Sign reversal of the Josephson inductance magnetochiral anisotropy and 0-π-like transitions in supercurrent diodes.

The recent discovery of the intrinsic supercurrent diode effect, and its prompt observation in a rich variety of systems, has shown that non-reciprocal supercurrents naturally emerge when both space-inversion and time-inversion symmetries are broken. In Josephson junctions, non-reciprocal supercurrent can be conveniently described in terms of spin-split Andreev states. Here we demonstrate a sign reversal of the Josephson inductance magnetochiral anisotropy, a manifestation of the supercurrent diode effect. The asymmetry of the Josephson inductance as a function of the supercurrent allows us to probe the current-phase relation near equilibrium, and to probe jumps in the junction ground state. Using a minimal theoretical model, we can then link the sign reversal of the inductance magnetochiral anisotropy to the so-called 0-π-like transition, a predicted but still elusive feature of multichannel junctions. Our results demonstrate the potential of inductance measurements as sensitive probes of the fundamental properties of unconventional Josephson junctions.

Electrical stimulation methods and protocols for the treatment of traumatic brain injury: a critical review of preclinical research.

BACKGROUND: Traumatic brain injury (TBI) is a leading cause of disabilities resulting from cognitive and neurological deficits, as well as psychological disorders. Only recently, preclinical research on electrical stimulation methods as a potential treatment of TBI sequelae has gained more traction. However, the underlying mechanisms of the anticipated improvements induced by these methods are still not fully understood. It remains unclear in which stage after TBI they are best applied to optimize the therapeutic outcome, preferably with persisting effects. Studies with animal models address these questions and investigate beneficial long- and short-term changes mediated by these novel modalities. METHODS: In this review, we present the state-of-the-art in preclinical research on electrical stimulation methods used to treat TBI sequelae. We analyze publications on the most commonly used electrical stimulation methods, namely transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), deep brain stimulation (DBS) and vagus nerve stimulation (VNS), that aim to treat disabilities caused by TBI. We discuss applied stimulation parameters, such as the amplitude, frequency, and length of stimulation, as well as stimulation time frames, specifically the onset of stimulation, how often stimulation sessions were repeated and the total length of the treatment. These parameters are then analyzed in the context of injury severity, the disability under investigation and the stimulated location, and the resulting therapeutic effects are compared. We provide a comprehensive and critical review and discuss directions for future research. RESULTS AND CONCLUSION: We find that the parameters used in studies on each of these stimulation methods vary widely, making it difficult to draw direct comparisons between stimulation protocols and therapeutic outcome. Persisting beneficial effects and adverse consequences of electrical simulation are rarely investigated, leaving many questions about their suitability for clinical applications. Nevertheless, we conclude that the stimulation methods discussed here show promising results that could be further supported by additional research in this field.

Deep Learning-Based Image Registration in Dynamic Myocardial Perfusion CT Imaging.

Registration of dynamic CT image sequences is a crucial preprocessing step for clinical evaluation of multiple physiological determinants in the heart such as global and regional myocardial perfusion. In this work, we present a deformable deep learning-based image registration method for quantitative myocardial perfusion CT examinations, which in contrast to previous approaches, takes into account some unique challenges such as low image quality with less accurate anatomical landmarks, dynamic changes of contrast agent concentration in the heart chambers and tissue, and misalignment caused by cardiac stress, respiration, and patient motion. The introduced method uses a recursive cascade network with a ventricle segmentation module, and a novel loss function that accounts for local contrast changes over time. It was trained and validated on a dataset of n = 118 patients with known or suspected coronary artery disease and/or aortic valve insufficiency. Our results demonstrate that the proposed method is capable of registering dynamic cardiac perfusion sequences by reducing local tissue displacements of the left ventricle (LV), whereas contrast changes do not affect the registration and image quality, in particular the absolute CT (HU) values of the entire CT sequence. In addition, the deep learning-based approach presented reveals a short processing time of a few seconds compared to conventional image registration methods, demonstrating its application potential for quantitative CT myocardial perfusion measurements in daily clinical routine.

Apparent prevalence and risk factors for udder skin diseases and udder edema in Bavarian dairy herds.

