Towards an improved methodology for automated readability prediction

Since the first half of the 20th century, readability formulas have been widely employed to automatically predict the readability of an unseen text. In this article, the formulas and the text characteristics they are composed of are evaluated in the context of large Dutch and English corpora. We describe the behaviour of the formulas and the text characteristics by means of correlation matrices and a principal component analysis, and test the methodological validity of the formulas by means of collinearity tests. Both the correlation matrices and the principal component analysis show that the formulas described in this paper strongly correspond, regardless of the language for which they were designed. Furthermore, the collinearity test reveals shortcomings in the methodology that was used to create some of the existing readability formulas. All of this leads us to conclude that a new readability prediction method is needed. We finally make suggestions to come to a cleaner methodology and present web applications that will help us collect data to compile a new gold standard for readability prediction

Ultrafast Photoclick Reaction for Selective 18F-Positron Emission Tomography Tracer Synthesis in Flow

The development of very fast, clean, and selective methods for indirect labeling in PET tracer synthesis is an ongoing challenge. Here we present the development of an ultrafast photoclick method for the synthesis of short-lived 18F-PET tracers based on the photocycloaddition reaction of 9,10-phenanthrenequinones with electron-rich alkenes. The respective precursors are synthetically easily accessible and can be functionalized with various target groups. Using a flow photo-microreactor, the photoclick reaction can be performed in 60 s, and clinically relevant tracers for prostate cancer and bacterial infection imaging were prepared to demonstrate practicality of the method

Folate Receptor-Beta Has Limited Value for Fluorescent Imaging in Ovarian, Breast and Colorectal Cancer

Aims Tumor-specific targeted imaging is rapidly evolving in cancer diagnosis. The folate receptor alpha (FR-alpha) has already been identified as a suitable target for cancer therapy and imaging. FR-alpha is present on similar to 40% of human cancers. FR-beta is known to be expressed on several hematologic malignancies and on activated macrophages, but little is known about FR-beta expression in solid tumors. Additional or simultaneous expression of FR-beta could help extend the indications for folate-based drugs and imaging agents. In this study, the expression pattern of FR-beta is evaluated in ovarian, breast and colorectal cancer. Methods FR-beta expression was analyzed by semi-quantitative scoring of immunohistochemical staining on tissue microarrays (TMAs) of 339 ovarian cancer patients, 418 breast cancer patients, on 20 slides of colorectal cancer samples and on 25 samples of diverticulitis. Results FR-beta expression was seen in 21% of ovarian cancer samples, 9% of breast cancer samples, and 55% of colorectal cancer samples. Expression was weak or moderate. Of the diverticulitis samples, 80% were positive for FR-beta expression in macrophages. FR-beta status neither correlated to known disease-related variables, nor showed association with overall survival and progression free survival in ovarian and breast cancer. In breast cancer, negative axillary status was significantly correlated to FR-beta expression (p=0.022). Conclusions FR-beta expression was low or absent in the majority of ovarian, breast and colorectal tumor samples. From the present study we conclude that the low FR-beta expression in ovarian and breast tumor tissue indicates limited practical use of this receptor in diagnostic imaging and therapeutic purposes. Due to weak expression, FR-beta is not regarded as a suitable target in colorectal cancer

In vitro imaging of bacteria using (18)F-fluorodeoxyglucose micro positron emission tomography

Positron emission tomography (PET) with fluorine-18-fluorodeoxyglucose ((18)F-FDG) can be applied to detect infection and inflammation. However, it was so far not known to what extent bacterial pathogens may contribute to the PET signal. Therefore, we investigated whether clinical isolates of frequently encountered bacterial pathogens take up (18)F-FDG in vitro, and whether FDG inhibits bacterial growth as previously shown for 2-deoxy-glucose. 22 isolates of Gram-positive and Gram-negative bacterial pathogens implicated in fever and inflammation were incubated with (18)F-FDG and uptake of (18)F-FDG was assessed by gamma-counting and µPET imaging. Possible growth inhibition by FDG was assayed with Staphylococcus aureus and the Gram-positive model bacterium Bacillus subtilis. The results show that all tested isolates accumulated (18)F-FDG actively. Further, (18)F-FDG uptake was hampered in B. subtilis pts mutants impaired in glucose uptake. FDG inhibited growth of S. aureus and B. subtilis only to minor extents, and this effect was abrogated by pts mutations in B. subtilis. These observations imply that bacteria may contribute to the signals observed in FDG-PET infection imaging in vivo. Active bacterial FDG uptake is corroborated by the fact that the B. subtilis phosphotransferase system is needed for (18)F-FDG uptake, while pts mutations protect against growth inhibition by FDG

