Vascular pathology of malignant cervical lymphadenopathy: qualitative and quantitative assessment with power Doppler ultrasound

BACKGROUND: Malignant vascular pathology has traditionally been studied with invasive angiography or in vitro immunohistochemistry. The objective of this study was to investigate the vascular patterns and vascular density of benign and malignant cervical lymphadenopathy using power Doppler ultrasound combined with a computed quantitative image processing system. METHODS: Investigations of 189 cervical lymph node lesions were undertaken prospectively using a 5-10 MHz linear array transducer in power mode. The types of vascular patterns displayed with power Doppler ultrasound, after sweep-scanning over the whole lymph node, were classified as hilar, spotted, peripheral, or mixed. Quantitative assessment of vascularity was made by sampling three parallel planes of each lymph node. A computed image processing system automatically calculated the density of vascular signals (called the "vascularity index" in this study) within the lymph node plane. RESULTS: Malignant lymph node lesions were shown to have higher vascularity indices (0.169+/-0.147, P < 0.01). The vascular patterns of benign lesions were mostly of avascular or hilar type (in 83% of cases). Malignant lesions were characterized by patterns of mixed (47%), spotted (20%), or peripheral type (11%). When vascular pattern (nonhilar type) and vascularity index (maximum > or = 0.09) were combined, the specificity for diagnosing malignant lymphadenopathy was as high as 97%. Variance in tumor vascularity was noted in both the benign and malignant groups. CONCLUSIONS: Power Doppler ultrasound combined with a computed image processing system provided an objective tool for assessing tumor vascularity quantitatively. Using this modality, the vascular pathology of malignant lymphadenopathy was found to be characterized by higher vascular density and aberrant vascular patterns.     

The "8-kD" cytoplasmic dynein light chain is required for nuclear migration and for dynein heavy chain localization in Aspergillus nidulans

The heavy chain of cytoplasmic dynein is required for nuclear migration in Aspergillus nidulans and other fungi. Here we report on a new gene required for nuclear migration, nudG, which encodes a homologue of the "8-kD" cytoplasmic dynein light chain (CDLC). We demonstrate that the temperature sensitive nudG8 mutation inhibits nuclear migration and growth at restrictive temperature. This mutation also inhibits asexual and sexual sporulation, decreases the intracellular concentration of the nudG CDLC protein and causes the cytoplasmic dynein heavy chain to be absent from the mycelial tip, where it is normally located in wild-type mycelia. Coimmunoprecipitation experiments with antibodies against the cytoplasmic dynein heavy chain (CDHC) and the nudG CDLC demonstrated that some fraction of the cytoplasmic dynein light chain is in a protein complex with the CDHC. Sucrose gradient sedimentation analysis, however, showed that not all of the NUDG protein is complexed with the heavy chain. A double mutant carrying a cytoplasmic dynein heavy chain deletion plus a temperature-sensitive nudG mutation grew no more slowly at restrictive temperature than a strain with only the CDHC deletion. This result demonstrates that the effect of the nudG mutation on nuclear migration and growth is mediated through an interaction with the CDHC rather than with some other molecule (e.g., myosin-V) with which the 8-kD CDLC might theoretically interact.     

Improving early prediction of academic failure using sentiment analysis on self‐evaluated comments

This study presents a model for the early identification of students who are likely to fail in an academic course. To enhance predictive accuracy, sentiment analysis is used to identify affective information from text‐based self‐evaluated comments written by students. Experimental results demonstrated that adding extracted sentiment information from student self‐evaluations yields a significant improvement in early‐stage prediction quality. The results also indicate the limited early‐stage predictive value of structured data, such as homework completion, attendance, and exam grades, due to data sparseness at the beginning of the course. Thus, applying sentiment analysis to unstructured data (e.g., self‐evaluation comments) can play an important role in improving the accuracy of early‐stage predictions. The findings present educators with an opportunity to provide students with real‐time feedback and support to help students become self‐regulated learners. Using the exploring results for improvement in teaching and learning initiatives is important to maintain students' performances and the effectiveness of the learning process.     

Engineering strength of fiber-reinforced soil estimated by swarm intelligence optimized regression system

Fiber-reinforced soil (FRS) has been used as a promising alternative material for civil and construction engineering. Shear strength of FRS is influenced complexly by many factors including fiber properties, soil properties, and stress conditions. This inherent complexity limits the ability of designers to assess shear strength parameters and has made it difficult to establish a mathematical model for accurately predicting the FRS shear strength. Accurately estimating the shear strength of FRS is vital for civil engineers in designing geotechnical structures and management. Thus, this work proposed a novel computational method, namely a swarm intelligence optimized regression (SIOR) system to estimate the peak shear strength of randomly distributed FRS. The SIOR system integrates a machine learning technique with an enhanced swarm intelligence algorithm to obtain reliable and good performance in prediction process. The real-world FRS dataset collected over the past 30 years was used to validate the proposed system. To demonstrate the capability of the proposed system, the SIOR modeling results were compared with those obtained using numeric predictive models. The analytical results confirm that the SIOR system is superior to a baseline machine learning model and empirical methods via cross-fold validation and hypothesis test with accuracy improvement from 44.7 to 99.7% in mean absolute percentage error. Therefore, the SIOR system can significantly improve the prediction accuracy and facilitate civil engineers in estimating the shear strength of the FRS.