The objective of this cross-sectional study was to assess the prevalence and risk factors for teat warts, udder edema, udder thigh dermatitis, and udder cleft dermatitis on Bavarian dairy farms. Udder health and hygiene scores of lactating cows were recorded on 152 farms in Bavaria, Germany. Management practices (e.g., housing, milking systems, and feeding regimens) were assessed with a comprehensive questionnaire. Adjusted prevalence estimates were determined using regression analysis with herd as the random effect. Mann-Whitney U or Fisher's exact on herd level and regression analyses on cow level were performed to determine risk factors. Of the 6,208 cows examined, 4.0% had teat warts, 1.1% udder edema, 0.2% udder thigh dermatitis, and 0.3% udder cleft dermatitis. The apparent median within-herd prevalence was less than 4% for all 4 diseases. Herd-level factors that were associated with the presence of teat warts on a farm were the proportion of cows with poor teat ends as well as conventional milking systems compared with milking robots. At a cow level, teat warts were associated with high somatic cell counts. Herds with poor depth (<5 cm) of bedding material and cows with days in milk less than 60 d had increased odds for udder edema. First-lactating cows had higher odds for udder thigh dermatitis. Freestall housing and comfort rubber mats were identified as risk factors for udder cleft dermatitis on a herd level. In conclusion, although most nonmastitis udder diseases were rarely observed in this study, some herd management practices and cow factors were associated with their presence on a farm or cow level. Future studies are needed to further investigate risk factors for each disease in more detail.

AEPUS: a tool for the Automated Extraction of Pennation angles in Ultrasound images with low Signal-to-noise ratio for plane-wave imaging.

The penetrating ability of ultrasound (US) com-bined with its real-time operation make it the perfect tool for investigating muscle contraction mechanics during complex functional tasks, e.g., locomotion. Changes in fascicle lengths and pennation angles of muscle fascicles strongly correlate with the capacity of skeletal muscles to produce forces, thereby represent fundamental parameters to be tracked. While the gold standard for extracting these features from US images is still based on manual annotation, the availability of recording devices capable of generating big data of muscle dynamics makes such manual approach unfeasible, setting the need for automated muscle images annotation tools. Existing approaches, however, are seriously limited, also in view of the continuous developments and technology ad-vancements for ultrafast US and plane-wave imaging. In fact, they rely on conventional (slow) B-mode imaging, make use of point tracking approaches (which often fail due to out-of-plane motion), or can only operate on very high quality images. To overcome all these limitations, we present AEPUS, an automated image labeling tool capable of extracting pennation angles from low quality images using a very small number of plane waves, therefore making it capable of exploiting all the benefits of ultrafast US. Clinical Relevance - Ultrasound is a standard research tool to investigate alterations of spastic muscles in children with Cerebral Palsy. We propose a reliable and time-efficient method to track muscle features in ultrasound images and support clinical biomechanists in their analyses.

CANreduce-SP-adding psychological support to web-based adherence-focused guided self-help for cannabis users: study protocol for a three-arm randomized control trial.

BACKGROUND: Cannabis is the most-frequently used illicit drug in Europe. Over the last few years in Spain, treatment demand has increased, yet most cannabis users do not seek treatment despite the related problems. A web-based self-help tool, like CANreduce 2.0, could help these users to control their consumption. METHODS: This study protocol describes a three-arm randomized controlled trial (RCT) comparing the effectiveness of three approaches, in terms of reducing cannabis use among problematic cannabis users, the first two treatment arms including the Spanish version of CANreduce 2.0 (an adherence-focused, guidance-enhanced, web-based self-help tool) (1) with and (2) without psychological support; and the third group (3) treatment as usual (TAU). Study hypotheses will be tested concerning the primary outcome: change in the number of days of cannabis use over the previous week, comparing assessments at 6 weeks and 3 and 6 months follow-up between groups and against baseline. Secondary outcomes related to cannabis use will be tested similarly. Mental disorders will be explored as predictors of adherence and outcomes. Analyses will be performed on an intention-to-treat basis, then verified by complete case analyses. DISCUSSION: This study will test how effective the Spanish version of CANreduce 2.0 (CANreduce-SP) is at reducing both the frequency and quantity of cannabis use in problematic users and whether adding psychological support increases its effectiveness. TRIAL REGISTRATION: This trial is registered with the Clinical Trials Protocol Registration and Results System (PRS) number: NCT04517474 . Registered 18 August 2020, (Archived by archive.is https://archive.is/N1Y64 ). The project commenced in November 2020 and recruitment is anticipated to end by November 2022.

Effect of Rashba and Dresselhaus spin-orbit coupling on supercurrent rectification and magnetochiral anisotropy of ballistic Josephson junctions.

