Convergent Neuroimaging and Molecular Signatures in Mild Cognitive Impairment and Alzheimer's Disease: A Data-Driven Meta-Analysis with N = 3,118.

The current study aimed to evaluate the susceptibility to regional brain atrophy and its biological mechanism in Alzheimer's disease (AD). We conducted data-driven meta-analyses to combine 3,118 structural magnetic resonance images from three datasets to obtain robust atrophy patterns. Then we introduced a set of radiogenomic analyses to investigate the biological basis of the atrophy patterns in AD. Our results showed that the hippocampus and amygdala exhibit the most severe atrophy, followed by the temporal, frontal, and occipital lobes in mild cognitive impairment (MCI) and AD. The extent of atrophy in MCI was less severe than that in AD. A series of biological processes related to the glutamate signaling pathway, cellular stress response, and synapse structure and function were investigated through gene set enrichment analysis. Our study contributes to understanding the manifestations of atrophy and a deeper understanding of the pathophysiological processes that contribute to atrophy, providing new insight for further clinical research on AD.

Cardiac Genetic Test Yields and Genotype-Phenotype Correlations from Large Cohort Investigated by Medical Examiner's Office.

BACKGROUND: Few reports describe the yield of postmortem genetic testing from medical examiners' offices or correlate genetic test results with autopsy-confirmed phenotypes from a large cohort. OBJECTIVES: To report results from cardiomyopathy- and cardiac arrhythmia-associated genetic testing in conjunction with autopsy findings of cases investigated at the United States' largest medical examiner office. METHODS: Postmortem cases tested from 2015 to 2022 with a cardiomyopathy- and cardiac arrhythmia-associated gene panel were reviewed. American College of Medical Genetics and Genomics/Association for Molecular Pathology guidelines were used to classify variant pathogenicity. Correlations of pathogenic/likely pathogenic variants (P/LPVs) with cardiac pathology were evaluated. RESULTS: The cohort included 1107 decedents of diverse ages and ethnicities. P/LPVs were detected in 87 (7.9%) cases, with 73 and 14 variants in cardiomyopathy and cardiac arrhythmia genes, respectively. Variants of uncertain significance were detected in 437 (39.5%) cases. The diagnostic yield (percentage of P/LPV) in decedents with cardiomyopathy (26.1%) was significantly higher than those without (P < 0.0001). The diagnostic yield was significantly lower in infants (0.7%) than older age groups (ranging from 1 to 74 years old, 5.7%-25.9%), which had no statistical difference between their yields. The diagnostic yields by cardiac autopsy findings were 54.0% for hypertrophic cardiomyopathy, 47.1% for arrhythmogenic cardiomyopathy, 20.0% for myocardial fibrosis, 19.0% for dilated cardiomyopathy, and 11.3% for myocarditis. Most P/LPVs were in MYBPC3, TTN, PKP2, SCN5A, MYH7, and FLNC. Ten P/LPVs were novel. CONCLUSIONS: Our results support the importance of performing postmortem genetic testing on decedents of all ages with cardiomyopathy, cardiac lesions insufficient to diagnosis a specific cardiomyopathy (e.g., myocardial fibrosis), and myocarditis. Combined postmortem cardiac examination and genetic analysis are advantageous in accurately determining the underlying cause of death and informing effective clinical care of family members.

Modified lentiviral globin gene therapy for pediatric ß0/ß0 transfusion-dependent ß-thalassemia: A single-center, single-arm pilot trial.

ß0/ß0 thalassemia is the most severe type of transfusion-dependent ß-thalassemia (TDT) and is still a challenge facing lentiviral gene therapy. Here, we report the interim analysis of a single-center, single-arm pilot trial (NCT05015920) evaluating the safety and efficacy of a ß-globin expression-optimized and insulator-engineered lentivirus-modified cell product (BD211) in ß0/ß0 TDT. Two female children were enrolled, infused with BD211, and followed up for an average of 25.5 months. Engraftment of genetically modified hematopoietic stem and progenitor cells was successful and sustained in both patients. No unexpected safety issues occurred during conditioning or after infusion. Both patients achieved transfusion independence for over 22 months. The treatment extended the lifespan of red blood cells by over 42 days. Single-cell DNA/RNA-sequencing analysis of the dynamic changes of gene-modified cells, transgene expression, and oncogene activation showed no notable adverse effects. Optimized lentiviral gene therapy may safely and effectively treat all ß-thalassemia.

