A causal analysis of environmental familiarity on navigation information needs

Digital technologies have recently become more advanced, allowing for the development of social networking sites and applications. Despite these advancements, phone calls and text messages still make up the largest proportion of mobile data usage. It is possible to study human communication behaviors and mobility patterns using the useful information that mobile phone data provide. Specifically, the digital traces left by the large number of mobile devices provide important information that facilitates a deeper understanding of human behavior and mobility configurations for researchers in various fields, such as criminology, urban sensing, transportation planning, and healthcare. Mobile phone data record significant spatiotemporal (i.e., geospatial and time-related data) and communication (i.e., call) information. These can be used to achieve different research objectives and form the basis of various practical applications, including human mobility models based on spatiotemporal interactions, real-time identification of criminal activities, inference of friendship interactions, and density distribution estimation. The present research primarily reviews studies that have employed mobile phone data to investigate, assess, and predict human communication and mobility patterns in the context of crime prevention. These investigations have sought, for example, to detect suspicious activities, identify criminal networks, and predict crime, as well as understand human communication and mobility patterns in urban sensing applications. To achieve this, a systematic literature review was conducted on crime research studies that were published between 2014 and 2022 and listed in eight electronic databases. In this review, we evaluated the most advanced methods and techniques used in recent criminology applications based on mobile phone data and the benefits of using this information to predict crime and detect suspected criminals. The results of this literature review contribute to improving the existing understanding of where and how populations live and socialize and how to classify individuals based on their mobility patterns. The results show extraordinary growth in studies that utilized mobile phone data to study human mobility and movement patterns compared to studies that used the data to infer communication behaviors. This observation can be attributed to privacy concerns related to acquiring call detail records (CDRs). Additionally, most of the studies used census and survey data for data validation. The results show that social network analysis tools and techniques have been widely employed to detect criminal networks and urban communities. In addition, correlation analysis has been used to investigate spatial–temporal patterns of crime, and ambient population measures have a significant impact on crime rates.

Based on benchmarking various methods and analyzing multiple real scRNA-seq datasets, a computational platform/workflow and a set of tips for best practices are developed for analyzing causal interactions or relationships in single cells.

A good deal of humility: Cochran on observational studies

Fifty-one learning strategies were developed and validated in this study to make language learners more knowledgeable about unfamiliar environments. The study tested the effectiveness of the strategies in improving the creativity of students. 75 university students participated in the study and they were divided into three groups: (1) students who learned in unfamiliar learning environments without familiarization strategies; (2) students who learned in unfamiliar learning environments and used familiarization strategies; and (3) students who learned in familiar learning environments. All students learned vocabulary, grammar, sentence patterns and how to write descriptive essays in class. After class, they applied newly learned knowledge by describing people, objects and situations in the real world using mobile devices. The content of students' essays was compared across three groups and the results showed that students in Group 2 and Group 3 had better creativity scores than students in Group 1. The results indicated that students' creativity is affected by their familiarity with the environment. Therefore, it is suggested that student familiarization with learning sites needs to be considered when designing learning activities in the real world. Educators should provide familiarization strategies to students when learning in unfamiliar learning environments.

Influence of residential built environment on human mobility in Xining: A mobile phone data perspective

Recent development of therapies targeting oncogenes have dramatically improved cancer care for subsets of patients and generated a wave of interest in cancer precision medicine. However, effective targeted therapies are scarce as we have a limited understanding of how drug responses are modulated by tumor genotype. Here, we utilize both human data and in vivo models to identify genetic drivers of therapy response in KRAS-driven non-small cell lung cancer patients treated with chemotherapy. We first present an integrative causal inference analysis of three clinical data resources: (1) the recently released Genomics Evidence Neoplasia Information Exchange, Biopharma Collaboration dataset (GENIE-BPC; n=197), (2) a selected set of patients from the Tempus Clinico-genomic Database (n=330), and (3) an additional cohort from Memorial Sloan Kettering Cancer Center (n=218). Each dataset was first analyzed separately using a causal inference pipeline. Doubly robust estimators for each variant were inferred within a counting process survival analysis model accounting for time-varying treatments and immortal time bias. Meta-analysis of the results from all three cohorts identified three commonly mutated and highly replicable genes with a significant effect on overall survival: KEAP1, SMARCA4, and CDKN2A. As further validation, we used our murine in vivo pharmacogenomics (PGx) platform that can quantify the effects of therapies across thousands of tumors of diverse genotypes. These tumors are initiated de novo in the native microenvironmental context in mice with an intact adaptive immune system. We tested a chemotherapy combination of carboplatin and pemetrexed in mice with KrasG12D-driven lung tumors and inactivation of each of 60 putative tumor suppressors. Treatment led to >75% reduction in tumor sizes relative to tumor suppressor inactivated (matched) vehicle-treated controls. These models identified causal effects for two out of the three candidates above: KEAP1 (resistance) and CDKN2A (sensitive). Moreover, our PGx platform identified additional candidate genes beyond those found using the clinical data, which had insufficient sample size for these rarely mutated genes. Together, we demonstrate how leveraging our PGx platform together with human data within a causal inference framework may improve the stratification of patients by their clinical outcomes, profoundly advancing the promise of precision medicine. Citation Format: David Amar, Erick Scott, Ian P. Winters, Gregory D. Wall, Dmitri A. Petrov, Monte M. Winslow, Joseph Juan, Ian K. Lai, Lafia Sebastian, Edwin A. Apilado, Gabriel Grenot, Vy B. Tran, Charles Rudin, Michael J. Rosen. An in vivo pharmacogenomics platform replicates and extends biomarkers of therapy response identified via causal inference analysis of clinical data. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5723.