     

Rechargeable Sodium-Based Hybrid Metal-Ion Batteries toward Advanced Energy Storage

Growing demands on energy storage devices have inspired a tremendous amount of research on rechargeable batteries. Future generations of rechargeable batteries are required to have high energy density, long lifespan, low cost, high safety, low environmental impact, and wide commercial affordability. To achieve these goals, significant efforts are underway to focus on electrolyte chemistry, electrode engineering, and new designs for energy storage systems. Herein, a comprehensive overview of an innovative sodium-based hybrid metal-ion battery (HMIBs) for advanced next-generation energy storage is presented. Recent advances on sodium-based HMIBs from the development of reformulated or novel materials associated with Na⁺ ions and other metal ions (such as Li⁺, K⁺, Mg²⁺, Zn²⁺, etc.), are summarized in this work. Daniell cell and “rocking-chair” type batteries are covered. Finally, the current challenges and future remedies in terms of the design and fabrication of new electrolytes, cathodes, and anodes for advanced HMIBs are discussed in this report.     

Interfacial Engineering of Bifunctional Niobium (V)-Based Heterostructure Nanosheet Toward High Efficiency Lean-Electrolyte Lithium–Sulfur Full Batteries

High-efficiency lithium–sulfur (Li–S) batteries depend on an advanced electrode structure that can attain high sulfur utilization at lean-electrolyte conditions and minimum amount of lithium. Herein, a twinborn holey Nb₄N₅–Nb₂O₅ heterostructure is designed as a dual-functional host for both redox–kinetics–accelerated sulfur cathode and dendrite-inhibited lithium anode simultaneously for long-cycling and lean-electrolyte Li–S full batteries. Benefiting from the accelerative polysulfides anchoring–diffusion–converting efficiency of Nb₄N₅–Nb₂O₅, polysulfide-shutting is significantly alleviated. Meanwhile, the lithiophilic nature of holey Nb₄N₅–Nb₂O₅ is applied as an ion-redistributor for homogeneous Li-ion deposition. Taking advantage of these merits, the Li–S full batteries present excellent electrochemical properties, including a minimum capacity decay rate of 0.025% per cycle, and a high areal capacity of 5.0 mAh cm⁻² at sulfur loading of 6.9 mg cm⁻², corresponding to negative to positive capacity ratio of 2.4:1 and electrolyte to sulfur ratio of 5.1 µL mg⁻¹. Therefore, this work paves a new avenue for boosting high-performances Li–S batteries toward practical applications.     

Multiple Access Control in a Centralized Full-Duplex Cognitive Machine Type Network with RF Energy Harvesting

Wireless Personal Communications - Machine type communication connecting machines in the Internet of Things (IoT) brings massive traffic and rapidly increases the demand for radio spectrum in...     

stc89c51单片机的厨房煤气泄露检测报警系统设计与实现

本篇论文是对煤气泄露的测量和报警进行的分析,气体传感器是作为系统的一个具体研究对象,该系统的重要核心为单片机据此组合成为一个具有结果显示、数据通信、数据采集、等多个不同功能的系统。对排气扇切断阀门、报警灯以及蜂鸣器进行有效控制,对煤气发生泄露的事故进行实时地监测以及有效控制监测系统状态。     

A Kinetic Model for Oxidation of Si–Al–O–N Materials

In this paper a new model has been proposed to describe the oxidation of Si–Al–O–N materials in both isothermal and non-isothermal conditions in the form of powders and pellets. All these formulae are analytic with a form of explicit function; therefore, they are not only easy to use but also enable the easy performance of a theoretical analysis. The application of these formulae to practical systems shows that this model is feasible; moreover, these formulae can not only be used to treat the oxidation for Si–Al–O–N materials but also be applied to treat reactions in other fields of materials.     

Modeling of an improved Chemical Vapor infiltration Process for Ceramic Composites Fabrication

A quasi-steady-state approach is applied to model the pressure-driven, temperature-gradient chemical vapor infiltration (improved CVI process) for ceramic matrix composites fabrication. The deposited matrix in this study is SiC which is converted from the thermal decomposition of methyltrichlorosilane gas under excess hydrogen. A three-dimensional unit cell is adopted to simulate the spatial arrangements of reinforcements in discontinuous fiber mats and three-dimensionally woven fabrics. The objectives of this paper are to predict (1) the temperature and density distributions in a fibrous preform during processing, (2) the advancement of the solidified front, (3) the total fabrication period, and (4) the vapor inlet pressure variation for maintaining a constant flow rate. Furthermore, the effect of boundary temperature and inlet pressure variations on the total proassing period is also studied. The fabrication temperature examined in this paper is in the range between 873 and 1473 K, and the pressure is from 1.0001 to 2.0000 atm (1.0134 × 10⁵ to 2.0265 × 10⁵ Pa). Based upon this analysis, the influence of the reactor condition on the density of the final product in a CVI process can be quantified.