Super-Resolution Ultrasound Reveals Cerebrovascular Impairment in a Mouse Model of Alzheimer's Disease.

Increasing evidence has suggested a link between cerebrovascular disease and the cognitive impairment associated with Alzheimer's disease. However, detailed descriptions of microvascular changes across brain regions and how they relate to other more traditional pathology have been lacking. Additionally, the efforts to elucidate the interplay between cerebral microvascular function and Alzheimer's disease progression are complicated by the necessity of probing deep-brain structures since early-stage Alzheimer's disease typically involves hippocampal pathology. The purpose of this study was to examine changes in microvascular dynamics in a mouse model of Alzheimer's disease using cohorts that were age-matched to wild-type controls. Data from both sexes were included in this study. Super-resolution ultrasound localization microscopy revealed microvascular functional and structural features throughout the whole brain depth to visualize and quantify. We found that functional decreases in hippocampal and entorhinal flow velocity preceded structural derangements in regional vascular density. Co-registered histological sectioning confirmed the regionalized perfusion deficits seen on ultrasound imaging, which were co-localized with amyloid beta plaque deposition. In addition to providing global vascular quantifications of deep brain structures with a high local resolution, this technology also permitted velocity-profile analysis of individual vessels and, in some cases, allowed for decoupling of arterial and venous flow contributions. These data suggest that microvascular pathology is an early and pervasive feature of Alzheimer's disease and may represent a novel therapeutic target for this disease.

Epidemiological Investigation of 387 Individuals Over 65 Years Old With Osteoporotic Fractures.

Context: With the rapidly aging population globally, osteoporosis (OP) has become a major public health problem, and fracture is a common complication of OP. Older adults, especially postmenopausal women, have a higher incidence of OP. Objective: The study intended to analyze the clinical information, epidemiological characteristics, treatments, and follow-up results of patients with osteoporotic fractures (OPFs) in adults over 65 years old, to provide data support for the prevention, treatment, and use of OPF focus groups in clinical practice. Design: The research team performed a retrospective analysis using electronic medical records and related imaging data of patients. Setting: The study took place at Hebei General Hospital in Hebei, China. Participants: Participants were 387 patients over 65 years old with osteoporotic fractures who had been admitted to the hospital between July 2012 and July 2018. Outcome Measures: The research team recorded participants' ages, genders, fracture causes, and fracture sites. The team performed a follow-up analysis on refractures, treatment with anti-osteoporotic drugs, exercise, and survival status within the 3 years after surgery. Results: The study's male-to-female ratio was 1:3.1, and the rate of osteoporotic fracture for females was significantly higher than that of males. The mean age of participants with fractures was 75.6 ± 8.5 years, and most fractures occurred in participants 78 to 85 years old. Of the 387 participants, 169 participants had hip fractures (43.67%); 98 had vertebral compression fractures (25.32%); 51 had distal radius and ulna fractures (13.18%); 42 had proximal humerus fractures (10.85%); and 27 had other fractures (6.98%). The number of women with fractures at each site was greater than the number of men, but the differences weren't statistically significant (P > .05). The main causes of injury were falls (71.58%), and the main place of the occurrence of injury was at home (65.6%). Of the 387 participants, 346 had surgical treatment (89.41%), and the effective rate of surgical treatment was 99.42%. Three years after surgery, the research team followed up with 235 participants, for a follow-up rate of 60.72%. Within the 3 years of the follow-up period, 61 participants had refractures (25.63%), 29 received treatment with regular anti-osteoporotic drugs (12.34%), 36 exercised twice or more a week (15.32%), and 32 had died for various reasons (13.62%). Conclusions: The study preliminarily described the epidemiological characteristics of 387 osteoporotic fractures in adults over 65 years old. More women had fractures than men; the hip was the most common fracture site, and falls were the main cause of injury. Most of the fractures occurred in the place of residence, and the refracture rate was 25.96% at three years after surgery.

Identification of circRNA-miRNA-mRNA networks contributes to explore underlying pathogenesis and therapy strategy of gastric cancer.

