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This study examines the effects of laser heating on the ignition and critical combustion characteristics, such as temperature and burn time, of individual iron particles. It provides time-dependent temperature profiles of laser-ignited particles across a broad spectrum of particle size distributions and oxygen concentrations obtained from experiments, which are a useful database for further development and validation of iron particle combustion models. The herein reported datasets significantly complement those existing in the literature. In the current study, the raw data are thoroughly re-evaluated using refined approaches. For the experimental canonical configuration of single iron particle combustion, an ignition system based on laser heating provides several advantages in overcoming temperature field uncertainties and facilitating controlled environments for optical diagnostics. Despite the inherent uncertainties in laser heating, associated with non-uniform intensity profiles and particle size variations, this study addresses the critical question of how these uncertainties affect in situ measurements of particle temperature and time to reach peak temperature. This study, conducted using a numerical model, reveals a dependence of the particle temperature after laser heating on particle size. However, this dependence does not significantly impact the key parameters of iron-particle combustion, such as the maximum temperature and burn time of the laser-ignited iron particle. The study also presents a comparison between the simulated particle temperature histories and those derived from two-color pyrometry measurements for a wide range of particle size distributions and oxygen concentrations. Notably, by implementing a laser-heating sub-model into an iron particle combustion model, assuming external-diffusion-limited oxidation only up to stoichiometric FeO, the temperature evolution up to the maximum temperature is reasonably captured for a wide range of particle sizes (20–53μm) and oxygen volumetric fractions (14–21vol% O2 mixed with N2). However, with increasing oxygen concentration, the external-diffusion limited model significantly overestimates the heating rate and subsequently the maximum particle temperature.

Abstract Background Cervical cancer screening strategies should ideally be informed by population-specific data. Strategies recommended for secondary prevention, are often inadequately studied in populations with high cervical disease burdens. This report describes the test performance measured against CIN2 + /CIN3 + histology in HIV-positive women (HPW) and HIV-negative women (HNW) with the aim to determine the most effective strategies to identify South African women at risk. Methods Primary screening using visual inspection, cytology and HPV DNA (cobas®) was performed in two South African provinces on 456 HPW and 639 HNW participating in the multicentric DiaVACCS trial. Histology was obtained for 91.7% screen-positive and 42.7% screen-negative participants, and unavailable histology was determined by multiple imputation to adjust for verification bias. Cross-sectional test performance was calculated for single and combination test strategies with and without intermediate risk categories using different cut-offs. Minimum acceptability for sensitivity and specificity, treatment and follow-up numbers were considered to evaluate strategies. Results The only single test to reach acceptability in HPW was cytology (LSIL) [sensitivity 71.2%; specificity 90.5%; treatment 33.4%]; in HNW only HPV (hr) qualified [sensitivity 68.2%; specificity 85.2%; treatment 23.5%]. The universally best performing strategy which also resulted in smaller treatment numbers without intermediate risk group was primary HPV(hr), with treatment of both HPV(16/18) and cytology (ASCUS +) [HPW: sensitivity 73.6%; specificity 89.7%; treatment 34.7%. HNW: sensitivity 59.1%; specificity 93.6%; treatment 13.9%]. DNA testing for hrHPV (any) and hrHPV (16/18) was the best universally acceptable strategy with an intermediate risk category (early follow-up) in HPW [sensitivity 82.1%; specificity 96.4%; treatment 17.1%; follow-up 31.4%] and HNW [sensitivity 68.2%; specificity 96.7%; treatment 7.6%; follow-up 15.9%]. In comparison, using both HPV (16/18) and cytology (ASCUS +) as secondary tests in hrHPV positive women, decreased follow-up [HPW 13.8%, HNW 9.6%], but increased treatment [HPW 34.7%, HNW 13.9%]. Conclusion Using hrHPV (any) as primary and both HPV16/18 and cytology as secondary tests, was universally acceptable without an intermediate risk group. Strategies with follow-up groups improved screening performance with smaller treatment numbers, but with effective management of the intermediate risk group as prerequisite.

Abstract Silicon-based spin qubits offer a potential pathway toward realizing a scalable quantum computer owing to their compatibility with semiconductor manufacturing technologies. Recent experiments in this system have demonstrated crucial technologies, including high-fidelity quantum gates and multiqubit operation. However, the realization of a fault-tolerant quantum computer requires a high-fidelity spin measurement faster than decoherence. To address this challenge, we characterize and optimize the initialization and measurement procedures using the parity-mode Pauli spin blockade technique. Here, we demonstrate a rapid (with a duration of a few μs) and accurate (with >99% fidelity) parity spin measurement in a silicon double quantum dot. These results represent a significant step forward toward implementing measurement-based quantum error correction in silicon.

