Prognostic Factors in Philadelphia Chromosome-Positive Acute Myeloid Leukemia Using Fluorescence in Situ Hybridization.

BACKGROUND: Philadelphia chromosome-positive acute myeloid leukemia (Ph+ AML) is a rare leukemia subtype first classified by the World Health Organization in 2016. The incidence of Ph+ AML is approximately 0.5 - 3%, and its prognosis is poor. Ph+ AML with additional chromosomal abnormalities in children has rarely been reported, and its treatment and prognosis remain uncertain. METHODS: We retrospectively analyzed 649 patients with AML from 2006 - 2021. Six (0.9%) patients with Ph+ AML were identified and treated with conventional chemotherapy. The clinical features and prognoses were retrospectively analyzed. RESULTS: Six cases of AML with a Ph chromosome were reported. One of the six individuals exhibited a biphenotypic immunophenotype, one exhibited a simple myeloid immunophenotype, and the other four exhibited myeloid and lymphoid expression. Karyotypic analysis (R banding) was performed in six cases, four of which were classical Ph chromosomal abnormalities, two of which had additional abnormalities outside the Ph chromosome. Fluorescence in situ hybridization (FISH) analysis using the BCR/ABL fusion gene distinguished that the BCR major breakpoint break in three cases was type P210 and the BCR minor breakpoint break in three cases was type P190. The complete remission rate of the six patients in this study using conventional chemotherapy was 60%, with a median survival time of 7.5 months. CONCLUSIONS: In summary, Ph+ AML is a heterogeneous disease often associated with additional chromosomal abnormalities. Ph+ AML is seen with a lymphoid immunophenotype and alterations in associated genes such as the IGH gene. Adults were predominantly P210 and two cases in children were both P190. Conventional treatments are less effective, and there are no standard treatment regimens.

Droplet Printing Enabled by Cavity Collapse Ejection.

The behavior of cavity collapse in liquids is of fundamental importance in natural and industrial applications. It is still challenging to use the phenomenon of cavity collapse ejection in on-demand droplet printing technology. In this study, we investigate the cavity collapse ejection phenomenon in the submillimeter to millimeter scale and demonstrate that the cavity capillary energy is a critical factor affecting the state of the generated jet. Based on this phenomenon, we developed a droplet printing technology that can print nanoliter satellite-free droplets from a millimeter-sized nozzle, which reduces the risk of nozzle clogging. Using this printing technology, we demonstrated the printing of a nanoparticle suspension with 60% mass loading. Finally, we also showcased the printing of various inks for different applications using this technology, demonstrating the printability of cavity collapse-ejection printing technology in functional inks and showing potential to be applied in scenarios such as bioassays, the electronics industry, and additive manufacturing.

Application of Air-Coupled Ground Penetrating Radar Based on F-K Filtering and BP Migration in High-Speed Railway Tunnel Detection.

As the number and length of high-speed railway tunnels increase in China, implicit defects such as insufficient lining thicknesses, voids, and poor compaction have become increasingly common, posing a serious threat to train operation safety. It is, therefore, imperative to conduct a comprehensive census of the defects within the tunnel linings. In response to this problem, this study proposes a high-speed railway tunnel detection method based on vehicle-mounted air-coupled GPR. Building on a forward simulation of air-coupled GPR, the study proposes the F-K filtering and BP migration algorithms based on the practical considerations of random noise and imaging interference from the inherent equipment. Through multi-dimensional quantitative comparisons, these algorithms are shown to improve the spectrum entropy values and instantaneous amplitude ratios by 4.6% and 11.6%; and 120% and 180%, respectively, over the mean and bandpass filtering algorithms, demonstrating their ability to suppress clutter and enhance the internal signal prominence of the lining. The experimental results are consistent with the forward simulation trends, and the verification using the ground-coupled GPR detection confirms that air-coupled GPR can meet the requirements of high-speed railway tunnel lining inspections. A comprehensive GPR detection model is proposed to lay the foundation for a subsequent defect census of high-speed railway tunnels.

Hepatic Steatosis: CT-Based Prevalence in Adults in China and the United States and Associations With Age, Sex, and Body Mass Index.

