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Extracting population-wise information from medical images, specifically in the neurological domain, is crucial to better understanding disease processes and progression. This is frequently done in a whole-brain voxel-wise manner, in which a population of patients and healthy controls are registered to a common co-ordinate space and a statistical test is performed on the distribution of image intensities for each location. Although this method has yielded a number of scientific insights, it is further from clinical applicability as the differences are often small and altogether do not permit for a performant classifier. In this paper, we take the opposite approach of using a performant classifier, specifically a traditional convolutional neural network, and then extracting insights from it which can be applied in a population-wise manner, a method we call voxel-based diktiometry. We have applied this method to diffusion tensor imaging (DTI) analysis for Parkinson’s Disease, using the Parkinson’s Progression Markers Initiative (PPMI) database. By using the network sensitivity information, we can decompose what elements of the DTI contribute the most to the network’s performance, drawing conclusions about diffusion biomarkers for Parkinson’s disease that are based on metrics which are not readily expressed in the voxel-wise approach.

By far the largest contribution to ion detectability in liquid chromatography-driven mass spectrometry-based proteomics is the efficient generation of peptide ions by the electrospray source. To maximize the transfer of peptides from liquid to a gaseous phase to allow molecular ions to enter the mass spectrometer at micro-spray flow rates, an efficient electrospray process is required. Here we describe superior performance of new Vacuum-Insulated-Probe-Heated-ElectroSpray-Ionization source (VIP-HESI) coupled with micro-spray flow rate chromatography and Bruker timsTOF PRO mass spectrometer. VIP-HESI significantly improves chromatography signals in comparison to nano-spray ionization using the CaptiveSpray source and provides increased protein detection with higher quantitative precision, enhancing reproducibility of sample injection amounts. Protein quantitation of human K562 lymphoblast samples displayed excellent chromatographic retention time reproducibility (

Aging manifests as progressive deterioration in cellular and systemic homeostasis, requiring systems-level perspectives to understand the gradual molecular dysregulation of underlying biological processes. Here, we report systems-level changes in the molecular regulation of biological processes under multiple lifespan-extending interventions in mice and across age in humans. In mouse cohorts, Differential Rank Conservation (DIRAC) analyses of liver proteomics and transcriptomics show that mechanistically distinct prolongevity interventions tighten the regulation of aging-related biological modules, including fatty acid metabolism and inflammation processes. An integrated analysis of liver transcriptomics with mouse genome-scale metabolic model supports the shifts in fatty acid metabolism. Additionally, the difference in DIRAC patterns between proteins and transcripts suggests biological modules which may be tightly regulated via cap-independent translation. In a human cohort spanning the majority of the adult lifespan, DIRAC analyses of blood proteomics and metabolomics demonstrate that regulation of biological modules does not monotonically loosen with age; instead, the regulatory patterns shift according to both chronological and biological ages. Our findings highlight the power of systems-level approaches to identifying and characterizing the biological processes involved in aging and longevity.

International audience; Purpose: Deep brain stimulation (DBS) is an interventional treatment for some neurological and neurodegenerative diseases. For example, in Parkinson's disease, DBS electrodes are positioned at particular locations within the basal ganglia to alleviate the patient's motor symptoms. These interventions depend greatly on a preoperative planning stage in which potential targets and electrode trajectories are identified in a preoperative MRI. Due to the small size and low contrast of targets such as the subthalamic nucleus (STN), their segmentation is a difficult task. Machine learning provides a potential avenue for development, but it has difficulty in segmenting such small structures in volumetric images due to additional problems such as segmentation class imbalance. Approach: We present a two-stage separable learning workflow for STN segmentation consisting of a localization step that detects the STN and crops the image to a small region and a segmentation step that delineates the structure within that region. The goal of this decoupling is to improve accuracy and efficiency and to provide an intermediate representation that can be easily corrected by a clinical user. This correction capability was then studied through a humancomputer interaction experiment with seven novice participants and one expert neurosurgeon. Results: Our two-step segmentation significantly outperforms the comparative registrationbased method currently used in clinic and approaches the fundamental limit on variability due to the image resolution. In addition, the human-computer interaction experiment shows that the additional interaction mechanism allowed by separating STN segmentation into two steps significantly improves the users' ability to correct errors and further improves performance. Conclusions: Our method shows that separable learning not only is feasible for fully automatic STN segmentation but also leads to improved interactivity that can ease its translation into clinical use.

Study question Are menstrual tampons an acceptable and comparable method to lower vaginal swabs (LVS) for analysing the vaginal microbiome in non-pregnant women? Summary answer Tampons capture a higher bacterial load, volunteers are familiar with them, thus are well-accepted, and can be efficiently frozen at -20 °C prior to analysis. What is known already The vaginal microbiome plays a key role in women’s reproductive health. The application of investigating this niche allows a greater understanding of the impact of the vaginal microbiota in diverse reproductive disorders including infertility, recurrent miscarriage, spontaneous preterm birth and sexually transmitted infections. Fear and embarrassment related to physicians taking a traditional vaginal swab using a speculum results in many female genitourinary health appointments to be missed in the UK and recruitment challenges into vaginal microbiota research studies. Growing evidence suggests a menstrual tampon to be a superior alternative biospecimen collection device to a vaginal swab. Study design, size, duration This was a prospective, cross-sectional observational study. Twenty-one healthy volunteers (average age 30 years) were recruited over a period of 6 weeks at the Centre for Women's Health Research, University of Liverpool, an academic, tertiary referral centre. Participants/materials, setting, methods The volunteers consented to wearing a tampon for 20 minutes, taking 2 LVS and completing an acceptability questionnaire. Samples were halved for immediate processing and freezing at -20 °C for 2-4 weeks. Real-time PCR using Femoflor-16 was carried out to identify microorganisms present in vaginal microbiota. Microbiota was classified by total bacterial mass and relative abundance of lactobacillus species. Statistical analysis was carried out using GraphPad PRISM 5, Microsoft Excel 2019 and IBM SPSS Statistics 27. Main results and the role of chance Of the 84 samples, four had insufficient material for the purpose of analysis. Three of these were LVS samples and the fourth was a frozen tampon sample. Mean tampon total bacterial mass was higher for fresh and frozen samples versus the mean LVS total bacterial mass. The Femoflor algorithm gave identical vaginal microbiota characterisation between the fresh tampon and fresh LVS in 68.4% (13/19) of cases. 92% (12/13) samples with over 80% relative abundance of lactobacillus in the LVS also had over 80% relative abundance in the tampon. More discrepancy was seen between samples within the 20-80% and 80% (normal) and a tampon with non-dominance of lactobacillus ( The questionnaires revealed that 90.5% of the volunteers highly rated using tampons (3 or more out of 5 on a Likert scale), with 52.4% rating tampon-use at 5/5. All participants (100%) stated that they would be happy to provide a tampon sample for clinical/research testing purposes in the future. Limitations, reasons for caution This study included 21 healthy volunteers; this is not necessarily representative of the entire UK population, but the tampon and LVS from the same volunteer were consistently comparable before and after freezing. We used a multiplex PCR assay to target common vaginal bacteria, inevitably some bacteria have not been characterised. Wider implications of the findings Self-collected tampons are suitable, well accepted biospecimen collection devices that may reduce barriers posed due to embarrassment, improve attendance in genitourinary medicine clinics and encourage participation in vaginal microbiota research. Freezing before processing reduces time and costs. A large study is necessary to ensure reproducibility and acceptability to patient population. Trial registration number Not Applicable

