Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust contour tracking in echocardiographic sequences

In this paper we present an evaluation of a robust visual image tracker on echocardiographic image sequences. We show how the tracking framework can be customised to define an appropriate shape-space that describes heart shape deformations that can be learnt from a training data set. We also investigate an energy-based temporal boundary enhancement method to improve image feature measurement. Preliminary results are presented demonstrating tracking on real normal heart motion data sequences and synthesised and real abnormal heart motion data sequences. We conclude by discussing some of our current research efforts.
From projective to Euclidean space under any practical situation, a criticism of self-calibration

For many practical applications it is important to relax the self-calibration conditions to allow for changing internal camera parameters (e.g. zooming/focusing.). Classical techniques failed for such conditions. We present the available constraints that allow us to right a projective calibration to a Euclidean one. Meanwhile, we found that the estimations of the internal parameters were rather inaccurate. We discuss theoretically this difficulty and above all the resulting effect on the 3D reconstruction. In fact, we show that the uncertainty on the focal length estimation leads to an Euclidean calibration up to a quasi anisotropic homothety whereas the error on the principal point can often be interpreted as a translation. Hopefully, the calibration we come up with, is quite acceptable for reconstruction of models.
Endovascular management of intracranial aneurysms during pregnancy in three cases and review of the literature

We present three cases of cerebral aneurysms (1 unruptured; 2 ruptured) treated with endovascular techniques in pregnancies. The first ruptured case is a 28-year-old female on 20th gestational week. After the endovascular coiling, the patient suffered persistent hemiparesis and delivered a healthy baby by cesarean section. The second ruptured case is a 25-year-old female on 36th week of pregnancy. She died of aneurysm re-rupture after delivery of a healthy baby by cesarean section. The third unruptured case is a 31-year-old woman on the 26th gestational week of pregnancy who died of a giant basilar tip aneurysm after stent-assisted coiling. Ruptured aneurysm obliteration should be prioritized followed by vaginal delivery or cesarean section. The decision regarding the treatment of unruptured aneurysms should be carefully considered on a case-by-case basis. Stent-assisted coiling may be applicable to aneurysm during pregnancy.
Scattering Networks for Hybrid Representation Learning

Scattering networks are a class of designed Convolutional Neural Networks (CNNs) with fixed weights. We argue they can serve as generic representations for modelling images. In particular, by working in scattering space, we achieve competitive results both for supervised and unsupervised learning tasks, while making progress towards constructing more interpretable CNNs. For supervised learning, we demonstrate that the early layers of CNNs do not necessarily need to be learned, and can be replaced with a scattering network instead. Indeed, using hybrid architectures, we achieve the best results with predefined representations to-date, while being competitive with end-to-end learned CNNs. Specifically, even applying a shallow cascade of small-windowed scattering coefficients followed by $1\times 1$1×1-convolutions results in AlexNet accuracy on the ILSVRC2012 classification task. Moreover, by combining scattering networks with deep residual networks, we achieve a single-crop top-5 error of 11.4 percent on ILSVRC2012. Also, we show they can yield excellent performance in the small sample regime on CIFAR-10 and STL-10 datasets, exceeding their end-to-end counterparts, through their ability to incorporate geometrical priors. For unsupervised learning, scattering coefficients can be a competitive representation that permits image recovery. We use this fact to train hybrid GANs to generate images. Finally, we empirically analyze several properties related to stability and reconstruction of images from scattering coefficients.
Neighborhood preserving embedding

Recently there has been a lot of interest in geometrically motivated approaches to data analysis in high dimensional spaces. We consider the case where data is drawn from sampling a probability distribution that has support on or near a submanifold of Euclidean space. In this paper, we propose a novel subspace learning algorithm called neighborhood preserving embedding (NPE). Different from principal component analysis (PCA) which aims at preserving the global Euclidean structure, NPE aims at preserving the local neighborhood structure on the data manifold. Therefore, NPE is less sensitive to outliers than PCA. Also, comparing to the recently proposed manifold learning algorithms such as Isomap and locally linear embedding, NPE is defined everywhere, rather than only on the training data points. Furthermore, NPE may be conducted in the original space or in the reproducing kernel Hilbert space into which data points are mapped. This gives rise to kernel NPE. Several experiments on face database demonstrate the effectiveness of our algorithm
Aortic arch and common carotid artery plaques with soft components pose a substantial risk of cerebral embolization during carotid stenting

