Complex hierarchical structures in single-cell genomics data unveiled by deep hyperbolic manifold learning.

With the advances in single-cell sequencing techniques, numerous analytical methods have been developed for delineating cell development. However, most are based on Euclidean space, which would distort the complex hierarchical structure of cell differentiation. Recently, methods acting on hyperbolic space have been proposed to visualize hierarchical structures in single-cell RNA-seq (scRNA-seq) data and have been proven to be superior to methods acting on Euclidean space. However, these methods have fundamental limitations and are not optimized for the highly sparse single-cell count data. To address these limitations, we propose scDHMap, a model-based deep learning approach to visualize the complex hierarchical structures of scRNA-seq data in low-dimensional hyperbolic space. The evaluations on extensive simulation and real experiments show that scDHMap outperforms existing dimensionality-reduction methods in various common analytical tasks as needed for scRNA-seq data, including revealing trajectory branches, batch correction, and denoising the count matrix with high dropout rates. In addition, we extend scDHMap to visualize single-cell ATAC-seq data.

PQSDC: a parallel lossless compressor for quality scores data via sequences partition and run-length prediction mapping.

MOTIVATION: The quality scores data (QSD) account for 70% in compressed FastQ files obtained from the short and long reads sequencing technologies. Designing effective compressors for QSD that counterbalance compression ratio, time cost, and memory consumption is essential in scenarios such as large-scale genomics data sharing and long-term data backup. This study presents a novel parallel lossless QSD-dedicated compression algorithm named PQSDC, which fulfills the above requirements well. PQSDC is based on two core components: a parallel sequences-partition model designed to reduce peak memory consumption and time cost during compression and decompression processes, as well as a parallel four-level run-length prediction mapping model to enhance compression ratio. Besides, the PQSDC algorithm is also designed to be highly concurrent using multicore CPU clusters. RESULTS: We evaluate PQSDC and four state-of-the-art compression algorithms on 27 real-world datasets, including 61.857 billion QSD characters and 632.908 million QSD sequences. (1) For short reads, compared to baselines, the maximum improvement of PQSDC reaches 7.06% in average compression ratio, and 8.01% in weighted average compression ratio. During compression and decompression, the maximum total time savings of PQSDC are 79.96% and 84.56%, respectively; the maximum average memory savings are 68.34% and 77.63%, respectively. (2) For long reads, the maximum improvement of PQSDC reaches 12.51% and 13.42% in average and weighted average compression ratio, respectively. The maximum total time savings during compression and decompression are 53.51% and 72.53%, respectively; the maximum average memory savings are 19.44% and 17.42%, respectively. (3) Furthermore, PQSDC ranks second in compression robustness among the tested algorithms, indicating that it is less affected by the probability distribution of the QSD collections. Overall, our work provides a promising solution for QSD parallel compression, which balances storage cost, time consumption, and memory occupation primely. AVAILABILITY AND IMPLEMENTATION: The proposed PQSDC compressor can be downloaded from https://github.com/fahaihi/PQSDC.

High Spatial Resolution 2D Imaging of Current Density and Pressure for Graphene Devices under High Pressure Using Nitrogen-Vacancy Centers in Diamond.

Current density imaging is helpful for discovering interesting electronic phenomena and understanding carrier dynamics, and by combining pressure distributions, several pressure-induced novel physics may be comprehended. In this work, noninvasive, high-resolution two-dimensional images of the current density and pressure gradient for graphene ribbon and hBN-graphene-hBN devices are explored using nitrogen-vacancy (NV) centers in diamond under high pressure. The two-dimensional vector current density is reconstructed by the vector magnetic field mapped by the near-surface NV center layer in the diamond. The current density images accurately and clearly reproduce the complicated structure and current flow of graphene under high pressure. Additionally, the spatial distribution of the pressure is simultaneously mapped, rationalizing the nonuniformity of the current density under high pressure. The current method opens a significant new avenue to investigate electronic transport and conductance variations in two-dimensional materials and electrical devices under high pressure as well as for nondestructive evaluation of semiconductor circuits.

