MPIGAN: An end-to-end deep based generative framework for high-resolution magnetic particle imaging reconstruction.

BACKGROUND: Magnetic particle imaging (MPI) is a recently developed, non-invasive in vivo imaging technique to map the spatial distribution of superparamagnetic iron oxide nanoparticles (SPIONs) in animal tissues with high sensitivity and speed. It is a challenge to reconstruct images directly from the received signals of MPI device due to the complex physical behavior of the nanoparticles. System matrix and X-space are two commonly used MPI reconstruction methods, where the former is extremely time-consuming and the latter usually produces blurry images. PURPOSE: Currently, we proposed an end-to-end machine learning framework to reconstruct high-resolution MPI images from 1-D voltage signals directly and efficiently. METHODS: The proposed framework, which we termed "MPIGAN", was trained on a large MPI simulation dataset containing 291 597 pairs of high-resolution 2-D phantom images and each image's corresponding voltage signals, so that it was able to accurately capture the nonlinear relationship between the spatial distribution of SPIONs and the received voltage signal, and realized high-resolution MPI image reconstruction. RESULTS: Experiment results showed that, MPIGAN exhibited remarkable abilities in high-resolution MPI image reconstruction. MPIGAN outperformed the traditional methods of system matrix and X-space in recovering the fine-scale structure of magnetic nanoparticles' spatial distribution and achieving enhanced reconstruction performance in both visual effects and quantitative assessments. Moreover, even when the received signals were severely contaminated with noise, MPIGAN could still generate high-quality MPI images. CONCLUSION: Our study provides a promising AI solution for end-to-end, efficient, and high-resolution magnetic particle imaging reconstruction.

Demographic and clinical feature disparity between progress and non-progress patients with vitiligo after COVID-19 vaccination: A cross-sectional study.

There have been several case reports regarding newly developed vitiligo following the coronavirus disease 19 (COVID-19) vaccination. However, the relationship between COVID-19 vaccine and vitiligo progression remains unclear. To explore the relationship between COVID-19 vaccine and vitiligo progression and its potential influencing factors, A cross-sectional study was conducted on 90 patients with vitiligo who received inactivated COVID-19 vaccination. Detailed information covering demographic characteristics (age and sex), vitiligo clinical features (disease subtypes, duration, stage and comorbidities) and disease activity was collected through an electronic questionnaire. Ninety patients with vitiligo included 44.4% males, with an average age of 38.1 years (standard deviation, SD = 15.0). Patients were divided into progress group (29, 32.2%) and normal group (61, 67.8%) based on whether they experienced vitiligo progression after inactivated COVID-19 vaccination. 41.3% of patients in the progress group experienced vitiligo progression within 1 week after vaccination, and disease progression mainly occurred after the first dose inoculation (20, 69.0%). Logistic regression revealed that patients aged <45 years (odds ratio (OR) was 0.87, 95% confidence interval (CI): 0.34-2.22) and male patients (OR = 0.84, 95% CI: 0.34-2.05) had lower risk for vitiligo progression, while patients with segmental vitiligo (SV) subtype (OR = 1.68, 95% CI: 0.53-5.33), with <5 years disease duration (OR = 1.32, 95% CI: 0.51-3.47) had higher risk for vitiligo progression after COVID-19 vaccination, but without statistical significance. Over 30% patients experienced vitiligo progression after inactivated COVID-19 vaccination, and female patients, elder age, shorter disease duration and SV subtype are potential risk factors for vitiligo progression.

Reconstruction of perceived face images from brain activities based on multi-attribute constraints.

Reconstruction of perceived faces from brain signals is a hot topic in brain decoding and an important application in the field of brain-computer interfaces. Existing methods do not fully consider the multiple facial attributes represented in face images, and their different activity patterns at multiple brain regions are often ignored, which causes the reconstruction performance very poor. In the current study, we propose an algorithmic framework that efficiently combines multiple face-selective brain regions for precise multi-attribute perceived face reconstruction. Our framework consists of three modules: a multi-task deep learning network (MTDLN), which is developed to simultaneously extract the multi-dimensional face features attributed to facial expression, identity and gender from one single face image, a set of linear regressions (LR), which is built to map the relationship between the multi-dimensional face features and the brain signals from multiple brain regions, and a multi-conditional generative adversarial network (mcGAN), which is used to generate the perceived face images constrained by the predicted multi-dimensional face features. We conduct extensive fMRI experiments to evaluate the reconstruction performance of our framework both subjectively and objectively. The results show that, compared with the traditional methods, our proposed framework better characterizes the multi-attribute face features in a face image, better predicts the face features from brain signals, and achieves better reconstruction performance of both seen and unseen face images in both visual effects and quantitative assessment. Moreover, besides the state-of-the-art intra-subject reconstruction performance, our proposed framework can also realize inter-subject face reconstruction to a certain extent.

