Notes

Colour Doppler photo involving retrobulbar circulation in various harshness of glaucoma optic neuropathy.
The Aging involving Adipocytes Improves Term regarding Pro-Inflammatory Cytokines Chronologically.
Problematic Utilization of Nitrous Oxide by simply Young Moroccan-Dutch Grown ups.

Numbers of GP locums in the NHS have grown in recent years, yet evidence on the scale and scope of the locum workforce in general practice is sparse.

To identify characteristics, geographical patterns, and drivers of GP locum use.

Observational study of routine data from general practices in England.

Descriptive analyses of national GP workforce data between December 2017 and September 2020 were conducted to determine the volume and geographical distribution of locum use and examine the characteristics of locums compared with other GP types. Locum full-time equivalent (FTE) was modelled using negative binomial regression and estimated incidence rate ratios (IRRs) for associations between outcome and characteristics of practices and population.

In December 2019, total locum FTE was 1217.9 compared with 33 996.6 for total GP FTE. Locums represented 3.3% of total GP FTE, which was fewer than other GP types. Median locum age was 42 years (interquartile range [IQR] 36 to 51) FTE and the majority were UK and those who served underperforming practices in rural areas. This is likely to reflect recruitment or high turnover challenges in these practices/areas and can provide a greater understanding of general practice workforce challenges in England.
There is little evidence about the relationship between aetiology, illness severity, and clinical course of respiratory tract infections (RTIs) in primary care. Understanding these associations would aid in the development of effective management strategies for these infections.

To investigate whether clinical presentation and illness course differ between RTIs where a viral pathogen was detected and those where a potential bacterial pathogen was found.

Post hoc analysis of data from a pragmatic randomised trial on the effects of oseltamivir in patients with flu-like illness in primary care (
= 3266) in 15 European countries.

Patient characteristics and their signs and symptoms of disease were registered at baseline. Nasopharyngeal (adults) or nasal and pharyngeal (children) swabs were taken for polymerase chain reaction analysis. Patients were followed up until 28 days after inclusion. Regression models and Kaplan-Meier curves were used to analyse the relationship between aetiology, clinical presen-like illness seems to have limited value. A wait-and-see policy in most of these patients with flu-like illness seems the best option.Transcranial magnetic stimulation (TMS) is a non-invasive method of brain stimulation that is receiving increasingly attentionTranscranial magnetic stimulation (TMS) is a non-invasive method of brain stimulation that is receiving increasingly attentionfor new clinical applications. Through electromagnetic induction cortical activity can be modulated and therapeuticeffects can be achieved in a variety of psychiatric and neurological conditions. According to the World Health Organization(WHO) depression is the most disabling disease in the world and 350 million people suffer from depression globally. Majordepression is the most common disorder to be treated with TMS and the first mental disorder for which TMS received approvalfrom the US Food and Drug Administration (FDA). We here introduce the basic principles of TMS, discuss the latestdata on safety and side effects, and present various TMS treatment protocols as well as treatment response predictors inmajor depressive disorder.Despite major advances in the treatment of mood disorders, major depression, a common mental disorder, remains a serious public health problem. Electroconvulsive therapy (ECT) regardless of the anesthetic agent used, is the most effective form of treatment in major depression and the gold standard therapy in treatment resistant depression. Ketamine is one of the anesthetic drugs approved by the Αmerican Psychiatric Association Task Force Report for use in ECT. However, it has been used infrequently as an anesthetic in ECT. The initial reports suggested that ketamine has antidepressant properties resulting in rapid antidepressant response when administered in subanesthetic dose (0.5 mg/kg) in slow intravenous injection in patients suffering from depression. In recent trials has been reported that ketamine as the only anesthetic or as an adjunctive to another anesthetic agent may enhance the antidepressant effect of ECT either by increasing efficacy or by producing a rapid antidepressant response. ECT with ketamine may also cause less cognitive side effects. The most notable limitations of these studies are the small number of patients enrolled and several methodological differences (patients characteristics, electrode placements, titration method, anesthetic agent used with ketamine). The results of the clinical trials have been summarized in six meta-analysis and suggest that ketamine when used as a sole anesthetic agent or as an adjunctive anesthetic in ECT may accelerates the antidepressant response but does not augment ECT efficacy. It also does not improve the cognitive profile of the treatment. Larger, double-blind randomized controlled trial are needed for a definite conclusion.Treatment-Resistant Depression (TRD) calls for the development of effective interventions for mood elevation and stabilization. Recently, both ketamine and its S-enantiomer (esketamine) have been investigated with successful clinical trials demonstrating effectiveness in TRD. More specifically, in 2019, intranasally administered esketamine, as opposed to the more effective intravenous ketamine, has been approved by the FDA as a treatment option for TRD. Treatment with esketamine, however, potentially comes with major adverse effects, including risk of psychosis, the possibility of abuse and dependence after repeated use, transient but non-negligible change in blood pressure and the heart rate, and potential toxicity on the urothelium and the liver. These risks are minimized when treatment is kept within the recommended dose range and the drug is administered by experienced medical personnel. Nevertheless, these risks appear to be offset by the effectiveness of esketamine in a wide range of depressive symptoms, such as anhedonia, anxiety, cognitive impairment, suicidality, and general dysfunction. This review highlights the need for more phase 4 clinical studies to evaluate esketamine's performance in real life, including long-term effectiveness and risk studies.