Exact Boundaries Research Articles

Quantitative Endogenous Fluorescence Analysis Discriminates Glioblastoma Tumor through Visible and NIR Excitation

Nowadays, the ability to diagnose brain tumors intraoperatively and delineate their margins, as accurately as possible, is of primordial importance during surgery. However, the exact tumor boundaries and targets are difficult to find due to the similar visual appearances especially at the margins, leading in many cases to poor surgical outcomes and a high risk of recurrences. To solve this dogma, our group tried to determine different tissue optical signatures such as optical parameters and endogenous fluorescence. For that, we conducted a comparative study to differentiate between healthy and tumorous tissues under one- and two-photon excitations on optical properties to explore several optical parameters. In addition, the study of endogenous fluorescence can successfully help with the discrimination between tissue types using one- and two-photon excitations. This works suggests that the multimodal analysis of optical signatures of tissues could be a reliable technique that can be used to differentiate between healthy and tumorous tissues. This can help in the future with the implementation of such techniques in vivo during surgery to help the surgeon with the decision that needs to be taken as for tissue resection.

Critical Assessment of the Biomarker Discovery and Classification Methods for Multiclass Metabolomics.

Multiclass metabolomics has been widely applied in clinical practice to understand pathophysiological processes involved in disease progression and diagnostic biomarkers of various disorders. In contrast to the binary problem, the multiclass classification problem is more difficult in terms of obtaining reliable and stable results due to the increase in the complexity of determining exact class decision boundaries. In particular, methods of biomarker discovery and classification have a significant effect on the multiclass model because different methods with significantly varied theories produce conflicting results even for the same dataset. However, a systematic assessment for selecting the most appropriate methods of biomarker discovery and classification for multiclass metabolomics is still lacking. Therefore, a comprehensive assessment is essential to measure the suitability of methods in multiclass classification models from multiple perspectives. In this study, five biomarker discovery methods and nine classification methods were assessed based on four benchmark datasets of multiclass metabolomics. The performance assessment of the biomarker discovery and classification methods was performed using three evaluation criteria: assessment a (cluster analysis of sample grouping), assessment b (biomarker consistency in multiple subgroups), and assessment c (accuracy in the classification model). As a result, 13 combining strategies with superior performance were selected under multiple criteria based on these benchmark datasets. In conclusion, superior strategies that performed consistently well are suggested for the discovery of biomarkers and the construction of a classification model for multiclass metabolomics.

Dispensing of quantum information beyond no-broadcasting theorem—is it possible to broadcast anything genuinely quantum?

No-broadcasting theorem is one of the most fundamental results in quantum information theory; it guarantees that the simplest attacks on any quantum protocol, based on eavesdropping and copying of quantum information, are impossible. Due to the fundamental importance of the no-broadcasting theorem, it is essential to understand the exact boundaries of this limitation. We generalize the standard definition of broadcasting by restricting the set of states which we want to broadcast and restricting the sets of measurements which we use to test the broadcasting. We show that in some of the investigated cases broadcasting is equivalent to commutativity, while in other cases commutativity is not necessary.

A bittersweet fate: detection of serotype switching in Pseudomonas aeruginosa.

High-risk clone types in Pseudomonas aeruginosa are problematic global multidrug-resistant clones. However, apart from their ability to resist antimicrobial treatment, not much is known about what sets these clones apart from the multitude of other clones. In high-risk clone ST111, it has previously been shown that replacement of the native serotype biosynthetic gene cluster (O4) by a different gene cluster (O12) by horizontal gene transfer and recombination may have contributed to the global success of this clone. However, the extent to which isolates undergo this type of serotype switching has not been adequately explored in P. aeruginosa. In the present study, a bioinformatics tool has been developed and utilized to provide a first estimate of serotype switching in groups of multidrug resistant (MDR) clinical isolates. The tool detects serotype switching by analysis of core-genome phylogeny and in silico serotype. Analysis of a national survey of MDR isolates found a prevalence of 3.9 % of serotype-switched isolates in high-risk clone types ST111, ST244 and ST253. A global survey of MDR isolates was additionally analysed, and it was found that 2.3 % of isolates had undergone a serotype switch. To further understand this process, we determined the exact boundaries of the horizontally transferred serotype O12 island. We found that the size of the serotype island correlates with the clone type of the receiving isolate and additionally we found intra-clone type variations in size and boundaries. This suggests multiple serotype switch events. Moreover, we found that the housekeeping gene gyrA is co-transferred with the O12 serotype island, which prompted us to analyse this allele for all serotype O12 isolates. We found that 95 % of ST111 O12 isolates had a resistant gyrA allele and 86 % of all O12 isolates had a resistant gyrA allele. The rates of resistant gyrA alleles in isolates with other prevalent serotypes are all lower. Together, these results show that the transfer and acquisition of serotype O12 in high-risk clone ST111 has happened multiple times and may be facilitated by multiple donors, which clearly suggests a strong selection pressure for this process. However, gyrA-mediated antibiotic resistance may not be the only evolutionary driver.

