First biochemical characterization of a novel ribonuclease from wild mushroom Amanita hemibapha

A 45-kDa ribonuclease (RNase) was purified from dried fruiting bodies of the wild mushroom Amanita hemibapha. It was adsorbed on DEAE-cellulose, S-sepharose, and finally purified on Superdex 75. The RNase exhibited maximal RNase activity at pH 5 and in a temperature range between 60-70°C. It demonstrated no ribonucleolytic activity toward four polyhomoribonucleotides. The amino acid sequence analysis (GDDETFWEHEWAK) showed this RNase was a ribonuclease T2-like RNase. It exhibited strong inhibitory activity against HIV-1 reverse transcriptase (HIV-1 RT) with an IC(50) of 17 μM

Model-based Control of the Current Density Profile in the Experimental Advanced Superconducting Tokamak (EAST)

As worldwide energy consumption increases, the world is facing the possibility of an energy shortage problem. While several approaches have been proposed to slow down this process, which include the improvement of the combustion efficiency of fossil fuels and the introduction of nuclear energy and renewable energy, such as solar, wind, and geothermal energy, a replacement for fossil fuels will eventually be needed. The energy that comes from a nuclear reaction, which includes nuclear fission and nuclear fusion, has a high energy production density (rate of energy produced divided by the area of the land needed to produce it) and produces no air pollution or greenhouse gases, which makes it a strong and attractive candidate. Compared with nuclear fission, the radioactive waste from nuclear fusion can be more easily disposed, the reactants in a nuclear fusion reaction are abundantly available in nature, and nuclear fusion poses no risk of a nuclear accident. For all these reasons, nuclear fusion is a potential solution for the energy shortage problem. However, there are many challenges that need to be conquered to achieve nuclear fusion. The primary challenge is to confine the hot reactants, whose temperatures are about one hundred million degrees Kelvin. At these temperatures, the reactants are in the plasma state and have enough kinetic energy to overcome the repelling electrostatic forces and fuse. One of the most promising approaches to confine the fusion plasma is magnetic confinement, where magnetic fields are used to confine the plasma through the Lorentz force. The tokamak is one of the fusion devices that exploit magnetic confinement. To demonstrate the viability of a nuclear fusion power plant, the International Thermonuclear Experimental Reactor (ITER) tokamak project is aimed at producing 500 megawatts power with 50 megawatts of input power, which will make it the first tokamak with net energy output. To be able to obtain the desired fusion gain, the ITER tokamak will need to operate at a temperature and a pressure so high that the plasma has a good chance of becoming unstable and difficult to confine. To address this issue, extensive research has been conducted on different fusion tokamaks around the world to find high performance operating scenarios characterized by a high fusion gain, good plasma confinement, plasma stability, and a dominant self-generated plasma current with the goal of developing candidate scenarios for ITER. The shape of the toroidal current density profile, or the safety factor profile ($q$-profile), impacts steady-state operation, magnetohydrodynamic (MHD) stability, and plasma performance. The plasma $\beta$, which is the ratio of the kinetic pressure of the plasma to the magnetic pressure (pressure exerted on plasma by the magnetic field), acts as an important economic factor in fusion power generation. Therefore, active control of the toroidal current density profile and plasma $\beta$ is one path towards advanced scenarios. This dissertation focuses on developing control solutions for regulating the current density profile, and to some extent the normalized plasma $\beta$ (denoted as $\beta_N$), on the Experimental Advanced Superconducting Tokamak (EAST) located at the Institute of Plasma Physics, Chinese Academy of Sciences (ASIPP), in Hefei, China. Towards this goal, a control-oriented, physics-based model has been developed for the current density profile evolution in EAST in response to available heating and current-drive (H\&CD) systems. The feasibility of reconstructing the internal plasma states, which may be crucial for feedback control, from measurements at the magnetic axis and at the plasma edge has been studied by using experimental data and exploiting the response model. Target scenarios (characterized by desired $q$-profile and $\beta_N$) have been developed by following a model-based finite-time optimization approach. Feedback controllers ranging from simpler Proportional-Integral-Derivative (PID) controllers to more complex model-based optimal controllers, derived from Linear-Quadratic-Regulator (LQR), $H_\infty$, and Model Predictive Control (MPC) theories, have been synthesized to counteract deviations from the desired target scenario. The overall control solution has been implemented in the Plasma Control System (PCS) and closed-loop $q$-profile regulation has been demonstrated for the first time ever in EAST in disturbance rejection and target tracking experiments

A Laccase with HIV-1 Reverse Transcriptase Inhibitory Activity from the Broth of Mycelial Culture of the Mushroom Lentinus tigrinus

