Miracle-dz/UniFAR-FTUs huggingface dataset:
Longitudinal Analyses of Mutational Subclonal Architecture and Tumor Subtypes in Recurrent Bladder Cancer

Longitudinal tumor sequencing of recurrent bladder cancer (BC) can facilitate the investigation of BC progression-associated genomic and transcriptomic alterations. In this study, we analyzed 18 tumor specimens including distant and locoregional metastases obtained during tumor progression for five BC patients using whole-exome and transcriptome sequencing. Along with the substantial level of intratumoral mutational heterogeneity across the cases, we observed that clonal mutations were enriched with known BC driver genes and apolipoprotein B mRNA editing enzyme, catalytic polypeptide (APOBEC)-associated mutation signatures compared with subclonal mutations, suggesting the genetic makeup for BC tumorigenesis associated with APOBEC deaminase activity was accomplished early in the cancer evolution. Mutation-based phylogenetic analyses also revealed temporal dynamics of mutational clonal architectures in which the number of mutational clones varied along the BC progression and notably was often punctuated by clonal sweeps associated with chemotherapy. The bulk-level transcriptome sequencing revealed frequent subtype switching in which transcriptionally defined BC subtypes may vary during tumor progression. Longitudinal whole-exome and transcriptome sequencing of recurrent BC may advance our understanding into the BC heterogeneity in terms of somatic mutations, cell clones and transcriptome-based tumor subtypes during disease progression.
Femtosecond Magnetooptics and the Orbital Model of the Ultrafast Magnetic Dynamics

The critical analysis of the magnetization reversal processes and magnetooptics of ferromagnets was carried out. The condition of the formation of nonequilibrium orbital momenta lpf after turning off the femtosecond laser pumping was found. The frequency range for processes involving lpf was estimated as 106 Hz to 1015 Hz. Such objects were not known in the magnetism before using the methods of femtosecond optics. The estimated frequency range covers all the processes related to femtosecond magnetism. Our qualitative analysis of these processes allows us to hope that their quantitative description does not require significant changes in the existing theories of magnetism.
Cell‐free DNA integrity correlates with lymph node metastasis and predicts short‐term postoperative recurrence in patients with proximal gastric carcinoma after curative gastrectomy

The predictive value of cell‐free DNA (cfDNA) integrity for lymph node metastasis and short‐term postoperative recurrence in patients with proximal gastric carcinoma (PGC) after curative gastrectomy remains unclear. From August 2019 to March 2021, the cfDNA integrity was assessed in 107 patients with proximal gastric carcinoma who underwent total gastrectomy (TG). The correlation between cfDNA integrity and lymph node metastasis was inspected, and the optimal cutoff value of cfDNA integrity for lymph node metastasis was determined. Meanwhile, the cfDNA integrity was identically detected in 91 proximal gastric cancer patients receiving proximal gastrectomy (PG), as well as 41 heathy individuals. The diagnostic value of cfDNA integrity for short‐term postoperative recurrence was assessed in both cohorts. The cfDNA integrity in healthy control was significantly higher than that in TG (p < .001) and PG (p = .0003) cohorts, but not statistically different between the latter two (p = .540). In the TG cohort, the cfDNA integrity was closely correlated to lymph node metastasis. Using the cutoff value of 0.873, the patients were stratified into the cfDNA integrity high and low groups; low cfDNA integrity was associated with a shorter disease free survival (DFS) both in the TG (p = .011) and PG (p = .041) cohorts. Moreover, cfDNA integrity was assessed to possess the most predictive value for postoperative recurrence by ROC curves in both cohorts. Therefore, the cfNDA integrity may correlate with lymph node metastasis in patients with PGC, and predict short‐term recurrence after curative gastrectomy.
Promising Energetic Polymers from Nanostructured Bacterial Cellulose

