Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations.

Machine learning interatomic potential (MLIP) overcomes the challenges of high computational costs in density-functional theory and the relatively low accuracy in classical large-scale molecular dynamics, facilitating more efficient and precise simulations in materials research and design. In this review, the current state of the four essential stages of MLIP is discussed, including data generation methods, material structure descriptors, six unique machine learning algorithms, and available software. Furthermore, the applications of MLIP in various fields are investigated, notably in phase-change memory materials, structure searching, material properties predicting, and the pre-trained universal models. Eventually, the future perspectives, consisting of standard datasets, transferability, generalization, and trade-off between accuracy and complexity in MLIPs, are reported.

High expression of RTEL1 predicates worse progression in gliomas and promotes tumorigenesis through JNK/ELK1 cascade.

Gliomas are the most common primary intracranial tumor worldwide. The maintenance of telomeres serves as an important biomarker of some subtypes of glioma. In order to investigate the biological role of RTEL1 in glioma. Relative telomere length (RTL) and RTEL1 mRNA was explored and regression analysis was performed to further examine the relationship of the RTL and the expression of RTEL1 with clinicopathological characteristics of glioma patients. We observed that high expression of RTEL1 is positively correlated with telomere length in glioma tissue, and serve as a poor prognostic factor in TERT wild-type patients. Further in vitro studies demonstrate that RTEL1 promoted proliferation, formation, migration and invasion ability of glioma cells. In addition, in vivo studies also revealed the oncogene role of RTEL1 in glioma. Further study using RNA sequence and phospho-specific antibody microarray assays identified JNK/ELK1 signaling was up-regulated by RTEL1 in glioma cells through ROS. In conclusion, our results suggested that RTEL1 promotes glioma tumorigenesis through JNK/ELK1 cascade and indicate that RTEL1 may be a prognostic biomarker in gliomas.

Deep learning-based workflow for automatic extraction of atria and epicardial adipose tissue on cardiac computed tomography in atrial fibrillation.

BACKGROUND: Preoperative estimation of the volume of the left atrium (LA) and epicardial adipose tissue (EAT) on computed tomography (CT) images is associated with an increased risk of atrial fibrillation (AF) recurrence. We aimed to design a deep learning-based workflow to provide reliable automatic segmentation of the atria, pericardium, and EAT for future applications in the management of AF. METHODS: This study enrolled 157 patients with AF who underwent first-time catheter ablation between January 2015 and December 2017 at Taipei Veterans General Hospital. Three-dimensional (3D) U-Net models of the LA, right atrium (RA), and pericardium were used to develop a pipeline for total, LA-EAT, and RA-EAT automatic segmentation. We defined fat within the pericardium as tissue with attenuation between -190 and -30 HU and quantified the total EAT. Regions between the dilated endocardial boundaries and endocardial walls of the LA or RA within the pericardium were used to detect voxels attributed to fat, thus estimating LA-EAT and RA-EAT. RESULTS: The LA, RA, and pericardium segmentation models achieved Dice coefficients of 0.960 ± 0.010, 0.945 ± 0.013, and 0.967 ± 0.006, respectively. The 3D segmentation models correlated well with the ground truth for the LA, RA, and pericardium (r=0.99 and p < 0.001 for all). The Dice coefficients of our proposed method for EAT, LA-EAT, and RA-EAT were 0.870 ± 0.027, 0.846 ± 0.057 and 0.841 ± 0.071, respectively. CONCLUSION: Our proposed workflow for automatic LA, RA, and EAT segmentation using 3D U-Nets on CT images is reliable in patients with AF.

Feasibility of Auto-Quantified Epicardial Adipose Tissue in Predicting Atrial Fibrillation Recurrence After Catheter Ablation.