Abstract Because of their long coherence time and compatibility with industrial foundry processes, electron spin qubits are a promising platform for scalable quantum processors. A full-fledged quantum computer will need quantum error correction, which requires high-fidelity quantum gates. Analyzing and mitigating gate errors are useful to improve gate fidelity. Here, we demonstrate a simple yet reliable calibration procedure for a high-fidelity controlled-rotation gate in an exchange-always-on Silicon quantum processor, allowing operation above the fault-tolerance threshold of quantum error correction. We find that the fidelity of our uncalibrated controlled-rotation gate is limited by coherent errors in the form of controlled phases and present a method to measure and correct these phase errors. We then verify the improvement in our gate fidelities by randomized benchmark and gate-set tomography protocols. Finally, we use our phase correction protocol to implement a virtual, high-fidelity, controlled-phase gate.

Abstract Background Tumour hypoxia is a negative predictive and prognostic biomarker in colorectal cancer typically assessed by invasive sampling methods, which suffer from many shortcomings. This retrospective proof-of-principle study explores the potential of MRI-derived imaging markers in predicting tumour hypoxia non-invasively in patients with colorectal liver metastases (CLM). Methods A single-centre cohort of 146 CLMs from 112 patients were segmented on preoperative T2-weighted (T2W) images and diffusion-weighted imaging (DWI). HIF-1 alpha immunohistochemical staining index (high/low) was used as a reference standard. Radiomic features were extracted, and machine learning approaches were implemented to predict the degree of histopathological tumour hypoxia. Results Radiomic signatures from DWI b200 (AUC = 0.79, 95% CI 0.61–0.93, p = 0.002) and ADC (AUC = 0.72, 95% CI 0.50–0.90, p = 0.019) were significantly predictive of tumour hypoxia. Morphological T2W TE75 (AUC = 0.64, 95% CI 0.42–0.82, p = 0.092) and functional DWI b0 (AUC = 0.66, 95% CI 0.46–0.84, p = 0.069) and b800 (AUC = 0.64, 95% CI 0.44–0.82, p = 0.071) images also provided predictive information. T2W TE300 (AUC = 0.57, 95% CI 0.33–0.78, p = 0.312) and b = 10 (AUC = 0.53, 95% CI 0.33–0.74, p = 0.415) images were not predictive of tumour hypoxia. Conclusions T2W and DWI sequences encode information predictive of tumour hypoxia. Prospective multicentre studies could help develop and validate robust non-invasive hypoxia-detection algorithms. Critical relevance statement Hypoxia is a negative prognostic biomarker in colorectal cancer. Hypoxia is usually assessed by invasive sampling methods. This proof-of-principle retrospective study explores the role of AI-based MRI-derived imaging biomarkers in non-invasively predicting tumour hypoxia in patients with colorectal liver metastases (CLM). Graphical Abstract

The capuchin monkey (Sapajus apella), a New World monkey species, exhibits prominent characteristics that make it an ideal model for neuroscience research. These characteristics include its phylogenetic traits, telencephalization coefficient, anatomical structures and pathways, genetic profile, immune responses, cognitive abilities, and complex behavioral repertoires. Traditionally, methodologies for stereotactic neurosurgery in research models have relied on the use of brain atlases. However, this approach can lead to errors due to the considerable variation in brain size and shape among individual monkeys. To address this issue, we developed a protocol for deriving individual coordinates for each monkey using a straightforward and relatively inexpensive method involving MRI imaging. Our protocol utilizes a specially designed, 3D-printed stereotactic head-holder that is safe to use with an MR magnet, non-invasive placement of fiducial markers, and post-processing with open-source software. This approach enhances MRI data visualization, improves anatomical targeting, and refines the design of neurosurgical experiments. Our technique could also prove beneficial in other areas of neuroscience research that require accurate calculation of stereotaxic coordinates. Furthermore, it could be useful for other nonhuman primate species for which brain atlases are typically unavailable.

Tomlins et al. develop an Immunotherapy Response Score (IRS) to predict clinical benefit from checkpoint inhibition across solid tumors. IRS integrates TMB and gene expression, is evaluable from FFPE material, and is predictive for real-world progression-free survival and overall survival in patients receiving PD-1/PD-L1 blockade therapy.