This study investigated the nitration of nanostructured bacterial cellulose (NBC). The NBC, obtained using symbiotic Medusomyces gisevii Sa-12 as the microbial producer and then freeze-dried, was nitrated herein by two methods, the first using mixed sulphuric–nitric acids (MA) and the second using concentrated nitric acid in the presence of methylene chloride (NA+MC). The synthesized samples of NBC nitrates (NBCNs) exhibited 11.77–12.27% nitrogen content, a viscosity of 1086 mPa·s or higher, 0.7–14.5% solubility in an alcohol–ester mixture, and 0.002% ash. Scanning electron microscopy showed that the nitration compacted the NBC structure, with the original reticulate pattern of the structure being preserved in full. Infrared spectroscopy for the presence of functional nitro groups at 1658–1659, 1280, 838–840, 749–751 and 693–694 cm−1 confirmed the synthesis of cellulose nitrates in particular. Thermogravimetric and differential thermal analyses showed the resultant NBCNs to have a high purity and high specific heats of decomposition of 6.94–7.08 kJ/g. The NBCN samples differ conceptually from plant-based cellulose nitrates by having a viscosity above 1086 mPa·s and a unique 3D reticulate structure that is retained during the nitration. The findings suggest that the NBCNs can be considered for use in novel high-tech materials and science-driven fields distinct from the application fields of plant-based cellulose nitrates. The NBCN sample obtained with NA+MC has the ability to generate an organogel when it is dissolved in acetone. Because of the said property, this NBCN sample can find use as a classical adhesive scaffold and an energetic gel matrix for creating promising energetic polymers.
Longitudinal Process of Setting and Achieving Activity- and Participation-Level Goals in Home Rehabilitation in Japan: A Qualitative Study Using Trajectory Equifinality Modeling

This study aimed to clarify the longitudinal goal negotiation and collaboration process of achieving activity- and participation-level goals. We conducted a qualitative study using the trajectory equifinality model. Nine occupational therapists with experience in setting and achieving activity- and participation-level goals were recruited and interviewed about their clients. We identified two phases and four pathways in the setting and attainment process for activity- and participation-level goals. Throughout the longitudinal goal-setting process, when the occupational therapist and client had difficulty discussing activity- and participation-level goals, the therapist respected the client’s expectations, explained the purpose of occupational therapy in detail, and conducted individual face-to-face interviews. When it was difficult to provide work-based interventions, the occupational therapist made flexible use of functional training, elemental movement training, occupation-based practice, and environmental modifications. The results of this study may assist in supporting clients to improve their activity and participation in home rehabilitation.
Analysis of Temperature Sensitivity Parameters in Hydrodynamic Model of Streamer

Temperature is an important environmental factor affecting streamer discharge. However, the mechanism and regularity of the influence are still unclear. This paper analyzes the mechanism of how the parameters in the hydrodynamic model are affected by temperature and the finite element method is used to solve three particle continuity equations along with Poisson’s equation simultaneously. The results show that the increase in temperature inhibits the ionization and the attachment process, and the reduction rate of the ionization coefficient is 40 times the attachment coefficient numerically, which means that the streamer is easier to initiate at low temperatures. The increase in temperature accelerates the diffusion and drift of electrons. This effect increases the average velocity of the streamer, and the average velocity increases by about 4.89m/( $\text{s}\cdot \text{K}$ ) with temperature, which means that the fast-moving charged particles at high temperature may affect the discharge signals. The essence of the influence of temperature on the physical quntities in the streamer discharge is its influence on ionization, attachment, diffusion, and drift effects. The results in this paper can comprehensively consider the effects and obtain the effect mechanism of temperature on the micro-process of the streamer, contributing to the research on the insulation design of power equipment and the influence of temperature on the discharge signals.
Scalable and Robust East-West Forwarding Framework for Hyperscale Clouds

With the broad deployment of distributed applications on clouds, east-west traffic is now dominating the majority of cloud networks. The existing communication solutions are tightly coupled with either the control plane (<italic>e.g.</italic>, preprogrammed model) or the location of compute nodes (<italic>e.g.</italic>, conventional gateway model). As a result, it is difficult to flexibly respond to the rapidly expanding networks and frequent abnormal events (<italic>e.g.</italic>, burst traffic and device failures). Accordingly, they may not provide high-performance east-west forwarding while ensuring scalability and robustness. To address this issue, we design Zeta, a scalable and robust east-west forwarding framework with gateway clusters for hyperscale clouds. Zeta abstracts the traffic forwarding capability as a Gateway Cluster Layer, decoupled from the logic of control plane and the location of compute nodes. Specifically, Zeta adopts gateway clusters to support large-scale networks and cope with burst traffic. Moreover, a transparent Multi IPs Migration is proposed for fast recovery from unpredictable failures. We implement Zeta based on eXpress Data Path (XDP) and evaluate its scalability and robustness through comprehensive experiments with up to 100k container instances. Our evaluation shows that Zeta reduces the 99% RTT by <inline-formula> <tex-math notation="LaTeX">$5.1 {\times }$ </tex-math></inline-formula> in burst video traffic, and reduces the gateway pure recovery delay by <inline-formula> <tex-math notation="LaTeX">$10.8 {\times }$ </tex-math></inline-formula> compared with the state-of-the-art solutions.
Minimax Learning Rate for Multi-dividing Ontology Algorithm ⋆

