Depth- and curvature-based quantitative susceptibility mapping analyses of cortical iron in Alzheimer's disease.

In addition to amyloid beta plaques and neurofibrillary tangles, Alzheimer's disease (AD) has been associated with elevated iron in deep gray matter nuclei using quantitative susceptibility mapping (QSM). However, only a few studies have examined cortical iron, using more macroscopic approaches that cannot assess layer-specific differences. Here, we conducted column-based QSM analyses to assess whether AD-related increases in cortical iron vary in relation to layer-specific differences in the type and density of neurons. We obtained global and regional measures of positive (iron) and negative (myelin, protein aggregation) susceptibility from 22 adults with AD and 22 demographically matched healthy controls. Depth-wise analyses indicated that global susceptibility increased from the pial surface to the gray/white matter boundary, with a larger slope for positive susceptibility in the left hemisphere for adults with AD than controls. Curvature-based analyses indicated larger global susceptibility for adults with AD versus controls; the right hemisphere versus left; and gyri versus sulci. Region-of-interest analyses identified similar depth- and curvature-specific group differences, especially for temporo-parietal regions. Finding that iron accumulates in a topographically heterogenous manner across the cortical mantle may help explain the profound cognitive deterioration that differentiates AD from the slowing of general motor processes in healthy aging.

Single Cell Analysis Reveals Novel Immune Perturbations in Fibrotic Hypersensitivity Pneumonitis.

Rationale: Fibrotic hypersensitivity pneumonitis is a debilitating interstitial lung disease driven by incompletely understood immune mechanisms. Objectives: To elucidate immune aberrations in fibrotic hypersensitivity pneumonitis in single-cell resolution. Methods: Single-cell 5' RNA sequencing was conducted on peripheral blood mononuclear cells and bronchoalveolar lavage cells obtained from 45 patients with fibrotic hypersensitivity pneumonitis, 63 idiopathic pulmonary fibrosis, 4 non-fibrotic hypersensitivity pneumonitis, and 36 healthy controls in the United States and Mexico. Analyses included differential gene expression (Seurat), transcription factor activity imputation (DoRothEA-VIPER), and trajectory analyses (Monocle3/Velocyto-scVelo-CellRank). Measurements and Main Results: Overall, 501,534 peripheral blood mononuclear cells from 110 patients and controls and 88,336 bronchoalveolar lavage cells from 19 patients were profiled. Compared to controls, fibrotic hypersensitivity pneumonitis has elevated classical monocytes (adjusted-p=2.5e-3) and are enriched in CCL3hi/CCL4hi and S100Ahi classical monocytes (adjusted-p<2.2e-16). Trajectory analyses demonstrate that S100Ahi classical monocytes differentiate into SPP1hi lung macrophages associated with fibrosis. Compared to both controls and idiopathic pulmonary fibrosis, fibrotic hypersensitivity pneumonitis patient cells are significantly enriched in GZMhi cytotoxic T cells. These cells exhibit transcription factor activities indicative of TGFß and TNFα/NFκB pathways. These results are publicly available at https://ildimmunecellatlas.org. Conclusions: Single-cell transcriptomics of fibrotic hypersensitivity pneumonitis patients uncovered novel immune perturbations, including previously undescribed increases in GZMhi cytotoxic CD4+ and CD8+ T cells - reflecting this disease's unique inflammatory T-cell driven nature - as well as increased S100Ahi and CCL3hi/CCL4hi classical monocytes also observed in idiopathic pulmonary fibrosis. Both cell populations may guide the development of new biomarkers and therapeutic interventions.

ATM-mediated double-strand break repair is required for meiotic genome stability at high temperature.

