ENV-624: A NEW HIGH-YIELDING BIO-DISPERSANT PRODUCER MUTATED FROM RHODOCOCCUS ERYTHROPOLIS STRAIN P6-4P

Preeminent effectiveness and feasibility of dispersants have been the key reasons for their widely serving as the response agents in oil spill responses. Moreover, dispersants can also overcome the limitation factors of other countermeasures like accessibility, weather conditions, sea states, and oil thickness. However, the public concerns of the usages of the chemically synthetic dispersants are also essential due to their toxicity and persistency in the ecosystem. Bio-dispersants can be a promising alternative as the proven features of lower toxicity and persistency while with high effectiveness, but its broad application prospects are currently restricted by the high production cost that is 3-10 times more than chemical synthetic ones because of the low productivity. Thus, a hyper bio-dispersant producer will be the desired coping strategy. An isolated bio-dispersant producer from NL offshore, Rhodococcus erythropolis strain P6-4P was selected for generating high-yielding producers by mutation. After UV mutagenesis, 21 enhanced mutants were selected through oil spreading screening method. Further productivity quantify test of critical micelle dilution (CMD) with higher resolution was conducted to these mutants. An outstanding mutant showed CMD as high as 225 while 15.4 is the CMD of the wild type strain, which means the new mutant is 14.6 times increase. The 16S rDNA sequencing results revealed that the 16 S ribosomal DNA of the mutant 100% matched with the original strain indicating the mutation occurred on other parts of the genome which will be identified through next-generation sequencing and comparative analysis in the future study. This mutated high-yielding strain was capable to significantly improve the production rate and the total yield of bio-dispersants. The yield of crude bio-dispersant was 54g per liter with 6 days incubation. At 4mg/uL crude product/crude oil ratio, the dispersion effectiveness was found comparable to Corexit 9500A at 1:25 (dispersant/crude oil ratio). Future works on further mutagenesis base on this new high-producing strain by novel mutation methods were also discussed

STUDY OF TWO MEDICINAL HERBS LEUCAS ASPERA AND CISTUS LAURIFOLIUS FOR THEIR PROSTAGLANDIN INHIBITORY AND ANTIOXIDANT COMPONENTS

研究科: 千葉大学大学院医学薬学府学位:千大院医薬博甲第薬6

Hot Compression Test and Microstructure Evolution in LZ50 Axle Steel

True strain-true stress curves of the LZ50 axle steel were obtained after hot compression tests had been performed on a Gleeble-3800 thermal simulator at strain rates of 0.01, 0.1, 1 and 5 s^(-1) and at deformation temperatures from 850 to 1,150 ℃. Following the data processing, the relationship between the flow stress and the deformation temperature of the material under different true strain conditions was analysed. On this basis and according to the influence of deformation factors, the constitutive equation of the Johnson-Cook flow stress model is established, and the model is modified according to the defects of the model, so that the improved model can effectively predict the mechanical behaviour in the range of high strain rates and temperatures. The dynamic material model (DMM) was used to generate the hot working diagram of the material. Through calculation and analysis, the optimum process area in terms of temperature was found to be in the range from 1,050 to 1,150 ℃ and in terms of strain rate in the rage from 1 to 5 s^(-1). Finally, the microstructure evolution of the compressed specimens under different strain rates and temperatures was studied in the metallographic analysis, which provided a theoretical basis and reference value for later damage

Flexibly-oriented double Cdc45-MCM-GINS intermediates during eukaryotic replicative helicase maturation

The core of the eukaryotic helicase MCM is loaded as an inactive double hexamer (DH). How it is assembled into two active Cdc45-MCM-GINS (CMG) helicases remains elusive. Here, we report that at the onset of S phase, both Cdc45 and GINS are loaded as dimers onto MCM DH, resulting in formation of double CMG (d-CMG). As S phase proceeds, d-CMGs gradually mature into two single CMG-centered replisome progression complexes (RPCs). Mass spectra reveal that RPA and DNA Pol α/primase co-purify exclusively with RPCs, but not with d-CMGs. Consistently, d-CMGs are not able to catalyze either the unwinding or de novo DNA synthesis, while RPCs can do both. Using single-particle electron microscopy, we have obtained 2D class averages of d-CMGs. Compared to MCM DHs, they display heterogeneous, flexibly orientated and partially loosened conformations with changed interfaces. The dumbbell-shaped d-CMGs are mediated by Ctf4, while other types of d-CMGs are independent of Ctf4. These data suggest CMG dimers as bona fide intermediates during MCM maturation, providing an additional quality control for symmetric origin activation and bidirectional replication

