Prognostic value of the geriatric nutritional risk index in patients with non-metastatic clear cell renal cell carcinoma: a propensity score matching analysis.

BACKGROUND: This study aimed to investigate the prognostic value of the geriatric nutritional risk index (GNRI) in patients with non-metastatic clear cell renal cell carcinoma (ccRCC) who underwent nephrectomy. METHODS: Patients with non-metastatic ccRCC who underwent nephrectomy between 2013 and 2021 were analyzed retrospectively. The GNRI was calculated within one week before surgery. The optimal cut-off value of GNRI was determined using X-tile software, and the patients were divided into a low GNRI group and a high GNRI group. The Kaplan-Meier method was used to compare the overall survival (OS), cancer-specific survival (CSS) and recurrence-free survival (RFS) between the two groups. Univariate and multivariate Cox proportional hazard models were used to determine prognostic factors. In addition, propensity score matching (PSM) was performed with a matching ratio of 1:3 to minimize the influence of confounding factors. Variables entered into the PSM model were as follows: sex, age, history of hypertension, history of diabetes, smoking history, BMI, tumor sidedness, pT stage, Fuhrman grade, surgical method, surgical approach, and tumor size. RESULTS: A total of 645 patients were included in the final analysis, with a median follow-up period of 37 months (range: 1-112 months). The optimal cut-off value of GNRI was 98, based on which patients were divided into two groups: a low GNRI group (≤ 98) and a high GNRI group (> 98). Kaplan-Meier analysis showed that OS (P < 0.001), CSS (P < 0.001) and RFS (P < 0.001) in the low GNRI group were significantly worse than those in the high GNRI group. Univariate and multivariate Cox analysis showed that GNRI was an independent prognostic factor of OS, CSS and RFS. Even after PSM, OS (P < 0.05), CSS (P < 0.05) and RFS (P < 0.05) in the low GNRI group were still worse than those in the high GNRI group. In addition, we observed that a low GNRI was associated with poor clinical outcomes in elderly subgroup (> 65) and young subgroup (≤ 65), as well as in patients with early (pT1-T2) and low-grade (Fuhrman I-II) ccRCC. CONCLUSION: As a simple and practical tool for nutrition screening, the preoperative GNRI can be used as an independent prognostic indicator for postoperative patients with non-metastatic ccRCC. However, larger prospective studies are necessary to validate these findings.

A gut microbiota rheostat forecasts responsiveness to PD-L1 and VEGF blockade in mesothelioma.

Malignant mesothelioma is a rare tumour caused by asbestos exposure that originates mainly from the pleural lining or the peritoneum. Treatment options are limited, and the prognosis is dismal. Although immune checkpoint blockade (ICB) can improve survival outcomes, the determinants of responsiveness remain elusive. Here, we report the outcomes of a multi-centre phase II clinical trial (MiST4, NCT03654833) evaluating atezolizumab and bevacizumab (AtzBev) in patients with relapsed mesothelioma. We also use tumour tissue and gut microbiome sequencing, as well as tumour spatial immunophenotyping to identify factors associated with treatment response. MIST4 met its primary endpoint with 50% 12-week disease control, and the treatment was tolerable. Aneuploidy, notably uniparental disomy (UPD), homologous recombination deficiency (HRD), epithelial-mesenchymal transition and inflammation with CD68+ monocytes were identified as tumour-intrinsic resistance factors. The log-ratio of gut-resident microbial genera positively correlated with radiological response to AtzBev and CD8+ T cell infiltration, but was inversely correlated with UPD, HRD and tumour infiltration by CD68+ monocytes. In summary, a model is proposed in which both intrinsic and extrinsic determinants in mesothelioma cooperate to modify the tumour microenvironment and confer clinical sensitivity to AtzBev. Gut microbiota represent a potentially modifiable factor with potential to improve immunotherapy outcomes for individuals with this cancer of unmet need.

A Novel Multi-modal Population-graph based Framework for Patients of Esophageal Squamous Cell Cancer Prognostic Risk Prediction.