© 2019 Objective: To investigate the effects of different positioning on the volume/location of the internal jugular vein (IJV) using 2-dimensional (2D) tracked ultrasound. Design: This was a prospective, observational study. Setting: Local research institute. Participants: Healthy volunteers. Interventions: Twenty healthy volunteers were scanned in the following 6 positions: (1) supine with head neutral, rotated 15 and 30 degrees to the left and (2) 5-, 10-, and 15-degree Trendelenburg position with head neutral. In each position the volunteer's neck was scanned using a 2D ultrasound probe tracked with a magnetic tracker. These spatially tracked 2D images were collected and reconstructed into a 3D volume of the IJV and carotid artery. This 3D ultrasound volume then was segmented to obtain a 3D surface on which measurements and calculations were performed. Measurements and Main Results: The measurements included average cross-section area (CSA), CSA along the length of IJV, and average overlap rate. CSA (mm2) in the supine and 5-, 10-, and 15-degree Trendelenburg positions were as follows: 86.7 ± 44.8, 104.3 ± 54.5, 119.1 ± 58.6, and 133.7 ± 53.3 (p < 0.0001). CSA enlarged with the increase of Trendelenburg degree. Neither Trendelenburg position nor head rotation showed a correlation with overlap rate. Conclusions: Trendelenburg position significantly increased the CSA of the IJV, thus facilitating IJV cannulation. This new 3D reconstruction method permits the creation of a 3D volume through a tracked 2D ultrasound scanning system with image acquisition and integration and may prove useful in providing the user with a “road map” of the vascular anatomy of a patient's neck or other anatomic structures.

International audience; Transcranial magnetic stimulation is a non-invasive therapeutic procedure in which specific cortical brain regions are stimulated in order to disrupt abnormal neural behaviour. This procedure requires the annotation of a number of cortical point targets which is often performed by a human expert. Nevertheless, there is a large degree of variability between experts that cannot be described readily using the existing zero-mean uni-modal error model common in computer-assisted interventions. This is due to the error between experts arising from a difference of type rather than a difference of degree, that is, experts are not necessarily picking the same point with some error, but are picking fundamentally different points. In order to model these types of localisation errors, this paper proposes a simple probabilistic model that uses the agreement between annotations as a basis, not requiring a ground-truth annotation to be strictly known. This work will allow for the localisation error in transcranial magnetic stimulation to be better described which may spur further developments in clinical training as well as machine learning for cortical point localisation. © 2022 SPIE.

Transcranial magnetic stimulation (TMS) is a growing therapy for a variety of psychiatric and neurological disorders that arise from or are modulated by cortical regions of the brain represented by singular 3D target points. These target points are often determined manually with assistance from a pre-operative T1-weighted MRI, although there is growing interest in automatic target point localisation using an atlas. However, both approaches can be time-consuming which has an effect on the clinical workflow, and the latter does not take into account patient variability such as the varying number of cortical gyri where these targets are located. This paper proposes a multi-resolution convolutional neural network for point localisation in MR images for a priori defined points in increasingly finely resolved versions of the input image. This approach is both fast and highly memory efficient, allowing it to run in high-throughput centres, and has the capability of distinguishing between patients with high levels of anatomical variability. Preliminary experiments have found the accuracy of this network to be $$7.26\pm 5.30$$ mm, compared to $$9.39\pm 4.63$$ mm for deformable registration and $$6.94\pm 5.10$$ mm for a human expert. For most treatment points, the human expert and proposed CNN statistically significantly outperform registration, but neither statistically significantly outperforms the other, suggesting that the proposed network has human-level performance. The human-level performance of this network indicates that it can improve TMS planning by automatically localising target points in seconds, avoiding more time-consuming registration or manual point localisation processes. This is particularly beneficial for out-of-hospital centres with limited computational resources where TMS is increasingly being administered.