Τhe Food and Drug Administration (FDA) approval of the use of S-ketamine in the form of nasal spray for the treatment of treatment-resistant depression, launched a new category of therapeutic agents in psychiatry. link= TGF-beta inhibitor review A well-known class of substances, psychedelics, are introduced with a 30-year delay in the treatment of mental disorders. Intravenous ketamine infusion has been studied in the treatment of depression since the 1990s. Here we present the current protocol for the treatment of ketamine infusion in patients with treatment-resistant depression and related clinical information.The lack of utter efficacy and fast action of commonly used antidepressants that selectively target the monoaminergic neurotransmission has led to the exploration of ketamine's actions. Ketamine's antidepressant effect was firstly described in 1973 and nowadays its therapeutic value as a fast- and long- lasting antidepressant has been extensively established. Ketamine is an antagonist of the N-Methyl-D-aspartate receptor (NMDAR) and its main mechanism of action via NMDAR inhibition expressed in GABAergic (gamma-Aminobutyric acid, GABA) interneurons may be relayed to its antidepressant effects. This review aims to describe the pharmacokinetic and pharmacodynamic profile of ketamine when used for treatment-resistant depression. Moreover, ketamine is a racemic mixture consisting of two enantiomers, R- and S- ketamine. We describe the pharmacology of esketamine, along with the guidelines for effective and safe intranasal administration of esketamine. Lastly, this review presents sex differences in preclinical and clinical studies of ketamine and esketamine administration.Major depressive disorder is a serious mental health disorder of high prevalence and the leading cause of disability worldwide. While there are several classes of therapeutic agents with proven antidepressant efficacy, only about 40-60% of patients respond to initial antidepressant monotherapy, and 30-40% of patients may even show resistance to treatment even under optimal antidepressant pharmacotherapy. Despite the existence of international guidelines, there are still no clear and widely accepted treatment algorithms, no established predictive biomarkers of response to treatment, while the management treatment- resistant depression is usually based on clinical experience. The present article offers a brief narrative review of studies published so far on the predictive quality of various blood-based peripheral biomarkers with respect to response to pharmacological, stimulation or behavioral treatment in patients with treatment-resistant depression. To summarize the results, there does not yet appear to be anpatient subgroups, the achievement of higher rates of stable remission, and the development of new precision drugs with minimal side effects.Depression represents the predominant mood pole in bipolar disorder. Bipolar depression typically has a poor response to antidepressant medication, and also involves the risk of polarity shifts, induction of mixed states, and / or rapid cycle induction. The diagnosis of bipolar depression can be delayed by 8 to 10 years. The reason for this delay is mainly the fact that both manic and hypomanic episodes appear lately in the course of the disorder. It is therefore necessary to diagnose this clinical entity as early as possible versus monopolar depression in order to treat it more effectively. This differential diagnosis is based on certain clinical features of bipolar depression, which are often difficult to be distinguished from those of monopolar depression and therefore it is necessary to know specific criteria that differentiate them to some extent qualitatively and / or quantitatively. Such characteristics are daily mood swings, multiple physical complaints, psychomotor retardation, psychotic elements (delusions and perceptual disorders mood congruent or noncongruent), the disturbance of certain bodily functions, including circadian rhythms, sexual desire, appetite, and disorders of sleep architecture. The treatment of bipolar depression is based on the options known from monopolar depression (such as the use of antidepressants, antipsychotics, and certain antiepileptic agents) and their combinations, while in recent years it has been enriched with new pharmaceutical agents and non-pharmacological approaches. TGF-beta inhibitor review New glutaminergic regulators dominate the new pharmacological agents' research, and among them the antidepressant effect of ketamine and esketamine at sub-anesthetic doses is being extensively investigated during recent years. Non-pharmacological approaches include methods such as electroconvulsive therapy, repetitive transcranial magnetic stimulation (rTMS), sleep deprivation, and phototherapy.Treatment resistant depression is associated with serious and persistent symptomatology, chronic course, reduced quality of life, high rates of comorbidity with medical conditions or other mental disorders, increased indirect health care cost, increased suicidality and risk for hospitalization, negative impact to patients' functioning and occupation and poor treatment outcomes in general. The concept of treatment resistant depression emerged in the 1970s to describe a group of patients suffering from major depressive disorder who failed to reach remission of symptoms after at least two trials with antidepressant (efficient regarding dosage, compliance, and duration). Despite the introduction of many antidepressants over the following years, a large proportion of depressed patients fail to respond to available treatments, and this constitutes a continuing therapeutic challenge.Treatment resistant depression (TRD) is a serious public health problem. It is estimated that around 20- 40% of patients with a major depressive episode (whether monopolar or bipolar) do not exhibit clinical response to the current treatment with antidepressants, that is at least 50% decline in the symptoms scale. Furthermore, about half of the patients with symptom amelioration present residual symptoms which continue to negatively affect their functioning and increase the chance of relapse. Therefore, only 