Host immunity plays an important role against oral microorganisms in periodontitis.
This study assessed the infiltrating immune cell subtypes in 133 healthy periodontal and 210 chronic periodontitis ...tissues from Gene Expression Omnibus (GEO) datasets using the CIBERSORT gene signature files.
Plasma cells, naive B cells and neutrophils were all elevated in periodontitis tissues, when compared to those in healthy controls. In contrast, memory B cells, resting dendritic, mast cells and CD4 memory cells, as well as activated mast cells, M1 and M2 macrophages, and follicular helper T cells, were mainly present in healthy periodontal tissues. Furthermore, these periodontitis tissues generally contained a higher proportion of activated CD4 memory T cells, while the other subtypes of T cells, including resting CD4 memory T cells, CD8 T cells, follicular helper T cells (T
) and regulatory T cells (Tregs), were relatively lower in periodontitis tissues, when compared to healthy tissues. The ratio of dendritic and mast cells and macrophages was lower in periodontitis tissues, when compared to healthy tissues. In addition, there was a significant negative association of plasma cells with most of the other immune cells, such as plasma cells vs. memory B cells (γ = - 0.84), plasma cells vs. resting dendritic cells (γ = - 0.64), plasma cells vs. resting CD4 memory T cells (γ = 0.50), plasma cells versus activated dendritic cells (γ = - 0.46), plasma cells versus T
(γ = - 0.46), plasma cells versus macrophage M2 cells (γ = - 0.43), or plasma cells versus macrophage M1 cells (γ = - 0.40), between healthy control and periodontitis tissues.
Plasma cells, naive B cells and neutrophils were all elevated in periodontitis tissues. The infiltration of different immune cell subtypes in the periodontitis site could lead the host immunity against periodontitis.
This letter presents a differential 5-bit switch-type phase shifter (PS) that is based on a 65-nm CMOS process. This PS uses a new differential PS unit, which includes a high-pass state and a ...low-pass state. The strengths of this PS unit include low-phase error and low-amplitude error between two states, which can effectively improve the performance of the entire 5-bit PS. The fabricated 5-bit PS has a low average insertion loss of <8.0 dB, low root mean square (rms) phase error of <5.9°, and low rms amplitude error of <0.22 dB in 35-41.9 GHz. It also has a compact size of 0.125 mm 2 .
In this brief, a frequency-reconfigurable phase shifter (PS) with a substrate-shield-based inductor for 5G applications is presented. This PS can switch its operating bands between 26.5-29.5 GHz and ...37-40 GHz by a single control voltage. Because of the switch-type topology, this PS has the benefit of zero power consumption. To verify the utility of this circuit, a 5-bit frequency-reconfigurable PS was designed and fabricated with a 65-nm CMOS process, where 4 bits out of the 5-bit PS employ the proposed frequency-reconfigurable PS. The 5-bit PS has the root mean square (RMS) phase error of 3.7°-12.2° and the RMS amplitude error of 0.4-0.96 dB in 26.5-29.5 GHz. After the control voltage was changed, The PS also had an RMS phase error of 1.6°-8° and an RMS amplitude error of 0.43-0.64 dB in another band (37-40 GHz). According to the aforementioned results, the proposed frequency-reconfigurable PS can support the frequency bands of n257 and n260, which were released by 3rd Generation Partnership Project (3GPP). Hence, the proposed PS is an effective solution for 5G millimeter wave applications.
This article discusses different topologies of switch‐type phase shifter units and the effects of such units on phase accuracy and loss variation for wideband operation. It is observed that the ...optimal selection of topology depends on the value of phase shift. This article discusses a Ka‐band 5‐bit switch‐type phase shifter MMIC with T‐type high‐pass topology, T‐type low‐pass topology, and high‐pass/low‐pass switched‐path‐type topology. This circuit was implemented through a 65‐nm CMOS process. This phase shifter has low RMS phase error of 2.4°–6.2° and low RMS amplitude error of 0.48–0.66 dB over the 32–41 GHz.
Abstract
The exceptional mechanical strength of medium/high-entropy alloys has been attributed to hardening in random solid solutions. Here, we evidence non-random chemical mixing in a CrCoNi alloy, ...resulting from short-range ordering. A data-mining approach of electron nanodiffraction enabled the study, which is assisted by neutron scattering, atom probe tomography, and diffraction simulation using first-principles theory models. Two samples, one homogenized and one heat-treated, are observed. In both samples, results reveal two types of short-range-order inside nanoclusters that minimize the Cr–Cr nearest neighbors (L1
2
) or segregate Cr on alternating close-packed planes (L1
1
). The L1
1
is predominant in the homogenized sample, while the L1
2
formation is promoted by heat-treatment, with the latter being accompanied by a dramatic change in dislocation-slip behavior. These findings uncover short-range order and the resulted chemical heterogeneities behind the mechanical strength in CrCoNi, providing general opportunities for atomistic-structure study in concentrated alloys for the design of strong and ductile materials.
