The Study of Protein Conformation in Solution Via Direct Sampling by Desorption Electrospray Ionization Mass Spectrometry

The direct sampling feature of liquid sample desorption electrospray ionization (DESI) allows the ionization of liquid samples without adding acids/organic solvents (i.e., without sample pretreatment). As a result, it provides a new approach for probing protein conformation in solution. In this study, it has been observed that native protein ions are generated from proteins in water by DESI. Interestingly, the intensities of the resulting protein ions appear to be higher than those generated by ESI of the proteins in water or in ammonium acetate. For protein solutions that already contain acids/organic solvents, DESI can be used to investigate the influences of these denaturants on protein conformations and the obtained results are in good agreement with spectroscopic data. In addition, online monitoring of protein conformational changes by DESI is feasible; for instance, heat-induced unfolding of ubiquitin can be traced with DESI in water without influences of organic solvents/acids. This DESI method provides a new alternative tool for the study of protein conformation in solution

Climate Change Impacts on Winter Wheat Yield in Northern China

Exploring the impacts of climate change on agriculture is one of important topics with respect to climate change. We quantitatively examined the impacts of climate change on winter wheat yield in Northern China using the Cobb–Douglas production function. Utilizing time-series data of agricultural production and meteorological observations from 1981 to 2016, the impacts of climatic factors on wheat production were assessed. It was found that the contribution of climatic factors to winter wheat yield per unit area (WYPA) was 0.762–1.921% in absolute terms. Growing season average temperature (GSAT) had a negative impact on WYPA for the period of 1981–2016. A 1% increase in GSAT could lead to a loss of 0.109% of WYPA when the other factors were constant. While growing season precipitation (GSP) had a positive impact on WYPA, as a 1% increase in GSP could result in 0.186% increase in WYPA, other factors kept constant. Then, the impacts on WYPA for the period 2021–2050 under two different emissions scenarios RCP4.5 and RCP8.5 were forecasted. For the whole study area, GSAT is projected to increase 1.37°C under RCP4.5 and 1.54°C under RCP8.5 for the period 2021–2050, which will lower the average WYPA by 1.75% and 1.97%, respectively. GSP is tended to increase by 17.31% under RCP4.5 and 22.22% under RCP8.5 and will give a rise of 3.22% and 4.13% in WYPA. The comprehensive effect of GSAT and GSP will increase WYPA by 1.47% under RCP4.5 and 2.16% under RCP8.5

SpacePulse: Combining Parameterized Pulses and Contextual Subspace for More Practical VQE

In this paper, we explore the integration of parameterized quantum pulses with the contextual subspace method. The advent of parameterized quantum pulses marks a transition from traditional quantum gates to a more flexible and efficient approach to quantum computing. Working with pulses allows us to potentially access areas of the Hilbert space that are inaccessible with a CNOT-based circuit decomposition. Compared to solving the complete Hamiltonian via the traditional Variational Quantum Eigensolver (VQE), the computation of the contextual correction generally requires fewer qubits and measurements, thus improving computational efficiency. Plus a Pauli grouping strategy, our framework, SpacePulse, can minimize the quantum resource cost for the VQE and enhance the potential for processing larger molecular structures

A Fe/Fe3O4/N-carbon composite with hierarchical porous structure and in situ formed N-doped graphene-like layers for high-performance lithium ion batteries

Fe/Fe3O4/N-carbon composite consisting of a porous carbon matrix containing a highly conductive N-doped graphene-like network and Fe/Fe3O4 nanoparticles was prepared. The porous carbon has a hierarchical structure which is inherited from rice husk and the N-doped graphene-like network formed in situ. When used as an anode material for lithium batteries, the composite delivered a reversible capacity of approximately 610 mA h g(-1) at a current density of 200 mA g(-1) even after 100 cycles, due to the synergism between the unique hierarchical porous structures, highly electrically conductive N-doped graphene- like networks and nanosized particles of Fe/Fe3O4. This work provides a simple approach to prepare N-doped porous carbon activated nanoparticle composites which could be used to improve the electrochemical performance of lithium ion batteries

Crop Yield and Temperature Changes in North China during 601–900 AD

Depending on the descriptions of crop yield and social response to crop failure/harvest from Chinese historical documents, we classified the crop yield of North China during 601–900 AD into six categories and quantified each category to be the crop yield grades. We found that the regional mean crop yield had a significant (P<0.01) negative trend at the rate of −0.24% per decade. The interannual, multiple-decadal, and century-scale variability accounted for ~47%, ~30%, and ~20% of the total variations of crop yield, respectively. The interannual variability was significantly (P<0.05) persistent across the entire period. The multiple-decadal variability was more dominant after 750 AD than that before 750 AD, while the century-scale variability was more dominant before 750 AD than that after 750 AD. The variations of crop yield could be partly explained by temperature changes. On one hand, the declining trend of crop yield cooccurred with the climate cooling trend from 601 to 900 AD; on the other hand, the crop yield was positively correlated with temperature changes at 30-year resolution with the correlation coefficient of 0.59 (P<0.1). These findings supported that high (low) crop yield occurred in the warming (cooling) climate