Prognostic risk prediction is pivotal for clinicians to appraise the patient's esophageal squamous cell cancer (ESCC) progression status precisely and tailor individualized therapy treatment plans. Currently, CT-based multi-modal prognostic risk prediction methods have gradually attracted the attention of researchers for their universality, which is also able to be applied in scenarios of preoperative prognostic risk assessment in the early stages of cancer. However, much of the current work focuses only on CT images of the primary tumor, ignoring the important role that CT images of lymph nodes play in prognostic risk prediction. Additionally, it is important to consider and explore the inter-patient feature similarity in prognosis when developing models. To solve these problems, we proposed a novel multi-modal population-graph based framework leveraging CT images including primary tumor and lymph nodes combined with clinical, hematology, and radiomics data for ESCC prognostic risk prediction. A patient population graph was constructed to excavate the homogeneity and heterogeneity of inter-patient feature embedding. Moreover, a novel node-level multi-task joint loss was proposed for graph model optimization through a supervised-based task and an unsupervised-based task. Sufficient experimental results show that our model achieved state-of-the-art performance compared with other baseline models as well as the gold standard on discriminative ability, risk stratification, and clinical utility. The core code is available at https://github.com/wuchengyu123/MPGSurv.

Long-short diffeomorphism memory network for weakly-supervised ultrasound landmark tracking.

Ultrasound is a promising medical imaging modality benefiting from low-cost and real-time acquisition. Accurate tracking of an anatomical landmark has been of high interest for various clinical workflows such as minimally invasive surgery and ultrasound-guided radiation therapy. However, tracking an anatomical landmark accurately in ultrasound video is very challenging, due to landmark deformation, visual ambiguity and partial observation. In this paper, we propose a long-short diffeomorphism memory network (LSDM), which is a multi-task framework with an auxiliary learnable deformation prior to supporting accurate landmark tracking. Specifically, we design a novel diffeomorphic representation, which contains both long and short temporal information stored in separate memory banks for delineating motion margins and reducing cumulative errors. We further propose an expectation maximization memory alignment (EMMA) algorithm to iteratively optimize both the long and short deformation memory, updating the memory queue for mitigating local anatomical ambiguity. The proposed multi-task system can be trained in a weakly-supervised manner, which only requires few landmark annotations for tracking and zero annotation for deformation learning. We conduct extensive experiments on both public and private ultrasound landmark tracking datasets. Experimental results show that LSDM can achieve better or competitive landmark tracking performance with a strong generalization capability across different scanner types and different ultrasound modalities, compared with other state-of-the-art methods.

Dark-field imaging and fluorescence dual-mode detection of microRNA-21 in living cells by core-satellite plasmonic nanoprobes.

The in situ precise quantification and simultaneous imaging of low abundance microRNAs (miRNAs) within living cells is critical for cancer diagnosis, yet it remains a significant challenge. Leveraging the excellent sensitivity and spatiotemporal resolution of dark-field microscopy (DFM) and fluorescence imaging, we have successfully devised a novel detection approach using dual-signal reporter probes (DSRPs). These probes allow for highly sensitive detection of miRNA-21 in living cells via toehold-mediated strand displacement cascades. The DSRPs were constructed by Au nanoparticles and Ag nanoclusters core-satellite nanostructures. After the recognition of miRNA-21, the strand displacement cascades were triggered, inducing the disassembly of the Au/Ag core-satellite nanostructure with apparent scattering intensity decrease and peak wavelength shifts. Additionally, the fluorescence of Ag clusters could be recovered and further enhanced when in close proximity to specific guanine-rich strands. The dual-signal response capability enables the accurate detection of miRNA-21 from 1 fM to 1 nM, with a limit of detection reached 0.75 fM. DFM and fluorescent imaging of living cells efficiently confirms the applicable detection of miRNA-21 in complex detection media. The biosensor based on DSRPs represents a promising nanoplatform for visual monitoring and imaging of biomolecules in living cells, even at the single particle level.

Transformaer-based model for lung adenocarcinoma subtypes.