Selective Reduction of Triple Bond via Proton-Coupled Electron Transfer for the Synthesis of α,ß-Unsaturated γ-Lactams.

Described herein is the development of a visible-light-induced photoredox 1,6-enyne reductive cyclization via selective reduction of a triple bond instead of an activated double bond. The selective 1,6-enyne radical cyclization/carbon═carbon double bond cleavage provided a straightforward route to structurally valuable α,ß-unsaturated γ-lactams. TEMPO-trap experiments, control experiments, and DFT calculations have offered evidence supporting the possible catalytic cycle.

Value of CT-Based Deep Learning Model in Differentiating Benign and Malignant Solid Pulmonary Nodules ≤ 8 mm.

RATIONALE AND OBJECTIVES: We examined the effectiveness of computed tomography (CT)-based deep learning (DL) models in differentiating benign and malignant solid pulmonary nodules (SPNs) ≤ 8 mm. MATERIALS AND METHODS: The study patients (n = 719) were divided into internal training, internal validation, and external validation cohorts; all had small SPNs and had undergone preoperative chest CTs and surgical resection. We developed five DL models incorporating features of the nodule and five different peri-nodular regions with the Multiscale Dual Attention Network (MDANet) to differentiate benign and malignant SPNs. We selected the best-performing model, which was then compared to four conventional algorithms (VGG19, ResNet50, ResNeXt50, and DenseNet121). Furthermore, another five DL models were constructed using MDANet to distinguish benign tumors from inflammatory nodules and the one performed best was selected out. RESULTS: Model 4, which incorporated the nodule and 15 mm peri-nodular region, best differentiated benign and malignant SPNs. The model had an area under the curve (AUC), accuracy, recall, precision, and F1-score of 0.730, 0.724, 0.711, 0.705, and 0.707 in the external validation cohort. Model 4 also performed better than the other four conventional algorithms. Model 8, which incorporated the nodule and 10 mm peri-nodular region, was the best model for distinguishing benign tumors from inflammatory nodules. The model had an AUC, accuracy, recall, precision, and F1-score of 0.871, 0.938, 0.863, 0.904, and 0.882 in the external validation cohort. CONCLUSION: The study concludes that CT-based DL models built with MDANet can accurately discriminate among small benign and malignant SPNs, benign tumors and inflammatory nodules.

Giant dielectric tunability in ferroelectric ceramics with ultralow loss by ion substitution design.

Due to their responsiveness to modulation by external direct current fields, dielectric tunable materials are extensively utilized in integrated components, such as ferroelectric phase shifters. Barium strontium titanate ceramics have been considered the most potential tunable materials for a long time. However, the significant dielectric loss and high voltage drive have limited their further applications. Recently, Bi6Ti5WO22 ceramic has regained attention for its high dielectric tunability with low loss. In this study, we judiciously introduce Nb5+ with a larger ionic radius, replacing Ti4+ and W6+. This successful substitution enables the modulation of the phase transition temperature of Bi6Ti5WO22 ceramics to room temperature, resulting in superior tunable properties. Specifically, the 0.7Bi6Ti5WO22-0.3Bi6Ti4Nb2O22 ceramics exhibit giant tunability (~75.6%) with ultralow loss (<0.002) under a low electric field (1.5 kV/mm). This tunability is twice that of barium strontium titanate ceramics with a similar dielectric constant and only one-tenth of the loss. Neutron powder diffraction and transmission-electron-microscopy illustrate the nanodomains and micro-strains influenced by ion substitution. Density functional theory simulation calculations reveal the contribution of ion substitution to polarization. The research provides an ideal substitute for tunable material and a general strategy for adjusting phase transition temperature to improve dielectric properties.

The Effects of Tremella fuciformis Polysaccharide on the Physicochemical, Multiscale Structure and Digestive Properties of Cyperus esculentus Starch.

