In this paper, a tunable mid-infrared metasurface based on VO
phase change material is proposed for temperature control. The proposed structure consisting of a VO
/SiO
/VO
cavity supports a thermally ...switchable Fabry-Perot-like resonance mode at the transparency window of the atmosphere. Theoretically, the radiative cooling power density of the proposed metasurface can be switched to four-fold as the device temperature is below/above the phase change temperature of VO
. Besides radiative cooling, a passive temperature control application based on this huge cooling power switching ability is theoretically demonstrated. We believe the proposed device can be applied for small radiative cooling and temperature control applications.
RLR-mediated type I IFN production plays a pivotal role in elevating host immunity for viral clearance and cancer immune surveillance. Here, we report that glycolysis, which is inactivated during RLR ...activation, serves as a barrier to impede type I IFN production upon RLR activation. RLR-triggered MAVS-RIG-I recognition hijacks hexokinase binding to MAVS, leading to the impairment of hexokinase mitochondria localization and activation. Lactate serves as a key metabolite responsible for glycolysis-mediated RLR signaling inhibition by directly binding to MAVS transmembrane (TM) domain and preventing MAVS aggregation. Notably, lactate restoration reverses increased IFN production caused by lactate deficiency. Using pharmacological and genetic approaches, we show that lactate reduction by lactate dehydrogenase A (LDHA) inactivation heightens type I IFN production to protect mice from viral infection. Our study establishes a critical role of glycolysis-derived lactate in limiting RLR signaling and identifies MAVS as a direct sensor of lactate, which functions to connect energy metabolism and innate immunity.
Display omitted
•Lactate inhibits RLR-mediated interferon production•This regulation occurs through direct sensing of lactate by MAVS•MAVS associates with hexokinase, but this association is disrupted by RIG-I•Targeting LDHA enhances type I IFN production and viral clearance
Lactate acts as a regulator of the adaptor MAVS, allowing a cross-regulation between antiviral signaling and energy metabolism
Akt: a key transducer in cancer Tsai, Pei-Jane; Lai, Yi-Hsin; Manne, Rajesh Kumar ...
Journal of biomedical science,
10/2022, Letnik:
29, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Growth factor signaling plays a pivotal role in diverse biological functions, such as cell growth, apoptosis, senescence, and migration and its deregulation has been linked to various human diseases. ...Akt kinase is a central player transmitting extracellular clues to various cellular compartments, in turn executing these biological processes. Since the discovery of Akt three decades ago, the tremendous progress towards identifying its upstream regulators and downstream effectors and its roles in cancer has been made, offering novel paradigms and therapeutic strategies for targeting human diseases and cancers with deregulated Akt activation. Unraveling the molecular mechanisms for Akt signaling networks paves the way for developing selective inhibitors targeting Akt and its signaling regulation for the management of human diseases including cancer.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Learning effectiveness is normally analyzed by data collection through tests or questionnaires. However, instant feedback is usually not available. Learners’ facial emotion and learning motivation ...has a positive relationship. Therefore, the system identifying learners’ facial emotions can provide feedback that teachers can understand students’ learning situation and provide help or improve teaching strategy. Studies have found that convolutional neural networks provide a good performance in basic facial emotion recognition. Convolutional neural networks do not require manual design features like traditional machine learning, they automatically learn the necessary features of the entire image. This article improves the FaceLiveNet network with low and high accuracy in basic emotion recognition, and proposes the framework of Dense_FaceLiveNet. We use Dense_FaceLiveNet for two-phases of transfer learning. First, from the relatively simple data JAFFE and KDEF basic emotion recognition model transferring to the FER2013 basic emotion dataset and obtained an accuracy of 70.02%. Secondly, using the FER2013 basic emotion recognition model transferring to learning emotion recognition model, the test accuracy rate is as high as 91.93%, which is 12.9% higher than the accuracy rate of 79.03% without using the transfer learning model, which proves that the use of transfer learning can effectively improve the recognition accuracy of learning emotion recognition model. In addition, in order to test the generalization ability of the Learning Emotion Recognition Model, videos recorded by students from a national university in Taiwan during class learning were used as test data. The original database of learning emotions did not consider that students would have exceptions such as over eyebrows, eyes closed and hand hold the chin etc. To improve this situation, after adding the learning emotion database to the images of the exceptions mentioned above, the model was rebuilt, and the recognition accuracy rate of the model was 92.42%. By comparing the output of maps, the rebuilt model does have the characteristics of success in learning images such as eyebrows, chins, and eyes closed. Furthermore, after combining all the students’ image data with the original learning emotion database, the model was rebuilt and obtained the accuracy rate reached 84.59%. The result proves that the Learning Emotion Recognition Model can achieve high recognition accuracy by processing the unlearned image through transfer learning. The main contribution is to design two-phase transfer learning for establishing the learning emotion recognition model and overcome the problem for small amounts of learning emotion data. Our experiment results have shown the performance improvement of two-phase transfer learning.