BACKGROUND: Lung cancer has the highest morbidity and mortality rate among all types of cancer. Histological subtypes serve as crucial markers for the development of lung cancer and possess significant clinical values for cancer diagnosis, prognosis, and prediction of treatment responses. However, existing studies only dichotomize normal and cancerous tissues, failing to capture the unique characteristics of tissue sections and cancer types. PURPOSE: Therefore, we have pioneered the classification of lung adenocarcinoma (LAD) cancer tissues into five subtypes (acinar, lepidic, micropapillary, papillary, and solid) based on section data in whole-slide image sections. In addition, a novel model called HybridNet was designed to improve the classification performance. METHODS: HybridNet primarily consists of two interactive streams: a Transformer and a convolutional neural network (CNN). The Transformer stream captures rich global representations using a self-attention mechanism, while the CNN stream extracts local semantic features to optimize image details. Specifically, during the dual-stream parallelism, the feature maps of the Transformer stream as weights are weighted and summed with those of the CNN stream backbone; at the end of the parallelism, the respective final features are concatenated to obtain more discriminative semantic information. RESULTS: Experimental results on a private dataset of LAD showed that HybridNet achieved 95.12% classification accuracy, and the accuracy of five histological subtypes (acinar, lepidic, micropapillary, papillary, and solid) reached 94.5%, 97.1%, 94%, 91%, and 99% respectively; the experimental results on the public BreakHis dataset show that HybridNet achieves the best results in three evaluation metrics: accuracy, recall and F1-score, with 92.40%, 90.63%, and 91.43%, respectively. CONCLUSIONS: The process of classifying LAD into five subtypes assists pathologists in selecting appropriate treatments and enables them to predict tumor mutation burden (TMB) and analyze the spatial distribution of immune checkpoint proteins based on this and other clinical data. In addition, the proposed HybridNet fuses CNN and Transformer information several times and is able to improve the accuracy of subtype classification, and also shows satisfactory performance on public datasets with some generalization ability.

Sketch-Supervised Histopathology Tumour Segmentation: Dual CNN-Transformer With Global Normalised CAM.

Deep learning methods are frequently used in segmenting histopathology images with high-quality annotations nowadays. Compared with well-annotated data, coarse, scribbling-like labelling is more cost-effective and easier to obtain in clinical practice. The coarse annotations provide limited supervision, so employing them directly for segmentation network training remains challenging. We present a sketch-supervised method, called DCTGN-CAM, based on a dual CNN-Transformer network and a modified global normalised class activation map. By modelling global and local tumour features simultaneously, the dual CNN-Transformer network produces accurate patch-based tumour classification probabilities by training only on lightly annotated data. With the global normalised class activation map, more descriptive gradient-based representations of the histopathology images can be obtained, and inference of tumour segmentation can be performed with high accuracy. Additionally, we collect a private skin cancer dataset named BSS, which contains fine and coarse annotations for three types of cancer. To facilitate reproducible performance comparison, experts are also invited to label coarse annotations on the public liver cancer dataset PAIP2019. On the BSS dataset, our DCTGN-CAM segmentation outperforms the state-of-the-art methods and achieves 76.68 % IOU and 86.69 % Dice scores on the sketch-based tumour segmentation task. On the PAIP2019 dataset, our method achieves a Dice gain of 8.37 % compared with U-Net as the baseline network.

GMRE-iUnet: Isomorphic Unet fusion model for PET and CT lung tumor images.

Lung tumor PET and CT image fusion is a key technology in clinical diagnosis. However, the existing fusion methods are difficult to obtain fused images with high contrast, prominent morphological features, and accurate spatial localization. In this paper, an isomorphic Unet fusion model (GMRE-iUnet) for lung tumor PET and CT images is proposed to address the above problems. The main idea of this network is as following: Firstly, this paper constructs an isomorphic Unet fusion network, which contains two independent multiscale dual encoders Unet, it can capture the features of the lesion region, spatial localization, and enrich the morphological information. Secondly, a Hybrid CNN-Transformer feature extraction module (HCTrans) is constructed to effectively integrate local lesion features and global contextual information. In addition, the residual axial attention feature compensation module (RAAFC) is embedded into the Unet to capture fine-grained information as compensation features, which makes the model focus on local connections in neighboring pixels. Thirdly, a hybrid attentional feature fusion module (HAFF) is designed for multiscale feature information fusion, it aggregates edge information and detail representations using local entropy and Gaussian filtering. Finally, the experiment results on the multimodal lung tumor medical image dataset show that the model in this paper can achieve excellent fusion performance compared with other eight fusion models. In CT mediastinal window images and PET images comparison experiment, AG, EI, QAB/F, SF, SD, and IE indexes are improved by 16.19%, 26%, 3.81%, 1.65%, 3.91% and 8.01%, respectively. GMRE-iUnet can highlight the information and morphological features of the lesion areas and provide practical help for the aided diagnosis of lung tumors.