In eukaryotes, meiotic recombination maintains genome stability and creates genetic diversity. The conserved Ataxia-Telangiectasia Mutated (ATM) kinase regulates multiple processes in meiotic homologous recombination, including DNA double-strand break (DSB) formation and repair, synaptonemal complex organization, and crossover formation and distribution. However, its function in plant meiotic recombination under stressful environmental conditions remains poorly understood. In this study, we demonstrate that ATM is required for the maintenance of meiotic genome stability under heat stress in Arabidopsis thaliana. Using cytogenetic approaches we determined that ATM does not mediate reduced DSB formation but does ensure successful DSB repair, and thus meiotic chromosome integrity, under heat stress. Further genetic analysis suggested that ATM mediates DSB repair at high temperature by acting downstream of the MRE11-RAD50-NBS1 (MRN) complex, and acts in a RAD51-independent but chromosome axis-dependent manner. This study extends our understanding on the role of ATM in DSB repair and the protection of genome stability in plants under high temperature stress.

Comamonas endophytica sp. nov., a novel indole acetic acid producing endophyte isolated from bamboo in China.

Two novel indole acetic acid-producing strains, 5MLIRT and D4N7, were isolated from Indosasa shibataeoides in Yongzhou, Hunan province, and Phyllostachys edulis in Hangzhou, Zhejiang province, respectively. Based on their 16S rRNA sequences, strains 5MLIRT and D4N7 were closely related to Comamonas antarcticus 16-35-5T (98.4 % sequence similarity), and the results of 92-core gene phylogenetic trees showed that strains 5MLIRT and D4N7 formed a phylogenetic lineage within the clade comprising Comamonas species. The complete genome size of strain 5MLIRT was 4.49 Mb including two plasmids, and the DNA G+C content was 66.5 mol%. The draft genome of strain D4N7 was 4.26 Mb with 66.7 mol% G+C content. The average nucleotide identity and digital DNA-DNA hybridization values among strain 5MLIRT and species in the genus Comamonas were all below the species delineation threshold. The colonies of strain 5MLIRT and D4N7 were circular with regular margins, convex, pale yellow and 1.0-2.0 mm in diameter when incubated at 30 °C for 3 days. Strains 5MLIRT and D4N7 grew optimally at 30 °C, pH 7.0 and 1.0 % NaCl. The respiratory isoprenoid quinone was ubiquinone-8. The major polar lipids were phosphatidylethanolamine, phosphatidylglycerol and diphosphatidylglycerol. Polyphasic analyses indicated that strains 5MLIRT and D4N7 could be distinguished from related validly named Comamonas species and represent a novel species of the genus Comamonas, for which the name Comamonas endophytica sp. nov. is proposed. The type strain is 5MLIRT (=ACCC 62069T=GDMCC 1.2958T=JCM 35331T).

iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) technology has enabled assessment of transcriptome-wide changes at single-cell resolution. Due to the heterogeneity in environmental exposure and genetic background across subjects, subject effect contributes to the major source of variation in scRNA-seq data with multiple subjects, which severely confounds cell type specific differential expression (DE) analysis. Moreover, dropout events are prevalent in scRNA-seq data, leading to excessive number of zeroes in the data, which further aggravates the challenge in DE analysis. RESULTS: We developed iDESC to detect cell type specific DE genes between two groups of subjects in scRNA-seq data. iDESC uses a zero-inflated negative binomial mixed model to consider both subject effect and dropouts. The prevalence of dropout events (dropout rate) was demonstrated to be dependent on gene expression level, which is modeled by pooling information across genes. Subject effect is modeled as a random effect in the log-mean of the negative binomial component. We evaluated and compared the performance of iDESC with eleven existing DE analysis methods. Using simulated data, we demonstrated that iDESC had well-controlled type I error and higher power compared to the existing methods. Applications of those methods with well-controlled type I error to three real scRNA-seq datasets from the same tissue and disease showed that the results of iDESC achieved the best consistency between datasets and the best disease relevance. CONCLUSIONS: iDESC was able to achieve more accurate and robust DE analysis results by separating subject effect from disease effect with consideration of dropouts to identify DE genes, suggesting the importance of considering subject effect and dropouts in the DE analysis of scRNA-seq data with multiple subjects.