Purpose Deep learning methods (DLMs) have recently been proposed to generate pseudo-CT (pCT) for magnetic resonance imaging (MRI) based dose planning. This study aims to evaluate and compare DLMs (U-Net and generative adversarial network [GAN]) using various loss functions (L2, single-scale perceptual loss [PL], multiscale PL, weighted multiscale PL) and a patch-based method (PBM). Methods and Materials Thirty-nine patients received a volumetric modulated arc therapy for prostate cancer (78 Gy). T2-weighted MRIs were acquired in addition to planning CTs. The pCTs were generated from the MRIs using 7 configurations: 4 GANs (L2, single-scale PL, multiscale PL, weighted multiscale PL), 2 U-Net (L2 and single-scale PL), and the PBM. The imaging endpoints were mean absolute error and mean error, in Hounsfield units, between the reference CT (CTref) and the pCT. Dose uncertainties were quantified as mean absolute differences between the dose volume histograms (DVHs) calculated from the CTref and pCT obtained by each method. Three-dimensional gamma indexes were analyzed. Results Considering the image uncertainties in the whole pelvis, GAN L2 and U-Net L2 showed the lowest mean absolute error (≤34.4 Hounsfield units). The mean errors were not different than 0 (P ≤ .05). The PBM provided the highest uncertainties. Very few DVH points differed when comparing GAN L2 or U-Net L2 DVHs and CTref DVHs (P ≤ .05). Their dose uncertainties were ≤0.6% for the prostate planning target Volume V95%, ≤0.5% for the rectum V70Gy, and ≤0.1% for the bladder V50Gy. The PBM, U-Net PL, and GAN PL presented the highest systematic dose uncertainties. The gamma pass rates were >99% for all DLMs. The mean calculation time to generate 1 pCT was 15 s for the DLMs and 62 min for the PBM. Conclusions Generating pCT for MRI dose planning with DLMs and PBM provided low-dose uncertainties. In particular, the GAN L2 and U-Net L2 provided the lowest dose uncertainties together with a low computation time.

The 1981 Dalit mass conversion to Islam at Meenakshipuram, Tamil Nadu, arguably began the Hindu Right's political rise. The conversion raises two different concepts for understanding mass conversion's relationship to democracy. Though it is commonly framed in terms of B. R. Ambedkar's thought, whereby conversion's core is an interior psychic process of changing principles to see the world differently, I suggest that Meenakshipuram's event may more appropriately be framed by E. V. Ramasami's [EVR] thought, whereby conversion's core is an exterior somatic process of changing appearances to be seen differently in the world. These concepts of conversion raise alternative engagements with issues of text, force, foreignness, time, and Marxism. The argument is prefaced by a discussion of freedom's typology (Berlin), subaltern representation (Spivak), and religious mass (Geertz/Asad), which I argue favors EVR's concept of conversion over Ambedkar's. Such issues are not unimportant in an age of rising right-wing populisms globally.

International audience; Purpose - To automatically identify regions where prostate cancer is suspected on multi-parametric magnetic resonance images (mp-MRI). Methods - A residual network was implemented based on segmentations from an expert radiologist on T2-weighted, apparent diffusion coefficient map, and high b-value diffusion-weighted images. Mp-MRIs from 346 patients were used in this study. Results - The residual network achieved a hit or miss accuracy of 93% for lesion detection, with an average Jaccard score of 71% that compared the agreement between network and radiologist segmentations. Conclusion - This paper demonstrated the ability for residual networks to learn features for prostate lesion segmentation.

International audience; Deep Brain Stimulation (DBS) is an increasingly common therapy for a large range of neurological disorders, such as abnormal movement disorders. The effectiveness of DBS in terms of controlling patient symptomatology has made this procedure increasingly used over the past few decades. Concurrently, the popularity of Machine Learning (ML), a subfield of artificial intelligence, has skyrocketed and its influence has more recently extended to medical domains such as neurosurgery. Despite its growing research interest, there has yet to be a literature review specifically on the use of ML in DBS. We have followed a fully systematic methodology to obtain a corpus of 73 papers. In each paper, we identified the clinical application, the type/amount of data used, the method employed, and the validation strategy, further decomposed into 12 different sub-categories. The papers overall illustrated some existing trends in how ML is used in the context of DBS, including the breath of the problem domain and evolving techniques, as well as common frameworks and limitations. This systematic review analyzes at a broad level how ML have been recently used to address clinical problems on DBS, giving insight into how these new computational methods are helping to push the state-of-the-art of functional neurosurgery. DBS clinical workflow is complex, involves many specialists, and raises several clinical issues which have partly been addressed with artificial intelligence. However, several areas remain and those that have been recently addressed with ML are by no means considered “solved” by the community nor are they closed to new and evolving methods. © 2021

International audience; Micro-electrode recording (MER) is a powerful way of localizing target structures during neurosur-gical procedures such as the implantation of deep brain stimulation electrodes, which is a common treatment for Parkinson's disease and other neurological disorders. While Micro-electrode Recording (MER) provides adjunctive information to guidance assisted by pre-operative imaging, it is not unanimously used in the operating room. The lack of standard use of MER may be in part due to its long duration, which can lead to complications during the operation, or due to high degree of expertise required for their interpretation. Over the past decade, various approaches addressing automating MER analysis for target localization have been proposed, which have mainly focused on feature engineering. While the accuracies obtained are acceptable in certain configurations, one issue with handcrafted MER features is that they do not necessarily capture more subtle differences in MER that could be detected auditorily by an expert neurophysiologist. In this paper, we propose and validate a deep learning-based pipeline for subthalamic nucleus (STN) localization with micro-electrode recordings motivated by the human auditory system. Our proposed Convolutional Neural Network (CNN), referred as SepaConvNet, shows improved accuracy over two comparative networks for locating the STN from one second MER samples.

