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.

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.

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.

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.

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.

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.

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.

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.

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.

Bioinspired self-assembled colloidal collectives drifting in three dimensions underwater.

Active matter systems feature a series of unique behaviors, including the emergence of collective self-assembly structures and collective migration. However, realizing collective entities formed by synthetic active matter in spaces without wall-bounded support makes it challenging to perform three-dimensional (3D) locomotion without dispersion. Inspired by the migration mechanism of plankton, we propose a bimodal actuation strategy in the artificial colloidal systems, i.e., combining magnetic and optical fields. The magnetic field triggers the self-assembly of magnetic colloidal particles to form a colloidal collective, maintaining numerous colloids as a dynamically stable entity. The optical field allows the colloidal collectives to generate convective flow through the photothermal effect, enabling them to use fluidic currents for 3D drifting. The collectives can perform 3D locomotion underwater, transit between the water-air interface, and have a controlled motion on the water surface. Our study provides insights into designing smart devices and materials, offering strategies for developing synthetic active matter capable of controllable collective movement in 3D space.

Untethered Micro/Nanorobots for Remote Sensing: Toward Intelligent Platform.

Untethered micro/nanorobots that can wirelessly control their motion and deformation state have gained enormous interest in remote sensing applications due to their unique motion characteristics in various media and diverse functionalities. Researchers are developing micro/nanorobots as innovative tools to improve sensing performance and miniaturize sensing systems, enabling in situ detection of substances that traditional sensing methods struggle to achieve. Over the past decade of development, significant research progress has been made in designing sensing strategies based on micro/nanorobots, employing various coordinated control and sensing approaches. This review summarizes the latest developments on micro/nanorobots for remote sensing applications by utilizing the self-generated signals of the robots, robot behavior, microrobotic manipulation, and robot-environment interactions. Providing recent studies and relevant applications in remote sensing, we also discuss the challenges and future perspectives facing micro/nanorobots-based intelligent sensing platforms to achieve sensing in complex environments, translating lab research achievements into widespread real applications.

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.

Horizontal and Vertical Coalescent Microrobotic Collectives Using Ferrofluid Droplets.

Many artificial miniature robotic collectives have been developed to overcome the inherent limitations of inadequate individual capabilities. However, the basic building blocks of the reported collectives are mainly in the solid state, where the morphological boundaries of internal individuals are clear and cannot genuinely merge. Miniature robotic collectives based on liquid units still need to be explored; such on-demand mergeable swarm systems are advantageous for adapting to the changing external environment. Here, a strategy to achieve a coalescent collective system we presented that exploits the ferrofluid droplets' splitting and coalescence properties to trigger the formation of horizontal multimodal and vertical gravity-resistant collectives and unveil pattern-enabled robotic functionalities. When subjected to a time-varying magnetic field, the droplet swarm exhibits a variety of morphologies ranging from horizontal collectives, including vortex-like, chain-like, and crystal-like patterns to vertical layer-upon-layer patterns. Using experiments and simulations, the formation and transformation of different morphological collectives are shown and their robust environmental adaptability are demonstrated. Potential applications of the multimodal droplet collectives are presented, including exploring an unknown environment, targeted object delivery, and fluid flow filtration in a lab-on-a-chip. This work may facilitate the design of microrobotic swarm systems and expand the range of materials for miniature robots.

Joint COVID-19 and influenza-like illness forecasts in the United States using internet search information.