Classifying breast cancer subtypes on multi-omics data via sparse canonical correlation analysis and deep learning.

BACKGROUND: Classifying breast cancer subtypes is crucial for clinical diagnosis and treatment. However, the early symptoms of breast cancer may not be apparent. Rapid advances in high-throughput sequencing technology have led to generating large number of multi-omics biological data. Leveraging and integrating the available multi-omics data can effectively enhance the accuracy of identifying breast cancer subtypes. However, few efforts focus on identifying the associations of different omics data to predict the breast cancer subtypes. RESULTS: In this paper, we propose a differential sparse canonical correlation analysis network (DSCCN) for classifying the breast cancer subtypes. DSCCN performs differential analysis on multi-omics expression data to identify differentially expressed (DE) genes and adopts sparse canonical correlation analysis (SCCA) to mine highly correlated features between multi-omics DE-genes. Meanwhile, DSCCN uses multi-task deep learning neural network separately to train the correlated DE-genes to predict breast cancer subtypes, which spontaneously tackle the data heterogeneity problem in integrating multi-omics data. CONCLUSIONS: The experimental results show that by mining the associations among multi-omics data, DSCCN is more capable of accurately classifying breast cancer subtypes than the existing methods.

Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation.

BACKGROUND: Driver genes play a vital role in the development of cancer. Identifying driver genes is critical for diagnosing and understanding cancer. However, challenges remain in identifying personalized driver genes due to tumor heterogeneity of cancer. Although many computational methods have been developed to solve this problem, few efforts have been undertaken to explore gene-patient associations to identify personalized driver genes. RESULTS: Here we propose a method called LPDriver to identify personalized cancer driver genes by employing linear neighborhood propagation model on individual genetic data. LPDriver builds personalized gene network based on the genetic data of individual patients, extracts the gene-patient associations from the bipartite graph of the personalized gene network and utilizes a linear neighborhood propagation model to mine gene-patient associations to detect personalized driver genes. The experimental results demonstrate that as compared to the existing methods, our method shows competitive performance and can predict cancer driver genes in a more accurate way. Furthermore, these results also show that besides revealing novel driver genes that have been reported to be related with cancer, LPDriver is also able to identify personalized cancer driver genes for individual patients by their network characteristics even if the mutation data of genes are hidden. CONCLUSIONS: LPDriver can provide an effective approach to predict personalized cancer driver genes, which could promote the diagnosis and treatment of cancer. The source code and data are freely available at https://github.com/hyr0771/LPDriver .

Hydrodynamics-Controlled Single-Particle Electrocatalysis.

Electrocatalysis is considered promising in renewable energy conversion and storage, yet numerous efforts rely on catalyst design to advance catalytic activity. Herein, a hydrodynamic single-particle electrocatalysis methodology is developed by integrating collision electrochemistry and microfluidics to improve the activity of an electrocatalysis system. As a proof-of-concept, hydrogen evolution reaction (HER) is electrocatalyzed by individual palladium nanoparticles (Pd NPs), with the development of microchannel-based ultramicroelectrodes. The controlled laminar flow enables the precise delivery of Pd NPs to the electrode-electrolyte interface one by one. Compared to the diffusion condition, hydrodynamic collision improves the number of active sites on a given electrode by 2 orders of magnitude. Furthermore, forced convection enables the enhancement of proton mass transport, thereby increasing the electrocatalytic activity of each single Pd NP. It turns out that the improvement in mass transport increases the reaction rate of HER at individual Pd NPs, thus a phase transition without requiring a high overpotential. This study provides new avenues for enhancing electrocatalytic activity by altering operating conditions, beyond material design limitations.

Singlet Fission in Pechmann Dyes: Planar Chromophore Design and Understanding.

