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1. Considerations for developing mitochondrial transplantation techniques for individualized medicine

   https://doi.org/10.2144/btn-2023-0072  

   Neikirk, Kit; Stephens, Dominique C; Beasley, Heather K; Marshall, Andrea G; Gaddy, Jennifer A; Damo, Steven M; Hinton, Antentor O (April 2024, BioTechniques)

   Tweetable abstract Mitochondrial transplantation has been used to treat various diseases associated with mitochondrial dysfunction. Here, we highlight the considerations in quality control mechanisms that should be considered in the context of mitochondrial transplantation. 

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2. New support for the Enhanced Mutualism Hypothesis for invasion

   https://doi.org/10.1111/nph.18377  

   Lekberg, Ylva; Callaway, Ragan M. (July 2022, New Phytologist)

   This article is a Commentary onYuet al. (2022),236: 1140–1153. 

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3. Algorithms for Computing Closest Points for Segments

   https://doi.org/10.4230/LIPIcs.STACS.2024.58  

   Wang, Haitao (March 2024, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)

   Beyersdorff, Olaf; Kanté, Mamadou Moustapha; Kupferman, Orna; Lokshtanov, Daniel (Ed.)

   Given a set P of n points and a set S of n segments in the plane, we consider the problem of computing for each segment of S its closest point in P. The previously best algorithm solves the problem in n^{4/3}2^{O(log^*n)} time [Bespamyatnikh, 2003] and a lower bound (under a somewhat restricted model) Ω(n^{4/3}) has also been proved. In this paper, we present an O(n^{4/3}) time algorithm and thus solve the problem optimally (under the restricted model). In addition, we also present data structures for solving the online version of the problem, i.e., given a query segment (or a line as a special case), find its closest point in P. Our new results improve the previous work. 

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4. Cross-State Travel for Cancer Care and Implications for Telehealth Reciprocity

   https://doi.org/10.1001/jamanetworkopen.2024.61021  

   Moen, Erika L; Wang, Qianfei; Liu, Lingbo; Wang, Fahui; Tosteson, Anna_N A; Smith, Rebecca E; Cowan, Lauren; Onega, Tracy (February 2025, JAMA Network Open)

   ImportancePatients often travel for cancer care, yet the extent to which patients cross state lines for cancer care is not well understood. This knowledge can have implications for policies that regulate telehealth access to out-of-state clinicians. ObjectiveTo quantify the extent of cross-state delivery of cancer services to patients with cancer. Design, Setting, and ParticipantsThis cross-sectional study analyzed fee-for-service Medicare claims data for beneficiaries (aged ≥66 years) with a diagnosis of breast, colon, lung, or pancreatic cancer between January 1, 2017, and December 31, 2020. Analyses were performed between January 1 and July 30, 2024. ExposurePatient rurality. Main Outcomes and MeasuresThe primary outcome of interest was receipt of cancer care across state lines. Frequencies of cancer services (surgery, radiation, and chemotherapy) were summarized by cancer type in relation to in-state vs out-of-state receipt of care based on state of residence for Medicare beneficiaries. Cross-state delivery of cancer services was also quantified by adjacent vs nonadjacent states and overall between-state flows for service utilization. ResultsThe study included 1 040 874 Medicare beneficiaries with cancer. The mean (SD) age of the study population was 76.5 (7.4) years. Most patients were female (68.2%) and urban residing (78.5%); one-quarter (25.9%) were aged between 70 and 74 years. In terms of race and ethnicity, 7.0% of patients identified as Black, 3.4% as Hispanic, and 85.5% as White. Overall, approximately 6.9% of cancer care was delivered across state lines, with the highest proportion (8.3%) occurring for surgical care, followed by radiation (6.7%) and chemotherapy (5.6%) services. Out of all cross-state care, 68.4% occurred in adjacent states. Frequency of cross-state cancer care increased with patient rurality. Compared with urban-residing patients, isolated rural-residing patients were 2.5 times more likely to cross state lines for surgical procedures (18.5% vs 7.5%), 3 times more likely to cross state lines for radiation therapy services (16.9% vs 5.7%), and almost 4 times more likely to cross state lines for chemotherapy services (16.3% vs 4.2%). Conclusions and RelevanceIn this cross-sectional study of Medicare claims data, a notable proportion of cancer services occurred across state lines, particularly for rural-residing patients. These results highlight the need for cross-state telehealth policies that recognize the prevalence of care delivery from geographically distant specialized oncology services. 

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5. Information criteria for model selection

   https://doi.org/10.1002/wics.1607  

   Zhang, Jiawei; Yang, Yuhong; Ding, Jie (February 2023, WIREs Computational Statistics)

   Abstract The rapid development of modeling techniques has brought many opportunities for data‐driven discovery and prediction. However, this also leads to the challenge of selecting the most appropriate model for any particular data task. Information criteria, such as the Akaike information criterion (AIC) and Bayesian information criterion (BIC), have been developed as a general class of model selection methods with profound connections with foundational thoughts in statistics and information theory. Many perspectives and theoretical justifications have been developed to understand when and how to use information criteria, which often depend on particular data circumstances. This review article will revisit information criteria by summarizing their key concepts, evaluation metrics, fundamental properties, interconnections, recent advancements, and common misconceptions to enrich the understanding of model selection in general. This article is categorized under:Data: Types and Structure > Traditional Statistical DataStatistical Learning and Exploratory Methods of the Data Sciences > Modeling MethodsStatistical and Graphical Methods of Data Analysis > Information Theoretic MethodsStatistical Models > Model Selection 

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