BACKGROUND: The aim of this study was to build an auto-segmented artificial intelligence model of the atria and epicardial adipose tissue (EAT) on computed tomography (CT) images, and examine the prognostic significance of auto-quantified left atrium (LA) and EAT volumes for AF.Methods and Results: This retrospective study included 334 patients with AF who were referred for catheter ablation (CA) between 2015 and 2017. Atria and EAT volumes were auto-quantified using a pre-trained 3-dimensional (3D) U-Net model from pre-ablation CT images. After adjusting for factors associated with AF, Cox regression analysis was used to examine predictors of AF recurrence. The mean (±SD) age of patients was 56±11 years; 251 (75%) were men, and 79 (24%) had non-paroxysmal AF. Over 2 years of follow-up, 139 (42%) patients experienced recurrence. Diabetes, non-paroxysmal AF, non-pulmonary vein triggers, mitral line ablation, and larger LA, right atrium, and EAT volume indices were linked to increased hazards of AF recurrence. After multivariate adjustment, non-paroxysmal AF (hazard ratio [HR] 0.6; 95% confidence interval [CI] 0.4-0.8; P=0.003) and larger LA-EAT volume index (HR 1.1; 95% CI 1.0-1.2; P=0.009) remained independent predictors of AF recurrence. CONCLUSIONS: LA-EAT volume measured using the auto-quantified 3D U-Net model is feasible for predicting AF recurrence after CA, regardless of AF type.

Direct Formation of Amide-Linked C-Glycosyl Amino Acids and Peptides via Photoredox/Nickel Dual Catalysis.

Glycoproteins account for numerous biological processes including those associated with diseases and infections. The advancement of glycopeptides has emerged as a promising strategy for unraveling biological pathways and discovering novel medicines. In this arena, a key challenge arises from the absence of efficient synthetic strategies to access glycopeptides and glycoproteins. Here, we present a highly concise approach to bridging saccharides with amino acids and peptides through an amide linkage. Our amide-linked C-glycosyl amino acids and peptides are synthesized through cooperative Ni-catalyzed and photoredox processes. The catalytic process generates a glycosyl radical and an amide carbonyl radical, which subsequently combine to yield the C-glycosyl products. The saccharide reaction partners encompass mono-, di-, and trisaccharides. All 20 natural amino acids, peptides, and their derivatives can efficiently undergo glycosylations with yields ranging from acceptable to high, demonstrating excellent stereoselectivities. As a substantial expansion of applications, we have shown that simple C-glycosyl amino acids can function as versatile building units for constructing C-glycopeptides with intricate spatial complexities.

Site-Selective C-O Bond Editing of Unprotected Saccharides.

Glucose and its polyhydroxy saccharide analogs are complex molecules that serve as essential structural components in biomacromolecules, natural products, medicines, and agrochemicals. Within the expansive realm of saccharides, a significant area of research revolves around chemically transforming naturally abundant saccharide units to intricate or uncommon molecules such as oligosaccharides or rare sugars. However, partly due to the presence of multiple hydroxyl groups with similar reactivities and the structural complexities arising from stereochemistry, the transformation of unprotected sugars to the desired target molecules remains challenging. One such formidable challenge lies in the efficient and selective activation and modification of the C-O bonds in saccharides. In this study, we disclose a modular 2-fold "tagging-editing" strategy that allows for direct and selective editing of C-O bonds of saccharides, enabling rapid preparation of valuable molecules such as rare sugars and drug derivatives. The first step, referred to as "tagging", involves catalytic site-selective installation of a photoredox active carboxylic ester group to a specific hydroxyl unit of an unprotected sugar. The second step, namely, "editing", features a C-O bond cleavage to form a carbon radical intermediate that undergoes further transformations such as C-H and C-C bond formations. Our strategy constitutes the most effective and shortest route in direct transformation and modification of medicines and other molecules bearing unprotected sugars.

Can uterine artery pulsatility index predict fetal chromosomal abnormality in early pregnancy loss? A retrospective cohort study.