Singlet fission in organic chromophores holds the potential for enhancing photovoltaic efficiencies beyond the single-junction limit. The most basic requirement of a singlet fission material is that it has a large energy gap between its first singlet and triplet excited states. Identifying such compounds is not simple and has been accomplished either through computational screening or by subtle modifications of previously known fission materials. Here, we propose an approach that leverages ground and excited-state aromaticity combined with double-bond conformation to establish simple qualitative design rules for predicting fundamental optical properties without the need for computational modeling. By investigating two Pechmann dye isomers, we demonstrate that although their planarity and degree of charge transfer are similar, singlet fission is active in the isomer with a trans-conformation, while the cis-isomer exhibits greater favorability for polaronic processes, experimentally validated using ultrafast and electron spin resonance spectroscopy. Our results offer a new design perspective that provides a rational framework for tailoring optoelectronic systems to specific applications such as singlet fission or triplet-triplet annihilation.

Mechanism Study of Molecular Trap in All-Organic Polystyrene-Based Dielectric Composite.

It is a huge challenge to explore how charge traps affect the electric breakdown of polymer-based dielectric composites. In this paper, alkane and aromatic molecules with different substituents are investigated according to DFT theoretical method. The combination of strong electron-withdrawing groups and aromatic rings can establish high electron affinity molecules. 4'-Nitro-4-dimethylaminoazobenzene (NAABZ) with a vertical electron affinity of 1.39 eV and a dipole moment of 10.15 D is introduced into polystyrene (PSt) to analyze the influence of charge traps on electric properties. Marcus charge transfer theory is applied to calculate the charge transfer rate between PSt and NAABZ. The nature of charge traps is elaborated from a dynamic perspective. The enhanced breakdown mechanism of polymers-based composites stems from the constraint of carrier mobility caused by the change in transfer rate. But the electrophile nature of high electron affinity filler can decrease the potential barriers at the metal-polymer interface. Simultaneously, the relationship between the electron affinity of fillers and the breakdown strength of polymer-based composites is nonlinear because of the presence of the inversion region. Based on the deep understanding of the molecular trap, this work provides the theoretical calculation for the design and development of high-performance polymer dielectrics.

Hidden Markov random field models for cell-type assignment of spatially resolved transcriptomics.

MOTIVATION: The recent development of spatially resolved transcriptomics (SRT) technologies has facilitated research on gene expression in the spatial context. Annotating cell types is one crucial step for downstream analysis. However, many existing algorithms use an unsupervised strategy to assign cell types for SRT data. They first conduct clustering analysis and then aggregate cluster-level expression based on the clustering results. This workflow fails to leverage the marker gene information efficiently. On the other hand, other cell annotation methods designed for single-cell RNA-seq data utilize the cell-type marker genes information but fail to use spatial information in SRT data. RESULTS: We introduce a statistical spatial transcriptomics cell assignment model, SPAN, to annotate clusters of cells or spots into known types in SRT data with prior knowledge of predefined marker genes and spatial information. The SPAN model annotates cells or spots from SRT data using predefined overexpressed marker genes and combines a mixture model with a hidden Markov random field to model the spatial dependency between neighboring spots. We demonstrate the effectiveness of SPAN against spatial and nonspatial clustering algorithms through extensive simulation and real data experiments. AVAILABILITY AND IMPLEMENTATION: https://github.com/ChengZ352/SPAN.

DxFormer: a decoupled automatic diagnostic system based on decoder-encoder transformer with dense symptom representations.