20-40% of patients (36.8% in STAR*D)1 who receive therapy for a major depressive episode for the first time exhibit remission (i.e., at least 70% decrease in symptom severity or HAMD score ≤7/MADRS score ≤10)2 - which is the goal of current treatments. Even when remission is achieved, though, there is often a long way to recovery and to the patient's return to the prior state of occupational and social functioning. Moreover, long-term medical treatment is needed in order to achieve and maintain the above. discussed in this Supplement issue of Psychiatriki.9.In this article, we focus on a biobjective hot strip mill (HSM) scheduling problem arising in the steel industry. Besides the conventional objective regarding penalty costs, we have also considered minimizing the total starting times of rolling operations in order to reduce the energy consumption for slab reheating. The problem is complicated by the inevitable uncertainty in rolling processing times, which means deterministic scheduling models will be ineffective. To obtain robust production schedules with satisfactory performance under all possible conditions, we apply the robust optimization (RO) approach to model and solve the scheduling problem. First, an RO model and an equivalent mixed-integer linear programming model are constructed to describe the HSM scheduling problem with uncertainty. Then, we devise an improved Benders' decomposition algorithm to solve the RO model and obtain exactly optimal solutions. Next, for coping with large-sized instances, a multiobjective particle swarm optimization algorithm with an embedded local search strategy is proposed to handle the biobjective scheduling problem and find the set of Pareto-optimal solutions. Finally, we conduct extensive computational tests to verify the proposed algorithms. Results show that the exact algorithm is effective for relatively small instances and the metaheuristic algorithm can achieve satisfactory solution quality for both small- and large-sized instances of the problem.Text classification has been widely explored in natural language processing. In this article, we propose a novel adaptive dense ensemble model (AdaDEM) for text classification, which includes local ensemble stage (LES) and global dense ensemble stage (GDES). To strengthen the classification ability and robustness of the enhanced layer, we propose a selective ensemble model based on enhanced attention convolutional neural networks (EnCNNs). To increase the diversity of the ensemble system, these EnCNNs are generated by using two manners 1) different sample subsets and 2) different granularity kernels. Then, an evaluation criterion that considers both accuracy and diversity is proposed in LES to obtain effective integration results. Furthermore, to make better use of information flow, we develop an adaptive dense ensemble structure with multiple enhanced layers in GDES to mitigate the issue that there may be redundant or invalid enhanced layers in the cascade structure. We conducted extensive experiments against state-of-the-art methods on multiple real-world datasets, including long and short texts, which has verified the effectiveness and generality of our method.Study of pairwise genetic interactions, such as mutually exclusive mutations, has led to understanding of underlying mechanisms in cancer. Investigation of various combinatorial motifs within networks of such interactions can lead to deeper insights into its mutational landscape and inform therapy development. One such motif called the Between-Pathway Model (BPM) represents redundant or compensatory pathways that can be therapeutically exploited. Finding such BPM motifs is challenging since most formulations require solving variants of the NP-complete maximum weight bipartite subgraph problem. In this paper we design an algorithm based on Integer Linear Programming (ILP) to solve this problem. In our experiments, our approach outperforms the best previous method to mine BPM motifs. Further, our ILP-based approach allows us to easily model additional application-specific constraints. We illustrate this advantage through a new application of BPM motifs that can potentially aid in finding combination therapies to combat cancer.To bridge the gap between the source and target domains in unsupervised domain adaptation (UDA), the most common strategy puts focus on matching the marginal distributions in the feature space through adversarial learning. However, such category-agnostic global alignment lacks of exploiting the class-level joint distributions, causing the aligned distribution less discriminative. To address this issue, we propose in this paper a novel margin preserving self-paced contrastive Learning (MPSCL) model for cross-modal medical image segmentation. link2 Unlike the conventional construction of contrastive pairs in contrastive learning, the domain-adaptive category prototypes are utilized to constitute the positive and negative sample pairs. With the guidance of progressively refined semantic prototypes, a novel margin preserving contrastive loss is proposed to boost the discriminability of embedded representation space. To enhance the supervision for contrastive learning, more informative pseudo-labels are generated in target domain in a self-paced way, thus benefiting the category-aware distribution alignment for UDA. Furthermore, the domain-invariant representations are learned through joint contrastive learning between the two domains. Extensive experiments on cross-modal cardiac segmentation tasks demonstrate that MPSCL significantly improves semantic segmentation performance, and outperforms a wide variety of state-of-the-art methods by a large margin.The booming Internet of Things makes smart healthcare a reality, while cloud-based medical storage systems solve the problems of large-scale storage and real-time access of medical data. The integrity of medical data outsourced in cloud-based medical storage systems has become crucial since only complete data can make a correct diagnosis, and public auditing protocol is a key technique to solve this problem. To guarantee the integrity of medical data and reduce the burden of the data owner, we propose an efficient privacy-preserving public auditing protocol for the cloud-based medical storage systems, which supports the functions of batch auditing and dynamic update of data. Detailed security analysis shows that our protocol