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Background Cobalt-ferrite nanoparticles (Co-Fe NPs) are attractive for nanotechnology-based therapies. Thus, exploring their effect on viability of seven different cell lines representing different organs of the human body is highly important. Methods The toxicological effects of Co-Fe NPs were studied by in-vitro exposure of A549 and NCIH441 cell-lines (lung), precision-cut lung slices from rat, HepG2 cell-line (liver), MDCK cell-line (kidney), Caco-2 TC7 cell-line (intestine), TK6 (lymphoblasts) and primary mouse dendritic-cells. Toxicity was examined following exposure to Co-Fe NPs in the concentration range of 0.05 -1.2 mM for 24 and 72 h, using Alamar blue, MTT and neutral red assays. Changes in oxidative stress were determined by a dichlorodihydrofluorescein diacetate based assay. Data analysis and predictive modeling of the obtained data sets were executed by employing methods of Knowledge Discovery from Data with emphasis on a decision tree model (J48). Results Different dose–response curves of cell viability were obtained for each of the seven cell lines upon exposure to Co-Fe NPs. Increase of oxidative stress was induced by Co-Fe NPs and found to be dependent on the cell type. A high linear correlation (R 2 =0.97) was found between the toxicity of Co-Fe NPs and the extent of ROS generation following their exposure to Co-Fe NPs. The algorithm we applied to model the observed toxicity belongs to a type of supervised classifier. The decision tree model yielded the following order with decrease of the ranking parameter: NP concentrations (as the most influencing parameter), cell type (possessing the following hierarchy of cell sensitivity towards viability decrease: TK6 > Lung slices > NCIH441 > Caco-2 = MDCK > A549 > HepG2 = Dendritic) and time of exposure, where the highest-ranking parameter (NP concentration) provides the highest information gain with respect to toxicity. The validity of the chosen decision tree model J48 was established by yielding a higher accuracy than that of the well-known “naive bayes” classifier. Conclusions The observed correlation between the oxidative stress, caused by the presence of the Co-Fe NPs, with the hierarchy of sensitivity of the different cell types towards toxicity, suggests that oxidative stress is one possible mechanism for the toxicity of Co-Fe NPs.

Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from video data. Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this tool by extracting interpretable behavioral features from videos of three different head-fixed mouse preparations, as well as a freely moving mouse in an open field arena, and show how these interpretable features can facilitate downstream behavioral and neural analyses. We also show how the behavioral features produced by our model improve the precision and interpretation of these downstream analyses compared to using the outputs of either fully supervised or fully unsupervised methods alone.

We introduce an intracranial EEG (iEEG) dataset collected as part of an adversarial collaboration between proponents of two theories of consciousness: Global Neuronal Workspace Theory and Integrated Information Theory. The data were recorded from 38 patients undergoing intracranial monitoring of epileptic seizures across three research centers using the same experimental protocol. Participants were presented with suprathreshold visual stimuli belonging to four different categories (faces, objects, letters, false fonts) in three orientations (front, left, right view), and for three durations (0.5, 1.0, 1.5 s). Participants engaged in a non-speeded Go/No-Go target detection task to identify infrequent targets with some stimuli becoming task-relevant and others task-irrelevant. Participants also engaged in a motor localizer task. The data were checked for its quality and converted to Brain Imaging Data Structure (BIDS). The de-identified dataset contains demographics, clinical information, electrode reconstruction, behavioral performance, and eye-tracking data. We also provide code to preprocess and analyze the data. This dataset holds promise for reuse in consciousness science and vision neuroscience to answer questions related to stimulus processing, target detection, and task-relevance, among many others.

We describe an architecture for organizing, integrating and sharing neurophysiology data within a single laboratory or across a group of collaborators. It comprises a database linking data files to metadata and electronic laboratory notes; a module collecting data from multiple laboratories into one location; a protocol for searching and sharing data and a module for automatic analyses that populates a website. These modules can be used together or individually, by single laboratories or worldwide collaborations. A modular architecture for managing and sharing electrophysiology, behavior, colony management and other data has been built to support individual laboratories or large consortia.

Understanding brain function relies on the collective work of many labs generating reproducible results. However, reproducibility has not been systematically assessed within the context of electrophysiological recordings during cognitive behaviors. To address this, we formed a multi-lab collaboration using a shared, open-source behavioral task and experimental apparatus. Experimenters in 10 laboratories repeatedly targeted Neuropixels probes to the same location (spanning secondary visual areas, hippocampus, and thalamus) in mice making decisions; this generated a total of 121 experimental replicates, a unique dataset for evaluating reproducibility of electrophysiology experiments. Despite standardizing both behavioral and electrophysiological procedures, some experimental outcomes were highly variable. A closer analysis uncovered that variability in electrode targeting hindered reproducibility, as did the limited statistical power of some routinely used electrophysiological analyses, such as single-neuron tests of modulation by individual task parameters. Reproducibility was enhanced by histological and electrophysiological quality-control criteria. Our observations suggest that data from systems neuroscience is vulnerable to a lack of reproducibility, but that across-lab standardization, including metrics we propose, can serve to mitigate this.