MOTIVATION: Symptom-based automatic diagnostic system queries the patient's potential symptoms through continuous interaction with the patient and makes predictions about possible diseases. A few studies use reinforcement learning (RL) to learn the optimal policy from the joint action space of symptoms and diseases. However, existing RL (or Non-RL) methods focus on disease diagnosis while ignoring the importance of symptom inquiry. Although these systems have achieved considerable diagnostic accuracy, they are still far below its performance upper bound due to few turns of interaction with patients and insufficient performance of symptom inquiry. To address this problem, we propose a new automatic diagnostic framework called DxFormer, which decouples symptom inquiry and disease diagnosis, so that these two modules can be independently optimized. The transition from symptom inquiry to disease diagnosis is parametrically determined by the stopping criteria. In DxFormer, we treat each symptom as a token, and formalize the symptom inquiry and disease diagnosis to a language generation model and a sequence classification model, respectively. We use the inverted version of Transformer, i.e. the decoder-encoder structure, to learn the representation of symptoms by jointly optimizing the reinforce reward and cross-entropy loss. RESULTS: We conduct experiments on three real-world medical dialogue datasets, and the experimental results verify the feasibility of increasing diagnostic accuracy by improving symptom recall. Our model overcomes the shortcomings of previous RL-based methods. By decoupling symptom query from the process of diagnosis, DxFormer greatly improves the symptom recall and achieves the state-of-the-art diagnostic accuracy. AVAILABILITY AND IMPLEMENTATION: Both code and data are available at https://github.com/lemuria-wchen/DxFormer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A benchmark for automatic medical consultation system: frameworks, tasks and datasets.

MOTIVATION: In recent years, interest has arisen in using machine learning to improve the efficiency of automatic medical consultation and enhance patient experience. In this article, we propose two frameworks to support automatic medical consultation, namely doctor-patient dialogue understanding and task-oriented interaction. We create a new large medical dialogue dataset with multi-level fine-grained annotations and establish five independent tasks, including named entity recognition, dialogue act classification, symptom label inference, medical report generation and diagnosis-oriented dialogue policy. RESULTS: We report a set of benchmark results for each task, which shows the usability of the dataset and sets a baseline for future studies. AVAILABILITY AND IMPLEMENTATION: Both code and data are available from https://github.com/lemuria-wchen/imcs21. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Experimental study of modified Tavis-Cummings model with directly-coupled superconducting artificial atoms.

The Tavis-Cummings model is intensively investigated in quantum optics and has important applications in generation of multi-atom entanglement. Here, we employ a superconducting circuit quantum electrodynamic system to study a modified Tavis-Cummings model with directly-coupled atoms. In our device, three superconducting artificial atoms are arranged in a chain with direct coupling through fixed capacitors and strongly coupled to a transmission line resonator. By performing transmission spectrum measurements, we observe different anticrossing structures when one or two qubits are resonantly coupled to the resonator. In the case of the two-qubit Tavis-Cummings model without qubit-qubit interaction, we observe two dips at the resonance point of the anticrossing. The splitting of these dips is determined by Δ λ=2g12+g32, where g1 and g3 are the coupling strengths between Qubit 1 and the resonator, and Qubit 3 and the resonator, respectively. The direct coupling J12 between the two qubits results in three dressed states in the two-qubit Tavis-Cummings model at the frequency resonance point, leading to three dips in the transmission spectrum. In this case, the distance between the two farthest and asymmetrical dips, arising from the energy level splitting, is larger than in the previous case. The frequency interval between these two dips is determined by the difference in eigenvalues (Δ λ=ε 1+-ε 1-), obtained through numerical calculations. What we believe as novel and intriguing experimental results may potentially advance quantum optics experiments, providing valuable insights for future research.

Hsa_circ_0005397 promotes hepatocellular carcinoma progression through EIF4A3.

