Thickness-Dependent Gilbert Damping and Soft Magnetism in Metal/Co-Fe-B/Metal Sandwich Structure.

The achievement of the low Gilbert damping parameter in spin dynamic modulation is attractive for spintronic devices with low energy consumption and high speed. Metallic ferromagnetic alloy Co-Fe-B is a possible candidate due to its high compatibility with spintronic technologies. Here, we report thickness-dependent damping and soft magnetism in Co-Fe-B films sandwiched between two non-magnetic layers with Co-Fe-B films up to 50 nm thick. A non-monotonic variation of Co-Fe-B film damping with thickness is observed, which is in contrast to previously reported monotonic trends. The minimum damping and the corresponding Co-Fe-B thickness vary significantly among the different non-magnetic layer series, indicating that the structure selection significantly alters the relative contributions of various damping mechanisms. Thus, we developed a quantitative method to distinguish intrinsic from extrinsic damping via ferromagnetic resonance measurements of thickness-dependent damping rather than the traditional numerical calculation method. By separating extrinsic and intrinsic damping, each mechanism affecting the total damping of Co-Fe-B films in sandwich structures is analyzed in detail. Our findings have revealed that the thickness-dependent damping measurement is an effective tool for quantitatively investigating different damping mechanisms. This investigation provides an understanding of underlying mechanisms and opens up avenues for achieving low damping in Co-Fe-B alloy film, which is beneficial for the applications in spintronic devices design and optimization.

HM_ADET: a hybrid model for automatic detection of eyelid tumors based on photographic images.

BACKGROUND: The accurate detection of eyelid tumors is essential for effective treatment, but it can be challenging due to small and unevenly distributed lesions surrounded by irrelevant noise. Moreover, early symptoms of eyelid tumors are atypical, and some categories of eyelid tumors exhibit similar color and texture features, making it difficult to distinguish between benign and malignant eyelid tumors, particularly for ophthalmologists with limited clinical experience. METHODS: We propose a hybrid model, HM_ADET, for automatic detection of eyelid tumors, including YOLOv7_CNFG to locate eyelid tumors and vision transformer (ViT) to classify benign and malignant eyelid tumors. First, the ConvNeXt module with an inverted bottleneck layer in the backbone of YOLOv7_CNFG is employed to prevent information loss of small eyelid tumors. Then, the flexible rectified linear unit (FReLU) is applied to capture multi-scale features such as texture, edge, and shape, thereby improving the localization accuracy of eyelid tumors. In addition, considering the geometric center and area difference between the predicted box (PB) and the ground truth box (GT), the GIoU_loss was utilized to handle cases of eyelid tumors with varying shapes and irregular boundaries. Finally, the multi-head attention (MHA) module is applied in ViT to extract discriminative features of eyelid tumors for benign and malignant classification. RESULTS: Experimental results demonstrate that the HM_ADET model achieves excellent performance in the detection of eyelid tumors. In specific, YOLOv7_CNFG outperforms YOLOv7, with AP increasing from 0.763 to 0.893 on the internal test set and from 0.647 to 0.765 on the external test set. ViT achieves AUCs of 0.945 (95% CI 0.894-0.981) and 0.915 (95% CI 0.860-0.955) for the classification of benign and malignant tumors on the internal and external test sets, respectively. CONCLUSIONS: Our study provides a promising strategy for the automatic diagnosis of eyelid tumors, which could potentially improve patient outcomes and reduce healthcare costs.

A Wide-Band Magnetoelectric Sensor Based on a Negative-Feedback Compensated Readout Circuit.