Human Mobility Data in the COVID-19 Pandemic: Characteristics, Applications, and Challenges

Sleep is important for processing memories and emotions. It is widely assumed that during sleep, particularly during slow-wave sleep (SWS), newly acquired memory traces are spontaneously reactivated, thereby supporting their consolidation and stabilization. Beyond memory traces, the Memories-of-Sleep (MemoSleep-) Hypothesis suggests that cognitive concepts are also reactivated during sleep. The repeated reactivations of these concepts potentially influence sleep quality by either impairing or improving sleep, depending on the particular content of these concepts. However, it remains unclear whether memory reactivations occur primarily during SWS or whether the non-rapid eye movement (NREM) sleep stage N2 is also involved. Additionally, the exact nature of sleep disturbances induced by unfamiliar environments, particularly over non-consecutive nights, has not yet been fully characterized and explained by the MemoSleep-Hypothesis. Finally, the influence of emotions on sleep within the context of the MemoSleep-Hypothesis remains to be clarified. This dissertation addresses these research gaps through three independent sleep studies. The first study investigated the contribution of memory reactivation during NREM sleep stage N2 compared to SWS. However, no significant difference was found between words that were externally reactivated during N2 and those reactivated during SWS. Moreover, there was no overall memory benefit from targeted memory reactivation (TMR) during sleep. The second study examined sleep disturbances during the first night in an unfamiliar environment, known as the First-Night Effect (FNE), as an example of the reactivation of stressful mental concepts during sleep. The results show that an FNE exists also in non-consecutive nights and even occurs in familiar environments. However, prior familiarization with the recording equipment was found to attenuate the FNE. The third study explored, for the first time, whether musical chords associated with varying emotional ratings can elicit different brain responses during sleep. The results confirmed that the sleeping brain is capable of differentially processing specific musical chords. The results of this dissertation challenge the specific role of SWS in memory reactivation during sleep. Additionally, they demonstrate that unfamiliar sleep environments generally impact sleep quality, however, a habituation occurs even over non-consecutive nights. Finally, these findings pave the way for novel approaches to using emotion-associated musical stimuli to influence activated mental concepts during sleep, providing new knowledge and a foundation for further refinements and future research directions within the framework of the MemoSleep-Hypothesis.

Background Spatial representations of the environment may change as a result of increased familiarity; yet there are few studies about this topic (Evans and Pezdek 1980; Thorndyke and Hayes-Roth 1982). These studies have suggested that familiar environments are represented in an allocentric way, whereas unfamiliar environments in an egocentric way. A representation is egocentric if maintains the perspective under which spatial information has been experienced. This means that access to spatial locations is not equally easy but depends on the relation between the required location and the organism. An allocentric representation is based on frames of reference independent of the organism’s position and centred on objects or environmental features. However, in these studies the factor “familiarity” was confused with the way of exploring the environment (real exploration for familiar participants, maps for unfamiliar participants), and consequently it was difficult to interpret the results unambiguously. Further, there are few studies based on locomotor exploration of real environments and they have shown that unfamiliar environments are represented egocentrically, although salient environmental axes may acquire a certain relevance (Iachini and Logie 2003; McNamara et al. 2003). Overall, in all these studies the patterns of results were interpreted as verifying the “allocentric” or “egocentric” view according to a specific theoretical definition and there was no direct measure of allocentric vs egocentric coding.

BackgroundHuman mobility behaviour modelling plays an essential role in the understanding and control of the spread of contagious diseases by limiting the contact among individuals, predicting the spatio-temporal evolution of an epidemic and inferring migration patterns. It informs programmatic and policy decisions for effective and efficient intervention. The objective of this research is to assess the human mobility pattern and analyse its implication for malaria disease epidemiology.MethodsIn this study, human mobility patterns in Benishangul-Gumuz and Gambella regions in Western Ethiopia were explored based on a cellular network mobility parameter (e.g., handover rate) via real world data. Anonymized data were retrieved for mobile active users with mobility related information. The data came from anonymous traffic records collected from all the study areas. For each cell, the necessary mobility parameter data per hour, week and month were collected. A scale factor was computed to change the mobility parameter value to the human mobility pattern. Finally, the relative human mobility probability for each scenario was estimated. MapInfo and Matlab softwares were used for visualization and analysis purposes. Hourly travel patterns in the study settings were compared with hourly malaria mosquito vector feeding behaviour.ResultsHeterogeneous human movement patterns were observed in the two regions with some areas showing typically high human mobility. Furthermore, the number of people entering into the two study regions was high during the highest malaria transmission season. Two peaks of hourly human movement, 8:00 to 9:00 and 16:00 to 18:00, emerged in Benishangul-Gumuz region while 8:00 to 10:00 and 16:00 to 18:00 were the peak hourly human mobility time periods in Gambella region. The high human movement in the night especially before midnight in the two regions may increase the risk of getting mosquito bite particularly by early biters depending on malaria linked human behaviour of the population.ConclusionsHigh human mobility was observed both within and outside the two regions. The population influx and efflux in these two regions is considerably high. This may specifically challenge the transition from malaria control to elimination. The daily mobility pattern is worth considering in the context of malaria transmission. In line with this malaria related behavioural patterns of humans need to be properly addressed.