Simultaneous breaking of inversion- and time-reversal symmetry in Josephson junction (JJ) leads to a possible violation of theI(φ) = -I(-φ) equality for the current-phase relation. This is known as anomalous Josephson effect and it produces a phase shiftφ0in sinusoidal current-phase relations. In ballistic JJs with non-sinusoidal current phase relation the observed phenomenology is much richer, including the supercurrent diode effect and the magnetochiral anisotropy (MCA) of Josephson inductance. In this work, we present measurements of both effects on arrays of JJs defined on epitaxial Al/InAs heterostructures. We show that the orientation of the current with respect to the lattice affects the MCA, possibly as the result of a finite Dresselhaus component. In addition, we show that the two-fold symmetry of the Josephson inductance reflects in the activation energy for phase slips.

Full model selection using regression trees for numeric predictions of biomarkers for metabolic challenges in dairy cows.

Dairy cows suffer poor metabolic adaptation syndrome (PMAS)1 during early post-calving periods caused by negative energy balance. Measurement of blood beta-hydroxy butyric acid (BHBA)2 and blood non-esterified fatty acids (NEFA)3 allow early and accurate detection of negative energy balance. Machine learning prediction of blood BHBA and blood NEFA using milk testing samples represents an opportunity to identify at-risk animals, using less labor than direct blood testing methods. Routine milk testing on modern dairies and computer record keeping provide an immense amount of data which can then be used in machine learning models. Previous research for predicting blood metabolites using Fourier-transform infrared spectroscopy (FTIR)4 milk data has focused mainly on individual models rather than a comparison among the models. Full model selection is the process of comparing different combinations of pre-processing methods, variable selection, and statistical learning algorithms to determine which model results in the lowest prediction error for a given dataset. For this project we used a full model selection approach with regression trees (rtFMS)5 . rtFMS uses the cross-validated performance of different model configurations to feed a regression tree for selecting a final model. A total of 384 possible model configurations (algorithms, predictors and data preprocessing options) for each outcome (blood BHBA and blood NEFA) were considered in the rtFMS technique. rtFMS allows direct comparison of multiple modeling approaches reducing bias due to empirical knowledge, modeling habits, or preferences, identifying the model with minimal root mean squared prediction error (RMSE)6 . An elastic net regression model was selected as the best performing model for both biomarkers. The input data for blood BHBA predictions were FTIR milk spectra, with a second derivative pre-processing, and a filter with 212 wave numbers, obtaining RMSE = 0.354 (0.328-0.392). The best performing model for blood NEFA had input data of FTIR milk spectra, with a second derivative pre-processing, and a filter with 212 wave numbers filter along with the time of milking, obtaining RMSE = 0.601 (0.564-0.654). The comparison of multiple modeling strategies, conducted by rtFMS, present an option for improved FTIR prediction models of blood BHBA and blood NEFA by reducing error due to human bias. The implementation of rtFMS to design future prediction models can guide model inputs and features. Our prediction models have the potential to increase early detection of metabolic disorders in dairy cows during the transition period.

Costs of Epilepsy in Austria: Unemployment as a primary driving factor.

PURPOSE: Epilepsy is one of the most common chronic neurological disorders, and long-term treatment with antiseizure medication is often central to its management. The costs of antiseizure medication are more evident than other disease-related costs; thus, we assessed the direct and indirect costs of epilepsy focusing on both drug expenditure and other cost-driving factors. METHODS: Outpatient records and questionnaires applied in a tertiary epilepsy centre in Vienna were used in this bottom-up cost-of-illness study to evaluate disease duration, age at onset, epilepsy syndrome, seizure frequency, sex, healthcare utilisation, diagnostic evaluations, antiseizure medication, and occupation. Cost data were clustered in a histogram-based data analysis, and multivariate regressions were performed to identify cost drivers. RESULTS: The average annual costs of 273 patients amounted to €9,256 ($10,459): €4,486 ($5,069) direct costs and €4,770 ($5,390) indirect costs. A histogram of semi-annual costs revealed distinct groups with low costs (< €2,500 = $2,825) and high costs (> €2,500 = $2,825). Seizure-free patients were clustered in the group with low costs; patients with ongoing seizures appeared more frequently in the group with high costs. Working patients were more often found in the group with low costs, whereas unemployed patients were more prevalent in the group with high costs. The regression analysis confirmed worklessness as the main cost driver. CONCLUSION: Non-productivity and poorly controlled disease with ongoing seizures are associated with higher costs in epilepsy. Providing high-level care and optimal drug treatment that enables patients to remain in work may help reduce the economic burden of epilepsy.