PURPOSE: The purpose of this study was to explore the expression and potential mechanism of hsa_circ_0005397 in hepatocellular carcinoma progression. METHODS: Quantitative reverse transcription-polymerase chain reaction(qRT-PCR) was used to measure the expression level of hsa_circ_0005397 and EIF4A3 from paired HCC tissues and cell lines. Western Blot (WB) and immunohistochemistry (IHC) were used to verify the protein level of EIF4A3. The specificity of primers was confirmed by agarose gel electrophoresis. Receiver Operating Characteristic (ROC) Curve was drawn to analyze diagnostic value. Actinomycin D and nuclear and cytoplasmic extraction assays were utilized to evaluate the characteristics of hsa_circ_0005397. Cell Counting kit-8 (CCK-8) and colony formation assays were performed to detect cell proliferation. Flow cytometry analysis was used to detect the cell cycle. Transwell assay was performed to determine migration and invasion ability. RNA-binding proteins (RBPs) of hsa_circ_0005397 in HCC were explored using bioinformatics websites. The relationship between hsa_circ_0005397 and Eukaryotic Translation Initiation Factor 4A3 (EIF4A3) was verified by RNA Binding Protein Immunoprecipitation (RIP) assays, correlation and rescue experiments. RESULTS: In this study, hsa_circ_0005397 was found to be significantly upregulated in HCC, and the good diagnostic sensitivity and specificity shown a potential diagnostic capability. Upregulated expression of hsa_circ_0005397 was significantly related to tumor size and stage. Hsa_circ_0005397 was circular structure which more stable than liner mRNA, and mostly distributed in the cytoplasm. Upregulation of hsa_circ_0005397 generally resulted in stronger proliferative ability, clonality, and metastatic potency of HCC cells; its downregulation yielded the opposite results. EIF4A3 is an RNA-binding protein of hsa_circ_0005397, which overexpressed in paired HCC tissues and cell lines. In addition, expression of hsa_circ_0005397 decreased equally when EIF4A3 was depleted. RIP assays and correlation assay estimated that EIF4A3 could interacted with hsa_circ_0005397. Knockdown of EIF4A3 could reverse hsa_circ_0005397 function in HCC progression. CONCLUSIONS: Hsa_circ_0005397 promotes progression of hepatocellular carcinoma through EIF4A3. These research findings may provide novel clinical value for hepatocellular carcinoma.

Fetal overgrowth and weight trajectories during infancy and adiposity in early childhood.

BACKGROUND: Large-for-gestational age (LGA), a marker of fetal overgrowth, has been linked to obesity in adulthood. Little is known about how infancy growth trajectories affect adiposity in early childhood in LGA. METHODS: In the Shanghai Birth Cohort, we followed up 259 LGA (birth weight >90th percentile) and 1673 appropriate-for-gestational age (AGA, 10th-90th percentiles) children on body composition (by InBody 770) at age 4 years. Adiposity outcomes include body fat mass (BFM), percent body fat (PBF), body mass index (BMI), overweight/obesity, and high adiposity (PBF >85th percentile). RESULTS: Three weight growth trajectories (low, mid, and high) during infancy (0-2 years) were identified in AGA and LGA subjects separately. BFM, PBF and BMI were progressively higher from low- to mid-to high-growth trajectories in both AGA and LGA children. Compared to the mid-growth trajectory, the high-growth trajectory was associated with greater increases in BFM and the odds of overweight/obesity or high adiposity in LGA than in AGA children (tests for interactions, all P < 0.05). CONCLUSIONS: Weight trajectories during infancy affect adiposity in early childhood regardless of LGA or not. The study is the first to demonstrate that high-growth weight trajectory during infancy has a greater impact on adiposity in early childhood in LGA than in AGA subjects. IMPACT: Large-for-gestational age (LGA), a marker of fetal overgrowth, has been linked to obesity in adulthood, but little is known about how weight trajectories during infancy affect adiposity during early childhood in LGA subjects. The study is the first to demonstrate a greater impact of high-growth weight trajectory during infancy (0-2 years) on adiposity in early childhood (at age 4 years) in subjects with fetal overgrowth (LGA) than in those with normal birth size (appropriate-for-gestational age). Weight trajectory monitoring may be a valuable tool in identifying high-risk LGA children for close follow-ups and interventions to decrease the risk of obesity.

Exploring the novel benefits of leucine: Protecting nitrite-induced liver damage in sub-adult grass carp (Ctenopharyngodon idella) through regulating mitochondria quality control.