BACKGROUND: Circular RNAs (circRNAs) are a new class of noncoding RNAs that have gained increased attention in human tumor research. However, the identification and function of circRNAs are largely unknown in the context of gastric cancer (GC). This study aims to identify novel circRNAs and determine their action networks in GC. METHODS: A comprehensive strategy of data mining, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and computational biology were conducted to discover novel circRNAs and to explore their potential mechanisms in GC. Promising therapeutic drugs for GC were determined by connectivity map (CMap) analysis. RESULTS: Six overlapped differentially expressed circRNAs (DECs) were screened from selected microarray and RNA-Seq datasets of GC, and the six DECs were then validated by sanger sequencing and RNase R treatment. Subsequent RT-qPCR analysis of GC samples confirmed decreased expressions of the six DECs (hsa_circ_0000390, hsa_circ_0000615, hsa_circ_0001438, hsa_circ_0002190, hsa_circ_0002449 and hsa_circ_0003120), all of which accumulated preferentially in the cytoplasm. MiRNA binding sites and AGO2 occupation of the six circRNAs were predicted using online databases, and circRNA-miRNA interactions including the six circRNAs and 33 miRNAs were determined. Then, 5320 target genes of the above 33 miRNAs and 1492 differently expressed genes (DEGs) from The Cancer Genome Atlas (TCGA) database were identified. After intersecting the miRNA target genes and the 889 downregulated DEGs, 320 overlapped target genes were acquired. The Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that these target genes were related to two critical tumor-associated signaling pathways. A protein-protein interaction network with the 320 target genes was constructed using STRING, and fifteen hubgenes (ATF3, BTG2, DUSP1, EGR1, FGF2, FOSB, GNAO1, GNAI1, GNAZ, GNG7, ITPR1, ITPKB, JUND, NR4A3, PRKCB) in the network were identified. Finally, bioactive chemicals (including vorinostat, trichostatin A and astemizole) based on the fifteen hubgenes were identifed as therapeutic agents for GC through the CMap analysis. CONCLUSIONS: This study provides a novel insight for further exploration of the pathogenesis and therapy of GC from the circRNA-miRNA-mRNA network perspective.

Surgical site infection following traumatic orthopaedic surgeries in geriatric patients: Incidence and prognostic risk factors.

Geriatric population is increasing rapidly worldwide, and fragility fracture and complication following orthopaedic surgery in elderly people have now become major challenges for surgeons. Further studies are required to identify potentially modifiable factors associated with surgical site infection (SSI) in geriatric patients. This retrospective, multicenter study was conducted at four level I hospitals in China. During the 31-month study period, a total of 2341 patients (65 years or older) underwent orthopaedic surgery and complete data were recorded from September 2015 to April 2018. Demographics information, medications and additional comorbidities, surgery-related variables, and laboratory indexes were extracted and analysed. Receiver-operating characteristic analysis was performed to detect the optimum threshold of continuous variables. Independent risk factors of SSI were identified by univariate and multivariate analyses. Finally, 63 patients suffered from wound infection within the follow-up period, indicating a 2.7% incidence rate of SSI. Statistical results showed that open injury (odds ratio [OR], 9.5; 95% confidence interval [CI], 5.4-16.7), American Society of Anesthesiologists classified III-IV score (OR, 2.2; 95% CI, 1.3-3.8), surgical duration of >132 minutes (OR, 2.9; 95% CI, 1.1-5.0), serum albumin (ALB) of <36.4 mg/L (OR, 2.0; 95% CI, 1.6-3.4), and blood glucose (GLU) of >118 mg/dL (OR, 3.1; 95% CI, 1.1-5.3) were independent risk factors of postoperative SSI. With the application of sensitive and modifiable variables such as surgical duration and the levels of ALB and GLU, more geriatric patients with sub-high risk of postoperative SSI could be identified.

Efficient Deployment of Key Nodes for Optimal Coverage of Industrial Mobile Wireless Networks.