Robust nontarget DNA-triggered catalytic hairpin assembly amplification strategy for the improved sensing of microRNA in complex biological matrices.

A simple but robust fluorescence strategy based on a nontarget DNA-triggered catalytic hairpin assembly (CHA) was constructed to probe microRNA-21 (miR-21). A short ssDNA rather than degradable target miRNA was employed as an initiator. Two molecular beacons needed to assist the CHA process were simplified to avoid unfavorable nonspecific interactions. In the presence of the target, the initiator was released from a partially duplex and triggered the cyclic CHA reaction, resulting in a significantly amplified optical readout. A wide linear range from 0.1 pM to 1000 pM for the sensing of miR-21 in buffer was achieved with a low detection limit of 0.76 pM. Fortunately, this strategy demonstrated an obviously improved performance for miR-21 detection in diluted serum. The fluorescence signals were enhanced remarkably and the sensitivity was further improved to 0.12 pM in 10% serum. The stability for miR-21 quantification and the capability for the analysis of single nucleotide polymorphisms (SNPs) were also improved greatly. More importantly, the biosensor could be applied to image miR-21 in different living tumor cells with high resolution, illustrating its promising potential for the assay of miRNAs in various complex situations for early-stage disease diagnosis and biological studies in cells.

Characterization of renal artery variation in patients with clear cell renal cell carcinoma and the predictive value of accessory renal artery in pathological grading of renal cell carcinoma: a retrospective and observational study.

OBJECTIVE: To explore the characteristics of renal artery variation in patients with renal cell carcinoma and to evaluate the predicting value of accessory renal artery in the pathological grading of renal cell carcinoma. METHODS: The clinicopathological data of patients with clear cell renal cell carcinoma diagnosed in the Department of Urology of the First Hospital of Shanxi Medical University from September 2019 to March 2023 were retrospectively analyzed. All patients underwent visual three-dimensional model reconstruction from computed tomography images. All kidneys were divided into two groups: the affected kidney and the healthy kidney, and the incidence of renal artery variation in the two groups was analyzed. Then, according to the existence of accessory renal artery in the affected kidney, the patients were divided into two groups, and the relationship between accessory renal artery and clinicopathological features of patients with clear cell renal cell carcinoma was analyzed. Finally, univariate and multivariate logistic regression analyses were performed to determine the predictors of Fuhrman grading of clear cell renal cell carcinoma, and the predictive ability of the model was evaluated by the receiver operating characteristic curve. RESULTS: The incidence of renal artery variation and accessory renal artery in the affected kidney was significantly higher than them in the healthy kidney. The patients with accessory renal artery in the affected kidney had larger tumor maximum diameter, higher Fuhrman grade and more exophytic growth. The presence of accessory renal artery on the affected kidney and the maximum diameter of tumor are independent predictors of high-grade renal cell carcinoma. The receiver operating characteristic curve suggests that the model has a good predictive ability. CONCLUSION: The existence of accessory renal artery on the affected kidney may be related to the occurrence and development of clear cell renal cell carcinoma, and can better predict Fuhrman grade of clear cell renal cell carcinoma. The finding provides a reference for the future diagnostic evaluation of RCC, and provides a new direction for the study of the pathogenesis of RCC.

Top 100 most-cited articles on renal cell carcinoma: A bibliometric analysis.