BACKGROUND. Calibrated CT fat fraction (FFCT) measurements derived from un-enhanced abdominal CT reliably reflect liver fat content, allowing large-scale population-level investigations of steatosis prevalence and associations. OBJECTIVE. The purpose of this study was to compare the prevalence of hepatic steatosis, as assessed by calibrated CT measurements, between population-based Chinese and U.S. cohorts, and to investigate in these populations the relationship of steatosis with age, sex, and body mass index (BMI). METHODS. This retrospective study included 3176 adults (1985 women and 1191 men) from seven Chinese provinces and 8748 adults (4834 women and 3914 men) from a single U.S. medical center, all drawn from previous studies. All participants were at least 40 years old and had undergone unenhanced abdominal CT in previous studies. Liver fat content measurements on CT were cross-calibrated to MRI proton density fat fraction measurements using phantoms and expressed as adjusted FFCT measurements. Mild, moderate, and severe steatosis were defined as adjusted FFCT of 5.0-14.9%, 15.0-24.9%, and 25.0% or more, respectively. The two cohorts were compared. RESULTS. In the Chinese and U.S. cohorts, the median adjusted FFCT for women was 4.7% and 4.8%, respectively, and that for men was 5.8% and 6.2%, respectively. In the Chinese and U.S. cohorts, steatosis prevalence for women was 46.3% and 48.7%, respectively, whereas that for men was 58.9% and 61.9%, respectively. Severe steatosis prevalence was 0.9% and 1.8% for women and 0.2% and 2.6% for men in the Chinese and U.S. cohorts, respectively. Adjusted FFCT did not vary across age decades among women or men in the Chinese cohort, although it increased across age decades among women and men in the U.S. cohort. Adjusted FFCT and BMI exhibited weak correlation (r = 0.312-0.431). Among participants with normal BMI, 36.8% and 38.5% of those in the Chinese and U.S. cohorts, respectively, had mild steatosis, and 3.0% and 1.5% of those in the Chinese and U.S. cohorts, respectively, had moderate or severe steatosis. Among U.S. participants with a BMI of 40.0 or greater, 17.7% had normal liver content. CONCLUSION. Steatosis and severe steatosis had higher prevalence in the U.S. cohort than in the Chinese cohort in both women and men. BMI did not reliably predict steatosis. CLINICAL IMPACT. The findings provide new information on the dependence of hepatic steatosis on age, sex, and BMI.

A low-cost self-dispersing method of droplet array generation enabled by a simple reusable mask for bioanalysis and bioassays.

Discontinuous dewetting is an attractive technique that can produce droplet array of specific volume, geometry and at predefined location on a substrate. Droplet array has great potential in bioanalysis such as high-throughput live cell screening, digital PCR, and drug candidates. Here, we propose a self-dispersing droplet array generation method, which has advantages of low cost, simple operation, and easy large-area production ability. Droplet array of specific volumes was generated on a polymethyl methacrylate (PMMA) substrate using a simple reusable polyimide (PI) adhesive mask. Experiment shows that the generated droplet array can be used to successfully capture single particles which obeys Poisson distribution in a high-throughput manner. Furthermore, a droplet-array sandwiching chip was created based on the self-dispersion method for rapid detection of human serum albumin (HSA) at wide range of 183-11,712 µg/mL with low reagent consumption of 2.2 µL, demonstrating its potential applications in convenient high-throughput bioanalysis and bioassays.

An Energy-Efficient Clustering Method for Target Tracking Based on Tracking Anchors in Wireless Sensor Networks.

As a key technology in wireless sensor networks (WSNs), target tracking plays an essential role in many applications. To improve energy efficiency, clustering is widely used in tracking to organize the network to achieve data fusion and reduce communication costs. Many existing studies make dynamic adjustments based on static clusters to track moving targets. However, the additional overhead caused by frequent cluster reconstruction and redundant data transmission is rarely considered. To address this issue, we propose a tracking-anchor-based clustering method (TACM) in this paper, in which tracking anchors are introduced to provide activation indications for sensors according to the target position. We use the rough fuzzy C-means (RFCM) algorithm to locate the anchors and use the membership table to activate sensors to form a cluster. Since there are no sending, receiving, and fusing data tasks for anchors, they are lightly burdened and can significantly reduce the frequency of being rotated. Moreover, the state of cluster members (CMs) is scheduled using the linear 0-1 programming to reduce redundant transmissions. The simulation results demonstrate that, compared with some existing clustering methods, the proposed TACM effectively reduces the energy consumption when tracking a moving target, thus prolonging the network lifetime.

