Genomic Transformation of the Picoeukaryote Ostreococcus tauri

Common problems hindering rapid progress in Plant Sciences include cellular, tissue and whole organism complexity, and notably the high level of genomic redundancy affecting simple genetics in higher plants. The novel model organism Ostreococcus tauri is the smallest free-living eukaryote known to date, and possesses a greatly reduced genome size and cellular complexity(1,2), manifested by the presence of just one of most organelles (mitochondrion, chloroplast, golgi stack) per cell, and a genome containing only ~8000 genes. Furthermore, the combination of unicellularity and easy culture provides a platform amenable to chemical biology approaches. Recently, Ostreococcus has been successfully employed to study basic mechanisms underlying circadian timekeeping(3-6). Results from this model organism have impacted not only plant science, but also mammalian biology(7). This example highlights how rapid experimentation in a simple eukaryote from the green lineage can accelerate research in more complex organisms by generating testable hypotheses using methods technically feasible only in this background of reduced complexity. Knowledge of a genome and the possibility to modify genes are essential tools in any model species. Genomic(1), Transcriptomic(8), and Proteomic(9) information for this species is freely available, whereas the previously reported methods(6,10) to genetically transform Ostreococcus are known to few laboratories worldwide. In this article, the experimental methods to genetically transform this novel model organism with an overexpression construct by means of electroporation are outlined in detail, as well as the method of inclusion of transformed cells in low percentage agarose to allow selection of transformed lines originating from a single transformed cell. Following the successful application of Ostreococcus to circadian research, growing interest in Ostreococcus can be expected from diverse research areas within and outside plant sciences, including biotechnological areas. Researchers from a broad range of biological and medical sciences that work on conserved biochemical pathways may consider pursuing research in Ostreococcus, free from the genomic and organismal complexity of larger model species

Large atom number Bose-Einstein condensate of sodium

We describe the setup to create a large Bose-Einstein condensate containing more than 120x10^6 atoms. In the experiment a thermal beam is slowed by a Zeeman slower and captured in a dark-spot magneto-optical trap (MOT). A typical dark-spot MOT in our experiments contains 2.0x10^10 atoms with a temperature of 320 microK and a density of about 1.0x10^11 atoms/cm^3. The sample is spin polarized in a high magnetic field, before the atoms are loaded in the magnetic trap. Spin polarizing in a high magnetic field results in an increase in the transfer efficiency by a factor of 2 compared to experiments without spin polarizing. In the magnetic trap the cloud is cooled to degeneracy in 50 s by evaporative cooling. To suppress the 3-body losses at the end of the evaporation the magnetic trap is decompressed in the axial direction.Comment: 11 pages, 12 figures, submitted to Review Of Scientific Instrument

Adverse effects of personalized automated feedback

In large classes with hundreds of students, it is rarely feasible to provide students with individual feedback on their performance. Automatically generated personalized feedback on students' performance might help to overcome this issue, but available empirical effect studies are inconclusive due to lack of methodological rigor. This study uses a repetitive randomized control experiment to explore whether automatically generated feedback is effective and for which students. Our results indicate that feedback does not have a positive effect on performance for all students. Some groups benefit from receiving personalized feedback, while others do not perform better than the control group. Students that perform average benefit most from receiving personalized feedback. However, lower-scoring students who received feedback tend to have lower attrition rates and if they participate at the final exam, their performance is not higher than the control group. Therefore, providing automated feedback is not something that should be undertaken mindlessly.</p

Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection

Accurate pulmonary nodule detection is a crucial step in lung cancer screening. Computer-aided detection (CAD) systems are not routinely used by radiologists for pulmonary nodule detection in clinical practice despite their potential benefits. Maximum intensity projection (MIP) images improve the detection of pulmonary nodules in radiological evaluation with computed tomography (CT) scans. Inspired by the clinical methodology of radiologists, we aim to explore the feasibility of applying MIP images to improve the effectiveness of automatic lung nodule detection using convolutional neural networks (CNNs). We propose a CNN-based approach that takes MIP images of different slab thicknesses (5 mm, 10 mm, 15 mm) and 1 mm axial section slices as input. Such an approach augments the two-dimensional (2-D) CT slice images with more representative spatial information that helps discriminate nodules from vessels through their morphologies. Our proposed method achieves sensitivity of 92.67% with 1 false positive per scan and sensitivity of 94.19% with 2 false positives per scan for lung nodule detection on 888 scans in the LIDC-IDRI dataset. The use of thick MIP images helps the detection of small pulmonary nodules (3 mm-10 mm) and results in fewer false positives. Experimental results show that utilizing MIP images can increase the sensitivity and lower the number of false positives, which demonstrates the effectiveness and significance of the proposed MIP-based CNNs framework for automatic pulmonary nodule detection in CT scans. The proposed method also shows the potential that CNNs could gain benefits for nodule detection by combining the clinical procedure.Comment: Submitted to IEEE TM

