Distributed Representations for Cognitive Control in Frontal Medial Cortex.

In natural and artificial neural networks, modularity and distributed structure afford complementary but competing benefits. The former allows for hierarchical representations that can flexibly recombine modules to address novel problems, whereas the latter can benefit from less constrained training, potentially uncovering fruitful statistical regularities. Here, we investigate these competing demands in the context of human sequential behavior.

Clinical, Dosimetric and Radiomic Features Predictive of Lung Toxicity After (Chemo)Radiotherapy.

Background: Treatment of locally advanced non small cell lung cancer (LA-NSCLC) is based on (chemo)radiotherapy, which may cause acute lung toxicity: radiation pneumonitis (RP). Its frequency seems to increase by the use of adjuvant durvalumab therapy.

Aims: To identify clinical, dosimetric, and radiomic factors associated with grade (G)≥2 RP and build a prediction model based on selected parameters.

Industry Perceptions and Experiences with the Access Consortium New Active Substance Work-Sharing Initiative (NASWSI): Survey Results and Recommendations.

The Access Consortium New Active Substance Work-Sharing Initiative, or "Access" for simplicity, allows regulatory authorities (RAs) of the Access Consortium countries to jointly review applications for the registration of new active substances or for new indications. Using a survey developed by the pharmaceutical industry trade associations of the five Access Consortium countries-Australia, Canada, Singapore, Switzerland, and the United Kingdom (UK)-this study gathered insights into the perceptions and experiences of the Access pathway held by affiliates of pharmaceutical companies. Understanding industry perceptions of Access is important for the success of the initiative, as participation is voluntary.

UroPredict: Machine learning model on real-world data for prediction of kidney cancer recurrence (UroCCR-120).

Renal cell carcinoma (RCC) is most often diagnosed at a localized stage, where surgery is the standard of care. Existing prognostic scores provide moderate predictive performance, leading to challenges in establishing follow-up recommendations after surgery and in selecting patients who could benefit from adjuvant therapy. In this study, we developed a model for individual postoperative disease-free survival (DFS) prediction using machine learning (ML) on real-world prospective data.

Contrastive image adaptation for acquisition shift reduction in medical imaging.

The domain shift, or acquisition shift in medical imaging, is responsible for potentially harmful differences between development and deployment conditions of medical image analysis techniques. There is a growing need in the community for advanced methods that could mitigate this issue better than conventional approaches. In this paper, we consider configurations in which we can expose a learning-based pixel level adaptor to a large variability of unlabeled images during its training, i.

A CT-Based Clinical, Radiological and Radiomic Machine Learning Model for Predicting Malignancy of Solid Renal Tumors (UroCCR-75).

Background: Differentiating benign from malignant renal tumors is important for patient management, and it may be improved by quantitative CT features analysis including radiomic.

Purpose: This study aimed to compare performances of machine learning models using bio-clinical, conventional radiologic and 3D-radiomic features for the differentiation of benign and malignant solid renal tumors using pre-operative multiphasic contrast-enhanced CT examinations.

Materials And Methods: A unicentric retrospective analysis of prospectively acquired data from a national kidney cancer database was conducted between January 2016 and December 2020.

The final frontier: ecological and evolutionary dynamics of a global parasite invasion.

Studying rapid biological changes accompanying the introduction of alien organisms into native ecosystems can provide insights into fundamental ecological and evolutionary theory. While powerful, this quasi-experimental approach is difficult to implement because the timing of invasions and their consequences are hard to predict, meaning that baseline pre-invasion data are often missing. Exceptionally, the eventual arrival of (hereafter Varroa) in Australia has been predicted for decades.

Modelling daily weight variation in honey bee hives.

A quantitative understanding of the dynamics of bee colonies is important to support global efforts to improve bee health and enhance pollination services. Traditional approaches focus either on theoretical models or data-centred statistical analyses. Here we argue that the combination of these two approaches is essential to obtain interpretable information on the state of bee colonies and show how this can be achieved in the case of time series of intra-day weight variation.

Machine-learning approach for prediction of pT3a upstaging and outcomes of localized renal cell carcinoma (UroCCR-15).

Objectives: To assess the impact of pathological upstaging from clinically localized to locally advanced pT3a on survival in patients with renal cell carcinoma (RCC), as well as the oncological safety of various surgical approaches in this setting, and to develop a machine-learning-based, contemporary, clinically relevant model for individual preoperative prediction of pT3a upstaging.

Materials And Methods: Clinical data from patients treated with either partial nephrectomy (PN) or radical nephrectomy (RN) for cT1/cT2a RCC from 2000 to 2019, included in the French multi-institutional kidney cancer database UroCCR, were retrospectively analysed. Seven machine-learning algorithms were applied to the cohort after a training/testing split to develop a predictive model for upstaging to pT3a.

Traces of a neonicotinoid pesticide stimulate different honey bee colony activities, but do not increase colony size or longevity.

How neonicotinoid contamination affects honey bees remains controversial. Studies have yielded contradictory results, and few have examined effects on colony development. Here we report the results of a comprehensive five-year study of the effects of the neonicotinoid imidacloprid on honey bee colonies.

Non-additive gene interactions underpin molecular and phenotypic responses in honey bee larvae exposed to imidacloprid and thymol.

Understanding the cumulative risk of chemical mixtures at environmentally realistic concentrations is a key challenge in honey bee ecotoxicology. Ecotoxicogenomics, including transcriptomics, measures responses in individual organisms at the molecular level which can provide insights into the mechanisms underlying phenotypic responses induced by one or more stressors and link impacts on individuals to populations. Here, fifth instar honey bee larvae were sampled from a previously reported field experiment exploring the phenotypic impacts of environmentally realistic chronic exposures of the pesticide imidacloprid (5 μg.