Objectives A higher rate of embolization is considered a disadvantage of carotid stenting (CAS), when compared with carotid endarterectomy. Plaques in the aortic arch (AA) and the common carotid artery (CCA) may be additional sources of embolization to stented internal carotid plaques during CAS. In this study, we aimed to investigate the relationship between these plaques and intracerebral embolization. Methods We analyzed the occurrence and composition of plaques in the AA and CCA by computed tomography angiography (CTA) in 101 consecutive cases of CAS. Cases of peri-procedural embolization were detected on diffusion-weighted imaging as lesions demonstrating diffusion restriction. We applied the χ2 and Fisher’s exact tests, as well as logistic regression models. Results The occurrence of plaques in the AA and CCA was significantly related to the appearance of new diffusion-weighted imaging lesions (p = 0.013 and p = 0.004, respectively). Patients with soft plaques in the AA or CCA had a significantly higher risk of embolization than those without plaques (p = 0.012 and p = 0.006, respectively). In contrast, homogeneously calcified plaques did not pose significantly higher risks. Conclusions Soft plaques in the AA and CCA result in a substantial risk of embolization during CAS. Use of a CTA examination of the AA and the CCA in patients with carotid stenosis may help to select lower-risk patients for CAS.
Neurointerventional management of carotid webs associated with recurrent and acute cerebral ischemic syndromes

Background A carotid web can be defined as an endoluminal shelf-like projection often noted at the origin of the internal carotid artery (ICA) just beyond the bifurcation. Diagnosis of a carotid web as an underlying cause of recurrent ischemic stroke is infrequent and easily misdiagnosed as an atheromatous plaque. Surgery has traditionally been used to resect symptomatic lesions while there is no enough evidence supporting medical therapy as the sole management. To our knowledge there is only one report about carotid artery stenting (CAS) as a definite management of carotid web and no previous reports of acute large-vessel occlusions undergoing mechanical thrombectomy in the setting of carotid web as the etiology. Case report We report two cases: The first presented with recurrent ischemic stroke in the same arterial territory and the other with an emergent left middle cerebral artery (MCA) occlusion that underwent endovascular mechanical thrombectomy in which initial computed tomographic angiograms (CTA) suggested carotid web etiologies. Following confirmation with digital subtraction angiography (DSA), both patients ultimately underwent endovascular carotid stenting instead of surgical resection for definitive carotid web treatment. Conclusions Carotid webs are a rare cause of ischemic stroke in young and middle-aged adults that can readily be identified by CTA. Endovascular management may include emergent mechanical thrombectomy for large-vessel thromboembolic complications, and for definitive treatment with carotid stenting across the carotid web as an alternative to surgical resection and medical management for secondary stroke prevention.
Multiple kernels for object detection

Our objective is to obtain a state-of-the art object category detector by employing a state-of-the-art image classifier to search for the object in all possible image sub-windows. We use multiple kernel learning of Varma and Ray (ICCV 2007) to learn an optimal combination of exponential χ2 kernels, each of which captures a different feature channel. Our features include the distribution of edges, dense and sparse visual words, and feature descriptors at different levels of spatial organization.
Robust visual tracking using &#x2113;1 minimization