Alterations of intracerebral connectivity in epilepsy patients with secondary bilateral synchrony.

INTRODUCTION: The aim of our study was to evaluate intracerebral network changes in epilepsy patients demonstrating secondary bilateral synchrony (SBS) in EEG by applying a new Diffusion Tensor Imaging (DTI) method using an energy-based global tracking algorithm. MATERIALS AND METHODS: 10 MRI negative epilepsy patients demonstrating SBS in 10-20 surface EEG were included. EEG findings were analyzed for irritative zones characterized by focal interictal epileptiform discharges (IEDs) triggering SBS. In addition, DTI including an energy-based global tracking algorithm was applied to analyze fiber tract alterations in irritative zones. To measure the deviation of a certain cortical connection in comparison to healthy controls, normalized differences of fiber tract streamline counts (SC) and their p-values were evaluated in comparison to corresponding fibers of the control group. RESULTS: In 6 patients the irritative zone initiating SBS was located in the frontal lobe, in 3 patients in the temporal lobe and in 1 patient in the region surrounding the right central sulcus. All patients demonstrated significantly altered SC in brain lobes where the irritative zone triggering SBS was located (p ≤ 0.05). Seven out of 10 patients demonstrated SC alterations in tracts connecting brain lobes between the ipsilateral and the contralateral hemisphere (p ≤ 0.05). CONCLUSION: Our data demonstrate that alterations in fiber tracts in irritative zones triggering SBS are not necessarily associated with intracerebral lesions visible in high resolution MRI. Our study gives evidence that diffusion tensor imaging is a promising non-invasive additive tool for intracerebral network analyses even in MRI-negative epilepsy patients.

Voxel-Based Morphometry-from Hype to Hope. A Study on Hippocampal Atrophy in Mesial Temporal Lobe Epilepsy.

BACKGROUND AND PURPOSE: Automated volumetry of the hippocampus is considered useful to assist the diagnosis of hippocampal sclerosis in temporal lobe epilepsy. However, voxel-based morphometry is rarely used for individual subjects because of high rates of false-positives. We investigated whether an approach with high dimensional warping to the template and nonparametric statistics would be useful to detect hippocampal atrophy in patients with hippocampal sclerosis. MATERIALS AND METHODS: We performed single-subject voxel-based morphometry with nonparametric statistics within the framework of Statistical Parametric Mapping to compare MRI from 26 well-characterized patients with temporal lobe epilepsy individually against a group of 110 healthy controls. The following statistical threshold was used: P < .05 corrected for multiple comparisons with family-wise error over the region of interest right and left hippocampus. RESULTS: The sensitivity for the detection of atrophy related to hippocampal sclerosis was 0.92 (95% CI, 0.67-0.99) for the right hippocampus and 0.60 (0.31-0.83) for the left, and the specificity for volume changes was 0.98 (0.93-0.99). All clusters of decreased hippocampal volumes were correctly lateralized to the seizure focus. Hippocampal volume decrease was in accordance with neuronal cell loss on histology reports. CONCLUSIONS: Nonparametric voxel-based morphometry is sensitive and specific for hippocampal atrophy in patients with mesial temporal lobe epilepsy and may be useful in clinical practice.

Lesion-Specific Language Network Alterations in Temporal Lobe Epilepsy.

BACKGROUND AND PURPOSE: Temporal lobe epilepsy, structural or nonlesional, may negatively affect language function. However, little is known about the lesion-specific influence on language networks. We hypothesized that different epileptogenic lesions are related to distinct alterations in the functional language connectome detected by fMRI. MATERIALS AND METHODS: One hundred one patients with epilepsy due to mesiotemporal sclerosis (21 left, 22 right), low-grade mesiotemporal tumors (12 left), or nonlesional temporal lobe epilepsy (22 left, 24 right) and 22 healthy subjects performed 3T task-based language fMRI. Task-based activation maps (laterality indices) and functional connectivity analysis (global and connectivity strengths between language areas) were correlated with language scores. RESULTS: Laterality indices based on fMRI activation maps failed to discriminate among patient groups. Functional connectivity analysis revealed the most extended language network alterations in left mesiotemporal sclerosis (involving the left temporal pole, left inferior frontal gyrus, and bilateral premotor areas). The other patient groups showed less extended but also predominantly ipsilesional network changes compared with healthy controls. Left-to-right hippocampal connectivity strength correlated positively with naming function (P = .01), and connectivity strength between the left Wernicke area and the left hippocampus was linked to verbal fluency scores (P = .01) across all groups. CONCLUSIONS: Different pathologies underlying temporal lobe epilepsy are related to distinct alterations of the functional language connectome visualized by fMRI functional connectivity analysis. Network analysis allows new insights into language organization and provides possible imaging biomarkers for language function. These imaging findings emphasize the importance of a personalized treatment strategy in patients with epilepsy.

