Distribution of Onryza maga (Leech, 1890) (Lepidoptera: Hesperiidae) with description of female genitalia and taxonomic notes

For more than twenty years, Hainan, Vietnam, Myanmar, Thailand, Malaysia, Singapore and Indonesia have been erroneously reported in Chinese literature as belonging to the distribution range of Onryza maga (Leech 1890). Based upon a careful survey of specimens and relevant literature, these regions are omitted from the known range of this species. Onryza maga maga is found from northeast Guizhou, south Henan and Qinling-Daba Mountains in Shaanxi of China, its occurrence in Hunan is confirmed. The adults are redescribed and the variability of wing patterns is discussed. Female genitalia are illustrated and described for the first time. Some biological information and an updated distribution map of the species are provided

Microscale Investigation of Arsenic Distribution and Species in Cement Product from Cement Kiln Coprocessing Wastes

To improve the understanding of the immobilization mechanism and the leaching risk of Arsenic (As) in the cement product from coprocessing wastes using cement kiln, distribution and species of As in cement product were determined by microscale investigation methods, including electron probe microanalysis (EPMA) and X-ray absorption spectroscopy. In this study, sodium arsenate crystals (Na3AsO412H2O) were mixed with cement production raw materials and calcined to produce cement clinker. Then, clinker was mixed water to prepare cement paste. EPMA results showed that As was generally distributed throughout the cement paste. As content in calcium silicate hydrates gel (C-S-H) was in low level, but higher than that in other cement mineral phases. This means that most of As is expected to form some compounds that disperse on the surfaces of cement mineral phases. Linear combination fitting (LCF) of the X-ray absorption near edge structure spectra revealed that As in the cement paste was predominantly As(V) and mainly existed as Mg3(AsO4)2, Ca3(AsO4)2, and Na2HAsO4

Bayesian Nested Neural Networks for Uncertainty Calibration and Adaptive Compression

Nested networks or slimmable networks are neural networks whose architectures can be adjusted instantly during testing time, e.g., based on computational constraints. Recent studies have focused on a "nested dropout" layer, which is able to order the nodes of a layer by importance during training, thus generating a nested set of sub-networks that are optimal for different configurations of resources. However, the dropout rate is fixed as a hyper-parameter over different layers during the whole training process. Therefore, when nodes are removed, the performance decays in a human-specified trajectory rather than in a trajectory learned from data. Another drawback is the generated sub-networks are deterministic networks without well-calibrated uncertainty. To address these two problems, we develop a Bayesian approach to nested neural networks. We propose a variational ordering unit that draws samples for nested dropout at a low cost, from a proposed Downhill distribution, which provides useful gradients to the parameters of nested dropout. Based on this approach, we design a Bayesian nested neural network that learns the order knowledge of the node distributions. In experiments, we show that the proposed approach outperforms the nested network in terms of accuracy, calibration, and out-of-domain detection in classification tasks. It also outperforms the related approach on uncertainty-critical tasks in computer vision.Comment: 16 pages, 10 figure

Mitigating catalyst deactivation in selective hydrogenation by enhancing dispersion and utilizing reaction heat effect

ACKNOWLEDGMENT This work was financially supported by National Key R&D Program of China (2021YFB3801600), National Natural Science Foundation of China (21908002), and Fundamental Research Funds for the Central Universities (buctrc201921, JD2223).Peer reviewedPostprin

Chiral charge density wave and backscattering-immune orbital texture in monolayer 1T-TiTe2

Non-trivial electronic states are attracting intense attention in low-dimensional physics. Though chirality has been identified in charge states with a scalar order parameter, its intertwining with charge density waves (CDW), film thickness and the impact on the electronic behaviors remain less well understood. Here, using scanning tunneling microscopy, we report a 2 x 2 chiral CDW as well as a strong suppression of the Te-5p hole-band backscattering in monolayer 1T-TiTe2. These exotic characters vanish in bilayer TiTe2 with a non-CDW state. Theoretical calculations approve that chirality comes from a helical stacking of the triple-q CDW components and therefore can persist at the two-dimensional limit. Furthermore, the chirality renders the Te-5p bands an unconventional orbital texture that prohibits electron backscattering. Our study establishes TiTe2 as a promising playground for manipulating the chiral ground states at the monolayer limit and provides a novel path to engineer electronic properties from an orbital degree.Comment: 21 pages, 5 figure

