Explore the vast range of titles available.

In this paper, machine learning (ML) algorithms are used for channel prediction in wireless communications. The performances of five ML algorithms are compared in terms of the prediction accuracy and the symbol error rate (SER) of different modulation schemes based on the prediction. The result shows that, for channel prediction, support vector machine (SVM) has the best performance in terms of accuracy and stability. For signal detection, SVM and linear regression (LR) have their own advantages in different ranges of signal to noise ratio (SNR). At high constellation size, ML methods give similar performances to existing scheme. From the numerical examples, the SERs based on SVM and LR can both reach lower than 10−3 in binary phase shift keying and 16‐ary quadrature amplitude modulation signalling, and can reach 1.13×10−2 $\\times 10^{-2}$and 4.28×10−3 $\\times 10^{-3}$in 16‐ary phase shift keying signalling respectively. In terms of prediction time, SVM is more efficient.

In general multiple medical devices orthogonal frequency-division multiplexing (OFDM) communication systems, all the interfering medical users are legitimate but will cause disturbance to the desired user. In this work, we evaluate three deep learning (DL) algorithms: fully connected deep neural networks, convolutional neural networks, and long short-term memory neural networks for signal processing and detection in uncoded multiple medical devices OFDM communications systems. The bit error rates (BER) of these DL methods are compared with the conventional linear minimum mean squared error (LMMSE) detector. Additionally, the relationships between the BER and signal-to-interference ratio, signal-to-noise ratio, the number of interferences, and modulation type are investigated. Numerical results show that DL methods outperform LMMSE under different multiple medical device interference situations and are robust when the wireless channel has high variability. Also, DL methods are proven to have strong anti-interference ability and are useful in multiple medical devices OFDM systems.

Wireless communications systems have become an irreplaceable part of daily life. In recent years, machine-learning (ML) algorithms have been widely applied in various industries. Particularly, they are often employed in wireless communications because of the correlations in the time and space dimensions of wireless signals, channels, and energy. In this thesis, the performances of different conventional ML algorithms, deep-learning (DL) algorithms, and adversarial attack methods are evaluated in a wireless powered communications (WPC) system, wireless channel prediction, and signal detection in a multiuser orthogonal frequency-division multiplexing (OFDM) communications system. Numerical results are presented to show the performances of these algorithms. First, for efficient operation of an energy harvester in a WPC system, four different ML algorithms are used to model the radio frequency energy data. Linear regression (LR) is found to have the highest accuracy and the most stable performance for energy prediction. Next, five conventional ML algorithms are compared for channel prediction and further signal detection based on the prediction. The support vector machine (SVM) is found to have the best performance in terms of prediction accuracy and stability. For signal detection, SVM and LR give similar or even better performances to the existing scheme at high constellation size. Then, three DL algorithms are proposed for signal detection in an uncoded multiuser OFDM communications system. Additionally, the relationships between the bit error rate (BER) and different factors are investigated. The DL methods outperform linear minimum mean-squared error, and they are robust when the channel has a high variability. Finally, different attack algorithms are evaluated against a DL-based multiuser OFDM detector. The BERs under these attack methods show that the perturbation efficiency of adversarial attacks is higher than general multiuser interference. Virtual adversarial method and the zeroth-order-optimization attack are the most efficient among the white- and black-box methods, respectively.

