Multiple Magneto-Optical Microrobotic Collectives with Selective Control in Three Dimensions Under Water.

Inspired by natural swarms, various methods are developed to create artificial magnetic microrobotic collectives. However, these magnetic collectives typically receive identical control inputs from a common external magnetic field, limiting their ability to operate independently. And they often rely on interfaces or boundaries for controlled movement, posing challenges for independent, three-dimensional(3D) navigation of multiple magnetic collectives. To address this challenge, self-assembled microrobotic collectives are proposed that can be selectively actuated in a combination of external magnetic and optical fields. By harnessing both actuation methods, the constraints of single actuation approaches are overcome. The magnetic field excites the self-assembly of colloids and maintains the self-assembled microrobotic collectives without disassembly, while the optical field drives selected microrobotic collectives to perform different tasks. The proposed magnetic-photo microrobotic collectives can achieve independent position and path control in the two-dimensional (2D) plane and 3D space. With this selective control strategy, the microrobotic collectives can cooperate in convection and mixing the dye in a confined space. The results present a systematic approach for realizing selective control of multiple microrobotic collectives, which can address multitasking requirements in complex environments.

The association of HBV infection and head and neck cancer: a systematic review and meta-analysis.

BACKGROUND: Hepatitis B virus (HBV) infections is an important public health problem worldwide and closely affect extrahepatic cancer. Several recent studies have investigated the relationship between HBV infection and head and neck cancer (HNC), but their findings were inconsistent.In order to address the limitations of small sample sizes, we conducted a meta-analysis to assess the association between HBV and HNC. METHODS: We systematically searched PubMed, Web of Science, Embase, Scopus, Cochrane Library, and China National Knowledge Infrastructure from inception to August 2023. Original articles published as a case-control or cohort study were included. HBV infection was identified by HBsAg, HBV DNA or ICD codes. Review articles, meeting abstracts, case reports, communications, editorials and letters were excluded, as were studies in a language other than English or Chinese. According to the MOOSE guidelines, frequencies reported for all dichotomous variables were extracted by two reviewers independently. Similarly, the outcomes of OR, RR or HR, and 95% CIs after adjusting for age and gender were collected. RESULTS: Thirteen relevant studies and 58,006 patients with HNC were included. Our analysis revealed a positive correlation between HBV and HNC (OR = 1.50; 95% CI: 1.28-1.77). After adjusting for age and gender, the similar result (OR = 1.30; 95% CI: 1.10-1.54) was obtained. Subgroup analysis further demonstrated a significant association between HBV infection and oral cancer (OR = 1.24; 95% CI: 1.05-1.47), as well as nasopharyngeal carcinoma (OR = 1.41; 95% CI: 1.26-1.58). However, due to the limited number of studies included, the statistical significance was not reached for cancer of the oropharynx (OR = 1.82; 95% CI: 0.66-5.05), hypopharynx (OR = 1.33; 95% CI: 0.88-2.00), and larynx (OR = 1.25; 95% CI: 0.69-2.24) after adjusting for age and gender. When excluding the interference of HIV/HCV, smoking and alcohol use, the final outcome (OR = 1.17; 95% CI: 1.01-1.35) got the same conclusion. CONCLUSIONS: Our study confirmed a positive relationship between HNC, specifically oral cancer and nasopharyngeal carcinoma, and HBV infection. However, further investigation is required at the molecular level to gather additional evidence in HNC.

Inference of dynamic systems from noisy and sparse data via manifold-constrained Gaussian processes.

Parameter estimation for nonlinear dynamic system models, represented by ordinary differential equations (ODEs), using noisy and sparse data, is a vital task in many fields. We propose a fast and accurate method, manifold-constrained Gaussian process inference (MAGI), for this task. MAGI uses a Gaussian process model over time series data, explicitly conditioned on the manifold constraint that derivatives of the Gaussian process must satisfy the ODE system. By doing so, we completely bypass the need for numerical integration and achieve substantial savings in computational time. MAGI is also suitable for inference with unobserved system components, which often occur in real experiments. MAGI is distinct from existing approaches as we provide a principled statistical construction under a Bayesian framework, which incorporates the ODE system through the manifold constraint. We demonstrate the accuracy and speed of MAGI using realistic examples based on physical experiments.

Downregulation of miR-892b inhibits the progression of osteoarthritis via targeting cyclin D1 and cyclin D2.

