Development of a human-size magnetic particle imaging device for sentinel lymph node biopsy of breast cancer

In this study, a novel human-size handheld magnetic particle imaging (MPI) system was developed for the high-precision detection of sentinel lymph nodes for breast cancer. The system consisted of a highly sensitive home-made MPI detection probe, a set of concentric coils pair for spatialization, a solenoid coil for uniform excitation at 8 [email protected] mT, and a full mirrored coil set positioned far away from the scanning area. The mirrored coils formed an extremely effective differential pickup structure which suppressed the system noise as high as 100 dB. The different combination of the inner and outer gradient current made the field free point (FFP) move in the Z direction with a uniform intensity of 0.54T/m, while the scanning in the XY direction was implemented mechanically. The third-harmonic signal of the Superparamagnetic Iron Oxide Nanoparticles (SPIONs) at the FFP was detected and then reconstructed synchronously with the current changes. Experiment results showed that the tomographic detection limit was 30 mm in the Z direction, and the sensitivity was about 10 μg Fe SPIONs at 40 mm distance with a spatial resolution of about 5 mm. In the rat experiment, 54 μg intramuscular injected SPIONs were detected successfully in the sentinel lymph node, in which the tracer content was about 1.2% total injected Fe. Additionally, the effective detection time window was confirmed from 4 to 6 min after injection. Relevant clinical ethics are already in the application process. Large mammalian SLNB MPI experiments and 3D preoperative SLNB imaging will be performed in the future

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

Evaluation Indicator System for China’s Agricultural Industrial Safety

On the basis of new characteristics and trend of China’s agricultural development in the post-WTO period, combining analysis of factors influencing agricultural industrial safety, this paper builds an evaluation indicator system for China’s agricultural industrial safety by scientific indicator system design method. This indicator system includes risk factor indicators (showing risk degree) and capacity factor indicators (showing guaranteeing ability), and consists of 7 subsystems: consumption safety, production safety, industrial controlling capacity, industrial development capacity, industrial development environment, government functions and industrial foundation condition. Risk factor is divided into 5 levels: higher risk, high risk, medium risk, low risk and lower risk; guarantee risk is also divided into five levels: strong, healthy, normal, weak and disabled. According to the overall evaluation score obtained from weighting sum, the agricultural industrial safety includes 5 types: very safe, safe, basically safe, not safe and hazardous. This evaluation indicator system is expected to providing theoretical reference for evaluating China’s agricultural industrial safety

Automatic labeling algorithms and their applications in weld seam of oil pipeline

Building information model (BIM) technology has become an important tool for construction practitioners to improve the engineering design, construction and management. However, it has not been widely used in petrochemical industry, such as oil pipeline construction. This paper presents an automatic labeling algorithm based on BIM technology. We used simulated annealing algorithm to optimize the overlap of pipe weld label positions, resulting in the sequential pipe welds. We used this algorithm in oil pipeline construction and verified its automatic labeling effect on pipe welds

DataSheet_1_The causal relationship between gut microbiota and inflammatory dermatoses: a Mendelian randomization study.docx