Joint inversion with petrophysical constraints using indicator functions and the extended alternating direction method of multipliers

Joint inversions often need to construct an objective function with multiple complex constraining terms, which usually increase the computational cost, take a long time to converge, and often are nondifferentiable. We have developed a joint inversion framework that implements petrophysical constraints by indicator functions and solves the optimization problem using the alternating direction method of multipliers (ADMM). An indicator function can describe arbitrary ranges and relationships of multiple physical property parameters by a feasible set. Objective functions involving multiple nondifferentiable terms are numerically not easy to solve, so we adopt an extended alternating direction method of multipliers (eADMM) that separates the terms as independent subproblems and select the most straightforward and efficient solving technique for each of them. With the eADMM, every objective function term can generate its own model, and the models are forced to converge through equality constraints. Such a mechanism brings further efficiency from parallelization and the flexibility of allowing the final inversion model to not strictly obey the model constraints because of uncertainties in the data and petrophysical information. Our approach is tested on two synthetic examples of joint inversion of gravity and magnetic data. The first example contains two blocks having the same magnetic susceptibility but different densities. Our synthetic inversions verify that the combination of the indicator function and eADMM method effectively recovers exact boundaries and values. The second example uses the data synthesized from a realistic kimberlite exploration project. Our joint inversion can distinguish two kimberlite facies, image their position, and even delineate the boundary between the two facies with much improved accuracy. Based on the same model, the robustness of our method also has been tested with inexact and incorrect petrophysical information. The simplicity, flexibility, effectiveness, and efficiency make our approach a good candidate for a wide range of applications in joint inversion.

Detection of Detached Ice-fragments at Martian Polar Scarps Using a Convolutional Neural Network

Repeated high-resolution imaging has revealed current mass wasting in the form of ice block falls at steep scarps of Mars. However, both the accuracy and efficiency of ice-fragments’ detection are limited when using conventional computer vision methods. Existing deep learning methods suffer from the problem of shadow interference and indistinguishability between classes. To address these issues, we proposed a deep learning-driven change detection model that focuses on regions of interest. A convolutional neural network simultaneously analyzed bitemporal images, i.e., pre- and postdetach images. An augmented attention module was integrated in order to suppress irrelevant regions such as shadows while highlighting the detached ice-fragments. A combination of dice loss and focal loss was introduced to deal with the issue of imbalanced classes and hard, misclassified samples. Our method showed a true positive rate of 84.2% and a false discovery rate of 16.9%. Regarding the shape of the detections, the pixel-based evaluation showed a balanced accuracy of 85% and an F1 score of 73.2% for the detached ice-fragments. This last score reflected the difficulty in delineating the exact boundaries of some events both by a human and the machine. Compared with five state-of-the-art change detection methods, our method can achieve a higher F1 score and surpass other methods in excluding the interference of the changed shadows. Assessing the detections of the detached ice-fragments with the help of previously detected corresponding shadow changes demonstrated the capability and robustness of our proposed model. Furthermore, the good performance and quick processing speed of our developed model allow us to efficiently study large-scale areas, which is an important step in estimating the ongoing mass wasting and studying the evolution of the martian polar scarps.

Weakly Supervised Instance Segmentation via Category-aware Centerness Learning with Localization Supervision

Deep convolutional neural networks (DCNN) trained with pixel-level segmentation masks achieve high performance in the task of instance segmentation. The difficulty of acquiring such annotation limits the application and popularization of the DCNN-based approaches. To address the issue, a weakly supervised approach is proposed in the paper which performs instance segmentation only with the supervision of bounding box or coarse localization annotation. A novel DCNN model is constructed which consists of two branches: the centerness branch and the segmentation branch. The former branch is to learn the semantically spatial importance over the areas of object instances under the localization supervision. Object proposals with exact boundaries are automatically generated and are then ranked under the guidance of the output of the centerness branch. The most matched instance proposal is assigned to each object, which is then used to supervise the segmentation branch. The losses are calculated by both the outputs of the two branches and the entire DCNN model is trained end-to-end. Experiments are extensively conducted to verify the effectiveness. With the supervision of precise bounding box annotation, our approach achieves state-of-the-art (SOTA) accuracy in the comparison with recent related works. And in the case of coarse localization annotation, our approach only deduces a slight reduction in accuracy, which significantly outperforms other approaches. The excellent performance demonstrates that our approach would be helpful to further alleviate the workload of image annotation while maintaining competitive accuracy.