In this paper we propose a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework. In this framework, occlusion, corruption and other challenging issues are addressed seamlessly through a set of trivial templates. Specifically, to find the tracking target at a new frame, each target candidate is sparsely represented in the space spanned by target templates and trivial templates. The sparsity is achieved by solving an ℓ1-regularized least squares problem. Then the candidate with the smallest projection error is taken as the tracking target. After that, tracking is continued using a Bayesian state inference framework in which a particle filter is used for propagating sample distributions over time. Two additional components further improve the robustness of our approach: 1) the nonnegativity constraints that help filter out clutter that is similar to tracked targets in reversed intensity patterns, and 2) a dynamic template update scheme that keeps track of the most representative templates throughout the tracking procedure. We test the proposed approach on five challenging sequences involving heavy occlusions, drastic illumination changes, and large pose variations. The proposed approach shows excellent performance in comparison with three previously proposed trackers.
A hand-held photometric stereo camera for 3-D modeling

This paper presents a simple yet practical 3-D modeling method for recovering surface shape and reflectance from a set of images. We attach a point light source to a hand-held camera to add a photometric constraint to the multi-view stereo problem. Using the photometric constraint, we simultaneously solve for shape, surface normal, and reflectance. Unlike prior approaches, we formulate the problem using realistic assumptions of a near light source, non-Lambertian surfaces, perspective camera model, and the presence of ambient lighting. The effectiveness of the proposed method is verified using simulated and real-world scenes.
Multivariate Extension of Matrix-Based Rényi's <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math><alternatives><mml:math><mml:mi>α</mml:mi></mml:math><inline-graphic xlink:href="yu-ieq1-2932976.gif"/></alternatives></inline-formula>-Order Entropy Functional

The matrix-based Rényi's <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math><alternatives><mml:math><mml:mi>α</mml:mi></mml:math><inline-graphic xlink:href="yu-ieq2-2932976.gif"/></alternatives></inline-formula>-order entropy functional was recently introduced using the normalized eigenspectrum of a Hermitian matrix of the projected data in a reproducing kernel Hilbert space (RKHS). However, the current theory in the matrix-based Rényi's <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math><alternatives><mml:math><mml:mi>α</mml:mi></mml:math><inline-graphic xlink:href="yu-ieq3-2932976.gif"/></alternatives></inline-formula>-order entropy functional only defines the entropy of a single variable or mutual information between two random variables. In information theory and machine learning communities, one is also frequently interested in multivariate information quantities, such as the multivariate joint entropy and different interactive quantities among multiple variables. In this paper, we first define the matrix-based Rényi's <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math><alternatives><mml:math><mml:mi>α</mml:mi></mml:math><inline-graphic xlink:href="yu-ieq4-2932976.gif"/></alternatives></inline-formula>-order joint entropy among multiple variables. We then show how this definition can ease the estimation of various information quantities that measure the interactions among multiple variables, such as interactive information and total correlation. We finally present an application to feature selection to show how our definition provides a simple yet powerful way to estimate a widely-acknowledged intractable quantity from data. A real example on hyperspectral image (HSI) band selection is also provided.
Video Anomaly Detection with Sparse Coding Inspired Deep Neural Networks

This paper presents an anomaly detection method that is based on a sparse coding inspired Deep Neural Networks (DNN). Specifically, in light of the success of sparse coding based anomaly detection, we propose a Temporally-coherent Sparse Coding (TSC), where a temporally-coherent term is used to preserve the similarity between two similar frames. The optimization of sparse coefficients in TSC with the Sequential Iterative Soft-Thresholding Algorithm (SIATA) is equivalent to a special stacked Recurrent Neural Networks (sRNN) architecture. Further, to reduce the computational cost in alternatively updating the dictionary and sparse coefficients in TSC optimization and to alleviate hyperparameters selection in TSC, we stack one more layer on top of the TSC-inspired sRNN to reconstruct the inputs, and arrive at an sRNN-AE. We further improve sRNN-AE in the following aspects: i) rather than using a predefined similarity measurement between two frames, we propose to learn a data-dependent similarity measurement between neighboring frames in sRNN-AE to make it more suitable for anomaly detection; ii) to reduce computational costs in the inference stage, we reduce the depth of the sRNN in sRNN-AE and, consequently, our framework achieves real-time anomaly detection; iii) to improve computational efficiency, we conduct temporal pooling over the appearance features of several consecutive frames for summarizing information temporally, then we feed appearance features and temporally summarized features into a separate sRNN-AE for more robust anomaly detection. To facilitate anomaly detection evaluation, we also build a large-scale anomaly detection dataset which is even larger than the summation of all existing datasets for anomaly detection in terms of both the volume of data and the diversity of scenes. Extensive experiments on both a toy dataset under controlled settings and real datasets demonstrate that our method significantly outperforms existing methods, which validates the effectiveness of our sRNN-AE method for anomaly detection. Codes and data have been released at https://github.com/StevenLiuWen/sRNN_TSC_Anomaly_Detection.
One-year efficacy and safety of the Trufill DCS Orbit and Orbit Galaxy detachable coils in the endovascular treatment of intracranial aneurysms: Results from the TRULINE study

