Soft phototactic swimmer based on self-sustained hydrogel oscillator.

Oscillations are widely found in living organisms to generate propulsion-based locomotion often driven by constant ambient conditions, such as phototactic movements. Such environment-powered and environment-directed locomotions may advance fully autonomous remotely steered robots. However, most man-made oscillations require nonconstant energy input and cannot perform environment-dictated movement. Here, we report a self-sustained soft oscillator that exhibits perpetual and untethered locomotion as a phototactic soft swimming robot, remotely fueled and steered by constant visible light. This particular out-of-equilibrium actuation arises from a self-shadowing-enabled negative feedback loop inherent in the dynamic light-material interactions, promoted by the fast and substantial volume change of the photoresponsive hydrogel. Our analytical model and governing equation unveil the oscillation mechanism and design principle with key parameters identified to tune the dynamics. On this autonomous oscillator platform, we establish a broadly applicable principle for converting a continuous input into a discontinuous output. The modular design can be customized to accommodate various forms of input energy and to generate diverse oscillatory behaviors. The hydrogel oscillator showcases agile life-like omnidirectional motion in the entire three-dimensional space with near-infinite degrees of freedom. The large force generated by the powerful and long-lasting oscillation can sufficiently overcome water damping and effectively self-propel away from a light source. Such a hydrogel oscillator-based all-soft swimming robot, named OsciBot, demonstrated high-speed and controllable phototactic locomotion. This autonomous robot is battery free, deployable, scalable, and integratable. Artificial phototaxis opens broad opportunities in maneuverable marine automated systems, miniaturized transportation, and solar sails

Security Enhancement Mechanism Based on Contextual Authentication and Role Analysis for 2G-RFID Systems

The traditional Radio Frequency Identification (RFID) system, in which the information maintained in tags is passive and static, has no intelligent decision-making ability to suit application and environment dynamics. The Second-Generation RFID (2G-RFID) system, referred as 2G-RFID-sys, is an evolution of the traditional RFID system to ensure better quality of service in future networks. Due to the openness of the active mobile codes in the 2G-RFID system, the realization of conveying intelligence brings a critical issue: how can we make sure the backend system will interpret and execute mobile codes in the right way without misuse so as to avoid malicious attacks? To address this issue, this paper expands the concept of Role-Based Access Control (RBAC) by introducing context-aware computing, and then designs a secure middleware for backend systems, named Two-Level Security Enhancement Mechanism or 2L-SEM, in order to ensure the usability and validity of the mobile code through contextual authentication and role analysis. According to the given contextual restrictions, 2L-SEM can filtrate the illegal and invalid mobile codes contained in tags. Finally, a reference architecture and its typical application are given to illustrate the implementation of 2L-SEM in a 2G-RFID system, along with the simulation results to evaluate how the proposed mechanism can guarantee secure execution of mobile codes for the system

Analysis of Hot Points on Data Mining Research of Medical in Foreign Countries

To promote the current development of medical data mining research, a quantitative statistics and qualitative analysis of the papers in the field of medical data mining technologies were made with the methodology of bibliometric and knowledge mapping, which were enlisted in the database of Web of Science analyzing the general situation of the papers about data mining from several aspects: period sequences, subject funds, countries and regions, core authors and research institutions, the hotspots and research frontiers. Our analysis exposed that the research of data mining in medical showed a multi-disciplinary integration of the development trend, but high-yield leading author group has not yet formed. It is important to note that scholars should raise awareness of clinical medical data mining as well as explore new research directions for further studying

Magnetic Ratcheting Cytometry Towards Manafacturing Scale Separations Of “Best In Class” Cart-T Cells

Adoptive cell therapies taking advantage of engineered Chimeric Antigen Receptors (CAR) or T-Cell Receptors (TCR) have shown incredible potential as “living drugs” that achieve personalized immunotherapies for cancer patients. However, variations in T cell transduction efficiency during genetic modification can lead to widely varied levels of expression[1] (~2-orders of magnitude) which can possibly dilute therapeutic effectiveness and potentially contribute to off-tumor toxicity[2]. While research has shown that isolation of cell sub-populations with tightly controlled expression could lead to improved therapies[3], limitations of current cell separation technologies prevent implementation at manufacturing scale workflows. Quantitative separation techniques (e.g. fluorescence assisted cell separation-FACS) do not scale for production of therapeutic doses, and magnetic assisted cell separation (MACS) techniques do not allow precise selection of cell sub-populations based on surface expression. Because of these limitations, enrichment of “best in class” CAR-T/TCR sub-populations at manufacturing scale throughputs remains impractical and non-economical. [1] Chang ZL, Silver PA, Chen YY. Identification and selective expansion of functionally superior T cells expressing chimeric antigen receptors. J Transl Med. 2015;13:161. doi:10.1186/s12967-015-0519-8. [2] Carels N, Spinassé LB, Tilli TM, Tuszynski JA. Toward precision medicine of breast cancer. Theor Biol Med Model. 2016;13:7. doi:10.1186/s12976-016-0035-4. [3] Berger C, Jensen MC, Lansdorp PM, Gough M, Elliott C, Riddell SR. Adoptive transfer of effector CD8+ T cells derived from central memory cells establishes persistent T cell memory in primates. J Clin Investig 2008;118: 294–305. Please click Additional Files below to see the full abstract