The Prognostic and Clinical Value of CD44 in Colorectal Cancer: A Meta-Analysis

Background: CD44 is widely used as a putative cancer stem cells (CSCs) marker for colorectal cancer (CRC). However, the prognostic role of CD44 in CRC remains controversial.Methods: We performed a systematic review and meta-analysis to evaluate the association of various CD44 isoforms and overall survival (OS) and clinicopathological features of CRC patients.Results: A total of 48 studies were included in the meta-analysis. Total CD44 isoforms overexpression was significantly correlated with worse OS of patients with CRC (HR = 1.32, 95% CI = 1.08–1.61, P = 0.007). In a stratified analysis, a higher level of either CD44v6 or CD44v2 had an unfavorable impact on OS (HRCD44v6 = 1.50, 95% CI = 1.10–2.14, P = 0.010; HRCD44v2 = 2.93, 95% CI = 1.49–5.77, P = 0.002). Additionally, CD44 was shown to be associated with some clinicopathological features, such as lymph node metastasis (ORCD44 = 1.56, 95% CI = 1.01–2.41, P = 0.044; ORCD44v6 = 1.97, 95% CI = 1.19–3.26, P = 0.008; ORTotal CD44 isoforms = 1.57, 95% CI = 1.15–2.14, P = 0.004), distant metastasis (ORCD44 = 2.90, 95% CI = 1.08–7.83, P = 0.035; ORTotal CD44 isoforms = 1.89, 95% CI = 1.02–3.53, P = 0.044). Moreover, a high level of CD44 showed a possible correlation with poor differentiation (ORTotal CD44 isoforms = 1.44, 95% CI = 1.00–2.08, P = 0.051), elevated level of CD44v6 tend to be correlated with tumor size (OR = 1.71, 95% CI = 0.99–2.96, P = 0.056).Conclusions: This meta-analysis demonstrated that CD44 overexpression might be an unfavorable prognostic factor for CRC patients and could be used to predict poor differentiation, lymph node metastasis and distant metastasis

GLM-130B: An Open Bilingual Pre-trained Model

We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model with 130 billion parameters. It is an attempt to open-source a 100B-scale model at least as good as GPT-3 (davinci) and unveil how models of such a scale can be successfully pre-trained. Over the course of this effort, we face numerous unexpected technical and engineering challenges, particularly on loss spikes and divergence. In this paper, we introduce the training process of GLM-130B including its design choices, training strategies for both efficiency and stability, and engineering efforts. The resultant GLM-130B model offers significant outperformance over GPT-3 175B (davinci) on a wide range of popular English benchmarks while the performance advantage is not observed in OPT-175B and BLOOM-176B. It also consistently and significantly outperforms ERNIE TITAN 3.0 260B -- the largest Chinese language model -- across related benchmarks. Finally, we leverage a unique scaling property of GLM-130B to reach INT4 quantization without post training, with almost no performance loss, making it the first among 100B-scale models and more importantly, allowing its effective inference on 4$\times$RTX 3090 (24G) or 8$\times$RTX 2080 Ti (11G) GPUs, the most affordable GPUs required for using 100B-scale models. The GLM-130B model weights are publicly accessible and its code, training logs, related toolkit, and lessons learned are open-sourced at \url{https://github.com/THUDM/GLM-130B/}.Comment: Accepted to ICLR 202

Downregulation of Brassica napus MYB69 (BnMYB69) increases biomass growth and disease susceptibility via remodeling phytohormone, chlorophyll, shikimate and lignin levels

MYB transcription factors are major actors regulating plant development and adaptability. Brassica napus is a staple oil crop and is hampered by lodging and diseases. Here, four B. napus MYB69 (BnMYB69s) genes were cloned and functionally characterized. They were dominantly expressed in stems during lignification. BnMYB69 RNA interference (BnMYB69i) plants showed considerable changes in morphology, anatomy, metabolism and gene expression. Stem diameter, leaves, roots and total biomass were distinctly larger, but plant height was significantly reduced. Contents of lignin, cellulose and protopectin in stems were significantly reduced, accompanied with decrease in bending resistance and Sclerotinia sclerotiorum resistance. Anatomical detection observed perturbation in vascular and fiber differentiation in stems, but promotion in parenchyma growth, accompanied with changes in cell size and cell number. In shoots, contents of IAA, shikimates and proanthocyanidin were reduced, while contents of ABA, BL and leaf chlorophyll were increased. qRT-PCR revealed changes in multiple pathways of primary and secondary metabolisms. IAA treatment could recover many phenotypes and metabolisms of BnMYB69i plants. However, roots showed trends opposite to shoots in most cases, and BnMYB69i phenotypes were light-sensitive. Conclusively, BnMYB69s might be light-regulated positive regulators of shikimates-related metabolisms, and exert profound influences on various internal and external plant traits