BACKGROUNDS: Osteoarthritis (OA) is an orthopedic inflammatory disease which can cause functional disability and chronic pain. MiRNAs are known to play important roles in OA. To identify the targets for the treatment of OA, bioinformatics analysis was performed to explore differentially expressed miRNAs between OA and normal samples. METHODS: Bioinformatics analysis was conducted to identify differentially expressed miRNAs. To mimic OA in vitro, primary chondrocytes were stimulated with IL-1ß. Meanwhile, flow cytometry was performed to detect the cell apoptosis and cycle distribution. In addition, protein and mRNA expressions were detected by Western blot and RT-qPCR, respectively. Finally, in vivo model of OA was constructed to investigate the function of miR-892b in OA. RESULTS: The data indicated that miR-892b was identified to be upregulated in OA samples. Additionally, miR-892b antagomir markedly reversed IL-1ß-induced growth decline of chondrocytes via inhibiting the apoptosis. IL-1ß notably elevated the expressions of MMP1 and MMP13 and downregulated the level of Aggrecan in chondrocytes, while miR-892b antagomir reversed these phenomena. Meanwhile, cyclin D1 and cyclin D2 were the direct targets of miR-892b. In addition, IL-1ß-induced G1 phase arrest in chondrocytes was partially abolished by of miR-892b antagomir. In vivo study indicated miR-892b antagomir could significantly alleviate the symptom of OA in a rat model. CONCLUSION: MiR-892b antagomir inhibits the progression of OA via targeting Cyclin D1 and Cyclin D2. Thus, our finding might supply a novel target for OA treatment.

Tire Speckle Interference Bubble Defect Detection Based on Improved Faster RCNN-FPN.

With the development of neural networks, object detection based on deep learning is developing rapidly, and its applications are gradually increasing. In the tire industry, detecting speckle interference bubble defects of tire crown has difficulties such as low image contrast, small object scale, and large internal differences of defects, which affect the detection precision. To solve these problems, we propose a new feature pyramid network based on Faster RCNN-FPN. It can fuse features across levels and directions to improve small object detection and localization, and increase object detection precision. The method has proven its effectiveness through cross-validation experiments. On a tire crown bubble defect dataset, the mAP [0.5:0.95] increased by 2.08% and the AP0.5 increased by 2.4% over the original network. The results show that the improved network significantly improves detecting tire crown bubble defects.

Unmasking the Actual COVID-19 Case Count.

This report presents a novel approach to estimate the total number of COVID-19 cases in the United States, including undocumented infections, by combining the Centers for Disease Control and Prevention's influenza-like illness surveillance data with aggregated prescription data. We estimated that the cumulative number of COVID-19 cases in the United States by 4 April 2020 was > 2.5 million.

Pre-existing autoimmune disease and the risk of immune-related adverse events among patients receiving checkpoint inhibitors for cancer.

INTRODUCTION: Patients with pre-existing autoimmune diseases have been excluded from clinical trials of immune checkpoint inhibitors (ICIs) for cancer. Real-world evidence is necessary to understand ICI safety in this population. METHODS: Patients treated with ICIs from 2011 to 2017 were identified using data from a large health insurer. Outcomes included time to (1) any hospitalization; (2) any hospitalization with an irAE diagnosis; and (3) outpatient corticosteroid treatment. The key exposure was pre-existing autoimmune disease, ascertained within 12 months before starting ICI treatment, and defined either by strict criteria (one inpatient or two outpatient claims at least 30 days apart) or relaxed criteria only (any claim, without meeting strict criteria). RESULTS: Of 4438 ICI-treated patients, pre-existing autoimmune disease was present among 179 (4%) by strict criteria, and another 283 (6%) by relaxed criteria only. In multivariable models, pre-existing autoimmune disease by strict criteria was not associated with all-cause hospitalization (HR 1.27, 95% CI 0.998-1.62), but it was associated with hospitalization with an irAE diagnosis (HR 1.81, 95% CI 1.21-2.71) and with corticosteroid treatment (HR 1.93, 95% CI 1.35-2.76). Similarly, pre-existing autoimmune disease by relaxed criteria only was not associated with all-cause hospitalization (HR 1.11, 95% CI 0.91-1.34), but was associated with hospitalization with an irAE diagnosis (HR 1.46, 95% CI 1.06-2.01) and corticosteroid treatment (HR 1.46, 95% CI 1.13-1.88). CONCLUSION: Pre-existing autoimmune disease was not associated with time to any hospitalization after initiating ICI therapy, but it was associated with a modest increase in hospitalizations with irAE diagnoses and with corticosteroid treatment.

An Identity Authentication Method Combining Liveness Detection and Face Recognition.