Swashplateless-elevon Actuation for a Dual-rotor Tail-sitter VTOL UAV

In this paper, we propose a novel swashplateless-elevon actuation (SEA) for dual-rotor tail-sitter vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAVs). In contrast to the conventional elevon actuation (CEA) which controls both pitch and yaw using elevons, the SEA adopts swashplateless mechanisms to generate an extra moment through motor speed modulation to control pitch and uses elevons solely for controlling yaw, without requiring additional actuators. This decoupled control strategy mitigates the saturation of elevons' deflection needed for large pitch and yaw control actions, thus improving the UAV's control performance on trajectory tracking and disturbance rejection performance in the presence of large external disturbances. Furthermore, the SEA overcomes the actuation degradation issues experienced by the CEA when the UAV is in close proximity to the ground, leading to a smoother and more stable take-off process. We validate and compare the performances of the SEA and the CEA in various real-world flight conditions, including take-off, trajectory tracking, and hover flight and position steps under external disturbance. Experimental results demonstrate that the SEA has better performances than the CEA. Moreover, we verify the SEA's feasibility in the attitude transition process and fixed-wing-mode flight of the VTOL UAV. The results indicate that the SEA can accurately control pitch in the presence of high-speed incoming airflow and maintain a stable attitude during fixed-wing mode flight. Video of all experiments can be found in youtube.com/watch?v=Sx9Rk4Zf7sQComment: 8 pages, 13 figure

Predicting 1-, 3-, 5-, and 8-year all-cause mortality in a community-dwelling older adult cohort: relevance for predictive, preventive, and personalized medicine

Background: Population aging is a global public health issue involving increased prevalence of age-related diseases, and concomitant burden on medical resources and the economy. Ninety-two diseases have been identified as age-related, accounting for 51.3% of the global adult disease burden. The economic cost per capita for older people over 60 years is 10 times that of the younger population. From the aspects of predictive, preventive, and personalized medicine (PPPM), developing a risk-prediction model can help identify individuals at high risk for all-cause mortality and provide an opportunity for targeted prevention through personalized intervention at an early stage. However, there is still a lack of predictive models to help community-dwelling older adults do well in healthcare. Objectives: This study aims to develop an accurate 1-, 3-, 5-, and 8-year all-cause mortality risk-prediction model by using clinical multidimensional variables, and investigate risk factors for 1-, 3-, 5-, and 8-year all-cause mortality in community-dwelling older adults to guide primary prevention. Methods: This is a two-center cohort study. Inclusion criteria: (1) community-dwelling adult, (2) resided in the districts of Chaonan or Haojiang for more than 6 months in the past 12 months, and (3) completed a health examination. Exclusion criteria: (1) age less than 60 years, (2) more than 30 incomplete variables, (3) no signed informed consent. The primary outcome of the study was all-cause mortality obtained from face-to-face interviews, telephone interviews, and the medical death database from 2012 to 2021. Finally, we enrolled 5085 community-dwelling adults, 60 years and older, who underwent routine health screening in the Chaonan and Haojiang districts, southern China, from 2012 to 2021. Of them, 3091 participants from Chaonan were recruited as the primary training and internal validation study cohort, while 1994 participants from Haojiang were recruited as the external validation cohort. A total of 95 clinical multidimensional variables, including demographics, lifestyle behaviors, symptoms, medical history, family history, physical examination, laboratory tests, and electrocardiogram (ECG) data were collected to identify candidate risk factors and characteristics. Risk factors were identified using least absolute shrinkage and selection operator (LASSO) models and multivariable Cox proportional hazards regression analysis. A nomogram predictive model for 1-, 3-, 5- and 8-year all-cause mortality was constructed. The accuracy and calibration of the nomogram prediction model were assessed using the concordance index (C-index), integrated Brier score (IBS), receiver operating characteristic (ROC), and calibration curves. The clinical validity of the model was assessed using decision curve analysis (DCA). Results: Nine independent risk factors for 1-, 3-, 5-, and 8-year all-cause mortality were identified, including increased age, male, alcohol status, higher daily liquor consumption, history of cancer, elevated fasting glucose, lower hemoglobin, higher heart rate, and the occurrence of heart block. The acquisition of risk factor criteria is low cost, easily obtained, convenient for clinical application, and provides new insights and targets for the development of personalized prevention and interventions for high-risk individuals. The areas under the curve (AUC) of the nomogram model were 0.767, 0.776, and 0.806, and the C-indexes were 0.765, 0.775, and 0.797, in the training, internal validation, and external validation sets, respectively. The IBS was less than 0.25, which indicates good calibration. Calibration and decision curves showed that the predicted probabilities were in good agreement with the actual probabilities and had good clinical predictive value for PPPM. Conclusion: The personalized risk prediction model can identify individuals at high risk of all-cause mortality, help offer primary care to prevent all-cause mortality, and provide personalized medical treatment for these high-risk individuals from the PPPM perspective. Strict control of daily liquor consumption, lowering fasting glucose, raising hemoglobin, controlling heart rate, and treatment of heart block could be beneficial for improving survival in elderly populations

Thermal Properties of Carbon Nanotube–Copper Composites for Thermal Management Applications

Carbon nanotube–copper (CNT/Cu) composites have been successfully synthesized by means of a novel particles-compositing process followed by spark plasma sintering (SPS) technique. The thermal conductivity of the composites was measured by a laser flash technique and theoretical analyzed using an effective medium approach. The experimental results showed that the thermal conductivity unusually decreased after the incorporation of CNTs. Theoretical analyses revealed that the interfacial thermal resistance between the CNTs and the Cu matrix plays a crucial role in determining the thermal conductivity of bulk composites, and only small interfacial thermal resistance can induce a significant degradation in thermal conductivity for CNT/Cu composites. The influence of sintering condition on the thermal conductivity depended on the combined effects of multiple factors, i.e. porosity, CNTs distribution and CNT kinks or twists. The composites sintered at 600°C for 5 min under 50 MPa showed the maximum thermal conductivity. CNT/Cu composites are considered to be a promising material for thermal management applications