High-resolution multi-shot diffusion imaging of structural networks in healthy neurocognitive aging.

Healthy neurocognitive aging has been associated with the microstructural degradation of white matter pathways that connect distributed gray matter regions, assessed by diffusion-weighted imaging (DWI). However, the relatively low spatial resolution of standard DWI has limited the examination of age-related differences in the properties of smaller, tightly curved white matter fibers, as well as the relatively more complex microstructure of gray matter. Here, we capitalize on high-resolution multi-shot DWI, which allows spatial resolutions < 1 mm3 to be achieved on clinical 3T MRI scanners. We assessed whether traditional diffusion tensor-based measures of gray matter microstructure and graph theoretical measures of white matter structural connectivity assessed by standard (1.5 mm3 voxels, 3.375 µl volume) and high-resolution (1 mm3 voxels, 1µl volume) DWI were differentially related to age and cognitive performance in 61 healthy adults 18-78 years of age. Cognitive performance was assessed using an extensive battery comprising 12 separate tests of fluid (speed-dependent) cognition. Results indicated that the high-resolution data had larger correlations between age and gray matter mean diffusivity, but smaller correlations between age and structural connectivity. Moreover, parallel mediation models including both standard and high-resolution measures revealed that only the high-resolution measures mediated age-related differences in fluid cognition. These results lay the groundwork for future studies planning to apply high-resolution DWI methodology to further assess the mechanisms of both healthy aging and cognitive impairment.

PEC-SERS Dual-Mode Detection of Foodborne Pathogens Based on Binding-Induced DNA Walker and C3N4/MXene-Au NPs Accelerator.

In this paper, a photoelectrochemical (PEC)-surface-enhanced Raman scattering (SERS) dual-mode biosensor is constructed coupled with a dual-recognition binding-induced DNA walker with a carbon nitride nanosheet (C3N4)/MXene-gold nanoparticles (C/M-Au NPs) accelerator, which is reliable and capable for sensitive and accurate detection of Staphylococcus aureus (S. aureus). Initially, a photoactive heterostructure is formed by combining C3N4 and MXene via a simple electrostatic self-assembly as they possess well-matched band-edge energy levels. Subsequently, in situ growth of gold nanoparticles on the formed surface results in better PEC performance and SERS activity, because of the synergistic effects of surface plasmon resonance and Schottky barrier. Furthermore, a three-dimensional, bipedal, and dual-recognition binding-induced DNA walker is introduced with the formation of Pb2+-dependent DNAzyme. In the presence of S. aureus, a significant quantity of intermediate DNA (I-DNA) is generated, which can open the hairpin structure of Methylene Blue-tagged hairpin DNA (H-MB) on the electrode surface, thereby enabling the switch of signals for the quantitative determination of S. aureus. The constructed PEC-SERS dual-mode biosensor that can be mutually verified under one reaction effectively addresses the problem of the low detection accuracy of traditional sensors. Experimental results revealed that the effective combination of PEC and SERS is achieved for amplification detection of S. aureus with a detection range of 5-108 CFU/mL (PEC) and 10-108 CFU/mL (SERS), and a detection of limit of 0.70 CFU/mL (PEC) and 1.35 CFU/mL (SERS), respectively. Therefore, this study offers a novel and effective dual-mode sensing strategy, which has important implications for bioanalysis and health monitoring.

PSMC5 regulates microglial polarization and activation in LPS-induced cognitive deficits and motor impairments by interacting with TLR4.