The winning score.

Incision lines placed at cosmetic boundaries and/or in rhytids during surgical procedures provide ideal concealment of scars. We suggest the use of a 30-gauge, half-inch needle or alternatively the back edge of a #15 scalpel blade to superficially score the skin to provide markings, which are fine enough to lie exactly within rhytids or at exact cosmetic boundaries such as the nasolabial fold or the junction of the cutaneous and vermillion lip. We measured the average depth of these scores to demonstrate that they are shallow enough to heal without scarring.

Learning Coulomb Diamonds in Large Quantum Dot Arrays

We introduce an algorithm that is able to find the facets of Coulomb diamonds in quantum dot arrays. We simulate these arrays using the constant-interaction model, and rely only on one-dimensional raster scans (rays) to learn a model of the device using regularized maximum likelihood estimation. This allows us to determine, for a given charge state of the device, which transitions exist and what the compensated gate voltages for these are. For smaller devices the simulator can also be used to compute the exact boundaries of the Coulomb diamonds, which we use to assess that our algorithm correctly finds the vast majority of transitions with high precision.

Noisy iris smoothing and segmentation scheme based on improved Wildes method.

In an automated iris recognition system, in order to get higher accuracy, we should have an efficient iris segmentation process. The reliability of accurate “iris recognition” system largely depends on the accuracy of segmentation process. Traditional “iris segmentation” methods are unable to detect the exact boundaries of iris and pupil, which is time consuming and also highly sensitive to noise. To overcome these problems, we have proposed an improved Wildes method (IWM) for segmentation in iris recognition system. The proposed algorithm consists of two major steps before applying Wildes method for segmentation: edge detection of iris and pupil from a noisy eye image with improved Canny with fuzzy logic (ICWFL) and removal of unwanted noise from above step with a hybrid restoration fusion filter (HRFF). A comparative study of various edge detection techniques is performed to prove the efficiency of ICWFL method. Similarly, the proposed method is tested with various noise densities from 10 to 95 dB. Also the working of the proposed HRFF is compared with some existing smoothing filters. Various experiments have been performed with the help of iris database of IIT_Delhi. Both visual and numerical results prove the efficiency of the proposed algorithm.

Fabrication and failure mechanisms of all-composite honeycomb sandwich cylinder under the axial compression

Winding-based method, an innovative technology, is proposed to successfully fabricate the all-composite honeycomb sandwich cylinder (CHSC). Excellent mechanical properties of CHSC can be achieved owning to continuous prepreg winding. The critical force of intracell buckling for exact hexagonal boundaries is proposed based on the energy principle, which has small difference between the simulation and experimental results. Three-dimensional failure maps are derived to reveal failure mechanisms of fabricated CHSC. Multi-failure mechanisms of CHSC under axial compression are explored by failure mechanism maps, which can more intuitively express the influence of dimensionless geometric parameters on failure modes. Failure modes of intracell buckling and face crushing were obtained by typical experiments under axial compression. The honeycomb sandwich cylinder prepared in this paper shows excellent bearing capacity compared to other types of sandwich cylinders. Winding-based method is expected to provide an important support for the fabrication of curved multifunctional honeycomb.

Geochemistry, U-Pb Zircon Ages and Hf Isotopes of Basement Rocks Beneath the Northeastern Margin of the Ordos Basin: Constraints on the Paleoproterozoic Evolution of the Western North China Craton

SHRIMP zircon ages, Hf-in-zircon isotopic compositions and whole rock geochemistry were analyzed on basement metamorphic rocks from drill cores collected from the northeastern margin of the Ordos Basin. Geochemical data from four metasedimentary rocks show large variations in major element compositions, but have similar REE patterns and trace element compositions, with ΣREE = 161.80 × 10−6~341.82 × 10−6, δEu = 0.26~0.63 and LaN/YbN = 3.44~25.38. SHRIMP zircon U-Pb dating of granitic gneiss yielded the magmatic zircon ages between 1856 ± 14 Ma and 2188 ± 11 Ma, with the upper intercept age of 2229 ± 88 Ma. The cores have εHf(t) values of −5.46 to +6.57, and Hf model ages vary from 2869 Ma to 2300 Ma. The analyses of metamorphic overgrowths on zircon grains yield an average metamorphic age of 1865 ± 17 Ma (MSWD = 3.5). The zircon cores have εHf(t) values of −3.98 to 1.95, and Hf model ages vary from 2782 to 2416 Ma. Combined with data from earlier studies, we draw the conclusion that the metamorphic rocks in the borehole were formed during middle Paleoproterozoic time and were involved in a major late Paleoproterozoic tectono-thermal event which might be attributable to the collision between the Western and Eastern Block along the TNCO (the Trans-North China Orogen). The basement rocks are petrologically, geochronologically and geochemically comparable with rocks from the Khondalite series. The fact that the material properties of basement rocks beneath the northeastern Ordos Basin are different from those of the Western Block indicates that the extent and exact boundaries of the Ordos Block need to be refined.

