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A previously healthy 26-year-old female presented with one month of worsening low back pain radiating to the right lower extremity. Magnetic resonance imaging (MRI) without contrast of the lumbar spine demonstrated enhancement of the right sacroiliac joint. Sacroiliac joint aspiration followed by culture and microbiology revealed Streptomyces griseus as the cause of infectious sacroiliitis. Streptomyces griseus is a part of the normal human flora that produces a plethora of secondary metabolites applied in various medications such as streptomycin. This represents the first described case of infectious sacroiliitis due to Streptomyces griseus in the literature. It is critical for spinal surgeons to consider fastidious organisms, such as Streptomyces griseus, on the differential diagnosis of sacroiliac joint pain, especially in patients with systemic symptoms and elevated inflammatory laboratory markers.

Reconfigurable intelligent surface (RIS) is a revolutionizing technology to achieve cost-effective communications. The active beamforming at the base station (BS) and the discrete phase shifts at RIS should be jointly designed to customize the propagation environment. However, current phase-shift setting methods ignore the non-separable property of phase shifts, degrading the performance, especially in cases with a large-sized RIS. To understand the problem characteristics related to the phase shifts and further tailor an eligible method with such characteristics, this paper, for the first time, analyzes the fitness landscape of the sum-rate maximization problem (maximizing the sum rate of users in a downlink multi-user multiple-input single-output system assisted by a RIS). Results show that the problem has a severe unstructured and rugged landscape, especially in cases with a large-sized RIS. This observation answers why current methods are ineligible and provides insightful guidance for designing a more intelligent method. With the landscape findings in mind, this paper introduces a niching genetic algorithm to solve the problem. In particular, the niching idea is employed to locate multiple local optima. These local optima act as stepping stones to facilitate approaching the global optima. Simulation results demonstrate that the proposed niching genetic algorithm obtains significant capacity gains over current methods in cases with large-sized RIS.

With the application of deep learning (DL) techniques, radar-based human activity recognition (HAR) attracts increasing attention thanks to its high accuracy and good privacy. However, training a DL model requires a large volume of data, and generally the trained model cannot be adapted to a new scenario. In this paper, we propose a supervised few-shot adversarial domain adaptation ( FS-ADA ) method for HAR, where only limited radar training data is collected from a new application scenario. We adopt the domain adaptation method to learn a common feature space between a pre-existing radar dataset and the newly acquired training data. We also design a multi-class discriminator network, which integrates the category classifier and the binary domain discriminator, to employ the supervised label information in the limited radar data for model training. Then, a multitask generative adversarial training mechanism is proposed to optimize FS-ADA . In this way, both domain-invariant and category-discriminative features can be extracted for HAR in a new scenario. Experimental results for two few-shot radar-based HAR tasks show that the proposed FS-ADA method is effective and outperforms state-of-the-art methods.

Congenital aortic arch anomalies and variants have been extensively characterized in the medical literature. Proper identification of these anomalies is important when surgical or percutaneous interventions are indicated.We present a case of a 48-year old male who presented to the emergency department with altered mental status. Magnetic resonance angiography (MRA) findings revealed an aberrant right subclavian artery (ARSA), early bifurcation of the right common carotid artery (CCA) with anomalous origin of the right vertebral artery (VA) from the right common carotid artery bifurcation, anomalous left vertebral artery originating from the aortic arch, and absent left common carotid artery with independent origins of the left external carotid artery (ECA) and internal carotid artery (ICA). No other abnormalities were identified, and the patient demonstrated no symptoms attributable to his vascular anomalies.To our knowledge, this unique combination of anomalies has never been reported in the literature. With an understanding of embryological pathways, even exceedingly rare anomalies like this one can be explained.

Network slicing is a core technique of fifth-generation (5G) systems and beyond. To maximize the number of accepted network slices with limited hardware resources, service providers must avoid over-provisioning of quality-of-service (QoS), which could prevent them from lowering capital expenditures (CAPEX)/operating expenses (OPEX) for 5G infrastructure. In this paper, we propose a sub-action aided double deep Q-network (SADDQN)-based network slicing algorithm for latency-aware services. Specifically, we model network slicing as a Markov decision process (MDP), where we consider virtual network function (VNF) placements to be the actions of the MDP, and define a reward function based on cost and service priority. Furthermore, we adopt the Dijkstra algorithm to determine the forwarding graph (FG) embedding for a given VNF placement and design a resource allocation algorithm – binary search assisted gradient descent (BSAGD) – to allocate resources to VNFs given the VNF-FG placement. For every service request, we first use the DDQN to choose an MDP action to determine the VNF placement (main action). Next, we employ the Dijkstra algorithm (first-phase sub-action) to find the shortest path for each pair of adjacent VNFs in the given VNF chain. Finally, we implement the BSAGD (second-phase sub-action) to realize this service with the minimum cost. The joint action results in an MDP reward that can be utilized to train the DDQN. Numerical evaluations show that, compared to state-of-the-art algorithms, the proposed algorithm can improve the cost-efficiency while giving priority to higher-priority services and maximizing the acceptance ratio.

Thanks to the ability of preventing the accumulation of errors, constrained adaptive filtering (CAF) algorithms have been widely applied. However, in practice, non-Gaussian noise may significantly degrade the filtering performance of CAFs derived from the second-order signal statistics. In this paper, we propose several constrained generalized maximum correntropy (CGMC) algorithms to overcome this problem, inspired by the robustness and flexibility of GMC to non-Gaussian noises. We first introduce a CGMC algorithm based on the gradient method. To improve its convergence rate with correlated inputs, we further propose a recursive CGMC (RCGMC) algorithm. For RCGMC, we conduct the convergence analysis, and characterize the theoretical transient mean square deviation (MSD) performance. Furthermore, we derive a low-complexity version of RCGMC by using the weighting method and the leading dichotomous coordinate descent (DCD) algorithm. Simulation results demonstrate the effectiveness of our proposed algorithms in non-Gaussian noise environment, and the consistency between the analytical and simulation results.