is secure under the defined security model. In addition, we have conducted extensive performance evaluations, and the results indicate that our protocol not only remarkably reduces the computational costs of both the data owner and the third-party auditor (TPA), but also significantly improves the communication efficiency between the TPA and the cloud server. Specifically, compared with other related work, the computational cost of the TPA in our protocol is negligible and the data owner saves more than 2/3 of computational cost. In addition, as the number of challenged blocks increases, our protocol saves nearly 90% of communication overhead between the TPA and the cloud server.It has been suggested that tactile intensity perception can be explained by a linear function of spike rate weighted by afferent type. Other than relying on mathematical models, verifying this experimentally is difficult due to the frequency tuning of different afferent types and changes in population recruitment patterns with vibrotactile frequency. To overcome these complexities, we used pulsatile mechanical stimuli which activate the same afferent population regardless of the repetition rate (frequency), generating one action potential per pulse. We used trains of different frequencies (20-200 Hz) to investigate perceived intensity. Subjects' magnitude ratings increased with pulse rate up to ∼100 Hz and plateaued beyond this frequency. TGF-beta inhibitor review This was true regardless of pulse amplitude, from small pulses that exclusively activated Pacinian (PC) afferents, to pulses large enough to activate other afferents including slowly adapting. Electrical stimulation, which activates afferents indiscriminately, plateaued at a similar frequency, although not in all subjects. As the plateauing did not depend on indentation magnitude and hence on afferent weights, we propose that the contribution of spike count to intensity perception is weighted by a function of frequency. This may explain why fine textures evoking high frequency vibrations of a small magnitude do not feel disproportionally intense.The encounter of large amounts of biological sequence data generated during the last decades and the algorithmic and hardware improvements have offered the possibility to apply machine learning techniques in bioinformatics. While the machine learning community is aware of the necessity to rigorously distinguish data transformation from data comparison and adopt reasonable combinations thereof, this awareness is often lacking in the field of comparative sequence analysis. With realization of the disadvantages of alignments for sequence comparison, some typical applications use more and more so-called alignment-free approaches. link2 In light of this development, we present a conceptual framework for alignment-free sequence comparison, which highlights the delineation of 1) the sequence data transformation comprising of adequate mathematical sequence coding and feature generation, from 2) the subsequent (dis-)similarity evaluation of the transformed data by means of problem-specific but mathematically consistent proximity measures. We consider coding to be an information-loss free data transformation in order to get an appropriate representation, whereas feature generation is inevitably information-lossy with the intention to extract just the task-relevant information. This distinction sheds light on the plethora of methods available and assists in identifying suitable methods in machine learning and data analysis to compare the sequences under these premises.A large number of people suffer from life-threatening cardiac abnormalities, and electrocardiogram (ECG) analysis is beneficial to determining whether an individual is at risk of such abnormalities. Automatic ECG classification methods, especially the deep learning based ones, have been proposed to detect cardiac abnormalities using ECG records, showing good potential to improve clinical diagnosis and help early prevention of cardiovascular diseases. However, the predictions of the known neural networks still do not satisfactorily meet the needs of clinicians, and this phenomenon suggests that some information used in clinical diagnosis may not be well captured and utilized by these methods. In this paper, we introduce some rules into convolutional neural networks, which help present clinical knowledge to deep learning based ECG analysis, in order to improve automated ECG diagnosis performance. Specifically, we propose a Handcrafted-Rule-enhanced Neural Network (called HRNN) for ECG classification with standard 12-lead ECG input, which consists of a rule inference module and a deep learning module. Experiments on two large-scale public ECG datasets show that our new approach considerably outperforms existing state-of-the-art methods. Further, our proposed approach not only can improve the diagnosis performance, but also can assist in detecting mislabelled ECG samples.Postural control is a complex feedback system that relies on vast array of sensory inputs in order to maintain a stable upright stance. The brain cortex plays a crucial role in the processing of this information and in the elaboration of a successful adaptive strategy to external stimulation preventing loss of balance and falls. In the present work, the participants postural control system was challenged by disrupting the upright stance via a mechanical skeletal muscle vibration applied to the calves. The EEG source connectivity method was used to investigate the cortical response to the external stimulation and highlight the brain network primarily involved in high-level coordination of the postural control system. The cortical network reconfiguration was assessed during two experimental conditions of eyes open and eyes closed and the network flexibility (i.e. its dynamic reconfiguration over time) was correlated with the sample entropy of the stabilogram sway. The results highlight two different cortical strategies in the alpha band the predominance of frontal lobe connections during open eyes and the strengthening of temporal-parietal network connections in the absence of visual cues. Furthermore, a high correlation emerges between the flexibility in the regions surrounding the right temporo-parietal junction and the sample entropy of the CoP sway, suggesting their centrality in the postural control system. These results open the possibility to