A universal karyotypic system for hexaploid and diploid Avena species brings oat cytogenetics into the genomics era.

BACKGROUND: The identification of chromosomes among Avena species have been studied by C-banding and in situ hybridization. However, the complicated results from several cytogenetic nomenclatures for identifying oat chromosomes are often contradictory. A universal karyotyping nomenclature system for precise chromosome identification and comparative evolutionary studies would be essential for genus Avena based on the recently released genome sequences of hexaploid and diploid Avena species. RESULTS: Tandem repetitive sequences were predicted and physically located on chromosomal regions of the released Avena sativa OT3098 genome assembly v1. Eight new oligonucleotide (oligo) probes for sequential fluorescence in situ hybridization (FISH) were designed and then applied for chromosome karyotyping on mitotic metaphase spreads of A. brevis, A. nuda, A. wiestii, A. ventricosa, A. fatua, and A. sativa species. We established a high-resolution standard karyotype of A. sativa based on the distinct FISH signals of multiple oligo probes. FISH painting with bulked oligos, based on wheat-barley collinear regions, was used to validate the linkage group assignment for individual A. sativa chromosomes. We integrated our new Oligo-FISH based karyotype system with earlier karyotype nomenclatures through sequential C-banding and FISH methods, then subsequently determined the precise breakage points of some chromosome translocations in A. sativa. CONCLUSIONS: This new universal chromosome identification system will be a powerful tool for describing the genetic diversity, chromosomal rearrangements and evolutionary relationships among Avena species by comparative cytogenetic and genomic approaches.

Comparisons are Odious? The neural basis of in-group and out-group social comparison among game players: An fMRI study.

Social comparison is an important way for individuals to define their social characteristics. Online games with a large amount of social information provide a convenient platform for social comparison between players. However, few studies have examined the neural basis of different social comparisons in game players. This study aims to explore the activation of brain regions triggered by social comparison in different contexts and the possible moderating effect of group identity. A total of 26 subjects participated in our experiment. We referred to the minimum group paradigm to evoke group identity and used the dot estimation paradigm to generate in-group or out-group social comparisons. The activation of brain regions was measured and analyzed. Compared to upward comparison, the fusiform gyrus, putamen, lentiform nucleus, precuneus, and precentral gyrus were significantly activated in downward comparison when the group identity of the comparison object was the same as that of the player. When the two had different identities, downward comparison significantly activated the angular gyrus, middle frontal gyrus, and superior frontal gyrus. However, the moderating effect of group identity was not significant. Further functional connectivity analysis based on the brain region activation results was performed. Our study has shown that social comparison in group contexts leads to the activation of different brain regions and provides neurophysiological evidence of social interaction among game players.

Synthesis of SiN/SiS-heterocycles via the reactions of a bis-silylene with isocyanate/isothiocyanate molecules.

Reactions of o-carborane-fused bis-silylene 1 with isocyanate/isothiocyanate molecules furnished a series of SiN/SiS-heterocycles, which show distinct styles of cyclization and were theoretically studied.

Clinical ocular surface characteristics and expression of MUC5AC in diabetics: a population-based study.

OBJECTIVES: To investigate the clinical characteristics and the expression of mucin 5AC (MUC5AC) in in diabetic and non-diabetic subjects with or without dry eye disease. METHODS: A total of 399 participants (796 eyes) aged 50-80 years were enrolled in this study. Participants were divided into 4 groups: non-diabetic without dry eye group (normal group), non-diabetic with dry eye group, diabetic without dry eye group and diabetic with dry eye group. Demographic information, fasting plasma glucose (FBG), and glycated haemoglobin A1C (HbA1C) data were collected. Additionally, ocular surface disease index (OSDI) questionnaire, signs of dry eye, tear osmolarity, and meibomian glands were evaluated. Tear MUC5AC expression and conjunctival goblet cells density (GCD) were tested. RESULTS: Compared with non-diabetic with dry eye group, diabetic with dry eye group showed significantly lower tear film osmolarity (TFO), but higher corneal fluorescein and conjunctival lissamine green staining scores. In comparison with diabetic without dry eye group, diabetic with dry eye group showed significantly higher TFO, corneal fluorescein and conjunctival lissamine green staining scorers. The MUC5AC concentration and GCD of diabetic with dry eye group was significantly lower than those of the non-diabetic with dry eye group. Diabetic subjects with higher HbA1c levels (≥7.8%) showed higher TFO and shorter fluorescein tear break time. CONCLUSION: Diabetics with dry eye exhibited notably higher corneal fluorescein and conjunctival lissamine green staining scores. Conjunctival goblet cells and MUC5AC were significantly reduced in diabetics. Higher TFO was associated with the duration of diabetes and HbA1c levels.

