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BACKGROUND AND PURPOSE: Transcranial MR imaging–guided focused ultrasound is a promising novel technique to treat multiple disorders and diseases. Planning for transcranial MR imaging–guided focused ultrasound requires both a CT scan for skull density estimation and treatment-planning simulation and an MR imaging for target identification. It is desirable to simplify the clinical workflow of transcranial MR imaging–guided focused ultrasound treatment planning. The purpose of this study was to examine the feasibility of deep learning techniques to convert MR imaging ultrashort TE images directly to synthetic CT of the skull images for use in transcranial MR imaging–guided focused ultrasound treatment planning. MATERIALS AND METHODS: The U-Net neural network was trained and tested on data obtained from 41 subjects (mean age, 66.4 ± 11.0 years; 15 women). The derived neural network model was evaluated using a k-fold cross-validation method. Derived acoustic properties were verified by comparing the whole skull-density ratio from deep learning synthesized CT of the skull with the reference CT of the skull. In addition, acoustic and temperature simulations were performed using the deep learning CT to predict the target temperature rise during transcranial MR imaging–guided focused ultrasound. RESULTS: The derived deep learning model generates synthetic CT of the skull images that are highly comparable with the true CT of the skull images. Their intensities in Hounsfield units have a spatial correlation coefficient of 0.80 ± 0.08, a mean absolute error of 104.57 ± 21.33 HU, and a subject-wise correlation coefficient of 0.91. Furthermore, deep learning CT of the skull is reliable in the skull-density ratio estimation (r = 0.96). A simulation study showed that both the peak target temperatures and temperature distribution from deep learning CT are comparable with those of the reference CT. CONCLUSIONS: The deep learning method can be used to simplify workflow associated with transcranial MR imaging–guided focused ultrasound.

The literature on the allocation of power for rule-making and law enforcement assumes that administrative agencies are ex-ante regulators, while courts are ex-post enforcers. However, the Supreme People’s Court of China makes proactive rules to govern economic affairs, functioning as a de facto market regulator. The empirical evidence on Chinese credit regulations suggests that the incentive level and information access interactively determine the rule-making power allocation between courts and agencies. This article argues that the additional delegation of proactive rule-making power to the judiciary is both a challenge and an opportunity for advancing China’s reform agenda. It proposes institutional rearrangements to correct ineffective incentives and to channel information into courts. The research highlights the hybrid role the Supreme People’s Court plays in policy implementation and dispute resolution, offering new insights into the design of efficient rule-making power allocation.

In order to research the influence of the control delay time on the vibration isolation system, the dynamic characteristic analysis of a two-degree-of-freedom vibration system, based on high-static-low-dynamic-stiffness vibration isolator (HSLDS-VI) with time-delayed feedback control is presented. The two-degree-of-freedom nonlinear vibration isolation dynamical system model comprising the delay time parameter is also derived. Then, the effects of feedback gain coefficient and delay parameter on the dynamic characteristics are analyzed with multiple scales method. The results demonstrate that the amplitude can be suppressed by the time-delay variation for a fixed feedback gain. The system can exhibit periodic motion or chaotic vibration when the feedback gain coefficient and time-delay parameter are adjusted, which is beneficial to the application of high-static-low-dynamic-stiffness vibration isolator.

Protoplast electrofusion between callus protoplasts of cultivar TMS60444 and mesophyll protoplasts of cultivar SC8 was performed as an approach for the genetic improvement of cassava. The fusion products were subsequently cultured in protoplast culture medium (TM2G) with gradual dilution for approximately 1–2 months. Then the protoplast-derived compact calli were transferred to suspension culture medium (SH) for suspension culture. The cultured products developed successively into embryos, mature embryos, and shoots on somatic embryo emerging medium (MSN), embryo maturation medium (CMM), and shoot elongation medium (CEM), respectively. And the shoots were then rooted on Murashige and Skoog (1962) medium (MS). Sixty-six cell lines were obtained and 12 of them developed into plantlets. Based on assessment of ploidy level and chromosome counting, four of these plantlets were tetraploid and the remaining eight were diploid. Based on assessment of ploidy level and simple sequence repeat (SSR) analysis, nine tetraploid cell lines, one diploid variant plant line and nine variant cell lines were obtained. The diploid variant plant line and the nine variant cell lines all showed partial loss of genetic material compared to that of the parent TMS60444, based on SSR patterns. These results showed that some new germplasm of cassava were created. In this study, a protocol for protoplast electrofusion was developed and validated. Another important conclusion from this work is the confirmation of a viable protocol for the regeneration of plants from cassava protoplasts. Going forward, we hope to provide technical guidance for cassava tissue culture, and also provide some useful inspiration and reference for further genetic improvement of cassava.

The tortuosity assessment of vessels and nerve fibres in ophthalmic images has drawn substantial attention, for its potentials in assisting various medical diagnoses. Numerous morphological tortuosity measures have been leveraged to quantify tortuosity from various perspectives, which warrants the simultaneous use of multiple measures with an aim to produce a robust and accurate assessment. This paper proposes an approach for the automated assessment of curvilinear structures' tortuosity. Starting with the generation of clusters of tortuosity density for each individual measure, labelled fuzzy sets are then extracted that enhances the readability of subsequent operations. Finally, results from multiple measures are aggregated by a nearest neighbour guided approach where weights are generated in a data-driven manner to explain the derived aggregations. Experimental results on both ophthalmic vessel and nerve images demonstrate the superior performance of the proposed method over those where measurements are used independently or aggregated using conventional averaging operators.