Abstract When developing models for clinical information retrieval and decision support systems, the discrete outcomes required for training are often missing. These labels need to be extracted from free text in electronic health records. For this extraction process one of the most important contextual properties in clinical text is negation, which indicates the absence of findings. We aimed to improve large scale extraction of labels by comparing three methods for negation detection in Dutch clinical notes. We used the Erasmus Medical Center Dutch Clinical Corpus to compare a rule-based method based on ContextD, a biLSTM model using MedCAT and (finetuned) RoBERTa-based models. We found that both the biLSTM and RoBERTa models consistently outperform the rule-based model in terms of F1 score, precision and recall. In addition, we systematically categorized the classification errors for each model, which can be used to further improve model performance in particular applications. Combining the three models naively was not beneficial in terms of performance. We conclude that the biLSTM and RoBERTa-based models in particular are highly accurate accurate in detecting clinical negations, but that ultimately all three approaches can be viable depending on the use case at hand.

Abstract Parkinson’s disease (PD) is characterised by the progressive loss of midbrain dopaminergic neurons and the presence of aggregated α-synuclein (α-syn). Pericytes and microglia, two non-neuronal cells contain α-syn in the human brain, however, their role in disease processes is poorly understood. Pericytes, found surrounding the capillaries in the brain are important for maintaining the blood–brain barrier, controlling blood flow and mediating inflammation. In this study, primary human brain pericytes and microglia were exposed to two different α-synuclein aggregates. Inflammatory responses were assessed using immunocytochemistry, cytometric bead arrays and proteome profiler cytokine array kits. Fixed flow cytometry was used to investigate the uptake and subsequent degradation of α-syn in pericytes. We found that the two α-syn aggregates are devoid of inflammatory and cytotoxic actions on human brain derived pericytes and microglia. Although α-syn did not induce an inflammatory response, pericytes efficiently take up and degrade α-syn through the lysosomal pathway but not the ubiquitin–proteasome system. Furthermore, when pericytes were exposed the ubiquitin proteasome inhibitor—MG132 and α-syn aggregates, there was profound cytotoxicity through the production of reactive oxygen species resulting in apoptosis. These results suggest that the observed accumulation of α-syn in pericytes in human PD brains likely plays a role in PD pathogenesis, perhaps by causing cerebrovascular instability, under conditions of cellular stress.

Following the ignition and solid-to-liquid phase transition of a fine (on the order of 10–100µm in diameter) iron particle, the self-sustained combustion of a liquid-phase droplet of iron and its oxides takes place. The objective of the current work is to develop an interpretive and explanatory model for the liquid-phase combustion of a single fine iron particle. A zero-dimensional physicochemical model is developed assuming fast internal processes, such that the combustion rate is limited by the rate of external oxygen (O2) transport. The model considers a particle covered by a shell of liquid-phase FeO enclosing a core of liquid-phase Fe. Stefan flow and diffusion are considered for the gas-transport of O2, while the gas-transport of gas-phase Fe and FeO are calculated via diffusion only. The outward gas-phase Fe and FeO consume inward-transported O2 to stoichiometrically form hematite (Fe2O3), and the remaining oxygen that reaches the particle surface is entirely consumed to form liquid-phase FeO. The time history of simulated particle temperature shows consistent overprediction of the peak particle temperature when compared to experimental temperature measurements, indicating that the assumption of fast internal kinetics may be incorrect. The model is also unable to capture the apparent slow cooling rate observed in experiments. A further analysis is performed through a heuristic model with a calibrated reaction-rate law, where the internal diffusion of reactive Fe and O ions may become rate-limiting. The calibration of the pre-exponential factor in the Arrhenius term to match the experimental peak temperature yielded good agreement of time to peak temperature, as well as the slow cooling rate. The heuristic model considering internal diffusion predicts a plateau in peak temperature with increasing oxygen concentration. Possible uncertainties of the models, as well as future work, are discussed.

We ask a basic question about how or if the existence of observable spacetime (not only its geometry) is predicted in solutions to the Einstein equations of general relativity (GR). Here the simplest case of the GR action containing the cosmological constant term and a constraint that generates the observable measurement condition are considered. We find that including the latter suggests that a cosmological constant serves as a sufficient requirement for enabling the existence of an observable spacetime.

Smyth et al. use tissue microarrays from Alzheimer’s disease (AD) patient brains to show that PDGF-BB:PDGFRβ signalling components are reduced in AD. They then use primary human brain pericytes to elucidate a pathway by which PDGF-BB:PDGFRβ signalling in brain pericytes is disrupted in AD, thus impairing the blood brain barrier.