Magnetoelectric (ME) sensors cannot effectively detect broadband magnetic field signals due to their narrow bandwidth, and existing readout circuits are unable to vary the bandwidth of the sensors. To expand the bandwidth, this paper introduces a negative-feedback readout circuit, fabricated by introducing a negative-feedback compensation circuit based on the direct readout circuit of the ME sensor. The negative-feedback compensation circuit contains a current amplifier, a feedback resistor, and a feedback coil. For this purpose, a Metglas/PVDF/Metglas ME sensor was prepared. Experimental measurements show that there is a six-fold difference between the maximum and minimum values of the ME voltage coefficients in the 6-39 kHz frequency band for the ME sensor without the negative-feedback compensation circuit when the sensor operates at the optimal bias magnetic field. However, the ME voltage coefficient in this band remains stable, at 900 V/T, after the charge amplification of the direct-reading circuit and the negative-feedback circuit. In addition, experimental results show that this negative-feedback readout circuit does not increase the equivalent magnetic noise of the sensor, with a noise level of 240 pT/√Hz in the frequency band lower than 25 kHz, 63 pT/√Hz around the resonance frequency of 30 kHz, and 620 pT/√Hz at 39 kHz. This paper proposes a negative-feedback readout circuit based on the direct readout circuit, which greatly increases the bandwidth of ME sensors and promotes the widespread application of ME sensors in the fields of broadband weak magnetic signal detection and DBS electrode positioning.

Effects of acupuncture on microglial polarization and the TLR4/TRIF/MyD88 pathway in a rat model of traumatic brain injury.

OBJECTIVE: Neuroinflammation caused by traumatic brain injury (TBI) can lead to neurological deficits. Acupuncture can inhibit neuroinflammation and promote nerve repair; however, the specific mechanism is still unclear. The purpose of this study was to explore whether acupuncture could modulate the M1 and M2 phenotypic polarization of microglia in a rat model of TBI via the toll-like receptor 4 (TLR4)/intracellular toll-interleukin-1 receptor (TIR) domain-containing adaptor inducing interferon-ß (TRIF)/myeloid differentiation factor 88 (MyD88) pathway. METHODS: A total of 90 adult male Sprague-Dawley (SD) rats, SPF grade, were randomly divided into a normal group, model group and acupuncture group. Each group was further divided into three subgroups (first, third, and fifth day groups) according to the treatment time (n = 10 rats/subgroup). We used the modified neurological severity score (mNSS) method to quantify neurological deficits before and after modeling. We used Nissl staining to observe the pathological changes in brain tissue, flow cytometry to detect the proportion of M1 and M2 polarized microglia in the injured area on the first, third and fifth day, and co-immunoprecipitation (Co-IP) to examine TLR4/TRIF/MyD88 expression in microglia on the first, third and fifth day, as well as expression of the amount of binding of TLR4 with TRIF and MyD88. RESULTS: Compared to the model group, mNSS in the acupuncture group gradually decreased and pathological morphology improved. The proportion of CD11b/CD86 positive cells was decreased, while that of CD11b/CD206 was increased in the acupuncture group. Expression of IP TLR4, IP TRIF and IP MyD88 also decreased in the acupuncture group. CONCLUSION: The results of this study demonstrate that one of the mechanisms through which acupuncture mitigates neuroinflammation and promotes nerve repair in TBI rats may be inhibition of M1 phenotypic polarization and promotion of M2 phenotypic polarization through inhibition of the TLR4/TRIF/MyD88 signaling pathway.

Acupuncture promotes nerve repair through the benign regulation of mTOR-mediated neuronal autophagy in traumatic brain injury rats.