Leucine is an essential amino acid for fish. The ability of leucine to resist stress in fish has not been reported. Nitrite is a common pollutant in the aquatic environment. Therefore, we investigated the effects of dietary leucine on growth performance and nitrite-induced liver damage, mitochondrial dysfunction, autophagy, and apoptosis for sub-adult grass carp. A total of 450 grass carp (615.91 ± 1.15 g) were selected and randomly placed into 18 net cages. The leucine contents of the six diets were 2.91, 5.90, 8.92, 11.91, 14.93, and 17.92 g/kg, respectively. After a 9-week feeding trial, the nitrite exposure experiment was set up for 96 h. These results indicated that dietary leucine significantly promoted FW, WG, PWG, and SGR of sub-adult grass carp (P < 0.05). Appropriate levels of dietary leucine (11.91-17.92 g/kg) decreased the activities of serum parameters (glucose, cortisol, and methemoglobin contents, glutamic oxaloacetic transaminase, glutamic pyruvic transaminase, and lactate dehydrogenase), the contents of reactive oxygen species (ROS), nitric oxide (NO) and peroxynitrite (ONOO-). In addition, appropriate levels of dietary leucine (11.91-17.92 g/kg) increased the mRNA levels of mitochondrial biogenesis genes (PGC-1α, Nrf1/2, TFAM), fusion-related genes (Opa1, Mfn1/2) (P < 0.05), and decreased the mRNA levels of caspase 3, caspase 8, caspase 9, fission-related gene (Drp1), mitophagy-related genes (Pink1, Parkin) and autophagy-related genes (Beclin1, Ulk1, Atg5, Atg7, Atg12) (P < 0.05). Appropriate levels of dietary leucine (8.92-17.92 g/kg) also increased the protein levels of AMP-activated protein kinase (AMPK), prostacyclin (p62) and decreased the protein levels of protein light chain 3 (LC3), E3 ubiquitin ligase (Parkin), and Cytochrome c (Cytc). Appropriate levels of leucine (8.92-17.92 g/kg) could promote growth performance and alleviate nitrite-induced mitochondrial dysfunction, autophagy, apoptosis for sub-adult grass carp. Based on quadratic regression analysis of PWG and serum GPT activity, dietary leucine requirements of sub-adult grass carp were recommended to be 12.47 g/kg diet and 12.55 g/kg diet, respectively.

Opportunities of Flexible and Portable Electrochemical Devices for Energy Storage: Expanding the Spotlight onto Semi-solid/Solid Electrolytes.

The ever-increasing demand for flexible and portable electronics has stimulated research and development in building advanced electrochemical energy devices which are lightweight, ultrathin, small in size, bendable, foldable, knittable, wearable, and/or stretchable. In such flexible and portable devices, semi-solid/solid electrolytes besides anodes and cathodes are the necessary components determining the energy/power performances. By serving as the ion transport channels, such semi-solid/solid electrolytes may be beneficial to resolving the issues of leakage, electrode corrosion, and metal electrode dendrite growth. In this paper, the fundamentals of semi-solid/solid electrolytes (e.g., chemical composition, ionic conductivity, electrochemical window, mechanical strength, thermal stability, and other attractive features), the electrode-electrolyte interfacial properties, and their relationships with the performance of various energy devices (e.g., supercapacitors, secondary ion batteries, metal-sulfur batteries, and metal-air batteries) are comprehensively reviewed in terms of materials synthesis and/or characterization, functional mechanisms, and device assembling for performance validation. The most recent advancements in improving the performance of electrochemical energy devices are summarized with focuses on analyzing the existing technical challenges (e.g., solid electrolyte interphase formation, metal electrode dendrite growth, polysulfide shuttle issue, electrolyte instability in half-open battery structure) and the strategies for overcoming these challenges through modification of semi-solid/solid electrolyte materials. Several possible directions for future research and development are proposed for going beyond existing technological bottlenecks and achieving desirable flexible and portable electrochemical energy devices to fulfill their practical applications. It is expected that this review may provide the readers with a comprehensive cross-technology understanding of the semi-solid/solid electrolytes for facilitating their current and future researches on the flexible and portable electrochemical energy devices.

The effects of repeated freezing and thawing on bovine sperm morphometry and function.