BACKGROUND: Early pregnancy loss (EPL) or spontaneous loss of an intrauterine pregnancy within the first trimester occurs commonly worldwide. It is useful to predict the possibility of fetal chromosomal abnormalities using other cheap and easily available markers. OBJECTIVE: This study aimed to evaluate whether the uterine artery pulsatility index (UtA-PI) can predict fetal chromosomal abnormality in early pregnancy loss (EPL). METHODS: This was a retrospective cohort study including 148 women who underwent dilation and curettage for missed abortion. The UtA-PI was measured and evaluated by transvaginal ultrasound. Abnormal UtA-PI was identified through the mean of left and right UA-PI ≥ 90th percentiles of the relevant values for the corresponding gestational age. Copy number variation sequencing (CNV-seq) was performed on EPL cases without maternal cell contamination. RESULTS: 107 (72.3%) cases were classified with normal UtA-PI, while 41 (27.7%) cases were classified with abnormal UtA-PI. The fetal chromosomal abnormality rate was significantly higher in cases with normal UtA-PI than in those with abnormal UtA-PI (67.3% vs 22.0%, P = 7.1 × 10-7). Compared to cases with abnormal UtA-PI, the risk of fetal chromosomal abnormalities in cases with normal UtA-PI increased with an odds ratio of 7.3 (95% confidence interval [CI]: 3.2‒17.0, P = 4 × 10-7). The predictive value of normal UtA-PI alone for fetal chromosomal abnormalities was shown to have an area under the curve of 0.67‒0.71 in our population. CONCLUSION: The UtA-PI seems to be lower and less likely to be elevated in EPL with fetal chromosomal abnormalities compared to those without aneuploidies. We suggest that UtA-PI should be examined in all EPL patients.

A novel electrochemical aptasensor based on core-shell nanomaterial labeling for simultaneous detection of acetamiprid and malathion.

At present, due to the coexistence of multiple pesticides in vegetables and the enhanced toxicity, a simultaneous detection method for multiple pesticides is urgently needed. In this work, two types of core-shell nanomaterials, Ag-Au core-shell nanoparticles (Ag@Au NPs) and Cu2O-Au core-shell nanoparticles (Cu2O@Au NPs), were synthesized and labeled with acetamiprid aptamer and malathion aptamer to prepare two novel electroactive signal probes, respectively. The two probes were hybridized on the surface of the electrode by the principle of base complementary pairing between the aptamers and the thiolated DNA oligonucleotide sequences, and a dual-signal electrochemical aptasensor for the simultaneous detection of acetamiprid and malathion was established by modified glassy carbon electrode (GCE). The limits of detection (LOD) were calculated to be 43.7 pg mL-1 for acetamiprid and 63.4 pg mL-1 for malathion. The aptasensor determined acetamiprid and malathion in spinach and rape with the recovery rates of 88.9%-112.5% and 98.0%-114.1%, respectively.

Accelerating the Discovery of Transition Metal Borides by Machine Learning on Small Data Sets.

Accurate and efficient prediction of the stability and structure-stability relationship is important to discover materials; however, it requires tremendous efforts via traditional trial-and-error schemes. Here, we presented a small-data set machine learning (ML) method to accelerate the discovery of promising ternary transition metal boride (MAB) candidates. Based on data sets obtained by ab initio calculations, we developed three robust neural networks to predict the decomposition energy (ΔHd) and assess the thermodynamic stability of 212-typed MABs (M2AB2). The quantitative relation between ΔHd and stability was unraveled by several composition-and-structure descriptors. Three hexagonal M2AB2, i.e., Nb2PB2, Nb2AsB2, and Zr2SB2, were discovered to be stable with negative ΔHd, and 75 metastable MABs were identified with ΔHd less than 70 meV/atom. Finally, the dynamical stability and mechanical properties of MABs were investigated by ab initio calculations, whose results further verified the reliability of our ML models. This work provided a ML approach on small data sets to accelerate the discovery of compounds and expanded the MAB phase family to VA and VIA groups.

Harmless process of organic matter in organosilicon waste residue by fluidization-like DDBD reactor: Temperature action and mechanism.

Herein, the non-hazardous application of low-temperature plasma technology in solid waste from the silicone industry was investigated by using a fluidization-like double dielectric barrier discharge plasma (DDBD) reactor. The results show ∼92.9% TOC in the organosilicon waste residue could be removed at the conditions (Discharge power: 7.0 W, S/G: 12.5 gminL-1, SIE: 158.0 JL-1), i.e. TOC content decreases from 166.0 g/kg to 11.8 g/kg. At the same time, the energy efficiency of the TOC removal rate reach ∼732.1 gkWh-1, and the temperature of the discharge zone is below 280 °C. According to the TG-MS analysis and infrared thermal imager, it is considered that the heat energy generated in the plasma treatment process can affect the decomposition of organic matter. On the other hand, the samples were characterized before and after treatment by BET, SEM, XRD, FTIR, and GC-MS. It was proposed the organic matter was firstly gasified under the action of plasma and thermal. Then, the active group will generate and react with the C-H, C-C, or C-Si by the bombardment of sufficient energy of charged particles, leading the organic matter further to decompose into small molecules, such as CH4, H2, CO, and CO2.