employ network-based flexibility metrics as markers of a healthy postural control system, with implications in the diagnosis and treatment of postural impairing diseases.This study evaluated the effect of change in background on steady state visually evoked potentials (SSVEP) and steady state motion visually evoked potentials (SSMVEP) based brain computer interfaces (BCI) in a small-profile augmented reality (AR) headset. A four target SSVEP and SSMVEP BCI was implemented using the Cognixion AR headset prototype. An active (AB) and a non-active background (NB) were evaluated. The signal characteristics and classification performance of the two BCI paradigms were studied. Offline analysis was performed using canonical correlation analysis (CCA) and complex-spectrum based convolutional neural network (C-CNN). Finally, the asynchronous pseudo-online performance of the SSMVEP BCI was evaluated. Signal analysis revealed that the SSMVEP stimulus was more robust to change in background compared to SSVEP stimulus in AR. The decoding performance revealed that the C-CNN method outperformed CCA for both stimulus types and NB background, in agreement with results in the literature. The average offline accuracies for W = 1 s of C-CNN were (NB vs. AB) SSVEP 82% ±15% vs. 60% ±21% and SSMVEP 71.4% ± 22% vs. 63.5% ± 18%. Additionally, for W = 2 s, the AR-SSMVEP BCI with the C-CNN method was 83.3% ± 27% (NB) and 74.1% ±22% (AB). The results suggest that with the C-CNN method, the AR-SSMVEP BCI is both robust to change in background conditions and provides high decoding accuracy compared to the AR-SSVEP BCI. This study presents novel results that highlight the robustness and practical application of SSMVEP BCIs developed with a low-cost AR headset.The machine learning (ML) life cycle involves a series of iterative steps, from the effective gathering and preparation of the data-including complex feature engineering processes-to the presentation and improvement of results, with various algorithms to choose from in every step. Feature engineering in particular can be very beneficial for ML, leading to numerous improvements such as boosting the predictive results, decreasing computational times, reducing excessive noise, and increasing the transparency behind the decisions taken during the training. Despite that, while several visual analytics tools exist to monitor and control the different stages of the ML life cycle (especially those related to data and algorithms), feature engineering support remains inadequate. In this paper, we present FeatureEnVi, a visual analytics system specifically designed to assist with the feature engineering process. Our proposed system helps users to choose the most important feature, to transform the original features into powerful alternatives, and to experiment with different feature generation combinations. link3 Additionally, data space slicing allows users to explore the impact of features on both local and global scales. FeatureEnVi utilizes multiple automatic feature selection techniques; furthermore, it visually guides users with statistical evidence about the influence of each feature (or subsets of features). The final outcome is the extraction of heavily engineered features, evaluated by multiple validation metrics. The usefulness and applicability of FeatureEnVi are demonstrated with two use cases and a case study. We also report feedback from interviews with two ML experts and a visualization researcher who assessed the effectiveness of our system.In this paper, we present ARCHIE++, a testing framework for conducting AR system testing and collecting user feedback in the wild. We begin by presenting a set of current trends in performing human testing of AR systems, identified by reviewing a selection of recent work from leading conferences in mixed reality, human factors, and mobile and pervasive systems. From the trends, we identify a set of challenges to be faced when attempting to adopt these practices to testing in the wild. These challenges are used to inform the design of our framework, which provides a cloud-enabled and device-agnostic way for AR systems developers to improve their knowledge of environmental conditions and to support scalability and reproducibility when testing in the wild. We then present a series of case studies demonstrating how ARCHIE++ can be used to support a range of AR testing scenarios, and demonstrate the limited overhead of the framework through a series of evaluations. We close with additional discussion on the design and utility of ARCHIE++ under various edge conditions.Omnidirectional videos have become a leading multimedia format for Virtual Reality applications. While live 360 videos offer a unique immersive experience, streaming of omnidirectional content at high resolutions is not always feasible in bandwidth-limited networks. While in the case of flat videos, scaling to lower resolutions works well, 360 video quality is seriously degraded because of the viewing distances involved in head-mounted displays. Hence, in this paper, we investigate first how quality degradation impacts the sense of presence in immersive Virtual Reality applications. Then, we are pushing the boundaries of 360 technology through the enhancement with multisensory stimuli. 48 participants experimented both 360 scenarios (with and without multisensory content), while they were divided randomly between four conditions characterised by different encoding qualities (HD, FullHD, 2.5K, 4K). The results showed that presence is not mediated by streaming at a higher bitrate. The trend we identified revealed however that presence is positively and significantly impacted by the enhancement with multisensory content. This shows that multisensory technology is crucial in creating more immersive experiences.This paper presents an edge-based defocus blur estimation method from a single defocused image. We first distinguish edges that lie at depth discontinuities (called depth edges, for which the blur estimate is ambiguous) from edges that lie at approximately constant depth regions (called pattern edges, for which the blur estimate is well-defined). Then, we estimate the defocus blur amount at pattern edges only, and explore an interpolation scheme based on guided filters that prevents data propagation across the detected depth edges to obtain a dense blur map with well-defined object boundaries. Both tasks (edge classification