Exploration of the Fertility Decision-Making Experiences of Women of Reproductive Age With Cancer: A Qualitative Systematic Review.

Women of reproductive age with cancer face unique considerations in terms of fertility. The related decision-making process is complicated, and insufficient support can lead to decisional conflict. The aim of this qualitative systematic review was to identify and integrate qualitative evidence regarding the fertility decision-making process of women of reproductive age undergoing treatment for cancer.

Manganese self-boosting hollow nanoenzymes with glutathione depletion for synergistic cancer chemo-chemodynamic therapy.

Chemodynamic therapy (CDT) has outstanding potential as a combination therapy to treat cancer. However, the effectiveness of CDT in the treatment of solid tumors is limited by the overexpression of glutathione (GSH) in the tumor microenvironment (TME). GSH overexpression diminishes oxidative stress and attenuates chemotherapeutic drug-induced apoptosis in cancer cells.

OBMO: One Bounding Box Multiple Objects for Monocular 3D Object Detection.

Compared to typical multi-sensor systems, monocular 3D object detection has attracted much attention due to its simple configuration. However, there is still a significant gap between LiDAR-based and monocular-based methods. In this paper, we find that the ill-posed nature of monocular imagery can lead to depth ambiguity.

Ecological risk evaluation and sensitivity analysis of heavy metals on soil organisms under human activities in the Tibet Plateau, China.

The Tibetan Plateau (TP), once considered a pristine environment, is now facing increased heavy metal pollution due to human activities, causing unprecedented ecological risks to soil organisms. However, little is known about the sensitivity and tolerance of different soil organisms to heavy metal toxicity in the high-altitude areas of the TP under the background of human activity intensity and future risk control priorities. In this study, we conducted an ecological risk assessment and threshold calculation for 10 heavy metals in soil for typical soil organisms, including Cd, Co, Cr, Cu, Ni, Pb, Zn, Mn, Sb, and Sn, using the species sensitivity distribution (SSD) method in the zone between Ranwu town and Renlongba glacier on the TP.

X-view: Non-egocentric Multi-View 3D Object Detector.

3D object detection algorithms for autonomous driving reason about 3D obstacles either from 3D birds-eye view or perspective view or both. Recent works attempt to improve the detection performance via mining and fusing from multiple egocentric views. Although the egocentric perspective view alleviates some weaknesses of the birds-eye view, the sectored grid partition becomes so coarse in the distance that the targets and surrounding context mix together, which makes the features less discriminative.

Toward Better Accuracy-Efficiency Trade-Offs: Divide and Co-Training.

The width of a neural network matters since increasing the width will necessarily increase the model capacity. However, the performance of a network does not improve linearly with the width and soon gets saturated. In this case, we argue that increasing the number of networks (ensemble) can achieve better accuracy-efficiency trade-offs than purely increasing the width.

Preoperative Changes of Lung Nodule on Computed Tomography and Their Relationship With Pathological Outcomes.

Background: Whether changes of lung nodules on computed tomography could bring us helpful information related to their pathological outcomes remained unclear.

Materials And Methods: This retrospective study was carried out among 1,185 cases of lung nodules in Shanghai Chest Hospital from January 2015 to April 2017, which did not shrink or disappear after preoperative follow-up over three months. Their imaging features, changes, and clinical characteristics were collected.

Progressive Transfer Learning.

Model fine-tuning is a widely used transfer learning approach in person Re-identification (ReID) applications, which fine-tuning a pre-trained feature extraction model into the target scenario instead of training a model from scratch. It is challenging due to the significant variations inside the target scenario, e.g.

Single-Cell Microwell Platform Reveals Circulating Neural Cells as a Clinical Indicator for Patients with Blood-Brain Barrier Breakdown.

Central nervous system diseases commonly occur with the destruction of the blood-brain barrier. As a primary cause of morbidity and mortality, stroke remains unpredictable and lacks cellular biomarkers that accurately quantify its occurrence and development. Here, we identify NeuN/CD45/DAPI phenotype nonblood cells in the peripheral blood of mice subjected to middle cerebral artery occlusion (MCAO) and stroke patients.

MALAT1 rs619586 A/G polymorphisms are associated with decreased risk of lung cancer.

Lung cancer is the leading cause of cancer-associated mortality worldwide. Genetic factors are reported to play important roles in lung carcinogenesis. To evaluate genetic susceptibility, we conducted a hospital-based case-control study on the effects of functional single nucleotide polymorphisms (SNPs) in long non-coding RNAs (lncRNAs) and microRNAs on lung cancer development.

High Dimensional Similarity Search With Satellite System Graph: Efficiency, Scalability, and Unindexed Query Compatibility.

Approximate nearest neighbor search (ANNS) in high-dimensional space is essential in database and information retrieval. Recently, there has been a surge of interest in exploring efficient graph-based indices for the ANNS problem. Among them, navigating spreading-out graph (NSG) provides fine theoretical analysis and achieves state-of-the-art performance.

Preoperative peripheral blood neutrophil-to-lymphocyte ratios (NLR) and platelet-to-lymphocyte ratio (PLR) related nomograms predict the survival of patients with limited-stage small-cell lung cancer.

Background: We aim to establish neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) related nomograms based on the clinical data and peripheral blood markers to predict the survivals of patients with limited-stage small-cell lung cancer (LS-SCLC).

Methods: A total of 299 LS-SCLC patients after surgery were enrolled in this study. Univariate and multivariate analyses were conducted to select independent prognostic factors to develop the nomograms and then subjected to bootstrap internal validation.

