Planar Polymer Optical Waveguide with Metal-Organic Framework Coating for Carbon Dioxide Sensing

An easily fabricated gas sensor based on planar polymer optical waveguides with an integrated zeolite imidazole framework-8 (ZIF-8) thin film is presented for carbon dioxide detection and sensing. The planar optical waveguides are made of polymethylmethacrylate and fabricated by hot embossing, which makes it flexible and cost-efficient. Thin ZIF-8 films are uniformly grown on the waveguides surface through a simple solution method, which is crucial for the envisioned production of metal organic framework-based sensing devices on a large scale. Experimental results show that the produced optical elements exhibit a sensitivity of ≈2.5 μW/5 vol% toward carbon dioxide (CO2) with very rapid response time (≈6 s) and excellent reversibility of adsorption and desorption of the gas molecules. The demonstrated planar polymer sensing devices provide the potential to develop flexible on-chip gas sensors in an inexpensive and reproducible way

When Do Program-of-Thoughts Work for Reasoning?

The reasoning capabilities of Large Language Models (LLMs) play a pivotal role in the realm of embodied artificial intelligence. Although there are effective methods like program-of-thought prompting for LLMs which uses programming language to tackle complex reasoning tasks, the specific impact of code data on the improvement of reasoning capabilities remains under-explored. To address this gap, we propose complexity-impacted reasoning score (CIRS), which combines structural and logical attributes, to measure the correlation between code and reasoning abilities. Specifically, we use the abstract syntax tree to encode the structural information and calculate logical complexity by considering the difficulty and the cyclomatic complexity. Through an empirical analysis, we find not all code data of complexity can be learned or understood by LLMs. Optimal level of complexity is critical to the improvement of reasoning abilities by program-aided prompting. Then we design an auto-synthesizing and stratifying algorithm, and apply it to instruction generation for mathematical reasoning and code data filtering for code generation tasks. Extensive results demonstrates the effectiveness of our proposed approach. Code will be integrated into the EasyInstruct framework at https://github.com/zjunlp/EasyInstruct.Comment: Work in progres

OceanGPT: A Large Language Model for Ocean Science Tasks

Ocean science, which delves into the oceans that are reservoirs of life and biodiversity, is of great significance given that oceans cover over 70% of our planet's surface. Recently, advances in Large Language Models (LLMs) have transformed the paradigm in science. Despite the success in other domains, current LLMs often fall short in catering to the needs of domain experts like oceanographers, and the potential of LLMs for ocean science is under-explored. The intrinsic reason may be the immense and intricate nature of ocean data as well as the necessity for higher granularity and richness in knowledge. To alleviate these issues, we introduce OceanGPT, the first-ever LLM in the ocean domain, which is expert in various ocean science tasks. We propose DoInstruct, a novel framework to automatically obtain a large volume of ocean domain instruction data, which generates instructions based on multi-agent collaboration. Additionally, we construct the first oceanography benchmark, OceanBench, to evaluate the capabilities of LLMs in the ocean domain. Though comprehensive experiments, OceanGPT not only shows a higher level of knowledge expertise for oceans science tasks but also gains preliminary embodied intelligence capabilities in ocean technology. Codes, data and checkpoints will soon be available at https://github.com/zjunlp/KnowLM.Comment: Work in progress. Project Website: https://zjunlp.github.io/project/OceanGPT

Massively parallel pyrosequencing-based transcriptome analyses of small brown planthopper (Laodelphax striatellus), a vector insect transmitting rice stripe virus (RSV)

<p>Abstract</p> <p>Background</p> <p>The small brown planthopper (<it>Laodelphax striatellus</it>) is an important agricultural pest that not only damages rice plants by sap-sucking, but also acts as a vector that transmits rice stripe virus (RSV), which can cause even more serious yield loss. Despite being a model organism for studying entomology, population biology, plant protection, molecular interactions among plants, viruses and insects, only a few genomic sequences are available for this species. To investigate its transcriptome and determine the differences between viruliferous and naïve <it>L. striatellus</it>, we employed 454-FLX high-throughput pyrosequencing to generate EST databases of this insect.</p> <p>Results</p> <p>We obtained 201,281 and 218,681 high-quality reads from viruliferous and naïve <it>L. striatellus</it>, respectively, with an average read length as 230 bp. These reads were assembled into contigs and two EST databases were generated. When all reads were combined, 16,885 contigs and 24,607 singletons (a total of 41,492 unigenes) were obtained, which represents a transcriptome of the insect. BlastX search against the NCBI-NR database revealed that only 6,873 (16.6%) of these unigenes have significant matches. Comparison of the distribution of GO classification among viruliferous, naïve, and combined EST databases indicated that these libraries are broadly representative of the <it>L. striatellus </it>transcriptomes. Functionally diverse transcripts from RSV, endosymbiotic bacteria <it>Wolbachia </it>and yeast-like symbiotes were identified, which reflects the possible lifestyles of these microbial symbionts that live in the cells of the host insect. Comparative genomic analysis revealed that <it>L. striatellus </it>encodes similar innate immunity regulatory systems as other insects, such as RNA interference, JAK/STAT and partial Imd cascades, which might be involved in defense against viral infection. In addition, we determined the differences in gene expression between vector and naïve samples, which generated a list of candidate genes that are potentially involved in the symbiosis of <it>L. striatellus </it>and RSV.</p> <p>Conclusions</p> <p>To our knowledge, the present study is the first description of a genomic project for <it>L. striatellus</it>. The identification of transcripts from RSV, <it>Wolbachia</it>, yeast-like symbiotes and genes abundantly expressed in viruliferous insect, provided a starting-point for investigating the molecular basis of symbiosis among these organisms.</p