Dynamic algorithm for fitness function greatly improves the optimization efficiency of frequency selective surface for better design of radar.

Multiple objectives optimization of frequency selective surface (FSS) structures is challenging in electromagnetic wave filter design. For example, one of the sub-objectives, the sidelobe level (SLL), is critical to directional anti-interference, which is complicated and becomes the bottleneck for radar design. Here, we established a dynamic algorithm for fitness function to automatically adjust the weights of multiple objectives in the optimization process of FSS structures. The dynamic algorithm could efficiently evaluate the achieving probability of sub-objectives according to the statistical analysis of the latest individual distribution so that the fitness function could automatically adjusted to focus on the sub-objective difficult to optimize, such as SLL. Computational results from the dynamic algorithm showed that the efficiency of multi-objective optimization was greatly improved by 213%, as compared to the fixed-weighted algorithm of the fitness function. Specifically for SLL, the efficiency rate increased even better, up to 315%. More interestingly, the FSS structures were most improved while picking median value or golden section value as the reference value. Taken together, the current study indicated that the dynamic algorithm with fitness function might be a better choice for FSS structural optimization with SLL suppression and potentially for the better design of lower SLL radar.

Attention-based generative adversarial network in medical imaging: A narrative review.

As a popular probabilistic generative model, generative adversarial network (GAN) has been successfully used not only in natural image processing, but also in medical image analysis and computer-aided diagnosis. Despite the various advantages, the applications of GAN in medical image analysis face new challenges. The introduction of attention mechanisms, which resemble the human visual system that focuses on the task-related local image area for certain information extraction, has drawn increasing interest. Recently proposed transformer-based architectures that leverage self-attention mechanism encode long-range dependencies and learn representations that are highly expressive. This motivates us to summarize the applications of using transformer-based GAN for medical image analysis. We reviewed recent advances in techniques combining various attention modules with different adversarial training schemes, and their applications in medical segmentation, synthesis and detection. Several recent studies have shown that attention modules can be effectively incorporated into a GAN model in detecting lesion areas and extracting diagnosis-related feature information precisely, thus providing a useful tool for medical image processing and diagnosis. This review indicates that research on the medical imaging analysis of GAN and attention mechanisms is still at an early stage despite the great potential. We highlight the attention-based generative adversarial network is an efficient and promising computational model advancing future research and applications in medical image analysis.

The involvement of ERK1/2 and p38 MAPK in the premature senescence of melanocytes induced by H2O2 through a p53-independent p21 pathway.

BACKGROUND: The pathogenesis of vitiligo is still unknown and oxidative stress is an important factor that can damage or incapacitate melanocytes. OBJECTIVE: To investigate the role of oxidative stress in the premature senescence of melanocytes and their transfer of melanosomes. METHODS: Cultured human melanocytes were treated with H2O2 after which cell viability and apoptosis were assessed. We investigated whether exposure to H2O2 induces premature senescence. RNA sequencing was used to screen aging-related signaling pathways. The expression of dendritic regulatory proteins, adhesion molecules and cell cytoskeletal proteins, as well as melanosome distribution were characterized. The ROS scavenger NAC was used to study the role of ROS in cell senescence and in melanosome transfer. RESULTS: Cell viability decreased progressively and cell apoptosis increased after treatment with H2O2. H2O2 treatment tended to induce premature senescence in melanocytes through a p53-independent p21 pathway. RNA sequencing analysis showed that H2O2 treatment induced the differential expression of MAPK signaling pathway components. Western blotting and qRT-PCR confirmed that H2O2 treatment increased the phosphorylation of ERK1/2 and p38 MAPK, which are involved in inducing the senescence of melanocytes, but not JNK. The expression of cell cytoskeleton and adhesion molecules decreased after H2O2 treatment. p21 siRNA treatment reversed these changes. Treatment with NAC improved the premature senescence and the impaired melanosome transfer induced by H2O2. CONCLUSION: H2O2 increases ROS levels, which activates the ERK1/2 and p38 MAPK pathways to induce the premature senescence of melanocytes through p21 via a p53-independent pathway and consequently disrupts melanosome transfer.

