Neuroimaging features of cognitive impairments in schizophrenia and major depressive disorder.

Cognitive dysfunctions are one of the key symptoms of schizophrenia (SZ) and major depressive disorder (MDD), which exist not only during the onset of diseases but also before the onset, even after the remission of psychiatric symptoms. With the development of neuroimaging techniques, these non-invasive approaches provide valuable insights into the underlying pathogenesis of psychiatric disorders and information of cognitive remediation interventions. This review synthesizes existing neuroimaging studies to examine domains of cognitive impairment, particularly processing speed, memory, attention, and executive function in SZ and MDD patients. First, white matter (WM) abnormalities are observed in processing speed deficits in both SZ and MDD, with distinct neuroimaging findings highlighting WM connectivity abnormalities in SZ and WM hyperintensity caused by small vessel disease in MDD. Additionally, the abnormal functions of prefrontal cortex and medial temporal lobe are found in both SZ and MDD patients during various memory tasks, while aberrant amygdala activity potentially contributes to a preference to negative memories in MDD. Furthermore, impaired large-scale networks including frontoparietal network, dorsal attention network, and ventral attention network are related to attention deficits, both in SZ and MDD patients. Finally, abnormal activity and volume of the dorsolateral prefrontal cortex (DLPFC) and abnormal functional connections between the DLPFC and the cerebellum are associated with executive dysfunction in both SZ and MDD. Despite these insights, longitudinal neuroimaging studies are lacking, impeding a comprehensive understanding of cognitive changes and the development of early intervention strategies for SZ and MDD. Addressing this gap is critical for advancing our knowledge and improving patient prognosis.

Early inoculation of an endophyte alters the assembly of bacterial communities across rice plant growth stages.

The core endophytes of plants are regarded as promising resources in future agroecosystems. How they affect the assembly of rice-related bacterial communities after early inoculation remains unclear. Here, we examined bacterial communities across 148 samples, including bulk and rhizosphere soils, sterilized roots, stems, and seeds at the seedling, tillering, booting, and maturity stages. Tissue cultured rice seedlings were inoculated with Xathomonas sacchari JR3-14, a core endophytic bacterium of rice seeds, before transplanting. The results revealed that α-diversity indices were significantly enhanced in the root and stem endosphere at the seedling stage. ß-diversity was altered at most plant developmental stages, except for the root and stem at the booting stage. Network complexity consequently increased in the root and stem across rice growth stages, other than the stem endosphere at the booting stage. Four abundant beneficial bacterial taxa, Bacillus, Azospira, Azospirillum, and Arthrobacter, were co-enriched during the early growth stage. Infer Community Assembly Mechanisms by Phylogenetic-bin-based null model analysis revealed a higher relative contribution of drift and other eco-evolutionary processes mainly in root compartments across all growth stages, but the opposite pattern was observed in stem compartments. IMPORTANCE Endophytic bacteria are regarded as promising environmentally friendly resources to promote plant growth and plant health. Some of microbes from the seed are able to be carried over to next generation, and contribute to the plant's ability to adapt to new environments. However, the effects of early inoculation with core microbes on the assembly of the plant microbiome are still unclear. In our study, we demonstrate that early inoculation of the rice seed core endophytic bacterium Xanthomonas sacchari could alter community diversity, enhance complexity degree of network structure at most the growth stages, and enrich beneficial bacteria at the seedling stage of rice. We further analyzed the evolutionary processes caused by the early inoculation. Our results highlight the new possibilities for research and application of sustainable agriculture by considering the contribution of seed endophytes in crop production and breeding.

Rhizobium acaciae sp. nov., a new nitrogen-fixing symbiovar isolated from root nodules of Acacia saligna in Tunisia.

