Predicting Alignment Risk to Prevent Localization Failure

During localization and mapping the success of point cloud registration can be compromised when there is an absence of geometric features or constraints in corridors or across doorways, or when the volumes scanned only partly overlap, due to occlusions or constrictions between subsequent observations. This work proposes a strategy to predict and prevent laser-based localization failure. Our solution relies on explicit analysis of the point cloud content prior to registration. A model predicting the risk of a failed alignment is learned by analysing the degree of spatial overlap between two input point clouds and the geometric constraints available within the region of overlap. We define a novel measure of alignability for these constraints. The method is evaluated against three real-world datasets and compared to baseline approaches. The experiments demonstrate how our approach can help improve the reliability of laser-based localization during exploration of unknown and cluttered man-made environments

The role of supply chain integration in achieving competitive advantage: A study of UK automobile manufacturers

The competitive nature of the global automobile industry has resulted in a battle for efficiency and consistency in supply chain management (SCM). For manufacturers, the diversified network of suppliers represents more than just a production system; it is a strategic asset that must be managed, evaluated, and revised in order to attain competitive advantage. One capability that has become an increasingly essential means of alignment and assessment is supply chain integration (SCI). Through such practices, manufacturers create informational capital that is inimitable, yet transferrable, allowing suppliers to participate in a mutually-beneficial system of performance-centred outcomes. From cost reduction to time improvements to quality control, the benefits of SCI extend throughout the supply chain lifecycle, providing firms with improved predictability, flexibility, and responsiveness. Yet in spite of such benefits, key limitations including exposure to risks, supplier failures, or changing competitive conditions may expose manufacturers to a vulnerable position that can severely impact value and performance. The current study summarizes the perspectives and predictions of managers within the automobile industry in the UK, highlighting a dynamic model of interdependency and interpolation that embraces SCI as a strategic resource. Full commitment to integration is critical to achieving improved outcomes and performance; therefore, firms seeking to integrate throughout their extended supply chain must be willing to embrace a less centralized locus of control

Reliable Monte Carlo Localization for Mobile Robots

Reliability is a key factor for realizing safety guarantee of full autonomous robot systems. In this paper, we focus on reliability in mobile robot localization. Monte Carlo localization (MCL) is widely used for mobile robot localization. However, it is still difficult to guarantee its safety because there are no methods determining reliability for MCL estimate. This paper presents a novel localization framework that enables robust localization, reliability estimation, and quick re-localization, simultaneously. The presented method can be implemented using similar estimation manner to that of MCL. The method can increase localization robustness to environment changes by estimating known and unknown obstacles while performing localization; however, localization failure of course occurs by unanticipated errors. The method also includes a reliability estimation function that enables us to know whether localization has failed. Additionally, the method can seamlessly integrate a global localization method via importance sampling. Consequently, quick re-localization from failures can be realized while mitigating noisy influence of global localization. Through three types of experiments, we show that reliable MCL that performs robust localization, self-failure detection, and quick failure recovery can be realized

Characterizing pre-transplant and post-transplant kidney rejection risk by B cell immune repertoire sequencing.

Studying immune repertoire in the context of organ transplant provides important information on how adaptive immunity may contribute and modulate graft rejection. Here we characterize the peripheral blood immune repertoire of individuals before and after kidney transplant using B cell receptor sequencing in a longitudinal clinical study. Individuals who develop rejection after transplantation have a more diverse immune repertoire before transplant, suggesting a predisposition for post-transplant rejection risk. Additionally, over 2 years of follow-up, patients who develop rejection demonstrate a specific set of expanded clones that persist after the rejection. While there is an overall reduction of peripheral B cell diversity, likely due to increased general immunosuppression exposure in this cohort, the detection of specific IGHV gene usage across all rejecting patients supports that a common pool of immunogenic antigens may drive post-transplant rejection. Our findings may have clinical implications for the prediction and clinical management of kidney transplant rejection

Emerging evidence for CHFR as a cancer biomarker : from tumor biology to precision medicine

Novel insights in the biology of cancer have switched the paradigm of a "one-size-fits-all" cancer treatment to an individualized biology-driven treatment approach. In recent years, a diversity of biomarkers and targeted therapies has been discovered. Although these examples accentuate the promise of personalized cancer treatment, for most cancers and cancer subgroups no biomarkers and effective targeted therapy are available. The great majority of patients still receive unselected standard therapies with no use of their individual molecular characteristics. Better knowledge about the underlying tumor biology will lead the way toward personalized cancer treatment. In this review, we summarize the evidence for a promising cancer biomarker: checkpoint with forkhead and ring finger domains (CHFR). CHFR is a mitotic checkpoint and tumor suppressor gene, which is inactivated in a diverse group of solid malignancies, mostly by promoter CpG island methylation. CHFR inactivation has shown to be an indicator of poor prognosis and sensitivity to taxane-based chemotherapy. Here we summarize the current knowledge of altered CHFR expression in cancer, the impact on tumor biology and implications for personalized cancer treatment

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Syngeneic animal models of tobacco-associated oral cancer reveal the activity of in situ anti-CTLA-4.

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. Tobacco use is the main risk factor for HNSCC, and tobacco-associated HNSCCs have poor prognosis and response to available treatments. Recently approved anti-PD-1 immune checkpoint inhibitors showed limited activity (≤20%) in HNSCC, highlighting the need to identify new therapeutic options. For this, mouse models that accurately mimic the complexity of the HNSCC mutational landscape and tumor immune environment are urgently needed. Here, we report a mouse HNSCC model system that recapitulates the human tobacco-related HNSCC mutanome, in which tumors grow when implanted in the tongue of immunocompetent mice. These HNSCC lesions have similar immune infiltration and response rates to anti-PD-1 (≤20%) immunotherapy as human HNSCCs. Remarkably, we find that >70% of HNSCC lesions respond to intratumoral anti-CTLA-4. This syngeneic HNSCC mouse model provides a platform to accelerate the development of immunotherapeutic options for HNSCC

Restriction Enzyme Generated Next-Generation Sequencing Libraries and Genetic Risk Modifiers of BRCA1 Mutation Carriers

Next-generation sequencing (NGS) is a high throughput technique used to sequence large amounts of DNA in a short amount of time. However, a limitation to NGS is that the generated data is in a single consensus sequence without distinguishing between variants on homologous chromosomes. Separating or phasing the variants from the maternal and paternal chromosomes can provide information about the genetic origin of disease and information about how DNA nucleotide alterations interact in cis. This dissertation explores a new technical method of using restriction enzymes during NGS library preparation and its ability to increase the amount of phasing information that can be derived from NGS data. This study provides evidence that increasing the fragment size of NGS libraries can increase the amount of variant phasing information derived from NGS data. BRCA1 is a well-known tumor suppressor that, when mutated, predisposes the mutation carrier to breast cancer. BRCA1 mutation carriers have a 44-75% risk of developing breast cancer by age 70. In this study, we used next-generation sequencing data to identify germline genetic variants that modify the risk of breast cancer in BRCA1 mutation carriers. With the use of both biological and statistical filters, five variants were identified that changed breast cancer risk in BRCA1 mutation carriers. Furthermore, it was shown that two of the affected genes alter the growth of BRCA1 mutation breast cell lines. Perhaps, more importantly, the two variants were shown to alter the function of the affected genes. This is the first study to provide functional evidence on how common genetic variants can modify the risk of breast cancer in BRCA1 mutation carriers