In this study, we have investigated the effects of Tremella fuciformis polysaccharide (TP) on the pasting, rheological, structural and in vitro digestive properties of Cyperus esculentus starch (CS). The results showed that the addition of TP significantly changed the pasting characteristics of CS, increased the pasting temperature and pasting viscosity, inhibited pasting, reduced the exudation of straight-chain starch and was positively correlated with the amount of TP added. The addition of the appropriate amount of TP could increase its apparent viscosity and enhance its viscoelasticity. The composite system of CS/TP exhibited higher short-range ordered structure and solid dense structure, which protected the crystal structure of CS, but was related to the amount of TP added. In addition, the introduction of TP not only decreased the in vitro digestion rate of CS and increased the content of slow-digestible starch (SDS) and resistant starch (RS), but also reduced the degree of digestion. Correlation studies established that TP could improve the viscoelasticity, relative crystallinity and short-range order of the CS/TP composite gel, maintain the integrity of the starch granule and crystalline structure, reduce the degree of starch pasting and strengthen the gel network structure of CS, which could help to lower the digestibility of CS.

Deep learning-based platform performs high detection sensitivity of intracranial aneurysms in 3D brain TOF-MRA: An external clinical validation study.

PURPOSE: To evaluate the diagnostic efficacy of a developed artificial intelligence (AI) platform incorporating deep learning algorithms for the automated detection of intracranial aneurysms in time-of-flight (TOF) magnetic resonance angiography (MRA). METHOD: This retrospective study encompassed 3D TOF MRA images acquired between January 2023 and June 2023, aiming to validate the presence of intracranial aneurysms via our developed AI platform. The manual segmentation results by experienced neuroradiologists served as the "gold standard". Following annotation of MRA images by neuroradiologists using InferScholar software, the AI platform conducted automatic segmentation of intracranial aneurysms. Various metrics including accuracy (ACC), balanced ACC, area under the curve (AUC), sensitivity (SE), specificity (SP), F1 score, Brier Score, and Net Benefit were utilized to evaluate the generalization of AI platform. Comparison of intracranial aneurysm identification performance was conducted between the AI platform and six radiologists with experience ranging from 3 to 12 years in interpreting MR images. Additionally, a comparative analysis was carried out between radiologists' detection performance based on independent visual diagnosis and AI-assisted diagnosis. Subgroup analyses were also performed based on the size and location of the aneurysms to explore factors impacting aneurysm detectability. RESULTS: 510 patients were enrolled including 215 patients (42.16 %) with intracranial aneurysms and 295 patients (57.84 %) without aneurysms. Compared with six radiologists, the AI platform showed competitive discrimination power (AUC, 0.96), acceptable calibration (Brier Score loss, 0.08), and clinical utility (Net Benefit, 86.96 %). The AI platform demonstrated superior performance in detecting aneurysms with an overall SE, SP, ACC, balanced ACC, and F1 score of 91.63 %, 92.20 %, 91.96 %, 91.92 %, and 90.57 % respectively, outperforming the detectability of the two resident radiologists. For subgroup analysis based on aneurysm size and location, we observed that the SE of the AI platform for identifying tiny (diameter＜3mm), small (3 mm ≤ diameter＜5mm), medium (5 mm ≤ diameter＜7mm) and large aneurysms (diameter ≥ 7 mm) was 87.80 %, 93.14 %, 95.45 %, and 100 %, respectively. Furthermore, the SE for detecting aneurysms in the anterior circulation was higher than that in the posterior circulation. Utilizing the AI assistance, six radiologists (i.e., two residents, two attendings and two professors) achieved statistically significant improvements in mean SE (residents: 71.40 % vs. 88.37 %; attendings: 82.79 % vs. 93.26 %; professors: 90.07 % vs. 97.44 %; P < 0.05) and ACC (residents: 85.29 % vs. 94.12 %; attendings: 91.76 % vs. 97.06 %; professors: 95.29 % vs. 98.82 %; P < 0.05) while no statistically significant change was observed in SP. Overall, radiologists' mean SE increased by 11.40 %, mean SP increased by 1.86 %, and mean ACC increased by 5.88 %, mean balanced ACC promoted by 6.63 %, mean F1 score grew by 7.89 %, and Net Benefit rose by 12.52 %, with a concurrent decrease in mean Brier score declined by 0.06. CONCLUSIONS: The deep learning algorithms implemented in the AI platform effectively detected intracranial aneurysms on TOF-MRA and notably enhanced the diagnostic capabilities of radiologists. This indicates that the AI-based auxiliary diagnosis model can provide dependable and precise prediction to improve the diagnostic capacity of radiologists.