Fermentation Products of Paenibacillus bovis sp. nov. BD3526 Alleviates the Symptoms of Type 2 Diabetes Mellitus in GK Rats

Gut microbiota is closely related to type 2 diabetes mellitus (T2DM). The gut microbiota of patients with T2DM is significantly different from that of healthy subjects in terms of bacterial composition and diversity. Here, we used the fermentation products of Paenibacillus bovis sp. nov. BD3526 to study the disease progression of T2DM in Goto-kakisaki (GK) rats. We found that the symptoms in GK rats fed the fermentation products of BD3526 were significantly improved. The 16S rRNA sequencing showed that the fermentation products of BD3526 had strong effects on the gut microbiota by increasing the content of Akkermansia. In addition, the interaction of the genus in the gut of the BD3526 group also significantly changed. Additional cytokine detection revealed that the fermentation products of BD3526 can reduce the inflammatory factors in the intestinal mucus of GK rats and thereby inhibit the inflammatory response and ameliorate the symptoms of T2DM

Case report: NUT carcinoma with MXI1::NUTM1 fusion characterized by abdominopelvic lesions and ovarian masses in a middle-aged female

BackgroundNuclear protein of the testis (NUT) carcinoma is a rare subset of poorly differentiated, highly aggressive malignancy defined by NUTM1 gene rearrangements. Only three NUT cases of probable ovarian origin have been reported.Case presentationWe report a case of NUT carcinoma in a 53-year-old female who presented with extensive abdominopelvic lesions and bilateral ovarian masses suggestive of advanced ovarian cancer. This patient was admitted to our hospital due to abdominal pain and distension for over two months. Imaging examinations suggested a possible malignancy of bilateral adnexal origin. This patient first underwent diagnostic laparoscopy. After receiving neoadjuvant chemotherapy, she underwent cytoreductive surgery. Surgical pathology showed infiltration of monotonous round tumor cells with no apparent differentiation characteristics. Immunohistochemistry (IHC) revealed nuclear expression of the NUT protein. And MXI1::NUTM1 fusion was identified by next-generation sequencing (NGS). Herein, we introduce an unusual NUT carcinoma and describe the clinical, imaging, and pathological features. In addition, we briefly reviewed the published literature and discussed the possibility of primary gynecological NUT carcinoma.ConclusionsIdentifying a NUT carcinoma arising from the abdominopelvic cavity is essential, and we underscore the need for NUT testing in undifferentiated malignant neoplasms that appear in this clinical setting. Although it is unclear from which origin this tumor arose, proper classification is essential for treatment planning

Towards Semantic e-Science for Traditional Chinese Medicine

<p>Abstract</p> <p>Background</p> <p>Recent advances in Web and information technologies with the increasing decentralization of organizational structures have resulted in massive amounts of information resources and domain-specific services in Traditional Chinese Medicine. The massive volume and diversity of information and services available have made it difficult to achieve seamless and interoperable e-Science for knowledge-intensive disciplines like TCM. Therefore, information integration and service coordination are two major challenges in e-Science for TCM. We still lack sophisticated approaches to integrate scientific data and services for TCM e-Science.</p> <p>Results</p> <p>We present a comprehensive approach to build dynamic and extendable e-Science applications for knowledge-intensive disciplines like TCM based on semantic and knowledge-based techniques. The semantic e-Science infrastructure for TCM supports large-scale database integration and service coordination in a virtual organization. We use domain ontologies to integrate TCM database resources and services in a semantic cyberspace and deliver a semantically superior experience including browsing, searching, querying and knowledge discovering to users. We have developed a collection of semantic-based toolkits to facilitate TCM scientists and researchers in information sharing and collaborative research.</p> <p>Conclusion</p> <p>Semantic and knowledge-based techniques are suitable to knowledge-intensive disciplines like TCM. It's possible to build on-demand e-Science system for TCM based on existing semantic and knowledge-based techniques. The presented approach in the paper integrates heterogeneous distributed TCM databases and services, and provides scientists with semantically superior experience to support collaborative research in TCM discipline.</p

EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models

Large Language Models (LLMs) usually suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to the outdated/noisy data. To this end, many knowledge editing approaches for LLMs have emerged -- aiming to subtly inject/edit updated knowledge or adjust undesired behavior while minimizing the impact on unrelated inputs. Nevertheless, due to significant differences among various knowledge editing methods and the variations in task setups, there is no standard implementation framework available for the community, which hinders practitioners to apply knowledge editing to applications. To address these issues, we propose EasyEdit, an easy-to-use knowledge editing framework for LLMs. It supports various cutting-edge knowledge editing approaches and can be readily apply to many well-known LLMs such as T5, GPT-J, LlaMA, etc. Empirically, we report the knowledge editing results on LlaMA-2 with EasyEdit, demonstrating that knowledge editing surpasses traditional fine-tuning in terms of reliability and generalization. We have released the source code on GitHub at https://github.com/zjunlp/EasyEdit, along with Google Colab tutorials and comprehensive documentation for beginners to get started. Besides, we present an online system for real-time knowledge editing, and a demo video at http://knowlm.zjukg.cn/easyedit.mp4.Comment: The project website is https://github.com/zjunlp/EasyEdi