Prey capture and meat-eating by the wild colobus monkey _Rhinopithecus bieti_ in Yunnan, China

If it is true that extant primates evolved from an insectivorous ancestor, then primate entomophagy would be a primitive trait. Many taxa, however, have undergone a dietary shift from entomophagy to phytophagy, evolving a specialised gut and dentition and becoming exclusive herbivores. The exclusively herbivorous taxa are the Malagasy families Indriidae and Lepilemuridae, and the Old World Monkey subfamily Colobinae, and among these meat-eating has not been observed except as an anomaly, with the sole exception of the Hanuman langur (_Semnopithecus entellus_), which feeds on insects seasonally, and a single observation of a nestling bird predated by wild Sichuan snub-nosed monkeys (_Rhinopithecus roxellana_). Here, we describe the regular capture of warm-blooded animals and the eating of meat by a colobine, the critically endangered Yunnan snub-nosed monkey (_Rhinopithecus bieti_). This monkey engages in scavenge hunting as a male-biased activity that may, in fact, be related to group structure and spatial spread. In this context, meat-eating can be regarded as an energy/nutrient maximization feeding strategy rather than as a consequence of any special characteristic of meat itself. The finding of meat-eating in forest-dwelling primates might provide new insights into the evolution of dietary habits in early humans

Analytical description of high-aperture STED resolution with 0-2π\pi vortex phase modulation

Stimulated emission depletion (STED) can achieve optical super-resolution, with the optical diffraction limit broken by the suppression on the periphery of the fluorescent focal spot. Previously, it is generally experimentally accepted that there exists an inverse square root relationship with the STED power and the resolution, yet without strict analytical description. In this paper, we have analytically verified the relationship between the STED power and the achievable resolution from vector optical theory for the widely used 0-2$\pi$ vortex phase modulation. Electromagnetic fields of the focal region of a high numerical aperture objective are calculated and approximated into polynomials, and analytical expression of resolution as a function of the STED intensity has been derived. As a result, the resolution can be estimated directly from the measurement of the saturation power of the dye and the STED power applied.Comment: (19 pages

3D GIS Modeling of Soft Geo-Objects: Taking Rainfall, Overland Flow, and Soil Erosion as an Example

In physics, objects can be divided into rigid and soft objects according to the object deformation capacity. Similarly, geo-object can also be classified into rigid geo-objects (e.g., building, urban) and soft geo-objects (e.g., mudflow, water, soil erosion). There are three types of approaches for 3D GIS modeling, i.e., surface-based, volume-based, and hybrids in terms of geometry. These approaches are suitable for representing rigid geo-objects, but they are not suitable to simulate the intrinsic properties of the soft geo-object, i.e., dynamics and deformation. And so far there are few GIS modeling methods for simulation of soft geo-objects. GIS flow elements (FEs) and GIS soft voxels (SVs) were proposed for 3D modeling of soft geo-objects. GIS flow elements can realistically represent the dynamics and stochastics of soft geo-objects, while GIS soft voxels simulate deformation of soft geo-objects. The authors discuss the implementation and computer programming of GIS flow elements and GIS soft voxels in this study. GIS FE and SV have been successfully applied in a case study toward the simulation of the process of rainfall, overland flow, and soil erosion. A software system has been designed and developed, which has the functions of data management, model computation, and 3D simulation

New understandings of the genetic regulatory relationship between non-coding RNAs and m6A modification

One of the most frequent epigenetic modifications of RNA in eukaryotes is N6 methyladenosine (m6A), which is mostly present in messenger RNAs. Through the influence of several RNA processing stages, m6A modification is a crucial approach for controlling gene expression, especially in cancer progression. It is universally acknowledged that numerous non-coding RNAs (ncRNAs), such as microRNAs, circular RNAs, long non-coding RNAs, and piRNAs, are also significantly affected by m6A modification, and the complex genetic regulatory relationship between m6A and ncRNAs plays a pivotal role in the development of cancer. The connection between m6A modifications and ncRNAs offers an opportunity to explore the oncogene potential regulatory mechanisms and suggests that m6A modifications and ncRNAs could be vital biomarkers for multiple cancers. In this review, we discuss the mechanisms of interaction between m6A methylation and ncRNAs in cancer, and we also summarize diagnostic and prognostic biomarkers for clinical cancer detection. Furthermore, our article includes some methodologies for identifying m6A sites when assessing biomarker potential

Risk Identification of Sudden Water Pollution on Fuzzy Fault Tree in Beibu-Gulf Economic Zone

AbstractSudden water pollution incident has the characteristics of instantaneity and uncertainty. Based on the characteristics, fuzzy fault tree analysis method was used to identify the potential risks of water pollution in Beibu-Gulf economic zone, and it also combined with the collected data and analysis results. The research results showed that the abnormal discharge of sewage was the main risk factor of the economic zone; the probability value of water pollution potential risk in this study area ranged from 4.6 percent to17.7 percent,which considered the random uncertainty and fuzzy uncertainty of the causes. This research could be considered as an instruction for future risk management, and it will play a great role in the healthy development of ecological environment

Rapeseed Oil Monoester of Ethylene Glycol Monomethyl Ether as a New Biodiesel

A novel biodiesel named rapeseed oil monoester of ethylene glycol monomethyl ether is developed. This fuel has one more ester group than the traditional biodiesel. The fuel was synthesized and structurally identified through FT-IR and P1PH NMR analyses. Engine test results show that when a tested diesel engine is fueled with this biodiesel in place of 0# diesel fuel, engine-out smoke emissions can be decreased by 25.0%–75.0%, CO emissions can be reduced by 50.0%, and unburned HC emissions are lessened significantly. However, NOx emissions generally do not change noticeably. In the area of combustion performance, both engine in-cylinder pressure and its changing rate with crankshaft angle are increased to some extent. Rapeseed oil monoester of ethylene glycol monomethyl ether has a much higher cetane number and shorter ignition delay, leading to autoignition 1.1°CA earlier than diesel fuel during engine operation. Because of certain amount of oxygen contained in the new biodiesel, the engine thermal efficiency is improved 13.5%–20.4% when fueled with the biodiesel compared with diesel fuel

LM-Combiner: A Contextual Rewriting Model for Chinese Grammatical Error Correction

Over-correction is a critical problem in Chinese grammatical error correction (CGEC) task. Recent work using model ensemble methods based on voting can effectively mitigate over-correction and improve the precision of the GEC system. However, these methods still require the output of several GEC systems and inevitably lead to reduced error recall. In this light, we propose the LM-Combiner, a rewriting model that can directly modify the over-correction of GEC system outputs without a model ensemble. Specifically, we train the model on an over-correction dataset constructed through the proposed K-fold cross inference method, which allows it to directly generate filtered sentences by combining the original and the over-corrected text. In the inference stage, we directly take the original sentences and the output results of other systems as input and then obtain the filtered sentences through LM-Combiner. Experiments on the FCGEC dataset show that our proposed method effectively alleviates the over-correction of the original system (+18.2 Precision) while ensuring the error recall remains unchanged. Besides, we find that LM-Combiner still has a good rewriting performance even with small parameters and few training data, and thus can cost-effectively mitigate the over-correction of black-box GEC systems (e.g., ChatGPT).Comment: Accepted to COLING 202