Multiplex strategy electrochemical platform based on self-assembly dual-site DNA tetrahedral scaffold for one-step detection of diazinon and profenofos.

In the work, based on self-assembly dual-site DNA tetrahedral scaffold (DTS), thionine (Thi), and 6-(Ferrocenyl)hexanethiol (Fc6S), a multiplex strategy electrochemical platform was fabricated for the simultaneous detection of profenofos (PFF) and diazinon (DZN). Thi and Fc6S were used to label aptamers for the synthesis of probes respectively. Notably, Thi and Fc6S engendered recognizable DPV peaks at different potentials to achieve simultaneous detection of PFF and DZN. In addition to increasing the conductivity of the electrode, the combination of carboxylic acid functionalized multi-walled carbon nanotubes and ferroferric oxide nanoparticles could also increase its higher specific surface area of the electrode interface to adsorb more DTS. Because of the mechanical rigidity of the DTS, the DTS could keep a complementary chain upright and provide more binding sites for aptamers, the binding efficiency between the complementary chain and 2 binding aptamers could be improved. Comparing the aptasensors performance of single-strand DNA with that of the DTS with complementary strands, the benefits of the DTS were highlighted in this system. Under optimal conditions, the detection limits of PFF and DZN were both 3.33 pg/mL and the detection ranges were both 1.00 × 101-1.00 × 107 pg/mL. Meanwhile, the recoveries of PFF and DZN were 87.15%-117.34% and 91.20%-114.19%, respectively. The aptasensor could realize the simultaneous detection of PFF and DZN in vegetables. Furthermore, the aptasensor also had good stability and selectivity. This strategy could provide a good reference for developing effective aptasensors for the simultaneous detection of other small molecules and toxins.

Solid-State Luminescent Materials with Multiple Emission Colors and Near-Unity Quantum Yield.

Developing solid luminescent materials with a unity quantum yield and tunable emission color is promising, although it is still a difficult task. A straightforward heat-treatment method has been developed to load 3,4,9,10-perylenetetracarboxylic dianhydride (PTCDA) into the matrix of boric acid (BA) to produce powders with a near-unity quantum yield and tunable emission color from yellow to green. Our results suggest that the emission of the powders originates from PTCDA, and the tunability of the emission color is caused by the hydrolysis of PTCDA in the alkaline environment. The near-unity quantum yield is attributed to the BA matrix, which confines PTCDA. In addition, the powder also shows excellent thermal stability that allows its application in light-emitting diodes. The above results are important for the development of solid-state luminescent materials for various applications, and also provide a clue for studying the emission properties of luminescent materials.

Catalytic ozonation of toluene over amorphous Cu-Mn bimetallic oxide: Influencing factors, degradation mechanism and pathways.

Herein, amorphous catalysts were employed to investigate the catalytic ozonation system, revealing the degradation mechanism and influencing factors (O3 concentration, temperature, and humidity) for toluene catalytic ozonation. Cu0.2MnOx exhibited the highest toluene oxidized and excellent stability (∼85% at 60 h) based on the suitable value of Oads/Olat and potent synergy between Cu with Mn. To explore the effect of factors, the change of fresh and post-reaction samples was compared as revealed in the relevant characterization results (SEM, XRD, BET, XPS, TGA), DRIFTS and GC-MS identified the intermediates and byproducts. The results show that appropriate temperature (100 °C) and O3 concentration (2100 ppm) can effectively enhance the number of reactive oxygen species. Although H2O can increase the production of ·OH to promote degradation, it is easier to quench the active sites on the surface of amorphous catalysts. During the reaction, the main role of Cu in Cu-Mn bimetallic oxides is adsorption of toluene and O3, formation of benzoic acid, and oxidation of short-chain products. As for the adjacent Mn, it works on the cleavage of O-O in O3 and the ring-opening of benzene. Then, the mainly catalytic ozonation pathway of toluene was proposed and followed the order: toluene, benzoic acid, benzene, maleic anhydride, short-chain carbon species, CO2, and H2O.

Novel colorimetric aptasensor based on MOF-derived materials and its applications for organophosphorus pesticides determination.