Here we report the discovery of a novel Sediminibacterium sequenced from laboratory cultures of freshwater stream cyanobacteria from sites in Southern California, grown in BG11 media. A de novo genome assembly of this bacterium yielded a complete chromosomal genome on a single contig of length 3.34 Mbps with a GC content of 39.4%. A first pass annotation identified ~3000 protein coding genes, with 98% completeness when compared against all prokaryotic and cyanobacterial gene families in BUSCO. A comprehensive phylogenomic species tree reconstruction using 100 of these protein coding genes placed the novel bacterium to be sister to a previously identified Sediminibacterium, which is over 20% divergent from our novel genome.

Navigation problems of unmanned air vehicles (UAVs) flying in a formation have been investigated recently, where collision avoidance is a significant issue to be addressed. In this paper, we study resource allocation and power control for radar sensing in a multi- unmanned aerial vehicle (multi-UAV) formation flight system where multiple UAVs simultaneously perform radar sensing. To cope with mutual radar interference among the UAVs, we formulate a joint channel allocation and UAV transmission power control problem to maximize the minimum signal-to-interference-plus-noise ratio (SINR) of the radar echo signals. We then propose a computationally practical method to solve this NP-hard problem by decomposing it into two sub-problems, i.e., channel allocation and transmission power control. An iterative channel allocation and power control algorithm (ICAPCA) is proposed to jointly solve these two sub-problems. We also propose a reduced-complexity greedy channel allocation algorithm (GCAA), which can also be used to provide an initial solution to ICAPCA. Simulation results show that the proposed ICAPCA and GCAA can improve the minimum SINR and radar sensing performance significantly.

Objectives Recent case-reports have described an atypical cerebral microbleed (CMB) topography after extracorporeal membrane oxygenation (ECMO). The objective of this study was to examine the prevalence, radiographic patterns, and clinical correlates of possibly-ECMO-related (PER) CMB. Materials and methods We performed a retrospective study of 307 consecutive patients receiving ECMO support at our tertiary-care University Hospital (2013–2018). PER CMB were defined as CMB present in corpus-callosum and/or middle cerebellar peduncle with/without involvement of other lobar/deep structures. Leukoaraiosis was quantified using the Wahlund age-related white matter changes scale. Patient characteristics were compared between cohorts with and without PER CMB. Results Forty patients (median age 60 years; 33% vv-ECMO and 67% va-ECMO) received at-least one MRI-brain within 3 months of ECMO support. CMB were present in 77.5% (n = 31) patients with 39% (n = 12), 17% (n = 5), and 44% (n = 14) having low ( 30 CMB) burden respectively. Among CMB-positive patients, 71% (n = 22) had PER CMB, with 91% of such cases demonstrating involvement of splenium. Leukoaraiosis did not corelate to PER CMB presence (p = 0.267) or burden (ρ = 0.09). Patients with PER CMB had higher rates of ischemic stroke (50 vs. 33%), intracranial hemorrhage (41 vs. 17%), and all-cause mortality (27 vs. 17%); with survivors demonstrating no differences in their discharge disposition or modified Rankin Score. Conclusions Post-ECMO cerebral microbleeds have a distinct distribution pattern that commonly involves the splenium of corpus-callosum. Their etiopathogenesis may be independent of microvascular lipohyalinosis. This requires further study in a larger sample-size.

The source number identification is an essential step in direction-of-arrival (DOA) estimation. Existing methods may provide a wrong source number due to inferior statistical properties (in low SNR or limited snapshots) or modeling errors (caused by relaxing sparse penalties), especially in impulsive noise. To address this issue, we propose a novel idea of simultaneous source number identification and DOA estimation. We formulate a multiobjective off-grid DOA estimation model to realize this idea, by which the source number can be automatically identified together with DOA estimation. In particular, the source number is properly exploited by the $l_0$ norm of impinging signals without relaxations, guaranteeing accuracy. Furthermore, we design a multiobjective bilevel evolutionary algorithm to solve the proposed model. The source number identification and sparse recovery are simultaneously optimized at the on-grid (lower) level. A forward search strategy is developed to further refine the grid at the off-grid (upper) level. This strategy does not need linear approximations and can eliminate the off-grid gap with low computational complexity. Simulation results demonstrate the outperformance of our method in terms of source number and root mean square error., This paper has been submitted to an Elsevier journal for peer-review

This paper studies the threat that an aerial eavesdropper can pose to terrestrial wireless communications, from an information-theoretic point of view. The achievable ergodic and the average $\epsilon $ -outage secrecy rates with no channel state information at the transmitter (i.e., with no CSIT) are analyzed for a transmitter–receiver pair on the ground, in the presence of an aerial eavesdropper which flies a random trajectory following a smooth turn (ST) mobility model in a three-dimensional (3D) space. The ST mobility model induces a uniform distribution (of the eavesdropper’s waypoints) within the considered 3D volume. Closed-form asymptotic approximations of the achievable secrecy rates are derived based on the almost sure convergence and non-trivial mathematical manipulations. Validated by simulations, our analysis is tight and reveals that the ground transmission is particularly vulnerable to aerial eavesdropping which can be carried out in a distance without being noticed. 3D spherical regions are identified, within which the secrecy rates vanish. This sheds useful insights to protect terrestrial wireless networks from aerial eavesdropping.

© 2012 IEEE. Licensed-assisted-access (LAA) is used to extend the LTE link into the unlicensed band. How to guarantee the quality-of-service (QoS) for LTE devices in the unlicensed band is a challenging problem due to the listen-before-talk contention access in 5-GHz unlicensed bands. In this letter, we quantitatively analyze the medium access control delay for tagged LAA eNBs and propose a delay-guaranteed admission control scheme. We consider the freezing time of busy slots caused by collision or successful transmission, and introduce the exponential backoff mechanism for delay analysis. Validated by simulation results, our method provides important insights into the system admission performance and fairness of access.