Molecular mechanisms of Phytophthora sojae avirulence effectors escaping host recognition.

Phytophthora sojae is a well-known destructive oomycete pathogen, which causes soybean stem and root rot and poses a serious threat to global food security. Growing soybean cultivars with the appropriate resistance to P. sojae (Rps) genes are the primary management strategy to reduce losses. In most Phytophthora pathosystems, host resistance protein encoded by a specific R gene in the plant recognizes corresponding RxLR effector protein, encoded by an avirulence gene. This gene-for-gene relationship has been exploited to help breeders and agronomists deploy soybean cultivars. To date, 6 Rps genes have been incorporated into commercial soybean germplasm and trigger plant immunity in response to 8 P. sojae avirulence effectors. The incorporation of Rps genes in the soybean population creates selection pressure in favor of novel pathotypes of P. sojae. The 8 avirulence genes evolved to evade the host immune system, driven by genetic selection pressures. Understanding the evading strategies has important reference value for the prevention and control of Phytophthora stem and root rot. This investigation primarily highlights the research on the strategies of P. sojae avirulence effector evasion of host recognition, looking forward to creating durable resistance genes and thereby enabling successful disease management.

Association of novel rare coding variants with juvenile idiopathic arthritis.

OBJECTIVE: Juvenile idiopathic arthritis (JIA) is the most common type of arthritis among children, but a few studies have investigated the contribution of rare variants to JIA. In this study, we aimed to identify rare coding variants associated with JIA for the genome-wide landscape. METHODS: We established a rare variant calling and filtering pipeline and performed rare coding variant and gene-based association analyses on three RNA-seq datasets composed of 228 JIA patients in the Gene Expression Omnibus against different sets of controls, and further conducted replication in our whole-exome sequencing (WES) data of 56 JIA patients. Then we conducted differential gene expression analysis and assessed the impact of recurrent functional coding variants on gene expression and signalling pathway. RESULTS: By the RNA-seq data, we identified variants in two genes reported in literature as JIA causal variants, as well as additional 63 recurrent rare coding variants seen only in JIA patients. Among the 44 recurrent rare variants found in polyarticular patients, 10 were replicated by our WES of patients with the same JIA subtype. Several genes with recurrent functional rare coding variants have also common variants associated with autoimmune diseases. We observed immune pathways enriched for the genes with rare coding variants and differentially expressed genes. CONCLUSION: This study elucidated a novel landscape of recurrent rare coding variants in JIA patients and uncovered significant associations with JIA at the gene pathway level. The convergence of common variants and rare variants for autoimmune diseases is also highlighted in this study.

Integrative analysis of genome-wide association studies identifies novel loci associated with neuropsychiatric disorders.

Neuropsychiatric disorders, such as autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), schizophrenia (SCZ), bipolar disorder (BIP), and major depressive disorder (MDD) share common clinical presentations, suggesting etiologic overlap. A substantial proportion of SNP-based heritability for neuropsychiatric disorders is attributable to genetic components, and genome-wide association studies (GWASs) focusing on individual diseases have identified multiple genetic loci shared between these diseases. Here, we aimed at identifying novel genetic loci associated with individual neuropsychiatric diseases and genetic loci shared by neuropsychiatric diseases. We performed multi-trait joint analyses and meta-analysis across five neuropsychiatric disorders based on their summary statistics from the Psychiatric Genomics Consortium (PGC), and further carried out a replication study of ADHD among 2726 cases and 16299 controls in an independent pediatric cohort. In the multi-trait joint analyses, we found five novel genome-wide significant loci for ADHD, one novel locus for BIP, and ten novel loci for MDD. We further achieved modest replication in our independent pediatric dataset. We conducted fine-mapping and functional annotation through an integrative multi-omics approach and identified causal variants and potential target genes at each novel locus. Gene expression profile and gene-set enrichment analysis further suggested early developmental stage expression pattern and postsynaptic membrane compartment enrichment of candidate genes at the genome-wide significant loci of these neuropsychiatric disorders. Therefore, through a multi-omics approach, we identified novel genetic loci associated with the five neuropsychiatric disorders which may help to better understand the underlying molecular mechanism of neuropsychiatric diseases.

