Single-cell and spatial transcriptomic analysis reveals tumor cell heterogeneity and underlying molecular program in colorectal cancer by Teng Wang, Zhaoming Chen, Wang Wang, Wang Wang, Heng Wang, Shenglong Li

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Forecasting Groundwater Level by Characterizing Multiple Spatial Dependencies of Environmental Factors Using Graph‐Based Deep Learning by Yinghan Wu, Gang Mei, Kaixuan Shao, Nengxiong Xu, Jianbing Peng

“…However, existing research has mostly overlooked the multiple spatial dependencies between monitoring wells, limiting the understanding of the added value that graph‐based models bring to groundwater dynamics prediction. …”
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Gene expression noise in spatial patterning: hunchback promoter structure affects noise amplitude and distribution in Drosophila segmentation. by David M Holloway, Francisco J P Lopes, Luciano da Fontoura Costa, Bruno A N Travençolo, Nina Golyandina, Konstantin Usevich, Alexander V Spirov

“…We have corroborated model noise predictions experimentally. The results indicate that WT (self-regulatory) Hb output noise is predominantly dependent on the transcription and translation dynamics of its own expression, rather than on Bcd fluctuations. …”
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Determinants of Fare Evasion in Urban Bus Lines: Case Study of a Large Database Considering Spatial Components by Susana Freiria, Nuno Sousa

“…Consideration of this spatial component made it possible to find moderate evidence for dissuasive effects of inspection actions in some models and of pockets of evasive tendencies in other models, which appear in the statistical error term. …”
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Temporal and Spatial Variations of Precipitation δ18O and Controlling Factors on the Pearl River Basin and Adjacent Regions by Yunfeng Ruan, Zhaofei Liu, Zhijun Yao, Rui Wang

“…Based on the precipitation  δ18O values from the datasets of the Global Network of Isotopes in Precipitation (GNIP), the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis data, and previous researches, we explored the temporal and spatial variations of precipitation  δ18O in a typical monsoon climate zone, the Pearl River basin (PRB), and adjacent regions. …”
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Spatial characterization of debris ejection from the interaction of a tightly focused PW-laser pulse with metal targets by I.-M. Vladisavlevici, C. Vlachos, J.-L. Dubois, D. Haddock, S. Astbury, A. Huerta, S. Agarwal, H. Ahmed, J. I. Apiñaniz, M. Cernaianu, M. Gugiu, M. Krupka, R. Lera, A. Morabito, D. Sangwan, D. Ursescu, A. Curcio, N. Fefeu, J. A. Pérez-Hernández, T. Vacek, P. Vicente, N. Woolsey, G. Gatti, M. D. Rodríguez-Frías, J. J. Santos, P. W. Bradford, M. Ehret

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Electromyography-Based Gesture Recognition With Explainable AI (XAI): Hierarchical Feature Extraction for Enhanced Spatial-Temporal Dynamics by Jungpil Shin, Abu Saleh Musa Miah, Sota Konnai, Shu Hoshitaka, Pankoo Kim

“…Each branch of the proposed model was designed to extract hierarchical features, capturing both global and detailed spatial-temporal relationships to ensure feature effectiveness. …”
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Systematic spatial bias in DNA microarray hybridization is caused by probe spot position-dependent variability in lateral diffusion. by Doris Steger, David Berry, Susanne Haider, Matthias Horn, Michael Wagner, Roman Stocker, Alexander Loy

“…Combining observations from a simplified single-probe block array format with predictions from a mathematical model, the mechanism responsible for this bias is found to be a position-dependent variation in lateral diffusion of target molecules. …”
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Effects of habitat characteristics in an anthropized landscape on the spatial behavior and abundance of a common chameleon (Chamaeleo chamaeleon) population by M. A. Farfán, J. Duarte, D. Romero, L. Colorado‐Pedrero, P. García‐Quevedo, R. Arroyo‐Morales, F. Díaz‐Ruiz

“…We show the effects of a highway on the spatial behavior and the abundance of a common chameleon population. …”
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Interactions of Therapeutic Antibodies With Presynaptically‐Released Misfolded Proteins in Neurodegenerative Diseases. A Spatial Monte‐Carlo Simulation Study by Jamie Goff, Maryam Khalifa, Shaina M. Short, Piet H. van derGraaf, Hugo Geerts

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Impact of climate change and social determinants on the spatial and epidemiological distribution of tuberculosis in the state of Pará: a perspective for COP 30 by G.A. Tavares-Cohén, A.P. Simões-Castro, C. Andrade-Sales, P.A Mendonça-Cavalcante, M.P. Moreira-Sena, B.C. Brito-Alves, U.G. El-Awar, A.W. Araújo-Sardinha, C.H.M. Azevedo-Ribeiro, J.L. Fernandes-Vieira, L.W. Pereira-Sena

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Estimating Rail Transit Passenger Flow Considering Built Environment Factors: A Case Study in Shenzhen by Wenjing Wang, Haiyan Wang, Jun Liu, Chengfa Liu, Shipeng Wang, Yong Zhang

