Estimating Crop Stomatal Conductance Through High-Throughput Plant Phenotyping

During photosynthesis and transpiration, crops exchange carbon dioxide and water with the atmosphere through stomata. When a crop experiences water stress, stomata are closed to reducing water loss. However, the closing of stomata also negatively affects the photosynthetic efficiency of the crop and leads to lower yields. Stomatal conductance (gs) quantifies the degree of stomatal opening and closing by using the rate of gas exchange between the crop and the atmosphere, which helps to understand the water status of the crop for better irrigation management. Unfortunately, gs measurement typically requires contact measuring instruments and manual collection in the field, which is time-consuming and labor-intensive. Thus, this study estimates gs in two ways. Firstly, plant phenotypic data and weather information were used to estimate gs for various types of crops. The plant phenotypic data were extracted from images captured by a thermal infrared camera, a multispectral camera, and a visible and near-infrared spectrometer integrated on field phenotyping platform. Weather information was obtained from a field weather station. The random forest regression (RFR) model performed the best with R2 of 0.69 and RMSE of 0.135 mol*m-2 *s-1 , while the model using weather parameters alone had R2 of 0.58 and RMSE of 0.161, and the model using phenotypic data alone had R2 values of 0.59 and RMSE of 0.158 mol*m-2 *s-1 . The results indicated that there was a complementary relationship between plant phenotypic data and weather information in estimating gs. The second aspect of the study was to estimate maize and soybean gs directly from near-infrared, thermal-infrared and RGB (Red Green Blue) images collected by the same platform. The results showed that the convolutional neural network (CNN) model outperformed the other models with an R2 of 0.52. In addition, adding soil moisture as a variable to the model improved its accuracy, which decreased the RMSE from 0.147 to 0.137 mol*m-2 *s-1 . This study highlights the potential of estimating gs from remote sensing and field phenotyping platforms to help growers obtain information about the water status of crops and plan irrigation more efficiently. Advisor: Yufeng G

Defending Large Language Models Against Jailbreaking Attacks Through Goal Prioritization

Large Language Models (LLMs) continue to advance in their capabilities, yet this progress is accompanied by a growing array of safety risks. While significant attention has been dedicated to exploiting weaknesses in LLMs through jailbreaking attacks, there remains a paucity of exploration into defending against these attacks. We point out a pivotal factor contributing to the success of jailbreaks: the inherent conflict between the goals of being helpful and ensuring safety. To counter jailbreaking attacks, we propose to integrate goal prioritization at both training and inference stages. Implementing goal prioritization during inference substantially diminishes the Attack Success Rate (ASR) of jailbreaking attacks, reducing it from 66.4% to 2.0% for ChatGPT and from 68.2% to 19.4% for Vicuna-33B, without compromising general performance. Furthermore, integrating the concept of goal prioritization into the training phase reduces the ASR from 71.0% to 6.6% for LLama2-13B. Remarkably, even in scenarios where no jailbreaking samples are included during training, our approach slashes the ASR by half, decreasing it from 71.0% to 34.0%. Additionally, our findings reveal that while stronger LLMs face greater safety risks, they also possess a greater capacity to be steered towards defending against such attacks. We hope our work could contribute to the comprehension of jailbreaking attacks and defenses, and shed light on the relationship between LLMs' capability and safety. Our code will be available at \url{https://github.com/thu-coai/JailbreakDefense_GoalPriority}.Comment: 14 page

Systematic screening of long intergenic noncoding RNAs expressed during chicken embryogenesis

ABSTRACT: Long noncoding RNAs (lncRNAs) have emerged as important regulators of many biological processes, including embryogenesis and development. To provide a systematic analysis of lncRNAs expressed during chicken embryogenesis, we used Iso-Seq and RNA-Seq to identify potential lncRNAs at embryonic stages from d 1 to d 8 of incubation: sequential stages covering gastrulation, somitogenesis, and organogenesis. The data characterized an expanded landscape of lncRNAs, yielding 45,410 distinct lncRNAs (31,282 genes). Amongst these, a set of 13,141 filtered intergenic lncRNAs (lincRNAs) transcribed from 9803 lincRNA gene loci, of which, 66.5% were novel, were further analyzed. These lincRNAs were found to share many characteristics with mammalian lincRNAs, including relatively short lengths, fewer exons, lower expression levels, and stage-specific expression patterns. Functional studies motivated by “guilt-by-association” associated individual lincRNAs with specific GO functions, providing an important resource for future studies of lincRNA function. Most importantly, a weighted gene co-expression network analysis suggested that genes of the brown module were specifically associated with the day 2 stage. LincRNAs within this module were co-expressed with proteins involved in hematopoiesis and lipid metabolism. This study presents the systematic identification of lincRNAs in developing chicken embryos and will serve as a powerful resource for the study of lincRNA functions