and blur estimation) are performed by deep convolutional neural networks (CNNs) that share weights to learn meaningful local features from multi-scale patches centered at edge locations. Experiments on naturally defocused images show that the proposed method presents qualitative and quantitative results that outperform state-of-the-art (SOTA) methods, with a good compromise between running time and accuracy.Deep learning has enabled significant improvements in the accuracy of 3D blood vessel segmentation. Open challenges remain in scenarios where labeled 3D segmentation maps for training are severely limited, as is often the case in practice, and in ensuring robustness to noise. Inspired by the observation that 3D vessel structures project onto 2D image slices with informative and unique edge profiles, we propose a novel deep 3D vessel segmentation network guided by edge profiles. Our network architecture comprises a shared encoder and two decoders that learn segmentation maps and edge profiles jointly. 3D context is mined in both the segmentation and edge prediction branches by employing bidirectional convolutional long-short term memory (BCLSTM) modules. 3D features from the two branches are concatenated to facilitate learning of the segmentation map. As a key contribution, we introduce new regularization terms that a) capture the local homogeneity of 3D blood vessel volumes in the presence of biomarkers; and b) ensure performance robustness to domain-specific noise by suppressing false positive responses. Experiments on benchmark datasets with ground truth labels reveal that the proposed approach outperforms state-of-the-art techniques on standard measures such as DICE overlap and mean Intersection-over-Union. The performance gains of our method are even more pronounced when training is limited. Furthermore, the computational cost of our network inference is among the lowest compared with state-of-the-art.Images synthesized using depth-image-based-rendering (DIBR) techniques may suffer from complex structural distortions. The goal of the primary visual cortex and other parts of brain is to reduce redundancies of input visual signal in order to discover the intrinsic image structure, and thus create sparse image representation. Human visual system (HVS) treats images on several scales and several levels of resolution when perceiving the visual scene. With an attempt to emulate the properties of HVS, we have designed the no-reference model for the quality assessment of DIBR-synthesized views. To extract a higher-order structure of high curvature which corresponds to distortion of shapes to which the HVS is highly sensitive, we define a morphological oriented Difference of Closings (DoC) operator and use it at multiple scales and resolutions. DoC operator nonlinearly removes redundancies and extracts fine grained details, texture of an image local structure and contrast to which HVS is highly sensitive. We introduce a new feature based on sparsity of DoC band. To extract perceptually important low-order structural information (edges), we use the non-oriented Difference of Gaussians (DoG) operator at different scales and resolutions. Measure of sparsity is calculated for DoG bands to get scalar features. To model the relationship between the extracted features and subjective scores, the general regression neural network (GRNN) is used. Quality predictions by the proposed DoC-DoG-GRNN model show higher compatibility with perceptual quality scores in comparison to the tested state-of-the-art metrics when evaluated on four benchmark datasets with synthesized views, IRCCyN/IVC image/video dataset, MCL-3D stereoscopic image dataset and IST image dataset.Training deep models for RGB-D salient object detection (SOD) often requires a large number of labeled RGB-D images. However, RGB-D data is not easily acquired, which limits the development of RGB-D SOD techniques. To alleviate this issue, we present a Dual-Semi RGB-D Salient Object Detection Network (DS-Net) to leverage unlabeled RGB images for boosting RGB-D saliency detection. We first devise a depth decoupling convolutional neural network (DDCNN), which contains a depth estimation branch and a saliency detection branch. The depth estimation branch is trained with RGB-D images and then used to estimate the pseudo depth maps for all unlabeled RGB images to form the paired data. The saliency detection branch is used to fuse the RGB feature and depth feature to predict the RGB-D saliency. Then, the whole DDCNN is assigned as the backbone in a teacher-student framework for semi-supervised learning. Moreover, we also introduce a consistency loss on the intermediate attention and saliency maps for the unlabeled data, as well as a supervised depth and saliency loss for labeled data. Experimental results on seven widely-used benchmark datasets demonstrate that our DDCNN outperforms state-of-the-art methods both quantitatively and qualitatively. We also demonstrate that our semi-supervised DS-Net can further improve the performance, even when using an RGB image with the pseudo depth map.With the increasing popularity of convolutional neural networks (CNNs), recent works on face-based age estimation employ these networks as the backbone. However, state-of-the-art CNN-based methods treat each facial region equally, thus entirely ignoring the importance of some facial patches that may contain rich age-specific information. In this paper, we propose a face-based age estimation framework, called Attention-based Dynamic Patch Fusion (ADPF). In ADPF, two separate CNNs are implemented, namely the AttentionNet and the FusionNet. The AttentionNet dynamically locates and ranks age-specific patches by employing a novel Ranking-guided Multi-Head Hybrid Attention (RMHHA) mechanism. The FusionNet uses the discovered patches along with the facial image to predict the age of the subject. Since the proposed RMHHA mechanism ranks the discovered patches based on their importance, the length of the learning path of each patch in the FusionNet is proportional to the amount of information it carries (the longer, the more important). ADPF also introduces a novel diversity loss to guide the training of the AttentionNet and reduce the overlap among patches so that the diverse and important patches are discovered. Through extensive experiments, we show that our proposed framework outperforms state-of-the-art methods on several