External manipulation of emission colour is of significance for scientific research and applications, however, the general stimulus-responsive colour modulation method requires both stringent control of microstructures and continously adjustment of particular stimuli conditions. Here, we introduce pathways to manipulate the kinetics of time evolution of both intensity and spectral characteristics of X-ray excited afterglow (XEA) by regioselective doping of lanthanide activators in core-shell nanostructures. Our work reported here reveals the following phenomena: 1. The XEA intensities of multiple lanthanide activators are significantly enhanced via incorporating interstitial Na + ions inside the nanocrystal structure. 2. The XEA intensities of activators exhibit diverse decay rates in the core and the shell and can largely be tuned separately, which enables us to realize a series of core@shell NPs featuring distinct time-dependent afterglow colour evolution. 3. A core/multi-shell NP structure can be designed to simultaneously generate afterglow, upconversion and downshifting to realize multimode time-dependent multicolour evolutions. These findings can promote the development of superior XEA and plentiful spectral manipulation, opening up a broad range of applications ranging from multiplexed biosensing, to high-capacity information encryption, to multidimensional displays and to multifunctional optoelectronic devices. X-ray activated afterglow nanomaterials are desirable components for advanced optoelectronic applications. Here, the authors present pathways to modulate the stimulus-responsive color emissions in lanthanide-doped fluoride core-shell nanoparticles.

Background and Purpose As a part of the natural course of ischemic stroke, hemorrhage transformation (HT) is also a serious complication after reperfusion treatment, which may affect the prognosis of ischemic stroke patients. White matter lesion (WML) refers to focal lesions on neuroimaging and has been suggested to indicate a higher risk of HT. This systematic review and meta-analysis aimed to summarize current evidence on the relation between WML and HT. Methods This systematic review was prepared with reference to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). We searched PubMed, Embase, Web of Science, and Cochrane Library for published studies about WML and HT in ischemic stroke patients. Odds ratios (ORs) and 95% confidence intervals (CIs) from eligible studies were combined to quantify the association between WML severity and the risk of HT. And the descriptive analysis was adopted to evaluate the influence of different WML distributions on predicting HT. Results A total of 2303 articles were identified after removing duplicates through database searching and 41 studies were included in our final analysis. The meta-analysis showed that WML presence was associated with HT (OR = 1.62, 95%CI 1.08-2.43, p = 0.019) and symptomatic intracerebral hemorrhage (sICH) (OR = 1.64, 95%CI 1.17-2.30, p = 0.004), and moderate-to-severe WML indicated a higher risk of HT (OR = 2.03, 95%CI 1.33-3.12, p = 0.001) and sICH (OR = 1.92, 95%CI 1.31-2.81, p < 0.001), respectively. Risk effects of increasing WML severity on both HT and ICH were revealed by the dose-response meta-analysis. In addition, both periventricular (5 of 7 articles) and deep (5 of 6 articles) WML were shown to be associated with HT. Conclusions WML is associated with overall HT and sICH in ischemic stroke patients, and more severe WML indicates a higher risk of HT and sICH. Besides, both periventricular WML and deep WML could be risk factors for HT.

(1) Introduction: Symptom spectrum can be of great diversity and heterogeneity in mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS) patients in clinical practice. Here, we report a case of MELAS presenting asymptomatic refractory hypotension with m.3243 A>G mutation. (2) Case representation: A 51-year-old male patient presented with a headache, vertigo, and difficulty in expression and understanding. The magnetic resonance imaging of the brain revealed an acute stroke-like lesion involving the left temporoparietal lobe. A definitive diagnosis of MELAS was given after the genetic test identified the chrM-3243 A>G mutation. The patient suffered recurrent stroke-like episodes in the 1-year follow-up. Notably, refractory hypotension was observed during hospitalizations, and no significant improvement in blood pressure was found after continuous use of vasopressor drugs and fluid infusion therapy. (3) Conclusions: We report a case of refractory hypotension which was unresponsive to fluid infusion therapy found in a patient with MELAS. Our case suggests that comprehensive management should be paid attention to during treatment. A further study on the pathological mechanism of the multisystem symptoms in MELAS would be beneficial to the treatment of patients.