“…The results show that the average house price is significantly negatively correlated with passenger flow. The GWR model considering the house price factor has a high prediction accuracy, revealing the spatial characteristics of the built-up environment in the administrative districts of Shenzhen, which has shifted from the industrial structure in the east to the commercial and residential structure in the west. …”
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Remotely Sensed Variables Predict Grassland Diversity Better at Scales Below 1,000 km as Opposed to Abiotic Variables That Predict It Better at Larger Scales by Yujin Zhao, Bernhard Schmid, Zhaoju Zheng, Yang Wang, Jin Wu, Yao Wang, Ziyan Chen, Xia Zhao, Dan Zhao, Yuan Zeng, Yongfei Bai

“…Here we used vegetation survey data from 1,609 field sites (>4,000 plots of 1 m2), remotely sensed data (ecosystem productivity and phenology, habitat heterogeneity, functional traits and spectral diversity), and abiotic data (water‐ and energy‐related, characterizing climate‐dominated environment) together with machine learning and spatial autoregressive models to predict and map grassland species richness per 100 m2 across the Mongolian Plateau at 500 m resolution. …”
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Revealing the effects of environmental and spatio-temporal variables on changes in Japanese sardine (Sardinops melanostictus) high abundance fishing grounds based on interpretable... by Yongchuang Shi, Lei Yan, Shengmao Zhang, Fenghua Tang, Shenglong Yang, Wei Fan, Haibin Han, Yang Dai

“…Results: 1) From 2014 to 2021, the annual catch showed an overall increasing trend and peaked at 220,009.063 tons in 2021; the total monthly catch increased and then decreased, with a peak of 76, 033.4944 tons (July), and the catch was mainly concentrated in the regions of 39.5°-43°N and 146.75°-155.75°E; 2) Catboost model predicted better than LightGBM and XGBoost models, with the highest values of accuracy and F1-score, 73.8% and 75.31%, respectively; 3) the overall importance ranking of the model’s built-in method differed significantly from that in the SHAP method, and the overall importance ranking of the spatial variables in the SHAP method increased. …”
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Principal Component Regression to Study the Impact of Economic Factors on Disadvantaged Communities by Narmadha M. Mohankumar, Milan Jain, Heng Wan, Sumitrra Ganguli, Kyle D. Wilson, David M. Anderson

“…Our study (1) identifies the most significant income groups and employment industries that impact DAC status (2) provides the probability of DAC status across census tracts and compares the predictive accuracy with widely used machine learning (ML) approaches, (3) obtains historical predictions of the probability of DAC status, (4) obtains spatial downscaling of DAC status across block groups. …”
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Relative importance of temporal and location features in predicting smoking events by Han Yang, Hang Yu, Michael Kotlyar, Sheena R. Dufresne, Serguei V. S. Pakhomov

“…This study examined the predictive value of temporal and spatial features available from smartphones. …”
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HyMePre: A Spatial–Temporal Pretraining Framework with Hypergraph Neural Networks for Short-Term Weather Forecasting by Fei Wang, Dawei Lin, Baojun Chen, Guodong Jing, Yi Geng, Xudong Ge, Daoming Wei, Ning Zhang

“…Accurate short-term weather forecasting plays a vital role in disaster response, agriculture, and energy management, where timely and reliable predictions are essential for decision-making. Graph neural networks (GNNs), known for their ability to model complex spatial structures and relational data, have achieved remarkable success in meteorological forecasting by effectively capturing spatial dependencies among distributed weather stations. …”
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Spatial–Temporal Semantic and Geographic Correlation Network for SAR Image Change Detection With Limited Training Data by Haolin Li, Bin Zou, Lamei Zhang, Jiang Qin

“…Existing supervised and semisupervised methods for synthetic aperture radar (SAR) image change detection are limited by the scarcity of labeled data and the challenges of modeling spatial–temporal correlations. To address these issues, we propose STSGNet, a graph contrastive learning-based framework that captures spatial–temporal correlations from large amounts of unlabeled data and leverages limited labeled data for fine-tuning, thereby enhancing the model’s change detection performance. …”
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Hybrid solver with deep learning for transport problem in porous media by Vladislav Trifonov, Egor Illarionov, Anton Voskresenskii, Musheg Petrosyants, Klemens Katterbauer

“…Extensive experiments with dynamic and static reservoir features revealed that improving predictive accuracy does not necessarily improve fluid modeling. …”
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S²RCFormer: Spatial-Spectral Residual Cross-Attention Transformer for Multimodal Remote Sensing Data Classification by Yifei Xu, Lingming Cao, Jialu Li, Wenlong Li, Yaochen Li, Yingjie Zong, Aichen Wang, Yuan Rao, Shuiguang Deng

“…With the advancement of remote sensing technology, more and more modalities are becoming available for land cover classification tasks, helping to address the issue of insufficiency and incompleteness caused by modeling on single-source remote sensing images. However, most existing remote sensing data classifiers are struggling to capture reliable and informative spatial and spectral dependencies and neglect the correlations and complementarity between different modalities. …”
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