A Vision-Based Guidance Algorithm for Entering Buildings Through Windows for Delivery Drones

This article introduces a novel vision-based guidance algorithm to make a camera-equipped drone capable of collision-free entrance to a building through its windows. To eliminate the reliance on any extra sensor (ultrasonic range finders, lidars, depth sensors, stereo cameras, etc.), which are frequently used in the literature, we introduce an image-based optimization scheme to detect the exact boundaries of a window portal section. The optimization is based on a common facade segmentation technique, referred to as projected profiles, and relies on the assumption that the window portal is one of the least light-reflective segments in a facade structure. In combination with a visual tracking algorithm, our method elevates the tracking performance to avoid any guidance failures due to tracking drifts. As a result, the drone gets a rich extent of navigation data during the flight enabling it to reliably enter through the window. The window portal detection is evaluated using two online facade datasets, suggesting a 75% success rate in finding the correct entrance passage. Also, it is shown that the visual tracking performance is improved up to 58% in the specific case of tracking an entrance portal. The efficacy and real-time capability of the overall vehicle guidance are experimentally demonstrated using a rotorcraft micro aerial vehicle.

Two-stage designs with small sample sizes

ABSTRACT When applying group-sequential designs in clinical trials with normally distributed outcomes, approximate critical values are often applied. Here, normally distributed test statistics are assumed which, however, are in fact t-distributed. For small sample sizes, the approximation may lead to a serious inflation of the type I error rate. Recently, a method for computing the exact critical boundaries assuring type I error rate control was proposed and the critical boundaries for Pocock- and O’Brien-Fleming-like group-sequential designs were provided. For designs with one interim analysis, we present six alternative designs, which also control the type I error rate and in addition allow flexible design modifications. We compare the characteristics of these 6 two-stage designs. It is shown that considerable sample size savings can be achieved by including futility stopping and by optimizing the designs. Therefore, for clinical trials with small sample sizes as, for example, in the area of rare diseases, optimal two-stage designs with futility stopping may be a valuable alternative to classical group-sequential designs.

Visualization of Band Shifting and Interlayer Coupling in WxMo1–xS2 Alloys Using Near-Field Broadband Absorption Microscopy

Beyond-diffraction-limit optical absorption spectroscopy provides in-depth information on the graded band structures of composition-spread and stacked two-dimensional materials, in which direct/indirect bandgap, interlayer coupling, and defects significantly modify their optoelectronic functionalities such as photoluminescence efficiency. We here visualize the spatially varying band structure of monolayer and bilayer transition metal dichalcogenide alloys by using near-field broadband absorption microscopy. The near-field spectral and spatial information manifests the excitonic band shift that results from the interplay of composition spreading and interlayer coupling. These results enable us to identify, notably, the top layer of the bilayer alloy as pure WS2. We also use the aberration-free near-field transmission images to demarcate the exact boundaries of alloyed and pure transition metal dichalcogenides. This technology can offer valuable insights on various layered structures in the era of "stacking science" in the quest of quantum optoelectronic devices.

The Role of Energy Scales for the Structure of Ionic Liquids at Electrified Interfaces: A Theory-Based Approach.

Ionic liquids offer unique bulk and interfacial characteristics as battery electrolytes. Our continuum approach naturally describes the electrolyte on a macroscale. An integral formulation for the molecular repulsion, which can be quantitatively determined by both experimental and theoretical methods, models the electrolyte on the nanoscale. In this article, we perform a systematic series expansion of this integral formulation, derive a description of chemical potentials in terms of higher-order concentration gradients, and rationalize the appearance of fourth-order derivative operators in modified Poisson equations, as recently proposed in this context. In this way, we formulate a rigorous multiscale methodology from atomistic quantum chemistry calculations to phenomenological continuum models. We apply our generalized framework to ionic liquids near electrified interfaces and perform analytical asymptotic analysis. Three energy scales describing electrostatic forces between ions, molecular repulsion, and thermal motion determine the shape and width of the long-ranging charged double layer. We classify the charge screening mechanisms dependent on the system parameters as dielectricity, ion size, interaction strength, and temperature. We find that the charge density of electrochemical double layers in ionic liquids either decays exponentially, for negligible molecular repulsion, or oscillates continuously. Charge ordering across several ion diameters occurs if the repulsion between molecules is comparable with thermal energy and Coulomb interactions. Eventually, phase separation of the bulk electrolyte into ionic layers emerges once the molecular repulsion becomes dominant. Our framework predicts the exact phase boundaries among these three phases as a function of temperature, dielectricity, and ion size.