An Improved Q-Learning-Based Sensor-Scheduling Algorithm for Multi-Target Tracking.

Target tracking is an essential issue in wireless sensor networks (WSNs). Compared with single-target tracking, how to guarantee the performance of multi-target tracking is more challenging because the system needs to balance the tracking resource for each target according to different target properties and network status. However, the balance of tracking task allocation is rarely considered in those prior sensor-scheduling algorithms, which may result in the degradation of tracking accuracy for some targets and additional system energy consumption. To address this issue, we propose in this paper an improved Q-learning-based sensor-scheduling algorithm for multi-target tracking (MTT-SS). First, we devise an entropy weight method (EWM)-based strategy to evaluate the priority of targets being tracked according to target properties and network status. Moreover, we develop a Q-learning-based task allocation mechanism to obtain a balanced resource scheduling result in multi-target-tracking scenarios. Simulation results demonstrate that our proposed algorithm can obtain a significant enhancement in terms of tracking accuracy and energy efficiency compared with the existing sensor-scheduling algorithms.

Deep learning model improves radiologists' performance in detection and classification of breast lesions.

OBJECTIVE: Computer-aided diagnosis using deep learning algorithms has been initially applied in the field of mammography, but there is no large-scale clinical application. METHODS: This study proposed to develop and verify an artificial intelligence model based on mammography. Firstly, mammograms retrospectively collected from six centers were randomized to a training dataset and a validation dataset for establishing the model. Secondly, the model was tested by comparing 12 radiologists' performance with and without it. Finally, prospectively enrolled women with mammograms from six centers were diagnosed by radiologists with the model. The detection and diagnostic capabilities were evaluated using the free-response receiver operating characteristic (FROC) curve and ROC curve. RESULTS: The sensitivity of model for detecting lesions after matching was 0.908 for false positive rate of 0.25 in unilateral images. The area under ROC curve (AUC) to distinguish the benign lesions from malignant lesions was 0.855 [95% confidence interval (95% CI): 0.830, 0.880]. The performance of 12 radiologists with the model was higher than that of radiologists alone (AUC: 0.852 vs. 0.805, P=0.005). The mean reading time of with the model was shorter than that of reading alone (80.18 s vs. 62.28 s, P=0.032). In prospective application, the sensitivity of detection reached 0.887 at false positive rate of 0.25; the AUC of radiologists with the model was 0.983 (95% CI: 0.978, 0.988), with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 94.36%, 98.07%, 87.76%, and 99.09%, respectively. CONCLUSIONS: The artificial intelligence model exhibits high accuracy for detecting and diagnosing breast lesions, improves diagnostic accuracy and saves time.

Simultaneous removal of Cr(VI) and acid orange 7 from water in pyrite-persulfate system.

As the industries advances at a fast pace, efficient and simultaneous removal of both heavy metals and organics from aqueous is essential to protecting public human health and environment. In this work, we used pyrite as reductant and catalyst for simultaneously reducing Cr(VI) and activating persulfate (PS) to degrade acid orange 7 (AO7). The results indicated that the simultaneous removal rate of AO7 and Cr(VI) by pyrite-PS was up to 100% within 60 min under acidic conditions. However, There was a competitive relationship between PS activation and Cr(VI) reduction for robbing Fe2+. At beginning of the reaction, the limited Fe2+ firstly activated persulfate rather than reduce Cr(VI). The effect of dosage of pyrite and PS on Cr(VI) reduction was more significant than that on AO7 degradation. Increased pyrite dosages from 1g·L-1 to 6 g L-1 resulted in enhanced Cr(VI) removal, and excessive PS (more than 0.4 g L-1) was not beneficial to Cr(VI) removal. Electron paramagnetic resonance (EPR) spectroscopy and radical scavenger studies demonstrated that sulfate (SO4-·), singlet oxygen (1O2) and superoxide radical (·O2-) were the crucial reactive oxygen species (ROS) in the pyrite-PS system rather than hydroxyl radical (·OH). This study showed that the pyrite-PS system could simultaneously remove AO7 and Cr(VI), which provided a new idea for the actual wastewater treatment.

