Interpretable Ensemble Learning for Materials Property Prediction with Classical Interatomic Potentials: Carbon as an Example

Machine learning (ML) is widely used to explore crystal materials and predict their properties. However, the training is time-consuming for deep-learning models, and the regression process is a black box that is hard to interpret. Also, the preprocess to transfer a crystal structure into the input of ML, called descriptor, needs to be designed carefully. To efficiently predict important properties of materials, we propose an approach based on ensemble learning consisting of regression trees to predict formation energy and elastic constants based on small-size datasets of carbon allotropes as an example. Without using any descriptor, the inputs are the properties calculated by molecular dynamics with 9 different classical interatomic potentials. Overall, the results from ensemble learning are more accurate than those from classical interatomic potentials, and ensemble learning can capture the relatively accurate properties from the 9 classical potentials as criteria for predicting the final properties

Elucidating DNA binding of dithienylethenes from molecular dynamics and dichroism spectra

DNA binding modes of the stereoisomeric rotamers of two dithenylethene derivatives (DTE1 and DTE2) representing candidate molecular photoswitches of great promise for photopharmacology and nanotechnology have been identified and characterized in terms of their binding energies and electronic circular dichroism (CD) responses. In the open form, two binding modes are identified namely minor-groove binding of the lowest-energy conformer with an anti-parallel arrangement of methyl groups and major-groove double-intercalation of the P-enantiomers of an intermediate-state rotamer. Only the latter binding mode is found to be enantiomerically selective and expected to have an overall negative linear dichroism (LD) as observed in the experiment for DTE1 (Angew. Chem., Int. Ed., 2013, 52, 4393). In the closed form, the most favorable binding mode is found to be minor groove binding. Also this binding mode is found to be enantiomerically selective and for DTE1, it is the M-enantiomer that binds the strongest, showing a positive theoretical signature CD band in the long wavelength region with origin in pyridinium ligands. The theoretical CD spectrum is found to be in good agreement with the experimental one, which provides an indirect evidence for a correct identification of the binding mode in the closed form

Exosome-Based Early Detection of Cancer and Parkinson’s Disease

Exosomes have emerged as novel biomarkers for disease diagnostics and prognosis. Exosomes are present in bodily fluids and closely resemble the contents of their parental cells; thus, they have a huge potential to serve as a liquid biopsy tool in the diagnosis of multiple diseases. In particular, tumor exosomes have the potential as biomarkers for the early detection of cancer since their contents reflect the genomic and metabolic abnormalities in their parental cells. With the development of techniques for high-throughput purification and isolation of exosomes and exosome content analysis, exosomal proteins is rapidly becoming an important tool for the early diagnosis of cancer. Exosomes are extracellular vesicles with a diameter of 30 - 150 nm. These nanovesicles are produced by almost all types of mammalian cells and cancer cells through fusion of an intermediate endocytic compartment, namely, multivesicular bodies, with the plasma membrane. In the introduction part of this thesis, I introduce the concept of the exosome, its definition, biogenesis and characteristics, and composition. Then I discuss the use of exosomes for cancer prognosis, and α-synuclein circulating exosomes as an example to explain how exosomal proteins work and its high specificity to Parkinson Disease. Finally, I discuss the different methods of isolating and collecting specific types of exosomes. One unanswered question is at what stage of disease and cancer development can the exosome-based method be useful for diagnosis. To test the practicability of exosome-based methods in cancer and diseases diagnosis, I will establish a mathematical model and analytically calculate the concentrations of cancer-specific exosomes based on tumor growth. Then I will predict how early the exosome-based method can detect cancer and other diseases considering the detection limits of current diagnostic technologies. In addition, I will introduce and discuss the parameters required to inspect and verify the feasibility of my mathematical model. This thesis is focused on two specific exosomes: α-synuclein circulating exosomes for Parkinson disease and HSP70 proteins for liver cancer. After the exosomes are extracted and purified, they can be resuspended into a small volume solution. Therefore, exosomal samples yield higher concentrations of biomarkers in a resuspension solution than those in blood. In the following, we show that it takes about 2.05 years for a parental cell to expand to a cell population that can secrete a baseline value of the α-synuclein circulating exosomes in Parkinson disease (PD) patients, and 0.73 years for a parental tumor cell to expand to a cell population that can secrete the baseline number of HSP70 proteins circulating exosomes in liver cancer patients. In conclusion, the mathematical model I established can help us predict how exosomal protein can be used to detect cancer and PD