Abstract Introduction Neutrophil accumulation is a well-established feature of Alzheimer’s disease (AD) and has been linked to cognitive impairment by modulating disease-relevant neuroinflammatory and vascular pathways. Neutrophils express high levels of the oxidant-generating enzyme myeloperoxidase (MPO), however there has been controversy regarding the cellular source and localisation of MPO in the AD brain. Materials and methods We used immunostaining and immunoassays to quantify the accumulation of neutrophils in human AD tissue microarrays and in the brains of APP/PS1 mice. We also used multiplexed immunolabelling to define the presence of NETs in AD. Results There was an increase in neutrophils in AD brains as well as in the murine APP/PS1 model of AD. Indeed, MPO expression was almost exclusively confined to S100A8-positive neutrophils in both human AD and murine APP/PS1 brains. The vascular localisation of neutrophils in both human AD and mouse models of AD was striking and driven by enhanced neutrophil adhesion to small vessels. We also observed rare infiltrating neutrophils and deposits of MPO around plaques. Citrullinated histone H3, a marker of neutrophil extracellular traps (NETs), was also detected in human AD cases at these sites, indicating the presence of extracellular MPO in the vasculature. Finally, there was a reduction in the endothelial glycocalyx in AD that may be responsible for non-productive neutrophil adhesion to the vasculature. Conclusion Our report indicates that vascular changes may drive neutrophil adhesion and NETosis, and that neutrophil-derived MPO may lead to vascular oxidative stress and be a relevant therapeutic target in AD.

Auxotrophic primates like human beings rely on exogenous dietary vitamin B9 supplementation to meet their metabolic demands. Folates play a crucial role in nucleotide synthesis and DNA methylation. Maternal folate deficiency causes several pregnancy-related complications, perinatal defects, and early childhood cognitive impairments. New evidence suggests excess FA is a potential risk factor resulting in unfavourable genomic and epigenomic alterations. Thus, it is essential to revisit the need to consistently monitor maternal folate levels during pregnancy. Yet, to date, no point-of-care folate-monitoring biosensor is commercially available. Here, we critically appraise the advances in folate biosensors to understand the translational gaps in biosensor design. Further, our review sheds light on the potential role of folate biosensors in strengthening maternal, perinatal, and child healthcare.

Abstract This review aims to identify factors causing heterogeneity in breast DWI–MRI and their impact on its value for identifying breast cancer patients with pathological complete response (pCR) on neoadjuvant systemic therapy (NST). A search was performed on PubMed until April 2020 for studies analyzing DWI for identifying breast cancer patients with pCR on NST. Technical and clinical study aspects were extracted and assessed for variability. Twenty studies representing 1455 patients/lesions were included. The studies differed with respect to study population, treatment type, DWI acquisition technique, post-processing (e.g., mono-exponential/intravoxel incoherent motion/stretched exponential modeling), and timing of follow-up studies. For the acquisition and generation of ADC-maps, various b-value combinations were used. Approaches for drawing regions of interest on longitudinal MRIs were highly variable. Biological variability due to various molecular subtypes was usually not taken into account. Moreover, definitions of pCR varied. The individual areas under the curve for the studies range from 0.50 to 0.92. However, overlapping ranges of mean/median ADC-values at pre- and/or during and/or post-NST were found for the pCR and non-pCR groups between studies. The technical, clinical, and epidemiological heterogeneity may be causal for the observed variability in the ability of DWI to predict pCR accurately. This makes implementation of DWI for pCR prediction and evaluation based on one absolute ADC threshold for all breast cancer types undesirable. Multidisciplinary consensus and appropriate clinical study design, taking biological and therapeutic variation into account, is required for obtaining standardized, reliable, and reproducible DWI measurements for pCR/non-pCR identification.

Pauli spin blockade (PSB) can be employed as a great resource for spin qubit initialisation and readout even at elevated temperatures but it can be difficult to identify. We present a machine learning algorithm capable of automatically identifying PSB using charge transport measurements. The scarcity of PSB data is circumvented by training the algorithm with simulated data and by using cross-device validation. We demonstrate our approach on a silicon field-effect transistor device and report an accuracy of 96% on different test devices, giving evidence that the approach is robust to device variability. Our algorithm, an essential step for realising fully automatic qubit tuning, is expected to be employable across all types of quantum dot devices.

