Simulation of chemical reaction dynamics on an NMR quantum computer

Quantum simulation can beat current classical computers with minimally a few tens of qubits and will likely become the first practical use of a quantum computer. One promising application of quantum simulation is to attack challenging quantum chemistry problems. Here we report an experimental demonstration that a small nuclear-magnetic-resonance (NMR) quantum computer is already able to simulate the dynamics of a prototype chemical reaction. The experimental results agree well with classical simulations. We conclude that the quantum simulation of chemical reaction dynamics not computable on current classical computers is feasible in the near future.Comment: 37 pages, 7 figure

Diverse Substrate Scaffolds by Multicomponent Reactions and Protein Structural Dynamics by Single-molecule FRET

This dissertation is scientifically divided into two main parts; Part Ⅰ describes the assessment of diverse substrate scaffolds using multicomponent reaction chemistry. The excellent maneuverability and efficiency of multicomponent reactions combined with high-throughput synthesis allow generating big synthesis data. This enables the deeper exploration of the chemical space in a fast, low-cost, and environment-friendly manner. Further coupling with high-throughput screening methods can lead to an acceleration in the early stage of drug discovery. Part Ⅱ demonstrates that bilobed proteins bearing a common primordial structural core diversified into transcription factors, enzymes, and extra-cytoplasmic transport-related proteins. The reason is that evolutionary modifications primarily at its termini enabled distinct structural dynamics. Substrate-binding domain 2, one of those bilobed proteins, undergoes a large conformational change upon ligand binding. The allosteric coupling of substrate-binding domain 2 is specific to L-glutamine, whereas altering structural dynamics can adapt protein for additional ligands. Understanding the molecular determinants of this specificity and/or intramolecular allosteric communication is critical to understanding protein evolution and the development of therapeutics. Drug design benefits not only from generating extensive compounds libraries but also from a better understanding of drug targets, especially very dynamic ones, since the visible pocket from a PDB structure may not exist

COVID-19 causes record decline in global CO2 emissions

The considerable cessation of human activities during the COVID-19 pandemic has affected global energy use and CO2 emissions. Here we show the unprecedented decrease in global fossil CO2 emissions from January to April 2020 was of 7.8% (938 Mt CO2 with a +6.8% of 2-{\sigma} uncertainty) when compared with the period last year. In addition other emerging estimates of COVID impacts based on monthly energy supply or estimated parameters, this study contributes to another step that constructed the near-real-time daily CO2 emission inventories based on activity from power generation (for 29 countries), industry (for 73 countries), road transportation (for 406 cities), aviation and maritime transportation and commercial and residential sectors emissions (for 206 countries). The estimates distinguished the decline of CO2 due to COVID-19 from the daily, weekly and seasonal variations as well as the holiday events. The COVID-related decreases in CO2 emissions in road transportation (340.4 Mt CO2, -15.5%), power (292.5 Mt CO2, -6.4% compared to 2019), industry (136.2 Mt CO2, -4.4%), aviation (92.8 Mt CO2, -28.9%), residential (43.4 Mt CO2, -2.7%), and international shipping (35.9Mt CO2, -15%). Regionally, decreases in China were the largest and earliest (234.5 Mt CO2,-6.9%), followed by Europe (EU-27 & UK) (138.3 Mt CO2, -12.0%) and the U.S. (162.4 Mt CO2, -9.5%). The declines of CO2 are consistent with regional nitrogen oxides concentrations observed by satellites and ground-based networks, but the calculated signal of emissions decreases (about 1Gt CO2) will have little impacts (less than 0.13ppm by April 30, 2020) on the overserved global CO2 concertation. However, with observed fast CO2 recovery in China and partial re-opening globally, our findings suggest the longer-term effects on CO2 emissions are unknown and should be carefully monitored using multiple measures

UTR introns, antisense RNA and differentially spliced transcripts between Plasmodium yoelii subspecies

Additional file 1. Evaluation of RNA quality from the two NSM parasite samples in agarose gel (a), and a flow chart of data processing and analysis (b)

Optimization Design of a Winch Suction Underwater Dredging Robot Using Orthogonal Experimental Design

In marine engineering and water conservancy projects, dredging often occurs due to siltaccumulation, which can impede the long-term development of water ecosystems and water storage systems. To enhance dredging efficiency and the performance of dredging machines, a novel type of winch suction underwater dredging robot was designed. Computational fluid dynamics software was utilized to establish a fluid model of the robot’s winch suction dredging device and conduct model simulation experiments. The simulation test results were used to investigate the factors influencing dredging performance and their impact laws. Five key factors—namely, the reamer rotational speed, reamer arrangement angle, water flow rate, inlet pipe diameter, and outlet pipe diameter—were selected for consideration. By setting up various sets of factor levels, the significant influence of different factors on dredging efficiency was examined. Analysis of variance was employed to analyse the results of the orthogonal experimental design, leading to the identification of optimal factor levels and the establishment of an optimal scheme group. The results of the optimal scheme were verified, demonstrating a 13.049% increase in dredging efficiency and a 19.23% decrease in powerconsumption of the sludge pump compared to the initial experimental setup. The performance of the optimal program surpassed that of all the experimental designs and met the design requirements

