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The multidisciplinary team approach has been employed internationally for decades, as an attempt to bring collaborative decision-making and concentrate clinical experience from multiple specialties on single patient cases in a systematic fashion. Adoption is not yet worldwide, but is increasing. The role of the histopathologist is central, providing vital information and context to clinical diagnosis and management. Our review summarizes some of the most relevant research on the topic of MDT usage and efficacy in relation to breast cancer, attempting to draw together its advantages and challenges. It is hoped that this review will make a contribution to the current international literature regarding multidisciplinary approaches in breast cancer care.

In this work, we present a novel approach to understand the quality of public transit system in resource constrained regions using user-generated contents. With growing urban population, it is getting difficult to manage travel demand in an effective way. This problem is more prevalent in developing cities due to lack of budget and proper surveillance system. Due to resource constraints, developing cities have limited infrastructure to monitor transport services. To improve the quality and patronage of public transit system, authorities often use manual travel surveys. But manual surveys often suffer from quality issues. For example, respondents may not provide all the detailed travel information in a manual travel survey. The survey may have sampling bias. Due to close-ended design (specific questions in the questionnaire), lots of relevant information may not be captured in a manual survey process. To address these issues, we investigated if user-generated contents, for example, Twitter data, can be used to understand service quality in Greater Mumbai in India, which can complement existing manual survey process. To do this, we assumed that, if a tweet is relevant to public transport system and contains negative sentiment, then that tweet expresses user’s dissatisfaction towards the public transport service. Since most of the tweets do not have any explicit geolocation, we also presented a model that does not only extract users’ dissatisfaction towards public transit system but also retrieves the spatial context of dissatisfaction and the potential causes that affect the service quality. It is observed that a Random Forest-based model outperforms other machine learning models, while yielding 0.97 precision and 0.88 F1-score.

Understanding travel behavior is critical for an effective urban planning as well as for enabling various context-aware service provisions to support mobility as a service (MaaS). Both applications rely on the sensor traces generated by travellers’ smartphones. These traces can be used to interpret travel modes, both for generating automated travel diaries as well as for real-time travel mode detection. Current approaches segment a trajectory by certain criteria, e.g., drop in speed. However, these criteria are heuristic, and, thus, existing approaches are subjective and involve significant vagueness and uncertainty in activity transitions in space and time. Also, segmentation approaches are not suited for real time interpretation of open-ended segments, and cannot cope with the frequent gaps in the location traces. In order to address all these challenges a novel, state based bottom-up approach is proposed. This approach assumes a fixed atomic segment of a homogeneous state, instead of an event-based segment, and a progressive iteration until a new state is found. The research investigates how an atomic state-based approach can be developed in such a way that can work in real time, near-real time and offline mode and in different environmental conditions with their varying quality of sensor traces. The results show the proposed bottom-up model outperforms the existing event-based segmentation models in terms of adaptivity, flexibility, accuracy and richness in information delivery pertinent to automated travel behavior interpretation.

The last UK breast cancer (BC) human epidermal growth factor receptor 2 (HER2) testing guideline recommendations were published in 2015. Since then, new data and therapeutic strategies have emerged. The American Society of Clinical Oncology (ASCO)/College of American Pathologists (CAP) published a focused update in 2018 that reclassified in situ hybridisation (ISH) Group 2 (immunohistochemistry (IHC) score 2+and HER2/chromosome enumeration probe 17 (CEP17) ratio ≥2.0 and HER2 copy number <4.0 signals/cell), as well as addressed other concerns raised by previous guidelines. The present article further refines UK guidelines, with specific attention to definitions of HER2 status focusing on eight key areas: (1) HER2 equivocal (IHC 2+) and assignment of the ASCO/CAP ISH group 2 tumours; (2) the definition of the group of BCs with low IHC scores for HER2 with emphasis on the distinction between IHC score 1+ (HER2-Low) from HER2 IHC score 0 (HER2 negative); (3) reporting cases showing HER2 heterogeneity; (4) HER2 testing in specific settings, including on cytological material; (5) repeat HER2 testing, (6) HER2 testing turnaround time targets; (7) the potential role of next generation sequencing and other diagnostic molecular assays for routine testing of HER2 status in BC and (8) use of image analysis to score HER2 IHC. The two tiered system of HER2 assessment remains unchanged, with first line IHC and then ISH limited to IHC equivocal cases (IHC score 2+) but emerging data on the relationship between IHC scores and levels of response to anti-HER2 therapy are considered. Here, we present the latest UK recommendations for HER2 status evaluation in BC, and where relevant, the differences from other published guidelines.

Discrimination (for instance, along the lines of race or gender) is often prohibited in auctions. This is legally enforced by preventing the seller from explicitly biasing the rules in favor of bidders from certain groups (for example, by subsidizing their bids). In this paper, we study the efficacy of this policy in the context of a single object: independent private value setting with heterogeneous bidders. We show that restricting the seller to using an anonymous, sealed bid auction format (or, simply, a symmetric auction) imposes virtually no restriction on her ability to discriminate. Our results highlight that the discrepancy between the superficial impartiality of the auction rules and the resulting fairness of the outcome can be extreme.

