Nitrogen, Cobalt Co-doped Fluorescent Magnetic Carbon Dots as Ratiometric Fluorescent Probes for Cholesterol and Uric Acid in Human Blood Serum

Detection of cholesterol and uric acid biomarkers is of great importance for clinical diagnosis of several serious diseases correlated with their variations in human blood serum. In this study, a new kind of well selective and highly sensitive ratiometric fluorescent probe for cholesterol and uric acid determination in human blood serum was innovatively developed on the basis of the inner filter effect (IFE) process of nitrogen, cobalt co-doped carbon dots (N,Co-CDs) with 2,3-diaminophenazine (DAP). DAP was the oxidative product during the oxidation reaction between ophenylenediamine and H2O2. Fluorescent magnetic N,Co-CDs possessing blue emission and magnetic property were prepared through a facile one-pot hydrothermal strategy by using citric acid, diethylenetriamine, and cobalt(II) chloride hexahydrate as precursors. N,Co-CDs exhibited good ferromagnetic property and excellent optical properties even in extremely harsh environmental conditions, implying the huge potential applications of such N,Co-CDs in biological areas. On the basis of the IFE process between N,Co-CDs and DAP, N,Co-CDs were applied to establish ratiometric fluorescent probes for the indirect detection of cholesterol and uric acid that participated in enzyme-catalyzed H2O2-generation reactions. The established IFEbased fluorescent probes exhibited relatively low detection limits of 3.6 nM for cholesterol and 3.4 nM for uric acid, respectively. The fluorescent probe was successfully utilized for the determination of cholesterol and uric acid in human blood serum with satisfying results, which provided an informed perspective on the applications of such doped CDs to explore the specific and sensitive nanoprobe in disease diagnoses and clinical therapy

Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision

The rapid evolution of Multi-modality Large Language Models (MLLMs) has catalyzed a shift in computer vision from specialized models to general-purpose foundation models. Nevertheless, there is still an inadequacy in assessing the abilities of MLLMs on low-level visual perception and understanding. To address this gap, we present Q-Bench, a holistic benchmark crafted to systematically evaluate potential abilities of MLLMs on three realms: low-level visual perception, low-level visual description, and overall visual quality assessment. a) To evaluate the low-level perception ability, we construct the LLVisionQA dataset, consisting of 2,990 diverse-sourced images, each equipped with a human-asked question focusing on its low-level attributes. We then measure the correctness of MLLMs on answering these questions. b) To examine the description ability of MLLMs on low-level information, we propose the LLDescribe dataset consisting of long expert-labelled golden low-level text descriptions on 499 images, and a GPT-involved comparison pipeline between outputs of MLLMs and the golden descriptions. c) Besides these two tasks, we further measure their visual quality assessment ability to align with human opinion scores. Specifically, we design a softmax-based strategy that enables MLLMs to predict quantifiable quality scores, and evaluate them on various existing image quality assessment (IQA) datasets. Our evaluation across the three abilities confirms that MLLMs possess preliminary low-level visual skills. However, these skills are still unstable and relatively imprecise, indicating the need for specific enhancements on MLLMs towards these abilities. We hope that our benchmark can encourage the research community to delve deeper to discover and enhance these untapped potentials of MLLMs. Project Page: https://vqassessment.github.io/Q-Bench.Comment: 25 pages, 14 figures, 9 tables, preprint versio

Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models

Multi-modality foundation models, as represented by GPT-4V, have brought a new paradigm for low-level visual perception and understanding tasks, that can respond to a broad range of natural human instructions in a model. While existing foundation models have shown exciting potentials on low-level visual tasks, their related abilities are still preliminary and need to be improved. In order to enhance these models, we conduct a large-scale subjective experiment collecting a vast number of real human feedbacks on low-level vision. Each feedback follows a pathway that starts with a detailed description on the low-level visual appearance (*e.g. clarity, color, brightness* of an image, and ends with an overall conclusion, with an average length of 45 words. The constructed **Q-Pathway** dataset includes 58K detailed human feedbacks on 18,973 images with diverse low-level appearance. Moreover, to enable foundation models to robustly respond to diverse types of questions, we design a GPT-participated conversion to process these feedbacks into diverse-format 200K instruction-response pairs. Experimental results indicate that the **Q-Instruct** consistently elevates low-level perception and understanding abilities across several foundational models. We anticipate that our datasets can pave the way for a future that general intelligence can perceive, understand low-level visual appearance and evaluate visual quality like a human. Our dataset, model zoo, and demo is published at: https://q-future.github.io/Q-Instruct.Comment: 16 pages, 11 figures, page 12-16 as appendi

Genome-Wide Association Studies Reveal Genetic Variation and Candidate Genes of Drought Stress Related Traits in Cotton (Gossypium hirsutum L.)

Cotton is an important industrial crop worldwide and upland cotton (Gossypium hirsutum L.) is most widely cultivated in the world. Due to ever-increasing water deficit, drought stress brings a major threat to cotton production. Thus, it is important to reveal the genetic basis under drought stress and develop drought tolerant cotton cultivars. To address this issue, in present study, 319 upland cotton accessions were genotyped by 55,060 single nucleotide polymorphisms (SNPs) from high-density CottonSNP80K array and phenotyped nine drought tolerance related traits. The two datasets were used to identify quantitative trait nucleotides (QTNs) for the above nine traits using multi-locus random-SNP-effect mixed linear model method. As a result, a total of 20 QTNs distributed on 16 chromosomes were found to be significantly associated with six drought tolerance related traits. Of the 1,326 genes around the 20 QTNs, 205 were induced after drought stress treatment, and 46 were further mapped to Gene ontology (GO) term “response to stress.” Taken genome-wide association study (GWAS) analysis, RNA-seq data and qRT-PCR verification, four genes, RD2 encoding a response to desiccation 2 protein, HAT22 encoding a homeobox-leucine zipper protein, PIP2 encoding a plasma membrane intrinsic protein 2, and PP2C encoding a protein phosphatase 2C, were proposed to be potentially important for drought tolerance in cotton. These results will deepen our understanding of the genetic basis of drought stress tolerance in cotton and provide candidate markers to accelerate the development of drought-tolerant cotton cultivars