Exosome-Based Early Detection of Cancer and Parkinson’s Disease

Exosomes have emerged as novel biomarkers for disease diagnostics and prognosis. Exosomes are present in bodily fluids and closely resemble the contents of their parental cells; thus, they have a huge potential to serve as a liquid biopsy tool in the diagnosis of multiple diseases. In particular, tumor exosomes have the potential as biomarkers for the early detection of cancer since their contents reflect the genomic and metabolic abnormalities in their parental cells. With the development of techniques for high-throughput purification and isolation of exosomes and exosome content analysis, exosomal proteins is rapidly becoming an important tool for the early diagnosis of cancer. Exosomes are extracellular vesicles with a diameter of 30 - 150 nm. These nanovesicles are produced by almost all types of mammalian cells and cancer cells through fusion of an intermediate endocytic compartment, namely, multivesicular bodies, with the plasma membrane. In the introduction part of this thesis, I introduce the concept of the exosome, its definition, biogenesis and characteristics, and composition. Then I discuss the use of exosomes for cancer prognosis, and α-synuclein circulating exosomes as an example to explain how exosomal proteins work and its high specificity to Parkinson Disease. Finally, I discuss the different methods of isolating and collecting specific types of exosomes. One unanswered question is at what stage of disease and cancer development can the exosome-based method be useful for diagnosis. To test the practicability of exosome-based methods in cancer and diseases diagnosis, I will establish a mathematical model and analytically calculate the concentrations of cancer-specific exosomes based on tumor growth. Then I will predict how early the exosome-based method can detect cancer and other diseases considering the detection limits of current diagnostic technologies. In addition, I will introduce and discuss the parameters required to inspect and verify the feasibility of my mathematical model. This thesis is focused on two specific exosomes: α-synuclein circulating exosomes for Parkinson disease and HSP70 proteins for liver cancer. After the exosomes are extracted and purified, they can be resuspended into a small volume solution. Therefore, exosomal samples yield higher concentrations of biomarkers in a resuspension solution than those in blood. In the following, we show that it takes about 2.05 years for a parental cell to expand to a cell population that can secrete a baseline value of the α-synuclein circulating exosomes in Parkinson disease (PD) patients, and 0.73 years for a parental tumor cell to expand to a cell population that can secrete the baseline number of HSP70 proteins circulating exosomes in liver cancer patients. In conclusion, the mathematical model I established can help us predict how exosomal protein can be used to detect cancer and PD

Nuclear and cytoplasmic specific RNA binding proteome enrichment and its changes upon ferroptosis induction

Abstract The key role of RNA-binding proteins (RBPs) in posttranscriptional regulation of gene expression is intimately tied to their subcellular localization. Here, we show a subcellular-specific RNA labeling method for efficient enrichment and deep profiling of nuclear and cytoplasmic RBPs. A total of 1221 nuclear RBPs and 1333 cytoplasmic RBPs were enriched and identified using nuclear/cytoplasm targeting enrichment probes, representing an increase of 54.4% and 85.7% compared with previous reports. The probes were further applied in the omics-level investigation of subcellular-specific RBP-RNA interactions upon ferroptosis induction. Interestingly, large-scale RBPs display enhanced interaction with RNAs in nucleus but reduced association with RNAs in cytoplasm during ferroptosis process. Furthermore, we discovered dozens of nucleoplasmic translocation candidate RBPs upon ferroptosis induction and validated representative ones by immunofluorescence imaging. The enrichment of Tricarboxylic acid cycle in the translocation candidate RBPs may provide insights for investigating their possible roles in ferroptosis induced metabolism dysregulation

DOMAS: a data management software framework for advanced light sources

In recent years, China's advanced light sources have entered a period of rapid construction and development. As modern X-ray detectors and data acquisition technologies advance, these facilities are expected to generate massive volumes of data annually, presenting significant challenges in data management and utilization. These challenges encompass data storage, metadata handling, data transfer and user data access. In response, the Data Organization Management Access Software (DOMAS) has been designed as a framework to address these issues. DOMAS encapsulates four fundamental modules of data management software, including metadata catalogue, metadata acquisition, data transfer and data service. For light source facilities, building a data management system only requires parameter configuration and minimal code development within DOMAS. This paper firstly discusses the development of advanced light sources in China and the associated demands and challenges in data management, prompting a reconsideration of data management software framework design. It then outlines the architecture of the framework, detailing its components and functions. Lastly, it highlights the application progress and effectiveness of DOMAS when deployed for the High Energy Photon Source (HEPS) and Beijing Synchrotron Radiation Facility (BSRF)

Association between gestational visit‐to‐visit blood pressure variability and adverse neonatal outcomes