Real-Time Moving Object Detection in High-Resolution Video Sensing.

This paper addresses real-time moving object detection with high accuracy in high-resolution video frames. A previously developed framework for moving object detection is modified to enable real-time processing of high-resolution images. First, a computationally efficient method is employed, which detects moving regions on a resized image while maintaining moving regions on the original image with mapping coordinates. Second, a light backbone deep neural network in place of a more complex one is utilized. Third, the focal loss function is employed to alleviate the imbalance between positive and negative samples. The results of the extensive experimentations conducted indicate that the modified framework developed in this paper achieves a processing rate of 21 frames per second with 86.15% accuracy on the dataset SimitMovingDataset, which contains high-resolution images of the size 1920 × 1080.

Woofer-tweeter deformable mirror driven by combined actuators with a piezoelectric unimorph and stack for astronomical application.

A woofer-tweeter deformable mirror (DM) driven by combined actuators with a piezoelectric unimorph and stack for astronomical applications is proposed. The piezoelectric unimorph "tweeter" part, made of a 200-µm-thick lead zirconate titanate film and 200-µm-thick silicon, has 234 separate elements for high-order correction. It is magnetically jointed with a seven-element "woofer" piezo-stack array, which is for low-order correction. The combined DM was fabricated and experimentally evaluated, showing a high resonant frequency near 1 kHz. The piezo-stack array together with the unimorph actuators enable the DM to produce wavefronts with RMS residue errors less than 20 nm. Experimental results indicate that the woofer-tweeter DM has the capability to compensate for the first 35 terms of Zernike aberrations with normalized RMS wavefront errors less than 20%. The woofer-tweeter DM has higher bandwidth than a conventional unimorph DM as well as simple structure, low cost, and good scalability, offering a potential alternative for large-aperture astronomical applications.

NovelmiRNA-25 inhibits AMPD2 in peripheral blood mononuclear cells of patients with systemic lupus erythematosus and represents a promising novel biomarker.

BACKGROUND: Systemic lupus erythematosus (SLE) is a multisystemic autoimmune disease with various clinical manifestations. MicroRNAs (miRNAs) and immunometabolism are recognized as key elements in SLE pathogenesis; however, the relationship between miRNAs in peripheral blood mononuclear cells (PBMCs) and metabolism in SLE remains unclear. METHODS: We detected PBMC miRNA and mRNA profiles from 3 pooled SLE patients and 3 healthy controls (HCs) using next-generation sequencing, predicted miRNA targets in dysregulated mRNAs, predicted functions and interactions of differentially expressed genes using bioinformatics analysis, validated candidate miRNAs using qRT-PCR, and investigated the association between the expression of candidate miRNAs and SLE clinical characteristics. Moreover, we validated the direct and transcriptional regulatory effect of NovelmiRNA-25 on adenosine monophosphate deaminase 2 (AMPD2) using a dual-luciferase reporter assay and western blot and confirmed AMPD2 mRNA and protein expression in PBMCs using qRT-PCR and western blot, respectively. RESULTS: Multilayer integrative analysis of microRNA and mRNA regulation showed that 10 miRNAs were down-regulated and 19 miRNAs were up-regulated in SLE patient PBMCs compared with HCs. Bioinformatics analysis of regulatory networks between miRNAs and mRNAs showed that 19 miRNAs were related to metabolic processes. Two candidate miRNAs, NovelmiRNA-25 and miR-1273h-5p, which were significantly increased in the PBMCs of SLE patients (P < 0.05), represented diagnostic biomarkers with sensitivities of 94.74% and 89.47%, respectively (area under the curve = 0.574 and 0.788, respectively). NovelmiRNA-25 expression in PBMCs was associated with disease activity in SLE patients, in both active and stable groups (P < 0.05). NovelmiRNA-25 overexpression downregulated AMPD2 expression in HEK293T cells through direct targeting of the AMPD2 3'UTR (P < 0.01), while inhibition of NovelmiRNA-25 activity led to increased AMPD2 expression (P < 0.01). NovelmiRNA-25 overexpression also downregulated AMPD2 protein expression in HEK293T cells; AMPD2 protein expression in SLE patient PBMCs was decreased. Our results show that differentially expressed miRNAs play an important role in SLE. CONCLUSIONS: Our data demonstrate a novel mechanism in SLE development that involves the targeting of AMPD2 expression by NovelmiRNA-25. miRNAs may serve as novel biomarkers for the diagnosis and evaluation of disease activity of SLE and represent potential therapeutic targets for this disease.