Luteolin is a flavonoid found in high concentrations in celery and green pepper, and acts as a neuroprotectant. PSMC5 (proteasome 26S subunit, ATPase 5) protein levels were reduced after luteolin stimulation in activated microglia. We aimed to determine whether regulating PSMC5 expression could inhibit neuroinflammation, and investigate the underlying mechanisms.BV2 microglia were transfected with siRNA PSMC5 before the addition of LPS (lipopolysaccharide, 1.0 µg/ml) for 24 h in serum free DMEM. A mouse model of LPS-induced cognitive and motor impairment was established to evaluate the neuroprotective effects of shRNA PSMC5. Intracerebroventricular administration of shRNA PSMC5 was commenced 7 days prior to i.p. injection of LPS (750 µg/kg). Treatments and behavioral experiments were performed once daily for 7 consecutive days. Behavioral tests and pathological/biochemical assays were performed to evaluate LPS-induced hippocampal damage. Molecular dynamics simulation was used to confirm the interaction between PSMC5 and TLR4 (Toll-like receptor 4) in LPS-stimulated BV2 microglia. SiRNA PSMC5 inhibited BV2 microglial activation, and suppressed the release of inflammatory factors (IL-1ß, COX-2, PGE2, TNF-α, and iNOS) upon after LPS stimulation in BV2 microglia. LPS increased IκB-α and p65 phosphorylation, which was attenuated by siRNA PSMC5. Behavioral tests and pathological/biochemical assays showed that shRNA PSMC5 attenuated LPS-induced cognitive and motor impairments, and restored synaptic ultrastructure and protein levels in mice. ShRNA PSMC5 reduced pro-inflammatory cytokine (TNF-α, IL-1ß, PGE2, and NO) levels in the serum and brain, and relevant protein factors (iNOS and COX-2) in the brain. Furthermore, shRNA PSMC5 upregulated the anti-inflammatory mediators interleukin IL-4 and IL-10 in the serum and brain, and promoted a pro-inflammation-to-anti-inflammation phenotype shift in microglial polarization. Mechanistically, shRNA PSMC5 significantly alleviated LPS-induced TLR4 expression. The polarization of LPS-induced microglial pro-inflammation phenotype was abolished by TLR4 inhibitor and in the TLR-4-/- mouse, as in shRNA PSMC5 treatment. PSMC5 interacted with TLR4 via the amino sites Glu284, Met139, Leu127, and Phe283. PSMC5 site mutations attenuated neuroinflammation and reduced pro-inflammatory factors by reducing TLR4-related effects, thereby reducing TLR4-mediated MyD88 (myeloid differentiation factor 88)-dependent activation of NF-κB. PSMC5 could be an important therapeutic target for treatment of neurodegenerative diseases involving neuroinflammation-associated cognitive deficits and motor impairments induced by microglial activation.

An improved asymmetric susceptibility tensor imaging model with frequency offset correction.

PURPOSE: To improve susceptibility tensor imaging (STI) reconstruction using the asymmetric STI model with the correction of non-bulk-magnetic-susceptibility (NBMS) effects. METHOD: A frequency offset term was introduced into the asymmetric STI model to account for the bias between measured MRI frequency signals and conventional susceptibility tensor models because of NBMS contributions. Experiments were conducted to compare the proposed model with conventional STI, conventional STI with the proposed frequency offset correction, and asymmetric STI on simulation, ex vivo mouse brain, and in vivo human brain data. RESULTS: In the simulation where NBMS contributions are head rotation-invariant, the proposed method achieves the lowest errors in mean magnetic susceptibility (MMS) and magnetic susceptibility anisotropy (MSA) and is more robust to noise in the estimation of principal eigenvector (PEV). When considering the head orientation dependency of NBMS contributions, the proposed method shows advantages in estimating MSA and PEV. On the mouse and human brain data, the proposed method produces more reliable MSA maps and more consistent white matter fiber directions when referring to those from DTI than the compared STI methods. CONCLUSION: The proposed method can reduce the effects of NBMS-related frequency shifts on the susceptibility tensors in the brain white matter. This study inspires STI reconstruction from the perspective of better modeling the sources of frequency shifts.

Interfered chromosome pairing at high temperature promotes meiotic instability in autotetraploid Arabidopsis.