© 2013 China Institute of Communications. Random access is a well-known multiple access method for uncoordinated communication nodes. Existing work mainly focuses on optimizing iterative access protocols, assuming that packets are corrupted once they are collided, or that feedback is available and can be exploited. In practice, a packet may still be able to be recovered successfully even when collided with other packets. System design and performance analysis under such a situation, particularly when the details of collision are taken into consideration, are less known. In this paper, we provide a framework for analytically evaluating the actual detection performance in a random temporal multiple access system where nodes can only transmit. Explicit expressions are provided for collision probability and signal to interference and noise ratio (SINR) when different numbers of packets are collided. We then discuss and compare two receiver options for the AP, and provide detailed receiver design for the premium one. In particular, we propose a synchronization scheme which can largely reduce the preamble length. We also demonstrate that system performance could be a convex function of preamble length both analytically and via simulation, as well as the forward error correction (FEC) coding rate.

Background: Hematologic toxicity (HT) commonly occurs during chemoradiation therapy (CRT) for esophageal cancer. We sought to determine radiation doses that correlate with declines in blood counts due to vertebral body (VB) irradiation during CRT. Methods: We analyzed 53 esophageal cancer patients who were treated with weekly neoadjuvant carboplatin, paclitaxel and RT with weekly complete blood counts (CBC) available during treatment. HTs were graded according to the Common Terminology Criteria for Adverse Events version 4.0 (CTCAE v4.0). Dose volume histogram (DVH) parameters of Vx, defined as percentage of entire bony vertebra (body, pedicles, laminae, processes) receiving at least x Gy of radiation, were collected for VB V 5 (VBV 5 ), VBV 10 –VBV 60 in increments of 10, and mean vertebral dose (MVD). Linear and logistic regressions were performed to identify associations between leukopenia nadirs and DVH parameters. Receiver operator curves identified thresholds to avoid grade ≥3 leukopenia. Results: A proportion of 32.1% of patients (n=17) developed grade 3 leukopenia and 5.7% (n=3) developed grade 4 leukopenia. VBV 5 , VBV 10 , VBV 20 , VBV 30 , and MVD were significantly associated with worsening leukopenia on univariate and multivariate analysis. Associations with leukopenia were not seen with VBV 40 and VBV 50 DVH values. Thresholds to avoid grade ≥3 leukopenia were VBV 10

We examine the effect of an exogenous increase in information asymmetry (as proxied by late filings of firms' Form 10-K) on bond prices. We find that bondholders react negatively to a late filing announcement but this negative reaction is conditional on whether late filing firms appropriate wealth from bondholders through shareholder distribution. Moreover, we find that the impact of financial distress and covenants on bond values is mainly driven by the wealth appropriation from bondholders. The results are robust to difference-in-difference analysis using treatment (i.e., late filing) and control (i.e., non-late-filing) samples based on propensity score matching. The results provide evidence that shareholder distribution as a specific form of wealth appropriation from bondholders to shareholders has a significant effect on bond values when financial information is not timely provided to capital markets.

PARP inhibitors exploit defects in DNA repair pathways to selectively target cancerous cells via PARP1 catalytic inhibition and PARP1 trapping onto the DNA. All known clinical PARP1 inhibitors bind at the same site at the catalytic center of the enzyme. However, despite this resemblance they show immensely different outcomes in terms of response rate in the clinic due to their varying degree of PARP trapping ability. Moreover, the first-generation PARP inhibitors were not optimized for selectivity across the PARP family potentially driving undesirable side effects, including intestinal toxicity from tankyrase inhibition or hematological toxicity from PARP2 inhibition. There has been strong rationale for the use of PARP inhibitors in neuro-oncology. However, the first-generation PARP inhibitors have limited CNS distribution as these drugs were not designed for brain penetration. Recently AstraZeneca has reported the discovery of AZD5305, a next generation PARP1 selective inhibitor and PARP1-DNA trapper which was not designed with a CNS penetrant profile. Given the unmet need of a brain penetrant PARP1 inhibitor, we set out to identify a highly potent and selective PARP1 inhibitor and trapper with CNS profile. In our next generation PARP1 inhibitor, we sought to retain the profile of AZD5305 and lower the efflux for CNS penetration. Despite the challenge of narrow SAR, we successfully used the structure- and property-based design approach to identify a brain penetrant PARP1 inhibitor and PARP1-DNA trapper. We used multiple medicinal chemistry maneuvers such as masking the hydrogen bond donors and core modifications to lower the efflux in order to achieve brain penetration. Further optimization of the nicotinamide mimetic core for potency and metabolic stability led us to the discovery of AZD9574.AZD9574 shows improved selectivity for PARP1 over PARP2 vs AZD5305 and retains its excellent selectivity over other PARP family members. It has low efflux in Caco2, MDCK-MDR1, and MDCK-MDR1-BCRP permeability assays and it also showed CNS penetration in rat and cynomolgus monkey. AZD9574 has excellent secondary pharmacology and acceptable physicochemical properties and good PK in preclinical species.In vitro, AZD9574 selectively inhibits the growth of BRCAm cell lines. Importantly, AZD9574 showed efficacy in an intracranial BRCA1m MDA-MB-436 xenograft model at doses of 3, 10 and 30 mg/kg QD, significantly extending the survival of tumor-bearing mice compared to vehicle control arm.In summary, AZD9574 is a next generation selective PARP1 inhibitor and trapper with CNS penetration. This profile makes it an ideal candidate for treating CNS malignancies or brain metastases that have a dependence on PARP inhibition either as single agent or in combination with other therapies. Citation Format: Avipsa Ghosh, Sudhir M. Hande, Amber Balazs, Derek Barratt, Sabina Cosulich, Barry Davies, Sébastien Degorce, Kevin Embrey, Sonja Gill, Anders Gunnarsson, Giuditta Illuzzi, Peter Johnström, Jordan Lane, Carrie Larner, Rachel Lawrence, Elisabetta Leo, Andrew Madin, Elizabeth Martin, Lisa McWilliams, Lenka O’Connor, Mark O’Connor, Jonathan Orme, Fiona Pachl, Martin Packer, Andy Pike, Philip Rawlins, Marianne Schimpl, Magnus Schou, Anna Staniszewska, Wenzhan Yang, James Yates, Andrew Zhang, XiaoLa Zheng, Stephen Fawell, Petra Hamerlik, Jeffrey Johannes. Structure-based and property-based drug design of AZD9574, a CNS penetrant PARP1 selective inhibitor and trapper [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6302.