Average motion of emerging solar active region polarities: II. Joy's law.

CONTEXT: The tilt of solar active regions described by Joy's law is essential for converting a toroidal field to a poloidal field in Babcock-Leighton dynamo models. In thin flux tube models the Coriolis force causes what we observe as Joy's law, acting on east-west flows as they rise towards the surface. AIMS: Our goal is to measure the evolution of the average tilt angle of hundreds of active regions as they emerge, so that we can constrain the origins of Joy's law. METHODS: We measured the tilt angle of the primary bipoles in 153 emerging active regions (EARs) in the Solar Dynamics Observatory Helioseismic Emerging Active Region survey. We used line-of-sight magnetic field measurements averaged over 6 h to define the polarities and measure the tilt angle up to four days after emergence. RESULTS: We find that at the time of emergence the polarities are on average aligned east-west, and that neither the separation nor the tilt depends on latitude. We do find, however, that EARs at higher latitudes have a faster north-south separation speed than those closer to the equator at the emergence time. After emergence, the tilt angle increases and Joy's law is evident about two days later. The scatter in the tilt angle is independent of flux until about one day after emergence, when we find that higher-flux regions have a smaller scatter in tilt angle than lower-flux regions. CONCLUSIONS: Our finding that active regions emerge with an east-west alignment is consistent with earlier observations, but is still surprising since thin flux tube models predict that tilt angles of rising flux tubes are generated below the surface. Previously reported tilt angle relaxation of deeply anchored flux tubes can be largely explained by the change in east-west separation. We conclude that Joy's law is caused by an inherent north-south separation speed present when the flux first reaches the surface, and that the scatter in the tilt angle is consistent with buffeting of the polarities by supergranulation.

Prediction model optimization using full model selection with regression trees demonstrated with FTIR data from bovine milk.

Predictive modeling is the development of a model that is best able to predict an outcome based on given input variables. Model algorithms are different processes that are used to define functions that transform the data within models. Common algorithms include logistic regression (LR), linear discriminant analysis (LDA), classification and regression trees (CART), naïve Bayes (NB), and k-nearest neighbor (KNN). Data preprocessing option, such as feature extraction and reduction, and model algorithms are commonly selected empirically in epidemiological studies even though these decisions can significantly affect model performance. Accordingly, full model selection (FMS) methods were developed to provide a systematic approach to select predictive modeling methods; however, current limitations of FMS, such as its dependency on user-selected hyperparameters, have prevented their routine incorporation into analyses for model performance optimization. Here we present the use of regression trees as an innovative method to apply FMS. Regression tree FMS (rtFMS) requires the development of a model for every combination of predictive modeling method options under consideration. The iterated, cross-validation performances of these models are then passed through a regression tree for selection of a final model. We demonstrate the benefits of rtFMS using a milk Fourier transform infrared spectroscopy dataset, wherein we build prediction models for two blood metabolic health parameters in dairy cows, nonesterified fatty acids (NEFA) and ß-hydroxybutyrate acid (BHBA). The goal for building NEFA and BHBA prediction models is to provide a milk-based screening tool for metabolic health in dairy cattle that can be incorporated automatically in milk analysis routines. These models could be used in conjunction with physical exams, cow side tests, and other indications to initiate medical intervention. In contrast to previously reported FMS methods, rtFMS is not a black box, is simple to implement and interpret, it does not have hyperparameters, and it illustrates the relative importance of modeling options. Additionally, rtFMS allows for indirect comparisons among models developed using different datasets. Finally, rtFMS eliminates user bias due to personal preference for certain methods and rtFMS removes the dependency on published comparisons of methods. Thus, rtFMS provides clear benefits over the empirical selection of data preprocessing options and model algorithms.