In recent years, industrial wireless networks (IWNs) have been transformed by the introduction of mobile nodes, and they now offer increased extensibility, mobility, and flexibility. Nevertheless, mobile nodes pose efficiency and reliability challenges. Efficient node deployment and management of channel interference directly affect network system performance, particularly for key node placement in clustered wireless networks. This study analyzes this system model, considering both industrial properties of wireless networks and their mobility. Then, static and mobile node coverage problems are unified and simplified to target coverage problems. We propose a novel strategy for the deployment of clustered heads in grouped industrial mobile wireless networks (IMWNs) based on the improved maximal clique model and the iterative computation of new candidate cluster head positions. The maximal cliques are obtained via a double-layer Tabu search. Each cluster head updates its new position via an improved virtual force while moving with full coverage to find the minimal inter-cluster interference. Finally, we develop a simulation environment. The simulation results, based on a performance comparison, show the efficacy of the proposed strategies and their superiority over current approaches.

CRF-based models of protein surfaces improve protein-protein interaction site predictions.

BACKGROUND: The identification of protein-protein interaction sites is a computationally challenging task and important for understanding the biology of protein complexes. There is a rich literature in this field. A broad class of approaches assign to each candidate residue a real-valued score that measures how likely it is that the residue belongs to the interface. The prediction is obtained by thresholding this score.Some probabilistic models classify the residues on the basis of the posterior probabilities. In this paper, we introduce pairwise conditional random fields (pCRFs) in which edges are not restricted to the backbone as in the case of linear-chain CRFs utilized by Li et al. (2007). In fact, any 3D-neighborhood relation can be modeled. On grounds of a generalized Viterbi inference algorithm and a piecewise training process for pCRFs, we demonstrate how to utilize pCRFs to enhance a given residue-wise score-based protein-protein interface predictor on the surface of the protein under study. The features of the pCRF are solely based on the interface predictions scores of the predictor the performance of which shall be improved. RESULTS: We performed three sets of experiments with synthetic scores assigned to the surface residues of proteins taken from the data set PlaneDimers compiled by Zellner et al. (2011), from the list published by Keskin et al. (2004) and from the very recent data set due to Cukuroglu et al. (2014). That way we demonstrated that our pCRF-based enhancer is effective given the interface residue score distribution and the non-interface residue score are unimodal.Moreover, the pCRF-based enhancer is also successfully applicable, if the distributions are only unimodal over a certain sub-domain. The improvement is then restricted to that domain. Thus we were able to improve the prediction of the PresCont server devised by Zellner et al. (2011) on PlaneDimers. CONCLUSIONS: Our results strongly suggest that pCRFs form a methodological framework to improve residue-wise score-based protein-protein interface predictors given the scores are appropriately distributed. A prototypical implementation of our method is accessible at http://ppicrf.informatik.uni-goettingen.de/index.html.

miR-106a mimics the nuclear factor-κB signalling pathway by targeting DR6 in rats with osteoarthritis.

Introduction: Osteoarthritis (OA) is a common inflammatory joint disease characterised by progressive cartilage destruction. Management of this condition remains a significant challenge, and new therapies are required. We investigated the protective effects of miR-106a mimics in a murine model of OA. Material and methods: This study was performed using both in vitro and in vivo OA models. Primary chondrocytes were isolated from female rats, with inflammation induced via treatment with lipopolysaccharide (LPS). Then the effects of a miR-106a mimic were examined based on the level of inflammatory cytokine production and apoptotic signalling following LPS stimulation. An in vivo rat model of OA was generated by injecting LPS into the anterior cruciate ligament, followed by treatment with miR-106a mimics. Then, inflammatory and apoptotic protein expression was assessed in the cartilage tissue. Results: Treatment with miR-106a mimic reduced the levels of inflammatory cytokines and apoptotic proteins in cartilage tissues following LPS-induced inflammation. Furthermore, the mimic ameliorated the expression of DR-6 mRNA and DR6, IκBα, and p65 proteins in chondrocytes. Similar effects were seen in the in vivo model, with the mimic attenuating expression of NF-κB, p65, IκBα, and DR6 proteins and improving histopathological outcomes in the chondrocytes of OA rats. Conclusions: Treatment with miR-106a mimic ameliorates inflammation in cartilage tissues of OA subjects by activating death receptor 6 via the NF-κB signalling pathway.

Three-Dimensional Shear Wave Elastography Using Acoustic Radiation Force and a 2-D Row-Column Addressing (RCA) Array.