Aims GFR estimated with the creatinine-based Chronic Kidney Disease Epidemiology Collaboration (CKD-EPICr ) equation is used to screen for diabetic kidney disease and assess its severity. We systematically reviewed the process and outcome of evaluating CKD-EPICr in estimating point GFR or GFR decline over time in adults with type 1 or type 2 diabetes. Methods In this systematic review, MEDLINE, Embase and Cochrane Central Register of Controlled Trials were searched up to August 2019. Observational studies comparing CKD-EPICr with measured GFR (mGFR) in adults with diabetes were included. Studies on people with kidney transplant, non-diabetes related kidney disease, pregnancy, potential kidney donors, and those with critical or other systematic illnesses were excluded. Two independent reviewers extracted data from published papers and disagreements were resolved by consensus. Risk-of-bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. (PROSPERO registration number: CRD42018108776). Results From the 2820 records identified, 29 studies (14 704 participants) were included. All studies were at risk of bias. Bias (eight different forms) ranged from -26 to 35 ml min-1 1.73 m-2 ; precision (five different forms) ranged between 9 and 63 ml min-1 1.73 m-2 ; accuracy (five different forms) ranged between 16% and 96%; the correlation coefficient between CKD-EPICr and mGFR (four different forms) ranged between 0.38 and 0.86; and the reduced major axis regression slope ranged between 0.8 and 1.8. Conclusions Qualitative synthesis of data suggested CKD-EPICr was inaccurate in estimating point GFR or GFR decline over time. Furthermore, a lack of consistency in the methods and processes of evaluating the diagnostic performance of CKD-EPICr limits reliable quantitative assessment. The equation needs to be improved in adults with diabetes.

International audience; One of the recent developments in deep learning is the ability to train extremely deep residual neural networks, knowing that each residual block produces only marginal changes to the data. The accumulation of these changes result in the network's improved performance, analogous to a complex but trainable iterative algorithm. This intuition can be merged with the underlying theory of probabilistic graphical models in which these iterative algorithms are common and share the underlying probabilistic and information theoretic basis as deep learning. Prior models have been proposed with limitations on the number of iterations allowed in the solution algorithm due to the linear memory growth during the training process. This paper presents a structured activation layer which implements a conditional random field along with an arbitrary iteration message-passing marginal probability estimation algorithm which requires constant, rather than linear, memory with respect to the number of iterations. In this activation layer, the segmentation labels can be specified hierarchically, incorporating a level of abstract structure and some basic geometrical knowledge directly and easily into the network. Thus, this layer allows for the separation of abstract knowledge brought in by the network designer (in the form of the hierarchical structure) from probabilistic priors learned by the neural network. Preliminary results comparing this activation function to softmax and a similar non-hierarchical activation function indicate that it significantly improves performance in segmentation problems. © 2020 SPIE. All rights reserved.

© 2017 IEEE. Sensitivity to phase deviations in MRI forms the basis of a variety of techniques, including magnetic susceptibility weighted imaging and chemical shift imaging. Current phase processing techniques fall into two families: those which process the complex image data with magnitude and phase coupled, and phase unwrapping-based techniques that first linearize the phase topology across the image. However, issues, such as low signal and the existence of phase poles, can lead both methods to experience error. Cyclic continuous max-flow (CCMF) phase processing uses primal-dual-variational optimization over a cylindrical manifold, which represent the inherent topology of phase images, increasing its robustness to these issues. CCMF represents a third distinct paradigm in phase processing, being the only technique equipped with the inherent topology of phase. CCMF is robust and efficient with at least comparable accuracy as the prior paradigms.

Microbes associated with the external and internal anatomy of three commercially available predatory mite species—Phytoseiulus persimilis, Typhlodromips (= Amblyseius) swirskii, and Neoseiulus (= Amblyseius) cucumeris—were examined using light microscopy, confocal laser scanning microscopy and fluorescence in-situ hybridization (FISH). Four microbe morphotypes were observed on external body regions. These included three microfungi-like organisms (named T1, T2 and T3) and rod-shaped bacteria (T4). Morphotypes showed unique distributions on the external body regions and certain microbes were found only on one host species. Microfungi-like T1 were present in all three species whereas T2 and T3 were present in only P. persimilis and T. swirskii, respectively. T1 and T2 microbes were most abundant on the ventral structures of the idiosoma and legs, most frequently associated with coxae, coxal folds, ventrianal shields and epigynal shields. T3 microbes were most abundant on legs and dorsal idiosoma. T4 microbes were less abundant and were attached to epigynal shields of N. cucumeris and T. swirskii. Significant differences in distribution between batches suggest temporal fluctuations in the microbiota of phytoseiids in mass-reared systems. FISH showed bacteria within the alimentary tract, in Malpighian tubules and anal atria. These may aid absorption of excretory products or maintaining gut physiology. We suggest a mechanism by which microbes may be transmitted to offspring and throughout populations. This study aims to improve our knowledge of this poorly understood area and highlights the necessity of understanding the microbiota of Acari.

International audience; As new radiotherapy treatment systems using MRI (rather than traditional CT) are being developed, the accurate calculation of dose maps from MR imaging has become an increasing concern. MRI provides good soft-tissue but, unlike CT, lacks the electron density information necessary for dose calculation. In this paper, we proposed a generative adversarial network (GAN) using a perceptual loss to generate pseudo-CTs for prostate MRI dose calculation. This network was evaluated and compared to a U-Net network, a patch-based (PBM) and an atlas-based methods (ARM). Influence of the perceptual loss was assessed by comparing this network to a GAN using a L2 loss. GANs and U-Nets are rather similar with slightly better results for GANs. The proposed GAN outperformed the PBM by 9% and the ARM by 13% in term of MAE in whole pelvis. This method could be used for online dose calculation in MRI-only radiotherapy.

© 2019 Applications of ultrasound guidance for epidural injections are hindered by poor needle and epidural space visualization. This work presents an augmented reality (AR) ultrasound guidance system that addresses challenges in both needle visualization during navigation and epidural space identification for needle positioning. In this system, (i) B-mode ultrasound and the needle are visualized in a 3-D AR environment for improved navigation, and (ii) A-mode ultrasound, obtained from a custom-made single-element transducer housed at the needle tip, is used to identify the epidural space for improved needle positioning. Performance of the system was evaluated against ultrasound-only guidance in a phantom study with novice operators and an expert anesthesiologist. The procedure success rate was higher with the AR system (100%) than ultrasound-only guidance (57%). The AR system has the potential to improve procedure outcomes in terms of success rate, time, needle path-length and usability.

