tRNASer(CGA) differentially regulates expression of wild-type and codon-modified papillomavirus L1 genes

Exogenous transfer RNAs (tRNAs) favor translation of bovine papillomavirus 1 wild-type (wt) L1 mRNA in in vitro translation systems (Zhou et al. 1999, J. Virol., 73, 4972-4982). We, therefore, investigated whether papillomavirus (PV) wt L1 protein expression could be enhanced in eukaryotic cells following exogenous tRNA supplementation. Both Chinese hamster ovary (CHO) and Cos1 cells, transfected with PV1 wt L1 genes, effectively transcribed the genes but did not translate them. However, L1 protein translation was demonstrated following co-transfection with the L1 gene and a gene expressing tRNA(Ser)(CGA). Cell lines, stably transfected with a bovine papillomavirus 1 (BPV1) wt L1 expression construct, produced L1 protein after the transfection of the tRNA(Ser)(CGA) gene, but not following the transfection with basal vectors, suggesting that tRNA(Ser)(CGA) gene enhanced wt L1 translation as a result of endogenous tRNA alterations and phosphorylation of translation initiation factors elF4E and elF2alpha in the tRNA(Ser)(CGA) transfected L1 cell lines. The tRNA(Ser)(CGA) gene expression significantly reduced translation of L1 proteins expressed from codon-modified (HB) PV L1 genes utilizing mammalian preferred codons, but had variable effects on translation of green fluorescent proteins (GFPs) expressed from six serine GFP variants. The changes of tRNA pools appear to match the codon composition of PV wt and HB L1 genes and serine GFP variants to regulate translation of their mRNAs. These findings demonstrate for the first time in eukaryotic cells that translation of the target genes can be differentially influenced by the provision of a single tRNA expression construct

Intelligent machines work in unstructured environments by differential neuromorphic computing

Efficient operation of intelligent machines in the real world requires methods that allow them to understand and predict the uncertainties presented by the unstructured environments with good accuracy, scalability and generalization, similar to humans. Current methods rely on pretrained networks instead of continuously learning from the dynamic signal properties of working environments and suffer inherent limitations, such as data-hungry procedures, and limited generalization capabilities. Herein, we present a memristor-based differential neuromorphic computing, perceptual signal processing and learning method for intelligent machines. The main features of environmental information such as amplification (>720%) and adaptation (<50%) of mechanical stimuli encoded in memristors, are extracted to obtain human-like processing in unstructured environments. The developed method takes advantage of the intrinsic multi-state property of memristors and exhibits good scalability and generalization, as confirmed by validation in two different application scenarios: object grasping and autonomous driving. In the former, a robot hand experimentally realizes safe and stable grasping through fast learning (in ~1 ms) the unknown object features (e.g., sharp corner and smooth surface) with a single memristor. In the latter, the decision-making information of 10 unstructured environments in autonomous driving (e.g., overtaking cars, pedestrians) is accurately (94%) extracted with a 40*25 memristor array. By mimicking the intrinsic nature of human low-level perception mechanisms, the electronic memristive neuromorphic circuit-based method, presented here shows the potential for adapting to diverse sensing technologies and helping intelligent machines generate smart high-level decisions in the real world.Comment: 16 pages, 5 figure

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science

A Novel Fuzzing Method for Zigbee Based on Finite State Machine

With the extensive application of Zigbee, some bodies of literature were devoted into finding the vulnerabilities of Zigbee by fuzzing. According to earlier test records, the majority of defects were exposed due to a series of testing cases. However, the context of malformed inputs is not taken account into the previous algorithms. In this paper, we propose a refined structure-based fuzzing algorithm for Zigbee based on FSM, FSM-fuzzing. Any malformed input in FSM-Fuzzing is injected to the tested sensor against a specific initial state. If the sensor transferred to the next state of FMS or crashed, there would be a defect of Zigbee in dealing with the input under the state. The final state of the sensor is verified by an UIO sequence. After a round of tests, the sensor is regressed to the specific state to prepars for receiving the next mutation. All of the states would be traversed in FSM-fuzzing. A fuzzing tool, ZFSM-fuzzer, is designed for evaluating the performance of FSM-fuzzing. Experiment results show that there is a vulnerability of Zigbee in dealing with the frames without destination addresses. Further, the quality of cases of FSM-fuzzing is higher than the previous algorithms. Therefore, FSM-fuzzing is powerful in finding the vulnerabilities of Zigbee