Acoustic radiation force (ARF)-based shear wave elastography (SWE) is a clinically available ultrasound imaging mode that noninvasively and quantitatively measures tissue stiffness. Current implementations of ARF-SWE are largely limited to 2-D imaging, which does not provide a robust estimation of heterogeneous tissue mechanical properties. Existing 3-D ARF-SWE solutions that are clinically available are based on wobbler probes, which cannot provide true 3-D shear wave motion detection. Although 3-D ARF-SWE based on 2-D matrix arrays have been previously demonstrated, they do not provide a practical solution because of the need for a high channel-count ultrasound system (e.g., 1024-channel) to provide adequate volume rates and the delicate circuitries (e.g., multiplexers) that are vulnerable to the long-duration "push" pulses. To address these issues, here we propose a new 3-D ARF-SWE method based on the 2-D row-column addressing (RCA) array which has a much lower element count (e.g., 256), provides an ultrafast imaging volume rate (e.g., 2000 Hz), and can withstand the push pulses. In this study, we combined the comb-push shear elastography (CUSE) technique with 2-D RCA for enhanced SWE imaging field-of-view (FOV). In vitro phantom studies demonstrated that the proposed method had robust 3-D SWE performance in both homogenous and inclusion phantoms. An in vivo study on a breast cancer patient showed that the proposed method could reconstruct 3-D elasticity maps of the breast lesion, which was validated using a commercial ultrasound scanner. These results demonstrate strong potential for the proposed method to provide a viable and practical solution for clinical 3-D ARF-SWE.

Context-aware deep learning enables high-efficacy localization of high concentration microbubbles for super-resolution ultrasound localization microscopy.

Ultrasound localization microscopy (ULM) enables deep tissue microvascular imaging by localizing and tracking intravenously injected microbubbles circulating in the bloodstream. However, conventional localization techniques require spatially isolated microbubbles, resulting in prolonged imaging time to obtain detailed microvascular maps. Here, we introduce LOcalization with Context Awareness (LOCA)-ULM, a deep learning-based microbubble simulation and localization pipeline designed to enhance localization performance in high microbubble concentrations. In silico, LOCA-ULM enhanced microbubble detection accuracy to 97.8% and reduced the missing rate to 23.8%, outperforming conventional and deep learning-based localization methods up to 17.4% in accuracy and 37.6% in missing rate reduction. In in vivo rat brain imaging, LOCA-ULM revealed dense cerebrovascular networks and spatially adjacent microvessels undetected by conventional ULM. We further demonstrate the superior localization performance of LOCA-ULM in functional ULM (fULM) where LOCA-ULM significantly increased the functional imaging sensitivity of fULM to hemodynamic responses invoked by whisker stimulations in the rat brain.

Localization Free Super-Resolution Microbubble Velocimetry Using a Long Short-Term Memory Neural Network.

Ultrasound localization microscopy is a super-resolution imaging technique that exploits the unique characteristics of contrast microbubbles to side-step the fundamental trade-off between imaging resolution and penetration depth. However, the conventional reconstruction technique is confined to low microbubble concentrations to avoid localization and tracking errors. Several research groups have introduced sparsity- and deep learning-based approaches to overcome this constraint to extract useful vascular structural information from overlapping microbubble signals, but these solutions have not been demonstrated to produce blood flow velocity maps of the microcirculation. Here, we introduce Deep-SMV, a localization free super-resolution microbubble velocimetry technique, based on a long short-term memory neural network, that provides high imaging speed and robustness to high microbubble concentrations, and directly outputs blood velocity measurements at a super-resolution. Deep-SMV is trained efficiently using microbubble flow simulation on real in vivo vascular data and demonstrates real-time velocity map reconstruction suitable for functional vascular imaging and pulsatility mapping at super-resolution. The technique is successfully applied to a wide variety of imaging scenarios, include flow channel phantoms, chicken embryo chorioallantoic membranes, and mouse brain imaging. An implementation of Deep-SMV is openly available at https://github.com/chenxiptz/SR_microvessel_velocimetry, with two pre-trained models available at https://doi.org/10.7910/DVN/SECUFD.

