). These systems are no longer divided into cells and each user is served by all APs simultaneously. This paper analyses cell-free systems assuming the small-scale fading characterized by the generalized η-μ and κ-μ distributions, which proved to fit more precisely simulated data. The analyses considers the achievable rate of the users, assuming a homogeneous small-scale fading scenario. The results show that the presence of the signal specular component, expressed by the к parameter, strongly affects the system performance.
NOx emissions of an opposed wall-fired pulverized coal utility boiler

The present paper was addressed to the effects of operational variables and coal properties on NOx emission level of a 600-MW opposed wall-fired pulverized coal utility boiler. The in situ experiments were performed to study the effects of operational variables on boiler performance by changing individual variables while the other variables were held nearly constant on the basis of the nominal load operation. It was found that the oxygen level was a significant parameter to affect the NOx emission. The impacts of coal properties on NOx emission indicated that NOx emission level decreased with the increase of the ratio of the nitrogen content to low heating value because the volatile matter content in coal significantly influenced the in-flame NOx formation. Coal volatile content was the dominant parameter to affect fuel NOx formation through affecting the reducing condition in the inner near-burner zone when the low NOx burners were applied in the boiler. Copyright © 2009 Curtin University of Technology and John Wiley & Sons, Ltd.
Structure and dynamics of biological liquid crystals

A review of thermodynamic and flow liquid crystal models is presented and applied to a wide range of biological liquid crystals (BLCs), including helicoidal plywoods, biopolymer solutions and in vivo liquid crystals. The key characteristics of liquid crystals (self-assembly, packing, defects, functionalities, processability) are discussed in relation to biological materials and the strong correspondence between different synthetic and biological materials is discussed. Viscoelastic models for nematic and chiral nematics are reviewed and discussed in terms of key parameters that facilitate understanding and quantitative information from experimental measurements. The range and consistency of the predictions demonstrates that the use of mesoscopic liquid crystal models is an efficient tool to develop the science and biomimetic applications of mesogenic biological materials.
Mechanistic Studies of Pd(II)−α-Diimine-Catalyzed Olefin Polymerizations1

Mechanistic studies of olefin polymerizations catalyzed by aryl-substituted α-diimine−Pd(II) complexes are presented. Syntheses of several cationic catalyst precursors, [(N∧N)Pd(CH3)(OEt2)]BAr‘4 (N∧N = aryl-substituted α-diimine, Ar‘ = 3,5-(CF3)2C6H3), are described. X-ray structural analyses of [ArNC(H)C(H)NAr]Pd(CH3)(Cl) and [ArNC(Me)C(Me)NAr]Pd(CH3)2 (Ar = 2,6-(iPr)2C6H3) illustrate that o-aryl substituents crowd axial sites in these square planar complexes. Low-temperature NMR studies show that the alkyl olefin complexes, (N∧N)Pd(R)(olefin)+, are the catalyst resting states and that the barriers to migratory insertions lie in the range 17−19 kcal/mol. Following migratory insertion, the cationic palladium alkyl complexes (N∧N)Pd(alkyl)+ formed are β-agostic species which exhibit facile metal migration along the chain (“chain walking”) via β-hydride elimination/readdition reactions. Model studies using palladium−n-propyl and −isopropyl systems provide mechanistic details of this process, which is respon.
Drug-loaded electrospun mats of poly(vinyl alcohol) fibres and their release characteristics of four model drugs

Mats of PVA nanofibres were successfully prepared by the electrospinning process and were developed as carriers of drugs for a transdermal drug delivery system. Four types of non-steroidal anti-inflammatory drug with varying water solubility property, i.e. sodium salicylate (freely soluble in water), diclofenac sodium (sparingly soluble in water), naproxen (NAP), and indomethacin (IND) (both insoluble in water), were selected as model drugs. The morphological appearance of the drug-loaded electrospun PVA mats depended on the nature of the model drugs. The 1H-nuclear magnetic resonance results confirmed that the electrospinning process did not affect the chemical integrity of the drugs. Thermal properties of the drug-loaded electrospun PVA mats were analysed by differential scanning calorimetry and thermogravimetric analysis. The molecular weight of the model drugs played a major role on both the rate and the total amount of drugs released from the as-prepared drug-loaded electrospun PVA mats, with the rate and the total amount of the drugs released decreasing with increasing molecular weight of the drugs. Lastly, the drug-loaded electrospun PVA mats exhibited much better release characteristics of the model drugs than drug-loaded as-cast films.
Decontamination of Chlorate in Longan Plantation Soils by Bio-Stimulation with Molasses Amendment