BACKGROUND: As the prolonged COVID-19 pandemic continues, severe seasonal Influenza (flu) may happen alongside COVID-19. This could cause a "twindemic", in which there are additional burdens on health care resources and public safety compared to those occurring in the presence of a single infection. Amidst the raising trend of co-infections of the two diseases, forecasting both Influenza-like Illness (ILI) outbreaks and COVID-19 waves in a reliable and timely manner becomes more urgent than ever. Accurate and real-time joint prediction of the twindemic aids public health organizations and policymakers in adequate preparation and decision making. However, in the current pandemic, existing ILI and COVID-19 forecasting models face shortcomings under complex inter-disease dynamics, particularly due to the similarities in symptoms and healthcare-seeking patterns of the two diseases. METHODS: Inspired by the interconnection between ILI and COVID-19 activities, we combine related internet search and bi-disease time series information for the U.S. national level and state level forecasts. Our proposed ARGOX-Joint-Ensemble adopts a new ensemble framework that integrates ILI and COVID-19 disease forecasting models to pool the information between the two diseases and provide joint multi-resolution and multi-target predictions. Through a winner-takes-all ensemble fashion, our framework is able to adaptively select the most predictive COVID-19 or ILI signals. RESULTS: In the retrospective evaluation, our model steadily outperforms alternative benchmark methods, and remains competitive with other publicly available models in both point estimates and probabilistic predictions (including intervals). CONCLUSIONS: The success of our approach illustrates that pooling information between the ILI and COVID-19 leads to improved forecasting models than individual models for either of the disease.

Data from the internet enables the presence of infectious diseases such as influenza (flu) to be tracked and monitored. During the ongoing COVID-19 pandemic people will also be infected with flu, impacting health care providers. Predicting both COVID-19 and flu outbreaks in a timely manner enables health care providers and policymakers to prepare for the outbreaks. In this work, we develop a model to jointly predict cases of both COVID-19 and influenza-like illness that can be used at national and state levels in the USA. Our approach is more accurate than alternative similar approaches that predict cases of a single disease, showing the value of predicting the incidence of multiple diseases at the same time.

Estimating and Assessing Differential Equation Models with Time-Course Data.

Ordinary differential equation (ODE) models are widely used to describe chemical or biological processes. This Article considers the estimation and assessment of such models on the basis of time-course data. Due to experimental limitations, time-course data are often noisy, and some components of the system may not be observed. Furthermore, the computational demands of numerical integration have hindered the widespread adoption of time-course analysis using ODEs. To address these challenges, we explore the efficacy of the recently developed MAGI (MAnifold-constrained Gaussian process Inference) method for ODE inference. First, via a range of examples we show that MAGI is capable of inferring the parameters and system trajectories, including unobserved components, with appropriate uncertainty quantification. Second, we illustrate how MAGI can be used to assess and select different ODE models with time-course data based on MAGI's efficient computation of model predictions. Overall, we believe MAGI is a useful method for the analysis of time-course data in the context of ODE models, which bypasses the need for any numerical integration.

Exploiting ferrofluidic wetting for miniature soft machines.

Miniature magnetic soft machines could significantly impact minimally invasive robotics and biomedical applications. However, most soft machines are limited to solid magnetic materials, whereas further progress also relies on fluidic constructs obtained by reconfiguring liquid magnetic materials, such as ferrofluid. Here we show how harnessing the wettability of ferrofluids allows for controlled reconfigurability and the ability to create versatile soft machines. The ferrofluid droplet exhibits multimodal motions, and a single droplet can be controlled to split into multiple sub-droplets and then re-fuse back on demand. The soft droplet machine can negotiate changing terrains in unstructured environments. In addition, the ferrofluid droplets can be configured as a liquid capsule, enabling cargo delivery; a wireless omnidirectional liquid cilia matrix capable of pumping biofluids; and a wireless liquid skin, allowing multiple types of miniature soft machine construction. This work improves small magnetic soft machines' achievable complexity and boosts their future biomedical applications capabilities.

Designable microfluidic ladder networks from backstepping microflow analysis for mass production of monodisperse microdroplets.