Background and purpose No series reported the mid-term results of Trufill DCS Orbit and Orbit Galaxy detachable coils with independent evaluation. We present the one-year safety and efficacy of these coils in real-life routine clinical practice. Methods A total of 167 patients with 167 aneurysms (39.1% ruptured) were enrolled in the prospective TRULINE study. The primary endpoint was the safety, assessed by the combined morbidity-mortality rate observed since the time of the procedure and up to one-year follow-up. For safety, primary analyses were performed on intent-to-treat population (attempted coils procedure) and all adverse events have been reviewed by an independent Data Safety Monitoring Board. For efficacy, primary analyses were performed on the per-protocol population (patients treated with more than 70% of Trufill coils and not retreated during the follow-up period) and an independent core laboratory evaluated angiographic results. Results At one-year post-procedure, neurologic impairment was observed in 6.5% (95% confidence interval: 3.5–11.8) of the patients, and 2.6% (95% confidence interval: 1.0–6.8) had a permanent neurological deterioration. Three deaths were observed, unrelated to the procedure or coils. At one year, complete occlusion was seen in 52 aneurysms (54.2%), neck remnant in 28 aneurysms (29.2%), and aneurysm remnant in 16 aneurysms (16.7%). During the one-year follow-up, the overall incidence of recurrence was 30.2% with a mean interval of 13.8 ± 4.5 months and the retreatment for major recanalization was needed in nine patients (6.3%). Conclusions The TRULINE study confirms that endovascular coiling with Trufill DCS Orbit and Orbit Galaxy detachable coils is safe and effective.
Risk factors for vasovagal reaction associated with cerebral angiography via femoral catheterisation

Objective To analyse the related risk factors for vasovagal reaction associated with cerebral angiography via femoral catheterisation and discuss treatment measures and prevention and control methods. Methods The data of 3107 patients on whom cerebral angiography was performed in two high-volume interventional neuroradiology centres within 8 years were retrospectively analysed. Patients were divided into the vasovagal reaction and non-vasovagal reaction groups according to whether vasovagal reaction developed. The two groups’ clinical data were analysed using univariate and multivariate logistic regression analysis to examine vasovagal reaction-related factors. Results Of the 3107 patients included, 127 developed vasovagal reaction (4.09%). Of the 127 patients who developed vasovagal reaction, 123, three and one had vasovagal reaction after sheath removal, during cerebral angiography and upon femoral artery compression device removal, respectively. Differences in age, body mass index, sex ratios, proportions of patients with hypertension, diabetes and hyperlipemia, and adoption rate of femoral artery compression device were not statistically significant between the two groups (P > 0.05). Compared with the control group, the vasovagal reaction group had a significantly longer preoperative fasting time (P < 0.05). Multivariate logistic regression analysis revealed preoperative fasting time as the only risk factor for vasovagal reaction that was associated with cerebral angiography (P < 0.001). Conclusion Excessive fasting time before cerebral angiography is the most important risk factor for vasovagal reaction associated with cerebral angiography.
The mthA Mutation Conferring Low-Level Resistance to Streptomycin Enhances Antibiotic Production in Bacillus subtilis by Increasing the S-Adenosylmethionine Pool Size

