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.

Multi-Modal Electrophysiological Source Imaging With Attention Neural Networks Based on Deep Fusion of EEG and MEG.

The process of reconstructing underlying cortical and subcortical electrical activities from Electroencephalography (EEG) or Magnetoencephalography (MEG) recordings is called Electrophysiological Source Imaging (ESI). Given the complementarity between EEG and MEG in measuring radial and tangential cortical sources, combined EEG/MEG is considered beneficial in improving the reconstruction performance of ESI algorithms. Traditional algorithms mainly emphasize incorporating predesigned neurophysiological priors to solve the ESI problem. Deep learning frameworks aim to directly learn the mapping from scalp EEG/MEG measurements to the underlying brain source activities in a data-driven manner, demonstrating superior performance compared to traditional methods. However, most of the existing deep learning approaches for the ESI problem are performed on a single modality of EEG or MEG, meaning the complementarity of these two modalities has not been fully utilized. How to fuse the EEG and MEG in a more principled manner under the deep learning paradigm remains a challenging question. This study develops a Multi-Modal Deep Fusion (MMDF) framework using Attention Neural Networks (ANN) to fully leverage the complementary information between EEG and MEG for solving the ESI inverse problem, which is termed as MMDF-ANN. Specifically, our proposed brain source imaging approach consists of four phases, including feature extraction, weight generation, deep feature fusion, and source mapping. Our experimental results on both synthetic dataset and real dataset demonstrated that using a fusion of EEG and MEG can significantly improve the source localization accuracy compared to using a single-modality of EEG or MEG. Compared to the benchmark algorithms, MMDF-ANN demonstrated good stability when reconstructing sources with extended activation areas and situations of EEG/MEG measurements with a low signal-to-noise ratio.

Individual and collective manipulation of multifunctional bimodal droplets in three dimensions.

Spatiotemporally controllable droplet manipulation is vital across numerous applications, particularly in miniature droplet robots known for their exceptional deformability. Despite notable advancements, current droplet control methods are predominantly limited to two-dimensional (2D) deformation and motion of an individual droplet, with minimal exploration of 3D manipulation and collective droplet behaviors. Here, we introduce a bimodal actuation strategy, merging magnetic and optical fields, for remote and programmable 3D guidance of individual ferrofluidic droplets and droplet collectives. The magnetic field induces a magnetic dipole force, prompting the formation of droplet collectives. Simultaneously, the optical field triggers isothermal changes in interfacial tension through Marangoni flows, enhancing buoyancy and facilitating 3D movements of individual and collective droplets. Moreover, these droplets can function autonomously as soft robots, capable of transporting objects. Alternatively, when combined with a hydrogel shell, they assemble into jellyfish-like robots, driven by sunlight. These findings present an efficient strategy for droplet manipulation, broadening the capabilities of droplet-based robotics.

Dietary silymarin improves performance by altering hepatic lipid metabolism and cecal microbiota function and its metabolites in late laying hens.

BACKGROUND: Liver lipid dysregulation is one of the major factors in the decline of production performance in late-stage laying hens. Silymarin (SIL), a natural flavonolignan extracted from milk thistle, is known for its hepatoprotective and lipid-lowering properties in humans. This study evaluates whether SIL can provide similar benefits to late-stage laying hens. A total of 480 68-week-old Lohmann Pink laying hens were randomly assigned into 5 groups, each group consisting of 6 replicates with 16 hens each. The birds received a basal diet either without silymarin (control) or supplemented with silymarin at concentrations of 250, 500, 750, or 1,000 mg/kg (SIL250, SIL500, SIL750, SIL1000) over a 12-week period. RESULTS: The CON group exhibited a significant decline in laying rates from weeks 9 to 12 compared to the initial 4 weeks (P = 0.042), while SIL supplementation maintained consistent laying rates throughout the study (P > 0.05). Notably, the SIL500 and SIL750 groups showed higher average egg weight than the CON group during weeks 5 to 8 (P = 0.049). The SIL750 group had a significantly higher average daily feed intake across the study period (P < 0.05), and the SIL500 group saw a marked decrease in the feed-to-egg ratio from weeks 5 to 8 (P = 0.003). Furthermore, the SIL500 group demonstrated significant reductions in serum ALT and AST levels (P < 0.05) and a significant decrease in serum triglycerides and total cholesterol at week 12 with increasing doses of SIL (P < 0.05). SIL also positively influenced liver enzyme expression (FASN, ACC, Apo-VLDL II, FXR, and CYP7A1; P < 0.05) and altered the cecal microbiota composition, enhancing species linked to secondary bile acid synthesis. Targeted metabolomics identified 9 metabolites predominantly involved in thiamin metabolism that were significantly different in the SIL groups (P < 0.05). CONCLUSIONS: Our study demonstrated that dietary SIL supplementation could ameliorate egg production rate in late stage laying hens, mechanistically, this effect was via improving hepatic lipid metabolism and cecal microbiota function to achieve. Revealed the potentially of SIL as a feed supplementation to regulate hepatic lipid metabolism dysregulation. Overall, dietary 500 mg/kg SIL had the best effects.

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.

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.

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.

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.

Deep Attention Q-Network for Personalized Treatment Recommendation.

Tailoring treatment for severely ill patients is crucial yet challenging to achieve optimal healthcare outcomes. Recent advances in reinforcement learning offer promising personalized treatment recommendations. However, they often rely solely on a patient's current physiological state, which may not accurately represent the true health status of the patient. This limitation hampers policy learning and evaluation, undermining the effectiveness of the treatment. In this study, we propose Deep Attention Q-Network for personalized treatment recommendation, leveraging the Transformer architecture within a deep reinforcement learning framework to efficiently integrate historical observations of patients. We evaluated our proposed method on two real-world datasets: sepsis and acute hypotension patients, demonstrating its superiority over state-of-the-art methods. The source code for our model is available at https://github.com/stevenmsm/RL-ICU-DAQN.