Refreezing the remaining genetic resources after in vitro fertilization (IVF) can conserve genetic materials. However, the precise damage inflicted by repeated freezing and thawing on bovine sperm and its underlying mechanism remain largely unexplored. Thus, this study investigates the impact of repeated freeze-thaw cycles on sperm. Our findings indicate that such cycles significantly reduce sperm viability and motility. Furthermore, the integrity of the sperm plasma membrane and acrosome is compromised during this process, exacerbating the advanced apoptosis triggered by oxidative stress. Additionally, transmission electron microscopy exposed severe damage to the plasma membranes of both the sperm head and tail. Notably, the "9 + 2" structure of the tail was disrupted, along with a significant decrease in the level of the axonemal protein DNAH10, leading to reduced sperm motility. IVF outcomes revealed that repeated freeze-thaw cycles considerably impair sperm fertilization capability, ultimately reducing the blastocyst rate. In summary, our research demonstrates that repeated freeze-thaw cycles lead to a decline in sperm viability and motility, attributed to oxidative stress-induced apoptosis and DNAH10-related dynamic deficiency. As a result, the utility of semen is compromised after repeated freezing.

Supramolecular CRISPR-OFF switches with host-guest chemistry.

CRISPR (Clustered Regularly Interspaced Short Palindromic Repeat) technology is a powerful tool in biology and medicine. However, the safety and application of this technology is hampered by excessive activity of CRISPR machinery. It is particularly important to develop methods for switching off CRISPR activity in human cells. The current study demonstrates the concept of supramolecular CRISPR-OFF switches by employing host-guest chemistry. We demonstrate that the CRISPR systems show considerable tolerance to adamantoylation on guide RNAs (gRNAs), whereas supramolecular complexation tremendously affects the function of adamantoyl gRNAs. Host-guest chemistry is demonstrated to be novel and effective tools to reduce unwanted excessive activities of CRISPR complexes in human cells. This work indicates considerable potential of supramolecular strategy for controlling and enhancing CRISPR systems.

Environmental antibiotics exposure and childhood obesity: A cross-sectional case-control study.

Children's exposures to environmental antibiotics are a major public health concern. However, limited data are available on the effects of environmental antibiotic exposures on childhood obesity. Our study aimed to explore this relationship. We conducted a cross-sectional case-control study nested in a population-based survey of primary school students, including 1855 obese and 1875 random selected control children. A total of 10 antibiotics in urine samples were measured by liquid chromatography-tandem mass spectrometry. Multivariable survey logistic regression was used to assess the associations between environmental antibiotics exposures and childhood obesity. After adjusting for potential confounders, increased odds of obesity were observed in children exposed to tetracycline (OR = 1.31, 95% CI: 1.09-1.57) and sulfamonomethoxine (OR = 1.43, 95% CI: 1-2.05). Comparing none (

Effect of Prophylactic Placement of Percutaneous Endoscopic Gastrostomy on the Surgical Prognosis in Patients With Oral Cancer: A Pilot Study.

This study assessed the application of prophylactic percutaneous endoscopic gastrostomy (pPEG) in patients with newly diagnosed oral cancer (OC) who underwent surgery and were ready for subsequential chemoradiation. In total, 113 patients who underwent OC surgical treatment were divided into pPEG (n = 26), nasogastric tube (n = 54), and oral feeding (n = 33) groups. Their baseline characteristics, surgery-related outcomes, and nutritional data were analyzed using the Wilcoxon rank-sum test and χ2 test. No statistical significance was shown among the 3 groups using different flaps for reconstructing OC-related defects (P > 0.05). Patients of the pPEG group had the lowest incidence of surgical site infection (SSI; 23.1%, P = 0.006), but the longest hospital stay (24.1 ± 7.9 d, P < 0.001). Prophylactic percutaneous endoscopic gastrostomy may decrease the frequency of SSI but increase the length of hospitalization in SSI OC. However, multicentric prospective clinical controlled trials with large sample sizes are needed to further validate our findings.