For the visual detection of four organophosphorus pesticides (OPs), a colorimetric aptasensor was developed based on aptamer-mediated bimetallic metal-organic frameworks (MOFs) nano-polymers. Fe-Co magnetic nanoparticles (MNPs) and Fe-N-C nanozymes were prepared based on pyrolytic reaction, and were labeled with broad spectrum aptamers and complementary chains of organophosphorus pesticides respectively. The hybridization of aptamers and complementary chains led to the formation of nano-polymers. In the presence of target pesticides, they competed with complementary chains for aptamers on Fe-Co MNPs, resulting in a large number of Fe-N-C nanozymes signal labels being released into the supernatant. Fe-N-C nanozymes showed similar activity to peroxidase and catalyzed the 3,3',5,5'-tetramethylbenzidine-hydrogen peroxide (TMB-H2O2) color system to turn the solution blue-green under mild conditions. The magnetic probes had good selectivity and sensitivity, and were easily separated by magnetic absorption. The sensor functioned well under optimal conditions, demonstrating good stability and specificity for four pesticides: phorate, profenofos, isocarbophos and omethoate, and the detection limits of four pesticides were as low as 0.16 ng/mL, 0.16 ng/mL, 0.03 ng/mL and 1.6 ng/mL respectively, and the recovery rate of OPs residue in vegetable samples was satisfactory. The work described here provided a simple, rapid and sensitive way to construct a biosensor.

Interference-resistant aptasensor with tetrahedral DNA nanostructure for profenofos detection based on the composites of graphene oxide and polyaniline.

In this work, an interference-resistant electrochemical aptasensor that could detect profenofos in vegetables was constructed based on complexes of graphene oxide and polyaniline (GO@PANI) and gold nanoparticles-tetrahedral DNA nanostructure (Au-TDN). Compared with a single chain aptamer, the tetrahedral DNA nanostructure is highly stable and allows the aptamer on this structure to stand in a highly ordered position on an electrode surface. Moreover, the AuNPs are biocompatible and can protect the activity of the aptamer, which can improve the assembly success rate of Au-TDN. Besides, the conductivity of PANI had been tremendously enhanced thanks to the existence of GO, which improved the dispersion of PANI. The GO@PANI was prepared by a chemical synthesis method, which had a large surface area and was able to adsorb many Au-TDN. Under optimal working parameters, the constructed aptasensor exhibited good electrochemical sensing performance with a detection limit of 10.50 pg/mL and a linear range of 1.0 × 102-1.0 × 107 pg/mL. In addition, it was employed in detecting profenofos in vegetables with a good recovery rate of 90.41-116.37 %. More importantly, the aptasensor also has excellent stability and high selectivity. This study provides a promising method to avoid interference in the detection of profenofos by sensors.

Ultrasensitive electrochemiluminescence aptasensor based on ABEI reduced silver nanoparticles for the detection of profenofos.

An ultrasensitive electrochemiluminescence (ECL) aptasensor for detection of profenofos was constructed by the reducibility and chemiluminescence property of N-(aminobutyl)-N-(ethylisoluminol) (ABEI). ABEI was used to reduce silver nitrate (AgNO3) to silver nanoparticles (AgNPs), which could be adsorbed on the lattice of graphene oxide (GO) to form ABEI-AgNPs-GO complex. This compound could achieve excellent luminescence. The aptamer (Apt) modified (5') by sulfhydryl groups could be immobilized on AgNPs to capture profenofos. When profenofos was present, the ECL signal of the aptasensor would be weakened. To further demonstrate the successful construction of the aptasensor, cyclic voltammetry tests were performed on an electrochemical workstation and an ECL analyzer, respectively. The standard curve and specificity experiment both showed that the sensor had the advantages of low limit of detection (LOD) and good specificity. Under the optimal conditions, the aptasensor had a good linear response for profenofos in the range of 1 × 10-1-1 × 104 ng/mL. It also had a LOD of 6.7 × 10-2 ng/mL and a correlation coefficient (R2) of 0.9991. The aptasensor had been successfully applied to the detection of profenofos in vegetables. The recovery range of the proposed ECL aptasensor was 98 % ~ 107.4 %.

Analysis of gene expression in early seed germination of rice: landscape and genetic regulation.

