Can Large Language Models Understand Real-World Complex Instructions?

Large language models (LLMs) can understand human instructions, showing their potential for pragmatic applications beyond traditional NLP tasks. However, they still struggle with complex instructions, which can be either complex task descriptions that require multiple tasks and constraints, or complex input that contains long context, noise, heterogeneous information and multi-turn format. Due to these features, LLMs often ignore semantic constraints from task descriptions, generate incorrect formats, violate length or sample count constraints, and be unfaithful to the input text. Existing benchmarks are insufficient to assess LLMs' ability to understand complex instructions, as they are close-ended and simple. To bridge this gap, we propose CELLO, a benchmark for evaluating LLMs' ability to follow complex instructions systematically. We design eight features for complex instructions and construct a comprehensive evaluation dataset from real-world scenarios. We also establish four criteria and develop corresponding metrics, as current ones are inadequate, biased or too strict and coarse-grained. We compare the performance of representative Chinese-oriented and English-oriented models in following complex instructions through extensive experiments. Resources of CELLO are publicly available at https://github.com/Abbey4799/CELLO

Human matrix metalloproteinases: An ubiquitarian class of enzymes involved in several pathological processes

Human matrix metalloproteinases (MMPs) belong to the M10 family of the MA clan of endopeptidases. They are ubiquitarian enzymes, structurally characterized by an active site where a Zn(2+) atom, coordinated by three histidines, plays the catalytic role, assisted by a glutamic acid as a general base. Various MMPs display different domain composition, which is very important for macromolecular substrates recognition. Substrate specificity is very different among MMPs, being often associated to their cellular compartmentalization and/or cellular type where they are expressed. An extensive review of the different MMPs structural and functional features is integrated with their pathological role in several types of diseases, spanning from cancer to cardiovascular diseases and to neurodegeneration. It emerges a very complex and crucial role played by these enzymes in many physiological and pathological processes

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Isolation and functional expression of a novel lipase gene isolated directly from oil-contaminated soil

A lipase gene SR1 encoding an extracellular lipase was isolated from oil-contaminated soil and expressed in Escherichia coli. The gene contained a 1845-bp reading frame and encoded a 615-amino-acid lipase protein. The mature part of the lipase was expressed with an N-terminal histidine tag in E. coli BL21, purified and characterized biochemically. The results showed that the purified lipase combines the properties of Pseudomonas chlororaphis and other Serratia lipases characterized so far. Its optimum pH and temperature for hydrolysis activity was pH 5.5-8.0 and 37°C respectively. The enzyme showed high preference for short chain substrates (556.3±2.8 U/µg for C10 fatty acid oil) and surprisingly it also displayed high activity for long-chain fatty acid. The deduced lipase SR1 protein is probably from Serratia, and is organized as a prepro-protein and belongs to the GXSXG lipase family

Optic Disc Segmentation Using Attention-Based U-Net and the Improved Cross-Entropy Convolutional Neural Network

Medical image segmentation is an important part of medical image analysis. With the rapid development of convolutional neural networks in image processing, deep learning methods have achieved great success in the field of medical image processing. Deep learning is also used in the field of auxiliary diagnosis of glaucoma, and the effective segmentation of the optic disc area plays an important assistant role in the diagnosis of doctors in the clinical diagnosis of glaucoma. Previously, many U-Net-based optic disc segmentation methods have been proposed. However, the channel dependence of different levels of features is ignored. The performance of fundus image segmentation in small areas is not satisfactory. In this paper, we propose a new aggregation channel attention network to make full use of the influence of context information on semantic segmentation. Different from the existing attention mechanism, we exploit channel dependencies and integrate information of different scales into the attention mechanism. At the same time, we improved the basic classification framework based on cross entropy, combined the dice coefficient and cross entropy, and balanced the contribution of dice coefficients and cross entropy loss to the segmentation task, which enhanced the performance of the network in small area segmentation. The network retains more image features, restores the significant features more accurately, and further improves the segmentation performance of medical images. We apply it to the fundus optic disc segmentation task. We demonstrate the segmentation performance of the model on the Messidor dataset and the RIM-ONE dataset, and evaluate the proposed architecture. Experimental results show that our network architecture improves the prediction performance of the base architectures under different datasets while maintaining the computational efficiency. The results render that the proposed technologies improve the segmentation with 0.0469 overlapping error on Messidor

The non-metabolizable glucose analog D-glucal inhibits aflatoxin biosynthesis and promotes kojic acid production in Aspergillus flavus

Background: Aflatoxins (AFs) are potent carcinogenic compounds produced by several Aspergillus species, which pose serious threats to human health. As sugar is a preferred carbohydrate source for AF production, we examined the possibility of using sugar analogs to inhibit AF biosynthesis. Results: We showed that although D-glucal cannot be utilized by A. flavus as the sole carbohydrate source, it inhibited AF biosynthesis and promoted kojic acid production without affecting mycelial growth when applied to a glucose-containing medium. The inhibition occurred before the production of the first stable intermediate, norsolorinic acid, suggesting a complete inhibition of the AF biosynthetic pathway. Further studies showed that exogenous D-glucal in culture led to reduced accumulation of tricarboxylic acid (TCA) cycle intermediates and reduced glucose consumption, indicating that glycolysis is inhibited. Expression analyses revealed that D-glucal suppressed the expression of AF biosynthetic genes but promoted the expression of kojic acid biosynthetic genes. Conclusions: D-glucal as a non-metabolizable glucose analog inhibits the AF biosynthesis pathway by suppressing the expression of AF biosynthetic genes. The inhibition may occur either directly through interfering with glycolysis, or indirectly through reduced oxidative stresses from kojic acid biosynthesis

A Cotton Annexin Protein AnxGb6 Regulates Fiber Elongation through Its Interaction with Actin 1

<div><p>Annexins are assumed to be involved in regulating cotton fiber elongation, but direct evidence remains to be presented. Here we cloned six Annexin genes (<i>AnxGb</i>) abundantly expressed in fiber from sea-island cotton (<i>G. barbadense</i>). qRT-PCR results indicated that all six <i>G. barbadense</i> annexin genes were expressed in elongating cotton fibers, while only the expression of <i>AnxGb6</i> was cotton fiber-specific. Yeast two hybridization and BiFC analysis revealed that AnxGb6 homodimer interacted with a cotton fiber specific actin GbAct1. Ectopic-expressed <i>AnxGb6</i> in <i>Arabidopsis</i> enhanced its root elongation without increasing the root cell number. Ectopic <i>AnxGb6</i> expression resulted in more F-actin accumulation in the basal part of the root cell elongation zone. Analysis of <i>AnxGb6</i> expression in three cotton genotypes with different fiber length confirmed that <i>AnxGb6</i> expression was correlated to cotton fiber length, especially fiber elongation rate. Our results demonstrated that AnxGb6 was important for fiber elongation by potentially providing a domain for F-actin organization.</p></div