AIMS: Recent investigations have already proved the neuroprotective efficacy of acupuncture in clinical practice in the treatment of neurological diseases, such as traumatic brain injury (TBI). Since growing evidence has suggested that neuronal autophagy was involved in multiple stages of TBI, this study aims to clarify the autophagy mediating mechanism underlying the neuroprotective effect of acupuncture in TBI rats. METHODS: Three experiments were carried out to detect changes in neuronal autophagy and identify the potential molecular mechanism underlying the neuroprotective effect of acupuncture for TBI treatment. Feeney's free-falling epidural impingement method was used to establish the moderate TBI rat model; modified neurological severity scoring (mNSS) was used for neurological recovery evaluation. Nissl and HE staining were used to examine the histopathological changes. Immunofluorescence was used to detect the LC3-positive cell rate. The transmission electron microscope (TEM) was used to investigate the morphology and quantity of autophagosomes. Western blotting was used to determine the protein expressions of LC3, p62, beclin1, mTOR, ULK1, p-mTOR, and p-ULK1. Quantitative real-time polymerase chain reaction (qRT-PCR) was used for gene expressions analysis of LC3 mRNA and p62 mRNA. Co-immunoprecipitation (CO-IP) method was used to identify the protein interaction of mTOR and ULK1. RESULTS: On Day 3 after TBI, acupuncture accelerated the removal of damaged cellular structures by promoting neuronal autophagy; on Day 7 and Day 14 after TBI, acupuncture inhibited neuronal autophagy, preventing excessive autophagy and thus alleviated nerve damage. In addition, the simultaneous treatment with 3-MA or rapamycin at different stages after TBI attenuated the effect of acupuncture. CONCLUSION: Acupuncture has a benign regulatory effect on neuronal autophagy in different stages of TBI, possibly through the mTOR/ULK1 pathway.

Effect of cultivation mode on bacterial and fungal communities of Dendrobium catenatum.

BACKGROUND: The orchid growth and development often associate with microbes. However, the interaction between plant performance and microbial communities within and surrounding plants is less understood. Dendrobium catenatum, which used to be an endangered orchid species, has become a billion dollar industry in China. Simulated natural cultivation modes, such as living tree epiphytic (LT) and cliff epiphytic (CE) cultivations, improve the production or quality of D. catenatum and contribute to the development of D. catenatum industry. In a previous study, morphological characteristics, anatomical structure, and main bioactive components (polysaccharides and ethanol-soluble extractives) of D. catenatum grown under LT and CE significantly differed from a facility cultivation mode, pot (PO) cultivation, were observed. Whether cultivation mode affects bacterial and fungal communities of D. catenatum, thereby affecting the chemical quality of this plant, need to be explored. RESULTS: Both three plant organs (leaf, stem, and root) and cultivating substrates obtained under three cultivation modes: living tree epiphytic (LT), cliff epiphytic (CE), and pot (PO) cultivation were examined by adopting high-throughput sequencing methods. Subsequently, bacterial and fungal correlations with D. catenatum main chemical components, stem polysaccharides and ethanol-soluble extractives and leaf phenols and flavonoids, were elucidated. The results showed that microbial communities of the plants and substrates are both influenced by the cultivation mode. However, the plants and their cultivating substrates exhibited different patterns of bacterial and fungal composition, with clearly distinguished dominant bacterial groups, but shared dominance among fungal groups. Bacteria and fungi differed in abundance, diversity, and community structure, depending on the cultivation environment and plant organ. Both bacterial and fungal communities were affected by cultivation mode and plant organ. In both plants and substrates, PO bacterial and fungal community structure differed significantly from those of LT and CE modes. Bacterial and fungal community structure differed significantly between roots and the other two plant organs examined (stems and leaves). Several bacteria and fungi were positively correlated with main chemical components in D. catenatum. CONCLUSIONS: The findings indicate that microbial communities of the plants and substrates were both influenced by the cultivation mode and plant organ, and some of them were positively correlated with main chemical components in D. catenatum. The research would enhance our understanding of interactions between Dendrobium and the microbial environment, and to provide a theoretical basis for the development of improved D. catenatum cultivation methods.

Design of a Radial Vortex-Based Spin-Torque Nano-Oscillator in a Strain-Mediated Multiferroic Nanostructure for BFSK/BASK Applications.