BackgroundObservational studies have shown that gut microbiota is closely associated with inflammatory dermatoses such as psoriasis, rosacea, and atopic dermatitis (AD). However, the causal relationship between gut microbiota and inflammatory dermatosis remains unclear.MethodsBased on Maximum Likelihood (ML), MR-Egger regression, Inverse Variance Weighted (IVW), MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO), Weighted Mode, and Weighted Median Estimator (WME) methods, we performed a bidirectional two-sample Mendelian randomization (MR) analysis to explore the causal relationship between gut microbiota and inflammatory dermatosis. The genome-wide association study (GWAS) summary data of gut microbiota came from the MiBioGen consortium, while the GWAS summary data of inflammatory dermatosis (including psoriasis, AD, rosacea, vitiligo, acne, and eczema) came from the FinnGen consortium and IEU Open GWAS project. Cochran’s IVW Q test tested the heterogeneity among instrumental variables (IVs). The horizontal pleiotropy was tested by MR-Egger regression intercept analysis and MR-PRESSO analysis.ResultsEventually, the results indicated that 5, 16, 17, 11, 15, and 12 gut microbiota had significant causal effects on psoriasis, rosacea, AD, vitiligo, acne, and eczema, respectively, including 42 protective and 34 risk causal relationships. Especially, Lactobacilli and Bifidobacteria at the Family and Genus Level, as common probiotics, were identified as protective factors for the corresponding inflammatory dermatoses. The results of reverse MR analysis suggested a bidirectional causal effect between AD and genus Eubacterium brachy group, vitiligo and genus Ruminococcaceae UCG004. The causal relationship between gut microbiota and psoriasis, rosacea, acne, and eczema is unidirectional. There was no significant heterogeneity among these IVs. In conclusion, this bidirectional two-sample MR study identified 76 causal relationships between the gut microbiome and six inflammatory dermatoses, which may be helpful for the clinical prevention and treatment of inflammatory dermatoses.</p

Visual Analytic Method for Students’ Association via Modularity Optimization

Students spend most of their time living and studying on campus, especially in Asia, and they form various types of associations in addition to those with classmates and roommates. It is necessary for university authorities to master these types of associations, so as to provide appropriate services, such as psychological guidance and academic advice. With the rapid development of the &ldquo;smart campus,&rdquo; many kinds of student behavior data are recorded, which provides an unprecedented opportunity to deeply analyze students&rsquo; associations. In this paper, we propose a visual analytic method to construct students&rsquo; association networks by computing the similarity of their behavior data. We discover student communities using the popular Louvain (or BGLL) algorithm, which can extract community structures based on modularity optimization. Using various visualization charts, we visualized associations among students so as to intuitively express them. We evaluated our method using the real behavior data of undergraduates in a university in Beijing. The experimental results indicate that this method is effective and intuitive for student association analysis

X3DFast model for classifying dairy cow behaviors based on a two-pathway architecture

Abstract Behavior is one of the important factors reflecting the health status of dairy cows, and when dairy cows encounter health problems, they exhibit different behavioral characteristics. Therefore, identifying dairy cow behavior not only helps in assessing their physiological health and disease treatment but also improves cow welfare, which is very important for the development of animal husbandry. The method of relying on human eyes to observe the behavior of dairy cows has problems such as high labor costs, high labor intensity, and high fatigue rates. Therefore, it is necessary to explore more effective technical means to identify cow behaviors more quickly and accurately and improve the intelligence level of dairy cow farming. Automatic recognition of dairy cow behavior has become a key technology for diagnosing dairy cow diseases, improving farm economic benefits and reducing animal elimination rates. Recently, deep learning for automated dairy cow behavior identification has become a research focus. However, in complex farming environments, dairy cow behaviors are characterized by multiscale features due to large scenes and long data collection distances. Traditional behavior recognition models cannot accurately recognize similar behavior features of dairy cows, such as those with similar visual characteristics, i.e., standing and walking. The behavior recognition method based on 3D convolution solves the problem of small visual feature differences in behavior recognition. However, due to the large number of model parameters, long inference time, and simple data background, it cannot meet the demand for real-time recognition of dairy cow behaviors in complex breeding environments. To address this, we developed an effective yet lightweight model for fast and accurate dairy cow behavior feature learning from video data. We focused on four common behaviors: standing, walking, lying, and mounting. We recorded videos of dairy cow behaviors at a dairy farm containing over one hundred cows using surveillance cameras. A robust model was built using a complex background dataset. We proposed a two-pathway X3DFast model based on spatiotemporal behavior features. The X3D and fast pathways were laterally connected to integrate spatial and temporal features. The X3D pathway extracted spatial features. The fast pathway with R(2 + 1)D convolution decomposed spatiotemporal features and transferred effective spatial features to the X3D pathway. An action model further enhanced X3D spatial modeling. Experiments showed that X3DFast achieved 98.49% top-1 accuracy, outperforming similar methods in identifying the four behaviors. The method we proposed can effectively identify similar dairy cow behaviors while improving inference speed, providing technical support for subsequent dairy cow behavior recognition and daily behavior statistics