Introduction: Early detection is critical to reduce cancer deaths as treating early stage cancers is more likely to be successful. We aimed to develop and validate a noninvasive blood-based diagnostic model capable of detecting multiple cancers by assessing the expression levels of serum microRNAs (miRNAs). Patients and Methods: Four microarray datasets assessing expression profiles of 2588 serum miRNAs with a standardized platform were assembled from Gene Expression Omnibus (GEO) totaling 7536 participants including 3604 cancer patients across 13 cancer types and 3932 non-cancer controls. A diagnostic model was built in a training set of 416 participants, including 208 lung cancer patients and 208 non-cancer controls matched for age, sex, and smoking history. The model performance was assessed in the validation sets consisting of all the remaining 7120 participants. Results: A 4-miRNA diagnostic model was developed from the lung cancer training set. The model showed 99% sensitivity and specificity in the lung cancer validation set (n=3328, 1358 cancer & 1970 non-cancer participants); and the sensitivity remained to be >99% for patients with stage 1 disease. When applied to the additional dataset of 3792 participants including 2038 cancer patients across 12 cancer types and 1754 independent non-cancer controls, the model showed high sensitivities ranging from 83⋅2% to 100% for biliary tract, bladder, colorectal, esophageal, gastric, glioma, liver, pancreatic, and prostate cancers, and reasonable sensitivities of 68⋅2% and 72⋅0% for ovarian cancer and sarcoma, respectively, while maintaining 99⋅3% specificity (Table 1). Conclusion: The study provided a proof-of-concept demonstrating that the 4-miRNA model has the potential to be developed into a simple, inexpensive and noninvasive blood test for early detection of multiple cancers with high accuracy. Table 1. Sensitivities of the 4-miRNA diagnostic model in 12 cancer types. N Sensitivity Lung Cancer 1358 99.3% Biliary Tract Cancer 40 97.5% Bladder Cancer 392 98.2% Colorectal Cancer 155 85.8% Esophageal Cancer 124 84.7% Gastric Cancer 150 100% Glioma 40 87.5% Liver Cancer 348 84.2% Ovarian Cancer 333 68.2% Pancreatic Cancer 149 83.2% Prostate Cancer 40 92.5% Sarcoma 132 72.0% Citation Format: Andrew Zhang, Hai Hu. Development and validation of a novel circulating cell-free microRNA diagnostic model with high accuracy for multi-cancer early detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5931.

3065 Background: Cancer early detection is critical to reduce mortality as treating early stage cancers is more likely to have better outcomes. We previously developed a diagnostic model based on 4 serum cell-free microRNAs (miRNAs) capable of detecting 12 cancer types with high accuracy (AACR 2022, Poster 2890; Manuscript submitted). In the current study, we aimed to validate this diagnostic model using independent serum microRNA microarray datasets. Methods: Four microarray datasets assessing the expression of 2588 serum miRNAs in patients with esophageal squamous cell carcinoma, gastric cancer, prostate cancer and glioma, as well as non-cancer healthy controls with a standardized platform were identified from Gene Expression Omnibus (GEO). The datasets were combined, cases that were redundant among them or with the cases used in our previous study (correlation > 0.99) were excluded. The 4-miRNA model was applied to the final combined dataset to calculate a diagnostic index and make prediction of cancer vs. no-cancer using previously determined algorithm and cut-point. The performance of the diagnostic model was assessed using Receiver Operating Characteristic (ROC) analysis, sensitivity and specificity. Results: After excluding redundant cases, the final combined dataset consisted of 3877 subjects, including 447 esophageal, 1267 gastric, 769 prostate and 196 glioma cancer patients, as well as 1198 healthy controls. The 4-miRNA model demonstrated an area under the curve (AUC) of 0.986, 0.995, 0.992 and 0.990 in the ROC analysis, with a sensitivity of 85.0%, 99.6%, 90.6% and 86.7% for esophageal, gastric, prostate and glioma cancers, respectively, while achieving a 99.1% specificity. These performance metrics were highly consistent to those reported in our previous study (Table). Conclusions: The study provided an independent validation of the previously developed 4-miRNA model, further demonstrating that the diagnostic model we developed has the potential to be developed into a simple, inexpensive and noninvasive blood test for multi-cancer early detection with high accuracy. [Table: see text]