Editorial for the Special Issue on Advanced Interconnect and Packaging, 2nd Edition.

Interconnect and packaging technologies are crucial aspects of modern electronics, and they are essential to achieve high performance, miniaturization and low power consumption of electronic equipment [...].

Engineering the Substrate Specificity of (S)-ß-Phenylalanine Adenylation Enzyme HitB.

Adenylation enzymes catalyze the selective incorporation of aminoacyl building blocks in the biosynthesis of nonribosomal peptides and related natural products. Although ß-amino acid units are one of the important aminoacyl building blocks in natural product biosynthesis, very little is known about the engineering of ß-amino acid adenylation enzymes. In this study, we engineered the substrate specificity of the (S)-ß-phenylalanine adenylation enzyme, HitB, involved in the biosynthesis of macrolactam polyketide hitachimycin. Based on the previously determined structure of HitB wild-type, we mutated Phe328 and Ser293, which are located near the meta and ortho position of the (S)-ß-phenylalanine moiety, respectively. As a result, the HitB F328V and F328L mutants efficiently activated meta-substituted (S)-ß-phenylalanine analogs, and the HitB T293G and T293S mutants efficiently activated ortho-substituted (S)-ß-phenylalanine analogs. Structural analysis of the HitB F328L and T293G mutants with the corresponding nonhydrolyzable intermediate analogs revealed an enlarged substrate binding pocket for (S)-ß-phenylalanine analogs, providing detailed insights into the structural basis for creating enzyme substrate promiscuity. Our findings may be useful for production of various ß-amino acid-containing natural product analogs.

Identification of restrictive molecules involved in oncolytic virotherapy using genome-wide CRISPR screening.

Oncolytic viruses (OVs) offer a novel approach to treat solid tumors; however, their efficacy is frequently suboptimal due to various limiting factors. To address this challenge, we engineered an OV containing targets for neuron-specific microRNA-124 and Granulocyte-macrophage colony-stimulating factor (GM-CSF), significantly enhancing its neuronal safety while minimally compromising its replication capacity. Moreover, we identified PARP1 as an HSV-1 replication restriction factor using genome-wide CRISPR screening. In models of glioblastoma (GBM) and triple-negative breast cancer (TNBC), we showed that the combination of OV and a PARP inhibitor (PARPi) exhibited superior efficacy compared to either monotherapy. Additionally, single-cell RNA sequencing (scRNA-seq) revealed that this combination therapy sensitized TNBC to immune checkpoint blockade, and the incorporation of an immune checkpoint inhibitor (ICI) further increased the survival rate of tumor-bearing mice. The combination of PARPi and ICI synergistically enhanced the ability of OV to establish durable tumor-specific immune responses. Our study effectively overcomes the inherent limitations of OV therapy, providing valuable insights for the clinical treatment of TNBC, GBM, and other malignancies.

Ni-Co Complex Ionic Synergistically Modifying Octahedron Lattice of Cordierite Ceramics for the Application of 5G Microwave-Millimeter-wave Antennas.

In this work, phase-pure Mg1.8(Ni1-xCox)0.2Al4Si5O18 (0 ≤ x ≤ 1) ceramics were synthesized by a high-temperature solid-state method. On the basis of Rietveld refinement data of X-ray powder diffraction and Phillips-Vechten-Levine theory, the atomic ionicity, lattice energy, and bond energy of the compound were calculated to explore their influence on the microwave dielectric properties of ceramics. The Mg1.8Ni0.1Co0.1Al4Si5O18 (x = 0.5) ceramic exhibited the best microwave dielectric properties: εr = 4.44, Qf = 73 539 GHz@13 GHz, and τf = -23.9 ppm/°C. (Ni1-xCox)2+ complex ionic doping, compared with only Ni2+ or Co2+, is beneficial for improving the symmetry of [Si4Al2O18] hexagonal rings and reducing distortion. Subsequently, 8 wt % TiO2 was added to Mg1.8Ni0.1Co0.1Al4Si5O18, resulting in a near-zero τf and high Qf values for the composite ceramic, with εr = 5.22, Qf = 58 449 GHz@13 GHz, and τf = -2.06 ppm/°C. Finally, a 5G millimeter-wave antenna with a central operating frequency of 25.52 GHz was designed and fabricated using the Mg1.8Ni0.1Co0.1Al4Si5O18-8 wt % TiO2 ceramics. Operating in the 24.7-26.0 GHz range, it demonstrated favorable radiation characteristics with a simulated efficiency of 85.2% and a gain of 4.58 dBi. The antenna's performance confirms the high potential of the cordierite composite for application in 5G communication systems.

