Miracle-dz/UniFAR-FTUs huggingface dataset:
Simtuzumab Is Ineffective for Patients With Bridging Fibrosis or Compensated Cirrhosis Caused by Nonalcoholic Steatohepatitis.

BACKGROUND & AIMS Lysyl oxidase-like 2 contributes to fibrogenesis by catalyzing cross-linkage of collagen. We evaluated the safety and efficacy of simtuzumab, a monoclonal antibody against lysyl oxidase-like 2, in two phase 2b trials of patients with advanced fibrosis caused by nonalcoholic steatohepatitis. METHODS We performed a double-blind study of 219 patients with bridging fibrosis caused by nonalcoholic steatohepatitis who were randomly assigned (1:1:1) to groups given weekly subcutaneous injections of simtuzumab (75 or 125 mg) or placebo for a planned duration of 240 weeks. We performed a separate study of 258 patients with compensated cirrhosis randomly assigned (1:1:1) to groups given intravenous infusions of simtuzumab (200 or 700 mg) or placebo every other week. The studies were performed from January 2013 through July 2014 at 80 sites in North America and Europe. Biopsy specimens were collected and analyzed at screening and at weeks 48 and 96; clinical information and serum levels of fibrosis biomarkers were collected throughout the study. The primary end point was change from baseline to week 96 in hepatic collagen content, measured by morphometry of liver specimens, in patients with bridging fibrosis; for patients with cirrhosis, the primary end point was change in hepatic venous pressure gradient from baseline to week 96. RESULTS The 2 studies were stopped after week 96 because of lack of efficacy. All 3 groups of patients with bridging fibrosis-including those given placebo-had significant decreases in hepatic collagen content, but there was no statistically significant difference in decrease between patients receiving simtuzumab 75 mg and those receiving placebo (-0.2%, 95% confidence interval [CI] -1.3 to 1.0, P = .77) or between patients receiving simtuzumab 125 mg and those receiving placebo (-0.4%, 95% CI -1.5 to 0.8, P = .52). In patients with cirrhosis, the mean difference in hepatic venous pressure gradient between the 2 simtuzumab groups and the placebo group was 0.1 mm Hg (95% CI -1.2 to 1.5, P = .84 for 200 mg; 95% CI -1.2 to 1.4, P = .88 for 700 mg). Simtuzumab did not significantly decrease fibrosis stage, progression to cirrhosis in patients with bridging fibrosis, or liver-related clinical events in patients with cirrhosis. Rates of adverse events were similar among groups. CONCLUSION In two phase 2b trials of patients with bridging fibrosis or compensated cirrhosis associated with nonalcoholic steatohepatitis, simtuzumab was ineffective in decreasing hepatic collagen content or hepatic venous pressure gradient, respectively. Clinicaltrials.govNCT01672866 and NCT01672879.
An Adaptive Framework to Tune the Coordinate Systems in Nature-Inspired Optimization Algorithms

The performance of many nature-inspired optimization algorithms (NIOAs) depends strongly on their implemented coordinate system. However, the commonly used coordinate system is fixed and not well suited for different function landscapes, NIOAs thus might not search efficiently. To overcome this shortcoming, in this paper we propose a framework, named ACoS, to adaptively tune the coordinate systems in NIOAs. In ACoS, an Eigen coordinate system is established by making use of the cumulative population distribution information, which can be obtained based on a covariance matrix adaptation strategy and an additional archiving mechanism. Since the population distribution information can reflect the features of the function landscape to some extent, NIOAs in the Eigen coordinate system have the capability to identify the modality of the function landscape. In addition, the Eigen coordinate system is coupled with the original coordinate system, and they are selected according to a probability vector. The probability vector aims to determine the selection ratio of each coordinate system for each individual, and is adaptively updated based on the collected information from the offspring. ACoS has been applied to two of the most popular paradigms of NIOAs, i.e., particle swarm optimization and differential evolution, for solving 30 test functions with 30D and 50D at the 2014 IEEE Congress on Evolutionary Computation. The experimental studies demonstrate its effectiveness.
Optimizing Top- $k$  Multiclass SVM via Semismooth Newton Algorithm