Table_1_The causal relationship between gut microbiota and inflammatory dermatoses: a Mendelian randomization study.xlsx

BackgroundObservational studies have shown that gut microbiota is closely associated with inflammatory dermatoses such as psoriasis, rosacea, and atopic dermatitis (AD). However, the causal relationship between gut microbiota and inflammatory dermatosis remains unclear.MethodsBased on Maximum Likelihood (ML), MR-Egger regression, Inverse Variance Weighted (IVW), MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO), Weighted Mode, and Weighted Median Estimator (WME) methods, we performed a bidirectional two-sample Mendelian randomization (MR) analysis to explore the causal relationship between gut microbiota and inflammatory dermatosis. The genome-wide association study (GWAS) summary data of gut microbiota came from the MiBioGen consortium, while the GWAS summary data of inflammatory dermatosis (including psoriasis, AD, rosacea, vitiligo, acne, and eczema) came from the FinnGen consortium and IEU Open GWAS project. Cochran’s IVW Q test tested the heterogeneity among instrumental variables (IVs). The horizontal pleiotropy was tested by MR-Egger regression intercept analysis and MR-PRESSO analysis.ResultsEventually, the results indicated that 5, 16, 17, 11, 15, and 12 gut microbiota had significant causal effects on psoriasis, rosacea, AD, vitiligo, acne, and eczema, respectively, including 42 protective and 34 risk causal relationships. Especially, Lactobacilli and Bifidobacteria at the Family and Genus Level, as common probiotics, were identified as protective factors for the corresponding inflammatory dermatoses. The results of reverse MR analysis suggested a bidirectional causal effect between AD and genus Eubacterium brachy group, vitiligo and genus Ruminococcaceae UCG004. The causal relationship between gut microbiota and psoriasis, rosacea, acne, and eczema is unidirectional. There was no significant heterogeneity among these IVs. In conclusion, this bidirectional two-sample MR study identified 76 causal relationships between the gut microbiome and six inflammatory dermatoses, which may be helpful for the clinical prevention and treatment of inflammatory dermatoses.</p

Characterization of Bacterial Differences Induced by Cleft-Palate-Related Spatial Heterogeneity

Background: Cleft palate (CP) patients have a higher prevalence of oral and respiratory tract bacterial infections than the general population. Nevertheless, characteristics of bacterial differences induced by CP-related anatomical heterogeneity are unknown. Methods: In this study, we systematically described the characteristics of bacteria in the oral and nasal niches in healthy children, CP children, healthy adolescents, CP adolescents, and postoperative adolescents by 454-pyrosequencing technology (V3–V6) to determine bacterial differences induced by CP. Results: Due to the CP-induced variations in spatial structure, the early establishment of microecology in CP children was different from that in healthy children. Nasal bacterial composition showed greater changes than in the saliva. Moreover, such discrepancy also appeared in CP and postoperative adolescents who had even undergone surgery > 10 years previously. Interestingly, we found by Lefse analysis that part of bacterial biomarkers in the nasal cavity of CP subjects was common oral flora, suggesting bacterial translocation between the oral and nasal niches. Therefore, we defined the oral–nasal translocation bacteria as O-N bac. By comparing multiple groups, we took the intersection sets of O-N bacs selected from CP children, CP adolescents, and postoperative adolescents as TS O-N bacs with time–character, including Streptococcus, Gemella, Alloprevotella, Neisseria, Rothia, Actinomyces, and Veillonella. These bacteria were at the core of the nasal bacterial network in CP subjects, and some were related to infectious diseases. Conclusions: CP would lead to significant and long-term differences in oral and nasal flora. TS O-N bacs migrating from the oral to the nasal might be the key stone causing nasal flora dysbiosis in the CP patients