As an important data structure model, ontology has become one of the core contents in information science. Multi-dividing ontology algorithm combines the advantages of graph structure and learning algorithms proved to have high efficiency. In this paper, we investigate some theoretical problems of ontology algorithm in multi-dividing setting. The relationship between two versions of low noise assumptions is established. The risk excess and Lq-error are given. Specifically, the upper bound and lower bound minimax learning rate are obtained based on assumptions we describe in Section 2.
Improved Performance of a Two-Stage Converter Topology Using Incremental Conductance-Based MPPT Design for Grid-Tied PV Systems

A DC-DC converter is used between the PV modules and the inverter in a two-stage grid-integrated photovoltaic (PV) system. The dynamic interactions between the maximum power point tracking (MPPT) controller, inverter, and DC-DC converter may affect the system’s performance. This grid-connected system model integrated the MPPT mechanism to maximize the output power of the photovoltaic array, regardless of the temperature, irradiation conditions, and load characteristics. The PV system’s output voltage is regulated using a control technique with multiple inputs. The multiple input controller accommodates the high-frequency switching action in the inner loop, and the PI controller in the outer loop is designed to improve the dynamic performance. The proposed incremental conductance MPPT-based two-stage converter topology is designed and simulated with MATLAB Simulink simulation tool to validate the enhanced performance and analyzed power quality of the current injected into the grid. The performance analysis of the proposed system indicates that the closed-loop performance can be improved in several ways, including a faster response with less overshoot.
A Study on the Impact of Big Data Management on Business Agility—The Moderating Role of Corporate Strategy and Environmental Uncertainty

With the increasing uncertainty in the market environment and the integration of new IT technologies in business operations management, the use of big data management practices to increase operational agility has become a major concern for enterprises. Based on theories of dynamic capabilities and resource base, this paper analyses how big data management can contribute to the growth of enterprise agility and value, while incorporating the impact of environmental uncertainty and corporate strategy. The study found that:1.Big data infrastructure development, application management and human resource development better characterize enterprise big data management practices and effectively contribute to enterprise agility;2.Corporate strategy significantly moderates the relationship between big data management and agility, with big data application management and human resource development having a greater impact on the agility of forward-looking companies than defender strategies;3.External environmental uncertainty can effectively moderate the relationship between Big Data management and enterprise agility, as the environment becomes more dynamic, Big Data management can enhance enterprise agility, while the increase of environmental hostility will lead to Big Data management reducing enterprise agility. From the three aspects of big data infrastructure construction, big data application management and big data human resources training, it explores the impact of enterprise big data management on enterprise agility, but does not conduct an in-depth analysis of the interaction between the above three aspects; Although the sample size of this study meets the requirements of the structural equation model test, the sample size of the analytical strategy type of enterprises is relatively small, so the analytical strategy is not included in the moderating effect test. Further research can be carried out in depth from the above two aspects.
A PARALLEL HYBRID OPTIMIZATION ALGORITHM FOR ROBUST AIRFOIL DESIGN

We present a hybrid algorithm that consists of a population based, stochastic, zero-order optimization algorithm and a gradient based optimization algorithm for robust airfoil design optimization. The gradient based optimization algorithm used in the hybrid is to repair solutions (to satisfy the equality constraints) for the problem in contrast to other hybrids where it is usually used to improve the final solution obtained by the stochastic algorithm. Approaches involving equality constraints are known to pose difficulties to existing stochastic methods. The inequality constraints in the present stochastic algorithm are handled via the concept of non-dominance, instead of scaling and aggregating the constraint violations. To demonstrate the behavior of the current proposed hybrid algorithm, we present results of two single objective and two multi-objective airfoil design optimization problems. For comparison, a result of an airfoil design using aggregation is included to highlight some of the limitations of aggregation based formulations used in robust design. To improve upon the computational cost for such computationally expensive problems, we have implemented the algorithm to run on multiple processors based on master-slave architecture.
Electron transport in silicon nanowires having different cross-sections

Abstract Transport phenomena in silicon nanowires with different cross-section are investigated using an Extended Hydrodynamic model, coupled to the Schrödinger-Poisson system. The model has been formulated by closing the moment system derived from the Boltzmann equation on the basis of the maximum entropy principle of Extended Thermodynamics, obtaining explicit closure relations for the high-order fluxes and the production terms. Scattering of electrons with acoustic and non polar optical phonons have been taken into account. The bulk mobility is evaluated for square and equilateral triangle cross-sections of the wire.
A Nonparametric Bayesian Approach for Bridge Reliability Assessment Using Structural Health Monitoring Data