Top-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> performance has recently received increasing attention in large data categories. Advances, like a top-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> multiclass support vector machine (SVM), have consistently improved the top-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> accuracy. However, the key ingredient in the state-of-the-art optimization scheme based upon stochastic dual coordinate ascent relies on the sorting method, which yields <inline-formula> <tex-math notation="LaTeX">$O(d\log d)$ </tex-math></inline-formula> complexity. In this paper, we leverage the semismoothness of the problem and propose an optimized top-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> multiclass SVM algorithm, which employs semismooth Newton algorithm for the key building block to improve the training speed. Our method enjoys a local superlinear convergence rate in theory. In practice, experimental results confirm the validity. Our algorithm is four times faster than the existing method in large synthetic problems; Moreover, on real-world data sets it also shows significant improvement in training time.
Different rhinologic diseases cause a similar multidimensional decrease in generic health‐related quality of life

Previous studies illustrated that chronic rhinosinusitis and allergic rhinitis represent individual and socioeconomic burdens to a patient. However, few studies exist on the health‐related quality of life (HRQoL) amongst other rhinologic patients. Our study investigated the generic HRQoL in different rhinologic diseases.
A ±0.3 ppm Oven-Controlled MEMS Oscillator Using Structural Resistance-Based Temperature Sensing

This paper presents a 77.7-MHz silicon microelectromechanical-systems oven-controlled oscillator (MEMS OCXO) that uses the structural resistance (<inline-formula> <tex-math notation="LaTeX">$R_{{{\text {Struc}}}}$ </tex-math></inline-formula>) of the resonator as an embedded temperature sensor. The <inline-formula> <tex-math notation="LaTeX">$R_{{\text {Struc}}}$ </tex-math></inline-formula> exhibits a large temperature coefficient of resistance and is used as a self-temperature sensor to accurately and locally monitor the temperature of the resonator. A high-Q capacitive cross-sectional Lamé-mode resonator fabricated using the nanogap high aspect-ratio combined poly- and single-crystal silicon process (HARPSS) is used as the frequency selective element. A silicon resistor micro-oven is implemented on the MEMS die adjacent to the resonator and the ensemble is wafer-level packaged in vacuum to yield a 2 mm <inline-formula> <tex-math notation="LaTeX">$\times2$ </tex-math></inline-formula> mm MEMS die. The micro-oven resistor is automatically controlled by the analog loop to provide active temperature stabilization for the resonator. A resistance temperature detector (RTD) circuit, high-gain loop filter, and heater amplifier are implemented as the analog micro-oven control loop. To further boost the frequency stability, a digital feedforward calibration path which uses the digitized RTD output to fine tune the phase shift of the sustaining amplifier is added to the system. The silicon MEMS OCXO achieves ±0.3-ppm frequency stability from −25 °C to 85 °C. The microresonator is interfaced with a sustaining amplifier implemented in Taiwan Semiconductor Manufacturing Company 0.35-s<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process, consuming 16 mA from a 3.2-V supply.
Effect of Low-Field High-Frequency nsPEFs on the Biological Behaviors of Human A375 Melanoma Cells