Nanoscale, automated, high throughput synthesis and screening for the accelerated discovery of protein modifiers

Hit finding in early drug discovery is often based on high throughput screening (HTS) of existing and historical compound libraries, which can limit chemical diversity, is time-consuming, very costly, and environmentally not sustainable. On-the-fly compound synthesis and in situ screening in a highly miniaturized and automated format has the potential to greatly reduce the medicinal chemistry environmental footprint. Here, we used acoustic dispensing technology to synthesize a library in a 1536 well format based on the Groebcke–Blackburn–Bienaymé reaction (GBB-3CR) on a nanomole scale. The unpurified library was screened by differential scanning fluorimetry (DSF) and cross-validated using microscale thermophoresis (MST) against the oncogenic protein–protein interaction menin–MLL. Several GBB reaction products were found as μM menin binder, and the structural basis of the interactions with menin was elucidated by co-crystal structure analysis. Miniaturization and automation of the organic synthesis and screening process can lead to an acceleration in the early drug discovery process, which is an alternative to classical HTS and a step towards the paradigm of continuous manufacturing

Nanoscale, automated, high throughput synthesis and screening for the accelerated discovery of protein modifiers

Hit finding in early drug discovery is often based on high throughput screening (HTS) of existing and historical compound libraries, which can limit chemical diversity, is time-consuming, very costly, and environmentally not sustainable. On-the-fly compound synthesis and in situ screening in a highly miniaturized and automated format has the potential to greatly reduce the medicinal chemistry environmental footprint. Here, we used acoustic dispensing technology to synthesize a library in a 1536 well format based on the Groebcke–Blackburn–Bienaymé reaction (GBB-3CR) on a nanomole scale. The unpurified library was screened by differential scanning fluorimetry (DSF) and cross-validated using microscale thermophoresis (MST) against the oncogenic protein–protein interaction menin–MLL. Several GBB reaction products were found as μM menin binder, and the structural basis of the interactions with menin was elucidated by co-crystal structure analysis. Miniaturization and automation of the organic synthesis and screening process can lead to an acceleration in the early drug discovery process, which is an alternative to classical HTS and a step towards the paradigm of continuous manufacturing

Retrospective cohort study based on the MIMIC-IV database: analysis of factors influencing all-cause mortality at 30 days, 90 days, 1 year, and 3 years in patients with different types of stroke

ObjectiveThis study aims to evaluate key factors influencing the short-term and long-term prognosis of stroke patients, with a particular focus on variables such as body weight, hemoglobin, electrolytes, kidney function, organ function scores, and comorbidities. Stroke poses a significant global health burden, and understanding its prognostic factors is crucial for clinical management.MethodsThis is a retrospective cohort study based on data from the MIMIC-IV database, including stroke patients from 2010 to 2020. A total of 5,110 patients aged 18 and older were included in the study. The exposure variables included body weight and hemoglobin levels, while the outcome variables were the 30-day, 90-day, 1-year, and 3-year mortality risks. Covariates included electrolyte levels, kidney function, organ function scores, and comorbidities. Random forest and gradient boosting tree models were employed for data analysis to assess mortality risk.ResultsKaplan–Meier survival analysis showed that ischemic stroke patients had the highest 30-day mortality rate at 8.5%, with only 20% 1-year survival. Traumatic subarachnoid hemorrhage patients had the best prognosis, with a 1-year survival rate of 60%. Multivariable Cox regression analysis revealed that each 1-point increase in the Charlson Comorbidity Index raised the 1-year and 3-year mortality risks by 1.39 times (95% CI: 1.10–1.56) and 1.44 times, respectively. Each 1-point increase in the SOFA score increased the 30-day, 90-day, 1-year, and 3-year mortality risks by 2.11 times, 2.03 times, and 1.84 times, respectively. Additionally, lower hemoglobin levels were significantly associated with increased mortality, with 30-day, 90-day, and 1-year mortality risks increasing by 3.33 times, 3.34 times, and 4.16 times, respectively (p < 0.005). Age ≥ 71 years, longer hospital stays, and organ dysfunction were also significant factors affecting mortality.ConclusionThis study highlights the critical role of stroke type, comorbidity index, SOFA score, hemoglobin levels, and length of hospital stay in stroke prognosis. These findings provide valuable insights for clinical risk assessment and the development of individualized treatment strategies, which may improve the management and outcomes of stroke patients. The predictive model constructed effectively assesses mortality risks in stroke patients, offering support for future clinical practice