age estimation benchmark datasets.We present an intravascular ultrasound (IVUS) transducer array designed to enable shear wave elasticity imaging (SWEI) of arteries for the detection and characterization of atherosclerotic soft plaques. Using a custom dicing fixture, we have fabricated single-element and axially-segmented array transducer prototypes from 4.6-Fr to 7.6-Fr piezoceramic tubes, respectively. Focused excitation of the array prototype at 4 MHz yielded a focal gain of 5× in intensity, for an estimated 60 mW/cm2 [Formula see text] and 1.6-MPa negative peak pressure at 4.5-mm range in water. The single-element transducer generated a peak radial displacement of [Formula see text] in a uniform elasticity phantom, with axial shear waves detectable by an external linear array probe up to 5 mm away from the excitation plane. In a vessel phantom with a soft inclusion, the array prototype generated peak displacements of 2.2 and [Formula see text] in the soft inclusion and vessel wall regions, respectively. A SWEI image of the vessel phantom was reconstructed, with measured shear wave speed (SWS) of 1.66 ± 0.91 m/s and 0.97 ± 0.59 m/s for the soft inclusion and vessel wall regions, respectively. The array prototype was also used to obtain a SWEI image of an ex vivo porcine artery, with a mean SWS of 3.97 ± 1.12 m/s. These results suggest that a cylindrical intravascular ultrasound (IVUS) transducer array could be made capable of SWEI for atherosclerotic plaque detection in coronary arteries.Transcranial focused ultrasound (tFUS) is increasingly used in experimental neuroscience due to its neuromodulatory effectiveness in animal studies. However, achieving multitarget tFUS in small animals is typically limited by transducer size, energy transfer efficiency, and brain volume. The objective of this work was to construct an ultrasound system for multitarget neuromodulation in small animals. First, a miniaturized high-powered 2-D array transducer was developed. The phase delay of each array element was calculated based on the multifocal time-reversal method, generating multiple foci simultaneously in a 3-D field. The effects of the axial focal length, interfocus spacing (lateral distance between the two focal centers), and the number of foci on the focal properties of the pressure field were examined through numerical simulations. In-vitro ultrasonic measurements and transcranial simulations on a rat skull were conducted. The minimum interfocus spacing separating two -6-dB foci and the peak full-width at half-maximum were positively correlated with axial focal length; the relative relationship between the interfocus spacing and pressure field properties was similar for each axial focal length. The maximum acoustic pressure and spatial average intensity at focus in deionized water were 2.21 MPa and 133 W/cm2, respectively. The simulated and experimental results were compared, demonstrating agreement in both peak position and focus shape. The ultrasound system can provide a neuroscientific platform for evaluating the feasibility of multitarget ultrasound stimulation treatment protocols, thus improving the understanding of functional neuroanatomy.Recent advances in contactless micromanipulation strategies have revolutionized prospects of robotic manipulators as next-generation tools for minimally invasive surgeries. In particular, acoustically powered phased arrays offer dexterous means of manipulation both in air and water. Inspired by these phased arrays, we present SonoTweezer a compact, low-power, and lightweight array of immersible ultrasonic transducers capable of trapping and manipulation of sub-mm sized agents underwater. Based on a parametric investigation with numerical pressure field simulations, we design and create a six-transducer configuration, which is small compared to other reported multi-transducer arrays (16-256 elements). Despite the small size of array, SonoTweezer can reach pressure magnitudes of 300 kPa at a low supply voltage of 25 V to the transducers, which is in the same order of absolute pressure as multi-transducer arrays. Subsequently, we exploit the compactness of our array as an end-effector tool for a robotic manipulator to demonstrate long-range actuation of sub-millimeter agents over a hundred times the agent's body length. Furthermore, a phase-modulation over its individual transducers allows our array to locally maneuver its target agents at sub-mm steps. The ability to manipulate agents underwater makes SonoTweezer suitable for clinical applications considering water's similarity to biological media, e.g., vitreous humor and blood plasma. Finally, we show trapping and manipulation of micro-agents under medical ultrasound (US) imaging modality. This application of our actuation strategy combines the usage of US waves for both imaging and micromanipulation.Deformable registration is a crucial step in many medical procedures such as image-guided surgery and radiation therapy. Most recent learning-based methods focus on improving the accuracy by optimizing the non-linear spatial correspondence between the input images. Therefore, these methods are computationally expensive and require modern graphic cards for real-time deployment. In this paper, we introduce a new Light-weight Deformable Registration network that significantly reduces the computational cost while achieving competitive accuracy. In particular, we propose a new adversarial learning with distilling knowledge algorithm that successfully leverages meaningful information from the effective but expensive teacher network to the student network. We design the student network such as it is light-weight and well suitable for deployment on a typical CPU. The extensively experimental results on different public datasets show that our proposed method achieves state-of-the-art accuracy while significantly faster than recent methods. We further show that the use of our adversarial learning algorithm is essential for a time-efficiency deformable registration method. Finally, our source code and trained models are available at https//github.com/aioz-ai/LDR_ALDK.Thyroid nodules are one of the most common nodular lesions. The incidence of thyroid cancer has increased rapidly in the past three decades and is one of the cancers with the highest incidence. As a non-invasive imaging modality, ultrasonography can identify benign and malignant thyroid nodules, and it can be