The frequency and intensity of natural disasters has significantly increased over the past decades and this trend is predicted to continue. Facing these possible and unexpected disasters, understanding and simulating of human emergency mobility following disasters will becomethe critical issue for planning effective humanitarian relief, disaster management, and long-term societal reconstruction. However, due to the uniquenessof various disasters and the unavailability of reliable and large scale human mobility data, such kind of research is very difficult to be performed. Hence, in this paper,we collect big and heterogeneous data (e.g. 1.6 million users' GPS records in three years, 17520 times of Japan earthquake data in four years, news reporting data, transportation network data and etc.) to capture and analyze human emergency mobility following different disasters. By mining these big data, we aim to understand what basic laws govern human mobility following disasters, and develop a general model of human emergency mobility for generating and simulating large amount of human emergency movements. The experimental results and validations demonstrate the efficiency of our simulation model, and suggest that human mobility following disasters may be significantly morepredictable and can be easier simulated than previously thought.

The analysis of movement data has been recently fostered by the widespread diffusion of new techniques and systems for monitoring, collecting and storing location-aware data, generated by a wealth of technological infrastructures, such as GPS positioning and wireless networks [2]. These have made available massive repositories of spatio-temporal data recording human mobile activities, such as location data from mobile phones, GPS tracks from mobile devices, etc.: is it possible to discover from these data useful and timely knowledge about human mobility? The GeoPKDD project [1], since 2005, investigated this direction of research; the lesson learned is that there is a long way to go from raw data of individual trajectories up to high-level collective mobility knowledge, capable of supporting the decisions of mobility and transportation managers. Such analysts reason about semantically rich concepts, such as systematic vs. occasional movement behavior and homework commuting patterns; accordingly, the mainstream analytical tools of transportation engineering, such as origin/destination matrices, are based on semantically rich data collected by means of field surveys and interviews. Clearly, the price to pay for this richness is hard: mass surveys are very expensive, so that their periodicity is very broad and obsolescence is rapid; poor data quality is also a plague: people tend to respond elusively and inaccurately. On the other extreme, automatically sensed mobility data record individual trajectories at mass level, in real time. Clearly, the price to pay here is exactly the lack of semantics in raw data: How to bridge this deficiency? Current generation of analysis systems and methods on mobility data are based on standard statistical techniques, such as movement distributions among the city areas. However, we claim that these are not sufficient to express movement oriented analysis. For example, detecting subgroups of users having similar movements is a behaviour not captured by standars statistic techniques, due to the strong variability among individuals movements.

In recent decades, the frequency and intensity of natural disasters has increased significantly, and this trend is expected to continue. Therefore, understanding and predicting human behavior and mobility during a disaster will play a vital role in planning effective humanitarian relief, disaster management, and long-term societal reconstruction. However, such research is very difficult to perform owing to the uniqueness of various disasters and the unavailability of reliable and large-scale human mobility data. In this study, we collect big and heterogeneous data (e.g., GPS records of 1.6 million users 1 over 3 years, data on earthquakes that have occurred in Japan over 4 years, news report data, and transportation network data) to study human mobility following natural disasters. An empirical analysis is conducted to explore the basic laws governing human mobility following disasters, and an effective human mobility model is developed to predict and simulate population movements. The experimental results demonstrate the efficiency of our model, and they suggest that human mobility following disasters can be significantly more predictable and be more easily simulated than previously thought.

An AI assistant to help review and improve causal reasoning in epidemiological documents

Spatio-temporal association data refers to spatial feature information flows that have spatio-temporal associations with each other and change over time. The mining of spatio-temporal related data has attracted much attention and has been widely used in many fields. At present, many researches of spatio-temporal data mining can only infer the association mode of spatio-temporal objects from the system. Therefore, based on the complex network theory, this paper proposed a spatio-temporal data networked node correlation inference method based on Bayesian method. Based on the network topology structure, the sample statistics method was designed according to time and space information. The conditional probability between network nodes was calculated so that it was applied to the node correlation causal inference. Finally, the method was applied to two typical spatio-temporal correlation data networks - seismic network and pest-relational network, and the prediction accuracy was 76% and 80%, respectively. In addition, this paper analyzed the relationship between the accuracy of the prediction and other factors such as test set size, structural core and magnitude limits.