Abstract Background The new concept of difficult-to-treat rheumatoid arthritis (D2T RA) refers to RA patients who remain symptomatic after several lines of treatment, resulting in a high patient and economic burden. During a hackathon, we aimed to identify and predict D2T RA patients in structured and unstructured routine care data. Methods Routine care data of 1873 RA patients were extracted from the Utrecht Patient Oriented Database. Data from a previous cross-sectional study, in which 152 RA patients were clinically classified as either D2T or non-D2T, served as a validation set. Machine learning techniques, text mining, and feature importance analyses were performed to identify and predict D2T RA patients based on structured and unstructured routine care data. Results We identified 123 potentially new D2T RA patients by applying the D2T RA definition in structured and unstructured routine care data. Additionally, we developed a D2T RA identification model derived from a feature importance analysis of all available structured data (AUC-ROC 0.88 (95% CI 0.82–0.94)), and we demonstrated the potential of longitudinal hematological data to differentiate D2T from non-D2T RA patients using supervised dimension reduction. Lastly, using data up to the time of starting the first biological treatment, we predicted future development of D2TRA (AUC-ROC 0.73 (95% CI 0.71–0.75)). Conclusions During this hackathon, we have demonstrated the potential of different techniques for the identification and prediction of D2T RA patients in structured as well as unstructured routine care data. The results are promising and should be optimized and validated in future research.

Abstract Methods for predicting circadian phase have been developed for healthy individuals. It is unknown whether these methods generalize to clinical populations, such as delayed sleep–wake phase disorder (DSWPD), where circadian timing is associated with functional outcomes. This study evaluated two methods for predicting dim light melatonin onset (DLMO) in 154 DSWPD patients using ~ 7 days of sleep–wake and light data: a dynamic model and a statistical model. The dynamic model has been validated in healthy individuals under both laboratory and field conditions. The statistical model was developed for this dataset and used a multiple linear regression of light exposure during phase delay/advance portions of the phase response curve, as well as sleep timing and demographic variables. Both models performed comparably well in predicting DLMO. The dynamic model predicted DLMO with root mean square error of 68 min, with predictions accurate to within ± 1 h in 58% of participants and ± 2 h in 95%. The statistical model predicted DLMO with root mean square error of 57 min, with predictions accurate to within ± 1 h in 75% of participants and ± 2 h in 96%. We conclude that circadian phase prediction from light data is a viable technique for improving screening, diagnosis, and treatment of DSWPD.

Quantum information processing is a key technology in the ongoing second quantum revolution, with a wide variety of hardware platforms competing toward its realization. An indispensable component of such hardware is a measurement device, i.e., a quantum detector that is used to determine the outcome of a computation. The act of measurement in quantum mechanics, however, is naturally invasive as the measurement apparatus becomes entangled with the system that it observes. This always leads to a disturbance in the observed system, a phenomenon called quantum measurement backaction, which should solely lead to the collapse of the quantum wave function and the physical realization of the measurement postulate of quantum mechanics. Here we demonstrate that backaction can fundamentally change the quantum system through the detection process. For quantum information processing, this means that the readout alters the system in such a way that a faulty measurement outcome is obtained. Specifically, we report a backaction-induced population switching, where the bare presence of weak, nonprojective measurements by an adjacent charge sensor inverts the electronic charge configuration of a semiconductor double quantum dot system. The transition region grows with measurement strength and is suppressed by temperature, in excellent agreement with our coherent quantum backaction model. Our result exposes backaction channels that appear at the interplay between the detector and the system environments, and opens new avenues for controlling and mitigating backaction effects in future quantum technologies.

Jansson et al. design a high content screening system to target inflammation in human brain cells of the blood–brain barrier (pericytes and endothelial cells) to identify inflammatory modifiers. They identify cardiac glycosides as powerful regulators of neuroinflammatory pathways in brain barrier tissues such as vasculature, meninges and choroid plexus.

Abstract In Alzheimer’s disease (AD), microglia are affected by disease processes, but may also drive pathogenesis. AD pathology-associated microglial populations have been identified with single-cell RNA-Seq, but have not been validated in human brain tissue with anatomical context. Here, we quantified myeloid cell markers to identify changes in AD pathology-associated microglial populations. We performed fluorescent immunohistochemistry on normal (n = 8) and AD (n = 8) middle temporal gyri, co-labelling the pan-myeloid cell marker, Iba1, with one of 11 markers of interest (MOIs): CD45, HLA-DR, CD14, CD74, CD33, CD206, CD32, CD163, P2RY12, TMEM119, L-Ferritin. Novel image analyses quantified the single-cell abundance of Iba1 and each MOI. Each cell was gated into one Iba1-MOI population (Iba1low MOIhigh, Iba1high MOIhigh, or Iba1high MOIlow) and the abundance of each population was compared between AD and control. Triple-labelling of L-Ferritin and Iba1 with a subset of MOIs was performed to investigate L-Ferritin-MOI co-expression on Iba1low cells. Iba1low MOIhigh myeloid cell populations delineated by MOIs CD45, HLA-DR, CD14, CD74, CD33, CD32, and L-Ferritin were increased in AD. Further investigation of the Iba1low MOIhigh populations revealed that their abundances correlated with tau, but not amyloid beta, load in AD. The Iba1low microglial population highly expressed L-Ferritin, reflecting microglial dysfunction. The L-Ferritinhigh CD74high HLA-DRhigh phenotype of the Iba1low population mirrors that of a human AD pathology-associated microglial subpopulation previously identified using single-cell RNA-Seq. Our high-throughput immunohistochemical data with anatomical context support the microglial dysfunction hypothesis of AD.