used for large-scale screening. In this study, inspired by the domain knowledge of sonographers when diagnosing ultrasound images, a local and global feature disentangled network (LoGo-Net) is proposed to classify benign and malignant thyroid nodules. This model imitates the dual-pathway structure of human vision and establishes a new feature extraction method to improve the recognition performance of nodules. We use the tissue-anatomy disentangled (TAD) block to connect the dual pathways, which decouples the cues of local and global features based on the self-attention mechanism. To verify the effectiveness of the model, we constructed a large-scale dataset and conducted extensive experiments. The results show that our method achieves an accuracy of 89.33%, which has the potential to be used in the clinical practice of doctors, including early cancer screening procedures in remote or resource-poor areas.Balun or trap circuits are critical components for suppressing common-mode currents flowing on the outer conductors of coaxial cables in RF coil systems for Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS). Common-mode currents affect coils' tuning and matching, induce losses, pick up extra noise from the surrounding environment, lead to undesired cross-talk, and cause safety concerns in animal and human imaging. First proposed for microwave antenna applications, the Lattice balun has been widely used in MRI coils. It has a small footprint and can be easily integrated with coil tuning/matching circuits. However, the Lattice balun is typically a single-tuned circuit and cannot be used for multi-nuclear MRI and MRS with two RF frequencies. This work describes a dual-tuned Lattice balun design that is suitable for multi-nuclear MRI/MRS. It was first analyzed theoretically to derive component values. RF circuit simulations were then performed to validate the theoretical analysis and provide guidance for practical construction. Based on the simulation results, a dual-tuned balun circuit was built for 7T 1H/23Na MRI and bench tested. The fabricated dual-tuned balun exhibits superior performance at the Larmor frequencies of both 1H and 23Na, with less than 0.15 dB insertion loss and better than 17 dB common-mode rejection ratio at both frequencies.Estimating time-varying graphical models are of paramount importance in various social, financial, biological, and engineering systems, since the evolution of such networks can be utilized for example to spot trends, detect anomalies, predict vulnerability, and evaluate the impact of interventions. Existing methods require extensive tuning of parameters that control the graph sparsity and temporal smoothness. Furthermore, these methods are computationally burdensome with time complexity O(NP^3) for P variables and N time points. As a remedy, we propose a low-complexity tuning-free Bayesian approach, named BASS. Specifically, we impose temporally-dependent spike-and-slab priors on the graphs such that they are sparse and varying smoothly across time. A variational inference algorithm is then derived to learn the graph structures from the data automatically. Owing to the pseudo-likelihood and the mean-field approximation, the time complexity of BASS is only O(NP^2). Additionally, by identifying the frequency-domain resemblance to the time-varying graphical models, we show that BASS can be extended to learning frequency-varying inverse spectral density matrices, and yields graphical models for multivariate stationary time series. link3 Numerical results on both synthetic and real data show that BASS can better recover the underlying true graphs, while being more efficient than the existing methods, especially for high-dimensional cases.
Using a musculoskeletal modelling framework, we aimed to (1) estimate knee joint loading using static optimization (SO); (2) explore different calibration functions in electromyogram (EMG)-informed models used in estimating knee load; and (3) determine, when using an EMG-informed stochastic method, if the measured joint loadings are solutions to the muscle redundancy problem when investigating only the uncertainty in muscle forces.

Musculoskeletal models for three individuals with instrumented knee replacements were generated. Muscle forces were calculated using SO, EMG-informed, and EMG-informed stochastic methods. Measured knee joint loads from the prostheses were compared to the SO and EMG-informed solutions. Root mean square error (RMSE) in joint load estimation was calculated, and the muscle force ranges were compared.

The RMSE ranged between 192-674 N, 152-487 N, and 7-108 N for the SO, the calibrated EMG-informed solution, and the best fit stochastic result, respectively. The stochastic method produced solution spaces encompassing the measured joint loading up to 98% of stance.

Uncertainty in muscle forces can account for total knee loading and it is recommended that, where possible, EMG measurements should be included to estimate knee joint loading.

This work shows that the inclusion of EMG-informed modelling allows for better estimation of knee joint loading when compared to SO.
This work shows that the inclusion of EMG-informed modelling allows for better estimation of knee joint loading when compared to SO.Dear Editor The European Baseline Series (EBS) of contact allergens is used throughout Europe as a screening test to diagnose contact allergy as a proxy for allergy contact dermatitis and other hypersensitivity skin diseases (1). Parabens are alkyl esters of p-hydroxybenzoic acid with antimicrobial effects used as preservatives in cosmetics, foods, and drugs that have been included in the so called "baseline series" (2) for more than 40 years. Parabens, which are considered allergologically safe biocides and are classified as safe by the US Food and Drug Administration (FDA) and the Scientific Committee on Consumer Safety (SCCS) in Europe (2-4), are frequently present in cosmetics (5). Despite extensive and progressively expanding use worldwide, studies confirm that parabens are seldom responsible for allergic contact dermatitis to cosmetics, and the frequency of sensitivity to parabens has been low and stable for many decades (2). The frequency of positive reactions to paraben mix is less than 0.5% in most clinical series, although it seems that when it occurs it is often of high clinical relevance (6).
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