Non-invasive Deep-Brain Imaging with 3D Integrated Photoacoustic Tomography and Ultrasound Localization Microscopy (3D-PAULM).

Photoacoustic computed tomography (PACT) is a proven technology for imaging hemodynamics in deep brain of small animal models. PACT is inherently compatible with ultrasound (US) imaging, providing complementary contrast mechanisms. While PACT can quantify the brain's oxygen saturation of hemoglobin (sO2), US imaging can probe the blood flow based on the Doppler effect. Further, by tracking gas-filled microbubbles, ultrasound localization microscopy (ULM) can map the blood flow velocity with sub-diffraction spatial resolution. In this work, we present a 3D deep-brain imaging system that seamlessly integrates PACT and ULM into a single device, 3D-PAULM. Using a low ultrasound frequency of 4 MHz, 3D-PAULM is capable of imaging the whole-brain hemodynamic functions with intact scalp and skull in a totally non-invasive manner. Using 3D-PAULM, we studied the mouse brain functions with ischemic stroke. Multi-spectral PACT, US B-mode imaging, microbubble-enhanced power Doppler (PD), and ULM were performed on the same mouse brain with intrinsic image co-registration. From the multi-modality measurements, we future quantified blood perfusion, sO2, vessel density, and flow velocity of the mouse brain, showing stroke-induced ischemia, hypoxia, and reduced blood flow. We expect that 3D-PAULM can find broad applications in studying deep brain functions on small animal models.

High-Volume-Rate 3-D Ultrasound Imaging Using Fast-Tilting and Redirecting Reflectors.

Three-dimensional ultrasound imaging has many advantages over 2-D imaging such as more comprehensive tissue evaluation and less operator dependence. However, developing a low-cost and accessible 3-D ultrasound solution with high volume rate and imaging quality remains a challenging task. Recently, we proposed a 3-D ultrasound imaging technique: fast acoustic steering via tilting electromechanical reflectors (FASTER), which uses a fast-tilting acoustic reflector to steer ultrafast plane waves elevationally to achieve high-volume-rate 3-D imaging with conventional 1-D transducers. However, the initial FASTER implementation requires a water tank for acoustic wave conduction and cannot be conveniently used for regular handheld scanning. To address these limitations, here, we developed a novel ultrasound probe clip-on device that encloses a fast-tilting reflector, a redirecting reflector, and an acoustic wave conduction medium. The new FASTER 3-D imaging device can be easily attached to or removed from clinical ultrasound transducers, allowing rapid transformation from 2-D to 3-D imaging. In vitro B-mode studies demonstrated that the proposed method provided comparable imaging quality to conventional, mechanical-translation-based 3-D imaging while offering a much faster volume rate (e.g., 300 versus  âˆ¼  10 Hz). We also demonstrated 3-D power Doppler (PD) and 3-D super-resolution ultrasound localization microscopy (ULM) with the FASTER device. An in vivo imaging study showed that the FASTER device could clearly visualize the 3-D anatomy of the basilic vein. These results suggest that the newly developed redirecting reflector and the clip-on device could overcome key hurdles for future clinical translation of the FASTER 3-D imaging technology.

High Volume Rate 3-D Ultrasound Imaging Using Fast-Tilting and Redirecting Reflectors.

