Effects of cage vs. net-floor mixed rearing system on goose spleen histomorphology and gene expression profiles

Due to the demands for both environmental protection and modernization of the goose industry in China, the traditional goose waterside rearing systems have been gradually transitioning to the modern intensive dryland rearing ones, such as the net-floor mixed rearing system (MRS) and cage rearing system (CRS). However, the goose immune responses to different dryland rearing systems remain poorly understood. This study aimed to investigate and compare the age-dependent effects of MRS and CRS on the splenic histomorphological characteristics and immune-related genes expression profiles among three economically important goose breeds, including Sichuan White goose (SW), Gang goose (GE), and Landes goose (LD). Morphological analysis revealed that the splenic weight and organ index of SW were higher under CRS than under MRS (p < 0.05). Histological observations showed that for SW and LD, the splenic corpuscle diameter and area as well as trabecular artery diameter were larger under MRS than under CRS at 30 or 43 weeks of age (p < 0.05), while the splenic red pulp area of GE was larger under CRS than under MRS at 43 weeks of age (p < 0.05). Besides, at 43 weeks of age, higher mRNA expression levels of NGF, SPI1, and VEGFA in spleens of SW were observed under MRS than under CRS (p < 0.05), while higher levels of HSPA2 and NGF in spleens of LD were observed under MRS than under CRS (p < 0.05). For GE, there were higher mRNA expression levels of MYD88 in spleens under CRS at 30 weeks of age (p < 0.05). Moreover, our correlation analysis showed that there appeared to be more pronounced positive associations between the splenic histological parameters and expression levels of several key immune-related genes under MRS than under CRS. Therefore, it is speculated that the geese reared under MRS might exhibit enhanced immune functions than those under CRS, particularly for SW and LD. Although these phenotypic differences are assumed to be associated with the age-dependent differential expression profiles of HSPA2, MYD88, NGF, SPI1, and VEGFA in the goose spleen, the underlying regulatory mechanisms await further investigations

VPS35 haploinsufficiency increases Alzheimer’s disease neuropathology

The retromer complex component VPS35 prevents activation of the BACE1 and Aβ production and thus plays an essential role in limiting Alzheimer’s disease neuropathology

Metformin can alleviate the symptom of patient with diabetic nephropathy through reducing the serum level of Hcy and IL-33

Interleukin-33 (IL-33) and homocysteine (Hcy) were found to be up-regulated in patients with diabetic nephropathy (DN), and the present study aimed to investigate whether metformin (MT) can influence the serum levels of IL-33 and Hcy in patients with DN

BACE1 Regulates Hippocampal Astrogenesis via the Jagged1-Notch Pathway

BACE1 is the sole secretase for generating β-amyloid (Aβ) in vivo and is being actively pursued as a drug target for the treatment of Alzheimer’s disease. Transmembrane BACE1 exerts its biological activity by cleaving its membrane-bound cellular substrates. Here, we reveal that BACE1 directly regulates the level of membrane-anchored full-length Jagged1 (Jag1), a signaling molecule important for the control of neurogenesis and astrogenesis, via interaction with its cognate Notch receptor. We show that shedding of Jag1 is reduced in BACE1 null mice and upregulated Jag1 enhances Notch signaling via cell-cell juxtacrine interactions. Additional biochemical assays confirmed that overexpression of BACE1 enhanced cleavage of Jag1. Consequently, BACE1 null mice exhibit a significant increase in astrogenesis with a corresponding decrease in neurogenesis in their hippocampi during early development. Hence, BACE1 appears to function as a signaling protease that controls the balance of neurogenesis and astrogenesis via the Jag1-Notch pathway