Comparison of pre-emptive butorphanol or metamizole with ketamine +medetomidine and s-ketamine + medetomidine anaesthesia in improving intraoperative analgesia in mice.

In accordance with the 'refinement' component of the 3Rs, the primary aim of this study was to investigate and compare ketamine + medetomidine (KM) and s-ketamine + medetomidine (SKM) anaesthetic protocols in C57BL/6J mice (both sexes). We sought to determine whether s-ketamine could provide adequate surgical tolerance at a 50% dose relative to that of ketamine racemate and whether antagonism of medetomidine could be initiated 15 min earlier. The second aim was to investigate the potential improvement in analgesia for both anaesthetic protocols by adding butorphanol or metamizole. Analgesia was tested via the pedal withdrawal reaction (PWR) to a painful stimulus. During anaesthesia, respiratory frequency, pulse oximetry, body temperature and PWR were monitored. Among the 16 mice in each group, the PWR was lost in all the KM + metamizole (35:56 ± 6:07 min), KM + butorphanol (43:45 ± 2:14 min) and SKM + butorphanol (24:03 ± 5:50 min) mice, 15 of the non-premedicated KM (37:00 ± 8:11 min) mice, and 9 of the pure SKM (20:00 ± 4:19 min) mice; the latter group increased to 11 mice (17:16 ± 5:10 min) with premedication of metamizole. In contrast to the racemic combination, s-ketamine at the dose used here did not lead to sufficient loss of the PWR. However, earlier partial antagonism of SKM resulted in a slightly shorter and qualitatively better recovery than later partial antagonism of SKM. The addition of metamizole or butorphanol to KM or SKM anaesthesia positively influences the analgesic quality. However, when butorphanol is added, controlled ventilation may be necessary, especially for male mice.

Identifying poor metabolic adaptation during early lactation in dairy cows using cluster analysis.

Currently, cows with poor metabolic adaptation during early lactation, or poor metabolic adaptation syndrome (PMAS), are often identified based on detection of hyperketonemia. Unfortunately, elevated blood ketones do not manifest consistently with indications of PMAS. Expected indicators of PMAS include elevated liver enzymes and bilirubin, decreased rumen fill, reduced rumen contractions, and a decrease in milk production. Cows with PMAS typically are higher producing, older cows that are earlier in lactation and have greater body condition score at the start of lactation. It was our aim to evaluate commonly used measures of metabolic health (input variables) that were available [i.e., blood ß-hydroxybutyrate acid, milk fat:protein ratio, blood nonesterified fatty acids (NEFA)] to characterize PMAS. Bavarian farms (n = 26) with robotic milking systems were enrolled for weekly visits for an average of 6.7 wk. Physical examinations of the cows (5-50 d in milk) were performed by veterinarians during each visit, and blood and milk samples were collected. Resulting data included 790 observations from 312 cows (309 Simmental, 1 Red Holstein, 2 Holstein). Principal component analysis was conducted on the 3 input variables, followed by K-means cluster analysis of the first 2 orthogonal components. The 5 resulting clusters were then ascribed to low, intermediate, or high PMAS classes based on their degree of agreement with expected PMAS indicators and characteristics in comparison with other clusters. Results revealed that PMAS classes were most significantly associated with blood NEFA levels. Next, we evaluated NEFA values that classify observations into appropriate PMAS classes in this data set, which we called separation values. Our resulting NEFA separation values [<0.39 mmol/L (95% confidence limits = 0.360-0.410) to identify low PMAS observations and ≥0.7 mmol/L (95% confidence limits = 0.650-0.775) to identify high PMAS observations] were similar to values determined for Holsteins in conventional milking settings diagnosed with hyperketonemia and clinical symptoms such as anorexia and a reduction in milk yield, as reported in the literature. Future studies evaluating additional clinical and laboratory data, breeds, and milking systems are needed to validate these finding. The aim of future studies would be to build a PMAS prediction model to alert producers of cows needing attention and help evaluate on-farm metabolic health management at the herd level.

Immune tolerance induced by platelet-targeted factor VIII gene therapy in hemophilia A mice is CD4 T cell mediated.

