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In this paper, I review literature that can build foundation for integrating allocative and productive efficiency research of transit provision in the United States. Both frameworks concern transit performance but use distinct methods and talk past each other. Productive efficiency research uses economic cost models to measure the economic structure of transit service — economies of scale, economies of scope, etc. — to inform aggregate system size and output. This research shows that bus transit reaches constant scale economies between 300 and 500 vehicles but says little about when and where to allot these resources. Allocative efficiency research uses cost allocation models that disaggregate transit systems into parts — generally spatial areas or times of service — to measure service output and pricing variability. This literature suggests that transit agencies underprice or over-allocate resources to suburban areas and peak travel times. Yet, allocative efficiency scholarship ignores aggregate economic factors. This isolation in research approach and scale of focus leaves unclear the union between how much transit to produce, and when and where to produce it. Both bodies of literature can benefit from representation of transit modes other than bus transit and methodological integration. Allocative efficiency research can benefit from the use of more granular data and spatiotemporally focused studies.

Purpose: A higher minimum monitor unit (minMU) for pencil-beam scanning proton beams in intensity-modulated proton therapy is preferred for more efficient delivery. However, plan quality may be compromised when the minMU is too large. This study aimed to identify the optimal minMU (OminMU) to improve plan delivery efficiency while maintaining high plan quality., Methods: We utilized clinical plans including six anatomic sites (brain, head and neck, breast, lung, abdomen, and prostate) from 23 patients previously treated with the Varian ProBeam system. The minMU of each plan was increased from the current clinical minMU of 1.1 to 3-24 MU depending on the daily prescribed dose (DPD). The dosimetric parameters of the plans were evaluated for consistency against a 1.1-minMU plan for target coverage as well as organs-at-risk dose sparing. DPD/minMU was defined as the ratio of DPD to minMU (cGy/MU) to find the OminMU by ensuring that dosimetric parameters did not differ by >1% compared to those of the 1.1-minMU plan., Results: All plans up to 5 minMU showed no significant dose differences compared to the 1.1-minMU plan. Plan qualities remained acceptable when DPD/minMU ≥35 cGy/MU. This suggests that the 35 cGy/MU criterion can be used as the OminMU, which implies that 5 MU is the OminMU for a conventional fraction dose of 180 cGy. Treatment times were decreased by an average of 32% (max 56%, min 7%) and by an average of 1.6 min when the minMU was increased from 1.1 to OminMU., Conclusion: A clinical guideline for OminMU has been established. The minMU can be increased by 1 MU for every 35 cGy of DPD without compromising plan quality for most cases analyzed in this study. Significant treatment time reduction of up to 56% was observed when the suggested OminMU is used., (© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Numerous noninvasive methods are currently being used to determine biomarkers for diseases such as cancer. However, these methods are not always precise and reliable. Thus, there is an unmet need for better diagnostic and prognostic biomarkers that will be used to diagnose cancer in early, more treatable stages of the disease. Exosomes are extracellular vesicles of endocytic origin released by the majority of cells. Exosomes contain and transport nucleic acids, proteins, growth factors, and cytokines from their parent cells to surrounding or even distant cells via circulation in biofluids. Exosomes have attracted the interest of researchers, as recent data indicate that exosome content may be indicative of disease stages and may contribute to disease progression via exosome-mediated extracellular communication. Therefore, the contents of these vesicles are being investigated as possible biomarkers for disease diagnosis and prognosis. The functions of exosomes and their contents in disease development are becoming clearer as isolation and analytical methods, such as RNA sequencing, advance. In this review, we discuss current advances and challenges in exosomal content analyses with emphasis on information that can be generated using RNA sequencing. We also discuss how the RNA sequencing of exosomes may be used to discover novel biomarkers for the detection of different stages for various cancers using specific microRNAs that were found to be differentially expressed between healthy controls and cancer-diagnosed subjects.