Listening to bluetooth beacons for epidemic risk mitigation.

The ongoing COVID-19 pandemic let to efforts to develop and deploy digital contact tracing systems to expedite contact tracing and risk notification. Unfortunately, the success of these systems has been limited, partly owing to poor interoperability with manual contact tracing, low adoption rates, and a societally sensitive trade-off between utility and privacy. In this work, we introduce a new privacy-preserving and inclusive system for epidemic risk assessment and notification that aims to address these limitations. Rather than capturing pairwise encounters between user devices as done by existing systems, our system captures encounters between user devices and beacons placed in strategic locations where infection clusters may originate. Epidemiological simulations using an agent-based model demonstrate that, by utilizing location and environmental information and interoperating with manual contact tracing, our system can increase the accuracy of contact tracing actions and may help reduce epidemic spread already at low adoption.

Pooled testing of traced contacts under superspreading dynamics.

Testing is recommended for all close contacts of confirmed COVID-19 patients. However, existing pooled testing methods are oblivious to the circumstances of contagion provided by contact tracing. Here, we build upon a well-known semi-adaptive pooled testing method, Dorfman's method with imperfect tests, and derive a simple pooled testing method based on dynamic programming that is specifically designed to use information provided by contact tracing. Experiments using a variety of reproduction numbers and dispersion levels, including those estimated in the context of the COVID-19 pandemic, show that the pools found using our method result in a significantly lower number of tests than those found using Dorfman's method. Our method provides the greatest competitive advantage when the number of contacts of an infected individual is small, or the distribution of secondary infections is highly overdispersed. Moreover, it maintains this competitive advantage under imperfect contact tracing and significant levels of dilution.

Large-scale randomized experiments reveals that machine learning-based instruction helps people memorize more effectively.

We perform a large-scale randomized controlled trial to evaluate the potential of machine learning-based instruction sequencing to improve memorization while allowing the learners the freedom to choose their review times. After controlling for the length and frequency of study, we find that learners for whom a machine learning algorithm determines which questions to include in their study sessions remember the content over ~69% longer. We also find that the sequencing algorithm has an effect on users' engagement.

Tracking progress towards malaria elimination in China: Individual-level estimates of transmission and its spatiotemporal variation using a diffusion network approach.

In order to monitor progress towards malaria elimination, it is crucial to be able to measure changes in spatio-temporal transmission. However, common metrics of malaria transmission such as parasite prevalence are under powered in elimination contexts. China has achieved major reductions in malaria incidence and is on track to eliminate, having reporting zero locally-acquired malaria cases in 2017 and 2018. Understanding the spatio-temporal pattern underlying this decline, especially the relationship between locally-acquired and imported cases, can inform efforts to maintain elimination and prevent re-emergence. This is particularly pertinent in Yunnan province, where the potential for local transmission is highest. Using a geo-located individual-level dataset of cases recorded in Yunnan province between 2011 and 2016, we introduce a novel Bayesian framework to model a latent diffusion process and estimate the joint likelihood of transmission between cases and the number of cases with unobserved sources of infection. This is used to estimate the case reproduction number, Rc. We use these estimates within spatio-temporal geostatistical models to map how transmission varied over time and space, estimate the timeline to elimination and the risk of resurgence. We estimate the mean Rc between 2011 and 2016 to be 0.171 (95% CI = 0.165, 0.178) for P. vivax cases and 0.089 (95% CI = 0.076, 0.103) for P. falciparum cases. From 2014 onwards, no cases were estimated to have a Rc value above one. An unobserved source of infection was estimated to be moderately likely (p>0.5) for 19/ 611 cases and high (p>0.8) for 2 cases, suggesting very high levels of case ascertainment. Our estimates suggest that, maintaining current intervention efforts, Yunnan is unlikely to experience sustained local transmission up to 2020. However, even with a mean of 0.005 projected up to 2020, locally-acquired cases are possible due to high levels of importation.

Enhancing human learning via spaced repetition optimization.