A 59 kDa laccase with inhibitory activity against HIV-1 reverse transcriptase (IC50 = 2.4 μM) was isolated from the broth of mycelial culture of the mushroom Lentinus tigrinus. The isolation procedure involved ion exchange chromatography on DEAE-cellulose and CM-cellulose, and gel filtration by fast protein liquid chromatography on Superdex 75. The laccase was adsorbed on both types of ion exchangers. About 95-fold purification was achieved with a 25.9% yield of the enzyme. The procedure resulted in a specific enzyme activity of 76.6 U/mg. Its N-terminal amino acid sequence was GIPDLHDLTV, which showed little similarity to other mushroom laccase and other Lentinus tigrinus strain laccase. Its characteristics were different from previously reported laccase of other Lentinus tigrinus strain. Maximal laccase activity was observed at a pH of 4 and at a temperature of 60°C, respectively. This study yielded the information about the potentially exploitable activities of Lentinus tigrinus laccase

Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection

Multi-label image classification is a fundamental but challenging task towards general visual understanding. Existing methods found the region-level cues (e.g., features from RoIs) can facilitate multi-label classification. Nevertheless, such methods usually require laborious object-level annotations (i.e., object labels and bounding boxes) for effective learning of the object-level visual features. In this paper, we propose a novel and efficient deep framework to boost multi-label classification by distilling knowledge from weakly-supervised detection task without bounding box annotations. Specifically, given the image-level annotations, (1) we first develop a weakly-supervised detection (WSD) model, and then (2) construct an end-to-end multi-label image classification framework augmented by a knowledge distillation module that guides the classification model by the WSD model according to the class-level predictions for the whole image and the object-level visual features for object RoIs. The WSD model is the teacher model and the classification model is the student model. After this cross-task knowledge distillation, the performance of the classification model is significantly improved and the efficiency is maintained since the WSD model can be safely discarded in the test phase. Extensive experiments on two large-scale datasets (MS-COCO and NUS-WIDE) show that our framework achieves superior performances over the state-of-the-art methods on both performance and efficiency.Comment: accepted by ACM Multimedia 2018, 9 pages, 4 figures, 5 table

Trypsin Isoinhibitors with Antiproliferative Activity toward Leukemia Cells from Phaseolus vulgaris cv “White Cloud Bean”

A purification protocol that comprised ion exchange chromatography on DEAE-cellulose, affinity chromatography on Affi-gel blue gel, ion exchange chromatography on SP-Sepharose, and gel filtration by FPLC on Superdex 75 was complied to isolate two trypsin inhibitors from Phaseolus vulgaris cv “White Cloud Bean”. Both trypsin inhibitors exhibited a molecular mass of 16 kDa and reduced the activity of trypsin with an IC50 value of about 0.6 μM. Dithiothreitol attenuated the trypsin inhibitory activity, signifying that an intact disulfide bond is indispensable to the activity. [Methyl-3H] thymidine incorporation by leukemia L1210 cells was inhibited with an IC50 value of 28.8 μM and 21.5 μM, respectively. They were lacking in activity toward lymphoma MBL2 cells and inhibitory effect on HIV-1 reverse transcriptase and fungal growth when tested up to 100 μM

The effect of proteoglycans inhibited by RNA interference on metastatic characters of human salivary adenoid cystic carcinoma

<p>Abstract</p> <p>Background</p> <p>Salivary adenoid cystic carcinoma (SACC) is one of the most common malignancies of salivary gland. Recurrence or/and early metastasis is its biological properties. In SACC, neoplastic myoepithelial cells secrete proteoglycans unconventionally full of the cribriform or tubular and glandular structures of SACC. Literatures have demonstrated that extracellular matrix provided an essential microenvironment for the biological behavior of SACC. However, there is rare study of the effect of proteoglycans on the potential metastasis of SACC.</p> <p>In this study, human xylosyltransferase-I (XTLY-I) gene, which catalyzes the rate-limited step of proteoglycans biosynthesis, was knocked down by RNA interference (RNAi) to inhibit the proteoglycans biosynthesis in SACC cell line with high tendency of lung metastasis (SACC-M). The impact of down-regulated proteoglycans on the metastasis characters of SACC-M cells was analyzed and discussed. This research could provide a new idea for the clinical treatment of SACC.</p> <p>Methods</p> <p>The eukaryotic expression vector of short hairpin RNA (shRNA) targeting XTLY-I gene was constructed and transfected into SACC-M cells. A stably transfectant cell line named SACC-M-WJ4 was isolated. The XTLY-I expression was measured by real-time PCR and Western blot; the reduction of proteoglycans was measured. The invasion and metastasis of SACC-M-WJ4 cells were detected; the effect of down-regulated proteoglycans on the potential lung metastasis of nude mice was observed, respectively.</p> <p>Results</p> <p>The shRNA plasmid targeting XTLY-I gene showed powerful efficiency of RNAi. The mRNA level of target gene decreased by 86.81%, the protein level was decreased by 80.10%, respectively. The silence of XTLY-I gene resulted in the reduction of proteoglycans significantly in SACC-M-WJ4 cells. The inhibitory rate of proteoglycans was 58.17% (24 h), 66.06% (48 h), 57.91% (72 h), 59.36% (96 h), and 55.65% (120 h), respectively. The reduction of proteoglycans suppressed the adhesion, invasion and metastasis properties of SACC-M cells, and decreased the lung metastasis of SACC-M cells markedly either.</p> <p>Conclusion</p> <p>The data suggested that the silence of XTLY-I gene in SACC-M cells could suppress proteoglycans biosynthesis and secretion significantly. The reduction of proteoglycans inhibited cell adhesion, invasion and metastasis of SACC-M cells. There is a close relationship between proteoglycans and the biological behavior of SACC.</p