BACKGROUND AND PURPOSE: Anterior circulation large-vessel occlusion stroke, one of the most devastating stroke subtypes, is associated with substantial economic burden. We aimed to identify predictors of increased acute care hospitalization costs associated with anterior circulation large-vessel occlusion stroke. MATERIALS AND METHODS: Comprehensive cost-tracking software was used to calculate acute care hospitalization costs for patients with anterior circulation large-vessel occlusion stroke admitted July 2012 to October 2014. Patient demographics and stroke characteristics were analyzed, including final infarct volume on follow-up neuroimaging. Predictors of hospitalization costs were determined using multivariable linear regression including subgroup cost analyses by treatment technique (endovascular, IV tPA-only, and no reperfusion therapy) and sensitivity analyses incorporating patients initially excluded due to early withdrawal of care. RESULTS: Three hundred forty-one patients (median age, 69 years; interquartile range, 57–80 years; median NIHSS score, 16; interquartile range, 13–21) were included in our primary analysis. Final infarct volume, parenchymal hematoma, baseline NIHSS score, ipsilateral carotid stenosis, age, and obstructive sleep apnea were significant predictors of acute care hospitalization costs. Final infarct volume alone accounted for 20.87% of the total cost variance. Additionally, final infarct volume was consistently the strongest predictor of increased cost in primary, subgroup, and sensitivity analyses. CONCLUSIONS: Final infarct volume was the strongest predictor of increased hospitalization costs in anterior circulation large-vessel occlusion stroke. Acute stroke therapies that reduce final infarct volume may not only improve clinical outcomes but may also prove cost-effective.

PURPOSE: To create a succinct yet comprehensive evidence-based structured report template for indeterminate renal masses characterized at CT and MRI. METHODS: This IRB-exempt, iterative, multi-institutional quality improvement project was informed by published data derived from a multi-institutional survey and a multi-institutional review of CT and MRI radiology reports. A two-stage blinded Delphi process by the 16-member 12-institution Society of Abdominal Radiology Disease-Focused Panel on Renal Cell Carcinoma was conducted to create a structured report template for indeterminate renal masses evaluated at CT and MRI. Individual reporting characteristics were scored by members as ‘core,’ ‘optional,’ or ‘exclude.’ Threshold for inclusion was ≥ 80% support. If < 80% members considered a characteristic a ‘core’ feature, but ≥ 80% considered it either ‘core’ or ‘optional,’ it was considered an ‘optional’ feature. If neither was the case, the characteristic was excluded. Free-text comments were permitted. Characteristics considered ‘core’ by 50–99% of respondents in Round 1 (i.e., nonunanimous support) and uninvestigated free-text comments were assessed in Round 2. Core and optional structured reporting templates were derived. RESULTS: The response rate was 100% in Round 1 (16/16) and Round 2 (16/16). In Round 1, 5 characteristics had unanimous support as ‘core’ features. Following Round 2, 13 characteristics had ≥ 80% support as ‘core’ features, and 10 characteristics had ≥ 80% support as ‘optional’ features. Structured report templates were derived. DISCUSSION: Structured ‘core’ and ‘optional’ templates for indeterminate renal masses at CT and MRI were derived, which may improve compliance with reporting preferred and essential imaging characteristics.

Purpose Specular and confocal microscopes are important tools to monitor the health of the corneal endothelium (CE), but their high costs significantly limit accessibility in low-resource environments. We developed and validated a low-cost, fully automated method to quantitatively evaluate the CE using smartphone-based specular microscopy. Methods A OnePlus 7 Pro smartphone attached to a Topcon SL-D701 slit-lamp was used to image the central corneal endothelium of 30 eyes using the specular reflection technique. A novel on-device image processing algorithm automatically computed endothelial cell density (ECD), percentage of hexagonal cells (HEX), and coefficient of variation (CV) values. These values were compared with the ECD, HEX, and CV generated by a Tomey EM-4000 specular microscope used to image the same set of eyes. Results No significant differences were found in ECD (2799 ± 156 cells/mm2 vs. 2779 ± 166 cells/mm2; P = 0.28) and HEX (52 ± 6% vs. 53 ± 6%; P = 0.50) computed by smartphone-based specular imaging and specular microscope, respectively. A statistically significant difference in CV (34 ± 3% vs. 30 ± 3%; P < 0.01) was found between the two methods. The concordance achieved between the smartphone-based method and the Tomey specular microscope is very similar to the concordance between two specular microscopes reported in the literature. Conclusions Smartphone-based specular imaging and automated analysis is a low-cost method to quantitatively evaluate the CE with accuracy comparable to the clinical standard. Translational Relevance This tool can be used to screen the CE in low-resource regions and prompt investigation of suspected corneal endotheliopathies.

LoRa serves as one of the most deployed technologies in Internet-of-Things-based information systems (IoT-IS), and self-motivated deployment is the key to the rollout of LoRa. Proper incentive can play an important role in encouraging the private deployment of LoRa, increasing coverage and promoting effective management of IoT-IS. However, existing incentive mechanisms have the vulnerabilities of insecure centralized architecture and excessive utility loss of LoRa Controllers and Gateways, due to asymmetric information between private owners of gateways and centralized controller (or service providers). Blockchain-based LoRa networks, as a promising solution, have not been comprehensively studied to address the vulnerabilities, let alone the other issues of security, scalability, and flexibility. In this paper, we propose a novel Dual-Chained LoRa-based information system (LoRa-IS) to provide globally cross-validated security. Behaviors, including state-of-the-art contract-theoretic incentive mechanism and new flow control protocol, can be secured with the tamper-resistance of Blockchains. Being part of the proposed incentive mechanism, the new self-driven flow control allows both the Dual-Chain system and the LoRa network to scale. To the best of our knowledge, the proposed system is the first comprehensive Blockchain-based LoRa-IS combined with contract theory. We also provide analysis and simulations, showing that our system can pay fair incentives under information asymmetry. With the new flow control, the system can optimize network coverage while improving the Blockchain scalability and flexibility.