Changes in environmental temperature affect multiple meiotic processes in flowering plants. Polyploid plants derived from whole-genome duplication (WGD) have enhanced genetic plasticity and tolerance to environmental stress but face challenges in organizing and segregating doubled chromosome sets. In this study, we investigated the impact of increased environmental temperature on male meiosis in autotetraploid Arabidopsis (Arabidopsis thaliana). Under low to mildly increased temperatures (5°C-28°C), irregular chromosome segregation universally occurred in synthetic autotetraploid Columbia-0 (Col-0). Similar meiotic lesions occurred in autotetraploid rice (Oryza sativa L.) and allotetraploid canola (Brassica napus cv Westar), but not in evolutionarily derived hexaploid wheat (Triticum aestivum). At extremely high temperatures, chromosome separation and tetrad formation became severely disordered due to univalent formation caused by the suppression of crossing-over. We found a strong correlation between tetravalent formation and successful chromosome pairing, both of which were negatively correlated with temperature elevation, suggesting that increased temperature interferes with crossing-over predominantly by impacting homolog pairing. We also showed that loading irregularities of axis proteins ASY1 and ASY4 co-localize on the chromosomes of the syn1 mutant and the heat-stressed diploid and autotetraploid Col-0, revealing that heat stress affects the lateral region of synaptonemal complex (SC) by impacting the stability of the chromosome axis. Moreover, we showed that chromosome axis and SC in autotetraploid Col-0 are more sensitive to increased temperature than those in diploid Arabidopsis. Taken together, our data provide evidence suggesting that WGD negatively affects the stability and thermal tolerance of meiotic recombination in newly synthetic autotetraploid Arabidopsis.

Enhanced grey wolf algorithm for automatic tuning of an ensemble neural network in predicting PCF optical properties.

The structural characteristics of photonic crystal fibers (PCFs) determine their optical properties. This paper introduces an enhanced Grey Wolf Optimization algorithm termed ACD-GWO, which proposes adaptive strategies, chaotic mapping and dimension-based approaches and integrates them into the Grey Wolf Optimization framework. The aim is to achieve efficient automatic adjustment of hyperparameters and architecture for ensemble neural networks. The resulting ensemble neural network demonstrates accurate and rapid prediction of optical properties in PCFs, including effective refractive index, effective mode area, dispersion, and confinement loss, based on the PCF's structural characteristics. Compared to random forest and feedforward neural network models, the ensemble neural network achieves higher accuracy with a mean squared error of 3.78 × 10-6. Additionally, the computational time is significantly reduced, with only 2.27 minutes required for training and 0.08 seconds for prediction, which is much faster than numerical simulation software. This will provide new possibilities for optical device design and performance optimization, driving cutting-edge research and practical applications in the field of optics.

Microbacterium nymphoidis sp. nov. and Microbacterium festucae sp. nov., two novel species with high plant-promoting potential isolated from wetland plants in China.

Two novel plant growth-promoting, rod-shaped, Gram-positive and non-motile rhizobacteria, W1NT and W2RT, were isolated from wetland plants Festuca elata and Nymphoides peltatum, respectively, in China. The results of the 16S rRNA sequence alignment analysis showed that they were related to Microbacterium, with the highest similarity to Microbacterium ketosireducens (98.7 %) and Microbacterium laevaniformans (98.5 %) for strain W1NT, and to Microbacterium terricola (98.1 %) and Microbacterium marinum (98.0 %) for strain W2RT. Phylogenetic analyses based on 16S rRNA gene sequences and 92 conserved concatenated proteins suggested that the two strains belong to the genus Microbacterium and were placed in two separate novel phylogenetic clades. The genome sizes of the two strains were 3.2 and 3.7 Mb, and the G+C contents were 71.7 and 68.5 mol%, respectively. The comparative genome results showed that the average nucleotide identity values between W1NT and W2RT and other species ranged from 73.5 to 83.6 %, and the digital DNA-DNA hybridization values ranged from 19.7 to 26.8 %. These two strains show physiological and biochemical features that differ from those of closely related species. Rhamnose, galactose and glucose were present in the characteristic sugar fractions of strains W1NT and W2RT. The peptidoglycan of strains W1NT and W2RT contained the amino acids ornithine, alanine and aspartic acid. C15 : 0 anteiso, C17 : 0 anteiso and C16 : 0 iso were the predominant cellular fatty acids in W1NT and W2RT. Phosphatidylglycerol and diphosphatidylglycerol are major polar lipid components. Strain W1NT not only formed bacterial biofilms but also had the ability to solubilize phosphorus and produce indole-3-acetic acid. Strain W2RT had siderophore-producing and lignin-degrading properties. Based on their genetic and phenotypic characteristics, strains W1NT and W2RT were classified as novel bacteria in the genus Microbacterium and designated as Microbacterium festucae sp. nov. (type strain W1NT=ACCC 61807T=GDMCC 1.2966T=JCM 35339T) and Microbacterium nymphoidis sp. nov. (type strain W2RT=ACCC 61808T=GDMCC 1.2967T=JCM 35340T).

