Global fitness profiling of fission yeast deletion strains by barcode sequencing

A genome-wide deletion library is a powerful tool for probing gene functions and one has recently become available for the fission yeast Schizosaccharomyces pombe. Here we use deep sequencing to accurately characterize the barcode sequences in the deletion library, thus enabling the quantitative measurement of the fitness of fission yeast deletion strains by barcode sequencing

Pinning Control Strategy of Multicommunity Structure Networks

In order to investigate the effects of community structure on synchronization, a pinning control strategy is researched in a class of complex networks with community structure in this paper. A feedback control law is designed based on the network community structure information. The stability condition is given and proved by using Lyapunov stability theory. Our research shows that as to community structure networks, there being a threshold hT≈5, when coupling strength bellows this threshold, the stronger coupling strength corresponds to higher synchronizability; vice versa, the stronger coupling strength brings lower synchronizability. In addition the synchronizability of overlapping and nonoverlapping community structure networks was simulated and analyzed; while the nodes were controlled randomly and intensively, the results show that intensive control strategy is better than the random one. The network will reach synchronization easily when the node with largest betweenness was controlled. Furthermore, four difference networks’ synchronizability, such as Barabási-Albert network, Watts-Strogatz network, Erdös-Rényi network, and community structure network, are simulated; the research shows that the community structure network is more easily synchronized under the same control strength

ANPL: Compiling Natural Programs with Interactive Decomposition

The advents of Large Language Models (LLMs) have shown promise in augmenting programming using natural interactions. However, while LLMs are proficient in compiling common usage patterns into a programming language, e.g., Python, it remains a challenge how to edit and debug an LLM-generated program. We introduce ANPL, a programming system that allows users to decompose user-specific tasks. In an ANPL program, a user can directly manipulate sketch, which specifies the data flow of the generated program. The user annotates the modules, or hole with natural language descriptions offloading the expensive task of generating functionalities to the LLM. Given an ANPL program, the ANPL compiler generates a cohesive Python program that implements the functionalities in hole, while respecting the dataflows specified in sketch. We deploy ANPL on the Abstraction and Reasoning Corpus (ARC), a set of unique tasks that are challenging for state-of-the-art AI systems, showing it outperforms baseline programming systems that (a) without the ability to decompose tasks interactively and (b) without the guarantee that the modules can be correctly composed together. We obtain a dataset consisting of 300/400 ARC tasks that were successfully decomposed and grounded in Python, providing valuable insights into how humans decompose programmatic tasks. See the dataset at https://iprc-dip.github.io/DARC

DIGITAL SOIL MAPPING FOR SMART AGRICULTURE: THE SOLIM METHOD AND SOFTWARE PLATFORMS

The key challenges faced by many of the existing digital soil mapping (DSM) techniques are the rigid requirements on the size of soil samples to extract the relationships needed and on the stationarity of the extracted relationships. These requirements limit the application of these DSM techniques. This paper provides an overview of the SoLIM approach and an introduction to the operation of SoLIM through the software platforms available. SoLIM is based on the Third Law of Geography, which calls for the comparison of similarity in geographic (environmental) configuration of a prototype and an unsampled location and then use this similarity to predict the value of a soil property at a given location. DSM under SoLIM approach removes requirements on the sample size and the stationarity assumption. In addition, the uncertainty computed based on the similarities can be used to improve the efficiency of error reduction efforts. The SoLIM approach has been implemented in two platforms: SoLIM Solutions and CyberSoLIM. The theoretical foundation and the availability of software platforms under SoLIM make DSM possible and convenient over large and complex geographic regions

Cannabidiol regulates apoptosis and autophagy in inflammation and cancer: A review

Cannabidiol (CBD) is a terpenoid naturally found in plants. The purified compound is used in the treatment of mental disorders because of its antidepressive, anxiolytic, and antiepileptic effects. CBD can affect the regulation of several pathophysiologic processes, including autophagy, cytokine secretion, apoptosis, and innate and adaptive immune responses. However, several authors have reported contradictory findings concerning the magnitude and direction of CBD-mediated effects. For example, CBD treatment can increase, decrease, or have no significant effect on autophagy and apoptosis. These variable results can be attributed to the differences in the biological models, cell types, and CBD concentration used in these studies. This review focuses on the mechanism of regulation of autophagy and apoptosis in inflammatory response and cancer by CBD. Further, we broadly elaborated on the prospects of using CBD as an anti-inflammatory agent and in cancer therapy in the future

Self-driven Grounding: Large Language Model Agents with Automatical Language-aligned Skill Learning

Large language models (LLMs) show their powerful automatic reasoning and planning capability with a wealth of semantic knowledge about the human world. However, the grounding problem still hinders the applications of LLMs in the real-world environment. Existing studies try to fine-tune the LLM or utilize pre-defined behavior APIs to bridge the LLMs and the environment, which not only costs huge human efforts to customize for every single task but also weakens the generality strengths of LLMs. To autonomously ground the LLM onto the environment, we proposed the Self-Driven Grounding (SDG) framework to automatically and progressively ground the LLM with self-driven skill learning. SDG first employs the LLM to propose the hypothesis of sub-goals to achieve tasks and then verify the feasibility of the hypothesis via interacting with the underlying environment. Once verified, SDG can then learn generalized skills with the guidance of these successfully grounded subgoals. These skills can be further utilized to accomplish more complex tasks which fail to pass the verification phase. Verified in the famous instruction following task set-BabyAI, SDG achieves comparable performance in the most challenging tasks compared with imitation learning methods that cost millions of demonstrations, proving the effectiveness of learned skills and showing the feasibility and efficiency of our framework

Direct Visualization of Irreducible Ferrielectricity in Crystals

In solids, charge polarity can one-to-one correspond to spin polarity phenomenologically, e.g. ferroelectricity/ferromagnetism, antiferroelectricity/antiferromagnetism, and even dipole-vortex/magnetic-vortex, but ferrielectricity/ferrimagnetism kept telling a disparate story in microscopic level. Since the definition of a charge dipole involves more than one ion, there may be multiple choices for a dipole unit, which makes most ferrielectric orders equivalent to ferroelectric ones, i.e. this ferrielectricity is not necessary to be a real independent branch of polarity. In this work, by using the spherical aberration-corrected scanning transmission electron microscope, we visualize a nontrivial ferrielectric structural evolution in BaFe2Se3, in which the development of two polar sub-lattices is out-of-sync, for which we term it as irreducible ferrielectricity. Such irreducible ferrielectricity leads to a non-monotonic behavior for the temperature-dependent polarization, and even a compensation point in the ordered state. Our finding unambiguously distinguishes ferrielectrics from ferroelectrics in solids.Comment: 15 figure