3-D ultrasound imaging has many advantages over 2-D imaging such as more comprehensive tissue evaluation and less operator dependence. Although many 3-D ultrasound imaging techniques have been developed in the last several decades, a low-cost and accessible solution with high imaging volume rate and imaging quality remains elusive. Recently we proposed a new, high volume rate 3-D ultrasound imaging technique: Fast Acoustic Steering via Tilting Electromechanical Reflectors (FASTER), which uses a water-immersible and fast-tilting acoustic reflector to steer ultrafast plane waves in the elevational direction to achieve high volume rate 3-D ultrasound imaging with conventional 1-D array transducers. However, the initial implementation of FASTER imaging only involves a single fast-tilting acoustic reflector, which is inconvenient to use because the probe cannot be held in the regular upright position. Also, conventional FASTER imaging can only be performed inside a water tank because of the necessity of using water for acoustic conduction. To address these limitations of conventional FASTER, here we developed a novel ultrasound probe clip-on device that encloses a fast-tilting reflector, a redirecting reflector, and an acoustic wave conduction medium. The new FASTER 3-D imaging device can be easily attached to or removed from clinical ultrasound transducers, allowing rapid transformation from 2-D to 3-D ultrasound imaging. In vitro B-mode imaging studies demonstrated that the proposed method provided comparable imaging quality (e.g., spatial resolution and contrast-to-noise ratio) to conventional, mechanical-translation-based 3-D imaging while providing a much faster 3-D volume rate (e.g., 300 Hz vs ∻10 Hz). In addition to B-mode imaging, we also demonstrated 3-D power Doppler imaging and 3-D super-resolution ultrasound localization microscopy with the newly developed FASTER device. An in vivo imaging study showed that the FASTER device could clearly visualize the 3-D anatomy of the basilic vein of a healthy volunteer, and customized beamforming was implemented to accommodate the speed of sound difference between the acoustic medium and the imaging object (e.g., soft tissue). These results suggest that the newly developed redirecting reflector and the clip-on device could overcome key hurdles for future clinical translation of the FASTER 3-D imaging technology.

High-Level Synthesis Design of Scalable Ultrafast Ultrasound Beamformer With Single FPGA.

Ultrafast ultrasound imaging is essential for advanced ultrasound imaging techniques such as ultrasound localization microscopy (ULM) and functional ultrasound (fUS). Current ultrafast ultrasound imaging is challenged by the ultrahigh data bandwidth associated with the radio frequency (RF) signal, and by the latency of the computationally expensive beamforming process. As such, continuous ultrafast data acquisition and beamforming remain elusive with existing software beamformers based on CPUs or GPUs. To address these challenges, the proposed work introduces a novel method of implementing an ultrafast ultrasound beamformer specifically for ultrafast plane wave imaging (PWI) on a field programmable gate array (FPGA) by using high-level synthesis. A parallelized implementation of the beamformer on a single FPGA was proposed by 1) utilizing a delay compression technique to reduce the delay profile size, which enables both run-time pre-calculated delay profile loading from external memory and delay reuse, 2) vectorizing channel data fetching which is enabled by delay reuse, and 3) using fixed summing networks to reduce consumption of logic resources. Our proposed method presents two unique advantages over current FPGA beamformers: 1) high scalability that allows fast adaptation to different FPGA resources and beamforming speed demands by using Xilinx High-Level Synthesis as the development tool, and 2) allow a compact form factor design by using a single FPGA to complete the beamforming instead of multiple FPGAs. Current Xilinx FPGAs provide the capabilities of connecting up to 1024 ultrasound channels with a single FPGA and the newest JESD204B interface analog front end (AFE). This channel count is much more than the channel count needed by current linear arrays, which normally have 128 or 256 channels. With the proposed method, a sustainable average beamforming rate of 4.83 G samples/second in terms of input raw RF sample was achieved. The resulting image quality of the proposed beamformer was compared with the software beamformer on the Verasonics Vantage system for both phantom imaging and in vivo imaging of a mouse brain. Multiple imaging schemes including B-mode, power Doppler and ULM were assessed to verify that the image quality was not compromised for speed.

Novel insights into the role of leaf in the cutting process of Camellia sinensis using physiological, biochemical and transcriptome analyses.

Cuttage is the preferred approach for rapid propagation of many species including tea plant (Camellia sinensis). Leaf serves as a key part of nodal cutting, but there is a lack of systematic research on its role in the cutting process. In this study, 24 tea cultivars were employed to prove the necessity of leaf and light during cuttage. Further leaf physiological parameters found that lower net photosynthesis rate probably promoted rooting. Phytohormone content detection showed that auxin content and composition pattern were related to rooting ability. Leaf transcriptome analyses of cuttings from a representative easy-to-root cultivar (cv. Echa 10) revealed that genes involved in carbohydrate metabolism, signal transduction, metabolite biosynthesis and transportation were differentially expressed during the rooting process. CsTSA1, CsYUC10, CsAUX1s, CsPIN3 and CsPIN5 were selected as the candidate genes, which possibly regulate the rooting of nodal cuttings. These results illustrate the necessity of the leaf in cuttage and provide molecular evidence that leaf is an important place for signal transduction, metabolite synthesis and transport during the rooting process.