Accurate channel parameter estimation is challenging for wideband millimeter-wave (mmWave) large-scale hybrid arrays, due to beam squint and much fewer radio frequency (RF) chains than antennas. This paper presents a novel joint delay and angle estimation approach for wideband mmWave fully-connected hybrid uniform cylindrical arrays. We first design a new hybrid beamformer to reduce the dimension of received signals on the horizontal plane by exploiting the convergence of the Bessel function, and to reduce the active beams in the vertical direction through preselection. The important recurrence relationship of the received signals needed for subspace-based angle and delay estimation is preserved, even with substantially fewer RF chains than antennas. Then, linear interpolation is generalized to reconstruct the received signals of the hybrid beamformer, so that the signals can be coherently combined across the whole band to suppress the beam squint. As a result, efficient subspace-based algorithm algorithms can be developed to estimate the angles and delays of multipath components. The estimated delays and angles are further matched and correctly associated with different paths in the presence of non-negligible noises, by putting forth perturbation operations. Simulations show that the proposed approach can approach the Cram\'{e}r-Rao lower bound (CRLB) of the estimation with a significantly lower computational complexity than existing techniques., Comment: Accepted by IEEE Transactions on Wireless Communications, 15 pages, 7 figures

Sensors have been playing an increasingly important role in smart cities. Using small roadside magnetic sensors provides a cost-efficient method for monitoring vehicle traffic. However, there are significant challenges associated with vehicle data misalignment due to the timing-offsets between sensors and missed or increased data because of vehicle lane-changing. In this paper, we propose a novel traffic information acquisition and vehicle state estimation scheme using multiple road magnetic sensors. To efficiently solve the multi-sensor registration problem in the presence of timing-offset, we develop a linear discrimination analysis method to achieve vehicle separation and classification. To handle the situation of lane-changing, we propose a data smoothing technique based on a multi-hypotheses tracker that exploits vehicle correlation. The road density effect on the probability of correct data association is investigated, with numerical and experimental results provided. The results show that our proposed scheme can effectively detect vehicles with a 95.5% accuracy rate. It also outperforms some other speed sensing methods in terms of the vehicle speed estimation accuracy.

Federated learning (FL) as a distributed machine learning (ML) technique has lately attracted increasing attention of healthcare stakeholders as FL is perceived as a promising decentralized approach to address data privacy and security concerns. The FL approach stores and maintains the privacy-sensitive data locally while allows multiple sites to train ML models collaboratively. We aim to describe the most recent real-world cases using the FL in both COVID-19 and non-COVID-19 scenarios and also highlight current limitations and practical challenges of FL.

Joint communication and radar sensing (JCR) represents an emerging research field aiming to integrate the above two functionalities into a single system, sharing a majority of hardware and signal processing modules and, in a typical case, sharing a single transmitted signal. It is recognised as a key approach in significantly improving spectrum efficiency, reducing device size, cost and power consumption, and improving performance thanks to potential close cooperation of the two functions. Advanced signal processing techniques are critical for making the integration efficient, from transmission signal design to receiver processing. This paper provides a comprehensive overview of JCR systems from the signal processing perspective, with a focus on state-of-the-art. A balanced coverage on both transmitter and receiver is provided for three types of JCR systems, communication-centric, radar-centric, and joint design and optimization., Comment: 18 pages, 3 figures, journal

© 2020 IEEE. In this paper, we present a technique for realizing reliable multi-user MIMO communications in the presence of interference in time-varying channels. The null space of interfering channels is estimated and exploited for interference mitigation. We first introduce an improved superframe structure to enable frequent tracking of user channels and the null space of interfering channels. The different natures of the received user signals and interference require different processing methods. We improve and compare several adaptive equalizers to deal with time-varying user channels, and propose to use a subspace-based tracking algorithm to handle time-varying interfering channels. We simulate the proposed tracking algorithms in various settings, including when the interference signals are correlated. Simulation results are provided and validate the effectiveness of the proposed technique.

PurposeSpecular and confocal microscopes are important tools to monitor the health of the corneal endothelium (CE), but their high costs significantly limit accessibility in low-resource environments. In our study, we developed and validated a low-cost, fully automated method to quantitatively evaluate the CE using smartphone-based specular microscopy.MethodsA OnePlus 7 Pro smartphone attached to a Topcon SL-D701 slit-lamp was used to image normal the central corneal endothelium using the specular reflection technique. Images were automatically processed on-device and endothelial cell density (ECD), percentage of hexagonal cells (HEX), and coefficient of variation (CV) values were determined using our novel image analysis algorithm. The morphometric parameters generated from the images taken by Tomey EM-4000 specular microscope were compared between the testing modalities.ResultsNo significant differences in ECD (2799 ± 156 cells/mm2 vs 2779 ± 166 cells/mm2; p=0.28) and HEX (52 ± 6% vs 53 ± 6%; p=0.50) computed by smartphone-based specular imaging and specular microscope, respectively, were found. A statistically significant difference in CV (34 ± 3% vs 30 ± 3%; pConclusionsSmartphone-based specular imaging and automated analysis is a low-cost method to quantitatively evaluate the CE with accuracy comparable to the clinical standard.Translational RelevanceThis tool can be used to screen the CE in low-resource regions and reveal the need for further investigation of suspected corneal endotheliopathies.

© 2020 IEEE. Deep learning has shown a strong potential in device-free human activity recognition (HAR). However, a fundamental challenge is ensuring accuracy, without re-training, when exposing a previously trained architecture to a new or unseen environment. To overcome the aforementioned challenge, this paper proposes an environment-robust channel state information (CSI) based HAR by leveraging the properties of a matching network (MatNet) and enhanced features (HAR-MN-EF). To improve the CSI quality, we propose a CSI cleaning and enhancement method (CSI-CE) that includes two key stages: activity-related information extraction (ARIE) and correlation feature extraction based on principal component analysis (CFE-PCA). The ARIE stage is able to effectively enhance the activity-dependent features whilst mitigating behavior-unrelated information. The CFE-PCA stage further improves the extracted features by filtering out the residual activity-unrelated data and the residual noise contained in signals from the former stage. The extracted features are then sequenced into the MatNet to create an environment-robust HAR. Experimental results confirm that an architecture trained by the proposed HAR-MN-EF can be directly adapted to a new environment, achieving reliable sensing accuracies without requiring additional effort.