Cross-ancestry genome-wide association studies of brain imaging phenotypes.

Genome-wide association studies of brain imaging phenotypes are mainly performed in European populations, but other populations are severely under-represented. Here, we conducted Chinese-alone and cross-ancestry genome-wide association studies of 3,414 brain imaging phenotypes in 7,058 Chinese Han and 33,224 white British participants. We identified 38 new associations in Chinese-alone analyses and 486 additional new associations in cross-ancestry meta-analyses at P < 1.46 × 10-11 for discovery and P < 0.05 for replication. We pooled significant autosomal associations identified by single- or cross-ancestry analyses into 6,443 independent associations, which showed uneven distribution in the genome and the phenotype subgroups. We further divided them into 44 associations with different effect sizes and 3,557 associations with similar effect sizes between ancestries. Loci of these associations were shared with 15 brain-related non-imaging traits including cognition and neuropsychiatric disorders. Our results provide a valuable catalog of genetic associations for brain imaging phenotypes in more diverse populations.

The mean Hounsfield unit range acquired from different slices produces superior predictive accuracy for pyonephrosis in obstructive uropathy.

PURPOSE: To determine the non-contrast computer tomography imaging features of pyonephrosis and evaluate the predictive value of Hounsfield units (HUs) in different hydronephrotic region slices. MATERIALS AND METHODS: We retrospectively reviewed data from patients with hydronephrosis who had renal-ureteral calculi. All patients were categorized into pyonephrosis and simple hydronephrosis groups. Baseline characteristics, the mean HU values in the maximal hydronephrotic region (uHU) slice, and the range of uHU in different slices (ΔuHU) were compared between the two groups. Univariate and multivariate analyses were performed to identify risk factors for pyonephrosis. RESULTS: Among the 181 patients enrolled in the current study, 71 patients (39.2%) were diagnosed with pyonephrosis. The mean dilated pelvis surface areas were comparable between patients with pyonephrosis and simple hydronephrosis (822.61 mm² vs. 877.23 mm², p=0.722). Collecting system debris (p=0.022), a higher uHU (p=0.038), and a higher ΔuHU (p<0.001) were identified as independent risk factors for pyonephrosis based on multivariate analysis. The ΔuHU sensitivity and specificity were 88.7% and 86.4%, respectively, at a cutoff value of 6.56 (p<0.001), whereas the sensitivity and specificity for detecting pyonephrosis at a uHU cutoff value of 7.96 was 50.7% and 70.9%, respectively (p=0.003). CONCLUSIONS: Non-contrast computer tomography was shown to accurately distinguish simple hydronephrosis from pyonephrosis in patients with obstructive uropathy. Evaluation of the ΔuHU in different slices may be more reliable than the uHU acquired from a single slice in predicting pyonephrosis.

Targeting Non-Alcoholic Fatty Liver Disease with Hawthorn Ethanol Extract (HEE): A Comprehensive Examination of Hepatic Lipid Reduction and Gut Microbiota Modulation.

Recent studies have highlighted the lipid-lowering ability of hawthorn ethanol extract (HEE) and the role played by gut flora in the efficacy of HEE. Our study sought to explore the effects of HEE on non-alcoholic fatty liver disease (NAFLD) in normal flora and pseudo germ-free mice. The results showed that HEE effectively diminished hepatic lipid accumulation, ameliorated liver function, reduced inflammatory cytokine levels and blood lipid profiles, and regulated blood glucose levels. HEE facilitated triglyceride breakdown, suppressed fatty acid synthesis, and enhanced intestinal health by modulating the diversity of the gut microbiota and the production of short-chain fatty acids in the gut. In addition, HEE apparently helps to increase the presence of beneficial genera of bacteria, thereby influencing the composition of the gut microbiota, and the absence of gut flora affects the efficacy of HEE. These findings reveal the potential of hawthorn for the prevention and treatment of NAFLD and provide new perspectives on the study of functional plants to improve liver health.