Mechanism and influence of different colors of opaque outdoor surfaces on cooling demand of malls

The present study investigated a specific mall in Chengdu with diverse opaque outdoor surface colors (black, dark grey, dark green, navy blue and yellow). It was revealed that different colors had a variation effect and energy-saving mechanism on the cooling energy saving ratio in hourly, daily, monthly and annual dimensions. These results revealed that for the whole year, the annual cooling load exhibited a differential change. For the whole day, as affected by meteorological factors, especially wind speed, and when the solar radiation is the same, the daily cooling energy saving amounts presented two branches. The global horizontal radiation has a significant positive influence on the daily cooling energy saving amount, but the daily cooling energy saving ratio was not significant. At the hourly level, due to the leading role of wind speed and the diffuse radiation-to-global solar radiation ratio, the hourly cooling energy-saving amount and hourly cooling energy-saving rate presented different branches. When the hourly horizontal solar radiation was the same, the lower the wind speed, the greater the cooling energy savings. Furthermore, the higher the diffuse radiation-to-global solar radiation ratio, the greater the cooling energy savings

Image1_Enhancing anti-neuroinflammation effect of X-ray-triggered RuFe-based metal-organic framework with dual enzyme-like activities.TIF

Traumatic spinal cord injury (SCI), often resulting from external physical trauma, initiates a series of complex pathophysiological cascades, with severe cases leading to paralysis and presenting significant clinical challenges. Traditional diagnostic and therapeutic approaches, particularly X-ray imaging, are prevalent in clinical practice, yet the limited efficacy and notable side effects of pharmacological treatments at the injury site continue to pose substantial hurdles. Addressing these challenges, recent advancements have been made in the development of multifunctional nanotechnology and synergistic therapies, enhancing both the efficacy and safety of radiographic techniques. In this context, we have developed an innovative nerve regeneration and neuroprotection nanoplatform utilizing an X-ray-triggered, on-demand RuFe metal-organic framework (P-RuFe) for SCI recovery. This platform is designed to simulate the enzymatic activities of catalase and superoxide dismutase, effectively reducing the production of reactive oxygen species, and to remove free radicals and reactive nitrogen species, thereby protecting cells from oxidative stress-induced damage. In vivo studies have shown that the combination of P-RuFe and X-ray treatment significantly reduces mortality in SCI mouse models and promotes spinal cord repair by inhibiting glial cell proliferation and neuroinflammation. P-RuFe demonstrates excellent potential as a safe, effective scavenger of reactive oxygen and nitrogen species, offering good stability, biocompatibility, and high catalytic activity, and thus holds promise for the treatment of inflammation-related diseases.</p

DataSheet1_Enhancing anti-neuroinflammation effect of X-ray-triggered RuFe-based metal-organic framework with dual enzyme-like activities.docx

Traumatic spinal cord injury (SCI), often resulting from external physical trauma, initiates a series of complex pathophysiological cascades, with severe cases leading to paralysis and presenting significant clinical challenges. Traditional diagnostic and therapeutic approaches, particularly X-ray imaging, are prevalent in clinical practice, yet the limited efficacy and notable side effects of pharmacological treatments at the injury site continue to pose substantial hurdles. Addressing these challenges, recent advancements have been made in the development of multifunctional nanotechnology and synergistic therapies, enhancing both the efficacy and safety of radiographic techniques. In this context, we have developed an innovative nerve regeneration and neuroprotection nanoplatform utilizing an X-ray-triggered, on-demand RuFe metal-organic framework (P-RuFe) for SCI recovery. This platform is designed to simulate the enzymatic activities of catalase and superoxide dismutase, effectively reducing the production of reactive oxygen species, and to remove free radicals and reactive nitrogen species, thereby protecting cells from oxidative stress-induced damage. In vivo studies have shown that the combination of P-RuFe and X-ray treatment significantly reduces mortality in SCI mouse models and promotes spinal cord repair by inhibiting glial cell proliferation and neuroinflammation. P-RuFe demonstrates excellent potential as a safe, effective scavenger of reactive oxygen and nitrogen species, offering good stability, biocompatibility, and high catalytic activity, and thus holds promise for the treatment of inflammation-related diseases.</p