INTRODUCTION Regional brain iron dyshomeostasis is observed in cerebral small vessel disease (cSVD) and other neurodegeneration processes. However, its spatial patterns, cognitive impact, and underlying pathological mechanisms remain unclear. METHODS Voxel‐based analysis of quantitative susceptibility mapping (QSM) was used to detect regional susceptibility changes, and their correlations with cognitive function were assessed using linear regression. We combined the microarray dataset from the Allen Human Brain Atlas (AHBA) to explore the pathological mechanisms of iron deposition patterns. RESULTS A total of 87 cSVD patients and 80 controls were included in the study. Increased QSM values in the bilateral putamen and caudate were associated with cognitive decline in cSVD. Gene set enrichment analysis revealed the enrichment of gene sets related to central nervous system integrity. DISCUSSION Iron deposition in deep gray matter may indicate cognitive changes in cSVD and could be linked to the disruption of brain structural and functional integrity. Highlights Increased susceptibility values, indicating focal iron deposition, were observed in the deep gray matter of patients with cerebral small vessel disease (cSVD). Regional iron concentration in the deep gray nuclei was associated with cognitive impairment in cSVD patients. Imaging transcriptomics suggests that cSVD‐related iron deposition is linked to the structural and functional integrity of the brain. An open‐source script for imaging transcriptomics focusing on regional gene expression was developed and proposed.

Background and Objectives Thalamic infarction (TI) can lead to gait disturbances (GDs), even in the absence of significant motor impairments. Understanding the characteristics of GDs in TI patients is crucial for developing targeted rehabilitation strategies. Nonetheless, very little is known about the detailed gait changes in TI patients. This study aimed to investigate and characterize these parameters in TI patients. Methods Ninety participants, including 45 subacute TI patients and 45 age‐sex‐matched healthy controls, were cross‐sectionally and consecutively included from West China Hospital, Sichuan University. A detailed set of spatiotemporal gait analyses was performed with forty‐one parameters as output, evaluated using the “ReadyGo” three‐dimensional motion balance testing system. Additionally, we analyzed the correlation between cerebral small vessel disease (CSVD) and gait parameters in TI and healthy controls (HC) and performed a Fisher z‐test to determine whether there was a significant difference. Results Variability, stride length, stride speed, and swing velocity significantly differed in the affected and unaffected sides of TI patients. TI patients exhibited differences in thirty‐eight gait parameters compared to controls. Coordination analysis revealed impairments in the timed up and go test, with longer total time, turn time, stand‐up time, and reduced stride speed. Additionally, deficits were noted in the Heel‐Knee‐Shin test and Finger‐Nose test. However, no differences were found in Romberg's test. Balance assessment showed variations in sit time, torso rocking degree, torso forward roll degree, and walking speed. The correlation between gait parameters and CSVD in TI and HC is presented. Additionally, it was found that total burden leads to a decrease in step width in TI patients and increases trunk sway degree in the tandem stance test in TI. Conclusion This study demonstrates distinct spatiotemporal gait impairment patterns, coordination, and balance deficits in TI patients. Additionally, our findings suggest that the mechanisms underlying GDs may differ between TI patients and HC in relation to CSVD. These findings emphasize the need for personalized rehabilitation strategies to target these specific GDs in TI patients. This study utilizes ReadyGo for the quantitative analysis of gait in TI patients and HC without obvious motor impairments, comparing differences between the unaffected and affected sides of TI patients, as well as between TI patients and HC, and reports the baseline gait parameters of TI patients.