Evaluation of conventional and deep learning based image harmonization methods in radiomics studies.

To evaluate the impact of image harmonization on outcome prediction models using radiomics.234 patients from the Brain Tumor Image Segmentation Benchmark (BRATS) dataset with T1 MRI were enrolled in this study. Images were harmonized to a reference image using histogram matching (H) and a generative adversarial network (GAN)-based method (H).

A Model-Strengthened Imaging Biomarker for Survival Prediction in EGFR-Mutated Non-small-cell Lung Carcinoma Patients Treated with Tyrosine Kinase Inhibitors.

Non-small-cell lung carcinoma is a frequent type of lung cancer with a bad prognosis. Depending on the stage and genomics, several therapeutical approaches are used. Tyrosine Kinase Inhibitors (TKI) may be successful for a time in the treatment of EGFR-mutated non-small cells lung carcinoma.

Role of 3D volume growth rate for drug activity evaluation in meningioma clinical trials: the example of the CEVOREM study.

Background: We aimed to improve the assessment of the drug activity in meningioma clinical trials based on the study of the 3D volume growth rate (3DVGR) in a series of aggressive meningiomas. We secondarily aimed to correlate 3DVGR study with patient outcome.

Methods: We performed a post hoc analysis based on volume data and 3DVGR extracted from CEVOREM study including 18 patients with 32 recurrent high-grade meningiomas and treated with everolimus and octreotide.

Spatial mechanistic modeling for prediction of the growth of asymptomatic meningiomas.

Background And Objective: Mathematical modeling of tumor growth draws interest from the medical community as they have the potential to improve patients' care and the use of public health resources. The main objectives of this work are to model the growth of meningiomas - slow-growing benign tumors requiring extended imaging follow-up - and to predict tumor volume and shape at a later desired time using only two times examinations.

Methods: We develop two variants of a 3D partial differential system of equations (PDE) which yield after a spatial integration systems of ordinary differential equations (ODE) that relate tumor volume with time.

Traces of a Neonicotinoid Induce Precocious Foraging and Reduce Foraging Performance in Honey Bees.

There is increasing worldwide concern about the impacts of pesticide residues on honey bees and bee colony survival, but how sublethal effects of pesticides on bees might cause colony failure remains highly controversial, with field data giving very mixed results. To explore how trace levels of the neonicotinoid pesticide imidacloprid impacted colony foraging performance, we equipped bees with RFID tags that allowed us to track their lifetime flight behavior. One group of bees was exposed to a trace concentration (5 μg/kg, ppb) of imidacloprid in sugar syrup while in the larval stage.

A prospective comparison of dynamic contrast-enhanced MRI and Cr-EDTA clearance for glomerular filtration rate measurement in 42 kidney transplant recipients.

Objectives: To evaluate the performance of dynamic contrast-enhanced MRI measurement of glomerular filtration rate (GFR) compared with the reference standard technique of urinary clearance of Cr-EDTA.

Patients And Methods: All kidney transplant recipients (KTRs) with an indication for non-urgent contrast-enhanced MRI at our institution were prospectively included between 2008 and 2012. Renographies were acquired by low-dose dynamic contrast-enhanced MRI (DCE-MRI) then fitted with a two-compartment pharmacokinetic model.

Long-term dynamics of honey bee colonies following exposure to chemical stress.

Pesticide residues have been linked to reduced bee health and increased honey bee colony failure. Most research to date has investigated the role of pesticides on individual honey bees, and it is still unclear how trace levels of pesticides change colony viability and productivity over seasonal time scales. To address this question we exposed standard bee colonies to chemical stressors known to have negative effects on individual bees, and measured the productivity of bee colonies across a whole year in two environments: near Tucson Arizona and Sydney Australia.

The development of honey bee colonies assessed using a new semi-automated brood counting method: CombCount.

Precise, objective data on brood and honey levels in honey bee colonies can be obtained through the analysis of hive frame photographs. However, accurate analysis of all the frame photographs from medium- to large-scale experiments is time-consuming. This limits the number of hives than can be practically included in honeybee studies.

Mathematical modeling of the proliferation gradient in multicellular tumor spheroids.

MultiCellular Tumor Spheroids are 3D cell cultures that can accurately reproduce the behavior of solid tumors. It has been experimentally observed that large spheroids exhibit a decreasing gradient of proliferation from the periphery to the center of these multicellular 3D models: the proportion of proliferating cells is higher in the periphery while the non-proliferating quiescent cells increase in depth. In this paper, we propose to investigate the key mechanisms involved in the establishment of this gradient with a Partial Differential Equations model that mimics the experimental set-up of growing spheroids under different nutrients supply conditions.

Using within-day hive weight changes to measure environmental effects on honey bee colonies.

Patterns in within-day hive weight data from two independent datasets in Arizona and California were modeled using piecewise regression, and analyzed with respect to honey bee colony behavior and landscape effects. The regression analysis yielded information on the start and finish of a colony's daily activity cycle, hive weight change at night, hive weight loss due to departing foragers and weight gain due to returning foragers. Assumptions about the meaning of the timing and size of the morning weight changes were tested in a third study by delaying the forager departure times from one to three hours using screen entrance gates.

Experimental estimation of stored stress within spherical microtissues : What can and cannot be inferred from cutting experiments.

Biological tissues accumulate mechanical stress during their growth. The mere measurement of the stored stress is not an easy task. We address here the spherical case and our experiments consist in performing an incision of a spherical microtissue (tumor spheroid) grown in vitro.