Intelligence has been considered as the major challenge in promoting economic potential and production efficiency of precision agriculture. In order to apply advanced deep-learning technology to ...complete various agricultural tasks in online and offline ways, a large number of crop vision datasets with domain-specific annotation are urgently needed. To encourage further progress in challenging realistic agricultural conditions, we present the CropDeep species classification and detection dataset, consisting of 31,147 images with over 49,000 annotated instances from 31 different classes. In contrast to existing vision datasets, images were collected with different cameras and equipment in greenhouses, captured in a wide variety of situations. It features visually similar species and periodic changes with more representative annotations, which have supported a stronger benchmark for deep-learning-based classification and detection. To further verify the application prospect, we provide extensive baseline experiments using state-of-the-art deep-learning classification and detection models. Results show that current deep-learning-based methods achieve well performance in classification accuracy over 99%. While current deep-learning methods achieve only 92% detection accuracy, illustrating the difficulty of the dataset and improvement room of state-of-the-art deep-learning models when applied to crops production and management. Specifically, we suggest that the YOLOv3 network has good potential application in agricultural detection tasks.
Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2, and outbreaks have occurred worldwide. Laboratory test results are an important basis for clinicians ...to determine patient condition and formulate treatment plans.
Fifty-two thousand six hundred forty-four laboratory test results with continuous values of adult inpatients who were diagnosed with COVID-19 and hospitalized in the Fifth Hospital in Wuhan between 16 January 2020 and 18 March 2020 were compiled. The first and last test results were compared between survivors and non-survivors with variance test or Welch test. Laboratory test variables with significant differences were then included in the temporal change analysis.
Among 94 laboratory test variables in 82 survivors and 25 non-survivors with COVID-19, white blood cell count, neutrophil count/percentage, mean platelet volume, platelet distribution width, platelet-large cell percentage, hypersensitive C-reactive protein, procalcitonin, D-dimer, fibrin (ogen) degradation product, middle fluorescent reticulocyte percentage, immature reticulocyte fraction, lactate dehydrogenase were significantly increased (P < 0.05), and lymphocyte count/percentage, monocyte percentage, eosinophil percentage, prothrombin activity, low fluorescent reticulocyte percentage, plasma carbon dioxide, total calcium, prealbumin, total protein, albumin, albumin-globulin ratio, cholinesterase, total cholesterol, nonhigh-density/low-density/small-dense-low-density lipoprotein cholesterol were significantly decreased in non-survivors compared with survivors (P < 0.05), in both first and last tests. Prothrombin time, prothrombin international normalized ratio, nucleated red blood cell count/percentage, high fluorescent reticulocyte percentage, plasma uric acid, plasma urea nitrogen, cystatin C, sodium, phosphorus, magnesium, myoglobin, creatine kinase (isoenzymes), aspartate aminotransferase, alkaline phosphatase, glucose, triglyceride were significantly increased (P < 0.05), and eosinophil count, basophil percentage, platelet count, thrombocytocrit, antithrombin III, red blood cell count, haemoglobin, haematocrit, total carbon dioxide, acidity-basicity, actual bicarbonate radical, base excess in the extracellular fluid compartment, estimated glomerular filtration rate, high-density lipoprotein cholesterol, apolipoprotein A1/ B were significantly decreased in non-survivors compared with survivors (P < 0.05), only in the last tests. Temporal changes in 26 variables, such as lymphocyte count/percentage, neutrophil count/percentage, and platelet count, were obviously different between survivors and non-survivors.
By the comprehensive usage of the laboratory markers with different temporal changes, patients with a high risk of COVID-19-associated death or progression from mild to severe disease might be identified, allowing for timely targeted treatment.
Near-infrared (NIR) spectroscopy as an emerging analytical technique was used for the first time to quantitatively detect the watercore degree and soluble solids content (SSC) in apple. To reduce the ...data processing time and meet the needs of practical application, the variable selection methods including synergy interval (SI), successive projections algorithm (SPA), genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) were used to identify the characteristic variables and simplify the models. The spectral variables closely related to the apple bioactive components were used for the establishment of the partial least squares (PLS) models. The predictive correlation coefficient (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) were used to estimate the performance of the models. The CARS-PLS models displayed the best prediction performance using 600–1000 nm spectra with Rp, RMSEP, and RPD values of 0.9562, 1.340% and 3.720 for apple watercore degree; 0.9808, 0.327 oBx and 4.845 for apple SSC, respectively. These results demonstrate the potential of the NIR transmittance spectroscopy technology for quantitative detection of SSC and watercore degree in apple fruit.
•Novel NIR transmittance spectroscopy quantitatively detected the degree of watercore in apple.•The characteristic spectral variables of apple watercore disease detection were studied.•Multiple variables selection simplified and improved the performance of models.•NIR spectroscopy is advantageous as the fast, non-destructive measurements.
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