Contrast-Free Super-Resolution Power Doppler (CS-PD) Based on Deep Neural Networks.

Super-resolution ultrasound microvessel imaging based on ultrasound localization microscopy (ULM) is an emerging imaging modality that is capable of resolving micrometer-scaled vessels deep into tissue. In practice, ULM is limited by the need for contrast injection, long data acquisition, and computationally expensive postprocessing times. In this study, we present a contrast-free super-resolution power Doppler (CS-PD) technique that uses deep networks to achieve super-resolution with short data acquisition. The training dataset is comprised of spatiotemporal ultrafast ultrasound signals acquired from in vivo mouse brains, while the testing dataset includes in vivo mouse brain, chicken embryo chorioallantoic membrane (CAM), and healthy human subjects. The in vivo mouse imaging studies demonstrate that CS-PD could achieve an approximate twofold improvement in spatial resolution when compared with conventional power Doppler. In addition, the microvascular images generated by CS-PD showed good agreement with the corresponding ULM images as indicated by a structural similarity index of 0.7837 and a peak signal-to-noise ratio (PSNR) of 25.52. Moreover, CS-PD was able to preserve the temporal profile of the blood flow (e.g., pulsatility) that is similar to conventional power Doppler. Finally, the generalizability of CS-PD was demonstrated on testing data of different tissues using different imaging settings. The fast inference time of the proposed deep neural network also allows CS-PD to be implemented for real-time imaging. These features of CS-PD offer a practical, fast, and robust microvascular imaging solution for many preclinical and clinical applications of Doppler ultrasound.

Prevalence and Biological Characteristics of Listeria Species Isolated from Livestock and Poultry Meat in Gansu Province, China.

Listeria monocytogenes is a widespread foodborne pathogen contaminating foods during their production or processing stages. Fresh meat is susceptible to such contamination if it is not properly preserved. Our study was conducted to reveal the level of contamination and prevalence of Listeria spp. present in livestock and poultry meat from Gansu province. A total of 1,387 samples were collected from five cities in Gansu Province according to standard sampling procedures, of which 174 samples (12.5%) were positive for Listeria species. Among them, 14 isolates of L. monocytogenes (1.0%), 150 isolates of Listeria innocua (10.8%), and ten isolates of Listeria welshimeri (0.7%) were identified by conventional bacteriological and molecular identification methods. All isolates were subjected to serological assays, antimicrobial susceptibility tests, growth curve assays, determination of biofilm-forming capacity, and cluster analysis of the 16S rRNA gene sequences. Four predominant serotypes of L. monocytogenes were identified, including 1/2a (35.7%, 5/14), 1/2b (14.3%, 2/14), 1/2c (42.9%, 6/14), and 4b (7.1%, 1/14). All L. monocytogenes isolates were resistant to tetracycline and cefoxitin. Most L. innocua isolates (63.6%, 14/22) and L. welshimeri (40%, 4/10) were resistant to tetracycline. The high biofilm-forming ability was observed among 1/2c and 1/2a serotype isolates. The cluster analysis of the 16S rRNA gene sequences revealed a close genetic relationship between the three Listeria species. This study fills the gap in the knowledge of livestock and poultry meat that carry Listeria in slaughterhouses and markets in Gansu Province.

Pixel-Oriented Adaptive Apodization for Plane-Wave Imaging Based on Recovery of the Complete Dataset.