Display omitted
•Compare the applicability of various CNN architecture to basic emotion recognition.•UUse transfer learning to build learning emotion recognition model and verify the recognition effective.
Aims and objectives
To examine the effects of the two‐month breathing‐based walking intervention and its follow‐up on anxiety, depression, dyspnoea and quality of life in patients with chronic ...obstructive pulmonary disease.
Background
Mind–body‐related exercises improve bio‐psychological symptoms and quality of life in chronic diseases, but these improvements are not proven for chronic obstructive pulmonary disease.
Design
This was a randomised controlled study and applied the Consolidated Standards of Reporting Trials (CONSORT) statement.
Methods
Outpatients diagnosed with chronic obstructive pulmonary disease were recruited from a medical centre in Taiwan and randomly assigned to two groups. The walking group (n = 42) received breathing, meditation and walking for two months, and the control group (n = 42) did not. Data from the outcomes of anxiety, depression, dyspnoea and quality of life were collected at baseline and in Month 1, Month 2 and Month 3. Clinical trial registration was done (ClinicalTrials.gov.: NCT03388489).
Findings
The results showed significant changes in anxiety, depression, dyspnoea and quality of life in the walking group across three months, compared to those in the control group and at baseline.
Conclusion
This breathing‐based walking intervention is promising to achieve bio‐psychological well‐being for patients with chronic obstructive pulmonary disease.
Relevance to clinical practice
This breathing‐based walking, as a mind–body exercise, could serve as an evidence‐based nursing care that contributes to improving anxiety, depression, dyspnoea and quality of life in stable chronic obstructive pulmonary disease outpatients. The feasibility and acceptability of the breathing‐based walking were met the requirement of the chronic obstructive pulmonary disease outpatients, which could be considered as home‐based exercise.
The aim of this study was to investigate the functional networks in subjects with reversible cerebral vasoconstriction syndrome (RCVS) using resting-state functional magnetic resonance imaging ...(rs-fMRI).
We prospectively recruited patients with RCVS and healthy controls (HCs) between February 2017 and April 2021. The rs-fMRI data were analyzed using graph theory methods. We compared node-based global and regional topological metrics (Bundle 1) and network-based intranetwork and internetwork connectivity (Bundle 2) between RCVS patients and HCs. We also explored the associations of clinical and vascular (ie, the Lindegaard index, LI) parameters with significant rs-fMRI metrics.
A total of 104 RCVS patients and 93 HCs were included in the final analysis. We identified significantly decreased local efficiency of the left dorsal anterior insula (dAI; p = 0.0005) in RCVS patients within 30 days after disease onset as compared to HCs, which improved 1 month later. RCVS patients also had increased global efficiency (p = 0.009) and decreased average degree centrality (p = 0.045), clustering coefficient (p = 0.033), and assortativity values (p = 0.003) in node-based analysis. In addition, patients with RCVS had increased internetwork connectivity of the default mode network (DMN) with the salience (p = 0.027) and dorsal attention (p = 0.016) networks. Significant correlations between LI and regional local efficiency in left dAI (r
= -0.418, p = 0.042) was demonstrated.
The significantly lower local efficiency of the left dAI, suggestive of impaired central autonomic modulation, was negatively correlated with vasoconstriction severity, which is highly plausible for the pathogenesis of RCVS. ANN NEUROL 2023.
Rodent infestations are a common problem that can result in several issues, including diseases, damage to property, and crop loss. Conventional methods of controlling rodent infestations often ...involve using mousetraps and applying rodenticides manually, leading to high manpower expenses and environmental pollution. To address this issue, we introduce a system for remotely monitoring rodent infestations using Internet of Things (IoT) nodes equipped with Long Range (LoRa) modules. The sensing nodes wirelessly transmit data related to rodent activity to a cloud server, enabling the server to provide real-time information. Additionally, this approach involves using images to auxiliary detect rodent activity in various buildings. By capturing images of rodents and analyzing their behavior, we can gain insight into their movement patterns and activity levels. By visualizing the recorded information from multiple nodes, rodent control personnel can analyze and address infestations more efficiently. Through the digital and quantitative sensing technology proposed at this stage, it can serve as a new objective indicator before and after the implementation of medication or other prevention and control methods. The hardware cost for the proposed system is approximately USD 43 for one sensor module and USD 17 for one data collection gateway (DCG). We also evaluated the power consumption of the sensor module and found that the 3.7 V 18,650 Li-ion batteries in series can provide a battery life of two weeks. The proposed system can be combined with rodent control strategies and applied in real-world scenarios such as restaurants and factories to evaluate its performance.