In this study, an advanced Kinect sensor was adopted to acquire infrared radiation (IR) images for liveness detection. The proposed liveness detection method based on infrared radiation (IR) images can deal with face spoofs. Face pictures were acquired by a Kinect camera and converted into IR images. Feature extraction and classification were carried out by a deep neural network to distinguish between real individuals and face spoofs. IR images collected by the Kinect camera have depth information. Therefore, the IR pixels from live images have an evident hierarchical structure, while those from photos or videos have no evident hierarchical feature. Accordingly, two types of IR images were learned through the deep network to realize the identification of whether images were from live individuals. In comparison with other liveness detection cross-databases, our recognition accuracy was 99.8% and better than other algorithms. FaceNet is a face recognition model, and it is robust to occlusion, blur, illumination, and steering. We combined the liveness detection and FaceNet model for identity authentication. For improving the application of the authentication approach, we proposed two improved ways to run the FaceNet model. Experimental results showed that the combination of the proposed liveness detection and improved face recognition had a good recognition effect and can be used for identity authentication.

Advances in using Internet searches to track dengue.

Dengue is a mosquito-borne disease that threatens over half of the world's population. Despite being endemic to more than 100 countries, government-led efforts and tools for timely identification and tracking of new infections are still lacking in many affected areas. Multiple methodologies that leverage the use of Internet-based data sources have been proposed as a way to complement dengue surveillance efforts. Among these, dengue-related Google search trends have been shown to correlate with dengue activity. We extend a methodological framework, initially proposed and validated for flu surveillance, to produce near real-time estimates of dengue cases in five countries/states: Mexico, Brazil, Thailand, Singapore and Taiwan. Our result shows that our modeling framework can be used to improve the tracking of dengue activity in multiple locations around the world.

Accurate estimation of influenza epidemics using Google search data via ARGO.

Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives. We propose an influenza tracking model, ARGO (AutoRegression with GOogle search data), that uses publicly available online search data. In addition to having a rigorous statistical foundation, ARGO outperforms all previously available Google-search-based tracking models, including the latest version of Google Flu Trends, even though it uses only low-quality search data as input from publicly available Google Trends and Google Correlate websites. ARGO not only incorporates the seasonality in influenza epidemics but also captures changes in people's online search behavior over time. ARGO is also flexible, self-correcting, robust, and scalable, making it a potentially powerful tool that can be used for real-time tracking of other social events at multiple temporal and spatial resolutions.

Using electronic health records and Internet search information for accurate influenza forecasting.

BACKGROUND: Accurate influenza activity forecasting helps public health officials prepare and allocate resources for unusual influenza activity. Traditional flu surveillance systems, such as the Centers for Disease Control and Prevention's (CDC) influenza-like illnesses reports, lag behind real-time by one to 2 weeks, whereas information contained in cloud-based electronic health records (EHR) and in Internet users' search activity is typically available in near real-time. We present a method that combines the information from these two data sources with historical flu activity to produce national flu forecasts for the United States up to 4 weeks ahead of the publication of CDC's flu reports. METHODS: We extend a method originally designed to track flu using Google searches, named ARGO, to combine information from EHR and Internet searches with historical flu activities. Our regularized multivariate regression model dynamically selects the most appropriate variables for flu prediction every week. The model is assessed for the flu seasons within the time period 2013-2016 using multiple metrics including root mean squared error (RMSE). RESULTS: Our method reduces the RMSE of the publicly available alternative (Healthmap flutrends) method by 33, 20, 17 and 21%, for the four time horizons: real-time, one, two, and 3 weeks ahead, respectively. Such accuracy improvements are statistically significant at the 5% level. Our real-time estimates correctly identified the peak timing and magnitude of the studied flu seasons. CONCLUSIONS: Our method significantly reduces the prediction error when compared to historical publicly available Internet-based prediction systems, demonstrating that: (1) the method to combine data sources is as important as data quality; (2) effectively extracting information from a cloud-based EHR and Internet search activity leads to accurate forecast of flu.

Detection of High-Frequency Oscillations from Intracranial EEG Data with Switching State Space Model.

High Frequency Oscillations (HFOs) is an important biomarker that can potentially pinpoint the epileptogenic zones (EZs). However, the duration of HFO is short with around 4 cycles, which might be hard to recognize when embedded within signals of lower frequency oscillatory background. In addition, annotating HFOs manually can be time-consuming given long-time recordings and up to hundreds of intracranial electrodes. We propose to leverage a Switching State Space Model (SSSM) to identify the HFOs events automatically and instantaneously without relying on extracting features from sliding windows. The effectiveness of the SSSM for HFOs detection is fully validated in the intracranial EEG recording from human subjects undergoing the presurgical evaluations and showed improved accuracy when capturing the HFOs occurrence and their duration.