In theory, coherent plane-wave compounding (CPWC) enables ultrafast ultrasound imaging while maintaining a high imaging quality that is comparable to conventional B-mode imaging based on focused beam transmissions. However, in practice, due to the imperfect synthetization of transmit focusing (e.g., heterogeneous speed of sound in tissue and limited range of steering angle), CPWC suffers from a variety of imaging artifacts resulting from side lobes, grating lobes, and axial lobes. This study focuses on addressing the issues of axial lobes for CPWC, which constitutes an important source of clutter that leads to the degradation of contrast ratio and contrast-to-noise ratio (CR and CNR) of CPWC. We first investigated the source of the axial lobes based on plane-wave propagation and the delay-and-sum (DAS) beamforming. We then proposed a new method that is based on pixel-oriented adaptive apodization (POAA) to eliminate the axial lobes throughout the entire field of view (FOV). POAA was first validated in a simulation study, followed by in vitro phantom experiments and an in vivo case study on a carotid artery from a healthy volunteer. In the simulation study, suppression of axial lobes by 120 dB was observed from wire targets, and an improvement of CNR by up to 60% was found in a cyst-mimicking digital phantom. In the phantom experiment, POAA showed an improvement in CNR by around 20% over conventional methods. The effectiveness of axial lobe suppression was finally demonstrated in vivo, where POAA showed a substantial suppression of clutters throughout the entire FOV.

Effects of biochar and arbuscular mycorrhizal fungi on winter wheat growth and soil N2O emissions in different phosphorus environments.

Introduction: Promoting crop growth and regulating denitrification process are two main ways to reduce soil N2O emissions in agricultural systems. However, how biochar and arbuscular mycorrhizal fungi (AMF) can regulate crop growth and denitrification in soils with different phosphorus (P) supplies to influence N2O emission remains largely unknown. Method: Here, an eight-week greenhouse and one-year field experiments biochar and/or AMF (only in greenhouse experiment) additions under low and high P environments were conducted to characterize the effects on wheat (Triticum aestivum L.) growth and N2O emission. Results: With low P supply, AMF addition decreased leaf Mn concentration (indicates carboxylate-releasing P-acquisition strategies), whereas biochar addition increased leaf Mn concentration, suggesting biochar and AMF addition regulated root morphological and physiological traits to capture P. Compared with low P supply, the high P significantly promoted wheat growth (by 16-34%), nutrient content (by 33-218%) and yield (by 33-41%), but suppressed soil N2O emissions (by 32-95%). Biochar and/or AMF addition exhibited either no or negative effects on wheat biomass and nutrient content in greenhouse, and biochar addition promoted wheat yield only under high P environment in field. However, biochar and/or AMF addition decreased soil N2O emissions by 24-93% and 32% in greenhouse and field experiments, respectively. This decrease was associated mainly with the diminished abundance of N2O-producing denitrifiers (nirK and nirS types, by 17-59%, respectively) and the increased abundance of N2O-consuming denitrifiers (nosZ type, by 35-65%), and also with the increased wheat nutrient content, yield and leaf Mn concentration. Discussion: These findings suggest that strengthening the plant-soil-microbe interactions can mitigate soil N2O emissions via manipulating plant nutrient acquisition and soil denitrification.

Optimizing deep neural networks to predict the effect of social distancing on COVID-19 spread.

Deep Neural Networks (DNN) form a powerful deep learning model that can process unprecedented volumes of data. The hyperparameters of DNN have a significant influence on its prediction performance. Evolutionary algorithms (EAs) form a heuristic-based approach that provides an opportunity to optimize deep learning models to obtain good performance. Therefore, we propose an evolutionary deep learning model called IPSO-DNN based on DNN for prediction and an improved Particle Swarm Optimization (IPSO) algorithm to optimize the kernel hyperparameters of DNN in a self-adaptive evolutionary way. In the IPSO algorithm, a micro population size setting is introduced to improve the search efficiency of the algorithm, and the generalized opposition-based learning strategy is used to guide the population evolution. In addition, the IPSO algorithm employs a self-adaptive update strategy to prevent premature convergence and then improves the exploitation and exploration parameter optimization performance of DNN. In this paper, we show that the IPSO algorithm provides an efficient approach for tuning the hyperparameters of DNN with saving valuable computational resources. We explore the proposed IPSO-DNN model to predict the effect of social distancing on the spread of COVID-19 based on the social distancing metrics. The preliminary experimental results reveal that the proposed IPSO-DNN model has the least computation cost and yields better prediction accuracy results when compared to the other models. The experiments of the IPSO-DNN model also illustrate that aggressive and extensive social distancing interventions are crucial to help flatten the COVID-19 epidemic curve in the United States.