Spaced repetition is a technique for efficient memorization which uses repeated review of content following a schedule determined by a spaced repetition algorithm to improve long-term retention. However, current spaced repetition algorithms are simple rule-based heuristics with a few hard-coded parameters. Here, we introduce a flexible representation of spaced repetition using the framework of marked temporal point processes and then address the design of spaced repetition algorithms with provable guarantees as an optimal control problem for stochastic differential equations with jumps. For two well-known human memory models, we show that, if the learner aims to maximize recall probability of the content to be learned subject to a cost on the reviewing frequency, the optimal reviewing schedule is given by the recall probability itself. As a result, we can then develop a simple, scalable online spaced repetition algorithm, MEMORIZE, to determine the optimal reviewing times. We perform a large-scale natural experiment using data from Duolingo, a popular language-learning online platform, and show that learners who follow a reviewing schedule determined by our algorithm memorize more effectively than learners who follow alternative schedules determined by several heuristics.

Acute Abdomen Secondary to Ruptured Epithelial Ovarian Cancer during Pregnancy: The Relevance of Teamwork / Abdome agudo secundário à ruptura do câncer do ovário epitelial durante a gravidez: a importância do trabalho em equipe

Abstract Acute abdomen secondary to epithelial ovarian cancer rupture during pregnancy is a rare event. Our aim is to present how the work of a coordinated multidisciplinary team in a case of ruptured epithelial ovarian cancer during pregnancy is feasible to obtain the best results possible. A 34-year-old woman during the 37th week of her first gestation presented with an acute abdomen. During laparotomy, a ruptured 16.5-cm left ovarian tumor was detected; the tumor was extirpated and sent to pathologic evaluation. In the meantime, a Kerr cesarean section was performed, and a healthy female neonate was born. The tumor was diagnosed as a cystadenocarcinoma; therefore, the family and the combined surgical team (obstetricians and a surgical oncologist) decided to complete a definitive radical ovarian cancer surgery: hysterectomy, right salpingooophorectomy, lymphadenectomy, omentectomy and appendectomy. The patient's postoperative evolution was uneventful, and she was sent to adjuvant chemotherapy.

Resumo O abdome agudo secundário à ruptura do câncer do ovário epitelial durante a gravidez é um evento raro. Nosso objetivo é apresentar como o trabalho de uma equipe multidisciplinar coordenada em um caso de ruptura do câncer de ovário epitelial durante a gravidez é viável para obter os melhores resultados possíveis. Umamulher de 34 anos de idade, durante a 37a semana de sua primeira gestação, apresentou um abdome agudo. Durante a laparotomia, foi detectado um tumor ovariano esquerdo com ruptura de 16,5 cm; O tumor foi extirpado e enviado para avaliação patológica. Enquanto isso, uma cesariana de Kerr foi feita, e uma recém-nascida saudável nasceu. O tumor foi diagnosticado como um cistoadenocarcinoma; então, a família e a equipe cirúrgica combinada (obstetras e oncologista cirúrgico) decidiram concluir uma cirurgia radical definitiva do câncer de ovário: histerectomia, salpingo-ooforectomia direita, linfadenectomia, omentectomia e apendicectomia. A evolução pós-operatória da paciente foi sem intercorrências, e ela foi enviada para quimioterapia adjuvante.

Acute Abdomen Secondary to Ruptured Epithelial Ovarian Cancer during Pregnancy: The Relevance of Teamwork.

Acute abdomen secondary to epithelial ovarian cancer rupture during pregnancy is a rare event. Our aim is to present how the work of a coordinated multidisciplinary team in a case of ruptured epithelial ovarian cancer during pregnancy is feasible to obtain the best results possible. A 34-year-old woman during the 37th week of her first gestation presented with an acute abdomen. During laparotomy, a ruptured 16.5-cm left ovarian tumor was detected; the tumor was extirpated and sent to pathologic evaluation. In the meantime, a Kerr cesarean section was performed, and a healthy female neonate was born. The tumor was diagnosed as a cystadenocarcinoma; therefore, the family and the combined surgical team (obstetricians and a surgical oncologist) decided to complete a definitive radical ovarian cancer surgery: hysterectomy, right salpingo-oophorectomy, lymphadenectomy, omentectomy and appendectomy. The patient's postoperative evolution was uneventful, and she was sent to adjuvant chemotherapy.