International audience; Deep brain stimulation (DBS) is an interventional treatment for Parkinson's disease which involves the precise positioning of stimulated electrodes within deep brain structures, such as the SubThalamic Nucleus (STN). Although originally identified via imaging, additional inter-operative guidance is necessary to localize the target anatomy. Analysis of Micro-Electrode Recordings (MERs) allows for a trained neurophysiologist to infer the underlying anatomy at a particular electrode position using human audition, although it is subjective and requires a high degree of expertise. Various approaches to assist MER analysis during DBS are proposed in the literature, including deep learning methods, which rely on a static input description, that is, a pre-defined number of features or input size. In this paper, we propose two dynamic deep learning approaches adaptable to the complexity of MERs signal, by using an arbitrary long listening time (in 1s chunks), while providing feedback to the neurophysiologist as to the model's certainty. We evaluated five different deep learning based classifiers which can use arbitrary length MERs for STN segmentation. We found that a Bayesian extension using the highlevel features from SepaConvNet performed the best, increasing the balanced accuracy to 83.5%. This work represents a step forward in integrating automated analysis of MERs into the DBS surgical workflow by automatically finding and exploiting possible efficiencies in MER acquisition.

The negative impact of chemotherapy on cognitive function in cancer patients has gained increasing attention in the last decade. Whilst the short-term acute effects on cognition are expected following chemotherapy, the persistence of such impairments in the long-term is still in question. This is despite clinical evidence indicating cognitive difficulties may persist well beyond treatment and affect quality of life. In the present study, we assessed the long-term (3 months) cognitive impact of chemotherapy in a mouse model intended to mimic the human female post-menopausal population receiving chemotherapy for breast cancer. Ovariectomized, female, C57BL/6J mice received two doses of Doxorubicin, Cyclophosphamide, and 5-Fluorouracil or saline vehicle (control), separated by one week. During this interval, mice received BrdU injections to label dividing cells. Results indicate a persistent impairment in learning and recall (1 h, 24 h and 48 h) on the Morris water maze, reduced survival and differentiation of new neurons (BrdU+/NeuN+), and a persistent decline in proliferation of new cells (Ki67+) in the dentate gyrus. Locomotor activity, motor performance, and anxiety-like behavior were unaffected. We further evaluated the efficacy of a diet enriched in omega-3-fatty acids (DHA + EPA + DPA), in reversing long-term chemotherapy deficits but no rescue was observed. The model described produces long-term cognitive and cellular impairments from chemotherapy that mimic those observed in humans. It could be useful for identifying mechanisms of action and to test further the ability of lifestyle interventions (e.g., diet) for ameliorating chemotherapy-induced cognitive impairments.

Cenozoic age detrital sequences in the Pilbara region of Western Australia are becoming a focus for engineering geological and hydrogeological investigations with an increasing number of final open-pit walls developed in these materials for iron ore mining. Historically, detrital sequences were classified chronostratigraphically. However, within each chronostratigraphic unit exist sub-units of variable engineering geological and hydrogeological character. As the majority of drill-hole data from Pilbara iron ore mines is derived from reverse circulation techniques, a methodology to identify the engineering geological units through downhole geophysics and geochemical assays was required to progress model development to the level of detail required for geotechnical and hydrogeological studies. The methodology entails a review of cored hole data and use of twin holes to assess the typical geochemical and geophysical signatures of units identified. Improved interpretation of reverse circulation drill-holes has resulted in the development of detailed 3D engineering geological models, which have improved the understanding of geological variability and engineering properties for geotechnical and hydrogeological studies.

It is proposed that oc pharyngealized onset consonants—that is, ‘type-A’ onset consonants—arose out of Proto-Sino-Tibetan plain consonants followed by geminate vowels separated by a pharyngeal fricative. When the first copy of the geminate vowel fell, the initial consonants formed clusters with the pharyngeal fricative, evolving into the oc pharyngealized consonants we reconstruct. In the Kuki-Chin branch of Tibeto-Burman, the pharyngeal fricative fell, and long vowels resulted. This proposal supposes a statistical correlation between Kuki-Chin long vowels and oc type-A words on the one hand, and between Kuki-Chin short vowels and oc type-B words on the other, as originally proposed by S. Starostin. A significant statistic bearing on forty-three probable Chinese-Kuki-Chin cognates supports this correlation. Thus reconstructed, a precursor language of Proto-Sino-Tibetan was aligned with Proto-Austronesian and Proto-Austroasiatic in exhibiting a surface constraint against monomoraic free words: by that constraint, the vowel of an underlying monosyllable was realized as a geminate with an intervening parasitic consonant such as a glottal stop or a pharyngeal fricative, while the vowels of a disyllable remained nongeminate. After reduction of disyllables to monosyllables, this process resulted in a pharyngealized vs. nonpharyngealized consonant distinction in oc. 論文提出對上古漢語咽化聲母（即“A類聲母”）來源的一個假設，認為咽化聲母來源於原始漢藏語的無標記輔音，並且此無標記輔音後跟隨著由咽部擦音[ʕ]分割的雙胞元音（geminate vowels）。在雙胞元音的前半部分脫落後，輔音聲母跟咽部擦音形成了複輔音。新形成的複輔音最終演變為上古漢語的咽化聲母。而在藏緬語庫基-欽（Kuki-Chin）語支中，則是咽化擦音脫落，使雙胞元音變為長元音。本文解釋了漢語與庫基-欽語中一個統計學的顯著相關性：一方面，庫基-欽語含長元音的詞與上古漢語含A類聲母的詞呈顯著相關；另一方面，庫基-欽語含短元音的詞與上古漢語含B類聲母的詞呈顯著相關，正如斯塔羅金（S. Starostin）所說。據此構擬，我們提出原始漢藏語、原始南島語與原始南亞語中的一個共同限制（constraint）：禁止在語流中出現單音拍（monomoraic）詞。在這一限制的作用下，單音節詞的單元音發生了雙胞化（germination），雙胞元音中間插入了喉塞音或咽部擦音之類的次聲輔音。而同時，雙音節詞的元音無變化。漢語經過雙音節詞的單音節化，就形成了無標記輔音與咽化輔音的音位對立。(This article is in English.)