Efficient acid hydrolysis for compound-specific δ15N analysis of amino acids for determining trophic positions.

Compound-specific isotope analysis of nitrogen in amino acids (CSIA-AA, δ15NAA) has gained increasing popularity for elucidating energy flow within food chains and determining the trophic positions of various organisms. However, there is a lack of research on the impact of hydrolysis conditions, such as HCl concentration and hydrolysis time, on δ15NAA analysis in biota samples. In this study, we investigated two HCl concentrations (6 M and 12 M) and four hydrolysis times (2 h, 6 h, 12 h, and 24 h) for hydrolyzing and derivatizing AAs in reference materials (Tuna) and biological samples of little egret (n = 4), night heron (n = 4), sharpbelly (n = 4) and Algae (n = 1) using the n-pivaloyl-iso-propyl (NPIP) ester approach. A Dowex cation exchange resin was used to purify amino acids before derivatization. We then determined δ15NAA values using gas chromatography-combustion-isotope ratio mass spectrometry (GC-C-IRMS). The results revealed no significant differences (p > 0.05) in δ15NAA values among samples treated with different HCl concentrations or hydrolysis times, particularly for δ15NGlx (range: 21.0-23.5‰) and δ15NPhe (range: 4.3-5.4‰) in Tuna (12 M). Trophic positions (TPs) calculated based on δ15NAA at 2 h (little egret: 2.9 ± 0.1, night heron: 2.8 ± 0.1, sharpbelly: 2.0 ± 0.1 and Algae: 1.3 ± 0.2) were consistent with those at 24 h (3.1 ± 0.1, 2.8 ± 0.1, 2.2 ± 0.1 and 1.1 ± 0.1, respectively), suggesting that a 2-h hydrolysis time and a 6 M HCl concentration are efficient pretreatment conditions for determining δ15NAA and estimating TP. Compared to the currently used hydrolysis conditions (24 h, 6 M), the proposed conditions (2 h, 6 M) accelerated the δ15NAA assay, making it faster, more convenient, and more efficient. Further research is needed to simplify the operational processes and reduce the time costs, enabling more efficient applications of CSIA-AA.

Dysregulated MiR-223-5p Modulates Inflammation and Oxidative Stress in Traumatic Spinal Cord Injury.

AIM: This study aimed to evaluate the miR-223-5p expression in patients with spinal cord injury (SCI) and to determine its role in the pathogenesis of SCI. METHODS: The serum miR-223-5p levels were analyzed using quantitative real-time polymerase chain reaction. The diagnostic accuracy of miR-223-5p was evaluated using the receiving operating characteristic curves. LPS-induced PC12 cells were established as an in vitro inflammatory cell model. Cell apoptosis, inflammation and oxidative stress were examined. The SCI rat model was constructed to evaluate the effects of miR-223-5p on inflammatory response and motor function in rats. RESULTS: MiR-223-5p expression was upregulated in SCI patients. MiR-223-5p expression in the complete SCI group was significantly higher than that in incomplete SCI group. ROC analysis showed that miR-223-5p can distinguish SCI patients from healthy volunteers. In vitro experiments demonstrated that LPS upregulated apoptosis and inflammation in PC12 cells. Treatment with miR-223-5p inhibitor alleviated the changes in LPS-induced PC12 cells . Inhibition of miR-223-5p can alleviate the activation of inflammatory response and the effects of SCI on the motor function in rats. CONCLUSIONS: MiR-223-5p is a potential diagnostic marker for SCI, and it can promote the SCI progression by regulating nerve cell survival, inflammation, and oxidative stress.

Comparative analysis of codon usage bias in chloroplast genomes of ten medicinal species of Rutaceae.