Hepatotoxicity assessment investigations on PFASs targeting L-FABP using binding affinity data and machine learning-based QSAR model.

Per- and polyfluoroalkyl substances (PFASs) are persistent organic pollutants that have been detected in various environmental media and human serum, but their safety assessment remains challenging. PFASs may accumulate in liver tissues and cause hepatotoxicity by binding to liver fatty acid binding protein (L-FABP). Therefore, evaluating the binding affinity of PFASs to L-FABP is crucial in assessing the potential hepatotoxic effects. In this study, two binding sites of L-FABP were evaluated, results suggested that the outer site possessed high affinity to polyfluoroalkyl sulfates and the inner site preferred perfluoroalkyl sulfonamides, overall, the inner site of L-FABP was more sensitive to PFASs. The binding affinity data of PFASs to L-FABP were used as training set to develop a machine learning model-based quantitative structure-activity relationship (QSAR) for efficient prediction of potentially hazardous PFASs. Further Bayesian Kernel Machine Regression (BKMR) model disclosed flexibility as the determinant molecular property on PFASs-induced hepatotoxicity. It can influence affinity of PFASs to target protein through affecting binding conformations directly (individual effect) as well as integrating with other molecular properties (joint effect). Our present work provided more understanding on hepatotoxicity of PFASs, which could be significative in hepatotoxicity gradation, administration guidance, and safer alternatives development of PFASs.

Effect of food matrix on rapid detection of Vibrio parahaemolyticus in aquatic products based on toxR gene.

Vibrio parahaemolyticus has become an important public threat to human health. Rapid and robust pathogen diagnostics are necessary for monitoring its outbreak and spreading. Herein, we report an assay for the detection of V. parahaemolyticus based on recombinase aided amplification (RAA) combined with lateral flow dipstick (LFD), namely RAA-LFD. The RAA-LFD took 20 min at 36~38 â„ƒ, and showed excellent specificity. It detected as low as 6.4 fg/µL of V. parahaemolyticus in genomic DNA, or 7.4 CFU/g spiked food samples with 4 h of enrichment. The limit of detection in shrimp (Litopenaeus Vannamei), fish (Carassius auratus), clam (Ruditapes philippinarum) evidenced that sensitivity was considerably affected by the food matrix. The presence of food matrix reduced the sensitivity of spiked food samples by 10 ~ 100 times. In the filed samples detection, RAA-LFD method showed good coincidence with GB4789.7-2013 method and PCR method at rates of 90.6% and 94.1%, respectively. RAA-LFD has high accuracy and sensitivity for the detection of V. parahaemolyticus, which can serve as a model tool to meet the growing need for point-of-care diagnosis of V. parahaemolyticus.

Microalgae tolerant of boron stress and bioresources accumulation during the boron removal process.