SALMON: Self-Alignment with Principle-Following Reward Models

Supervised Fine-Tuning (SFT) on response demonstrations combined with Reinforcement Learning from Human Feedback (RLHF) constitutes a powerful paradigm for aligning LLM-based AI agents. However, a significant limitation of such an approach is its dependency on high-quality human annotations, making its application to intricate tasks challenging due to difficulties in obtaining consistent response demonstrations and in-distribution response preferences. This paper presents a novel approach, namely SALMON (Self-ALignMent with principle-fOllowiNg reward models), to align base language models with minimal human supervision, using only a small set of human-defined principles, yet achieving superior performance. Central to our approach is a principle-following reward model. Trained on synthetic preference data, this model can generate reward scores based on arbitrary human-defined principles. By merely adjusting these principles during the RL training phase, we gain full control over the preferences with the reward model, subsequently influencing the behavior of the RL-trained policies, and eliminating the reliance on the collection of online human preferences. Applying our method to the LLaMA-2-70b base language model, we developed an AI assistant named Dromedary-2. With only 6 exemplars for in-context learning and 31 human-defined principles, Dromedary-2 significantly surpasses the performance of several state-of-the-art AI systems, including LLaMA-2-Chat-70b, on various benchmark datasets. We have open-sourced the code and model weights to encourage further research into aligning LLM-based AI agents with enhanced supervision efficiency, improved controllability, and scalable oversight.Comment: Project page: https://github.com/IBM/SALMO

Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision

Recent AI-assistant agents, such as ChatGPT, predominantly rely on supervised fine-tuning (SFT) with human annotations and reinforcement learning from human feedback (RLHF) to align the output of large language models (LLMs) with human intentions, ensuring they are helpful, ethical, and reliable. However, this dependence can significantly constrain the true potential of AI-assistant agents due to the high cost of obtaining human supervision and the related issues on quality, reliability, diversity, self-consistency, and undesirable biases. To address these challenges, we propose a novel approach called SELF-ALIGN, which combines principle-driven reasoning and the generative power of LLMs for the self-alignment of AI agents with minimal human supervision. Our approach encompasses four stages: first, we use an LLM to generate synthetic prompts, and a topic-guided method to augment the prompt diversity; second, we use a small set of human-written principles for AI models to follow, and guide the LLM through in-context learning from demonstrations (of principles application) to produce helpful, ethical, and reliable responses to user's queries; third, we fine-tune the original LLM with the high-quality self-aligned responses so that the resulting model can generate desirable responses for each query directly without the principle set and the demonstrations anymore; and finally, we offer a refinement step to address the issues of overly-brief or indirect responses. Applying SELF-ALIGN to the LLaMA-65b base language model, we develop an AI assistant named Dromedary. With fewer than 300 lines of human annotations (including < 200 seed prompts, 16 generic principles, and 5 exemplars for in-context learning). Dromedary significantly surpasses the performance of several state-of-the-art AI systems, including Text-Davinci-003 and Alpaca, on benchmark datasets with various settings.Comment: Accepted at NeurIPS 2023 (Spotlight). Project page: https://github.com/IBM/Dromedar

Multimodal magnetic resonance imaging on brain structure and function changes in subjective cognitive decline: a mini-review

Subjective cognitive decline (SCD) is the initial stage of Alzheimer’s disease (AD). Early identification of SCD and its risk factors is of great importance for targeted interventions and for delaying the onset of AD. We reviewed the relevant literature on structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), functional magnetic resonance imaging (fMRI), and other techniques regarding SCD research in recent years. This study applied sMRI and fMRI techniques to explore abnormal brain structures and functions, which may help provide a basis for SCD diagnosis

HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environments

Recent advances in high-fidelity virtual environments serve as one of the major driving forces for building intelligent embodied agents to perceive, reason and interact with the physical world. Typically, these environments remain unchanged unless agents interact with them. However, in real-world scenarios, agents might also face dynamically changing environments characterized by unexpected events and need to rapidly take action accordingly. To remedy this gap, we propose a new simulated embodied benchmark, called HAZARD, specifically designed to assess the decision-making abilities of embodied agents in dynamic situations. HAZARD consists of three unexpected disaster scenarios, including fire, flood, and wind, and specifically supports the utilization of large language models (LLMs) to assist common sense reasoning and decision-making. This benchmark enables us to evaluate autonomous agents' decision-making capabilities across various pipelines, including reinforcement learning (RL), rule-based, and search-based methods in dynamically changing environments. As a first step toward addressing this challenge using large language models, we further develop an LLM-based agent and perform an in-depth analysis of its promise and challenge of solving these challenging tasks. HAZARD is available at https://vis-www.cs.umass.edu/hazard/.Comment: ICLR 2024. The first two authors contributed equally to this wor