Practical m

In collaborative data publishing (CDP), an m-adversary attack refers to a scenario where up to m malicious data providers collude to infer data records contributed by other providers. Existing solutions either rely on a trusted third party (TTP) or introduce expensive computation and communication overheads. In this paper, we present a practical distributed k-anonymization scheme, m-k-anonymization, designed to defend against m-adversary attacks without relying on any TTPs. We then prove its security in the semihonest adversary model and demonstrate how an extension of the scheme can also be proven secure in a stronger adversary model. We also evaluate its efficiency using a commonly used dataset

Adult image detection combining bovw based on region of interest and color moments

Abstract. To prevent pornography from spreading on the Internet effectively, we propose a novel method of adult image detection which combines bag-ofvisual-words (BoVW) based on region of interest (ROI) and color moments (CM). The goal of BoVW is to automatically mine the local patterns of adult contents, called visual words. The usual BoVW method clusters visual words from the patches in the whole image and adopts the weighting schemes of hard assignment. However, there are many background noises in the whole image and soft-weighting scheme is better than hard assignment. Therefore, we propose the method of BoVW based on ROI, which includes two perspectives. Firstly, we propose to create visual words in ROI for adult image detection. The representative power of visual words can be improved because the patches in ROI are more indicative to adult contents than those in the whole image. Secondly, soft-weighting scheme is adopted to detect adult images. Moreover, CM is selected by evaluating some commonly-used global features to be combined with BoVW based on ROI. The experiments and the comparison with the state-of-the-art methods show that our method is able to remarkably improve the performance of adult image detection

Evaluation of a village-based digital health kiosks program: A protocol for a cluster randomized clinical trial

Background To address disparities in healthcare quality and access between rural and urban areas in China, reforms emphasize strengthening primary care and digital health utilization. Yet, evidence on digital health approaches in rural areas is lacking. Objective This study will evaluate the effectiveness of Guangdong Second Provincial General Hospital's Digital Health Kiosk program, which uses the Dingbei telemedicine platform to connect rural clinicians to physicians in upper-level health facilities and provide access to artificial intelligence-enabled diagnostic support. We hypothesize that our interventions will increase healthcare utilization and patient satisfaction, decrease out-of-pocket costs, and improve health outcomes. Methods This cluster randomized control trial will enroll clinics according to a partial factorial design. Clinics will be randomized to either a control arm with clinician medical training, a second arm additionally receiving Dingbei telemedicine training, or a third arm with monetary incentives for patient visits conducted through Dingbei plus all prior interventions. Clinics in the second and third arm will then be orthogonally randomized to a social marketing arm that targets villager awareness of the kiosk program. We will use surveys and Dingbei administrative data to evaluate clinic utilization, revenue, and clinician competency, as well as patient satisfaction and expenses. Results We have received ethical approval from Guangdong Second Provincial General Hospital (IRB approval number: GD2H-KY IRB-AF-SC.07-01.1), Peking University (IRB00001052-21007), and the University of North Carolina at Chapel Hill (323385). Study enrollment began April 2022. Conclusions This study has the potential to inform future telemedicine approaches and assess telemedicine as a method to address disparities in healthcare access. Trial registration number: ChiCTR210005387

Low escape-rate genome safeguards with minimal molecular perturbation of Saccharomyces cerevisiae

As the use of synthetic biology both in industry and in academia grows, there is an increasing need to ensure biocontainment. There is growing interest in engineering bacterial- and yeast-based safeguard (SG) strains. First-generation SGs were based on metabolic auxotrophy; however, the risk of cross-feeding and the cost of growth-controlling nutrients led researchers to look for other avenues. Recent strategies include bacteria engineered to be dependent on nonnatural amino acids and yeast SG strains that have both transcriptional- and recombinational-based biocontainment. We describe improving yeast Saccharomyces cerevisiae-based transcriptional SG strains, which have near-WT fitness, the lowest possible escape rate, and nanomolar ligands controlling growth. We screened a library of essential genes, as well as the best-performing promoter and terminators, yielding the best SG strains in yeast. The best constructs were fine-tuned, resulting in two tightly controlled inducible systems. In addition, for potential use in the prevention of industrial espionage, we screened an array of possible "decoy molecules" that can be used to mask any proprietary supplement to the SG strain, with minimal effect on strain fitness

Artificial intelligence-aided rapid and accurate identification of clinical fungal infections by single-cell Raman spectroscopy

Integrating artificial intelligence and new diagnostic platforms into routine clinical microbiology laboratory procedures has grown increasingly intriguing, holding promises of reducing turnaround time and cost and maximizing efficiency. At least one billion people are suffering from fungal infections, leading to over 1.6 million mortality every year. Despite the increasing demand for fungal diagnosis, current approaches suffer from manual bias, long cultivation time (from days to months), and low sensitivity (only 50% produce positive fungal cultures). Delayed and inaccurate treatments consequently lead to higher hospital costs, mobility and mortality rates. Here, we developed single-cell Raman spectroscopy and artificial intelligence to achieve rapid identification of infectious fungi. The classification between fungi and bacteria infections was initially achieved with 100% sensitivity and specificity using single-cell Raman spectra (SCRS). Then, we constructed a Raman dataset from clinical fungal isolates obtained from 94 patients, consisting of 115,129 SCRS. By training a classification model with an optimized clinical feedback loop, just 5 cells per patient (acquisition time 2 s per cell) made the most accurate classification. This protocol has achieved 100% accuracies for fungal identification at the species level. This protocol was transformed to assessing clinical samples of urinary tract infection, obtaining the correct diagnosis from raw sample-to-result within 1 h