BACKGROUND: To analyze the top 100 most-cited articles on renal cell carcinoma (RCC) using bibliometric methods based on the Web of Science core collection database and to explore the research status, hotspots, and emerging trends in RCC. METHODS: The literature on RCC was searched in the Web of Science core collection database using a specific search strategy, and the types of literature were limited to articles and reviews, with no restrictions to language and publication date. The top 100 articles with the highest number of citations were extracted after the manual screening. The publication year, the number of citations, authors, country, institution, journal, and keywords of these articles were collected and analyzed. Descriptive statistics and visual analysis were performed using Microsoft Excel, VOSviewer, CiteSpace, R, and SPSS. RESULTS: The number of citations of the top 100 articles varied from 541 to 4530, with a median citation count of 807.5, and the citation rates ranged from 13.8 to 448.4 citations per year. Motzer RJ (n = 22), Escudier B (n = 13), Rini BI (n = 13), and Hutson TE (n = 11) were major contributors to this research area, with Motzer RJ publishing 16 articles as the first author. The US (n = 73), France (n = 5), Canada (n = 4), and Sweden (n = 4) were the leading countries for RCC studies. MEMORIAL SLOAN KETTERING CANCER CENTER (n = 22) was the institution with the highest number of publications. These 100 articles were derived from 24 journals, and the New England Journal of Medicine had the largest number of articles published (n = 18, impact factor = 91.245). The keyword co-occurrence network analysis showed that research hotspots in this field included molecular mechanisms of RCC development and progression, surgical treatment, targeted drug-related clinical trials, and immunotherapy. CONCLUSION: We analyzed the top 100 articles with the highest number of citations in the field of RCC and identified the influential authors, countries, institutions, and journals in this field. This study also presented the current research status, hotspots, and future trends in RCC.

Preoperative serum low-density lipoprotein cholesterol is an independent prognostic factor in patients with renal cell carcinoma after nephrectomy.

BACKGROUND: Little is known about the association between the preoperative low-density lipoprotein cholesterol (LDL-C) level and prognosis in patients with renal cell carcinoma (RCC) after nephrectomy, and its prognostic value needs to be elucidated. METHODS: The clinical and follow-up data of 737 RCC patients who underwent nephrectomy were retrospectively analyzed. The optimal cut-off LDL-C level was determined using X-tile, and then patients were divided into low and high LDL-C groups. The association between LDL-C levels and survival of RCC patients was assessed using the Kaplan-Meier method and Cox regression analysis. RESULTS: The optimal cut-off LDL-C level was 1.93 mmol/L, and patients were divided into the low (≤ 1.93 mmol/L) and high LDL-C (> 1.93 mmol/L) groups. The Kaplan-Meier analysis showed that patients in the low LDL-C group had significantly shorter overall survival (OS), cancer-specific survival (CSS) and recurrence-free survival (RFS) than those in the high LDL-C group (P = 0.001, P = 0.001, and P = 0.003, respectively). The COX univariate analysis showed that the preoperative LDL-C level was closely associated with OS, CSS, and RFS in RCC patients (P = 0.002, P = 0.003, and P = 0.005, respectively). The multivariate analysis showed that the preoperative LDL-C level was an independent factor for predicting survival (OS, CSS and RFS) in RCC patients after nephrectomy. The low preoperative LDL-C levels predicted worse OS (hazard ratio [HR]: 2.337; 95% confidence interval [CI]: 1.192-4.581; P = 0.013), CSS (HR: 3.347; 95% CI: 1.515-7.392; P = 0.003), and RFS (HR: 2.207; 95% CI: 1.178-4.132; P = 0.013). CONCLUSIONS: The preoperative LDL-C level is an independent factor for the prognosis of RCC patients after nephrectomy, and low preoperative LDL-C levels predict worse survival (OS, CSS, and RFS).

Exploring the mechanistic pathways of how social network influences social norms in adolescent smoking prevention interventions.