During the healing process, the functional gradient attachment of the rotator cuff (RC) tendon-bone interface fails to regenerate, which severely impedes load transfer and stress dissipation, thereby increasing the risk of retears. As a result, the treatment of rotator cuff tears remains a significant clinical challenge. In this study, a dual-crosslinked hyaluronic acid/polyethylene glycol (HA/PEG) hydrogel scaffold was synthesized using hyaluronic acid and polyethylene glycol as base materials. Manganese-doped mesoporous silica nanoparticles (Mn-MSN) were incorporated into the hydrogel system to fabricate a manganese-based mesoporous silica nanoparticle-loaded dual-crosslinked hydrogel (Mn-MSN@Gel). The physicochemical properties of Mn-MSN@Gel, including porosity, elemental distribution, mechanical properties, biodegradability, and biocompatibility, were systematically characterized. The ion release profiles of Si and Mn were evaluated to assess sustained delivery. Rheological properties and self-healing capabilities were examined to determine injectability and in vivo stability. In vitro, the effects of Mn-MSN@Gel on cell migration, proliferation, and differentiation were assessed using rat bone marrow mesenchymal stem cells (rat-BMSCs) and tendon-derived stem cells (rat-TDSCs). The expression of osteogenic, tenogenic, oxidative stress-related, and inflammatory cytokine genes was analyzed. In vivo, a rat rotator cuff repair model was established to evaluate the biomechanical properties and tissue regeneration at the tendon-bone interface (TBI) following Mn-MSN@Gel injection. Characterization demonstrated that Mn-MSN@Gel possesses a porous three-dimensional structure with uniform distribution of silicon, oxygen, and manganese elements, enabling sustained and slow release of Si and Mn ions. Additionally, the composite material exhibited excellent mechanical properties, biodegradability, and biocompatibility, while promoting cell migration/proliferation and accelerating regeneration of the tendon-bone interface. Mn-MSN@Gel enhanced the expression of osteogenic differentiation genes (Runx2, Alp, Sox9) in rat-BMSCs, upregulated tenogenic differentiation markers (Scx, Tnmd, Col3a1), and downregulated Mmp3 expression in rat-TDSCs. Furthermore, Mn-MSN@Gel modulated genes related to oxidative stress (Nrf2, Gpx4, Sod2) and inflammatory cytokines (IL-6, IL-10, Tnf-α), exhibiting anti-inflammatory effects and alleviating oxidative stress damage. In the rat rotator cuff repair model, Mn-MSN@Gel injection significantly improved postoperative biomechanical properties and promoted tissue regeneration at the TBI. The self-healing and injectable properties of Mn-MSN@Gel ensure precise delivery and stable integration in vivo. By combining mechanical support with sustained release of bioactive ions, Mn-MSN@Gel provides a comprehensive therapeutic strategy for regenerative repair of the tendon-bone interface. Its biocompatibility and bioactivity facilitate cell recruitment, migration, and lineage-specific differentiation, which are crucial for reconstructing the functional gradient structure of the TBI. The anti-inflammatory and antioxidant effects further contribute to a favorable healing microenvironment. Overall, these findings indicate that Mn-MSN@Gel is a foundational biomaterial with significant therapeutic potential for enhancing structural regeneration and functional recovery of the TBI following rotator cuff injury.

Background and objective Neuro‐ophthalmologic symptoms and retinal changes have been increasingly observed following thalamic stroke, and there is mounting evidence indicating distinct alterations occurring in the vision‐related functional network. However, the intrinsic correlations between these changes are not yet fully understood. Our objective was to explore the altered patterns of functional network connectivity and retina parameters, and their correlations with visual performance in patients with thalamic stroke. Methods We utilized resting‐state functional MRI to obtain multi‐modular functional connectivity (FC), and optical coherence tomography‐angiography to measure various retina parameters, such as the retinal nerve fiber layer (RNFL), ganglion cell‐inner plexiform layer (GCIPL), superficial vascular complex (SVC), and deep vascular complex. Visual acuity (VA) was used as a metric for visual performance. Results We included 46 patients with first‐ever unilateral thalamic stroke (mean age 59.74 ± 10.02 years, 33 males). Significant associations were found between FC of attention‐to‐default mode and SVC, RNFL, and GCIPL, as well as between FC of attention‐to‐visual and RNFL (p < .05). Both RNFL and GCIPL exhibited significant associations with FC of visual‐to‐visual (p < .05). Only GCIPL showed an association with VA (p = .038). Stratified analysis based on a disease duration of 6 months revealed distinct and significant linking patterns in multi‐modular FC and specific retina parameters, with varying correlations with VA in each subgroup. Conclusion These findings provide valuable insight into the neural basis of the associations between brain network dysfunction and impaired visual performance in patients with thalamic stroke. Our novel findings have the potential to inform future targeted and individualized therapies. However, further comprehensive studies are necessary to validate our results.