Generalizability of Self-Supervised Training Models for Digital Pathology: A Multicountry Comparison in Colorectal Cancer.

PURPOSE: In this multicountry study, we aim to explore the effectiveness of self-supervised learning (SSL) in colorectal cancer (CRC)-related predictive tasks using large amount of unlabeled digital pathology imaging data. METHODS: We adopted SimSiam to conduct self-supervised pretraining on two large whole-slide image CRC data sets from the United States and Australia. The SSL pretrained encoder is then used in several predictive tasks, including supervised predictive tasks (tissue classification, microsatellite instability v microsatellite stability classification), and weakly supervised predictive tasks (polyp type classification and adenoma grading, and 5-year survival prediction). Performance on the tasks was compared between models using SSL pretraining and those using ImageNet pretraining, and performance for one-country pretraining was compared with two-country pretraining. RESULTS: We demonstrate that SSL pretraining outperforms ImageNet pretraining in predictive tasks, that is, SSL pretraining outperforms the ImageNet pretraining by 3.01% of F1 score on average over supervised predictive tasks and 1.53% of AUC on average over weakly supervised predictive tasks. Furthermore, two-country SSL pretraining has shown more stable performance than single-country pretraining, that is, two-country pretraining outperforms at least one of the single-country pretrainings by 1.93% of F1 on average over supervised predictive tasks and 1.36% of AUC on average over weakly-supervised predictive tasks. CONCLUSION: We find that using unlabeled image data for SSL pretraining in CRC related tasks is more effective than using ImageNet pretraining. Furthermore, SSL pretraining using data from multiple countries achieve more stable performance and better generalization than single-country pretraining.

SiP-heterocycles derived from a bulky phosphanylsilylene.

Bis(1-adamantyl)phosphanylsilylene 1 was reacted with ArCCR (Ar = Ph, 4-iPr-C6H4, 3-F-C6H4; R = H, Ph) at 80 °C under microwave irradiation to afford fluorescence-active SiP-heterocycles 3a-d, which may undergo unique isomerizations starting from silirene intermediates. Moreover, the treatment of 1 with AdCP furnished a heavy congener of cyclopentadiene (4), whose formation involves cleavage of the Si(II)-P bond that is rarely observed in silylene chemistry.

dMIL-Transformer: Multiple Instance Learning Via Integrating Morphological and Spatial Information for Lymph Node Metastasis Classification.

Automated classification of lymph node metastasis (LNM) plays an important role in the diagnosis and prognosis. However, it is very challenging to achieve satisfactory performance in LNM classification, because both the morphology and spatial distribution of tumor regions should be taken into account. To address this problem, this article proposes a two-stage dMIL-Transformer framework, which integrates both the morphological and spatial information of the tumor regions based on the theory of multiple instance learning (MIL). In the first stage, a double Max-Min MIL (dMIL) strategy is devised to select the suspected top-K positive instances from each input histopathology image, which contains tens of thousands of patches (primarily negative). The dMIL strategy enables a better decision boundary for selecting the critical instances compared with other methods. In the second stage, a Transformer-based MIL aggregator is designed to integrate all the morphological and spatial information of the selected instances from the first stage. The self-attention mechanism is further employed to characterize the correlation between different instances and learn the bag-level representation for predicting the LNM category. The proposed dMIL-Transformer can effectively deal with the thorny classification in LNM with great visualization and interpretability. We conduct various experiments over three LNM datasets, and achieve 1.79%-7.50% performance improvement compared with other state-of-the-art methods.