Controllable mass production of monodisperse droplets plays a key role in numerous fields ranging from scientific research to industrial application. Microfluidic ladder networks show great potential in mass production of monodisperse droplets, but their design with uniform microflow distribution remains challenging due to the lack of a rational design strategy. Here an effective design strategy based on backstepping microflow analysis (BMA) is proposed for the rational development of microfluidic ladder networks for mass production of controllable monodisperse microdroplets. The performance of our BMA rule for rational microfluidic ladder network design is demonstrated by using an existing analogism-derived rule that is widely used for the design of microfluidic ladder networks as the control group. The microfluidic ladder network designed by the BMA rule shows a more uniform flow distribution in each branch microchannel than that designed by the existing rule, as confirmed by single-phase flow simulation. Meanwhile, the microfluidic ladder network designed by the BMA rule allows mass production of droplets with higher size monodispersity in a wider window of flow rates and mass production of polymeric microspheres from such highly monodisperse droplet templates. The proposed BMA rule provides new insights into the microflow distribution behaviors in microfluidic ladder networks based on backstepping microflow analysis and provides a rational guideline for the efficient development of microfluidic ladder networks with uniform flow distribution for mass production of highly monodisperse droplets. Moreover, the BMA method provides a general analysis strategy for microfluidic networks with parallel multiple microchannels for rational scale-up.

Probing Fast Transformation of Magnetic Colloidal Microswarms in Complex Fluids.

The rapidly transformed morphology of natural swarms enables fast response to environmental changes. Artificial microswarms can reconfigure their swarm patterns like natural swarms, which have drawn extensive attention due to their active adaptability in complex environments. However, as a prerequisite for biomedical applications of microswarms in confined environments, achieving on-demand control of pattern transformation rates remains a challenge. In this work, we report a strategy for optimizing pattern transformation rates of colloidal microswarms by coordinating the inner interactions. The influences of magnetic field parameters on pattern transformation rates are theoretically and experimentally studied, which elucidates the mechanism for optimal transformation rate control. The feasibility of the strategy is then validated in viscous Newtonian fluids and non-Newtonian biofluids. Moreover, the strategy is further validated in dynamic flow environments, exhibiting a promising future for practical applications in targeted delivery tasks with an optimal pattern transformation manner.

COVID-19 forecasts using Internet search information in the United States.

As the COVID-19 ravaging through the globe, accurate forecasts of the disease spread are crucial for situational awareness, resource allocation, and public health decision-making. Alternative to the traditional disease surveillance data collected by the United States (US) Centers for Disease Control and Prevention (CDC), big data from Internet such as online search volumes also contain valuable information for tracking infectious disease dynamics such as influenza epidemic. In this study, we develop a statistical model using Internet search volume of relevant queries to track and predict COVID-19 pandemic in the United States. Inspired by the strong association between COVID-19 death trend and symptom-related search queries such as "loss of taste", we combine search volume information with COVID-19 time series information for US national level forecasts, while leveraging the cross-state cross-resolution spatial temporal framework, pooling information from search volume and COVID-19 reports across regions for state level predictions. Lastly, we aggregate the state-level frameworks in an ensemble fashion to produce the final state-level 4-week forecasts. Our method outperforms the baseline time-series model, while performing reasonably against other publicly available benchmark models for both national and state level forecast.

COVID-19 hospitalizations forecasts using internet search data.

As the COVID-19 spread over the globe and new variants of COVID-19 keep occurring, reliable real-time forecasts of COVID-19 hospitalizations are critical for public health decisions on medical resources allocations. This paper aims to forecast future 2 weeks national and state-level COVID-19 new hospital admissions in the United States. Our method is inspired by the strong association between public search behavior and hospitalization admissions and is extended from a previously-proposed influenza tracking model, AutoRegression with GOogle search data (ARGO). Our LASSO-penalized linear regression method efficiently combines Google search information and COVID-19 related time series information with dynamic training and rolling window prediction. Compared to other publicly available models collected from COVID-19 forecast hub, our method achieves substantial error reduction in a retrospective out-of-sample evaluation from Jan 4, 2021, to Dec 27, 2021. Overall, we showed that our method is flexible, self-correcting, robust, accurate, and interpretable, making it a potentially powerful tool to assist healthcare officials and decision making for the current and future infectious disease outbreaks.