Perceptive mobile network (PMN) is a recently proposed next-generation network that integrates radar sensing into communication. One major challenge for realizing sensing in PMNs is how to deal with spatially-separated asynchronous transceivers. The asynchrony between sensing receiver and transmitter will cause both timing offsets (TOs) and carrier frequency offsets (CFOs) and lead to degraded sensing accuracy in both ranging and velocity measurements. In this paper, we propose an uplink sensing scheme for PMNs with asynchronous transceivers, targeting at resolving the sensing ambiguity and improving the sensing accuracy. We first adopt a cross-antenna cross-correlation (CACC) operation to remove the sensing ambiguity associated with both TOs and CFOs. Without sensing ambiguity, both actual propagation delay and actual Doppler frequency of multiple targets can be obtained using CACC outputs. To exploit the redundancy of the CACC outputs and reduce the complexity, we then propose a novel mirrored-MUSIC algorithm, which halves the number of unknown parameters to be estimated, for obtaining actual values of delays and Doppler frequencies. Finally, we propose a high-resolution angles-of-arrival (AoAs) estimation algorithm, which jointly processes all measurements from spatial, temporal, and frequency domains. The proposed AoAs estimation algorithm can achieve significantly higher estimation accuracy than that of using samples from the spatial domain only. We also derive the theoretical mean-square-error of the proposed algorithms. Numerical results are provided and validate the effectiveness of the proposed scheme.

The COVID-19 pandemic swept across the world, altering the structure and existence of graduate medical education programs across all disciplines. Orthopaedic residency programs can adapt during these unprecedented times to continue providing meaningful education to trainees and to continue providing high-quality patient care, all while keeping both residents and patients safe from disease. The purpose of this review was to evaluate the literature and describe evidence-based changes that can be made in an orthopaedic residency program to ensure patient and resident safety while sustaining the principles of graduate medical education during the COVID-19 pandemic. We describe measures that can be enacted now or during future pandemics, including workforce and occupational modifications, personal protective equipment, telemedicine, online didactic education, resident wellness, return to elective surgery, and factors affecting medical students and fellows. After a review of these strategies, programs can make changes for sustainable improvements and adapt to be ready for second-wave events or future pandemics. Level of Evidence Level V.

The development of mobile-health technology has the potential to revolutionize personalized medicine. Biomedical sensors (e.g., wearables) can assist with determining treatment plans for individuals, provide quantitative information to healthcare providers, and give objective measurements of health, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures, as well as knowledge of the temporal and spatial properties of the data. Thus, interpreting biomedical sensor data needs to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. This framework corrects for covariates to provide accurate assessments of the significance of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool, MhealthCI, around a specific implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the software implementation of MhealthCI to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity and also compare the performance to other models. Specifically, we show how the framework is able to evaluate an exercise intervention’s effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets., Author summary In this paper, we propose and describe a robust and flexible modeling framework called MhealthCI based on the Bayesian structural time series, for which we have found to excel at analyzing diverse biosensor data. While Bayesian modeling is often employed in various fields such as finance, marketing, and weather forecasting, it is rarely used in biomedicine, specifically for biosensor and wearable data relating to human health and behavior. We use and apply this framework with the goal of interpreting and quantifying the causal impact of an intervention, a widespread goal of biomedicine. We describe the diversity of data types to which it could apply, provide intuition to its mechanics, collect relevant data in various fields, provide a wrapper tool around well-known R packages that prepares and registers diverse biosensor data to be analyzed, and finally apply the method to showcase its strength in quantifying the impact of interventions.

Metrology measurements of the top copper contact in the main chip area is critical to predict the subsequent electrical performance in advanced 3D NAND technology nodes. Conventional CDSEM is used in the determination of top copper CD while the accurate measurement of copper depth remains challenging. In this paper, we propose a new approach that successfully explored the use of machine learning to combine the advantages of optical Critical Dimension (OCD) and picosecond ultrasonic technology (PULSE™) for high volume manufacturing (HVM) measurements in the main chip area. Results demonstrate that by using machine learning, we were able to combine the PULSE reference with cross-section microscopy results to successfully train the OCD data set. OCD measurements are rapid at

Device-free WiFi sensing utilizing channel state information (CSI) is attractive for human activity recognition (HAR). However, several challenging problems are yet to be resolved, e.g., difficulty in extracting proper features from input signals, susceptibility to the phase shift of CSI and difficulty in identifying similar behaviors (e.g., lying and standing). In this paper, we aim to tackle these problems by proposing a novel scheme for CSI-based HAR that uses activity filter-based deep learning network (HAR-AF-DLN) with enhanced correlation features. We first develop a novel CSI compensation and enhancement (CCE) method to compensate for the timing offset between the WiFi transmitter and receiver, enhance activity-related signals and reduce the dimension of inputs to DLN. Then, we design a novel activity filter (AF) to differentiate similar activities (e.g., standing and lying) based on the enhanced CSI correlation features obtained from CCE. Extensive simulation results demonstrate that our proposed HAR-AF-DLN scheme outperforms state-of-the-art methods with significantly improved recognition accuracy (especially for similar activities) and notably reduced training time.

© 2020 IEEE. Joint communication and radar sensing (JCAS) integrates communication and radar sensing into one system, sharing one transmitted signal. In this paper, we study a JCAS system that uses a dedicated low-cost single-antenna receiver for sensing. We provide sensing parameter estimation algorithms for the JCAS system, and investigate the optimization of the precoding matrix to balance communication and sensing performance. A MUSIC-based estimation approach is proposed to obtain time delays and angle-of-arrivals of targets. A weighted signal optimization to balance between communication and sensing is then proposed. Numerical results are provided and verify the effectiveness of the proposed scheme.

