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Consistent segmentation of COVID-19 patient's CT scans across multiple time points is essential to assess disease progression and response to therapy accurately. Existing automatic and interactive segmentation models for medical images only use data from a single time point (static). However, valuable segmentation information from previous time points is often not used to aid the segmentation of a patient's follow-up scans. Also, fully automatic segmentation techniques frequently produce results that would need further editing for clinical use. In this work, we propose a new single network model for interactive segmentation that fully utilizes all available past information to refine the segmentation of follow-up scans. In the first segmentation round, our model takes 3D volumes of medical images from two-time points (target and reference) as concatenated slices with the additional reference time point segmentation as a guide to segment the target scan. In subsequent segmentation refinement rounds, user feedback in the form of scribbles that correct the segmentation and the target's previous segmentation results are additionally fed into the model. This ensures that the segmentation information from previous refinement rounds is retained. Experimental results on our in-house multiclass longitudinal COVID-19 dataset show that the proposed model outperforms its static version and can assist in localizing COVID-19 infections in patient's follow-up scans., Comment: 10 pages, 11 figures, 4 tables

Retailers face significant pressure to improve revenue, margins and market share by applying price optimisation models. These are mathematical models that calculate how demand varies at different price levels, then combine that data with information on costs and inventory levels to recommend prices that will improve revenue and profits. These models have been around for a while-so what is different now? We have identified three important changes: (1) Data: availability of internal and external real-time data such as traffic to a website, consumers making buy/no buy decisions and competitor pricing strategies; (2) Analytics: advances in machine learning and ease of access (R, Python) have enabled the development of systems that learn on the fly about consumer behaviour and preferences and generate effective estimates of demand-price relationships; and (3) Automation: increase in computing speed enables real-time optimisation of prices of hundreds of competing products sold by the same retailer. We take advantage of these new opportunities by showing how they were applied at Boston-based flash sales retailer Rue La La, online market maker Groupon, and the largest online retailer in Latin America, B2W Digital (B2W). While all these examples are of on-line businesses which have readily available data and can change prices dynamically, we have also implemented similar methods for brick-and-mortar retailed in applications such as promotional pricing, new product introduction, and assortment optimisation with similar business impacts. Thus, beyond applications to price optimisations, these new trends enable companies to revolutionise their business from procurement to supply chain all the way to revenue management. Keywords: analytics, machine learning, price theory, online retail, forecasting

Autism spectrum disorder (ASD) has been attributed to genetic and non-genetic risk factors. Of the non-genetic factors, prenatal and perinatal complications have been extensively investigated, though few associations have been replicated consistently. We selected 2,562 families with at least one individual with ASD and one unaffected sibling. We investigated the relationships between 29 prenatal and perinatal complications and ASD, while considering the influences of confounding factors, comorbid conditions, and different ASD definitions. Although many complications were associated with ASD in the pairwise comparisons, only haematological disorders of the newborn and lower Apgar scores remained significant after adjusting for the effects of the confounders. After removing individuals with congenital anomalies, only 5-minute Apgar scores were associated with ASD. In conclusion, after considering confounding effects and four ASD definitions, several perinatal complications were associated with ASD with moderate effect sizes. Furthermore, comorbid conditions with ASD appear to be intertwined in these relationships.

Autism spectrum disorder (ASD) has been attributed to genetic and non-genetic risk factors. Of the non-genetic factors, prenatal and perinatal complications have been extensively investigated, though few associations have been replicated consistently. We selected 2,562 families with at least one individual with ASD and one unaffected sibling. We investigated the relationships between 29 prenatal and perinatal complications and ASD, while considering the influences of confounding factors, comorbid conditions, and different ASD definitions. Although many complications were associated with ASD in the pairwise comparisons, only haematological disorders of the newborn and lower Apgar scores remained significant after adjusting for the effects of the confounders. After removing individuals with congenital anomalies, only 5-minute Apgar scores were associated with ASD. In conclusion, after considering confounding effects and four ASD definitions, several perinatal complications were associated with ASD with moderate effect sizes. Furthermore, comorbid conditions with ASD appear to be intertwined in these relationships.

Autism spectrum disorder (ASD) has been attributed to genetic and non-genetic risk factors. Of the non-genetic factors, prenatal and perinatal complications have been extensively investigated, though few associations have been replicated consistently. We selected 2,562 families with at least one individual with ASD and one unaffected sibling. We investigated the relationships between 29 prenatal and perinatal complications and ASD, while considering the influences of confounding factors, comorbid conditions, and different ASD definitions. Although many complications were associated with ASD in the pairwise comparisons, only haematological disorders of the newborn and lower Apgar scores remained significant after adjusting for the effects of the confounders. After removing individuals with congenital anomalies, only 5-minute Apgar scores were associated with ASD. In conclusion, after considering confounding effects and four ASD definitions, several perinatal complications were associated with ASD with moderate effect sizes. Furthermore, comorbid conditions with ASD appear to be intertwined in these relationships.

Autism spectrum disorder (ASD) has been attributed to genetic and non-genetic risk factors. Of the non-genetic factors, prenatal and perinatal complications have been extensively investigated, though few associations have been replicated consistently. We selected 2,562 families with at least one individual with ASD and one unaffected sibling. We investigated the relationships between 29 prenatal and perinatal complications and ASD, while considering the influences of confounding factors, comorbid conditions, and different ASD definitions. Although many complications were associated with ASD in the pairwise comparisons, only haematological disorders of the newborn and lower Apgar scores remained significant after adjusting for the effects of the confounders. After removing individuals with congenital anomalies, only 5-minute Apgar scores were associated with ASD. In conclusion, after considering confounding effects and four ASD definitions, several perinatal complications were associated with ASD with moderate effect sizes. Furthermore, comorbid conditions with ASD appear to be intertwined in these relationships.