Readability Annotation: Replacing the Expert by the Crowd

This paper investigates two strategies for collecting readability assessments, an Expert Readers application intended to collect fine-grained readability assessments from language experts and a Sort by Readability application designed to be intuitive and open for everyone having internet access. We show that the data sets resulting from both annotation strategies are very similar. We conclude that crowdsourcing is a viable alternative to the opinions of language experts for readability prediction.

Flow-controlled ventilation decreases mechanical power in postoperative ICU patients

Background: Mechanical power (MP) is the energy delivered by the ventilator to the respiratory system and combines factors related to the development of ventilator-induced lung injury (VILI). Flow-controlled ventilation (FCV) is a new ventilation mode using a constant low flow during both inspiration and expiration, which is hypothesized to lower the MP and to improve ventilation homogeneity. Data demonstrating these effects are scarce, since previous studies comparing FCV with conventional controlled ventilation modes in ICU patients suffer from important methodological concerns. Objectives: This study aims to assess the difference in MP between FCV and pressure-controlled ventilation (PCV). Secondary aims were to explore the effect of FCV in terms of minute volume, ventilation distribution and homogeneity, and gas exchange. Methods: This is a physiological study in post-cardiothoracic surgery patients requiring mechanical ventilation in the ICU. During PCV at baseline and 90 min of FCV, intratracheal pressure, airway flow and electrical impedance tomography (EIT) were measured continuously, and hemodynamics and venous and arterial blood gases were obtained repeatedly. Pressure–volume loops were constructed for the calculation of the MP. Results: In 10 patients, optimized FCV versus PCV resulted in a lower MP (7.7 vs. 11.0 J/min; p = 0.004). Although FCV did not increase overall ventilation homogeneity, it did lead to an improved ventilation of the dependent lung regions. A stable gas exchange at lower minute volumes was obtained. Conclusions: FCV resulted in a lower MP and improved ventilation of the dependent lung regions in post-cardiothoracic surgery patients on the ICU. Trial registration Clinicaltrials.gov identifier: NCT05644418. Registered 1 December 2022, retrospectively registered.</p

Using the crowd for readability prediction

While human annotation is crucial for many natural language processing tasks, it is often very expensive and time-consuming. Inspired by previous work on crowdsourcing, we investigate the viability of using non-expert labels instead of gold standard annotations from experts for a machine learning approach to automatic readability prediction. In order to do so, we evaluate two different methodologies to assess the readability of a wide variety of text material: A more traditional setup in which expert readers make readability judgments and a crowdsourcing setup for users who are not necessarily experts. To this purpose two assessment tools were implemented: a tool where expert readers can rank a batch of texts based on readability, and a lightweight crowdsourcing tool, which invites users to provide pairwise comparisons. To validate this approach, readability assessments for a corpus of written Dutch generic texts were gathered. By collecting multiple assessments per text, we explicitly wanted to level out readers' background knowledge and attitude. Our findings show that the assessments collected through both methodologies are highly consistent and that crowdsourcing is a viable alternative to expert labeling. This is a good news as crowdsourcing is more lightweight to use and can have access to a much wider audience of potential annotators. By performing a set of basic machine learning experiments using a feature set that mainly encodes basic lexical and morpho-syntactic information, we further illustrate how the collected data can be used to perform text comparisons or to assign an absolute readability score to an individual text. We do not focus on optimising the algorithms to achieve the best possible results for the learning tasks, but carry them out to illustrate the various possibilities of our data sets. The results on different data sets, however, show that our system outperforms the readability formulas and a baseline language modelling approach. We conclude that readability assessment by comparing texts is a polyvalent methodology, which can be adapted to specific domains and target audiences if required