© 2017, CARS. Purpose: Real-time ultrasound has become a crucial aspect of several image-guided interventions. One of the main constraints of such an approach is the difficulty in interpretability of the limited field of view of the image, a problem that has recently been addressed using mixed reality, such as augmented reality and augmented virtuality. The growing popularity and maturity of mixed reality has led to a series of informal guidelines to direct development of new systems and to facilitate regulatory approval. However, the goals of mixed reality image guidance systems and the guidelines for their development have not been thoroughly discussed. The purpose of this paper is to identify and critically examine development guidelines in the context of a mixed reality ultrasound guidance system through a case study. Methods: A mixed reality ultrasound guidance system tailored to central line insertions was developed in close collaboration with an expert user. This system outperformed ultrasound-only guidance in a novice user study and has obtained clearance for clinical use in humans. A phantom study with 25 experienced physicians was carried out to compare the performance of the mixed reality ultrasound system against conventional ultrasound-only guidance. Despite the previous promising results, there was no statistically significant difference between the two systems. Results: Guidelines for developing mixed reality image guidance systems cannot be applied indiscriminately. Each design decision, no matter how well justified, should be the subject of scientific and technical investigation. Iterative and small-scale evaluation can readily unearth issues and previously unknown or implicit system requirements. Conclusions: We recommend a wary eye in development of mixed reality ultrasound image guidance systems emphasizing small-scale iterative evaluation alongside system development. Ultimately, we recommend that the image-guided intervention community furthers and deepens this discussion into best practices in developing image-guided interventions.

Purpose: To measure regional specific ventilation with free-breathing hydrogen 1 (1H) magnetic resonance (MR) imaging without exogenous contrast material and to investigate correlations with hyperpolarized helium 3 (3He) MR imaging and pulmonary function test measurements in healthy volunteers and patients with asthma. Materials and Methods: Subjects underwent free-breathing 1H and static breath-hold hyperpolarized 3He MR imaging as well as spirometry and plethysmography; participants were consecutively recruited between January and June 2017. Free-breathing 1H MR imaging was performed with an optimized balanced steady-state free-precession sequence; images were retrospectively grouped into tidal inspiration or tidal expiration volumes with exponentially weighted phase interpolation. MR imaging volumes were coregistered by using optical flow deformable registration to generate 1H MR imaging-derived specific ventilation maps. Hyperpolarized 3He MR imaging- and 1H MR imaging-derived specific ventilation maps were coregistered to quantify regional specific ventilation within hyperpolarized 3He MR imaging ventilation masks. Differences between groups were determined with the Mann-Whitney test and relationships were determined with Spearman (ρ) correlation coefficients. Statistical analyses were performed with software. Results: Thirty subjects (median age: 50 years; interquartile range [IQR]: 30 years), including 23 with asthma and seven healthy volunteers, were evaluated. Both 1H MR imaging-derived specific ventilation and hyperpolarized 3He MR imaging-derived ventilation percentage were significantly greater in healthy volunteers than in patients with asthma (specific ventilation: 0.14 [IQR: 0.05] vs 0.08 [IQR: 0.06], respectively, P < .0001; ventilation percentage: 99% [IQR: 1%] vs 94% [IQR: 5%], P < .0001). For all subjects, 1H MR imaging-derived specific ventilation correlated with plethysmography-derived specific ventilation (ρ = 0.54, P = .002) and hyperpolarized 3He MR imaging-derived ventilation percentage (ρ = 0.67, P < .0001) as well as with forced expiratory volume in 1 second (FEV1) (ρ = 0.65, P = .0001), ratio of FEV1 to forced vital capacity (ρ = 0.75, P < .0001), ratio of residual volume to total lung capacity (ρ = -0.68, P < .0001), and airway resistance (ρ = -0.51, P = .004). 1H MR imaging-derived specific ventilation was significantly greater in the gravitational-dependent versus nondependent lung in healthy subjects (P = .02) but not in patients with asthma (P = .1). In patients with asthma, coregistered 1H MR imaging specific ventilation and hyperpolarized 3He MR imaging maps showed that specific ventilation was diminished in corresponding 3He MR imaging ventilation defects (0.05 ± 0.04) compared with well-ventilated regions (0.09 ± 0.05) (P < .0001). Conclusion: 1H MR imaging-derived specific ventilation correlated with plethysmography-derived specific ventilation and ventilation defects seen by using hyperpolarized 3He MR imaging. © RSNA, 2018 Online supplemental material is available for this article.

© 2015 Elsevier B.V. The incorporation of intensity, spatial, and topological information into large-scale multi-region segmentation has been a topic of ongoing research in medical image analysis. Multi-region segmentation problems, such as segmentation of brain structures, pose unique challenges in image segmentation in which regions may not have a defined intensity, spatial, or topological distinction, but rely on a combination of the three. We propose a novel framework within the Advanced segmentation tools (ASETS). 22ASETS website: http://www.advancedsegmentationtools.org/, which combines large-scale Gaussian mixture models trained via Kohonen self-organizing maps, with deformable registration, and a convex max-flow optimization algorithm incorporating region topology as a hierarchy or tree. Our framework is validated on two publicly available neuroimaging datasets, the OASIS and MRBrainS13 databases, against the more conventional Potts model, achieving more accurate segmentations. Each component is accelerated using general-purpose programming on graphics processing Units to ensure computational feasibility.