Potassium chlorate is widely used as an active substance for flower induction in longan plantation fields for off-season production of longan fruits in northern Thailand. Contamination of groundwater with residual chlorate in soil is a cause for concern because of its toxicity to human health. Based on our previous finding that the addition of glucose or sucrose to soil was effective for accelerating the disappearance of residual chlorate in soil, the effect of the addition of molasses, which contains a high concentration of sucrose, as a substitute for glucose or sucrose was examined in laboratory and pot experiments. Under laboratory conditions, the addition of molasses to soil at the concentrations of 100 to 200 g kg−1 soil strikingly enhanced the rate of disappearance of chlorate applied at 341 mg kg−1 soil. Addition of diluted molasses was also effective for the accelerated disappearance of chlorate in soil when 33 g kg−1 soil of molasses was added repeatedly. The effect of repeated addition of diluted molasses to soil on the decontamination of residual chlorate in soil was also confirmed in an outdoor pot experiment. These results may lead to the development of a practical method of cleaning-up chlorate-polluted soil in longan plantation fields.
The Role of Electrical Vehicles for Power Quality and Security during Outages in the Distribution Grid

The power grid is facing challenges to ensure the power quality and security while new and distributed generation units are increasing in the power grid. The share of electrical vehicles (EVs) are increasing in recent period to reduce the emissions and efficient use of resources. The EVs can play a sustainable role in grid stability through power infeed to the grid and act as a storage unit. However, the role of EVs in outage scenarios requires more investigations to ensure the quality and security prospective. This paper sets out to outline the key factors and their inter-dependencies during a power outage for a distribution grid with EVs. For this investigation, various grid simulations are conducted, and the results are presented.
Topology Agnostic Bounds on Minimum Requirements for Network Failure Identification

In Boolean Network Tomography (BNT), node identifiability is a crucial property that reflects the possibility of unambiguously classifying the state of the nodes of a network as ’working’ or ’failed’ through end-to-end measurement paths. Designing a monitoring scheme satisfying network identifiability is an NP problem. In this article, we provide theoretical bounds on the minimum number of necessary measurement paths to guarantee identifiability of a given number of nodes. The bounds take into consideration two different classes of routing schemes (arbitrary and consistent routing) as well as quality of service (QoS) requirements. We formally prove the tightness of such bounds for the arbitrary routing scheme, and provide an algorithmic approach to the design of network topologies and path deployment that meet the discussed limits. Due to the computational complexity of the optimal solution, We evaluate the tightness of our lower bounds by comparing their values with an upper bound, obtained by a state-of-the-art heuristic for node identifiability. For our experiments we run extensive simulations on both synthetic and real network topologies, for which we show that the two bounds are close to each other, despite the fact that the provided lower bounds are topology agnostic.
Simulation Study of Single Event Burnout Hardening Technique on Power UMOSFET Using P-island Layer

In this work, TCAD simulation has been applied to study the Power U- shape metal-oxide-semiconductor field- effect transistor (P-island Power UMOSFET) with p-type islands to determine its sensitivity to Single Event Burnout (SEB). In the drift area, the p-type buried islands suppress the strong electric field at the bottom of the trench and prevent SEB. For the appropriate device design, the effect of doping concentration, thickness, length, and position of the p-islands on the breakdown voltage and the threshold voltage is studied. Using optimized parameters for the P-island, the hardened PI-UMOS obtains unchanged electrical characteristics from the standard UMOSFET. The outcome of the SEB simulations shows that the electrical field in the hardened structure is decreased by 60.12% relative to a standard structure and that the survivability of the SEB has also improved substantially by about 30.1%. Hence, Power UMOSFET with P-islands offers high SEB survivability for space and atmospheric applications.
Fault-Tolerant Tracking Control for Heterogeneous Multi-Agent Systems

This paper focuses on the fault-tolerant tracking problem in networks of leader-following heterogeneous linear multi-agent systems. Firstly, a fault estimation observer is constructed to estimate simultaneously the actuator faults and the followers’ states. Then, a fault-tolerant tracking controller is proposed. The latter is based on a dynamic internal reference for each agent and exploits the estimated faults. Thus, it is at first shown that the fault-tolerant tracking problem can be indirectly solved through the consensus tracking of the defined internal references. Then, a sufficient condition of consensus tracking for the internal references is proposed. Finally, a sufficient and necessary condition is derived for the fault-tolerant tracking control of linear heterogeneous multi-agent systems. The effectiveness of the proposed approach is illustrated with a numerical simulation.
Distributed Localization for Multi-Robot Systems in Presence of Unlocalizable Robots