ABSTRACT Certain Strr mutations that confer low-level streptomycin resistance result in the overproduction of antibiotics by Bacillus subtilis. Using comparative genome-sequencing analysis, we successfully identified this novel mutation in B. subtilis as being located in the mthA gene, which encodes S-adenosylhomocysteine/methylthioadenosine nucleosidase, an enzyme involved in the S-adenosylmethionine (SAM)-recycling pathways. Transformation experiments showed that this mthA mutation was responsible for the acquisition of low-level streptomycin resistance and overproduction of bacilysin. The mthA mutant had an elevated level of intracellular SAM, apparently acquired by arresting SAM-recycling pathways. This increase in the SAM level was directly responsible for bacilysin overproduction, as confirmed by forced expression of the metK gene encoding SAM synthetase. The mthA mutation fully exerted its effect on antibiotic overproduction in the genetic background of rel+ but not the rel mutant, as demonstrated using an mthA relA double mutant. Strikingly, the mthA mutation activated, at the transcription level, even the dormant ability to produce another antibiotic, neotrehalosadiamine, at concentrations of 150 to 200 μg/ml, an antibiotic not produced (<1 μg/ml) by the wild-type strain. These findings establish the significance of SAM in initiating bacterial secondary metabolism. They also suggest a feasible methodology to enhance or activate antibiotic production, by introducing either the rsmG mutation to Streptomyces or the mthA mutation to eubacteria, since many eubacteria have mthA homologues.
Multidrug Efflux Pump MdtBC of Escherichia coli Is Active Only as a B2C Heterotrimer

ABSTRACT RND (resistance-nodulation-division) family transporters in Gram-negative bacteria frequently pump out a wide range of inhibitors and often contribute to multidrug resistance to antibiotics and biocides. An archetypal RND pump of Escherichia coli, AcrB, is known to exist as a homotrimer, and this construction is essential for drug pumping through the functionally rotating mechanism. MdtBC, however, appears different because two pump genes coexist within a single operon, and genetic deletion data suggest that both pumps must be expressed in order for the drug efflux to occur. We have expressed the corresponding genes, with one of them in a His-tagged form. Copurification of MdtB and MdtC under these conditions showed that they form a complex, with an average stoichiometry of 2:1. Unequivocal evidence that only the trimer containing two B protomers and one C protomer is active was obtained by expressing all possible combinations of B and C in covalently linked forms. Finally, conversion into alanine of the residues, known to form a proton translocation pathway in AcrB, inactivated transport only when made in MdtB, not when made in MdtC, a result suggesting that MdtC plays a different role not directly involved in drug binding and extrusion.
Heterologous Expression of Bartonella Adhesin A in Escherichia coli by Exchange of Trimeric Autotransporter Adhesin Domains Results in Enhanced Adhesion Properties and a Pathogenic Phenotype

ABSTRACT Human-pathogenic Bartonella henselae causes cat scratch disease and vasculoproliferative disorders. An important pathogenicity factor of B. henselae is the trimeric autotransporter adhesin (TAA) Bartonella adhesin A (BadA), which is modularly constructed, consisting of a head, a long and repetitive neck-stalk module, and a membrane anchor. BadA is involved in bacterial autoagglutination, binding to extracellular matrix proteins and host cells, and in proangiogenic reprogramming. The slow growth of B. henselae and limited tools for genetic manipulation are obstacles for detailed examination of BadA and its domains. Here, we established a recombinant expression system for BadA mutants in Escherichia coli allowing functional analysis of particular BadA domains. Using a BadA mutant lacking 21 neck-stalk repeats (BadA HN23), the BadA HN23 signal sequence was exchanged with that of E. coli OmpA, and the BadA membrane anchor was additionally replaced with that of Yersinia adhesin A (YadA). Constructs were cloned in E. coli, and hybrid protein expression was detected by immunoblotting, fluorescence microscopy, and flow cytometry. Functional analysis revealed that BadA hybrid proteins mediate autoagglutination and binding to collagen and endothelial cells. In vivo, expression of this BadA construct correlated with higher pathogenicity of E. coli in a Galleria mellonella infection model.