Abstract The authors aimed to explore the association between visit‐to‐visit blood pressure variability (BPV) in pregnant women and adverse neonatal outcomes. The study included 52 891 pregnant women. BPV was calculated as standard deviation (SD) and coefficient of variation (CV) of systolic blood pressure (SBP) or diastolic blood pressure (DBP). All participants were divided into four groups by the quartiles of BPV. When comparing the highest quartiles to the lowest quartiles of DBP SD in all participants, the fully adjusted ORs were 1.19 (95% CI 1.11–1.27, p for trend < .001) for fetal distress, 1.32 (95% CI 1.14–1.54, p for trend < .001) for small for gestational age, 1.32 (95% CI 1.06–1.63, p for trend = .003) for 1‐min Apgar score ≤ 7. When comparing the highest quartiles to the lowest quartiles of DBP CV, ORs were 1.22 (95% CI 1.14–1.30, p for trend < .001) for fetal distress, 1.38 (95% CI 1.17–1.61, p for trend < .001) for small for gestational age, 1.43 (95% CI 1.14–1.79, p for trend < .001) for 1‐min Apgar score ≤ 7. ORs for preterm birth and 5‐min Apgar score ≤ 7 were not statistically significant. However, in participants with gestational hypertension or preeclampsia, ORs for preterm birth were 2.80 (95% CI 1.99–3.94, p for trend < .001) in DBP SD and 3.25 (95% CI 2.24–4.72, p for trend < .001) in DBP CV when extreme quartiles were compared. In conclusion, higher visit‐to‐visit BPV was associated with adverse neonatal outcomes

Contrast‐enhanced CT‐based radiomic analysis for determining the response to anti‐programmed death‐1 therapy in esophageal squamous cell carcinoma patients: A pilot study

Abstract Background In view of the fact that radiomics features have been reported as predictors of immunotherapy to various cancers, this study aimed to develop a prediction model to determine the response to anti‐programmed death‐1 (anti‐PD‐1) therapy in esophageal squamous cell carcinoma (ESCC) patients from contrast‐enhanced CT (CECT) radiomics features. Methods Radiomic analysis of images was performed retrospectively for image samples before and after anti‐PD‐1 treatment, and efficacy analysis was performed for the results of two different time node evaluations. A total of 68 image samples were included in this study. Quantitative radiomic features were extracted from the images, and the least absolute shrinkage and selection operator method was applied to select radiomic features. After obtaining selected features, three classification models were used to establish a radiomics model to predict the ESCC status and efficacy of therapy. A cross‐validation strategy utilizing three folds was employed to train and test the model. Performance evaluation of the model was done using the area under the curve (AUC) of receiver operating characteristic, sensitivity, specificity, and precision metric. Results Wavelet and area of gray level change (log‐sigma) were the most significant radiomic features for predicting therapy efficacy. Fifteen radiomic features from the whole tumor and peritumoral regions were selected and comprised of the fusion radiomics score. A radiomics classification was developed with AUC of 0.82 and 0.884 in the before and after‐therapy cohorts, respectively. Conclusions The combined model incorporating radiomic features and clinical CECT predictors helps to predict the response to anti‐PD‐1therapy in patients with ESCC

A Computational and Chemical Design Strategy for Manipulating Glycan‐Protein Recognition

Abstract Glycans are complex biomolecules that encode rich information and regulate various biological processes, such as fertilization, host‐pathogen binding, and immune recognition, through interactions with glycan‐binding proteins. A key driving force for glycan‐protein recognition is the interaction between the π electron density of aromatic amino acid side chains and polarized C─H groups of the pyranose (termed the CH–π interaction). However, the relatively weak binding affinity between glycans and proteins has hindered the application of glycan detection and imaging. Here, computational modeling and molecular dynamics simulations are employed to design a chemical strategy that enhances the CH–π interaction between glycans and proteins by genetically incorporating electron‐rich tryptophan derivatives into a lectin PhoSL, which specifically recognizes core fucosylated N‐linked glycans. This significantly enhances the binding affinity of PhoSL with the core fucose ligand and enables sensitive detection and imaging of core fucosylated glycans in vitro and in xenograft tumors in mice. Further, the study showed that this strategy is applicable to improve the binding affinity of GafD lectin for N‐acetylglucosamine‐containing glycans. The approach thus provides a general and effective way to manipulate glycan‐protein recognition for glycoscience applications

Dual-Target Electrochemical Biosensing Based on DNA Structural Switching on Gold Nanoparticle-Decorated MoS₂ Nanosheets

A MoS₂-based electrochemical aptasensor has been developed for the simultaneous detection of thrombin and adenosine triphosphate (ATP) based on gold nanoparticles-decorated MoS₂ (AuNPs–MoS₂) nanocomposites. Two different aptamer probes labeled with redox tags were simultaneously immobilized on an AuNPs–MoS₂ film modified electrode via Au–S bonds. The aptamers presented structural switches with the addition of target molecules (thrombin and ATP), resulting in methylene blue (MB) far from or ferrocene (Fc) close to the electrode surface. Therefore, a dual signaling detection strategy was developed, which featured both “signal-on” and “signal-off” elements in the detection system because of the target-induced structure switching. This proposed aptasensor could simultaneously determine ATP and thrombin as low as 0.74 nM ATP and 0.0012 nM thrombin with high selectivity, respectively. In addition, thrombin and ATP could act as inputs to activate an AND logic gate