Transport mode information is essential for understanding people’s movement behavior and travel demand estimation. Current approaches extract travel information once the travel is complete. Such approaches are limited in terms of generating just-in-time information for a number of mobility based applications, e.g., real time mode specific patronage estimation. In order to detect the transport modalities from GPS trajectories, various machine learning approaches have already been explored. However, the majority of them produce only a single conclusion from a given set of evidences, ignoring the uncertainty of any mode classification. Also, the existing machine learning approaches fall short in explaining their reasoning scheme. In contrast, a fuzzy expert system can explain its reasoning scheme in a human readable format along with a provision of inferring different outcome possibilities, but lacks the adaptivity and learning ability of machine learning. In this paper, a novel hybrid knowledge driven framework is developed by integrating a fuzzy logic and a neural network to complement each other’s limitations. Thus the aim of this paper is to automate the tuning process in order to generate an intelligent hybrid model that can perform effectively in near-real time mode detection using GPS trajectory. Tests demonstrate that a hybrid knowledge driven model works better than a purely knowledge driven model and at per the machine learning models in the context of transport mode detection.

* Context.--The International Collaboration on Cancer Reporting (ICCR), supported by major pathology and cancer organizations, aims at the standardization of evidence-based pathology reporting of different types of cancers, with the inclusion of all parameters deemed to be relevant for best patient care and future data collection. Lymph node metastasis is one of the most important prognostic factors in breast cancer. Objective.--To produce a histopathology reporting guide by a panel of recognized experts from the fields of pathology and surgery with elements deemed to be core (required) and noncore (recommended) to report when assessing regional lymph nodes of patients with breast cancer. Data Sources.--Published literature, previous guidelines/recommendations, and current cancer staging principles were the basis of the data set drafted by the expert panel. This was discussed in a series of teleconferences and email communications. The draft data set was then made available for public consultation through the ICCR Web site. After this consultation and ICCR ratification, the data set was finalized. Conclusions.--The ICCR has published a data set for the reporting of surgically removed lymph nodes (including sentinel lymph node biopsy, axillary lymph node dissection, targeted axillary surgery, and lymph node sampling specimens) for breast tumors. This is part of a series of 4 ICCR breast cancer-related data sets. It includes 10 core elements along with 2 noncore elements. This should allow for synoptic reporting, which is more precise, uniform, and complete than nonsynoptic reporting, and leads to improved patient outcomes. doi: 10.5858/arpa.2022-0060-RA

Background Wild waterfowl are considered as the natural reservoir for avian influenza (AI) viruses. Bangladesh has been experiencing highly pathogenic avian influenza (HPAI) outbreaks since 2007, mostly in chickens and occasionally in ducks. Ducks play an important role in the persistence and genetic recombination of AI viruses. This paper presents the results of serological and virological monitoring of AI in domestic ducks in 2013 in the north-east region of Bangladesh. Results A total of 871 and 662 serum samples and 909 and 302 pairs of cloacal and oropharyngeal swabs from domestic ducks of Mymensingh and Sylhet division, respectively, were analysed. Antibodies to type A influenza virus were detected by blocking ELISA in 60.73 and 47.73% serum samples of Mymensingh and Sylhet division, respectively. On haemagglutination-inhibition (HI) test 17.5% of ELISA positive serum samples were found to be seropositive to H5 avian influenza virus. Five cloacal swabs and one oropharyngeal swab were positive for M gene of type A influenza virus by real time RT-PCR (rRT-PCR), but all of them were negative for H5 influenza virus. Three of the six viruses were successfully characterized as H1N5, H2N5 and H7N5 subtype of AI virus, the other three remained uncharacterized. On sequencing and phylogenetic analysis the HA and NA genes were found to be of Eurasian avian lineage. The H7 virus had cleavage site motif of low pathogenic virus. Conclusions Low pathogenic avian influenza viruses were detected from apparently healthy domestic ducks. A small proportion of domestic ducks were found seropositive to H5 AI virus.

In breast cancer, the quality of the pathology services is of paramount importance as inevitably, the pathologist makes the confirmatory diagnosis and provides prognostic and predictive information, informing treatment plans directly. Various national and international organisations provide a pathology reporting minimum dataset (MDS) to ensure consistency in reporting. While the use of MDS promotes clarity, there may be specific areas requiring the pathologist’s input for individual patients and hence pathologists need to be aware of the clinical utility of pathology data to help tailor individualised patient treatment. In this article, we provide numerous examples of the role of pathology data in determining next steps in the patient pathway that are applicable to both the diagnostic and treatment pathways, including neoadjuvant treatment pathways. We also briefly discuss the important role and thereby the clinical utility of pathology data during the COVID-19 pandemic providing a template for the similar scenarios in the future if required.

We study dominant strategy incentive compatibility in a mechanism design setting with contingent contracts where the payoff of each agent is observed by the principal and can be contracted upon. Our main focus is on the class of linear contracts (one of the most commonly used contingent contracts) which consist of a transfer and a flat rate of profit sharing. We characterize outcomes implementable by linear contracts and provide a foundation for them by showing that, in finite type spaces, every social choice function that can be implemented using a more general nonlinear contingent contract can also be implemented using a linear contract. We then qualitatively describe the set of implementable outcomes. We show that a general class of social welfare criteria can be implemented. This class contains social choice functions (such as the Rawlsian) which cannot be implemented using (uncontingent) transfers. Under additional conditions, we show that only social choice functions in this class are implementable.