Complementary Pseudo Labels for Unsupervised Domain Adaptation On Person Re-Identification.

In recent years, supervised person re-identification (re-ID) models have received increasing studies. However, these models trained on the source domain always suffer dramatic performance drop when tested on an unseen domain. Existing methods are primary to use pseudo labels to alleviate this problem.

Association between gene polymorphisms and diabetic retinopathy risk: a meta-analysis.

A close association between peroxisome proliferator-activated receptor-γ2 (PPAR-γ2) and the development of diabetic retinopathy (DR) has been previously suggested. Herein, a meta-analysis was conducted to explore the association between polymorphisms and DR risk by performing a systematic search and quantitative analysis. Overall, fourteen articles involving 10,527 subjects were included.

ES-Net: Erasing Salient Parts to Learn More in Re-Identification.

As an instance-level recognition problem, re-identification (re-ID) requires models to capture diverse features. However, with continuous training, re-ID models pay more and more attention to the salient areas. As a result, the model may only focus on few small regions with salient representations and ignore other important information.

Association between the pri-miR-26a-1 rs7372209 C>T polymorphism and cancer susceptibility: multivariate analysis and trial sequential analysis.

MiR-26 has been suggested to play a tumor-suppressive role in cancer development, which could be influenced by the mutate pri-miR-26ª-1. Molecular epidemiological studies have demonstrated some inconsistent associations between pri-miR-26ª-1 rs7372209 C>T polymorphism and cancer risk. We therefore performed this meta-analysis with multivariate statistic method to comprehensively evaluate the associations between rs7372209 C>T polymorphism and cancer risk.

Query-biased Self-attentive Network for Query-focused Video Summarization.

This paper addresses the task of query-focused video summarization, which takes user queries and long videos as inputs and generates query-focused video summaries. Compared to video summarization, which mainly concentrates on finding the most diverse and representative visual contents as a summary, the task of query-focused video summarization considers the user's intent and the semantic meaning of generated summary. In this paper, we propose a method, named query-biased self-attentive network (QSAN) to tackle this challenge.

SIF: Self-Inspirited Feature Learning for Person Re-identification.

The re-identification (ReID) task has received increasing studies in recent years and its performance has gained significant improvement. The progress mainly comes from searching for new network structures to learn person representations. Most of these networks are trained using the classic stochastic gradient descent optimizer.

Moment Retrieval via Cross-Modal Interaction Networks with Query Reconstruction.

Moment retrieval aims to localize the most relevant moment in an untrimmed video according to the given natural language query. Existing works often only focus on one aspect of this emerging task, such as the query representation learning, video context modeling or multi-modal fusion, thus fail to develop a comprehensive system for further performance improvement. In this paper, we introduce a novel Cross-Modal Interaction Network (CMIN) to consider multiple crucial factors for this challenging task, including the syntactic dependencies of natural language queries, long-range semantic dependencies in video context and the sufficient cross-modal interaction.

Multicenter, prospective, observational study of a novel technique for preoperative pulmonary nodule localization.

Objectives: Minimally invasive surgery provides an ideal method for pathologic diagnosis and curative intent of small pulmonary nodules (SPNs); however, the main problem with thoracoscopic resection is the difficulty in locating the nodules. The goal of this study was to determine the safety and feasibility of a new localization technique tailored for SPNs.

Methods: A computed tomography (CT)-guided technique, which has a tri-colored suture and claw with 4 fishhook-shaped hooks, was designed to localize SPN preoperatively.

Fabrication and sensing application of a silver nanoshell array structure by a micro-flow injection method.

A two-dimensional periodic metallic spherical shell array structure with controllable geometric parameters was fabricated on the target substrate by microsphere templating and magnetron sputtering. The micro-flow injection method was used to prepare a two-dimensional colloidal microsphere template, and reactive ion etching (RIE) was used to change the spherical spacing. The geometric parameters and spectral characteristics of the spherical shell array structure were analyzed with the simulation software FDTD solutions.

Long-Form Video Question Answering via Dynamic Hierarchical Reinforced Networks.

Open-ended long-form video question answering is a challenging task in visual information retrieval, which automatically generates a natural language answer from the referenced long-form video contents according to a given question. However, the existing works mainly focus on short-form video question answering, due to the lack of modeling semantic representations from long-form video contents. In this paper, we introduce a dynamic hierarchical reinforced network for open-ended long-form video question answering, which employs an encoder-decoder architecture with a dynamic hierarchical encoder and a reinforced decoder.

Multi-Turn Video Question Answering via Hierarchical Attention Context Reinforced Networks.

Multi-turn video question answering is a challenging task in visual information retrieval, which generates the accurate answer from the referenced video contents according to the visual conversation context and given question. However, the existing visual question answering methods mainly tackle the problem of single-turn video question answering, which may be ineffectively applied for multi-turn video question answering directly, due to the insufficiency of modeling the sequential conversation context. In this paper, we study the problem of multi-turn video question answering from the viewpoint of multi-stream hierarchical attention context reinforced network learning.

On the Diversity of Conditional Image Synthesis with Semantic Layouts.

Many image processing tasks can be formulated as translating images between two image domains, such as colorization, super-resolution, and conditional image synthesis. In most of these tasks, an input image may correspond to multiple outputs. However, current existing approaches only show minor stochasticity of the outputs.

A Better Way to Attend: Attention with Trees for Video Question Answering.

We propose a new attention model for video question answering. The main idea of the attention models is to locate on the most informative parts of the visual data. The attention mechanisms are quite popular these days.