Numerous semiochemicals have been isolated from several species of astigmatid mites with various identified or unidentified functions. Alarm pheromonal activity is widespread with neryl formate and neral, being the most common compounds eliciting alarm response in conspecifics. The cosmopolitan astigmatid mite Suidasia medanensis (= S. pontifica) Oudemans (Acari: Suidasidae) has been reported to use neral as an alarm pheromone, but neral can also act as an allomone towards predators of oribatid mites. Suidasia medanensis can be utilised as a factitious prey for mass-rearing of the phytoseiid predatory mite Amblyseius (= Typhlodromips) swirskii (Athias-Henriot) (Acari: Phytoseiidae), which is used for biological control of insect and mite pests in protected crops. This study investigated the potential defence properties of the S. medanensis volatiles against A. swirskii, comparing the repellency to pollen-reared (naive) vs. S. medanensis-reared (experienced) predators using a synthetic blend of the isomers neral and geranial (1:1) as a model compound. In a repellency bioassay, the synthetic blend elicited a significant repellence to A. swirskii with no difference between naive and experienced predators. During capture success studies, S. medanensis under repeated attack could release sufficient quantities of the defence volatile to deter 1–5 attacks from A. swirskii, whereas hexane-treated S. medanensis artificially depleted of volatiles were significantly more vulnerable to an attack. This is the first report of an astigmatid defence volatile with repellent activity to a phytoseiid mite and the starting point to understanding semiochemical interactions in any current or novel factitious predator-prey mass-rearing system.

This letter presents novel behaviour-based tracking of people in low-resolution using instantaneous priors mediated by head-pose. We extend the Kalman Filter to adaptively combine motion information with an instantaneous prior belief about where the person will go based on where they are currently looking. We apply this new method to pedestrian surveillance, using automatically-derived head pose estimates, although the theory is not limited to head-pose priors. We perform a statistical analysis of pedestrian gazing behaviour and demonstrate tracking performance on a set of simulated and real pedestrian observations. We show that by using instantaneous `intentional' priors our algorithm significantly outperforms a standard Kalman Filter on comprehensive test data.

© 2015, CARS. Purpose: Several medical imaging modalities exhibit inherent scaling among the acquired data: The scale in an ultrasound image varies with the speed of sound and the scale of the range data used to reconstruct organ surfaces is subject to the scanner distance. In the context of surface-based registration, these scaling factors are often assumed to be isotropic, or as a known prior. Accounting for such anisotropies in scale can potentially dramatically improve registration and calibrations procedures that are essential for robust image-guided interventions. Methods: We introduce an extension to the ordinary iterative closest point (ICP) algorithm, solving for the similarity transformation between point-sets comprising anisotropic scaling followed by rotation and translation. The proposed anisotropic-scaled ICP (ASICP) incorporate a novel use of Mahalanobis distance to establish correspondence and a new solution for the underlying registration problem. The derivation and convergence properties of ASICP are presented, and practical implementation details are discussed. Because the ASICP algorithm is independent of shape representation and feature extraction, it is generalizable for registrations involving scaling. Results: Experimental results involving the ultrasound calibration, registration of partially overlapping range data, whole surfaces, as well as multi-modality surface data (intraoperative ultrasound to preoperative MR) show dramatic improvement in fiducial registration error. Conclusion: We present a generalization of the ICP algorithm, solving for a similarity transform between two point-sets by means of anisotropic scales, followed by rotation and translation. Our anisotropic-scaled ICP algorithm shares many traits with the ordinary ICP, including guaranteed convergence, independence of shape representation, and general applicability.

Objective To inform the design of a pivotal randomized controlled trial of prophylactic intra-aortic balloon counterpulsation (IABC) in patients undergoing coronary artery bypass graft (CABG) at high risk of postoperative low cardiac output syndrome (LCOS). Design Inception cohort study. Setting A total of 13 established cardiac centers in Australia, Canada, New Zealand, and the United Kingdom. Participants Adult patients were eligible for inclusion if they were listed for CABG surgery and had 2 or more LCOS risk factors (low ejection fraction, severe left main coronary artery disease, redo sternotomy, unstable angina). Interventions Outcomes of interest were a composite outcome of in-hospital mortality, postoperative acute myocardial infarction (AMI), acute kidney injury (AKI), or stroke as well as 6-month vital status and quality of life using the EuroQol 5-dimensional questionnaire (EQ5D). Measurements and Main Results The study included 136 participants over a 29-month period. Overall, in-hospital and 6-month mortality occurred in 7 (5%) and 11 (8%) participants, respectively. The composite outcome occurred in 60 (44%). The mean increase in EQ5D summary index at 6 months was 0.10 (standard deviation 0.24, p = 0.01). Perioperative AMI, AKI, or stroke significantly decreased the odds of a clinically meaningful improvement in quality of life (odds ratio 0.32; 95% confidence interval 0.13-0.79; p = 0.014). Preoperative IABC was used in 39 participants and did not predict postoperative outcomes. Conclusions The study identified a group of patients at risk of LCOS in whom CABG surgery was associated with a substantial burden of perioperative morbidity. Preoperative IABC use was variable, supporting the need for further research.

© 2017, Springer Science+Business Media New York. Label ordering, the specification of subset–superset relationships for segmentation labels, has been of increasing interest in image segmentation as they allow for complex regions to be represented as a collection of simple parts. Recent advances in continuous max-flow segmentation have widely expanded the possible label orderings from binary background/foreground problems to extendable frameworks in which the label ordering can be specified. This article presents Directed Acyclic Graph Max-Flow image segmentation which is flexible enough to incorporate any label ordering without constraints. This framework uses augmented Lagrangian multipliers and primal–dual optimization to develop a highly parallelized solver implemented using GPGPU. This framework is validated on synthetic, natural, and medical images illustrating its general applicability.

This systematic review examined longitudinal associations between weight change (weight gain and loss) and both physical and mental aspects of health-related quality of life (HRQOL) compared with stable weight in adults and children of the general population. MEDLINE, EMBASE, PsycINFO and PubMed databases were searched. Longitudinal observational studies measuring HRQOL with six predefined instruments were synthesized according to type of association: weight change and change in HRQOL (change-on-change association) and weight change and HRQOL at follow-up (predictive association). Twenty studies of adults (n = 15) or children (n = 5) were included. Fifteen studies used the SF-12 or SF-36. Results of nine studies in adults examining the change-on-change association were combined through a tallying of 606 analyses. Weight gain was most often associated with reduced physical, but not mental HRQOL, across all baseline body mass index categories and in both men and women. Weight loss may be associated with improved physical, but not mental HRQOL, among adults with overweight and obesity. Weight gain was more strongly associated with HRQOL than weight loss, implicating a greater need for preventative strategies to tackle obesity. Results in children and for the predictive association generally reflected these findings but require further research.