Deep CNNs with Robust LBP Guiding Pooling for Face Recognition.

Pooling layer in Convolutional Neural Networks (CNNs) is designed to reduce dimensions and computational complexity. Unfortunately, CNN is easily disturbed by noise in images when extracting features from input images. The traditional pooling layer directly samples the input feature maps without considering whether they are affected by noise, which brings about accumulated noise in the subsequent feature maps as well as undesirable network outputs. To address this issue, a robust Local Binary Pattern (LBP) Guiding Pooling (G-RLBP) mechanism is proposed in this paper to down sample the input feature maps and lower the noise impact simultaneously. The proposed G-RLBP method calculates the weighted average of all pixels in the sliding window of this pooling layer as the final results based on their corresponding probabilities of being affected by noise, thus lowers the noise impact from input images at the first several layers of the CNNs. The experimental results show that the carefully designed G-RLBP layer can successfully lower the noise impact and improve the recognition rates of the CNN models over the traditional pooling layer. The performance gain of the G-RLBP is quite remarkable when the images are severely affected by noise.

A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance.

The Unscented Kalman filter (UKF) may suffer from performance degradation and even divergence while mismatch between the noise distribution assumed as a priori by users and the actual ones in a real nonlinear system. To resolve this problem, this paper proposes a robust adaptive UKF (RAUKF) to improve the accuracy and robustness of state estimation with uncertain noise covariance. More specifically, at each timestep, a standard UKF will be implemented first to obtain the state estimations using the new acquired measurement data. Then an online fault-detection mechanism is adopted to judge if it is necessary to update current noise covariance. If necessary, innovation-based method and residual-based method are used to calculate the estimations of current noise covariance of process and measurement, respectively. By utilizing a weighting factor, the filter will combine the last noise covariance matrices with the estimations as the new noise covariance matrices. Finally, the state estimations will be corrected according to the new noise covariance matrices and previous state estimations. Compared with the standard UKF and other adaptive UKF algorithms, RAUKF converges faster to the actual noise covariance and thus achieves a better performance in terms of robustness, accuracy, and computation for nonlinear estimation with uncertain noise covariance, which is demonstrated by the simulation results.

A Novel Loss Recovery and Tracking Scheme for Maneuvering Target in Hybrid WSNs.

Tracking a mobile target, which aims to timely monitor the invasion of specific target, is one of the most prominent applications in wireless sensor networks (WSNs). Traditional tracking methods in WSNs only based on static sensor nodes (SNs) have several critical problems. For example, to void the loss of mobile target, many SNs must be active to track the target in all possible directions, resulting in excessive energy consumption. Additionally, when entering coverage holes in the monitoring area, the mobile target may be missing and then its state is unknown during this period. To tackle these problems, in this paper, a few mobile sensor nodes (MNs) are introduced to cooperate with SNs to form a hybrid WSN due to their stronger abilities and less constrained energy. Then, we propose a valid target tracking scheme for hybrid WSNs to dynamically schedule the MNs and SNs. Moreover, a novel loss recovery mechanism is proposed to find the lost target and recover the tracking with fewer SNs awakened. Furthermore, to improve the robustness and accuracy of the recovery mechanism, an adaptive unscented Kalman filter (AUKF) algorithm is raised to dynamically adjust the process noise covariance. Simulation results demonstrate that our tracking scheme for maneuvering target in hybrid WSNs can not only track the target effectively even if the target is lost but also maintain an excellent accuracy and robustness with fewer activated nodes.

A Robust Real Time Direction-of-Arrival Estimation Method for Sequential Movement Events of Vehicles.