Objective: To evaluate the effect of low-field high-frequency nanosecond pulsed electric fields (nsPEFs) on multiple biological behaviors of human A375 melanoma cells and to optimize suitable parameters for further study and clinical use. Methods: An nsPEF generator was developed to generate appropriate pulses. Cell apoptosis and the cell cycle were evaluated by flow cytometry. The CCK-8 assay was performed to explore the effect of nsPEFs on the viability of A375 melanoma cells. Cell migration was assessed using a Transwell Boyden Chamber. The proliferation of A375 melanoma cells was determined by the cloning efficacy test. Furthermore, the nude mouse tumorigenicity assay was used to detect the effectiveness of nsPEFs in vivo. Results: The nsPEFs with our tested parameters failed to induce apoptosis of A375 melanoma cells, though nsPEFs with high pulse duration (500 ns) induced necrosis. However, the viability and migration of A375 melanoma cells were significantly inhibited by nsPEFs. nsPEFs also suppressed the proliferation of A375 melanoma cells by restricting cells in G0/G1 phase. Moreover, animal experiments demonstrated that nsPEFs inhibited the growth of melanoma in vivo. Conclusion: Low-field high-frequency nsPEFs failed to induce apoptosis but effectively inhibited the growth of melanoma via affecting other biological behaviors of melanoma cells, such as cell viability, proliferation, and migration. Significance: This study investigated the influence of low-field high-frequency nsPEFs on melanoma through evaluating their effects on multiple biological behaviors and is helpful in the treatment of melanoma and other tumors.
A Three-Layered Mutually Reinforced Model for Personalized Citation Recommendation

Fast-growing scientific papers pose the problem of rapidly and accurately finding a list of reference papers for a given manuscript. Citation recommendation is an indispensable technique to overcome this obstacle. In this paper, we propose a citation recommendation approach via mutual reinforcement on a three-layered graph, in which each paper, author or venue is represented as a vertex in the paper layer, author layer, and venue layer, respectively. For personalized recommendation, we initiate the random walk separately for each query researcher. However, this has a high computational complexity due to the large graph size. To solve this problem, we apply a three-layered interactive clustering approach to cluster related vertices in the graph. Personalized citation recommendations are then made on the subgraph, generated by the clusters associated with each researcher’s needs. When evaluated on the ACL anthology network, DBLP, and CiteSeer ML data sets, the performance of our proposed model-based citation recommendation approach is comparable with that of other state-of-the-art citation recommendation approaches. The results also demonstrate that the personalized recommendation approach is more effective than the nonpersonalized recommendation approach.
Fisher Information Matrix of Unipolar Activation Function-Based Multilayer Perceptrons

The multilayer perceptrons (MLPs) are widely used in many fields, however, singularities in the parameter space may seriously influence the learning dynamics of MLPs and cause strange learning behaviors. Given that the singularities are the subspaces of the parameter space where the Fisher information matrix (FIM) degenerates, the FIM plays a key role in the study of the singular learning dynamics of the MLPs. In this paper, we obtain the analytical form of the FIM for unipolar activation function-based MLPs where the input subjects to the Gaussian distribution with general covariance matrix and the unipolar error function is chosen as the activation function. Then three simulation experiments are taken to verify the validity of the obtained results.
Adaptive Sliding Mode Consensus Tracking for Second-Order Nonlinear Multiagent Systems With Actuator Faults

This paper investigates the consensus tracking problem of second-order nonlinear multiagent systems (MAS) with disturbance and actuator fault by the sliding mode control method. The communication topology of the MAS is directed and only part of the followers have access to the leader’s information. First, a discontinuous sliding mode tracking protocol is studied for consensus tracking of the MAS. Second, to address the shortcoming of chattering and difficulty of setting the control gain in the discontinuous protocol, a continuous sliding mode tracking protocol with an adaptive mechanism is developed. The adaptive mechanism will adjust the gain of the control automatically and enable the tracking protocol to work well without prior knowledge of the MAS. Third, the performance of the adaptive sliding mode protocol for consensus tracking of the MAS in the presence of actuator faults of biased fault and partial loss of effectiveness fault is further investigated. Finally, numerical simulations are performed to illustrate the efficiency of the theoretical results.
Adapting Stochastic Block Models to Power-Law Degree Distributions