This paper considers the distributed localization problem for multi-robot systems in the plane with bearing measurements. A necessary and sufficient condition for individual robot to check triangular localizability is provided in terms of a geometric condition. Then a distributed orthogonal algorithm is presented for verifying whether a robot is triangularly localizable. In addition, this paper develops a distributed conjugate residual algorithm, which can solve the localization problem in a more efficient manner. Numerical simulations demonstrate the effectiveness of the approach.
Finite-time Newton seeking control for a class of unknown static maps

This paper proposes a Newton seeking control design that achieves finite-time practical stability of the optimum of unknown multivariable static maps. The Newton seeking system is shown to yield an averaged finite-time stable Newton descent algorithm with a finite-time stable equilibrium at the optimum. A classical averaging theorem due to Krasnosel’skii and Krein is used to demonstrate that the trajectories of the Newton seeking system approximate the trajectories of the averaged finite-time stable system. The analysis shows that the finite-time Newton-seeking technique achieves finite-time practical stability of the optimum of the cost function. A simulation study is used to demonstrate the effectiveness of the design method.
Survey of Human–Robot Collaboration in Industrial Settings: Awareness, Intelligence, and Compliance

Industrial robots working in isolation in a highly automated system are valued for their high productivity. The shortcomings of these pure robotic cells become more apparent when flexibility in production is required to respond to varying production volumes and customized product demands. Complete automation is highly productive, but it is costly to set up and difficult to change. On the other hand, manual production, although flexible, is slower and prone to human errors. Hence, in industry, smarter automation methods that leverage the dexterity, flexibility, and decision-making capability of a human to speed, precision, and power of a robot are required. In industry, the need for flexibility in production has resulted in the acceptance of human–robot collaboration (HRC) as a viable alternative. The objective of this survey is to address the main challenges in HRC (safety, trust-in-automation, and productivity), safety measures, types of HRC, technical standards, and conceptual categorization of HRC: awareness, intelligence, and compliance.
GEM: Online Globally Consistent Dense Elevation Mapping for Unstructured Terrain

Online dense mapping gives a representation of the unstructured terrain, which is indispensable for safe robotic motion planning. In this article, we propose such an elevation mapping system, namely GEM, to generate a dense local elevation map in constant real time for fast responsive local planning, and maintain a globally consistent dense map for path routing at the same time. We model the global elevation map as a collection of submaps. When the trajectory estimation of the robot is corrected by simultaneous localization and mapping (SLAM), only relative poses between submaps are updated without rebuilding the submap. As a result, this deformable global dense map representation is able to keep the global consistency online. Besides, we accelerate the local mapping by integrating traversability analysis into the mapping system to save the computation cost by obstacle awareness. The system is implemented by CPU-GPU coordinated processing to guarantee constant real-time performance for in-time handling of dynamic obstacles. Substantial experimental results on both simulated and real-world data set validate the efficiency and effectiveness of GEM.
A Novel Brain Tumor Segmentation Approach Based on Deep Convolutional Neural Network and Level Set

In recent years deep convolutional Neural Network (DCNN) gets a big success in brain tumor segmentation. But there are artifacts in the border region of segmentation results using DCNNs. To solve this question, we propose a hybrid model including DCNNs and traditional segmentation methods. First, we use U-Net and ResU-Net network in coarse segmentation. In order to deepen the network levels and improve the network performance, we add residual module to U-Net and comprise the ResU-Net. Second, we use level set in fine segmentation of tumor boundary. We take the intersection of the coarse segmentation outputs of U-Net and ResU-Net as input of level set module. The aim of taking the intersection of U-Net and ResU-Net outputs is to get better initialization information for the level set algorithm and accelerate the evolution of level set functions. The proposed approach is validated on the BraTS 2018 challenge dataset. The metrics used to evaluate the segmentation results are: Dice, Specificity, Sensitivity, Hausdorff distances (HD). We compare our approach with U-Net, ResU-Net and some other methods. The experimental results indicate our approach is better than some other deep networks.