Research Progress of Breast Tissue Marker Clips and Their Application in Neoadjuvant Therapy for Breast Cancer

Currently, breast cancer being of rapidly increasing incidence rates and as the most commonly diagnosed malignant tumor in breast surgery, has attracted much attention. Neoadjuvant therapy (NAT) has been proved to be beneficial for reducing tumor size and breast-conserving surgery. As a new type of metal localization marker, breast tissue marker clips can be used to precisely locate tumor tissue and improve cure rates. This review focuses on the marker clips and their significance in the diagnosis and treatment of neoadjuvant therapy for breast cancer, hoping to provide more clinical bases for research and promote this technology

Parallel Longest Increasing Subsequence and van Emde Boas Trees

This paper studies parallel algorithms for the longest increasing subsequence (LIS) problem. Let $n$ be the input size and $k$ be the LIS length of the input. Sequentially, LIS is a simple problem that can be solved using dynamic programming (DP) in $O(n\log n)$ work. However, parallelizing LIS is a long-standing challenge. We are unaware of any parallel LIS algorithm that has optimal $O(n\log n)$ work and non-trivial parallelism (i.e., $\tilde{O}(k)$ or $o(n)$ span). This paper proposes a parallel LIS algorithm that costs $O(n\log k)$ work, $\tilde{O}(k)$ span, and $O(n)$ space, and is much simpler than the previous parallel LIS algorithms. We also generalize the algorithm to a weighted version of LIS, which maximizes the weighted sum for all objects in an increasing subsequence. To achieve a better work bound for the weighted LIS algorithm, we designed parallel algorithms for the van Emde Boas (vEB) tree, which has the same structure as the sequential vEB tree, and supports work-efficient parallel batch insertion, deletion, and range queries. We also implemented our parallel LIS algorithms. Our implementation is light-weighted, efficient, and scalable. On input size $10^9$, our LIS algorithm outperforms a highly-optimized sequential algorithm (with $O(n\log k)$ cost) on inputs with $k\le 3\times 10^5$. Our algorithm is also much faster than the best existing parallel implementation by Shen et al. (2022) on all input instances.Comment: to be published in Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA '23

Towards V2I Age-aware Fairness Access: A DQN Based Intelligent Vehicular Node Training and Test Method

Vehicles on the road exchange data with base station (BS) frequently through vehicle to infrastructure (V2I) communications to ensure the normal use of vehicular applications, where the IEEE 802.11 distributed coordination function (DCF) is employed to allocate a minimum contention window (MCW) for channel access. Each vehicle may change its MCW to achieve more access opportunities at the expense of others, which results in unfair communication performance. Moreover, the key access parameters MCW is the privacy information and each vehicle are not willing to share it with other vehicles. In this uncertain setting, age of information (AoI) is an important communication metric to measure the freshness of data, we design an intelligent vehicular node to learn the dynamic environment and predict the optimal MCW which can make it achieve age fairness. In order to allocate the optimal MCW for the vehicular node, we employ a learning algorithm to make a desirable decision by learning from replay history data. In particular, the algorithm is proposed by extending the traditional DQN training and testing method. Finally, by comparing with other methods, it is proved that the proposed DQN method can significantly improve the age fairness of the intelligent node.Comment: This paper has been accepted by Chinese Journal of Electronics. Simulation codes have been provided at: https://github.com/qiongwu86/Age-Fairnes

Optimization of the lightning warning model for distribution network lines based on multiple meteorological factor thresholds