Highly accurate localization is important for wire-less communications. In this paper, we propose a new tensor-based positioning method for 5G wideband mmWave massive MIMO systems. We first develop an extended multidimensional interpolation (E-MI)-based method as the preprocessing step to suppress the frequency-dependence of the array steering vectors. By using this method, the data across the whole frequency band can be processed jointly, and the high temporal resolution offered by wideband mmWave signals can be exploited. Then, we propose a parameter decoupling (PD)-based tensor multiparameter estimation algorithm. This algorithm can suppress the noises in all of temporal, spatial and frequency domains, and thus all the parameters can be precisely estimated. A simplified perturbation term (S-PT)-based method is also presented to match the estimated parameters at low complexity. Based on the quasi-optical property of mmWave signals, we propose a novel method to compute the 3D coordinates of the target. Simulation results demonstrate the effectiveness of the proposed positioning method in the end.

The novel coronavirus pandemic, also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has placed an immense strain on healthcare systems across the entire world. Consequently, multiple federal and state governments have placed restrictions on hospitals such as limiting "elective surgery" and recommending social or physical distancing. We review the literature on several areas that have been affected including surgical selection, inpatient care, and physician well-being. These areas affecting inpatient paradigms include surgical priority, physical or social distancing, file sharing for online clinical communications, and physician wellness. During this crisis, it is important that orthopaedic departments place an emphasis on personnel safety and slowing the spread of the virus so that the department can still maintain vital functions. Physical distancing and emerging technologies such as inpatient telemedicine and online file sharing applications can enable orthopaedic programs to still function while attempting to protect medical staff and patients from the novel coronavirus spread. This literature review sought to provide evidence-based guidance to orthopaedic departments during an unprecedented time. Orthopaedic surgeons should follow the Centers for Disease Control and Prevention guidelines, wear personal protective equipment (PPE) when appropriate, have teams created using physical distancing, understand the department's policy on elective surgery, and engage in routines which enhance physician wellness.

© 1967-2012 IEEE. Wide deployment of radar sensors on automotive vehicles can potentially lead to a severe interference problem. Such interference has been characterized without considering directional antenna patterns, which could lead to results significantly larger than the actual ones. In this paper, we study the mean power of effective echo signals and interference, by considering both front- and side- mounted radars equipped with directional antennas. We employ the stochastic geometry method to characterize the randomness of vehicles and hence radars in both two-lane and multi-lane scenarios, and derive closed-form expressions for the mean interference by approximating the radiation pattern by Gaussian waveforms. Simulation results are shown to match the analytical results very well, and insights are obtained for the impact of radar parameters on interference. Based on the interference analysis, we aim to minimize the total transmission power of each vehicle with constraints on the required signal to interference and noise ratio. An optimal solution is obtained based on linear programming techniques and corroborated by simulation results.

© 2002-2012 IEEE. The sparse code multiple access (SCMA) is a promising candidate for bandwidth-efficient next generation wireless communications, since it can support more users than the number of resource elements. On the same note, faster-than-Nyquist (FTN) signaling can also be used to improve the spectral efficiency. Hence in this paper, we consider a combined uplink FTN-SCMA system in which the data symbols corresponding to a user are further packed using FTN signaling. As a result, a higher spectral efficiency is achieved at the cost of introducing intentional inter-symbol interference (ISI). To perform joint channel estimation and detection, we design a low complexity iterative receiver based on the factor graph framework. In addition, to reduce the signaling overhead and transmission latency of our SCMA system, we intrinsically amalgamate it with grant-free scheme. Consequently, the active and inactive users should be distinguished. To address this problem, we extend the aforementioned receiver and develop a new algorithm for jointly estimating the channel state information, detecting the user activity and for performs data detection. In order to further reduce the complexity, an energy minimization based approximation is employed for restricting the user state to Gaussian. Finally, a hybrid message passing algorithm is conceived. Our Simulation results show that the FTN-SCMA system relying on the proposed receiver design has a higher throughput than conventional SCMA scheme at a negligible performance loss.

© 1997-2012 IEEE. Physical layer security is vital to millimeter-wave communications enabled by large-scale arrays, particularly the energy-efficient lens antenna arrays (LAAs). However, the broad application of LAAs can be hindered by the lack of a proper understanding of the secrecy performance. This letter derives an asymptotic closed-form expression for the secrecy rate of LAA, despite the critical challenges including the coupling of unknown lens beam responses. With the new secrecy rate analysis, the optimal power assignment for the legitimate transmission is achieved, leading to the maximization of LAA secrecy. This power assignment is unprecedentedly studied in LAA due to the previous absence of an analytical secrecy rate. Simulations validate the accuracy of the analysis over wide ranges of system parameters.

Nearly 80% of cirrhotic patients diagnosed with hepatocellular carcinoma (HCC) are not eligible for surgical resection and instead undergo local-regional treatment. After therapy for HCC, patients undergo imaging surveillance to assess treatment efficacy and identify potential sites of progressive tumor elsewhere within the liver. Accurate interpretation of posttreatment imaging is essential for guiding further management decisions, and radiologists must understand expected treatment-specific imaging findings for each of the local-regional therapies. Of interest, expected imaging findings seen after radiation-based therapies (transarterial radioembolization and stereotactic body radiation therapy) are different than those seen after thermal ablation and transarterial chemoembolization. Given differences in expected posttreatment imaging findings, the current radiologic treatment response assessment algorithms used for HCC (modified Response Evaluation Criteria in Solid Tumors classification, European Association for the Study of Liver Diseases criteria, and Liver Imaging and Reporting Data System Treatment Response Algorithm) must be applied cautiously for radiation-based therapies in which persistent arterial phase hyperenhancement in the early posttreatment period is common and expected. This article will review the concept of tumor response assessment for HCC, the forms of local-regional therapy for HCC, and the expected posttreatment findings for each form of therapy. Keywords: Abdomen/GI, Liver, MR-Imaging, Treatment Effects, Tumor Response © RSNA, 2020.