Halide perovskites have many important optoelectronic properties, including high emission efficiency, high absorption coefficients, color purity, and tunable emission wavelength, which makes these ...materials promising for optoelectronic applications. However, the inability to precisely control large-scale patterned growth of halide perovskites limits their potential toward various device applications. Here, we report a patterning method for the growth of a cesium lead halide perovskite single crystal array. Our approach consists of two steps: (1) cesium halide salt arrays patterning and (2) chemical vapor transport process to convert salt arrays into single crystal perovskite arrays. Characterizations including energy-dispersive X-ray spectroscopy and photoluminescence have been employed to confirm the chemical compositions and the optical properties of the as-synthesized perovskite arrays. This patterning method enables the patterning of single crystal cesium lead halide perovskite arrays with tunable spacing (from 2 to 20 μm) and crystal size (from 200 nm to 1.2 μm) in high production yield (almost every pixel in the array is successfully grown with converted perovskite crystals). Our large-scale patterning method renders a platform for the study of fundamental properties and opportunities for perovskite-based optoelectronic applications.
Cancer stem cells (CSCs) play an important role in cancer treatment resistance and disease progression. Identifying an effective anti‐CSC agent may lead to improved disease control. We used ...CSC‐associated gene signatures to identify drug candidates that may inhibit CSC growth by reversing the CSC gene signature. Thiostrepton, a natural cyclic oligopeptide antibiotic, was the top‐ranked candidate. In non–small‐cell lung cancer (NSCLC) cells, thiostrepton inhibited CSC growth in vitro and reduced protein expression of cancer stemness markers, including CD133, Nanog and Oct4A. In addition, metastasis‐associated Src tyrosine kinase signalling, cell migration and epithelial‐to‐mesenchymal transition (EMT) were all inhibited by thiostrepton. Mechanistically, thiostrepton treatment led to elevated levels of tumour suppressor miR‐98. Thiostrepton combined with gemcitabine synergistically suppressed NSCLC cell growth and induced apoptosis. The inhibition of NSCLC tumours and CSC growth by thiostrepton was also demonstrated in vivo. Our findings indicate that thiostrepton, an established drug identified in silico, is an inhibitor of CSC growth and a potential enhancer of chemotherapy in NSCLC.
Objectives
To investigate the structural changes of hippocampus and amygdala and their relationships with migraine frequency and prognosis.
Methods
Hippocampus and amygdala volumes were measured by ...3-T brain magnetic resonance imaging (MRI) in 31 controls and 122 migraine patients who were categorized into eight groups by headache frequency: group 1 (1–2 headache days/month), 2 (3–4), 3 (5–7), 4 (8–10), 5 (11–14), 6 (15–19), 7 (20–24), and 8 (25–30). Headache frequency was reassessed 2 years later and a frequency reduction ≥50% was regarded a good outcome.
Results
Hippocampus and amygdala volumes fluctuated in patient groups but did not differ from the controls. In migraine patients, the bilateral hippocampus volumes peaked in group 3. The volumes and headache frequencies correlated positively in groups 2–3 on bilateral sides (L: r = 0.44, p = 0.007; R: r = 0.35, p = 0.037), and negatively in groups 3–7 on the left side (5–24 days/month; L: r = −0.31, p = 0.004) and groups 3–8 on the right side (r = −0.31, p = 0.002). The left amygdala volume also peaked in group 3, and correlated with headache frequency in groups 1–3 (r = 0.34, p = 0.020) and groups 3–6 (r = −0.30, p = 0.012). The volumetric changes of the right amygdala with headache frequency did not reach statistical significance. At 2-year follow-up, the right hippocampus volume was positively associated with a good migraine outcome after adjustment of headache frequency (OR 4.72, p = 0.024).
Conclusions
Hippocampus and amygdala display a structural plasticity linked to both headache frequency and clinical outcome of migraine.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
PDF