The Multi-Omics Architecture of Juvenile Idiopathic Arthritis.

Juvenile idiopathic arthritis (JIA) is highly heterogeneous in terms of etiology and clinical presentation with ambiguity in JIA classification. The advance of high-throughput omics technologies in recent years has gained us significant knowledge about the molecular mechanisms of JIA. Besides a minor proportion of JIA cases as monogenic, most JIA cases are polygenic disease caused by autoimmune mechanisms. A number of HLA alleles (including both HLA class I and class II genes), and 23 non-HLA genetic loci have been identified of association with different JIA subtypes. Omics technologies, i.e., transcriptome profiling and epigenomic analysis, contributed significant knowledge on the molecular mechanisms of JIA in addition to the genetic approach. New molecular knowledge on different JIA subtypes enables us to reconsider the JIA classification, but also highlights novel therapeutic targets to develop a cure for the devastating JIA.

A Conserved Glycoside Hydrolase Family 7 Cellobiohydrolase PsGH7a of Phytophthora sojae Is Required for Full Virulence on Soybean.

Phytopathogens deploy glycoside hydrolases (GHs) to disintegrate plant cell walls for nutrition and invasion. However, the pathogenic mechanisms of the majority of GHs in virulence remain unknown, especially in oomycetes. In this study, a Phytophthora sojae gene encodes a GH7 family cellobiohydrolase, named PsGH7a, was identified. PsGH7a was highly induced during the cyst germination and infection stages. PsGH7a is conserved in oomycetes, and shares a high amino acid sequence identity (>85%) within Phytophthora genus. The recombinant PsGH7a catalyzes the hydrolysis of ß-1,4-glucan and avicel, which represent the major components of cellulose in plant cell wall. The mutation of catalytic residue Glu236 to alanine resulted in a lower catalytic activity. In addition, the PsGH7a promotes Phytophthora invasion, while the mutant can not. Notably, PsGH7a protein triggers hypersensitive cell death in diverse plants. PsGH7a knockout mutants were generated via CRISPR/Cas9 system, to investigate its biological function. Compared to wild-type strain P6497, the mutants showed reduced virulence on susceptible soybean, indicates PsGH7a is indispensable to P. sojae virulence.

A twelve-gene signature for survival prediction in malignant melanoma patients.

BACKGROUND: Melanoma is defined as a highly mutational heterogeneous disease containing numerous alternations of the molecule. However, due to the phenotypically and genetically heterogeneity of malignant melanoma, conventional clinical characteristics remain restricted or limited in the ability to accurately predict individual outcomes and survival. This study aimed to establish an accurate gene expression signature to predict melanoma prognosis. METHODS: In this study, we established an RNA sequencing-based 12-gene signature data of melanoma patients obtained from 2 independent databases: the Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. We evaluated the quality of each gene to predict survival conditions in each database by employing univariate and multivariate regression models. A prognostic risk score based on a prognostic signature was determined. This prognostic gene signature further classified patients into low-risk and high-risk groups. RESULTS: Based on a prognostic signature, a prognostic risk score was determined. This prognostic gene signature further divided the patients into low-risk and high-risk groups. In the chemotherapy and radiotherapy groups of the TCGA cohort and V-raf murine sarcoma viral oncogene homolog B1 (BRAF) expression group in the GEO cohort, patients in the low-risk group had a longer survival duration compared to patients in the high-risk group. Nevertheless, the immunotherapy group in the TCGA database and neuroblastoma RAS viral oncogene homolog (NRAS) expression group in the GEO database had no significant differences in statistics. Moreover, this gene signature was associated with patient prognosis regardless of whether the Breslow depth was greater than or less than 3.75 mm. Stratified gene set enrichment analysis (GSEA) revealed that certain immune-related pathways, such as the T-cell signaling pathway, chemokine signaling pathway, and primary immunodeficiency, were significantly enriched in the low-risk group of both TCGA and GEO cohorts. This information implied the immune-related properties of the 12-gene signature. CONCLUSIONS: Our study emphasizes the significance of the gene expression signature in that it may be an indispensable prognostic predictor in melanoma and has great potential for application in personalized treatment.

