MicroRNA-like RNAs from the same miRNA precursors play a role in cassava chilling responses

Abstract MicroRNAs (miRNAs) are known to play important roles in various cellular processes and stress responses. MiRNAs can be identified by analyzing reads from high-throughput deep sequencing. The reads realigned to miRNA precursors besides canonical miRNAs were initially considered as sequencing noise and ignored from further analysis. Here we reported a small-RNA species of phased and half-phased miRNA-like RNAs different from canonical miRNAs from cassava miRNA precursors detected under four distinct chilling conditions. They can form abundant multiple small RNAs arranged along precursors in a tandem and phased or half-phased fashion. Some of these miRNA-like RNAs were experimentally confirmed by re-amplification and re-sequencing, and have a similar qRT-PCR detection ratio as their cognate canonical miRNAs. The target genes of those phased and half-phased miRNA-like RNAs function in process of cell growth metabolism and play roles in protein kinase. Half-phased miR171d.3 was confirmed to have cleavage activities on its target gene P-glycoprotein 11, a broad substrate efflux pump across cellular membranes, which is thought to provide protection for tropical cassava during sharp temperature decease. Our results showed that the RNAs from miRNA precursors are miRNA-like small RNAs that are viable negative gene regulators and may have potential functions in cassava chilling responses

BOOST: Harnessing Black-Box Control to Boost Commonsense in LMs' Generation

Large language models (LLMs) such as GPT-3 have demonstrated a strong capability to generate coherent and contextually relevant text. However, amidst their successes, a crucial issue persists: their generated outputs still lack commonsense at times. Moreover, fine-tuning the entire LLM towards more commonsensical outputs is computationally expensive if not infeasible. In this paper, we present a computation-efficient framework that steers a frozen Pre-Trained Language Model (PTLM) towards more commonsensical generation (i.e., producing a plausible output that incorporates a list of concepts in a meaningful way). Specifically, we first construct a reference-free evaluator that assigns a sentence with a commonsensical score by grounding the sentence to a dynamic commonsense knowledge base from four different relational aspects. We then use the scorer as the oracle for commonsense knowledge, and extend the controllable generation method called NADO to train an auxiliary head that guides a fixed PTLM to better satisfy the oracle. We test our framework on a series of GPT-2-, Flan-T5-, and Alpaca-based language models (LMs) on two constrained concept-to-sentence benchmarks. Human evaluation results demonstrate that our method consistently leads to the most commonsensical outputs.Comment: EMNLP 202

Simulation of Wind Power Integration with Modular Multilevel Converter-Based High Voltage Direct Current

The growing demand for large-capacity long distance transmission of wind power has boosted the development of flexible direct current (DC) transmission technology. To facilitate wind power integration, this paper designs a modular multilevel converter (MMC) for steady-state operation, using the parameters of the demonstration DC transmission project of offshore wind power in Sheyang County, eastern China\u27s Jiangsu Province. Relying on the simulation platform of PSCAD/EMTDC, the authors analyzed the proposed control theory, and verified that, under different working conditions (e.g., changing wind speed), the MMC-based high voltage direct current (MMC-HVDC) transmission system can integrate the wind power safely and efficiently. In addition, the authors discussed how to enhance the fault ride-through (FRT), a prominent problem in wind power operation, of the flexible DC system containing wind power, from the perspective of alternating current (AC) fault and DC fault

Protecting the Intellectual Property of Diffusion Models by the Watermark Diffusion Process

Diffusion models have emerged as state-of-the-art deep generative architectures with the increasing demands for generation tasks. Training large diffusion models for good performance requires high resource costs, making them valuable intellectual properties to protect. While most of the existing ownership solutions, including watermarking, mainly focus on discriminative models. This paper proposes WDM, a novel watermarking method for diffusion models, including watermark embedding, extraction, and verification. WDM embeds the watermark data through training or fine-tuning the diffusion model to learn a Watermark Diffusion Process (WDP), different from the standard diffusion process for the task data. The embedded watermark can be extracted by sampling using the shared reverse noise from the learned WDP without degrading performance on the original task. We also provide theoretical foundations and analysis of the proposed method by connecting the WDP to the diffusion process with a modified Gaussian kernel. Extensive experiments are conducted to demonstrate its effectiveness and robustness against various attacks

Comparación de la recategorización del sustantivo entre el español y el chino mandarín1

In the framework of Relevance Theory, this paper analyzes the noun recategorization and makes a comparison between Spanish and Mandarin Chinese. Through interlinguistic comparison, it can be confirmed that although there are some differences in noun categories and new interpretations generated through recategorization mechanism, the coercion operation principles in the two languages are the same. Whether in Spanish or Chinese, when there is a conflict between conceptual coding and procedural coding in the process of noun recategorization, it is always solved by procedural coding. In addition, in both languages, the receiver’s knowledge related to nouns is very important for the construction of coercive context

Free Triiodothyronine Levels Are Associated with Diabetic Nephropathy in Euthyroid Patients with Type 2 Diabetes

Objective. To investigate the association of thyroid function and diabetic nephropathy (DN) in euthyroid patients with type 2 diabetes. Methods. A total of 421 patients were included in this cross-sectional study. The following parameters were assessed: anthropometric measurements, fast plasma glucose, serum creatinine, lipid profile, HbA1c, free triiodothyronine (FT3), free thyroxine, thyroid-stimulating hormone levels, and urinary albumin-to-creatinine ratio (UACR). Patients with UACR of ≥30 mg/g were defined as those suffering from DN. Results. Of the 421 patients, 203 (48.2%) suffered from DN, and no difference was found between males and females. The patients with DN yielded significantly lower FT3 levels than those without DN (P<0.01). The prevalence of DN showed a significantly decreasing trend across the three tertiles based on FT3 levels (59.6%, 46.4%, and 38.6%, P<0.01). After adjustment for gender and age, FT3 levels were found to correlate positively with estimated glomerular filtration rate (P=0.03) and negatively with UACR (P<0.01). Multiple linear regression analysis showed that FT3 level was independently associated with UACR (β=-0.18, t=-3.70, and P<0.01). Conclusion. Serum FT3 levels are inversely associated with DN in euthyroid patients with type 2 diabetes, independent of traditional risk factors