We know little about how smoking prevention interventions might leverage social network structures to enhance protective social norms. In this study we combined statistical and network science methods to explore how social networks influence social norms related to adolescent smoking in school-specific settings in Northern Ireland and Colombia. Pupils (12-15 years old) participated in two smoking prevention interventions in both countries (n = 1344). A Latent Transition Analysis identified three groups characterized by descriptive and injunctive norms towards smoking. We employed a Separable Temporal Random Graph Model to analyze homophily in social norms and conducted a descriptive analysis of the changes in the students' and their friends' social norms over time to account for social influence. The results showed that students were more likely to be friends with others who had social norms against smoking. However, students with social norms favorable towards smoking had more friends with similar views than the students with perceived norms against smoking, underlining the importance of network thresholds. Our results support the notation that the ASSIST intervention takes advantage of friendship networks to leverage greater change in the students' smoking social norms than the Dead Cool intervention, reiterating that social norms are subject to social influence.

Evaluation of the metabolism of PEP06, an endostatin-RGDRGD 30-amino-acid polypeptide and a promising novel drug for targeting tumor cells.

PEP06 is a novel endostatin-Arg-Gly-Asp-Arg-Gly-Asp (RGDRGD) 30-amino-acid polypeptide featuring a terminally fused RGDRGD hexapeptide at the N terminus. The active endostatin fragment of PEP06 directly targets tumor cells and exerts an antitumoral effect. However, little is known about the kinetics and degradation products of PEP06 in vitro or in vivo. In this study, we investigated the in vitro metabolic stability of PEP06 after it was incubated with living cells obtained from animals of different species; we further identified the degradation characteristics of its cleavage products. PEP06 underwent rapid enzymatic degradation in multiple types of living cells, and the liver, kidney, and blood play important roles in the metabolism and clearance of the peptides resulting from the molecular degradation of PEP06. We identified metabolites of PEP06 using full-scan mass spectrometry (MS) and tandem MS (MS2), wherein 43 metabolites were characterized and identified as the degradation metabolites from the parent peptide, formed by successive losses of amino acids. The metabolites were C and N terminal truncated products of PEP06. The structures of 11 metabolites (M6, M7, M16, M17, M21, M25, M33, M34, M39, M40, and M42) were further confirmed by comparing the retention times of similar full MS spectrum and MS2 spectrum information with reference standards for the synthesized metabolites. We have demonstrated the metabolic stability of PEP06 in vitro and identified a series of potentially bioactive downstream metabolites of PEP06, which can support further drug research.

Bilateral occurrence of sperm granulomas in the left spermatic cord and on the right epididymis: A case report.

BACKGROUND: Sperm granuloma is a rare disease in clinical andrology and its incidence is still unclear worldwide. According to the existing literature, sperm granuloma often occurs unilaterally. Clinical and ultrasound features are similar to epididymal tuberculosis, chronic epididymitis and other diseases. Sperm granuloma is usually diagnosed based on postoperative histopathological and immunohistochemical examination. CASE SUMMARY: A 46-year-old man was admitted to the hospital due to the presence of a left scrotal mass for 3 mo and aggravation of pain for 1 wk. The lesions at both sites were surgically resected. Postoperative pathological examination showed that the left spermatic cord mass and the right epididymal mass were consistent with sperm granuloma. The sperm granulomas then recurred 3 mo after surgery. There is little change in the local mass so far. CONCLUSION: The case report is helpful for our understanding of this disease. In clinical diagnosis, it should be distinguished from epididymal tuberculosis, chronic epididymitis and other diseases. Color Doppler ultrasound can be used as a preferred examination method but postoperative pathological examination is still needed for diagnosis.

CANet: Context Aware Network for Brain Glioma Segmentation.

Automated segmentation of brain glioma plays an active role in diagnosis decision, progression monitoring and surgery planning. Based on deep neural networks, previous studies have shown promising technologies for brain glioma segmentation. However, these approaches lack powerful strategies to incorporate contextual information of tumor cells and their surrounding, which has been proven as a fundamental cue to deal with local ambiguity. In this work, we propose a novel approach named Context-Aware Network (CANet) for brain glioma segmentation. CANet captures high dimensional and discriminative features with contexts from both the convolutional space and feature interaction graphs. We further propose context guided attentive conditional random fields which can selectively aggregate features. We evaluate our method using publicly accessible brain glioma segmentation datasets BRATS2017, BRATS2018 and BRATS2019. The experimental results show that the proposed algorithm has better or competitive performance against several State-of-The-Art approaches under different segmentation metrics on the training and validation sets.