Radial vortex-based spin torque nano-oscillators (RV-STNOs) have attracted extensive attention as potential nano microwave signal generators due to their advantages over other topological states, such as their higher oscillation, higher microwave power, and lower power consumption. However, the current driving the oscillation frequency of the STNOs must be limited in a small range of adjustment, which means less data transmission channels. In this paper, a new RV-STNO system is proposed with a multiferroic nanostructure, which consists of an ultrathin magnetic multilayer and a piezoelectric layer. Phase diagrams of oscillation frequency and amplitude with respect to piezostrain and current are obtained through micromagnetic simulation. The results show that the threshold current density of -4000-ppm compressive strain-assisted RV-STNOs is reduced from 2 × 109 A/m2 to 2 × 108 A/m2, showing one order of magnitude lower than that of conventional current-driven nano-oscillators. Meanwhile, the range of oscillation frequency adjustment is significantly enhanced, and there is an increased amplitude at the low oscillation point. Moreover, a promising digital binary frequency-shift key (BFSK) and binary amplitude-shift key (BASK) modulation technique is proposed under the combined action of current pulse and piezostrain pulse. They can transmit bit signals and show good modulation characteristics with a minimal transient state. These results provide a reference for developing the next generation of spintronic nano-oscillators with a wide frequency range and low power consumption, showing potential for future wireless communication applications.

The Influence of Acupuncture Parameters on Efficacy and the Possible Use of Acupuncture in Combination with or as a Substitute for Drug Therapy in Patients with Ulcerative Colitis.

Background: Ulcerative colitis (UC) is an inflammatory disease of the colonic mucosa, which is accompanied by chronic, idiopathic characteristics. Acupuncture may be an effective therapy for UC. Here we focused on manual acupuncture and electroacupuncture (MA/EA), two widely used and studied acupuncture interventions, to probe the effects of acupuncture parameters on clinical efficacy in patients with UC and the use of MA/EA alone or with other drugs to support their wider adoption in clinical practice. Methods: The PubMed, Cochrane Library, Web of Science, Embase, China National Knowledge Infrastructure Database, and Wanfang databases were searched from inception to April 27, 2021. Randomized clinical trials (RCTs) published in Chinese or English were included, and subgroup analyses were performed according to acupuncture parameter, acupuncture type, and control medicine type. The risk of bias was assessed using the Cochrane Risk of Bias tool and modified Jadad scale, and Review Manager 5.4 and Stata 14.0 were used to perform a meta-analysis. Sources of heterogeneity were explored; sensitivity analysis was performed; and the GRADE methodology was used to assess the evidence level. Results: Sixteen studies (1454 individuals) were included. Retention of the needle [10-30 minutes (RR 1.18, 95% CI [1.11, 1.26], P < 0.01; heterogeneity: χ 2 = 6.25, df = 6 (P=0.40), I2 = 4%)], the frequency of MA [once every other day (RR 1.21, 95% CI [1.08, 1.35], P < 0.01; heterogeneity: χ 2 = 0.80, df = 1 (P=0.37), I2 = 0%)], and the length of treatment [8 weeks (RR 1.35, 95% CI [1.01, 1.81], P=0.04)] improved clinical efficacy at the end of treatment compared with medications alone. MA (RR 1.18, 95% CI [1.11, 1.25], P < 0.01; heterogeneity: χ 2 = 6.19, df = 7 (P=0.52), I2 = 0%) increased clinical efficacy compared with medications. Furthermore, MA plus medications (RR 1.26, 95% CI [1.13, 1.40], P < 0.01; heterogeneity: χ 2 = 0.95, df = 2 (P=0.62), I2 = 0%) and EA plus medications (RR 1.36, 95% CI [1.13, 1.63], P < 0.01; heterogeneity: χ 2 = 0.13, df = 1 (P=0.72), I2 = 0%) both dramatically improved clinical efficacy. The clinical efficacy of MA plus mesalazine or MA plus metronidazole and sulfasalazine was greater than with mesalazine or metronidazole and sulfasalazine alone. Similarly, EA plus sulfasalazine was more effective than sulfasalazine alone. MA/EA resulted in fewer adverse reactions than medical therapies. The use of MA plus medications significantly reduced Baron scores. GRADE evaluations indicated that the evidence strength was moderate to low but mostly low. Conclusions: Our study provides the latest evidence to allow us to speculate about the possible optimal MA parameters to treat patients with UC. The low number of adverse reactions and high efficacy make MA/EA a possible supplement to or replacement for traditional UC drugs. The variable parameter settings preferred by patients and acupuncturists may be an important factor limiting the wider clinical deployment of acupuncture as a potential UC therapy.