Background Patients with a history of peripheral arterial disease (PAD) undergoing coronary artery bypass grafting (CABG) exhibit higher rates of complications. There are conflicting data on the survival benefits for bilateral thoracic artery (BITA) grafting compared with left internal thoracic artery (LITA) CABG in patients with PAD. The aim of the study was to explore the influence of the use of BITA grafts vs. LITA for CABG on post-operative acute lower limb ischemia (ALLI) and main post-operative complications in patients with concomitant PAD. Methods We used a propensity-score (PS) based analysis to compare outcomes between the two surgical procedures, BITA and LITA. The inverse probability of treatment weighting PS technique was applied to adjust for pre- and intra-operative confounders, and to get optimal balancing of the pre-operative data. The primary outcome was the estimate of postoperative ALLI. Secondary outcomes included overall death and death of cardiac causes within 30 days of surgery, stroke and acute kidney disease (AKD). Results The study population consisted of 1961 patients. The LITA procedure was performed in 1768 patients whereas 193 patients underwent a BITA technique. The estimate of ALLI was 14% higher in the BITA compared to the LITA ( p  < 0.001) group. Thirty-day mortality, cardiac death, occurrence of stroke and AKI did not differ significantly between the groups. Conclusions The use of both ITAs led to a significant increase in ALLI. This result was most likely caused by the complete disruption of the ITA collateral providing additional blood supply to the lower extremities. Based on our data, BITA should be used with extreme caution in PAD patients. Further research on this topic is necessary to confirm our findings.

Background This study was undertaken to compare the accuracy of chronic kidney disease-epidemiology collaboration (eGFR CKD-EPI ) to modification of diet in renal disease (eGFR MDRD ) and the Cockcroft-Gault formulas of Creatinine clearance (C CG ) equations in predicting post coronary artery bypass grafting (CABG) mortality. Methods Data from 4408 patients who underwent isolated CABG over a 11-year period were retrieved from one institutional database. Discriminatory power was assessed using the c-index and comparison between the scores’ performance was performed with DeLong, bootstrap, and Venkatraman methods. Calibration was evaluated with calibration curves and associated statistics. Results The discriminatory power was higher in eGFR CKD-EPI than eGFR MDRD and C CG (Area under Curve [AUC]:0.77, 0.55 and 0.52, respectively). Furthermore, eGFR CKD-EPI performed worse in patients with an eGFR ≤29 ml/min/1.73m 2 (AUC: 0.53) while it was not influenced by higher eGFRs, age, and body size. In contrast, the MDRD equation was accurate only in women (calibration statistics p  = 0.72), elderly patients ( p  = 0.53) and subjects with severe impairment of renal function ( p  = 0.06) whereas C CG was not significantly biased only in patients between 40 and 59 years ( p  = 0.6) and with eGFR 45–59 ml/min/1.73m 2 ( p  = 0.32) or ≥ 60 ml/min/1.73m 2 ( p  = 0.48). Conclusions In general, CKD-EPI gives the best prediction of death after CABG with unsatisfactory accuracy and calibration only in patients with severe kidney disease. In contrast, the CG and MDRD equations were inaccurate in a clinically significant proportion of patients.

This archive lists 8000 Bronze Age British axeheads, alongside associated compositional analyses, isotopic measurements and radiocarbon dates. It integrates several major existing data collection efforts and published catalogues, whilst also providing a self-consistent basic typology. It is archived as four related flat-sheet text files and could be reused to support quantitative assessment of geographic and temporal patterns in metalwork style, deposition, recovery, hoard co-occurrence and/or metallurgical compositions to name just a few salient topics.

Understanding brain function relies on the collective work of many labs generating reproducible results. However, reproducibility has not been systematically assessed within the context of electrophysiological recordings during cognitive behaviors. To address this, we formed a multi-lab collaboration using a shared, open-source behavioral task and experimental apparatus. Experimenters in 10 laboratories repeatedly targeted Neuropixels probes to the same location (spanning secondary visual areas, hippocampus, and thalamus) in mice making decisions; this generated a total of 121 experimental replicates, a unique dataset for evaluating reproducibility of electrophysiology experiments. Despite standardizing both behavioral and electrophysiological procedures, some experimental outcomes were highly variable. A closer analysis uncovered that variability in electrode targeting hindered reproducibility, as did the limited statistical power of some routinely used electrophysiological analyses, such as single-neuron tests of modulation by individual task parameters. Reproducibility was enhanced by histological and electrophysiological quality-control criteria. Our observations suggest that data from systems neuroscience is vulnerable to a lack of reproducibility, but that across-lab standardization, including metrics we propose, can serve to mitigate this.

The neural representations of prior information about the state of the world are poorly understood. To investigate this issue, we examined brain-wide Neuropixels recordings and widefield calcium imaging collected by the International Brain Laboratory. Mice were trained to indicate the location of a visual grating stimulus, which appeared on the left or right with prior probability alternating between 0.2 and 0.8 in blocks of variable length. We found that mice estimate this prior probability and thereby improve their decision accuracy. Furthermore, we report that this subjective prior is encoded in at least 20% to 30% of brain regions which, remarkably, span all levels of processing, from early sensory areas (LGd, VISp) to motor regions (MOs, MOp, GRN) and high level cortical regions (ACCd, ORBvl). This widespread representation of the prior is consistent with a neural model of Bayesian inference involving loops between areas, as opposed to a model in which the prior is incorporated only in decision making areas. This study offers the first brain-wide perspective on prior encoding at cellular resolution, underscoring the importance of using large scale recordings on a single standardized task.Competing Interest StatementThe authors have declared no competing interest.Footnotes* New mice data has been added. Also, a new spikesorting was run* https://int-brain-lab.github.io/iblenv/index.html