Effect of fibers on self-healing properties of microbial mineralized cement mortars

In this study, we selected cement mortar as the research object, used the expanded perlite (EP) which adsorbed bacteria as the self-healing agent, and mixed basalt fibers to improve the properties. The effects of different dosages and sizes of self-healing agent and basalt fibers on the mechanical properties and self-healing properties of cement mortar were investigated by compressive strength, SEM, EDS, XRD, and optical microscopy tests. The results of the study showed that the bacteria were able to survive in cement mortar using expanded perlite as a carrier and induced the generation of calcium carbonate precipitates to fill the cracks. The dosage of the healing agent is proportional to the amount of healing products generated, which can significantly improve the self-healing performance of cracks in mortar. Fibers can bond the material, play the role of bridging, and become the adsorption carrier of bacterial metabolic precipitates, which is beneficial to the dense bonding of the products. The addition of appropriate amount of basalt fiber can simultaneously improve the self-healing properties and compressive strength of mortar. The simultaneous addition of healing agent and basalt fiber can realize the complementary advantages. By adding a small amount of healing agent and a moderate amount of fiber, not only can achieve 100% self-healing performance, but also improve the compressive strength of mortar. This study provides useful theoretical guidance for the design, preparation, and application of concrete

Blinded by Generated Contexts: How Language Models Merge Generated and Retrieved Contexts When Knowledge Conflicts?

While auxiliary information has become a key to enhancing Large Language Models (LLMs), relatively little is known about how LLMs merge these contexts, specifically contexts generated by LLMs and those retrieved from external sources. To investigate this, we formulate a systematic framework to identify whether LLMs' responses are attributed to either generated or retrieved contexts. To easily trace the origin of the response, we construct datasets with conflicting contexts, i.e., each question is paired with both generated and retrieved contexts, yet only one of them contains the correct answer. Our experiments reveal a significant bias in several LLMs (GPT-4/3.5 and Llama2) to favor generated contexts, even when they provide incorrect information. We further identify two key factors contributing to this bias: i) contexts generated by LLMs typically show greater similarity to the questions, increasing their likelihood of being selected; ii) the segmentation process used in retrieved contexts disrupts their completeness, thereby hindering their full utilization in LLMs. Our analysis enhances the understanding of how LLMs merge diverse contexts, offers valuable insights for advancing current LLM augmentation methods, and highlights the risk of generated misinformation for retrieval-augmented LLMs.Comment: Accepted at ACL 2024 Main, Homepage (https://tan-hexiang.github.io/Blinded_by_Generated_Contexts/

Continuous Piecewise-Affine Based Motion Model for Image Animation

Image animation aims to bring static images to life according to driving videos and create engaging visual content that can be used for various purposes such as animation, entertainment, and education. Recent unsupervised methods utilize affine and thin-plate spline transformations based on keypoints to transfer the motion in driving frames to the source image. However, limited by the expressive power of the transformations used, these methods always produce poor results when the gap between the motion in the driving frame and the source image is large. To address this issue, we propose to model motion from the source image to the driving frame in highly-expressive diffeomorphism spaces. Firstly, we introduce Continuous Piecewise-Affine based (CPAB) transformation to model the motion and present a well-designed inference algorithm to generate CPAB transformation from control keypoints. Secondly, we propose a SAM-guided keypoint semantic loss to further constrain the keypoint extraction process and improve the semantic consistency between the corresponding keypoints on the source and driving images. Finally, we design a structure alignment loss to align the structure-related features extracted from driving and generated images, thus helping the generator generate results that are more consistent with the driving action. Extensive experiments on four datasets demonstrate the effectiveness of our method against state-of-the-art competitors quantitatively and qualitatively. Code will be publicly available at: https://github.com/DevilPG/AAAI2024-CPABMM