Abstract Background Sensitive and reliable molecular diagnostics is needed to guide therapeutic decisions for cancer patients. Although less material becomes available for testing, genetic markers are rapidly expanding. Simultaneous detection of predictive markers, including mutations, gene amplifications and MSI, will save valuable material, time and costs. Methods Using a single-molecule molecular inversion probe (smMIP)-based targeted next-generation sequencing (NGS) approach, we developed an NGS panel allowing detection of predictive mutations in 33 genes, gene amplifications of 13 genes and microsatellite instability (MSI) by the evaluation of 55 microsatellite markers. The panel was designed to target all clinically relevant single and multiple nucleotide mutations in routinely available lung cancer, colorectal cancer, melanoma, and gastro-intestinal stromal tumor samples, but is useful for a broader set of tumor types. Results The smMIP-based NGS panel was successfully validated and cut-off values were established for reliable gene amplification analysis (i.e. relative coverage ≥3) and MSI detection (≥30% unstable loci). After validation, 728 routine diagnostic tumor samples including a broad range of tumor types were sequenced with sufficient sensitivity (2.4% drop-out), including samples with low DNA input (

The attentional blink (AB) phenomenon reveals a bottleneck of human information processing: the second of two targets is often missed when they are presented in rapid succession among distractors. In our previous work, we showed that the size of the AB can be changed by applying transcranial direct current stimulation (tDCS) over the left dorsolateral prefrontal cortex (lDLPFC) (London & Slagter, Journal of Cognitive Neuroscience, 33, 756-68, 2021). Although AB size at the group level remained unchanged, the effects of anodal and cathodal tDCS were negatively correlated: if a given individual's AB size decreased from baseline during anodal tDCS, their AB size would increase during cathodal tDCS, and vice versa. Here, we attempted to replicate this finding. We found no group effects of tDCS, as in the original study, but we no longer found a significant negative correlation. We present a series of statistical measures of replication success, all of which confirm that both studies are not in agreement. First, the correlation here is significantly smaller than a conservative estimate of the original correlation. Second, the difference between the correlations is greater than expected due to sampling error, and our data are more consistent with a zero-effect than with the original estimate. Finally, the overall effect when combining both studies is small and not significant. Our findings thus indicate that the effects of lDPLFC-tDCS on the AB are less substantial than observed in our initial study. Although this should be quite a common scenario, null findings can be difficult to interpret and are still under-represented in the brain stimulation and cognitive neuroscience literatures. An important auxiliary goal of this paper is therefore to provide a tutorial for other researchers, to maximize the evidential value from null findings.

Borsboom and colleagues have recently proposed a “network theory” of psychiatric disorders that conceptualizes psychiatric disorders as relatively stable networks of causally interacting symptoms. They have also claimed that the network theory should include non-symptom variables such as environmental factors. How are environmental factors incorporated in the network theory, and what kind of explanations of psychiatric disorders can such an “extended” network theory provide? The aim of this article is to critically examine what explanatory strategies the network theory that includes both symptoms and environmental factors can accommodate. We first analyze how proponents of the network theory conceptualize the relations between symptoms and between symptoms and environmental factors. Their claims suggest that the network theory could provide insight into the causal mechanisms underlying psychiatric disorders. We assess these claims in light of network analysis, Woodward’s interventionist theory, and mechanistic explanation, and show that they can only be satisfied with additional assumptions and requirements. Then, we examine their claim that network characteristics may explain the dynamics of psychiatric disorders by means of a topological explanatory strategy. We argue that the network theory could accommodate topological explanations of symptom networks, but we also point out that this poses some difficulties. Finally, we suggest that a multilayer network account of psychiatric disorders might allow for the integration of symptoms and non-symptom factors related to psychiatric disorders and could accommodate both causal/mechanistic and topological explanations.