Rutaceae family comprises economically important plants due to their extensive applications in spices, food, oil, medicine, etc. The Rutaceae plants is able to better utilization through biotechnology. Modern biotechnological approaches primarily rely on the heterologous expression of functional proteins in different vectors. However, several proteins are difficult to express outside their native environment. The expression potential of functional genes in heterologous systems can be maximized by replacing the rare synonymous codons in the vector with preferred optimal codons of functional genes. Codon usage bias plays a critical role in biogenetic engineering-based research and development. In the current study, 727 coding sequences (CDSs) obtained from the chloroplast genomes of ten Rutaceae plant family members were analyzed for codon usage bias. The nucleotide composition analysis of codons showed that these codons were rich in A/T(U) bases and preferred A/T(U) endings. Analyses of neutrality plots, effective number of codons (ENC) plots, and correlations between ENC and codon adaptation index (CAI) were conducted, which revealed that natural selection is a major driving force for the Rutaceae plant family's codon usage bias, followed by base mutation. In the ENC vs. CAI plot, codon usage bias in the Rutaceae family had a negligible relationship with gene expression level. For each sample, we screened 12 codons as preferred and high-frequency codons simultaneously, of which GCU encoding Ala, UUA encoding Leu, and AGA encoding Arg were the most preferred codons. Taken together, our study unraveled the synonymous codon usage pattern in the Rutaceae family, providing valuable information for the genetic engineering of Rutaceae plant species in the future.

Sub-2 nm Microstrained High-Entropy-Alloy Nanoparticles Boost Hydrogen Electrocatalysis.

High-entropy alloys (HEAs) confine multifarious elements into the same lattice, leading to intense lattice distortion effect. The lattice distortion tends to induce local microstrain at atomic level and thus affect surface adsorptions toward different adsorbates in various electrocatalytic reactions, yet remains unexplored. Herein, this work reports a class of sub-2 nm IrRuRhMoW HEA nanoparticles (NPs) with distinct local microstrain induced by lattice distortion for boosting alkaline hydrogen oxidation (HOR) and evolution reactions (HER). This work demonstrates that the distortion-rich HEA catalysts with optimized electronic structure can downshift the d-band center and generate uncoordinated oxygen sites to enhance the surface oxophilicity. As a result, the IrRuRhMoW HEA NPs show a remarkable HOR kinetic current density of 8.09 mA µg-1 PGM at 50 mV versus RHE, 8.89, 22.47 times higher than those of IrRuRh NPs without internal strain and commercial Pt/C, respectively, which is the best value among all the reported non-Pt based catalysts. IrRuRhMoW HEA NPs also display great HER performances with a turnover frequency (TOF) value of 5.93 H2 s-1 at 70 mV versus RHE, 4.6-fold higher than that of Pt/C catalyst, exceeding most noble metal-based catalysts. Experimental characterizations and theoretical studies collectively confirm that weakened hydrogen (Had) and enhanced hydroxyl (OHad) adsorption are achieved by simultaneously modulating the hydrogen adsorption binding energy and surface oxophilicity originated from intensified ligand effect and microstrain effect over IrRuRhMoW HEA NPs, which guarantees the remarkable performances toward HOR/HER.

Study on the differential diagnosis of benign and malignant breast lesions using a deep learning model based on multimodal images.

OBJECTIVE: To establish a multimodal model for distinguishing benign and malignant breast lesions. MATERIALS AND METHODS: Clinical data, mammography, and MRI images (including T2WI, diffusion-weighted images (DWI), apparent diffusion coefficient (ADC), and DCE-MRI images) of 132 benign and breast cancer patients were analyzed retrospectively. The region of interest (ROI) in each image was marked and segmented using MATLAB software. The mammography, T2WI, DWI, ADC, and DCE-MRI models based on the ResNet34 network were trained. Using an integrated learning method, the five models were used as a basic model, and voting methods were used to construct a multimodal model. The dataset was divided into a training set and a prediction set. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the model were calculated. The diagnostic efficacy of each model was analyzed using a receiver operating characteristic curve (ROC) and an area under the curve (AUC). The diagnostic value was determined by the DeLong test with statistically significant differences set at P < 0.05. RESULTS: We evaluated the ability of the model to classify benign and malignant tumors using the test set. The AUC values of the multimodal model, mammography model, T2WI model, DWI model, ADC model and DCE-MRI model were 0.943, 0.645, 0.595, 0.905, 0.900, and 0.865, respectively. The diagnostic ability of the multimodal model was significantly higher compared with that of the mammography and T2WI models. However, compared with the DWI, ADC, and DCE-MRI models, there was no significant difference in the diagnostic ability of these models. CONCLUSION: Our deep learning model based on multimodal image training has practical value for the diagnosis of benign and malignant breast lesions.