Enabling Self-passivation by Attaching Small Grains on Surfaces of Large Grains toward High-Performance Perovskite LEDs.

This paper reports a new method to generate stable and high-brightness electroluminescence (EL) by subsequently growing large/small grains at micro/nano scales with the configuration of attaching small grains on the surfaces of large grains in perovskite (MAPbBr3) films by mixing two precursor solutions (PbBr2 + MABr and Pb(Ac)2·3H2O + MABr). Consequently, the small and large grains serve, respectively, as passivation agents and light-emitting centers, enabling self-passivation on the defects located on the surfaces of light-emitting large grains. Furthermore, the light-emitting states become linearly polarized with maximal polarization of 30.8%, demonstrating a very stable light emission (49,119 cd/m2 with EQE = 11.31%) and a lower turn-on bias (1.9 V) than the bandgap (2.25V) in the perovskite LEDs (ITO/PEDOT:PSS/MAPbBr3/TPBi[50 nm]/LiF[0.7 nm]/Ag). Therefore, mixing large/small grains with the configuration of attaching small grains on the surfaces of large grains by mixing two precursor solutions presents a new strategy to develop high-performance perovskite LEDs.

Highly Efficient Charge Collection in Bulk-Heterojunction Organic Solar Cells by Anomalous Hole Transfer and Improved Interfacial Contact.

The dilute donor-fullerene bulk heterojunction (BHJ) has been proven to be an efficient architecture of organic solar cells. However, the hole-extraction pathway and the origin of the high open-circuit voltage ( VOC) in this peculiar architecture remains elusive. Direct evidence is provided here that the photogenerated holes can be extracted via the acceptor phase even under the operating conditions. Meanwhile VOC is found to be closely correlated with the surface composition at the MoO3/BHJ interface. Extending these findings into device optimization, more than 37% enhancement is achieved in a prototype BHJ device. These results evoke renewed insight into the underlying physics in organic solar cells.

Hydroxy-pentanones production by Bacillus sp. H15-1 and its complete genome sequence.

Acyloins are useful organic compounds with reactive adjacent hydroxyl group and carbonyl group. Current research is usually constrained to acetoin (i.e. 3-hydroxy-2-butanone) and the biological production of other acyloins was scarcely reported. In this study, two hydroxy-pentanone metabolites (3-hydroxy-2-pentanone and 2-hydroxy-3-pentanone) of Bacillus sp. H15-1 were identified by gas chromatography-mass spectrometry and authentic standards. Then the complete genome of this strain was sequenced and de novo assembled to a single circular chromosome of 4,162,101bp with a guanine-cytosine content of 46.3%, but no special genes were found for the biosynthesis of the hydroxy-pentanones. Since hydroxy-pentanones are the homologues of acetoin, the two genes alsD and alsS (encoding α-acetolactate decarboxylase and α-acetolactate synthase, respectively) responsible for acetoin formation in this strain were respectively expressed in Escherichia coli. The purified enzymes were found to be capable of transforming pyruvate and 2-oxobutanoate to the two hydroxy-pentanones. This study extends the knowledge on the biosynthesis of acyloins and provides helpful information for further utilizing Bacillus sp. H15-1 as a source of valuable acyloins.

High-Efficiency Selective Electron Tunnelling in a Heterostructure Photovoltaic Diode.

A heterostructure photovoltaic diode featuring an all-solid-state TiO2/graphene/dye ternary interface with high-efficiency photogenerated charge separation/transport is described here. Light absorption is accomplished by dye molecules deposited on the outside surface of graphene as photoreceptors to produce photoexcited electron-hole pairs. Unlike conventional photovoltaic conversion, in this heterostructure both photoexcited electrons and holes tunnel along the same direction into graphene, but only electrons display efficient ballistic transport toward the TiO2 transport layer, thus leading to effective photon-to-electricity conversion. On the basis of this ipsilateral selective electron tunnelling (ISET) mechanism, a model monolayer photovoltaic device (PVD) possessing a TiO2/graphene/acridine orange ternary interface showed ∼86.8% interfacial separation/collection efficiency, which guaranteed an ultrahigh absorbed photon-to-current efficiency (APCE, ∼80%). Such an ISET-based PVD may become a fundamental device architecture for photovoltaic solar cells, photoelectric detectors, and other novel optoelectronic applications with obvious advantages, such as high efficiency, easy fabrication, scalability, and universal availability of cost-effective materials.