Automatic segmentation of cattle rib-eye area in ultrasound images using the UNet++ deep neural network

• Deep Neural Networks perform well the semantic segmentation task in ultrasound images. • UNet ++ leads out better results inside the group of the Deep Neural Networks tested. • Cattle Rib-Eye Area can be mapped using the UNet++ neural network with high accuracy. Ultrasound imaging is commonly used to estimate the size of various cuts of meat or quality traits in live animals. Unfortunately, ultrasound images are known for having large amount of visual noise, which can make it difficult to define the exact boundaries or shapes of the regions of the interest in these images. Therefore, new strategies related to the digital image processing field are required to improve the process of obtaining information from these groups of images. In this context, artificial intelligence, through deep learning methods particularly, has proved to be an optimized and efficient strategy, but that has not yet been investigated in the cattle rib-eye area. This paper aims to investigate the feasibility of applying the Unet++ deep neural network to automatic segmentation of cattle rib-eye area in ultrasound images. Additionally, several well established deep learning semantic segmentation models are compared with Unet++ performance. These architectures are FCN, U-Net, SegNet, and Deeplab v3+. The models were tested on a dataset composed of gray scale images of cattle ultrasound. All models showed excellent results in both location and boundaries. Best results showed 97.37 % in IoU, 1.14 cm 2 in MAE and coefficient of determination ( R 2 ) of 0.999 . The labeled rib-eye area dataset used in this study is available for future research.

Features of the choice of information technologies for the development of agriculture

The introduction of digital technologies in the context of limited investment resources in agriculture requires an informed choice of specific goods and services in a complex saturated market. Traditional methods of expert assessments often lead to inconsistency of expert opinions and difficulty in making decisions. Therefore, the purpose of the article is to develop and test a methodology for choosing digital technologies for agriculture (using the example of software for performing cadastral works). To achieve the goal, the methodology of quadratic penalties was used. At the first stage of the study, the most important criteria for evaluating software were selected (performance, analytical capabilities, taking into account Russian legislation). These are functions that are not obvious to a non-professional buyer, which significantly affect the efficiency of cadastral work on agricultural land. At the second stage of the study, several variants of the programs were directly evaluated and the most effective ones were selected (with a minimum square-law penalty). Their use will allow not only drawing up documents for cadastral registration, but also to determine the exact boundaries of the fields for fair taxation, work planning. The research results can be used to substantiate decisions on the choice of certain digital technologies by agricultural enterprises.

Utility of intraoperative real-time near-infrared fluorescence surgery for spinal schwannoma.

The authors report the first cases of fluorescence-guided spinal surgery of schwannomas using near-infrared fluorescence imaging with the delayed window indocyanine (ICG) green (DWIG) technique for accurate real-time intraoperative tumor visualization.Patients with intradural spinal schwannomas received 0.5 mg/kg ICG at the beginning of surgery. After 1 hour, using the DWIG technique, near-infrared spectroscopy (NIRS) detected the spinal schwannomas, showing the exact tumor location and boundaries. DWIG with NIRS microscopy confirmed the exact location of spinal schwannomas before and after opening of the dura mater, thereby facilitating successful tumor dissection from the surrounding tissues, tumor resection, and confirmation of tumor removal.The video can be found here: https://stream.cadmore.media/r10.3171/2021.10.FOCVID21158

Semi-Discretization for Fractional-Order Feedback Systems with Input Delay

In this paper, we apply the semi-discretization method to controllable single-input linear systems with input delay and fractional-order feedback. This requires the fractional derivative to be discretized in a way that the resulting discrete map is linear and time-invariant. To this end, three different techniques are investigated, namely, the short-memory principle, the application of adaptive time steps, and the exponential approximation of the derivative weights. For all three approaches, we construct a linear map that describes the dynamics of the approximate semi-discrete system. The matrices of these linear maps can be used to investigate the stability of the system. As an example, stability charts are determined for the fractional-order proportional-derivative delayed control of the inverted pendulum, and the numerically obtained stability charts are compared with the exact stability boundaries.