Carve3D: Improving Multi-view Reconstruction Consistency for Diffusion Models with RL Finetuning

Multi-view diffusion models, obtained by applying Supervised Finetuning (SFT) to text-to-image diffusion models, have driven recent breakthroughs in text-to-3D research. However, due to the limited size and quality of existing 3D datasets, they still suffer from multi-view inconsistencies and Neural Radiance Field (NeRF) reconstruction artifacts. We argue that multi-view diffusion models can benefit from further Reinforcement Learning Finetuning (RLFT), which allows models to learn from the data generated by themselves and improve beyond their dataset limitations during SFT. To this end, we introduce Carve3D, an improved RLFT algorithm coupled with a novel Multi-view Reconstruction Consistency (MRC) metric, to enhance the consistency of multi-view diffusion models. To measure the MRC metric on a set of multi-view images, we compare them with their corresponding NeRF renderings at the same camera viewpoints. The resulting model, which we denote as Carve3DM, demonstrates superior multi-view consistency and NeRF reconstruction quality than existing models. Our results suggest that pairing SFT with Carve3D's RLFT is essential for developing multi-view-consistent diffusion models, mirroring the standard Large Language Model (LLM) alignment pipeline. Our code, training and testing data, and video results are available at: https://desaixie.github.io/carve-3d.Comment: 22 pages, 16 figures. Our code, training and testing data, and video results are available at: https://desaixie.github.io/carve-3d. This paper has been accepted to CVPR 2024. v2: incorporated changes from the CVPR 2024 camera-ready versio

Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm

In recent years, quantum computing has emerged as a transformative force in the field of combinatorial optimization, offering novel approaches to tackling complex problems that have long challenged classical computational methods. Among these, the Quantum Approximate Optimization Algorithm (QAOA) stands out for its potential to efficiently solve the Max-Cut problem, a quintessential example of combinatorial optimization. However, practical application faces challenges due to current limitations on quantum computational resource. Our work optimizes QAOA initialization, using Graph Neural Networks (GNN) as a warm-start technique. This sacrifices affordable computational resource on classical computer to reduce quantum computational resource overhead, enhancing QAOA's effectiveness. Experiments with various GNN architectures demonstrate the adaptability and stability of our framework, highlighting the synergy between quantum algorithms and machine learning. Our findings show GNN's potential in improving QAOA performance, opening new avenues for hybrid quantum-classical approaches in quantum computing and contributing to practical applications

Decomposition and comparative analysis of depressive symptoms between older adults living alone and with others in China

ObjectiveThis research dealt with investigating and measuring the contribution of the factors that impact depression in older adults living alone vs. those living with others (hereafter referred to as “not alone”) in China.DesignThis investigation adopts a cross-sectional research design. The dataset employed for this study comprises data from 2018 the Chinese Longitudinal Health Longevity Survey (CLHLS).SettingThe research involved data sourced from China, specifically from 23 of its provinces. From the 8th CLHLS, 12,197 older adults were selected who met the study requirements.MeasuresBinary logistic regression models were established to delve into the primary factors impacting the depressive symptoms of the individuals. Furthermore, Fairlie models were employed to assess these factors between older adults living alone and those not living alone. This approach facilitated an in-depth analysis of their respective contributions.ResultsIt was observed that the demographic of Chinese older adults exhibited depressive symptoms at a rate of 11.92%. Older adults who resided alone (15.76%) exhibited a higher prevalence of depressive symptoms in comparison to their counterparts living in not-alone settings (11.15%). Employing Fairlie decomposition analysis, it was determined that this observed disparity in depressive symptoms, amounting to 55.33% of the overall difference, could be primarily attributed to distinct factors. This encompassed variance in marital status (20.55%), years of school (4.63%), self-reported local income status (7.25%), self-reported sleep status (17.56%), and self-reported health status (4.24%).ConclusionThe resulting data indicated that depressive symptoms exhibited an elevated prevalence in older adults living alone than in those living not alone. This discrepancy was predominantly attributed to variance in socioeconomic marital status, years of school, self-reported local income status, self-reported sleep status, and self-reported health status by living alone vs. not alone. Mitigating these influential factors could help develop targeted and meticulous intervention strategies, precisely tailored to improve the mental well-being of older adults at high risk