Foreword for the Collection of Papers from the Workshop on the Analysis of Census Noisy Measurement Files and Differential Privacy

The 2022 Workshop on the Analysis of Census Noisy Measurement Files and Differential Privacy brought together research experts from many domains of social sciences, demography, public policy, statistics, and computer science to address key challenges in the use of the differentially private Census noisy measurement files to support social research and policy decisions

Nudge theories and strategies influencing adult health behaviors and outcomes in COPD management: a systematic review

ObjectiveChronic obstructive pulmonary disease (COPD) is a chronic respiratory disease with high prevalence and mortality, and self-management is a key component for better outcomes of COPD. Recently, nudging has shown promising potential in COPD management. In the present study, we conducted a systematic review to collate the list of nudges and identified the variables that influence nudging.MethodsWe undertook a systematic review. We employed database searches and snowballing. Data from selected studies were extracted. The risk of bias was assessed using the Cochrane Effective Practice and Organization of Care risk of bias tool. The study is registered with PROSPERO, CRD42023427051.ResultsWe retrieved 4,022 studies from database searches and 38 studies were included. By snowballing, 5 additional studies were obtained. Nudges were classified into four types: social influence, gamification, reminder, and feedback. Medication adherence, inhalation technique, physical activity, smoking cessation, vaccination administration, exercise capacity, self-efficacy, pulmonary function, clinical symptoms, and quality of life were analyzed as targeted health behaviors and outcomes. We found medication adherence was significantly improved by reminders via mobile applications or text materials, as well as feedback based on devices. Additionally, reminders through text materials greatly enhance inhalation techniques and vaccination in patients.ConclusionThis review demonstrates nudging can improve the health behaviors of patients with COPD and shows great potential for certain outcomes, particularly medication adherence, inhalation techniques, and vaccination. Additionally, the delivery modes, the patient characteristics, and the durations and seasons of interventions may influence the successful nudge-based intervention.Clinical trial registrationThis review has been registered in the international Prospective Registry of Systematic Evaluation (PROSPERO) database (identifier number CRD42023427051)

A Salmonella Small Non-Coding RNA Facilitates Bacterial Invasion and Intracellular Replication by Modulating the Expression of Virulence Factors

Small non-coding RNAs (sRNAs) that act as regulators of gene expression have been identified in all kingdoms of life, including microRNA (miRNA) and small interfering RNA (siRNA) in eukaryotic cells. Numerous sRNAs identified in Salmonella are encoded by genes located at Salmonella pathogenicity islands (SPIs) that are commonly found in pathogenic strains. Whether these sRNAs are important for Salmonella pathogenesis and virulence in animals has not been reported. In this study, we provide the first direct evidence that a pathogenicity island-encoded sRNA, IsrM, is important for Salmonella invasion of epithelial cells, intracellular replication inside macrophages, and virulence and colonization in mice. IsrM RNA is expressed in vitro under conditions resembling those during infection in the gastrointestinal tract. Furthermore, IsrM is found to be differentially expressed in vivo, with higher expression in the ileum than in the spleen. IsrM targets the mRNAs coding for SopA, a SPI-1 effector, and HilE, a global regulator of the expression of SPI-1 proteins, which are major virulence factors essential for bacterial invasion. Mutations in IsrM result in disregulation of expression of HilE and SopA, as well as other SPI-1 genes whose expression is regulated by HilE. Salmonella with deletion of isrM is defective in bacteria invasion of epithelial cells and intracellular replication/survival in macrophages. Moreover, Salmonella with mutations in isrM is attenuated in killing animals and defective in growth in the ileum and spleen in mice. Our study has shown that IsrM sRNA functions as a pathogenicity island-encoded sRNA directly involved in Salmonella pathogenesis in animals. Our results also suggest that sRNAs may represent a distinct class of virulence factors that are important for bacterial infection in vivo

Unmanned Aerial Vehicle Object Detection Based on Information-Preserving and Fine-Grained Feature Aggregation