Three bacterial strains, 1AS11T, 1AS12 and 1AS13, members of the new symbiovar salignae and isolated from root nodules of Acacia saligna grown in Tunisia, were characterized using a polyphasic approach. All three strains were assigned to the Rhizobium leguminosarum complex on the basis of rrs gene analysis. Phylogenetic analysis based on 1734 nucleotides of four concatenated housekeeping genes (recA, atpD, glnII and gyrB) showed that the three strains were distinct from known rhizobia species of the R. leguminosarum complex and clustered as a separate clade within this complex. Phylogenomic analysis of 92 up-to-date bacterial core genes confirmed the unique clade. The digital DNA-DNA hybridization and blast-based average nucleotide identity values for the three strains and phylogenetically related Rhizobium species ranged from 35.9 to 60.0% and 87.16 to 94.58 %, which were lower than the 70 and 96% species delineation thresholds, respectively. The G+C contents of the strains were 60.82-60.92 mol% and the major fatty acids (>4 %) were summed feature 8 (57.81 %; C18 : 1 ω7c) and C18 : 1 ω7c 11-methyl (13.24%). Strains 1AS11T, 1AS12 and 1AS13 could also be differentiated from their closest described species (Rhizobium indicum, Rhizobium laguerreae and Rhizobium changzhiense) by phenotypic and physiological properties as well as fatty acid content. Based on the phylogenetic, genomic, physiological, genotypic and chemotaxonomic data presented in this study, strains 1AS11T, 1AS12 and 1AS13 represent a new species within the genus Rhizobium and we propose the name Rhizobium acaciae sp. nov. The type strain is 1AS11T (=DSM 113913T=ACCC 62388T).

Full-color photon-counting single-pixel imaging.

We propose and experimentally demonstrate a high-efficiency single-pixel imaging (SPI) scheme by integrating time-correlated single-photon counting (TCSPC) with time-division multiplexing to acquire full-color images at an extremely low light level. This SPI scheme uses a digital micromirror device to modulate a sequence of laser pulses with preset delays to achieve three-color structured illumination, then employs a photomultiplier tube into the TCSPC module to achieve photon-counting detection. By exploiting the time-resolved capabilities of TCSPC, we demodulate the spectrum-image-encoded signals, and then reconstruct high-quality full-color images in a single round of measurement. Based on this scheme, strategies such as single-step measurement, high-speed projection, and undersampling can further improve imaging efficiency.

MR imaging of thymomas: a combined radiomics nomogram to predict histologic subtypes.

OBJECTIVES: Accurately predicting the WHO classification of thymomas is urgently needed to optimize individualized therapeutic strategies. We aimed to develop and validate a combined radiomics nomogram for personalized prediction of histologic subtypes in patients with thymomas. METHODS: A total of 182 thymoma patients were divided into training (n = 128) and test (n = 54) cohorts. Radiomics features were extracted from T2-weighted, T2-weighted fat suppression, and diffusion-weighted images to establish a radiomics signature in the training cohort. Multivariate logistic regression analysis was used to develop a combined radiomics nomogram that incorporated clinical, conventional MR imaging variables, apparent diffusion coefficient (ADC) value, and radiomics signature. The efficacy of clinical, conventional MR imaging, or ADC model was also evaluated respectively. The performances of different models were compared by receiver operating characteristic analysis and Delong test. The discrimination, calibration, and clinical usefulness of the combined radiomics nomogram were assessed. RESULTS: The radiomics signature, consisting of 14 features, achieved favorable predictive efficacy in differentiating low-risk from high-risk thymomas, outperforming clinical, conventional MR imaging, and ADC models. The combined radiomics nomogram incorporating tumor shape, ADC value, and radiomics signature yielded the best performance (training cohort: area under the curve [AUC] = 0.946, test cohort: AUC = 0.878). The calibration curve and decision curve analysis indicated the clinical utility of the combined radiomics nomogram. CONCLUSIONS: The radiomics signature is a useful tool that can be used to predict histologic subtypes of thymomas. The combined radiomics nomogram improved the individualized subtype prediction in patients with thymomas. KEY POINTS: • Fourteen robust features were selected to develop a radiomics signature for preoperative prediction of thymoma subtype. • MRI-based radiomics signature can differentiate low-risk thymomas from high-risk thymomas with favorable predictive efficacy compared with clinical, conventional MR imaging, and ADC models. • Combined radiomics nomogram based on tumor shape, ADC value, and radiomics signature could improve the individualized subtype prediction in patients with thymomas.