O abdome agudo secundário à ruptura do câncer do ovário epitelial durante a gravidez é um evento raro. Nosso objetivo é apresentar como o trabalho de uma equipe multidisciplinar coordenada em um caso de ruptura do câncer de ovário epitelial durante a gravidez é viável para obter os melhores resultados possíveis. Uma mulher de 34 anos de idade, durante a 37a semana de sua primeira gestação, apresentou um abdome agudo. Durante a laparotomia, foi detectado um tumor ovariano esquerdo com ruptura de 16,5 cm; O tumor foi extirpado e enviado para avaliação patológica. Enquanto isso, uma cesariana de Kerr foi feita, e uma recém-nascida saudável nasceu. O tumor foi diagnosticado como um cistoadenocarcinoma; então, a família e a equipe cirúrgica combinada (obstetras e oncologista cirúrgico) decidiram concluir uma cirurgia radical definitiva do câncer de ovário: histerectomia, salpingo-ooforectomia direita, linfadenectomia, omentectomia e apendicectomia. A evolução pós-operatória da paciente foi sem intercorrências, e ela foi enviada para quimioterapia adjuvante.

Solar Radiation Stress in Natural Acidophilic Biofilms of Euglena mutabilis Revealed by Metatranscriptomics and PAM Fluorometry.

The daily photosynthetic performance of a natural biofilm of the extreme acidophilic Euglena mutabilis from Río Tinto (SW, Spain) under full solar radiation was analyzed by means of pulse amplitude-modulated (PAM) fluorescence measurements and metatrascriptomic analysis. Natural E. mutabilis biofilms undergo large-scale transcriptomic reprogramming during midday due to a dynamic photoinhibition and solar radiation stress. Photoinhibition is due to UV radiation and not to light intensity, as revealed by PAM fluorometry analysis. In order to minimize the negative effects of solar radiation, our data supports the presence of a circadian rhythm in this euglenophyte that increases their opportunity to survive. Differential gene expression throughout the day (at 12:00, 20:00 and night) was monitored by massive Illumina parallel sequencing of metatranscriptomic libraries. The transcription pattern was altered in genes involved in Photosystem II stability and repair, UV damaged DNA repair, non-photochemical quenching and oxidative stress, supporting the photoinhibition detected by PAM fluorometry at midday.

Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm.

Information spreads across social and technological networks, but often the network structures are hidden from us and we only observe the traces left by the diffusion processes, called cascades. Can we recover the hidden network structures from these observed cascades? What kind of cascades and how many cascades do we need? Are there some network structures which are more difficult than others to recover? Can we design efficient inference algorithms with provable guarantees? Despite the increasing availability of cascade-data and methods for inferring networks from these data, a thorough theoretical understanding of the above questions remains largely unexplored in the literature. In this paper, we investigate the network structure inference problem for a general family of continuous-time diffusion models using an [Formula: see text]-regularized likelihood maximization framework. We show that, as long as the cascade sampling process satisfies a natural incoherence condition, our framework can recover the correct network structure with high probability if we observe O(d3 log N) cascades, where d is the maximum number of parents of a node and N is the total number of nodes. Moreover, we develop a simple and efficient soft-thresholding inference algorithm, which we use to illustrate the consequences of our theoretical results, and show that our framework outperforms other alternatives in practice.

Scalable Influence Estimation in Continuous-Time Diffusion Networks.

If a piece of information is released from a media site, can we predict whether it may spread to one million web pages, in a month ? This influence estimation problem is very challenging since both the time-sensitive nature of the task and the requirement of scalability need to be addressed simultaneously. In this paper, we propose a randomized algorithm for influence estimation in continuous-time diffusion networks. Our algorithm can estimate the influence of every node in a network with |V| nodes and |ε| edges to an accuracy of ε using n = O(1/ε2) randomizations and up to logarithmic factors O(n|ε|+n|V|) computations. When used as a subroutine in a greedy influence maximization approach, our proposed algorithm is guaranteed to find a set of C nodes with the influence of at least (1 - 1/e) OPT - 2Cε , where OPT is the optimal value. Experiments on both synthetic and real-world data show that the proposed algorithm can easily scale up to networks of millions of nodes while significantly improves over previous state-of-the-arts in terms of the accuracy of the estimated influence and the quality of the selected nodes in maximizing the influence.