The current Mycobacterium bovis BCG vaccine provides inconsistent protection against pulmonary infection with Mycobacterium tuberculosis. Immunity induced by subcutaneous immunization with BCG wanes and does not promote early recruitment of T cell to the lungs after M. tuberculosis infection. Delivery of Tuberculosis (TB) vaccines to the lungs may increase and prolong immunity at the primary site of M. tuberculosis infection. Pulmonary immunization with recombinant influenza A viruses (rIAVs) expressing an immune-dominant M. tuberculosis CD4+ T cell epitope (PR8-p25 and X31-p25) stimulates protective immunity against lung TB infection. Here, we investigated the potential use of rIAVs to improve the efficacy of BCG using simultaneous immunization (SIM) and prime-boost strategies. SIM with parenteral BCG and intranasal PR8-p25 resulted in equivalent protection to BCG alone against early, acute and chronic M. tuberculosis infection. Boosting BCG with rIAVs increased the frequency of IFN-γ-secreting specific T cells (p

Inflammation is a common feature of neurological diseases. During neuroinflammation, neutrophils are recruited to the brain vasculature, where myeloperoxidase can produce hypochlorous acid and the less well-studied oxidant hypothiocyanous acid (HOSCN). In this study, we exposed primary brain endothelial cells (BECs) to HOSCN and observed a rapid loss of transendothelial electrical resistance (TEER) at sublethal concentrations. Decreased barrier function was associated with a loss of tight junctions at cellular contacts and a concomitant loss of dynamic microtubules. Both tight junction and cytoskeletal disruptions were visible within 30 min of exposure, whereas significant loss of TEER took more than 1 h. The removal of the HOSCN after 30 min prevented subsequent barrier dysfunction. These results indicate that BECs are sensitive to HOSCN, resulting in the eventual loss of barrier function. We hypothesise that this mechanism may be relevant in neutrophil transmigration, with HOSCN facilitating blood–brain barrier opening at the sites of egress. Furthermore, this mechanism may be a way through which neutrophils, residing in the vasculature, can influence neuroinflammation in diseases.

The application of spin based qubits requests understanding and control of spin relaxation time T 1 which remains challenging. Here the authors experimentally demonstrate the spin relaxation mechanism via hyper fine interaction and long spin-relaxation time T 1 ~ 57 s for a single electron spin in GaAs quantum dot.

Abstract Background Microglia play critical roles in the brain during homeostasis and pathological conditions. Understanding the molecular events underpinning microglial functions and activation states will further enable us to target these cells for the treatment of neurological disorders. The transcription factor PU.1 is critical in the development of myeloid cells and a major regulator of microglial gene expression. In the brain, PU.1 is specifically expressed in microglia and recent evidence from genome-wide association studies suggests that reductions in PU.1 contribute to a delayed onset of Alzheimer’s disease (AD), possibly through limiting neuroinflammatory responses. Methods To investigate how PU.1 contributes to immune activation in human microglia, microarray analysis was performed on primary human mixed glial cultures subjected to siRNA-mediated knockdown of PU.1. Microarray hits were confirmed by qRT-PCR and immunocytochemistry in both mixed glial cultures and isolated microglia following PU.1 knockdown. To identify attenuators of PU.1 expression in microglia, high throughput drug screening was undertaken using a compound library containing FDA-approved drugs. NanoString and immunohistochemistry was utilised to investigate the expression of PU.1 itself and PU.1-regulated mediators in primary human brain tissue derived from neurologically normal and clinically and pathologically confirmed cases of AD. Results Bioinformatic analysis of gene expression upon PU.1 silencing in mixed glial cultures revealed a network of modified AD-associated microglial genes involved in the innate and adaptive immune systems, particularly those involved in antigen presentation and phagocytosis. These gene changes were confirmed using isolated microglial cultures. Utilising high throughput screening of FDA-approved compounds in mixed glial cultures we identified the histone deacetylase inhibitor vorinostat as an effective attenuator of PU.1 expression in human microglia. Further characterisation of vorinostat in isolated microglial cultures revealed gene and protein changes partially recapitulating those seen following siRNA-mediated PU.1 knockdown. Lastly, we demonstrate that several of these PU.1-regulated genes are expressed by microglia in the human AD brain in situ. Conclusions Collectively, these results suggest that attenuating PU.1 may be a valid therapeutic approach to limit microglial-mediated inflammatory responses in AD and demonstrate utility of vorinostat for this purpose.