Stochastic block models (SBMs) have been playing an important role in modeling clusters or community structures of network data. But, it is incapable of handling several complex features ubiquitously exhibited in real-world networks, one of which is the power-law degree characteristic. To this end, we propose a new variant of SBM, termed power-law degree SBM (PLD-SBM), by introducing degree decay variables to explicitly encode the varying degree distribution over all nodes. With an exponential prior, it is proved that PLD-SBM approximately preserves the scale-free feature in real networks. In addition, from the inference of variational E-Step, PLD-SBM is indeed to correct the bias inherited in SBM with the introduced degree decay factors. Furthermore, experiments conducted on both synthetic networks and two real-world datasets including Adolescent Health Data and the political blogs network verify the effectiveness of the proposed model in terms of cluster prediction accuracies.
Adaptive Critic Designs for Event-Triggered Robust Control of Nonlinear Systems With Unknown Dynamics

This paper develops a novel event-triggered robust control strategy for continuous-time nonlinear systems with unknown dynamics. To begin with, the event-triggered robust nonlinear control problem is transformed into an event-triggered nonlinear optimal control problem by introducing an infinite-horizon integral cost for the nominal system. Then, a recurrent neural network (RNN) and adaptive critic designs (ACDs) are employed to solve the derived event-triggered nonlinear optimal control problem. The RNN is applied to reconstruct the system dynamics based on collected system data. After acquiring the knowledge of system dynamics, a unique critic network is proposed to obtain the approximate solution of the event-triggered Hamilton–Jacobi–Bellman equation within the framework of ACDs. The critic network is updated by using simultaneously historical and instantaneous state data. An advantage of the present critic network update law is that it can relax the persistence of excitation condition. Meanwhile, under a newly developed event-triggering condition, the proposed critic network tuning rule not only guarantees the critic network weights to converge to optimums but also ensures nominal system states to be uniformly ultimately bounded. Moreover, by using Lyapunov method, it is proved that the derived optimal event-triggered control (ETC) guarantees uniform ultimate boundedness of all the signals in the original system. Finally, a nonlinear oscillator and an unstable power system are provided to validate the developed robust ETC scheme.
Type Regression Testing to Detect Breaking Changes in Node.js Libraries

The npm repository contains JavaScript libraries that are used by millions of software developers. Its semantic versioning system relies on the ability to distinguish between breaking and non-breaking changes when libraries are updated. However, the dynamic nature of JavaScript often causes unintended breaking changes to be detected too late, which undermines the robustness of the applications. We present a novel technique, type regression testing, to automatically determine whether an update of a library implementation affects the types of its public interface, according to how the library is being used by other npm packages. By leveraging available test suites of clients, type regression testing uses a dynamic analysis to learn models of the library interface. Comparing the models before and after an update effectively amplifies the existing tests by revealing changes that may affect the clients. Experimental results on 12 widely used libraries show that the technique can identify type-related breaking changes with high accuracy. It fully automatically classifies at least 90% of the updates correctly as either major or as minor or patch, and it detects 26 breaking changes among the minor and patch updates.
An Exact Algorithm for Sorting by Weighted Preserving Genome Rearrangements

The preserving Genome Sorting Problem (pGSP) asks for a shortest sequence of rearrangement operations that transforms a given gene order into another given gene order by using rearrangement operations that preserve common intervals, i.e., groups of genes that form an interval in both given gene orders. The wpGSP is the weighted version of the problem were each type of rearrangement operation has a weight and a minimum weight sequence of rearrangement operations is sought. An exact algorithm – called CREx2 – is presented, which solves the wpGSP for arbitrary gene orders and the following types of rearrangement operations: inversions, transpositions, inverse transpositions, and tandem duplication random loss operations. CREx2 has a (worst case) exponential runtime, but a linear runtime for problem instances where the common intervals are organized in a linear structure. The efficiency of CREx2 and its usefulness for phylogenetic analysis is shown empirically for gene orders of fungal mitochondrial genomes.
Robust Zeroing Neural-Dynamics and Its Time-Varying Disturbances Suppression Model Applied to Mobile Robot Manipulators