Improving the Generalizability of Infantile Cataracts Detection via Deep Learning-Based Lens Partition Strategy and Multicenter Datasets.

Infantile cataract is the main cause of infant blindness worldwide. Although previous studies developed artificial intelligence (AI) diagnostic systems for detecting infantile cataracts in a single center, its generalizability is not ideal because of the complicated noises and heterogeneity of multicenter slit-lamp images, which impedes the application of these AI systems in real-world clinics. In this study, we developed two lens partition strategies (LPSs) based on deep learning Faster R-CNN and Hough transform for improving the generalizability of infantile cataracts detection. A total of 1,643 multicenter slit-lamp images collected from five ophthalmic clinics were used to evaluate the performance of LPSs. The generalizability of Faster R-CNN for screening and grading was explored by sequentially adding multicenter images to the training dataset. For the normal and abnormal lenses partition, the Faster R-CNN achieved the average intersection over union of 0.9419 and 0.9107, respectively, and their average precisions are both > 95%. Compared with the Hough transform, the accuracy, specificity, and sensitivity of Faster R-CNN for opacity area grading were improved by 5.31, 8.09, and 3.29%, respectively. Similar improvements were presented on the other grading of opacity density and location. The minimal training sample size required by Faster R-CNN is determined on multicenter slit-lamp images. Furthermore, the Faster R-CNN achieved real-time lens partition with only 0.25 s for a single image, whereas the Hough transform needs 34.46 s. Finally, using Grad-Cam and t-SNE techniques, the most relevant lesion regions were highlighted in heatmaps, and the high-level features were discriminated. This study provides an effective LPS for improving the generalizability of infantile cataracts detection. This system has the potential to be applied to multicenter slit-lamp images.

Automatic classification of heterogeneous slit-illumination images using an ensemble of cost-sensitive convolutional neural networks.

BACKGROUND: Lens opacity seriously affects the visual development of infants. Slit-illumination images play an irreplaceable role in lens opacity detection; however, these images exhibited varied phenotypes with severe heterogeneity and complexity, particularly among pediatric cataracts. Therefore, it is urgently needed to explore an effective computer-aided method to automatically diagnose heterogeneous lens opacity and to provide appropriate treatment recommendations in a timely manner. METHODS: We integrated three different deep learning networks and a cost-sensitive method into an ensemble learning architecture, and then proposed an effective model called CCNN-Ensemble [ensemble of cost-sensitive convolutional neural networks (CNNs)] for automatic lens opacity detection. A total of 470 slit-illumination images of pediatric cataracts were used for training and comparison between the CCNN-Ensemble model and conventional methods. Finally, we used two external datasets (132 independent test images and 79 Internet-based images) to further evaluate the model's generalizability and effectiveness. RESULTS: Experimental results and comparative analyses demonstrated that the proposed method was superior to conventional approaches and provided clinically meaningful performance in terms of three grading indices of lens opacity: area (specificity and sensitivity; 92.00% and 92.31%), density (93.85% and 91.43%) and opacity location (95.25% and 89.29%). Furthermore, the comparable performance on the independent testing dataset and the internet-based images verified the effectiveness and generalizability of the model. Finally, we developed and implemented a website-based automatic diagnosis software for pediatric cataract grading diagnosis in ophthalmology clinics. CONCLUSIONS: The CCNN-Ensemble method demonstrates higher specificity and sensitivity than conventional methods on multi-source datasets. This study provides a practical strategy for heterogeneous lens opacity diagnosis and has the potential to be applied to the analysis of other medical images.