Enzymatic synthesis of aroma acetoin fatty acid esters by immobilized Candida antarctica lipase B.

OBJECTIVE: To enzymatically synthesize aroma acetoin fatty acid esters, useful as flavor and fragrance ingredients in foods. RESULTS: Immobilized Candida antarctica lipase B (CALB), performed significantly better than lipases from Rhizopus niveus and Candida rugosa in carrying out the esterification of acetoin and fatty acids. C4-C12 straight chain fatty acids were suitable acyl donors and CALB had a strong preference for longer straight chains up to ten carbon atoms. Higher temperatures, 40-60 °C, and higher acetoin/fatty acid molar ratios favored the conversion. The maximum yield of acetoin octanoate obtained was (51 ± 1) % after 24 h reaction time in hexane with 0.25 M octanoic acid, 5:1 excess acetoin and an enzyme concentration of 6 g/mol fatty acid at 60 °C. The enzyme activity declined at a steady rate during reuse at 60 °C and after the 10th cycle, 65 % of initial activity was still be retained. CONCLUSION: This is the first report of acetoin fatty acid ester synthesis by biological method and CALB has been shown to be effective for the lipase-catalyzed esterification of acetion and C4-C12 straight chain fatty acids.

Biodegradation of C5-C 8 fatty acids and production of aroma volatiles by Myroides sp. ZB35 isolated from activated sludge.

In the effluents of a biologically treated wastewater from a heavy oil-refining plant, C5-C8 fatty acids including pentanoic acid, hexanoic acid, heptanoic acid, octanoic acid, and 2-methylbutanoic acid are often detected. As these residual fatty acids can cause further air and water pollution, a new Myroides isolate ZB35 from activated sludge was explored to degrade these C5-C8 fatty acids in this study. It was found that the biodegradation process involved a lag phase that became prolonged with increasing acyl chain length when the fatty acids were individually fed to this strain. However, when fed as a mixture, the ones with longer acyl chains were found to become more quickly assimilated. The branched 2-methylbutanoic acid was always the last one to be depleted among the five fatty acids under both conditions. Metabolite analysis revealed one possible origin of short chain fatty acids in the biologically treated wastewater. Aroma volatiles including 2-methylbutyl isovalerate, isoamyl 2-methylbutanoate, isoamyl isovalerate, and 2-methylbutyl 2-methylbutanoate were subsequently identified from ZB35 extracts, linking the source of the fruity odor to these esters excreted by Myroides species. To our best knowledge, this is the first finding of these aroma esters in bacteria. From a biotechnological viewpoint, this study has revealed the potential of Myroides species as a promising source of aroma esters attractive for food and fragrance industries.

Monte Carlo simulation based on dynamic disorder model in organic semiconductors: from coherent to incoherent transport.

The dynamic disorder model for charge carrier transport in organic semiconductors has been extensively studied in recent years. Although it is successful on determining the value of bandlike mobility in the organic crystalline materials, the incoherent hopping, the typical transport characteristic in amorphous molecular semiconductors, cannot be described. In this work, the decoherence process is taken into account via a phenomenological parameter, say, decoherence time, and the projective and Monte Carlo method are applied for this model to determine the waiting time and thus the diffusion coefficient. It is obtained that the type of transport is changed from coherent to incoherent with a sufficiently short decoherence time, which indicates the essential role of decoherence time in determining the type of transport in organics. We have also discussed the spatial extent of carriers for different decoherence time, and the transition from delocalization (carrier resides in about 10 molecules) to localization is observed. Based on the experimental results of spatial extent, we estimate that the decoherence time in pentacene has the order of 1 ps. Furthermore, the dependence of diffusion coefficient on decoherence time is also investigated, and corresponding experiments are discussed.