Rural Areas on Their Way to a Smart Village - Experiences from Living Labs in Bavaria

This paper presents an overview of the approaches and experiences from existing living labs: german rural villages in which several digital solutions had been developed and implemented. The test villages have been selected based on a competition and are funded by the Bavarian state government in the project Digitales Dorf (Engl. digital village). Started in 2016 several measures had been taken to push digitalization in these rural areas with the goal to create equivalent living conditions to urban areas. The research question is how digitalization enhances the value of rural areas and which methods can be used to overcome the digitalization gap with a transferable and simple approach. This paper focuses on the transformation process rather than digital solutions, and presents requirements and best practices to promote digitalization in rural environments, their municipal processes and traditional approaches in everyday lif

Using surveillance data to monitor entry into care of newly diagnosed HIV-infected persons: San Francisco, 2006–2007

<p>Abstract</p> <p>Background</p> <p>Linkage to care after HIV diagnosis is associated with both clinical and public health benefits. However, ensuring and monitoring linkage to care by public health departments has proved to be a difficult task. Here, we report the usefulness of routine monitoring of CD4 T cell counts and plasma HIV viral load as measures of entry into care after HIV diagnosis.</p> <p>Methods</p> <p>Since July 1, 2006, the San Francisco Department of Public Health (SFDPH) incorporated monitoring initial primary care visit into standard HIV public health investigation for newly diagnosed HIV-infected patients in select clinics. Entry into care was defined as having at least one visit to a primary HIV care provider after the initial diagnosis of HIV infection. Investigators collected reports from patients, medical providers, laboratories and reviewed medical records to determine the date of the initial health care visit after HIV diagnosis. We identified factors associated with increased likelihood of entering care after HIV diagnosis.</p> <p>Results</p> <p>One -hundred and sixty new HIV-infected cases were diagnosed between July 1, 2006 and June 30, 2007. Routine surveillance methods found that 101 of those cases entered HIV medical care and monitoring of CD4 T cell counts and plasma HIV viral load confirmed entry to care of 25 more cases, representing a 25% increase over routine data collection methods. We found that being interviewed by a public health investigator was associated with higher odds of entry into care after HIV diagnosis (OR 18.86 [1.83–194.80], p = .001) compared to cases not interviewed. Also, HIV diagnosis at the San Francisco county hospital versus diagnosis at the county municipal STD clinic was associated with higher odds of entry into care (OR 101.71 [5.29–1952.05], p < .001).</p> <p>Conclusion</p> <p>The time from HIV diagnosis to initial CD4 T cell count, CD4 T cell value and HIV viral load testing may be appropriate surveillance measures for evaluating entry into care, as well as performance outcomes for local public health departments' HIV testing programs. Case investigation performed by the public health department or case management by clinic staff was associated with increased and shorter time to entry into HIV medical care.</p

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Optimizing Inventory Replenishment for Seasonal Demand with Discrete Delivery Times

This study investigates replenishment planning in the case of discrete delivery time, where demand is seasonal. The study is motivated by a case study of a soft drinks company in Germany, where data concerning demand are obtained for a whole year. The investigation focused on one type of apple juice that experiences a peak in demand during the summer. The lot-sizing problem reduces the ordering and the total inventory holding costs using a mixed-integer programming (MIP) model. Both the lot size and cycle time are variable over the planning horizon. To obtain results faster, a dynamic programming (DP) model was developed, and run using R software. The model was run every week to update the plan according to the current inventory size. The DP model was run on a personal computer 35 times to represent dynamic planning. The CPU time was only a few seconds. Results showed that initial planning is difficult to follow, especially after week 30, and the service level was only 92%. Dynamic planning reached a higher service level of 100%. This study is the first to investigate discrete delivery times, opening the door for further investigations in the future in other industries

Dynamic Lead-Time Forecasting Using Machine Learning in a Make-to-Order Supply Chain

This paper investigates the dynamic forecasting of lead-time, which can be performed by a logistics company for optimizing temporal shipment consolidation. Shipment consolidation is usually utilized to reduce outbound shipments costs, but it can increase the lead time. Forecasting in this paper is performed in a make-to-order supply chain using real data, where the logistics company does not know the internal production data of manufacturers. Forecasting was performed in several steps using machine-learning methods such as linear regression and logistic regression. The last step checks if the order will come in the next delivery week or not. Forecasting is evaluated after each shipment delivery to check the possibility of delaying the current arriving orders for a certain customer until the next week or making the delivery to the customer immediately. The results showed reasonable accuracy expressed in different ways, and one of them depends on a type I error with an average value of 0.07. This is the first paper that performs dynamic forecasting for the purpose of shipment temporal consolidation optimization in the consolidation center

A roadmap to becoming a smart village: Experiences from living labs in rural Bavaria

This paper illustrates the measures and digital integrations being made in the course of digitalization, using the example of existing rural pilot communities in Bavaria, Germany. The participating communities were selected as part of the government-funded project "Digitales Dorf" (Engl. digital village). Since 2016, digital solutions as well as complementary actions have been identified and implemented to make everyday life in the community equal to that in the city: the main intention is to push digitalization to create equivalent living conditions to urban areas. This paper is intended to provide an overview of the requirements and steps that need to be taken in digital transformation, in order to develop a generalized blueprint for other communities. Furthermore, it introduces the pilot projects, provides an insight into best practices to promote digitalization in traditional rural areas, and focuses on the transformation process rather than on digital solutions

Exploiting Smart Meter Water Consumption Measurements for Human Activity Event Recognition

Human activity event recognition (HAER) within a residence is a topic of significant interest in the field of ambient assisted living (AAL). Commonly, various sensors are installed within a residence to enable the monitoring of people. This work presents a new approach for HAER within a residence by (re-)using measurements from commercial smart water meters. Our approach is based on the assumption that changes in water flow within a residence, specifically the transition from no flow to flow above a certain threshold, indicate human activity. Using a separate, labeled evaluation data set from three households that was collected under controlled/laboratory-like conditions, we assess the performance of our HAER method. Our results showed that the approach has a high precision (0.86) and recall (1.00). Within this work, we further recorded a new open data set of water consumption data in 17 German households with a median sample rate of 0.083¯ Hz to demonstrate that water flow data are sufficient to detect activity events within a regular daily routine. Overall, this article demonstrates that smart water meter data can be effectively used for HAER within a residence