Lightning is one of the frequent natural disasters, which seriously affects the secure and stable operation of the power system, especially the distribution network lines with weak reliability. In order to improve the power supply reliability of the distribution network, higher requirements are put forward for the accuracy of lightning warning. Therefore, this paper establishes a lightning warning model based on comprehensive multi-meteorological factor thresholds and analyzes the meteorological factor data such as atmospheric field strength, echo intensity, echo-top height, and vertical cumulative liquid water content under thunderstorm weather. The threshold value of each factor warning is obtained, and the corresponding threshold weight is calculated by the entropy weight method. According to the weight of each threshold, the comprehensive threshold index of lightning warning is obtained, and the lightning warning is based on this index. A total of 105 lightning data from May to June 2022 in Nanchang city were analyzed as samples. The thresholds of atmospheric field strength, echo intensity, echo-top height, and vertical cumulative liquid water content were 1.2 kV/m, 40 dBZ, 8 km, and 5.2 kg·m−2, respectively. The corresponding weights of each factor were 0.4188, 0.2056, 0.2105, and 0.165, respectively. This model was used to warn a thunderstorm event in July 2022 in Nanchang area. The success rate of the model warning was 0.91, the false alarm rate (FAR) was 0.11, and the critical success index (CSI) was 0.80. Compared with the single-factor threshold lightning warning model, the warning FAR is decreased by 6%, and CSI is increased by 14% while ensuring the high warning success rate

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Robust Prescribed-Time ESO-Based Practical Predefined-Time SMC for Benthic AUV Trajectory-Tracking Control with Uncertainties and Environment Disturbance

The aim of this study is to address the trajectory-tracking control problem of benthic autonomous underwater vehicles (AUVs) subjected to model uncertainties and extra disturbance. In order to estimate lumped uncertainties and reconstruction speed information, this paper designs a robust prescribed-time extended state observer (RPTESO), and its prescribed time can be directly designed as an explicit parameter, without relying on the initial state of the system and complex parameter settings. In addition, an adaptive law is designed to improve the robustness of RPTSEO and reduce overshoot on the premise of ensuring convergence speed. Then, a non-singular robust practical predefined-time sliding mode control (RPPSMC) considering the hydrodynamic characteristics of AUV is designed, and the predefined time can be directly set by an explicit parameter. The RPPSMC is designed based on the lumped uncertainties estimated using RPTESO, so as to improve the control accuracy of the controller in a complex environment. Theoretical analysis and simulations demonstrated the effectiveness and superiority of the proposed method

Social Capital and Participant Retention in Online Mental Health Community: Quantifying the Relative Effect of Bridging and Bonding Social Capital

We examine the effect of social capital on participant retention in online mental health community, and disentangle the effect of bridging and bonding social capital on participant retention in this paper. Specifically, we derive participant profile data and activity data for 15 years from a Chinese online mental health community and construct social networks based on reply relationship for every half year. Following prior studies, bridging social capital and bonding social capital are measured by structural holes and network closure respectively. We conduct survival analysis to examine whether social capital has effect on participant retention, and use panel Logit model to capture the efficacy of different types of social capital. Results show that social capital significantly improves participant retention rate; bridging social capital has positive effect on participant retention, whereas bonding social capital has negative effect on participant retention

Janus nanoarchitectures: From structural design to catalytic applications

Janus nanoarchitectures, an emerging class of nanostructures named after the Roman god having two faces, have been considered as a fascinating class of nanomaterials for promising applications in various areas, such as optical imaging, emulsion stabilizers, catalysis, drug delivery, etc. The asymmetric structures or counterparts of Janus nanostructures provide access to construct a single unit with multifunctional properties, and thus allow the design of nanocomposites with a possible synergistic effect, especially for catalytic reactions. In the last decade, Janus nanomaterials have been successfully applied in the field of catalysis, by providing solutions to some complex situations, such as biphasic reactions, catalysts recovery, self-propelled movements, and biocompatible catalysis. In this review, we intend to highlight the recent progress of Janus nanoarchitectures for the growing field of catalytic applications. Herein, the fabrication and catalytic applications of Janus nanoarchitectures are critically reviewed in terms of three categories of compositions, i.e., polymeric, inorganic, and polymeric/inorganic Janus nanostructures. Specifically, typical applications of Janus nanoarchitectures in micro/nano motors, interfacial catalysis, and photocatalytic reactions are summarized and discussed. An outlook of the future applications and possible further study of Janus nanomaterials is also provided