General deep learning methods achieve high-level semantic feature representation by aggregating hierarchical features, which performs well in object detection tasks. However, issues arise with general deep learning methods in UAV-based remote sensing image object detection tasks. Firstly, general feature aggregation methods such as stride convolution may lead to information loss in input samples. Secondly, common FPN methods introduce conflicting information by directly fusing feature maps from different levels. These shortcomings limit the model’s detection performance on small and weak targets in remote sensing images. In response to these concerns, we propose an unmanned aerial vehicle (UAV) object detection algorithm, IF-YOLO. Specifically, our algorithm leverages the Information-Preserving Feature Aggregation (IPFA) module to construct semantic feature representations while preserving the intrinsic features of small objects. Furthermore, to filter out irrelevant information introduced by direct fusion, we introduce the Conflict Information Suppression Feature Fusion Module (CSFM) to improve the feature fusion approach. Additionally, the Fine-Grained Aggregation Feature Pyramid Network (FGAFPN) facilitates interaction between feature maps at different levels, reducing the generation of conflicting information during multi-scale feature fusion. The experimental results on the VisDrone2019 dataset demonstrate that in contrast to the standard YOLOv8-s, our enhanced algorithm achieves a mean average precision (mAP) of 47.3%, with precision and recall rates enhanced by 6.3% and 5.6%, respectively

Immune Checkpoint Inhibitors for Patients with Microsatellite Instability-High Colorectal Cancer: Protocol of a Pooled Analysis of Clinical Trials

Introduction Colorectal cancer (CRC) is the third most common cause of cancer and the second leading cause of cancer-related deaths worldwide. Microsatellite instability-high (MSI-H) is a distinct molecular subtype of CRC that occurs in approximately 15% of all cases. Recently, immune checkpoint inhibitors (ICIs) have emerged as a promising therapeutic approach for patients with MSI-H colorectal cancer, exhibiting higher response rates than standard chemotherapies. To assess the effectiveness and safety of ICIs for the treatment of patients with MSI-H CRC, we propose a comprehensive pooled analysis of clinical trial data. Methods and Analysis A systematic search of multiple electronic databases, including PubMed, EMBASE, Cochrane Library, and Clinicaltrials.gov, will be conducted from their inception until September, 2023 to identify eligible randomized controlled trials (RCTs) and non-randomized studies. Inclusion criteria comprise studies of adult patients with histologically confirmed MSI-H CRC treated with immune checkpoint inhibitors, with a comparison to a control group receiving conventional therapies. Outcomes of interest will be overall survival (OS), progression-free survival (PFS), objective response rate (ORR), disease control rate (DCR), and incidence of treatment-related adverse events (AEs). The Cochrane Risk of Bias tool and the Risk of Bias in Non-randomized Studies of Interventions (ROBINS-I) tool will be employed to evaluate the methodological quality of included studies. A random-effects model using the DerSimonian and Laird method will be applied for pooling the effect estimates, calculating hazard ratios (HRs) or risk ratios (RRs) with their corresponding 95% confidence intervals (CIs). Heterogeneity will be assessed using I² statistics, and subgroup analysis and meta-regression will be performed to explore potential effect modifiers in case of substantial heterogeneity. Publication bias will be evaluated with funnel plots and Egger's test. Sensitivity analysis will be conducted to assess the robustness of the results. Discussion This meta-analysis will synthesize available evidence from clinical trials on immune checkpoint inhibitors in treating MSI-H colorectal cancer. The findings will offer valuable information about the effectiveness and safety of ICIs in this patient population, contributing to the refinement of clinical guidelines and enhancing the decision-making process for healthcare providers, policy-makers, and patients. The comprehensive analysis of subgroups and sensitivity allows for an in-depth understanding of potential effect modification, providing essential directions for future research. Ethics and dissemination This study will involve the use of published data; hence, ethical approval is not required. The results of the study will be disseminated through publications in peer-reviewed journals and presentations at relevant conferences. The findings will potentially impact clinical decision-making and contribute to the development of evidence-based treatment recommendations for patients with MSI-H colorectal cancer

G-CAS: Greedy Algorithm-Based Security Event Correlation System for Critical Infrastructure Network

The attacks on the critical infrastructure network have increased sharply, and the strict management measures of the critical infrastructure network have caused its correlation analysis technology for security events to be relatively backward; this makes the critical infrastructure network’s security situation more severe. Currently, there is no common correlation analysis technology for the critical infrastructure network, and most technologies focus on expanding the dimension of data analysis, but with less attention to the optimization of analysis performance. The analysis performance does not meet the practical environment, and real-time analysis is even more impossible; as a result, the efficiency of security threat detection is greatly declined. To solve this issue, we propose the greedy tree algorithm, a correlation analysis approach based on the greedy algorithm, which optimizes event analysis steps and significantly improves the performance, so the real-time correlation analysis can be realized. We first verify the performance of the algorithm through formalization, and then the G-CAS (Greedy Correlation Analysis System) is implemented based on this algorithm and is applied in a real critical infrastructure network, which outperformed the current mainstream products.</jats:p