Essentials The immune response is a significant concern in gene therapy. Platelet-targeted gene therapy can restore hemostasis and induce immune tolerance. CD4 T cell compartment is tolerized after platelet gene therapy. Preconditioning regimen affects immune tolerance induction in platelet gene therapy. SUMMARY: Background Immune responses are a major concern in gene therapy. Our previous studies demonstrated that platelet-targeted factor VIII (FVIII) (2bF8) gene therapy together with in vivo drug selection of transduced cells can rescue the bleeding diathesis and induce immune tolerance in FVIIInull mice. Objective To investigate whether non-selectable 2bF8 lentiviral vector (LV) for the induction of platelet-FVIII expression is sufficient to induce immune tolerance and how immune tolerance is induced after 2bF8LV gene therapy. Methods Platelet-FVIII expression was introduced by 2bF8LV transduction and transplantation. FVIII assays and tail bleeding tests were used to confirm the success of platelet gene therapy. Animals were challenged with rhF8 to explore if immune tolerance was induced after gene therapy. Treg cell analysis, T-cell proliferation assay and memory B-cell-mediated ELISPOT assay were used to investigate the potential mechanisms of immune tolerance. Results We showed that platelet-FVIII expression was sustained and the bleeding diathesis was restored in FVIIInull mice after 2bF8LV gene therapy. None of the transduced recipients developed anti-FVIII inhibitory antibodies in the groups preconditioned with 660 cGy irradiation or busulfan plus ATG treatment even after rhF8 challenge. Treg cells significantly increased in 2bF8LV-transduced recipients and the immune tolerance developed was transferable. CD4+ T cells from treated animals failed to proliferate in response to rhF8 re-stimulation, but memory B cells could differentiate into antibody secreting cells in 2bF8LV-transduced recipients. Conclusion 2bF8LV gene transfer without in vivo selection of manipulated cells can introduce immune tolerance in hemophilia A mice and this immune tolerance is CD4+ T cell mediated.

Automatic multimodal detection for long-term seizure documentation in epilepsy.

OBJECTIVE: This study investigated sensitivity and false detection rate of a multimodal automatic seizure detection algorithm and the applicability to reduced electrode montages for long-term seizure documentation in epilepsy patients. METHODS: An automatic seizure detection algorithm based on EEG, EMG, and ECG signals was developed. EEG/ECG recordings of 92 patients from two epilepsy monitoring units including 494 seizures were used to assess detection performance. EMG data were extracted by bandpass filtering of EEG signals. Sensitivity and false detection rate were evaluated for each signal modality and for reduced electrode montages. RESULTS: All focal seizures evolving to bilateral tonic-clonic (BTCS, n=50) and 89% of focal seizures (FS, n=139) were detected. Average sensitivity in temporal lobe epilepsy (TLE) patients was 94% and 74% in extratemporal lobe epilepsy (XTLE) patients. Overall detection sensitivity was 86%. Average false detection rate was 12.8 false detections in 24h (FD/24h) for TLE and 22 FD/24h in XTLE patients. Utilization of 8 frontal and temporal electrodes reduced average sensitivity from 86% to 81%. CONCLUSION: Our automatic multimodal seizure detection algorithm shows high sensitivity with full and reduced electrode montages. SIGNIFICANCE: Evaluation of different signal modalities and electrode montages paces the way for semi-automatic seizure documentation systems.

Reduced electrode arrays for the automated detection of rhythmic and periodic patterns in the intensive care unit: Frequently tried, frequently failed?

OBJECTIVE: To investigate the effect of systematic electrode reduction from a common 10-20 EEG system on pattern detection sensitivity (SEN). METHODS: Two reviewers rated 17130 one-minute segments of 83 prospectively recorded cEEGs according to the ACNS standardized critical care EEG terminology (CCET), including burst suppression patterns (BS) and unequivocal electrographic seizures. Consensus annotations between reviewers were used as a gold standard to determine pattern detection SEN and specificity (SPE) of a computational algorithm (baseline, 19 electrodes). Electrodes were than reduced one by one in four different variations. SENs and SPEs were calculated to determine the most beneficial assembly with respect to the number and location of electrodes. RESULTS: High automated baseline SENs (84.99-93.39%) and SPEs (90.05-95.6%) were achieved for all patterns. Best overall results in detecting BS and CCET patterns were found using the "hairline+vertex" montage. While the "forehead+behind ear" montage showed an advantage in detecting ictal patterns, reaching a 15% drop of SEN with 10 electrodes, all montages could detect BS sufficiently if at least nine electrodes were available. CONCLUSION: For the first time an automated approach was used to systematically evaluate the effect of electrode reduction on pattern detection SEN in cEEG. SIGNIFICANCE: Prediction of the expected detection SEN of specific EEG patterns with reduced EEG montages in ICU patients.