Genomic analysis of the domestication and post-Spanish conquest evolution of the llama and alpaca

Background Despite their regional economic importance and being increasingly reared globally, the origins and evolution of the llama and alpaca remain poorly understood. Here we report reference genomes for the llama, and for the guanaco and vicuña (their putative wild progenitors), compare these with the published alpaca genome, and resequence seven individuals of all four species to better understand domestication and introgression between the llama and alpaca. Results Phylogenomic analysis confirms that the llama was domesticated from the guanaco and the alpaca from the vicuña. Introgression was much higher in the alpaca genome (36%) than the llama (5%) and could be dated close to the time of the Spanish conquest, approximately 500 years ago. Introgression patterns are at their most variable on the X-chromosome of the alpaca, featuring 53 genes known to have deleterious X-linked phenotypes in humans. Strong genome-wide introgression signatures include olfactory receptor complexes into both species, hypertension resistance into alpaca, and fleece/fiber traits into llama. Genomic signatures of domestication in the llama include male reproductive traits, while in alpaca feature fleece characteristics, olfaction-related and hypoxia adaptation traits. Expression analysis of the introgressed region that is syntenic to human HSA4q21, a gene cluster previously associated with hypertension in humans under hypoxic conditions, shows a previously undocumented role for PRDM8 downregulation as a potential transcriptional regulation mechanism, analogous to that previously reported at high altitude for hypoxia-inducible factor 1α. Conclusions The unprecedented introgression signatures within both domestic camelid genomes may reflect post-conquest changes in agriculture and the breakdown of traditional management practices

Single nucleus genome sequencing reveals high similarity among nuclei of an endomycorrhizal fungus

Nuclei of arbuscular endomycorrhizal fungi have been described as highly diverse due to their asexual nature and absence of a single cell stage with only one nucleus. This has raised fundamental questions concerning speciation, selection and transmission of the genetic make-up to next generations. Although this concept has become textbook knowledge, it is only based on studying a few loci, including 45S rDNA. To provide a more comprehensive insight into the genetic makeup of arbuscular endomycorrhizal fungi, we applied de novo genome sequencing of individual nuclei of Rhizophagus irregularis. This revealed a surprisingly low level of polymorphism between nuclei. In contrast, within a nucleus, the 45S rDNA repeat unit turned out to be highly diverged. This finding demystifies a long-lasting hypothesis on the complex genetic makeup of arbuscular endomycorrhizal fungi. Subsequent genome assembly resulted in the first draft reference genome sequence of an arbuscular endomycorrhizal fungus. Its length is 141 Mbps, representing over 27,000 protein-coding gene models. We used the genomic sequence to reinvestigate the phylogenetic relationships of Rhizophagus irregularis with other fungal phyla. This unambiguously demonstrated that Glomeromycota are more closely related to Mucoromycotina than to its postulated sister Dikarya

Why the Wind Curtailment of Northwest China Remains High

The total grid-connected installed capacity of wind power in northwest China has grown from 16,260 MW in 2013 to 43,290 MW in 2016; an increase of 88.7% each year. However, this region has suffered from increasingly serious wind curtailment since 2014, and the wind curtailment amount accounts for nearly a half of China’s total. The wind curtailment rate of Gansu Province, Xinjiang Uygur Autonomous Region and Ningxia Hui Autonomous Region in this area has increased and remains high. This paper constructs an analytical model to explore the reasons of the high wind curtailment of these three provinces from the four aspects of the wind power supply capacity, demand, grid transmission capacity, power system flexibility and market mechanism and laws. The results show that the relationship between the wind energy distribution and supply and the local load is incompatible, which is the source causing the high wind curtailment in northwest China. On the one hand, the game between the local government and developers has driven the development of wind power bases. On the other hand, the electricity sector is growing slowly and oversupply of electricity is seen in many areas of China. The wind power grid of northwest China not only faces limit of grid transmission capacity, but also constraint of insufficient flexibility of the electricity system. Presently, China has not set up a market mechanism and subsidy mechanism for the peak load adjustment, thus the thermal power companies lack motivation to voluntarily adjust the peak load. Moreover, the regional segregation and market barriers are also obstacles for the wind power outward transmission

The Design and Implementation of the Leaf Area Index Sensor

The quick and accurate acquisition of crop growth parameters on a large scale is important for agricultural management and food security. The combination of photographic and wireless sensor network (WSN) techniques can be used to collect agricultural information, such as leaf area index (LAI), over long distances and in real time. Such acquisition not only provides farmers with photographs of crops and suggestions for farmland management, but also the collected quantitative parameters, such as LAI, can be used to support large scale research in ecology, hydrology, remote sensing, etc. The present research developed a Leaf Area Index Sensor (LAIS) to continuously monitor the growth of crops in several sampling points, and applied 3G/WIFI communication technology to remotely collect (and remotely setup and upgrade) crop photos in real-time. Then the crop photos are automatically processed and LAI is estimated based on the improved leaf area index of Lang and Xiang (LAILX) algorithm in LAIS. The research also constructed a database of images and other information relating to crop management. The leaf length and width method (LAILLW) can accurately measure LAI through direct field harvest. The LAIS has been tested in several exemplary applications, and validation with LAI from LAILLW. The LAI acquired by LAIS had been proved reliable