Machine learning of genomic features in organotropic metastases stratifies progression risk of primary tumors

Metastatic cancer is associated with poor patient prognosis but its spatiotemporal behavior remains unpredictable at early stage. Here we develop MetaNet, a computational framework that integrates clinical and sequencing data from 32,176 primary and metastatic cancer cases, to assess metastatic risks of primary tumors. MetaNet achieves high accuracy in distinguishing the metastasis from the primary in breast and prostate cancers. From the prediction, we identify Metastasis-Featuring Primary (MFP) tumors, a subset of primary tumors with genomic features enriched in metastasis and demonstrate their higher metastatic risk and shorter disease-free survival. In addition, we identify genomic alterations associated with organ-specific metastases and employ them to stratify patients into various risk groups with propensities toward different metastatic organs. This organotropic stratification method achieves better prognostic value than the standard histological grading system in prostate cancer, especially in the identification of Bone-MFP and Liver-MFP subtypes, with potential in informing organ-specific examinations in follow-ups.

Semantic Enhancement of Human Urban Activity Chain Construction Using Mobile Phone Signaling Data

Data-driven urban human activity mining has become a hot topic of urban dynamic modeling and analysis. Semantic activity chain modeling with activity purpose provides scientific methodological support for the analysis and decision-making of human behavior, urban planning, traffic management, green sustainable development, etc. However, the spatial and temporal uncertainty of the ubiquitous mobile sensing data brings a huge challenge for modeling and analyzing human activities. Existing approaches for modeling and identifying human activities based on massive social sensing data rely on a large number of valid supervised samples or limited prior knowledge. This paper proposes an effective methodology for building human activity chains based on mobile phone signaling data and labeling activity purpose semantics to analyze human activity patterns, spatiotemporal behavior, and urban dynamics. We fully verified the effectiveness and accuracy of the proposed method in human daily activity process construction and activity purpose identification through accuracy comparison and spatial-temporal distribution exploration. This study further confirms the possibility of using big data to observe urban human spatiotemporal behavior.

Spatial-temporal behavior of benthic communities in the Terminos Lagoon, Mexico: Possible areas of protection and conservation

Terminos Lagoon is classified as a Flora and Fauna Protection Area due to the high biodiversity it registers, however, given its size, it makes total protection difficult. The objective of this study was to detect possible specific areas of conservation within the lagoon, for which an analysis of the spatiotemporal behavior of biodiversity was carried out (based on geostatistics) and thereby establish key regions of laguna de Terminos for their protection and recovery. Monthly samplings (2016-2017) were carried out by trawling with a shrimp net in 17 stations classified in 4 regions during dry (February-May), rain (June-September) and wind/winter (October-January). The organisms were identified up to the species level and the indices of abundance, richness and diversity were applied to carry out the interpolation and generation of maps. 17,950 organisms (382.9 kg) were collected and 103 species were identified. According to the interpolation of minimum curvature, at the temporal level, the rainy season was the one that presented the highest values in terms of diversity and richness, while at the spatial level, region 1 (adjacent area of Boca Atasta and Palizada river) it was the most representative during the three climatic seasons for both indices. In terms of abundance, region 2 was the one characterized by presenting the highest values. In conclusion, regions 1 and 2 represent areas of great ecological importance for the balance of biodiversity, which is why they are key areas that should be protected in Terminos Lagoon. This information would contribute significantly to knowing the state of the habitat, since it provides us with knowledge of the biological conditions of the ecosystem. Keywords: Coastal lagoons, onterpolation, Protected Natural Area, Gulf of Mexico, diversity, richness.

Noise-Induced Versus Intrinsic Oscillation in Ecological Systems

Studies of populations oscillating through time have a long history in ecology as these dynamics can help provide insights into the causes of population regulation. A particularly difficult challenge is determining the relative role of deterministic versus stochastic forces in producing this oscillatory behavior. Another classic ecological study area is the study of spatial synchrony which also has helped unravel underlying population dynamic principles. One possible approach to understanding the causes of population cycles is based on the idea that a focus on spatiotemporal behavior, oscillations in coupled populations, can provide much further insight into the relative role of deterministic versus stochastic forces. Using ideas based on concepts from statistical physics, we develop results showing that in a system with coupling between adjacent populations, a study of spatial synchrony provides much information about the underlying causes of oscillations. Novel, to ecology, measures of spatial synchrony are a key step.

Exploring the Spatiotemporal Patterns of Residents’ Daily Activities Using Text-Based Social Media Data: A Case Study of Beijing, China

The use of social media data provided powerful data support to reveal the spatiotemporal characteristics and mechanisms of human activity, as it integrated rich spatiotemporal and textual semantic information. However, previous research has not fully utilized its semantic and spatiotemporal information, due to its technical and algorithmic limitations. The efficiency of the deep mining of textual semantic resources was also low. In this research, a multi-classification of text model, based on natural language processing technology and the Bidirectional Encoder Representations from Transformers (BERT) framework is constructed. The residents’ activities in Beijing were then classified using the Sina Weibo data in 2019. The results showed that the accuracy of the classifications was more than 90%. The types and distribution of residents’ activities were closely related to the characteristics of the activities and holiday arrangements. From the perspective of a short timescale, the activity rhythm on weekends was delayed by one hour as compared to that on weekdays. There was a significant agglomeration of residents’ activities that presented a spatial co-location cluster pattern, but the proportion of balanced co-location cluster areas was small. The research demonstrated that location conditions, especially the microlocation condition (the distance to the nearest subway station), were the driving factors that affected the resident activity cluster patterns. In this research, the proposed framework integrates textual semantic analysis, statistical method, and spatial techniques, broadens the application areas of social media data, especially text data, and provides a new paradigm for the research of residents’ activities and spatiotemporal behavior.

Bouncing behavior of sub-four minute milers

Elite middle distance runners present as a unique population in which to explore biomechanical phenomena in relation to running speed, as their training and racing spans a broad spectrum of paces. However, there have been no comprehensive investigations of running mechanics across speeds within this population. Here, we used the spring-mass model of running to explore global mechanical behavior across speeds in these runners. Ten elite-level 1500 m and mile runners (mean 1500 m best: 3:37.3 ± 3.6 s; mile: 3:54.6 ± 3.9 s) and ten highly trained 1500 m and mile runners (mean 1500 m best: 4:07.6 ± 3.7 s; mile: 4:27.4 ± 4.1 s) ran on a treadmill at 10 speeds where temporal measures were recorded. Spatiotemporal and spring-mass characteristics and their corresponding variation were calculated within and across speeds. All spatiotemporal measures changed with speed in both groups, but the changes were less substantial in the elites. The elite runners ran with greater approximated vertical forces (+ 0.16 BW) and steeper impact angles (+ 3.1°) across speeds. Moreover, the elites ran with greater leg and vertical stiffnesses (+ 2.1 kN/m and + 3.6 kN/m) across speeds. Neither group changed leg stiffness with increasing speeds, but both groups increased vertical stiffness (1.6 kN/m per km/h), and the elite runners more so (further + 0.4 kN/m per km/h). The elite runners also demonstrated lower variability in their spatiotemporal behavior across speeds. Together, these findings suggested that elite middle distance runners may have distinct global mechanical patterns across running speeds, where they behave as stiffer, less variable spring-mass systems compared to highly trained, but sub-elite counterparts.