Siamese Local and Global Networks for Robust Face Tracking.

Convolutional neural networks (CNNs) have achieved great success in several face-related tasks, such as face detection, alignment and recognition. As a fundamental problem in computer vision, face tracking plays a crucial role in various applications, such as video surveillance, human emotion detection and human-computer interaction. However, few CNN-based approaches are proposed for face (bounding box) tracking. In this paper, we propose a face tracking method based on Siamese CNNs, which takes advantages of powerful representations of hierarchical CNN features learned from massive face images. The proposed method captures discriminative face information at both local and global levels. At the local level, representations for attribute patches (i.e:, eyes, nose and mouth) are learned to distinguish a face from another one, which are robust to pose changes and occlusions. At the global level, representations for each whole face are learned, which take into account the spatial relationships among local patches and facial characters, such as skin color and nevus. In addition, we build a new largescale challenging face tracking dataset to evaluate face tracking methods and to facilitate the research forward in this field. Extensive experiments on the collected dataset demonstrate the effectiveness of our method in comparison to several state-of-theart visual tracking methods.

MECHANISMS Study: Using Game Theory to Assess the Effects of Social Norms and Social Networks on Adolescent Smoking in Schools-Study Protocol.

This proof of concept study harnesses novel transdisciplinary insights to contrast two school-based smoking prevention interventions among adolescents in the UK and Colombia. We compare schools in these locations because smoking rates and norms are different, in order to better understand social norms based mechanisms of action related to smoking. We aim to: (1) improve the measurement of social norms for smoking behaviors in adolescents and reveal how they spread in schools; (2) to better characterize the mechanisms of action of smoking prevention interventions in schools, learning lessons for future intervention research. The A Stop Smoking in Schools Trial (ASSIST) intervention harnesses peer influence, while the Dead Cool intervention uses classroom pedagogy. Both interventions were originally developed in the UK but culturally adapted for a Colombian setting. In a before and after design, we will obtain psychosocial, friendship, and behavioral data (e.g., attitudes and intentions toward smoking and vaping) from ~300 students in three schools for each intervention in the UK and the same number in Colombia (i.e., ~1,200 participants in total). Pre-intervention, participants take part in a Rule Following task, and in Coordination Games that allow us to assess their judgments about the social appropriateness of a range of smoking-related and unrelated behaviors, and elicit individual sensitivity to social norms. After the interventions, these behavioral economic experiments are repeated, so we can assess how social norms related to smoking have changed, how sensitivity to classroom and school year group norms have changed and how individual changes are related to changes among friends. This Game Theoretic approach allows us to estimate proxies for norms and norm sensitivity parameters and to test for the influence of individual student attributes and their social networks within a Markov Chain Monte Carlo modeling framework. We identify hypothesized mechanisms by triangulating results with qualitative data from participants. The MECHANISMS study is innovative in the interplay of Game Theory and longitudinal social network analytical approaches, and in its transdisciplinary research approach. This study will help us to better understand the mechanisms of smoking prevention interventions in high and middle income settings.

Gradient vector flow with mean shift for skin lesion segmentation.

Image segmentation is an important task in the analysis of dermoscopy images since the extraction of skin lesion borders provides important cues for accurate diagnosis. In recent years, gradient vector flow based algorithms have demonstrated their merits in image segmentation. However, due to the compromise of internal and external energy forces within the partial differential equation these methods commonly lead to under- or over-segmentation problems. In this paper, we introduce a new mean shift based gradient vector flow (GVF) algorithm that drives the internal/external energies towards the correct direction. The proposed segmentation method incorporates a mean shift operation within the standard GVF cost function. Theoretical analysis proves that the proposed algorithm converges rapidly, while experimental results on a large set of diverse dermoscopy images demonstrate that the presented method accurately determines skin lesion borders in dermoscopy images.