The predatory mite Typhlodromips swirskii (Athias–Henriot) is commonly used to suppress pest populations of thrips and whitefly in commercial greenhouses. Many generalist phytoseiid mites can be reared on astigmatid factitious prey. This study investigated the life table parameters of T. swirskii to the astigmatid mite Suidasia medanensis (Oudemans) and the capture success ratio of T. swirskii to different life stages of the prey. Juvenile development time and survival was 5.01 ± 0.10 days and 93 %, respectively. The intrinsic (r m ) and finite (λ) rates of increase were 0.222 and 1.249, respectively, with average oviposition rate of 1.71 ± 0.07 eggs/female/day. The capture success ratio of T. swirskii to S. medanensis was: eggs > freeze killed adults > nymphs > live adults. Typhlodromips swirskii was concluded to exhibit good population growth rates with S. medanensis as prey and, a prey population with predominance of eggs and nymphs to be advantageous to the predator due to an unidentified defence mechanism of adult prey.

© Springer International Publishing AG 2017. Periodicity is an important characteristic in many types of video sequences, particularly in medical applications where the cardiac and respiratory cycles are of special significance. Simple spectral analysis or band-pass filtering is often insufficient to extract the periodic signal. Here, we propose modeling the periodic and background components using nested dynamic linear models. These models can approximate the periodic and background time series in a wide range of video sequences. A likelihood ratio test can be used to find regions of the video exhibiting periodicity. Our experiments suggested this technique is suitable for a variety of applications using different imaging modalities, including ultrasound, MRI and natural video.

© 2016 IEEE. Optimization techniques have been widely used in deformable registration, allowing for the incorporation of similarity metrics with regularization mechanisms. These regularization mechanisms are designed to mitigate the effects of trivial solutions to ill-posed registration problems and to otherwise ensure the resulting deformation fields are well-behaved. This paper introduces a novel deformable registration (DR) algorithm, RANCOR, which uses iterative convexification to address DR problems under nonsmooth total-variation regularization. Initial comparative results against four state-of-the-art registration algorithms and under smooth regularization, respectively, are presented using the Internet Brain Segmentation Repository (IBSR) database.

On 21 November 1938, a South Indian Jewish lawyer named Abraham Barak Salem attended “a special prayer recited for the German Jews’ persecution by Hitler and [the] Nazis” at his synagogue in the port city of Cochin. Salem is known as the “Jewish Gandhi” for his leadership of non-violent satyagraha campaigns to end caste-based discrimination within his local Indian Jewish community—of which he occupied the lowest rung or meshuchrarim. The special prayer was responding to Kristallnacht, the widespread violence against Jews in Nazi Germany and Austria foreshadowing Hitler’s Final Solution. Kristallnacht’s global profile demanded a response from Gandhi himself, who had hitherto remained silent on the Jews’ position in fascist Europe. On 26 November 1938, Gandhi published “The Jews” in which he claimed “[m]y sympathies are all with the Jews,” dismissed a Jewish Palestine as “a crime against humanity,” and encouraged Jews to engage in non-violent satyagraha against the Nazis

Although it is well documented that infectious diseases can pose threats to biodiversity, the potential long-term consequences of pathogen exposure on individual fitness and its effects on population viability have rarely been studied. We tested the hypothesis that pathogen exposure causes physiological carry-over effects with a pathogen that is uniquely suited to this question because the infection period is specific and time limited. The fungus Pseudogymnoascus destructans causes white-nose syndrome (WNS) in hibernating bats, which either die due to the infection while hibernating or recover following emergence from hibernation. The fungus infects all exposed individuals in an overwintering site simultaneously, and bats that survive infection during hibernation clear the pathogen within a few weeks following emergence. We quantified chronic stress during the active season, when bats are not infected, by measuring cortisol in bat claws. Free-ranging Myotis lucifugus who survived previous exposure to P. destructans had significantly higher levels of claw cortisol than naïve individuals. Thus, cryptic physiological carry-over effects of pathogen exposure may persist in asymptomatic, recovered individuals. If these effects result in reduced survival or reproductive success, they could also affect population viability and even act as a third stream in the extinction vortex. For example, significant increases in chronic stress, such as those indicated here, are correlated with reduced reproductive success in a number of species. Future research should directly explore the link between pathogen exposure and the viability of apparently recovered populations to improve understanding of the true impacts of infectious diseases on threatened populations.

© 2016 SPIE. Image-based ultrasound to magnetic resonance image (US-MRI) registration can be an invaluable tool in image-guided neuronavigation systems. State-of-the-art commercial and research systems utilize image-based registration to assist in functions such as brain-shift correction, image fusion, and probe calibration. Since traditional US-MRI registration techniques use reconstructed US volumes or a series of tracked US slices, the functionality of this approach can be compromised by the limitations of optical or magnetic tracking systems in the neurosurgical operating room. These drawbacks include ergonomic issues, line-of-sight/magnetic interference, and maintenance of the sterile field. For those seeking a US vendor-agnostic system, these issues are compounded with the challenge of instrumenting the probe without permanent modification and calibrating the probe face to the tracking tool. To address these challenges, this paper explores the feasibility of a real-time US-MRI volume registration in a small virtual craniotomy site using a single slice. We employ the Linear Correlation of Linear Combination (LC2) similarity metric in its patch-based form on data from MNI's Brain Images for Tumour Evaluation (BITE) dataset as a PyCUDA enabled Python module in Slicer. By retaining the original orientation information, we are able to improve on the poses using this approach. To further assist the challenge of US-MRI registration, we also present the BOXLC2 metric which demonstrates a speed improvement to LC2, while retaining a similar accuracy in this context.