On Knowledge Editing in Federated Learning: Perspectives, Challenges, and Future Directions

As Federated Learning (FL) has gained increasing attention, it has become widely acknowledged that straightforwardly applying stochastic gradient descent (SGD) on the overall framework when learning over a sequence of tasks results in the phenomenon known as ``catastrophic forgetting''. Consequently, much FL research has centered on devising federated increasing learning methods to alleviate forgetting while augmenting knowledge. On the other hand, forgetting is not always detrimental. The selective amnesia, also known as federated unlearning, which entails the elimination of specific knowledge, can address privacy concerns and create additional ``space'' for acquiring new knowledge. However, there is a scarcity of extensive surveys that encompass recent advancements and provide a thorough examination of this issue. In this manuscript, we present an extensive survey on the topic of knowledge editing (augmentation/removal) in Federated Learning, with the goal of summarizing the state-of-the-art research and expanding the perspective for various domains. Initially, we introduce an integrated paradigm, referred to as Federated Editable Learning (FEL), by reevaluating the entire lifecycle of FL. Secondly, we provide a comprehensive overview of existing methods, evaluate their position within the proposed paradigm, and emphasize the current challenges they face. Lastly, we explore potential avenues for future research and identify unresolved issues.Comment: 7 pages, 1 figure, 2 tabel

VPA mediates bidirectional regulation of cell cycle progression through the PPP2R2A-Chk1 signaling axis in response to HU

Cell cycle checkpoint kinases play a pivotal role in protecting against replicative stress. In this study, valproic acid (VPA), a histone deacetylase inhibitor (HDACi), was found to promote breast cancer MCF-7 cells to traverse into G2/M phase for catastrophic injury by promoting PPP2R2A (the B-regulatory subunit of Phosphatase PP2A) to facilitate the dephosphorylation of Chk1 at Ser317 and Ser345. By contrast, VPA protected normal 16HBE cells from HU toxicity through decreasing PPP2R2A expression and increasing Chk1 phosphorylation. The effect of VPA on PPP2R2A was at the post-transcription level through HDAC1/2. The in vitro results were affirmed in vivo. Patients with lower PPP2R2A expression and higher pChk1 expression showed significantly worse survival. PPP2R2A D197 and N181 are essential for PPP2R2A-Chk1 signaling and VPA-mediated bidirectional effect on augmenting HU-induced tumor cell death and protecting normal cells

ARES: A Parallel Discrete Ordinates Transport Code for Radiation Shielding Applications and Reactor Physics Analysis

ARES is a multidimensional parallel discrete ordinates particle transport code with arbitrary order anisotropic scattering. It can be applied to a wide variety of radiation shielding calculations and reactor physics analysis. ARES uses state-of-the-art solution methods to obtain accurate solutions to the linear Boltzmann transport equation. A multigroup discretization is applied in energy. The code allows multiple spatial discretization schemes and solution methodologies. ARES currently provides diamond difference with or without linear-zero flux fixup, theta weighted, directional theta weighted, exponential directional weighted, and linear discontinuous finite element spatial differencing schemes. Discrete ordinates differencing in angle and spherical harmonics expansion of the scattering source are adopted. First collision source method is used to eliminate or mitigate the ray effects. Traditional source iteration and Krylov iterative method preconditioned with diffusion synthetic acceleration are applied to solve the linear system of equations. ARES uses the Koch-Baker-Alcouffe parallel sweep algorithm to obtain high parallel efficiency. Verification and validation for the ARES transport code system have been done by lots of benchmarks. In this paper, ARES solutions to the HBR-2 benchmark and C5G7 benchmarks are in excellent agreement with published results. Numerical results are presented which demonstrate the accuracy and efficiency of these methods

Influence of Water on Tribolayer Growth When Lubricating Steel with a Fluorinated Phosphonium Dicyanamide Ionic Liquid

This work aims to elucidate the role of environmental humidity on the tribological behavior of steel surfaces lubricated with an ionic liquid comprised of a fluorinated phosphonium cation—tributyl-3,3,4,4,5,5,6,6,7,7,8,8,8-tridecafluoro-octyl-phosphonium—and a dicyanamide anion (i.e. N(CN)2−). Ball-on-disk tribotests were carried out at room temperature and at various levels of relative humidity (RH). Water was found to be required to promote the formation of a tribofilm over the contact area. The reaction layer exhibited a patchy morphology, which resembles that observed formed with conventional antiwear additives such as ZnDTP. A surface-chemical analysis of the tribofilm indicated that the tribofilm is composed of fluorides, oxides, and phosphates, pointing to a stress-induced degradation of the ions and corrosion of the sliding counterparts, which is enabled by the presence of water at the sliding interface