1,682 research outputs found
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 hTâ5, 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 networksâ synchronizability, such as BarabĂĄsi-Albert network, Watts-Strogatz network, Erdös-RĂ©nyi 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
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