Boron (B) industry and consuming produce large amounts of B-containing wastewater. Low tolerance of microorganisms and plants resulted in the biological removal of B was limited. Microalgae show high adaptability in adverse environments. Whether microalgae able to be utilized in B removal meanwhile produce bioresources, and the B tolerant mechanisms and regulation pathway of microalgae are unclear. In this study, the cell growth, B removal, and lipid/starch production of Chlorella regularis under different levels of B stress (0.5, 10, 25, and 50 mg/L) were examined. The mechanisms of signal perception and response were explored by transcriptome and network analysis. Microalgae tolerated 25 mg/L high B stress, cell growth showed no decline and biomass reach up to 4.5 g/L. Microalgae took in B with 3.35 mg/g and bonded them to protein and carbon components in cells, the B removal capability was higher than some special adsorbents. Microalgae produced 188.65 mg/(L∙d) lipids and 305.35 mg/(L∙d) starch. The mitogen-activated protein-kinase signaling pathway was involved in the B tolerance of microalgae and regulated B efflux, glycolysis, and lipid/starch accumulation to relieve B stress. This study provides potential biological technique for B removal in wastewater and promotes new insight into signal role in toxic pollutants biological treatment.

Research progress on icariin, a traditional Chinese medicine extract, in the treatment of asthma.

Bronchial asthma is a common chronic airway disease, and long-term management of asthma is the focus and challenge of clinical treatment. Glucocorticoids are often used as the first choice for the treatment of asthma. However, the occurrence of hormone dependence, hormone resistance, and local and systemic adverse reactions caused by hormone application also creates problems for the treatment of asthma. Finding new, safe, and effective therapeutic drugs is an important research direction at present. Icariin is an effective ingredient of the traditional Chinese medicine, Epimedium. It has various biological attributes such as anti-inflammatory and antioxidative activities, and immune regulation. It has high safety and a wide range of clinical applications. Icariin has the characteristics of multitargeted intervention in the treatment of asthma. Here, we review the specific mechanisms of icariin in treating asthma, and icariin is considered a novel therapy in controlling asthma; however, the mechanism is still worth further investigation.

Sparse Sliding-Window Kernel Recursive Least-Squares Channel Prediction for Fast Time-Varying MIMO Systems.

Accurate channel state information (CSI) is important for MIMO systems, especially in a high-speed scenario, fast time-varying CSI tends to be out of date, and a change in CSI shows complex nonlinearities. The kernel recursive least-squares (KRLS) algorithm, which offers an attractive framework to deal with nonlinear problems, can be used in predicting nonlinear time-varying CSI. However, the network structure of the traditional KRLS algorithm grows as the training sample size increases, resulting in insufficient storage space and increasing computation when dealing with incoming data, which limits the online prediction of the KRLS algorithm. This paper proposed a new sparse sliding-window KRLS (SSW-KRLS) algorithm where a candidate discard set is selected through correlation analysis between the mapping vectors in the kernel Hilbert spaces of the new input sample and the existing samples in the kernel dictionary; then, the discarded sample is determined in combination with its corresponding output to achieve dynamic sample updates. Specifically, the proposed SSW-KRLS algorithm maintains the size of the kernel dictionary within the sample budget requires a fixed amount of memory and computation per time step, incorporates regularization, and achieves online prediction. Moreover, in order to sufficiently track the strongly changeable dynamic characteristics, a forgetting factor is considered in the proposed algorithm. Numerical simulations demonstrate that, under a realistic channel model of 3GPP in a rich scattering environment, our proposed algorithm achieved superior performance in terms of both predictive accuracy and kernel dictionary size than that of the ALD-KRLS algorithm. Our proposed SSW-KRLS algorithm with M=90 achieved 2 dB NMSE less than that of the ALD-KRLS algorithm with v=0.001, while the kernel dictionary was about 17% smaller when the speed of the mobile user was 120 km/h.

Development of an isothermal recombinase-aided amplification assay for the rapid and visualized detection of Klebsiella pneumoniae.