Rejuvenating classical brain electrophysiology source localization methods with spatial graph Fourier filters for source extents estimation.

EEG/MEG source imaging (ESI) aims to find the underlying brain sources to explain the observed EEG or MEG measurement. Multiple classical approaches have been proposed to solve the ESI problem based on different neurophysiological assumptions. To support clinical decision-making, it is important to estimate not only the exact location of the source signal but also the extended source activation regions. Existing methods may render over-diffuse or sparse solutions, which limit the source extent estimation accuracy. In this work, we leverage the graph structures defined in the 3D mesh of the brain and the spatial graph Fourier transform (GFT) to decompose the spatial graph structure into sub-spaces of low-, medium-, and high-frequency basis. We propose to use the low-frequency basis of spatial graph filters to approximate the extended areas of brain activation and embed the GFT into the classical ESI methods. We validated the classical source localization methods with the corresponding improved version using GFT in both synthetic data and real data. We found the proposed method can effectively reconstruct focal source patterns and significantly improve the performance compared to the classical algorithms.

A novel method-based reinforcement learning with deep temporal difference network for flexible double shop scheduling problem.

This paper studies the flexible double shop scheduling problem (FDSSP) that considers simultaneously job shop and assembly shop. It brings about the problem of scheduling association of the related tasks. To this end, a reinforcement learning algorithm with a deep temporal difference network is proposed to minimize the makespan. Firstly, the FDSSP is defined as the mathematical model of the flexible job-shop scheduling problem joined to the assembly constraint level. It is translated into a Markov decision process that directly selects behavioral strategies according to historical machining state data. Secondly, the proposed ten generic state features are input into the deep neural network model to fit the state value function. Similarly, eight simple constructive heuristics are used as candidate actions for scheduling decisions. From the greedy mechanism, optimally combined actions of all machines are obtained for each decision step. Finally, a deep temporal difference reinforcement learning framework is established, and a large number of comparative experiments are designed to analyze the basic performance of this algorithm. The results showed that the proposed algorithm was better than most other methods, which contributed to solving the practical production problem of the manufacturing industry.

3D Motion Manipulation for Micro- and Nanomachines: Progress and Future Directions.

In the past decade, micro- and nanomachines (MNMs) have made outstanding achievements in the fields of targeted drug delivery, tumor therapy, microsurgery, biological detection, and environmental monitoring and remediation. Researchers have made significant efforts to accelerate the rapid development of MNMs capable of moving through fluids by means of different energy sources (chemical reactions, ultrasound, light, electricity, magnetism, heat, or their combinations). However, the motion of MNMs is primarily investigated in confined two-dimensional (2D) horizontal setups. Furthermore, three-dimensional (3D) motion control remains challenging, especially for vertical movement and control, significantly limiting its potential applications in cargo transportation, environmental remediation, and biotherapy. Hence, an urgent need is to develop MNMs that can overcome self-gravity and controllably move in 3D spaces. This review delves into the latest progress made in MNMs with 3D motion capabilities under different manipulation approaches, discusses the underlying motion mechanisms, explores potential design concepts inspired by nature for controllable 3D motion in MNMs, and presents the available 3D observation and tracking systems.

Effects of dietary supplementation with Radix Isatidis polysaccharide on egg quality, immune function, and intestinal health in hens.

This study aimed to investigate the effects of supplementing laying hen diets with Radix Isatidis Polysaccharide (RIPS) on egg quality, immune function, and intestinal health. The research was conducted using 288 Hyland Brown hens, which were randomly assigned to four dietary treatments: control (without RIPS), low dose (200 g/t), medium dose (500 g/t), and high dose (1000 g/t) of RIPS. Each dietary treatment was administered to eight replicates of nine hens for nine weeks. The results revealed that RIPS inclusion in diets significantly improved egg quality parameters such as egg shape index, yolk color, haugh unit, and protein height (P < 0.05). Additionally, RIPS supplementation enhanced immune function as evidenced by an alteration in serum biochemical parameters, an increase in the spleen index, and a decrease in the liver index. Further, an evaluation of intestinal health showed that RIPS fortified the intestinal barrier, thus increasing the population of beneficial intestinal bacteria and reducing the abundance of harmful ones. Such mechanisms promoted intestinal health, digestion, and nutrient absorption, ultimately leading to enhanced egg quality. In conclusion, supplementing laying hen diets with RIPS has been demonstrated to improve egg quality by boosting immunity and optimizing intestinal digestion and absorption.

