A Cultural Evolutionary Model for the Law of Abbreviation.

Efficiency principles are increasingly called upon to study features of human language and communication. Zipf's law of abbreviation is widely seen as a classic instance of a linguistic pattern brought about by language users' search for efficient communication. The "law"-a recurrent correlation between the frequency of words and their brevity-is a near-universal principle of communication, having been found in all of the hundreds of human languages where it has been tested, and a few nonhuman communication systems as well.

Multi-modal fusion and feature enhancement U-Net coupling with stem cell niches proximity estimation for voxel-wise GBM recurrence prediction.

We aim to develop a Multi-modal Fusion and Feature Enhancement U-Net (MFFE U-Net) coupling with stem cell niche proximity estimation to improve voxel-wise Glioblastoma (GBM) recurrence prediction.57 patients with pre- and post-surgery magnetic resonance (MR) scans were retrospectively solicited from 4 databases. Post-surgery MR scans included two months before the clinical diagnosis of recurrence and the day of the radiologicaly confirmed recurrence.

Fusion of deterministically generated photonic graph states.

Entanglement has evolved from an enigmatic concept of quantum physics to a key ingredient of quantum technology. It explains correlations between measurement outcomes that contradict classical physics and has been widely explored with small sets of individual qubits. Multi-partite entangled states build up in gate-based quantum-computing protocols and-from a broader perspective-were proposed as the main resource for measurement-based quantum-information processing.

The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights.

Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters.

Adverse radiation effect versus tumor progression following stereotactic radiosurgery for brain metastases: Implications of radiologic uncertainty.

Background: Adverse radiation effect (ARE) following stereotactic radiosurgery (SRS) for brain metastases is challenging to distinguish from tumor progression. This study characterizes the clinical implications of radiologic uncertainty (RU).

Methods: Cases reviewed retrospectively at a single-institutional, multi-disciplinary SRS Tumor Board between 2015-2022 for RU following SRS were identified.

Tungsten Filled 3-Dimensional Printed Lung Blocks for Total Body Irradiation.

Purpose: Lung blocks for total-body irradiation are commonly used to reduce lung dose and prevent radiation pneumonitis. Currently, molten Cerrobend containing toxic materials, specifically lead and cadmium, is poured into molds to construct blocks. We propose a streamlined method to create 3-dimensional (3D)-printed lung block shells and fill them with tungsten ball bearings to remove lead and improve overall accuracy in the block manufacturing workflow.

Expectations of Processing Ease, Informativeness, and Accuracy Guide Toddlers' Processing of Novel Communicative Cues.

Discovering the meaning of novel communicative cues is challenging and amounts to navigating an unbounded hypothesis space. Several theories posit that this problem can be simplified by relying on positive expectations about the cognitive utility of communicated information. These theories imply that learners should assume that novel communicative cues tend to have low processing costs and high cognitive benefits.

Puzzling out graphic codes.

This response takes advantage of the diverse and wide-ranging series of commentaries to clarify some aspects of the target article, and flesh out other aspects. My central point is a plea to take graphic codes seriously as codes, rather than as a kind of visual art or as a byproduct of spoken language; only in this way can the puzzle of ideography be identified and solved. In this perspective, I argue that graphic codes do not derive their expressive power from iconicity alone (unlike visual arts), and I clarify the peculiar relationship that ties writing to spoken language.

Statistical signals of copying are robust to time- and space-averaging.

Cattle brands (ownership marks left on animals) are subject to forces influencing other graphic codes: the copying of constituent parts, pressure for distinctiveness and pressure for complexity. The historical record of cattle brands in some US states is complete owing to legal registration, providing a unique opportunity to assess how sampling processes leading to time- and space-averaging influence our ability to make inferences from limited datasets in fields like archaeology. In this preregistered study, we used a dataset of ~81,000 Kansas cattle brands (1990-2016) to explore two aspects: (1) the relative influence of copying, pressure for distinctiveness and pressure for complexity on the creation and diffusion of brand components; and (2) the effects of time- and space-averaging on statistical signals.

Zipf's Law of Abbreviation holds for individual characters across a broad range of writing systems.

Zipf's Law of Abbreviation - the idea that more frequent symbols in a code are simpler than less frequent ones - has been shown to hold at the level of words in many languages. We tested whether it holds at the level of individual written characters. Character complexity is similar to word length in that it requires more cognitive and motor effort for producing and processing more complex symbols.

Development and multi-institutional validation of a convolutional neural network to detect vertebral body mis-alignments in 2D x-ray setup images.

Background: Misalignment to the incorrect vertebral body remains a rare but serious patient safety risk in image-guided radiotherapy (IGRT).

Purpose: Our group has proposed that an automated image-review algorithm be inserted into the IGRT process as an interlock to detect off-by-one vertebral body errors. This study presents the development and multi-institutional validation of a convolutional neural network (CNN)-based approach for such an algorithm using patient image data from a planar stereoscopic x-ray IGRT system.

The puzzle of ideography.

An ideography is a general-purpose code made of pictures that do not encode language, which can be used autonomously - not just as a mnemonic prop - to encode information on a broad range of topics. Why are viable ideographies so hard to find? I contend that self-sufficient graphic codes need to be narrowly specialized. Writing systems are only an apparent exception: At their core, they are notations of a spoken language.