Light and circadian regulation of clock components aids flexible responses to environmental signals

The circadian clock measures time across a 24h period, increasing fitness by phasing biological processes to the most appropriate time of day. The interlocking feedback loop mechanism of the clock is conserved across species; however, the number of loops varies. Mathematical and computational analyses have suggested that loop complexity affects the overall flexibility of the oscillator, including its responses to entrainment signals. We used a discriminating experimental assay, at the transition between different photoperiods, in order to test this proposal in a minimal circadian network (in Ostreococcus tauri) and a more complex network (in Arabidopsis thaliana). Transcriptional and translational reporters in O.tauri primarily tracked dawn or dusk, whereas in A.thaliana, a wider range of responses were observed, consistent with its more flexible clock. Model analysis supported the requirement for this diversity of responses among the components of the more complex network. However, these and earlier data showed that the O.tauri network retains surprising flexibility, despite its simple circuit. We found that models constructed from experimental data can show flexibility either from multiple loops and/or from multiple light inputs. Our results suggest that O.tauri has adopted the latter strategy, possibly as a consequence of genomic reduction

The natural language processing of radiology requests and reports of chest imaging:Comparing five transformer models’ multilabel classification and a proof-of-concept study

Background: Radiology requests and reports contain valuable information about diagnostic findings and indications, and transformer-based language models are promising for more accurate text classification. Methods: In a retrospective study, 2256 radiologist-annotated radiology requests (8 classes) and reports (10 classes) were divided into training and testing datasets (90% and 10%, respectively) and used to train 32 models. Performance metrics were compared by model type (LSTM, Bertje, RobBERT, BERT-clinical, BERT-multilingual, BERT-base), text length, data prevalence, and training strategy. The best models were used to predict the remaining 40,873 cases’ categories of the datasets of requests and reports. Results: The RobBERT model performed the best after 4000 training iterations, resulting in AUC values ranging from 0.808 [95% CI (0.757–0.859)] to 0.976 [95% CI (0.956–0.996)] for the requests and 0.746 [95% CI (0.689–0.802)] to 1.0 [95% CI (1.0–1.0)] for the reports. The AUC for the classification of normal reports was 0.95 [95% CI (0.922–0.979)]. The predicted data demonstrated variability of both diagnostic yield for various request classes and request patterns related to COVID-19 hospital admission data. Conclusion: Transformer-based natural language processing is feasible for the multilabel classification of chest imaging request and report items. Diagnostic yield varies with the information in the requests

Observation of shock waves in a large Bose-Einstein condensate

We observe the formation of shock waves in a Bose-Einstein condensate containing a large number of sodium atoms. The shock wave is initiated with a repulsive, blue-detuned light barrier, intersecting the BEC, after which two shock fronts appear. We observe breaking of these waves when the size of these waves approaches the healing length of the condensate. At this time, the wave front splits into two parts and clear fringes appear. The experiment is modeled using an effective 1D Gross-Pitaevskii-like equation and gives excellent quantitative agreement with the experiment, even though matter waves with wavelengths two orders of magnitude smaller than the healing length are present. In these experiments, no significant heating or particle loss is observed.Comment: 7 pages, 7 figure

Cavity Optomechanical Magnetometer

A cavity optomechanical magnetometer is demonstrated where the magnetic field induced expansion of a magnetostrictive material is transduced onto the physical structure of a highly compliant optical microresonator. The resulting motion is read out optically with ultra-high sensitivity. Detecting the magnetostrictive deformation of Terfenol-D with a toroidal whispering gallery mode (TWGM) resonator a peak sensitivity of 400 nT/Hz^.5 was achieved with theoretical modelling predicting that sensitivities of up to 500 fT/Hz^.5 may be possible. This chip-based magnetometer combines high-sensitivity and large dynamic range with small size and room temperature operation