BACKGROUND: Seed germination is a crucial process, which determines the initiation of seed plant life cycle. The early events during this important life cycle transition that called early seed germination is defined as initially water uptake plus radicle growing out of the covering seed layers. However, a specific genome-wide analysis of early seed germination in rice is still obscure. RESULTS: In this study, the physiological characteristics of rice seed during seed germination are determined to define key points of early seed germination. Transcriptome analyses of early phase of seed germination provided deeper insight into the genetic regulation landscape. Many genes involved in starch-to-sucrose transition were differentially expressed, especially alpha-amylase 1b and beta-amylase 2, which were predominantly expressed. Differential exon usage (DEU) genes were identified, which were significantly enriched in the pathway of starch and sucrose metabolism, indicating that DEU events were critical for starch-to-sucrose transition at early seed germination. Transcription factors (TFs) were also dramatic expressed, including the abscisic acid (ABA) responsive gene, OsABI5, and gibberellic acid (GA) responsive genes, GAI. Moreover, GAI transactivated GA responsive gene, GAMYB in vivo, indicating a potential pathway involved in early seed germination process. In addition, CBL-interacting protein kinase (CIPK) genes, such as CIPK13, CIPK14 and CIPK17 were potentially interacted with other proteins, indicating its pivotal role at early seed germination. CONCLUSION: Taken together, gene regulation of early seed germination in rice was complex and protein-to-gene or protein-to-protein interactions were indispensable.

Broad-specificity time-resolved fluorescent immunochromatographic strip for simultaneous detection of various organophosphorus pesticides based on indirect probe strategy.

The massive use of organophosphorus pesticides (OPs) poses a great threat to food safety, human health and environmental protection. As there are many kinds of pesticides, their detection is facing a severe challenge. The simultaneous detection of multiple organophosphorus pesticides in one test is a problem to be solved at present. In this paper, a time-resolved fluorescent immunochromatographic (TRFIA) strip is prepared by using broad-specificity antibodies (Abs) of OPs as the recognition element. Abs were connected to europium oxide latex microspheres using sheep anti-mouse antibodies (SaMIgG) to form an indirect probe. This strategy could effectively realize signal amplification, and could save the amount and protect the activity of Abs. After the detection, the color change of the test line (T-line) was observed to make qualitative judgment under UV-light (365 nm). Then, the images of the positive sample were analyzed by using ImageJ to complete the quantitative detection. Under optimal construction and operating conditions, the limit of detection of the strip could reach 0.53 ng g-1. And the TRFIA strip performed well in the additive test of vegetable samples. It is inexpensive to prepare, convenient to carry, and easy to operate. More importantly, it improves the detection efficiency and meets the needs of rapid field testing of a large number of samples.

A novel label-free electrochemiluminescence aptasensor using a tetrahedral DNA nanostructure as a scaffold for ultrasensitive detection of organophosphorus pesticides in a luminol-H2O2 system.

In this work, a new type of Au-tetrahedral DNA nanostructure (Au-TDN) was originally proposed and successfully applied in an electrochemiluminescence aptasensor to detect organophosphorus pesticides (Ops). The aptamers modified with -SH could be covalently bonded with gold nanoparticles (AuNPs) to form a tetrahedron structure, and there were independent probes at each vertex of the tetrahedron, which could increase the probability of specific binding with Ops. The originally designed structure could not only maintain a stable tetrahedral configuration, but also combined with the target to improve the sensitivity of the sensor. Meanwhile, silver nanoparticles (AgNPs) could catalyze the chemical reaction between luminol and H2O2 to generate a variety of intermediates called reactive oxygen species (ROS) for signal enhancement. Factors that had important influences on the aptasensor, such as the concentration of Au-TDN, the incubation time, and the pH value of the buffer, were optimized in this trial. According to the final results, the limit of detection (LOD) of 3 pg mL-1 (S/N = 3) for methyl parathion, the LOD of 0.3 pg mL-1 (S/N = 3) for parathion and the LOD of 0.03 pg mL-1 (S/N = 3) for phoxim were obtained, respectively. Moreover, the novel tetrahedral structure could be replaced by different types of aptamers to expand its application range and lay a foundation for the development of portable rapid detection devices for pesticide residues.