Parameters estimation of sequential movement events of vehicles is facing the challenges of noise interferences and the demands of portable implementation. In this paper, we propose a robust direction-of-arrival (DOA) estimation method for the sequential movement events of vehicles based on a small Micro-Electro-Mechanical System (MEMS) microphone array system. Inspired by the incoherent signal-subspace method (ISM), the method that is proposed in this work employs multiple sub-bands, which are selected from the wideband signals with high magnitude-squared coherence to track moving vehicles in the presence of wind noise. The field test results demonstrate that the proposed method has a better performance in emulating the DOA of a moving vehicle even in the case of severe wind interference than the narrowband multiple signal classification (MUSIC) method, the sub-band DOA estimation method, and the classical two-sided correlation transformation (TCT) method.

Design of an Acoustic Target Intrusion Detection System Based on Small-Aperture Microphone Array.

Automated surveillance of remote locations in a wireless sensor network is dominated by the detection algorithm because actual intrusions in such locations are a rare event. Therefore, a detection method with low power consumption is crucial for persistent surveillance to ensure longevity of the sensor networks. A simple and effective two-stage algorithm composed of energy detector (ED) and delay detector (DD) with all its operations in time-domain using small-aperture microphone array (SAMA) is proposed. The algorithm analyzes the quite different velocities between wind noise and sound waves to improve the detection capability of ED in the surveillance area. Experiments in four different fields with three types of vehicles show that the algorithm is robust to wind noise and the probability of detection and false alarm are 96.67% and 2.857%, respectively.

An Acoustic Signal Enhancement Method Based on Independent Vector Analysis for Moving Target Classification in the Wild.

In this paper, we study how to improve the performance of moving target classification by using an acoustic signal enhancement method based on independent vector analysis (IVA) in the unattended ground sensor (UGS) system. Inspired by the IVA algorithm, we propose an improved IVA method based on a microphone array for acoustic signal enhancement in the wild, which adopts a particular multivariate generalized Gaussian distribution as the source prior, an adaptive variable step strategy for the learning algorithm and discrete cosine transform (DCT) to convert the time domain observed signals to the frequency domain. We term the proposed method as DCT-G-IVA. Moreover, we design a target classification system using the improved IVA method for signal enhancement in the UGS system. Different experiments are conducted to evaluate the proposed method for acoustic signal enhancement by comparing with the baseline methods in our classification system under different wild environments. The experimental results validate the superiority of the DCT-G-IVA enhancement method in the classification system for moving targets in the presence of dynamic wind noise.

An Effective and Robust Decentralized Target Tracking Scheme in Wireless Camera Sensor Networks.

In this paper, we propose an effective and robust decentralized tracking scheme based on the square root cubature information filter (SRCIF) to balance the energy consumption and tracking accuracy in wireless camera sensor networks (WCNs). More specifically, regarding the characteristics and constraints of camera nodes in WCNs, some special mechanisms are put forward and integrated in this tracking scheme. First, a decentralized tracking approach is adopted so that the tracking can be implemented energy-efficiently and steadily. Subsequently, task cluster nodes are dynamically selected by adopting a greedy on-line decision approach based on the defined contribution decision (CD) considering the limited energy of camera nodes. Additionally, we design an efficient cluster head (CH) selection mechanism that casts such selection problem as an optimization problem based on the remaining energy and distance-to-target. Finally, we also perform analysis on the target detection probability when selecting the task cluster nodes and their CH, owing to the directional sensing and observation limitations in field of view (FOV) of camera nodes in WCNs. From simulation results, the proposed tracking scheme shows an obvious improvement in balancing the energy consumption and tracking accuracy over the existing methods.

Vehicle Counting and Moving Direction Identification Based on Small-Aperture Microphone Array.

The varying trend of a moving vehicle's angles provides much important intelligence for an unattended ground sensor (UGS) monitoring system. The present study investigates the capabilities of a small-aperture microphone array (SAMA) based system to identify the number and moving direction of vehicles travelling on a previously established route. In this paper, a SAMA-based acoustic monitoring system, including the system hardware architecture and algorithm mechanism, is designed as a single node sensor for the application of UGS. The algorithm is built on the varying trend of a vehicle's bearing angles around the closest point of approach (CPA). We demonstrate the effectiveness of our proposed method with our designed SAMA-based monitoring system in various experimental sites. The experimental results in harsh conditions validate the usefulness of our proposed UGS monitoring system.