This paper proposes a novel robust zeroing neural-dynamics (RZND) approach as well as its associated model for solving the inverse kinematics problem of mobile robot manipulators. Unlike existing works based on the assumption that neural network models are free of external disturbances, four common forms of time-varying disturbances suppressed by the proposed RZND model are investigated in this paper. In addition, theoretical analyses on the antidisturbance performance are presented in detail to prove the effectiveness and robustness of the proposed RZND model with time-varying disturbances suppressed for solving the inverse kinematics problem of mobile robot manipulators. That is, the RZND model converges toward the exact solution of the inverse kinematics problem of mobile robot manipulators with bounded or zero-oriented steady-state position error. Moreover, simulation studies and comprehensive comparisons with existing neural network models, e.g., the conventional Zhang neural network model and the gradient-based recurrent neural network model, together with extensive tests with four common forms of time-varying disturbances substantiate the efficacy, robustness, and superiority of the proposed RZND approach as well as its time-varying disturbances suppression model for solving the inverse kinematics problem of mobile robot manipulators.
Automated EEG Artifact Handling With Application in Driver Monitoring

Automated analyses of electroencephalographic (EEG) signals acquired in naturalistic environments are becoming increasingly important in areas such as brain–computer interfaces and behavior science. However, the recorded EEG in such environments is often heavily contaminated by motion artifacts and eye movements. This poses new requirements on artifact handling. The objective of this paper is to present an automated EEG artifacts handling algorithm, which will be used as a preprocessing step in a driver monitoring application. The algorithm, named Automated aRTifacts handling in EEG (ARTE), is based on wavelets, independent component analysis, and hierarchical clustering. The algorithm is tested on a dataset obtained from a driver sleepiness study including 30 drivers and 540 30-min 30-channel EEG recordings. The algorithm is evaluated by a clinical neurophysiologist, by quantitative criteria (signal quality index, mean square error, relative error, and mean absolute error), and by demonstrating its usefulness as a preprocessing step in driver monitoring, here exemplified with driver sleepiness classification. All results are compared with a state-of-the-art algorithm called FORCe. The quantitative and expert evaluation results show that the two algorithms are comparable, and that both algorithms significantly reduce the impact of artifacts in recorded EEG signals. When artifact handling is used as a preprocessing step in driver sleepiness classification, the classification accuracy increased by 5% when using ARTE and by 2% when using FORCe. The advantage with ARTE is that it is data driven and does not rely on additional reference signals or manually defined thresholds, making it well suited for use in dynamic settings where unforeseen and rare artifacts are commonly encountered.
Synthetic Ultra-High-Resolution Millimeter-Wave Imaging for Skin Cancer Detection

This work introduces, for the first time, a millimeter-wave imaging system with a “synthetic” ultra-wide imaging bandwidth of 98 GHz to provide the ultra-high resolutions required for early-stage skin cancer detection. The proposed approach consists of splitting the required ultra-wide imaging bandwidth into four sub-bands, and assigning each sub-band to a separate imaging element, i.e., an antenna radiator. Each of the sub-band antennas transmits and receives signals only at its corresponding sub-band. The captured signals are then combined and processed to form the image of the target. For each sub-band, a Vivaldi tapered slot antenna fed with a combination of substrate-integrated waveguide and coplanar waveguide is designed and microfabricated. Design techniques are also provided for the four similarly-shaped sub-band antennas for achieving excellent impedance matches ($S_{11}$ < –10 dB) and nearly constant gains of 10 dBi over the entire 12–110 GHz bandwidth. The design procedure is validated by comparing the simulated results with measurements performed on the fabricated prototypes. Excellent agreements are obtained between simulations and measurements. Finally, the feasibility of detecting early-stage skin tumors in three dimensions is experimentally verified by employing the sub-band antennas in a synthetic ultra-wideband imaging system with a bandwidth of 98 GHz. Two separate setups, each comprising a dispersive skin-mimicking phantom as well as two dispersive spherical tumors, are constructed for imaging experiments. Lateral and axial resolutions of 200 μm are confirmed, and a successful reconstruction of the spherical tumors is achieved in both cases.