BACKGROUND: Klebsiella pneumoniae is a zoonotic opportunistic pathogen, leading to severe infections in dairy cows and humans. Efficient, on-site and accurate detection of K. pneumoniae is necessary to reduce the harm of cow mastitis and human infections. The objective of this study was to establish a recombinase-aided amplification (RAA) method combined with lateral flow dipstick (LFD) for rapid detection of K. pneumoniae. RESULTS: The primer concentration, incubation temperature and incubation time of the RAA reaction were optimized. When the primer concentration was 100 nmol L-1 , the strongest band could be obtained by incubation at 37 °C for 20 min. The RAA-LFD method had high specificity to K. pneumoniae and showed no cross-reaction with other pathogens. In addition, the detection limit of RAA-LFD for K. pneumoniae was 20 fg genomic DNA and 2.5 × 102 CFU mL-1 of bacteria in pure culture, which is 100 times higher than that of polymerase chain reaction (PCR) detection. Moreover, the RAA-LFD method can detect K. pneumoniae at initial concentrations as low as 2.5 CFU per 25 mL in artificially spiked milk samples after at least incubation for 6 h. Importantly, RAA-LFD had a high agreement with a test accuracy of 96.9%, compared with the biochemical identification method. Also, the detection accuracy of RAA-LFD was higher than that of the PCR assay (95.3%). CONCLUSIONS: The results demonstrated that the RAA-LFD assay is an accurate, sensitive, simple and point-of-use detection method for K. pneumoniae, which could be used as a potential application in the research laboratory and for disease diagnosis. © 2021 Society of Chemical Industry.

Numerical Study of Fin-and-Tube Heat Exchanger in Low-Pressure Environment: Air-Side Heat Transfer and Frictional Performance, Entropy Generation Analysis, and Model Development.

Heat transfer and frictional performance at the air-side is predominant for the application and optimization of finned tube heat exchangers. For aerospace engineering, the heat exchanger operates under negative pressure, whereas the general prediction models of convective heat transfer coefficient and pressure penalty for this scenario are rarely reported. In the current study, a numerical model is developed to determine the air-side heat transfer and frictional performance. The influence of air pressure (absolute pressure) is discussed in detail, and the entropy generation considering the effect of heat transfer and pressure drop are analyzed. Furthermore, prediction models of air-side thermal and frictional factors are also developed. The results indicate that both the convective heat transfer coefficient and pressure penalty decrease significantly with decreasing air pressure, and the air-side heat transfer coefficient is decreased by 64.6~73.3% at an air pressure of 25 kPa compared with normal environment pressure. The entropy generation by temperature difference accounts for the highest proportion of the total entropy generation. The prediction correlations of Colburn j-factor and friction factor f show satisfactory accuracy with the absolute mean deviations of 7.48% and 9.42%, respectively. This study can provide a reference for the practical application of fined tube heat exchangers under a negative pressure environment.

MoDL-QSM: Model-based deep learning for quantitative susceptibility mapping.

Quantitative susceptibility mapping (QSM) has demonstrated great potential in quantifying tissue susceptibility in various brain diseases. However, the intrinsic ill-posed inverse problem relating the tissue phase to the underlying susceptibility distribution affects the accuracy for quantifying tissue susceptibility. Recently, deep learning has shown promising results to improve accuracy by reducing the streaking artifacts. However, there exists a mismatch between the observed phase and the theoretical forward phase estimated by the susceptibility label. In this study, we proposed a model-based deep learning architecture that followed the STI (susceptibility tensor imaging) physical model, referred to as MoDL-QSM. Specifically, MoDL-QSM accounts for the relationship between STI-derived phase contrast induced by the susceptibility tensor terms (χ13, χ23 and χ33) and the acquired single-orientation phase. The convolutional neural networks are embedded into the physical model to learn a regularization term containing prior information. χ33 and phase induced by χ13 and χ23 terms were used as the labels for network training. Quantitative evaluation metrics were compared with recently developed deep learning QSM methods. The results showed that MoDL-QSM achieved superior performance, demonstrating its potential for future applications.