Integrating structural health monitoring (SHM) data into reliability assessment has increasingly been practiced in the condition evaluation of in-service bridges over the past decade. The selection of probability distribution models for load- and resistance-related random variables is a prerequisite for monitoring-based reliability assessment. However, the underlying probabilistic assumptions of the used models could be restrictive and unverifiable especially when dealing with real-world heterogeneous monitoring data, weakening the confidence on the estimated reliability index. This study aims to develop a nonparametric Bayesian model with the Dirichlet process prior for bridge reliability assessment, where the model order constraint can be released such that the complexity of the model adapts to the observed data. Reliability analysis via the nonparametric Bayesian model allows the aleatory uncertainty and the epistemic uncertainty arising from monitoring data to be concurrently accounted for in the formulated reliability index. A numerical example is presented to verify the effectiveness of the nonparametric Bayesian model for dealing with multimodal data. The feasibility of the proposed approach for reliability assessment is then demonstrated with one-year strain monitoring data acquired from a large-scale bridge instrumented with the SHM system.
Over Two-Fold Photoluminescence Enhancement from Single-Walled Carbon Nanotubes Induced by Oxygen Doping

Covalent functionalization of single-walled carbon nanotubes (SWCNTs) is a promising way to improve their photoluminescent (PL) brightness and thus make them applicable as a base material for infrared light emitters. We report as high as over two-fold enhancement of the SWCNT PL brightness by using oxygen doping via the UV photodissociation of hypochlorite ions. By analyzing the temporal evolution of the PL and Raman spectra of SWCNTs in the course of the doping process, we conclude that the enhancement of SWCNTs PL brightness depends on the homogeneity of induced quantum defects distribution over the SWCNT surface.
Self-Supervised Surface Defect Localization via Joint De-Anomaly Reconstruction and Saliency-Guided Segmentation

Anomaly localization plays one of the significant role in practical industrial applications but still faces some unique challenges due to the rarity and diversity of anomalies. To address this issue, we propose a self-supervised surface defect localization method via joint de-anomaly reconstruction and saliency-guided segmentation, also named JDRSS. Considering the severe lack of anomalous images, an approach for synthesizing anomalous samples is proposed to simulate different types of anomalies, which results in a variety of realistic and diverse anomalous images. To promote the reconstruction quality to the normal reference, we develop a novel autoencoder-based reconstruction network by applying the bundled de-anomaly constraint to refrain both the embedding space and the reconstruction space from the interference of abnormality. Furthermore, in order to accurately localize the surface defect, an anomalous saliency map-guided segmentation network is proposed, in which the residuals from the reconstructed image and input are dexterously injected into each segmentation layer as spatial attention, thus enhancing the sensitivity to anomalies. We have conducted extensive experiments on the MVTec anomaly detection (MVTec AD) dataset and the proposed model achieves considerable performance for different classes of anomaly localization.
High-Speed Time-Domain Diffuse Optical Tomography With a Sensitivity Equation-Based Neural Network

Steady progress in time-domain diffuse optical tomography (TD-DOT) technology is allowing for the first time the design of low-cost, compact, and high-performance systems, thus promising more widespread clinical TD-DOT use, such as for recording brain tissue hemodynamics. TD-DOT is known to provide more accurate values of optical properties and physiological parameters compared to its frequency-domain or steady-state counterparts. However, achieving high temporal resolution is still difficult, as solving the inverse problem is computationally demanding, leading to relatively long reconstruction times. The runtime is further compromised by processes that involve ‘nontrivial’ empirical tuning of reconstruction parameters, which increases complexity and inefficiency. To address these challenges, we present a new reconstruction algorithm that combines a deep-learning approach with our previously introduced sensitivity-equation-based, non-iterative sparse optical reconstruction (SENSOR) code. The new algorithm (called SENSOR-NET) unfolds the iterations of SENSOR into a deep neural network. In this way, we achieve high-resolution sparse reconstruction using only learned parameters, thus eliminating the need to tune parameters prior to reconstruction empirically. Furthermore, once trained, the reconstruction time is not dependent on the number of sources or wavelengths used. We validate our method with numerical and experimental data and show that accurate reconstructions with 1 mm spatial resolution can be obtained in under 20 milliseconds regardless of the number of sources used in the setup. This opens the door for real-time brain monitoring and other high-speed DOT applications.