BACE1 Deficiency Causes Abnormal Neuronal Clustering in the Dentate Gyrus

BACE1 is validated as Alzheimer's β-secretase and a therapeutic target for Alzheimer's disease. In examining BACE1-null mice, we discovered that BACE1 deficiency develops abnormal clusters of immature neurons, forming doublecortin-positive neuroblasts, in the developing dentate gyrus, mainly in the subpial zone (SPZ). Such clusters were rarely observed in wild-type SPZ and not reported in other mouse models. To understand their origins and fates, we examined how neuroblasts in BACE1-null SPZ mature and migrate during early postnatal development. We show that such neuroblasts are destined to form Prox1-positive granule cells in the dentate granule cell layer, and mainly mature to form excitatory neurons, but not inhibitory neurons. Mechanistically, higher levels of reelin potentially contribute to abnormal neurogenesis and timely migration in BACE1-null SPZ. Altogether, we demonstrate that BACE1 is a critical regulator in forming the dentate granule cell layer through timely maturation and migration of SPZ neuroblasts

A B5G Non-Terrestrial-Network (NTN) and Hybird Constellation Based Data Collection System (DCS)

In beyond 5G (B5G) non-terrestrial network (NTN) systems, satellite technologies play an important role. Especially for data collection systems (DCS), low-earth orbit satellites have many advantages. Such as global coverage, low latency, and high efficiency. As a miniaturization technology, CubeSat has attracted extensive attention from a large number of scholars. Satellite constellations can coordinate for distributed tasks. This paper proposes a B5G NTN-based data collection system. A CubeSat constellation achieves global coverage as the basic space platform for DCS. The 5G terrestrial network is used as the data bearer network of the gateway station. A traffic load balance strategy is proposed to optimize the system’s efficiency. As a unified hardware platform, software-defined radio (SDR) is compatible with various sensor data models. Finally, the design was verified by a series of experiments

A complete phase diagram for dark-bright coupled plasmonic systems: applicability of Fano’s formula

Although coupled plasmonic systems have been extensively studied in the past decades, their theoretical understanding is still far from satisfactory. Here, based on experimental and numerical studies on a series of symmetry-broken nano-patch plasmonic resonators, we found that Fano’s formula, widely used in modeling such systems previously, works well for one polarization but completely fails for another polarization. In contrast, a two-mode coupled-mode theory (CMT) can interpret all experimental results well. This motivated us to employ the CMT to establish a complete phase diagram for such coupled plasmonic systems, which not only revealed the diversified effects and their governing physics in different phase regions, but more importantly, also justifies the applicabilities of two simplified models (including Fano’s formula) derived previously. Our results present a unified picture for the distinct effects discovered in such systems, which can facilitate people’s understanding of the governing physics and can design functional devices facing requests for diversified applications

Research on Entity and Relationship Extraction with Small Training Samples for Cotton Pests and Diseases

The extraction of entities and relationships is a crucial task in the field of natural language processing (NLP). However, existing models for this task often rely heavily on a substantial amount of labeled data, which not only consumes time and labor but also hinders the development of downstream tasks. Therefore, with a focus on enhancing the model’s ability to learn from small samples, this paper proposes an entity and relationship extraction method based on the Universal Information Extraction (UIE) model. The core of the approach is the design of a specialized prompt template and schema on cotton pests and diseases as one of the main inputs to the UIE, which, under its guided fine-tuning, enables the model to subdivide the entity and relationship in the corpus. As a result, the UIE-base model achieves an accuracy of 86.5% with only 40 labeled training samples, which really solves the problem of the existing models that require a large amount of manually labeled training data for knowledge extraction. To verify the generalization ability of the model in this paper, experiments are designed to compare the model with four classical models, such as the Bert-BiLSTM-CRF. The experimental results show that the F1 value on the self-built cotton data set is 1.4% higher than that of the Bert-BiLSTM-CRF model, and the F1 value on the public data set is 2.5% higher than that of the Bert-BiLSTM-CRF model. Furthermore, experiments are designed to verify that the UIE-base model has the best small-sample learning performance when the number of samples is 40. This paper provides an effective method for small-sample knowledge extraction