Research in dementia diagnosis typically involves a range of data modalities and also, the use of cognitive assessments, aiming at the development of approaches that are non-invasive, time-saving and economical. Given the existing diversity of prevalent cognitive assessment factors it is useful to assess and exploit the effectiveness of such cognitive features, while working towards the establishment of a methodology for making informed choice of such factors in practical use. As an initial approach, this paper employs the powerful Fuzzy-Rough Feature Selection (FRFS) technique to support such an analysis, by varying the underlying similarity functions and search strategies employed by FRFS. Evaluated on a benchmark from the renowned Alzheimer’s Disease Neuroimaging Initiative repository, experimental results demonstrate the significance and predictive capabilities of different cognitive assessments in working with a variety of popular classifiers.

PURPOSE: To develop an isotropic high-resolution stack-of-spirals UTE sequence for pulmonary imaging at 0.55 Tesla by leveraging a combination of robust respiratory-binning, trajectory correction, and concomitant-field corrections. METHODS: A stack-of-spirals golden-angle UTE sequence was used to continuously acquire data for 15.5 minutes. The data was binned to a stable respiratory phase based on superoinferior readout self-navigator signals. Corrections for trajectory errors and concomitant field artifacts, along with image reconstruction with conjugate gradient SENSE, were performed inline within the Gadgetron framework. Finally, data were retrospectively reconstructed to simulate scan times of 5, 8.5, and 12 minutes. Image quality was assessed using signal-to-noise, image sharpness, and qualitative reader scores. The technique was evaluated in healthy volunteers, patients with coronavirus disease 2019 infection, and patients with lung nodules. RESULTS: The technique provided diagnostic quality images with parenchymal lung SNR of 3.18 ± 0.0.60, 4.57 ± 0.87, 5.45 ± 1.02, and 5.89 ± 1.28 for scan times of 5, 8.5, 12, and 15.5 minutes, respectively. The respiratory binning technique resulted in significantly sharper images (p < 0.001) as measured with relative maximum derivative at the diaphragm. Concomitant field corrections visibly improved sharpness of anatomical structures away from iso-center. The image quality was maintained with a slight loss in SNR for simulated scan times down to 8.5 minutes. Inline image reconstruction and artifact correction were achieved in

Modern deep neural networks are able to beat human annotators in several medical image processing tasks. In practical manual annotation for medical image segmentation tasks, the labels of annotators often show inter-observer variability (IOV) which is mainly caused by annotators’ different understandings of expertise. In order to build a trustworthy segmentation system, robust models should consider how to capture uncertainty in samples and labels. Different from the conventional way of handling IOV with label fusion such as majority voting, a fuzzy integral based ensemble framework of multiple deep learning models for optic disc segmentation is proposed. Each component segmentation model is trained with respect to an annotator. Then, a powerful nonlinear aggregation function, the Choquet integral, is employed in form of a neural network to integrate the segmentation results of multiple annotators. The proposed method is validated on the public RIM-ONE dataset consisting of 169 fundus images and each image is annotated by 5 experts. Compared with conventional segmentation ensemble methods, the proposed methods achieves a higher Dice score (98.69%).

Escape is an evolutionarily conserved and essential avoidance response. Considered to be innate, most studies on escape responses focused on hard-wired circuits. We report here that peptidergic signaling is an integral and necessary component of the Caenorhabditis elegans escape circuit. Combining genetic screening, electrophysiology and calcium imaging, we reveal that a neuropeptide NLP-18 and its cholecystokinin receptor CKR-1 enable the escape circuit to execute a full omega (Ω) turn, the last motor step where the animal robustly steers away from its original trajectory. We demonstrate in vivo and in vitro that CKR-1 is a Gαq protein coupled receptor for NLP-18. in vivo, NLP-18 is mainly secreted by the gustatory sensory neuron (ASI) to activate CKR-1 in the head motor neuron (SMD) and the turn-initiating interneuron (AIB). Removal of NLP-18, removal of CKR-1, or specific knockdown of CKR-1 in SMD or AIB neurons lead to shallower turns hence less robust escape steering. Consistently, elevation of head motor neuron (SMD)’s Ca2+ transients during escape steering is attenuated upon the removal of NLP-18 or CKR-1. in vitro, synthetic NLP-18 directly evokes CKR-1-dependent currents in oocytes and CKR-1-dependent Ca2+ transients in SMD. Thus, cholecystokinin signaling modulates an escape circuit to generate robust escape steering.

The secondary active transporter NBCe1 couples the transmembrane movement of Na

As current develop trends in network architecture and key technologies are enabling the vision of the heterogeneous sharing network, the necessity intensifies for addressing real-life aspects in massive scales. Effectively control the network topology remains a key design requirement for heterogeneous resource sharing network. In this work, we exploit previous theoretical results on the network topology control and the decentralized nature of the blockchain, proposing a network topology control algorithm based on a positive evaluation method for resource sharing network, and was demonstrated for controlling a dynamic network topology. Furthermore, we established the positive evaluation method to evaluate the node's reliability by virtual credit values and behavior of the nodes. The simulation results and performance analysis showed that the proposed algorithm can effectively delay the expansion speed of network topology, thereby increase the feasibility and effectiveness of topology control.