Chemotherapy-induced cognitive impairment, also known as “chemobrain,” is a common side effect. The purpose of this study was to examine whether resveratrol, a natural polyphenol that has nootropic effects, could prevent chemobrain and its underlying mechanisms. Mice received three injections of docetaxel, adriamycin, and cyclophosphamide (DAC) in combination, a common chemotherapy regimen, at two-day intervals within one week. Resveratrol (50 and 100 mg/kg per day) was orally administered for three weeks, beginning one week before the DAC treatment. Water maze test and manganese-enhanced magnetic resonance imaging were used to evaluate animals' cognitive performance and brain neuronal activity, respectively. Blood and brain tissues were collected for measurement of cytokines, cytokine regulators, and biomarkers for neuroplasticity. DAC treatment produced a striking cognitive impairment. Cotreatment with 100 mg/kg resveratrol ameliorated DAC-induced cognitive impairment and decreases in prefrontal and hippocampal neuronal activity. Mice co-treated with both doses of resveratrol displayed significantly lower levels of the proinflammatory cytokines tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6), but markedly higher levels of the anti-inflammatory cytokines IL-4 and IL-10 in several sera and brain tissues than those co-treated with vehicle. Resveratrol modulated the cytokine-regulating pathway peroxisome proliferator activated receptor (PPAR)-γ/nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), and protected against DAC-induced decreases in the expression of the neuroplasticity biomarkers, brain-derived neurotrophic factor (BDNF), tropomyosin receptor kinase B (TrkB), amino acid neurotransmitter receptors, and calmodulin-dependent protein kinase II (CaMKII). These results demonstrate the efficacy of resveratrol in preventing chemobrain and its association with cytokine modulation and neuroprotection.

Abstract Background Pericytes and endothelial cells are critical cellular components of the blood-brain barrier (BBB) and play an important role in neuroinflammation. To date, the majority of inflammation-related studies in endothelia and pericytes have been carried out using immortalised cell lines or non-human-derived cells. Whether these are representative of primary human cells is unclear and systematic comparisons of the inflammatory responses of primary human brain-derived pericytes and endothelia has yet to be performed. Methods To study the effects of neuroinflammation at the BBB, primary brain endothelial cells and pericytes were isolated from human biopsy tissue. Culture purity was examined using qPCR and immunocytochemistry. Electrical cell-substrate impedance sensing (ECIS) was used to determine the barrier properties of endothelial and pericyte cultures. Using immunocytochemistry, cytometric bead array, and ECIS, we compared the responses of endothelia and pericytes to a panel of inflammatory stimuli (IL-1β, TNFα, LPS, IFN-γ, TGF-β1, IL-6, and IL-4). Secretome analysis was performed to identify unique secretions of endothelia and pericytes in response to IL-1β. Results Endothelial cells were pure, moderately proliferative, retained the expression of BBB-related junctional proteins and transporters, and generated robust TEER. Both endothelia and pericytes have the same pattern of transcription factor activation in response to inflammatory stimuli but respond differently at the secretion level. Secretome analysis confirmed that endothelia and pericytes have overlapping but distinct secretome profiles in response to IL-1β. We identified several cell-type specific responses, including G-CSF and GM-CSF (endothelial-specific), and IGFBP2 and IGFBP3 (pericyte-specific). Finally, we demonstrated that direct addition of IL-1β, TNFα, LPS, and IL-4 contributed to the loss of endothelial barrier integrity in vitro. Conclusions Here, we identify important cell-type differences in the inflammatory response of brain pericytes and endothelia and provide, for the first time, a comprehensive profile of the secretions of primary human brain endothelia and pericytes which has implications for understanding how inflammation affects the cerebrovasculature.

Summary: The brain cancer medulloblastoma consists of different transcriptional subgroups. To characterize medulloblastoma at the phosphoprotein-signaling level, we performed high-throughput peptide phosphorylation profiling on a large cohort of SHH (Sonic Hedgehog), group 3, and group 4 medulloblastomas. We identified two major protein-signaling profiles. One profile was associated with rapid death post-recurrence and resembled MYC-like signaling for which MYC lesions are sufficient but not necessary. The second profile showed enrichment for DNA damage, as well as apoptotic and neuronal signaling. Integrative analysis demonstrated that heterogeneous transcriptional input converges on these protein-signaling profiles: all SHH and a subset of group 3 patients exhibited the MYC-like protein-signaling profile; the majority of the other group 3 subset and group 4 patients displayed the DNA damage/apoptotic/neuronal signaling profile. Functional analysis of enriched pathways highlighted cell-cycle progression and protein synthesis as therapeutic targets for MYC-like medulloblastoma. : Using peptide phosphorylation profiling, Zomerman et al. identify two medulloblastoma phosphoprotein-signaling profiles that have prognostic value and are potentially targetable. They find that these profiles extend across transcriptome-based subgroup borders. This suggests that diverse genetic information converges on common protein-signaling pathways and highlights protein-signaling as a unique information layer. Keywords: medulloblastoma, protein-signaling, protein synthesis, MYC, TP53, proteome, phosphoproteome