Cambridge as a place in economics

Cambridge as a geographical reference often crops up in the characterisation of the economic theories and approaches that developed in Cambridge (UK) between the 1920s and the 1960s with the contribution of economists who did not always share the same interests, background or attitudes, but who all lived and worked – for considerable periods of time – in that particular corner of the world. In order to reconstruct the Cambridge of those years and explore the space it represented for economics we have selected a group of economists and a span of time – essentially between the two wars, with a few encroachments in the years following on the death of Keynes. Cambridge was not only a place, but also a play of magnetic forces, drawing together and driving apart, where ideas emerged from an environment formed through intense human and professional relations, a well defined cultural tradition and a way of its own of organising work and study. We present the dramatis personae and the background to their actions, and consider the characteristics of intellectual and personal communication on the basis of which we are led to define the Cambridge economists examined more as a `group' than a school

Recommendation Systems: An Insight Into Current Development and Future Research Challenges

Research on recommendation systems is swiftly producing an abundance of novel methods, constantly challenging the current state-of-the-art. Inspired by advancements in many related fields, like Natural Language Processing and Computer Vision, many hybrid approaches based on deep learning are being proposed, making solid improvements over traditional methods. On the downside, this flurry of research activity, often focused on improving over a small number of baselines, makes it hard to identify reference methods and standardized evaluation protocols. Furthermore, the traditional categorization of recommendation systems into content-based, collaborative filtering and hybrid systems lacks the informativeness it once had. With this work, we provide a gentle introduction to recommendation systems, describing the task they are designed to solve and the challenges faced in research. Building on previous work, an extension to the standard taxonomy is presented, to better reflect the latest research trends, including the diverse use of content and temporal information. To ease the approach toward the technical methodologies recently proposed in this field, we review several representative methods selected primarily from top conferences and systematically describe their goals and novelty. We formalize the main evaluation metrics adopted by researchers and identify the most commonly used benchmarks. Lastly, we discuss issues in current research practices by analyzing experimental results reported on three popular datasets

A Survey on Text Classification Algorithms: From Text to Predictions

In recent years, the exponential growth of digital documents has been met by rapid progress in text classification techniques. Newly proposed machine learning algorithms leverage the latest advancements in deep learning methods, allowing for the automatic extraction of expressive features. The swift development of these methods has led to a plethora of strategies to encode natural language into machine-interpretable data. The latest language modelling algorithms are used in conjunction with ad hoc preprocessing procedures, of which the description is often omitted in favour of a more detailed explanation of the classification step. This paper offers a concise review of recent text classification models, with emphasis on the flow of data, from raw text to output labels. We highlight the differences between earlier methods and more recent, deep learning-based methods in both their functioning and in how they transform input data. To give a better perspective on the text classification landscape, we provide an overview of datasets for the English language, as well as supplying instructions for the synthesis of two new multilabel datasets, which we found to be particularly scarce in this setting. Finally, we provide an outline of new experimental results and discuss the open research challenges posed by deep learning-based language models

Surgical resection of oral cancer: en-bloc versus discontinuous approach

Objectives: In the past literature agreed on treating oral carcinomas, using an \u201cen-bloc\u201d resection (EBR) but recently minimally invasive transoral surgery has spread as the preferable treatment for selected cases. This latter technique, which is performed with a discontinuous resection (DR), allows for a satisfactory postoperative quality of life (QoL) maintaining good survival rates. Materials and methods: In this study, we analyzed data about 147 surgically treated patients with oral cancer involving tongue and floor of the mouth. The sample was divided according to the surgical approach: EBR and DR group which were compared in terms of recurrence, overall survival, disease-free survival, and QoL. Results: In the DR group, survival analysis showed better results in term of survival, locoregional control, and postoperative anxiety, while the other QoL scores were similar in the two groups. Conclusion: The more invasive approach does not correlate to a better outcome. In selected cases, DR is an oncologically safe technique; EBR is still a valid option to treat advanced oral cancers

Disposable chlorine dioxide wipes for high-level disinfection in the ENT department: A systematic review

Background: Nasopharyngoscope reprocessing methods should be effective, rapid and reproducible with moderate cost. Tristel Trio Wipes system (TTWS) is a manual reprocessing method based on chlorine dioxide that has lately emerged in ENT department. This review aims to collect evidence on this system. Methods: The PubMed, Web of Science and Cochrane Library databases were searched for all the studies on TTWS or one of its components. Data were grouped according to the study type. Results: Ten articles were included in the review. TTWS ensured high-level disinfection in laboratory and clinical setting. Although the limitations of the manual systems, TTWS proved to be faster than automated endoscope reprocessing (AER) and safe for patients and health-care workers. TTWS represented cheaper system than AER or sheaths in low- and medium-volume centers. Conclusion: TTWS could be a valid, safe and fast HLD method for nasopharyngoscopes, with reasonable costs for medium-low reprocessing volumes

Wet-dry-wet drug screen leads to the synthesis of TS1, a novel compound reversing lung fibrosis through inhibition of myofibroblast differentiation

Therapies halting the progression of fibrosis are ineffective and limited. Activated myofibroblasts are emerging as important targets in the progression of fibrotic diseases. Previously, we performed a high-throughput screen on lung fibroblasts and subsequently demonstrated that the inhibition of myofibroblast activation is able to prevent lung fibrosis in bleomycin-treated mice. High-throughput screens are an ideal method of repurposing drugs, yet they contain an intrinsic limitation, which is the size of the library itself. Here, we exploited the data from our “wet” screen and used “dry” machine learning analysis to virtually screen millions of compounds, identifying novel anti-fibrotic hits which target myofibroblast differentiation, many of which were structurally related to dopamine. We synthesized and validated several compounds ex vivo (“wet”) and confirmed that both dopamine and its derivative TS1 are powerful inhibitors of myofibroblast activation. We further used RNAi-mediated knock-down and demonstrated that both molecules act through the dopamine receptor 3 and exert their anti-fibrotic effect by inhibiting the canonical transforming growth factor β pathway. Furthermore, molecular modelling confirmed the capability of TS1 to bind both human and mouse dopamine receptor 3. The anti-fibrotic effect on human cells was confirmed using primary fibroblasts from idiopathic pulmonary fibrosis patients. Finally, TS1 prevented and reversed disease progression in a murine model of lung fibrosis. Both our interdisciplinary approach and our novel compound TS1 are promising tools for understanding and combating lung fibrosis

Do demographic and clinical features and comorbidities affect the risk of spread to an additional body site in functional motor disorders?

The aim of this study is to assess changes in the body distribution and the semeiology of functional motor disorder (FMD) in patients who reported only one or more than one body site affected at FMD onset. Data were obtained from the Italian Registry of Functional Motor Disorders, which included patients with a diagnosis of clinically definite FMDs. The relationship between FMD features and spread to other body sites was estimated by multivariate Cox regression analysis. We identified 201 (49%) patients who reported only one body site affected at FMD onset and 209 (51%) who reported multiple body sites affected at onset. FMD spread from the initial site to another site in 43/201 (21.4%) patients over 5.7 ± 7.1 years in those with only one site affected at FMD onset; FMD spread to an another body site in 29/209 (13.8%) over 5.5 ± 6.5 years. The spread of FMD was associated with non-motor functional symptoms and psychiatric comorbidities only in the patients with one body site affected at FMD onset. Our findings provide novel insight into the natural history of FMD. The number of body sites affected at onset does not seem to have a consistent influence on the risk of spread. Furthermore, our findings suggest that psychiatric comorbidities and non-motor functional symptoms may predict the spread of FMD symptoms, at least in patients with one body site affected at onset