Quality Assurance for AI-Based Applications in Radiation Therapy.

Recent advancements in artificial intelligence (AI) in the domain of radiation therapy (RT) and their integration into modern software-based systems raise new challenges to the profession of medical physics experts. These AI algorithms are typically data-driven, may be continuously evolving, and their behavior has a degree of (acceptable) uncertainty due to inherent noise in training data and the substantial number of parameters that are used in the algorithms. These characteristics request adaptive, and new comprehensive quality assurance (QA) approaches to guarantee the individual patient treatment quality during AI algorithm development and subsequent deployment in a clinical RT environment.

Knowledge-based planning algorithm for lung SBRT with robust Bayesian stochastic frontier analysis and missing data management.

Purpose: A knowledge-based planning technique is developed based on Bayesian stochastic frontier analysis. A novel missing data management is applied in order to handle missing organs-at-risk and work with a complete dataset.

Methods: Geometric metrics are used to predict DVH metrics for lung SBRT with a retrospective database of 299 patients.

Predicting Adverse Radiation Effects in Brain Tumors After Stereotactic Radiotherapy With Deep Learning and Handcrafted Radiomics.

Introduction: There is a cumulative risk of 20-40% of developing brain metastases (BM) in solid cancers. Stereotactic radiotherapy (SRT) enables the application of high focal doses of radiation to a volume and is often used for BM treatment. However, SRT can cause adverse radiation effects (ARE), such as radiation necrosis, which sometimes cause irreversible damage to the brain.

Automated detection and segmentation of non-small cell lung cancer computed tomography images.

Detection and segmentation of abnormalities on medical images is highly important for patient management including diagnosis, radiotherapy, response evaluation, as well as for quantitative image research. We present a fully automated pipeline for the detection and volumetric segmentation of non-small cell lung cancer (NSCLC) developed and validated on 1328 thoracic CT scans from 8 institutions. Along with quantitative performance detailed by image slice thickness, tumor size, image interpretation difficulty, and tumor location, we report an in-silico prospective clinical trial, where we show that the proposed method is faster and more reproducible compared to the experts.

Adverse radiation effect and freedom from progression following repeat stereotactic radiosurgery for brain metastases.

Objective: The authors previously evaluated risk and time course of adverse radiation effects (AREs) following stereotactic radiosurgery (SRS) for brain metastases, excluding lesions treated after prior SRS. In the present analysis they focus specifically on single-fraction salvage SRS to brain metastases previously treated with SRS or hypofractionated SRS (HFSRS), evaluating freedom from progression (FFP) and the risk and time course of AREs.

Methods: Brain metastases treated from September 1998 to May 2019 with single-fraction SRS after prior SRS or HFSRS were analyzed.

Prospective Clinical Validation of Virtual Patient-Specific Quality Assurance of Volumetric Modulated Arc Therapy Radiation Therapy Plans.

Purpose: Performing measurement-based patient-specific quality assurance (PSQA) is recognized as a resource-intensive and time inefficient task in the radiation therapy treatment workflow. Paired with technological refinements in modern radiation therapy, research toward measurement-free PSQA has seen increased interest during the past 5 years. However, these efforts have not been clinically implemented or prospectively validated in the United States.

Cultural Evolution of Precise and Agreed-Upon Semantic Conventions in a Multiplayer Gaming App.

The amount of information conveyed by linguistic conventions depends on their precision, yet the codes that humans and other animals use to communicate are quite ambiguous: they may map several vague meanings to the same symbol. How does semantic precision evolve, and what are the constraints that limit it? We address this question using a multiplayer gaming app, where individuals communicate with one another in a scaled-up referential game. Here, the goal is for a sender to use black and white symbols to communicate colors.

An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognostication.

Despite widespread adoption of electronic health records (EHRs), most hospitals are not ready to implement data science research in the clinical pipelines. Here, we develop MEDomics, a continuously learning infrastructure through which multimodal health data are systematically organized and data quality is assessed with the goal of applying artificial intelligence for individual prognosis. Using this framework, currently composed of thousands of individuals with cancer and millions of data points over a decade of data recording, we demonstrate prognostic utility of this framework in oncology.

Resection with intraoperative cesium-131 brachytherapy as salvage therapy for recurrent brain tumors.

Objective: The authors' objective was to examine the safety and efficacy of salvage intracranial cesium-131 brachytherapy in combination with resection of recurrent brain tumors.

Methods: The authors conducted a retrospective chart review of consecutive patients treated with intraoperative intracranial cesium-131 brachytherapy at a single institution. Permanent suture-stranded cesium-131 seeds were implanted in the resection cavity after maximal safe tumor resection.

A systematic review and meta-analysis of liver tumor position variability during SBRT using various motion management and IGRT strategies.

Purpose: To suggest PTV margins for liver SBRT with different motion management strategies based on a systematic review and meta-analysis.

Methods: In accordance with Preferred-Reporting-Items-for-Systematic-Reviews-and-Meta-Analyses (PRISMA), a systematic review in PubMed, Embase and Medline databases was performed for liver tumor position variability. From an initial 533 studies published before October 2020, 36 studies were categorized as 18 free-breathing (FB; n = 401), 9 abdominal compression (AC; n = 145) and 9 breath-hold (BH; n = 126).