Dual-Functional Laser-Guided Magnetic Nanorobot Collectives against Gravity for On-Demand Thermo-Chemotherapy of Peritoneal Metastasis.

Combining hyperthermic intraperitoneal chemotherapy with cytoreductive surgery is the main treatment modality for peritoneal metastatic (PM) carcinoma despite the off-target effects of chemotherapy drugs and the ineluctable side effects of total abdominal heating. Herein, a laser-integrated magnetic actuation system that actively delivers doxorubicin (DOX)-grafted magnetic nanorobot collectives to the tumor site in model mice for local hyperthermia and chemotherapy is reported. With intraluminal movements controlled by a torque-force hybrid magnetic field, these magnetic nanorobots gather at a fixed point coinciding with the position of the localization laser, moving upward against gravity over a long distance and targeting tumor sites under ultrasound imaging guidance. Because aggregation enhances the photothermal effect, controlled local DOX release is achieved under near-infrared laser irradiation. The targeted on-demand photothermal therapy of multiple PM carcinomas while minimizing off-target tissue damage is demonstrated. Additionally, a localization/treatment dual-functional laser-integrated magnetic actuation system is developed and validated in vivo, offering a potentially clinically feasible drug delivery strategy for targeting PM and other intraluminal tumors.

Microfluidic fabrication of monodisperse microcapsules with gas cores.

A facile strategy for efficient and continuous fabrication of monodisperse gas-core microcapsules with controllable sizes and excellent ultrasound-induced burst performances is developed based on droplet microfluidics and interfacial polymerization. Monodisperse gas-in-oil-in-water (G/O/W) double emulsion droplets with a gas core and monomer-contained oil layer are fabricated in the upstream of a microfluidic device as templates, and then water-soluble monomers are added into the aqueous continuous phase in the downstream to initiate rapid interfacial polymerization at the O/W interfaces to prepare monodisperse gas-in-oil-in-solid (G/O/S) microcapsules with gas cores. The sizes of both microbubbles and G/O/W droplet templates can be precisely controlled by adjusting the gas supply pressure and the fluid flow rates. Due to the very thin shells of G/O/S microcapsules fabricated via interfacial polymerization, the sizes of the resultant G/O/S microcapsules are almost the same as those of the G/O/W droplet templates, and the microcapsules exhibit excellent deformable properties and ultrasound-induced burst performances. The proposed strategy provides a facile and efficient route for controllably and continuously fabricating monodisperse microcapsules with gas cores, which are highly desired for biomedical applications.

Insect-Scale Biped Robots Based on Asymmetrical Friction Effect Induced by Magnetic Torque.

Multimodal and controllable locomotion in complex terrain is of great importance for practical applications of insect-scale robots. Robust locomotion plays a particularly critical role. In this study, a locomotion mechanism for magnetic robots based on asymmetrical friction effect induced by magnetic torque is revealed and defined. The defined mechanism overcomes the design constraints imposed by both robot and substrate structures, enabling the realization of multimodal locomotion on complex terrains. Drawing inspiration from human walking and running locomotion, a biped robot based on the mechanism is proposed, which not only exhibits rapid locomotion across substrates with varying friction coefficients but also achieves precise locomotion along patterned trajectories through programmed controlling. Furthermore, apart from its exceptional locomotive capabilities, the biped robot demonstrates remarkable robustness in terms of load-carrying and weight-bearing performance. The presented locomotion and mechanism herein introduce a novel concept for designing magnetic robots while offering extensive possibilities for practical applications in insect-scale robotics.

County augmented transformer for COVID-19 state hospitalizations prediction.

The prolonged COVID-19 pandemic has tied up significant medical resources, and its management poses a challenge for the public health care decision making. Accurate predictions of the hospitalizations are crucial for the decision makers to make informed decision for the medical resource allocation. This paper proposes a method named County Augmented Transformer (CAT). To generate accurate predictions of four-week-ahead COVID-19 related hospitalizations for every states in the United States. Inspired by the modern deep learning techniques, our method is based on a self-attention model (known as the transformer model) that is actively used in Natural Language Processing. Our transformer based model can capture both short-term and long-term dependencies within the time series while enjoying computational efficiency. Our model is a data based approach that utilizes the publicly available information including the COVID-19 related number of confirmed cases, deaths, hospitalizations data, and the household median income data. Our numerical experiments demonstrate the strength and the usability of our model as a potential tool for assisting the medical resources allocation.