Anemia is a serious nutritional deficiency among infants and toddlers in rural China. However, it is unclear how the anemia status changes among China's rural children as they age. This study investigates the prevalence of anemia as children grow from infancy to preschool-age, as well as the dynamic anemia status of children over time. We conducted longitudinal surveys of 1170 children in the Qinba Mountain Area of China in 2013, 2015 and 2017. The results show that 51% of children were anemic in infancy (6-12 months), 24% in toddlerhood (22-30 months) and 19% at preschool-age (49-65 months). An even larger share of children (67%) suffered from anemia at some point over the course of study. The data also show that although only 4% of children were persistently anemic from infancy to preschool-age, 8% of children saw their anemia status deteriorate. We further found that children may be at greater risk for developing anemia, or for having persistent anemia, during the period between toddlerhood and preschool-age. Combined with the finding that children with improving anemia status showed higher cognition than persistently anemic children, there is an urgent need for effective nutritional interventions to combat anemia as children grow, especially between toddlerhood and preschool age.

Hybrids between biopolymeric materials and low-cost conductive carbon-based materials are interesting materials for applications in electronics, potentially reducing the need for materials that generate environmentally harmful electronic waste. Herein we investigate a scalable ball-milling method to form graphene nanoplatelets (GNPs) by milling graphite flakes with aqueous dispersions of proteins or protein nanofibrils (PNFs). Aqueous GNP dispersions with high concentrations (up to 3.2 mg mL-1) are obtained under appropriate conditions. The PNFs/proteins help to exfoliate graphite and stabilize the resulting GNP dispersions by electrostatic repulsion. PNFs are prepared from hen egg white lysozyme (HEWL) and β-lactoglobulin (BLG). The GNP dispersions can be processed into free-standing films having an electrical conductivity of up to 110 S m-1. Alternatively, the GNP dispersions can be drop-cast on PET substrates, resulting in mechanically flexible films having an electrical conductivity of up to 65 S m-1. The drop-cast films are investigated regarding their thermoelectric properties, having Seebeck coefficients of about 50 μV K-1. By annealing drop-cast films and thus carbonizing residual PNFs, an increase of electrical conductivity, coupled with a modest decrease in Seebeck coefficient, is obtained resulting in materials displaying power factors of up to 4.6 μW m-1 K-2.

Imbalanced Alfvénic turbulence is a universal process playing a crucial role in energy transfer in space,  astrophysical, and laboratory plasmas. A fundamental and long-lasting question about the imbalanced Alfvénic turbulence is how and through which mechanism the energy transfers between scales. Here, we show that the energy transfer of imbalanced Alfvénic turbulence is completed by coherent interactions between Alfvén waves and co-propagating anomalous fluctuations. These anomalous fluctuations are generated by nonlinear couplings instead of linear reflection. We also reveal that the energy transfer of the waves and the anomalous fluctuations is carried out mainly through local-scale and large-scale nonlinear interactions, respectively, responsible for their bifurcated power-law spectra. This work unveils the energy transfer physics of imbalanced Alfvénic turbulence, and advances the understanding of imbalanced Alfvénic turbulence observed by Parker Solar Probe in the inner heliosphere.

The lack of dedicated tools in commercial planning systems currently restricts efficient review and planning for re-irradiation. The aim of this study was to develop an automated re-irradiation planning framework based on cumulative doses.

Nicotinamide adenine dinucleotide (NAD) and its reduced form are indispensable cofactors in life. Diverse NAD mimics have been developed for applications in chemical and biological sciences. Nicotinamide cytosine dinucleotide (NCD) has emerged as a non-natural cofactor to mediate redox transformations, while cells are fed with chemically synthesized NCD. Here, we create NCD synthetase (NcdS) by reprograming the substrate binding pockets of nicotinic acid mononucleotide (NaMN) adenylyltransferase to favor cytidine triphosphate and nicotinamide mononucleotide over their regular substrates ATP and NaMN, respectively. Overexpression of NcdS alone in the model host Escherichia coli facilitated intracellular production of NCD, and higher NCD levels up to 5.0 mM were achieved upon further pathway regulation. Finally, the non-natural cofactor self-sufficiency was confirmed by mediating an NCD-linked metabolic circuit to convert L-malate into D-lactate. NcdS together with NCD-linked enzymes offer unique tools and opportunities for intriguing studies in chemical biology and synthetic biology.

Cell polarization toward an attractant is influenced by both physical and chemical factors. Most existing mathematical models are based on reaction-diffusion systems and only focus on the chemical process occurring during cell polarization. However, membrane tension has been shown to act as a long-range inhibitor of cell polarization. Here, we present a cell polarization model incorporating the interplay between Rac GTPase, filamentous actin (F-actin), and cell membrane tension. We further test the predictions of this model by performing single cell measurements of the spontaneous polarization of cancer stem cells (CSCs) and non-stem cancer cells (NSCCs), as the former have lower cell membrane tension. Based on both our model and the experimental results, cell polarization is more sensitive to stimuli under low membrane tension, and high membrane tension improves the robustness and stability of cell polarization such that polarization persists under random perturbations. Furthermore, our simulations are the first to recapitulate the experimental results described by Houk et al., revealing that aspiration (elevation of tension) and release (reduction of tension) result in a decrease in and recovery of the activity of Rac-GTP, respectively, and that the relaxation of tension induces new polarity of the cell body when a cell with the pseudopod-neck-body morphology is severed.

Drug repositioning, meanings finding new uses for existing drugs, which can accelerate the processing of new drugs research and development. Various computational methods have been presented to predict novel drug-disease associations for drug repositioning based on similarity measures among drugs and diseases. However, there are some known associations between drugs and diseases that previous studies not utilized.

The DNA damage response is a highly orchestrated process. The involvement of the DNA damage response factors in DNA damage response depends on their biochemical reactions with each other and with chromatin. Using online live-cell imaging combined with heavy ion microbeam irradiation, we studied the response of the scaffold protein X-ray repair cross-complementary protein 1 (XRCC1) at the localized DNA damage in charged particle irradiated HT1080 cells expressing XRCC1-tagged RFP. The results showed that XRCC1 was recruited to the DNA damage with ultrafast kinetics in a poly ADP-ribose polymerase-dependent manner. The consecutive reaction model well explained the response of XRCC1 at ion hits, and we found that the XRCC1 recruitment was faster and dissociation was slower in the G2 phase than those in the G1 phase. The fractionated irradiation of the same cells resulted in accelerated dissociation at the previous damage sites, and the dissociated XRCC1 immediately recycled with a higher recruitment efficiency. Our data revealed XRCC1's new rescue mechanism and its high turnover in DNA damage response, which benefits our understanding of the biochemical mechanism in DNA damage response.

As patient breathing irregularities can introduce a large uncertainty in targeting the internal tumor volume (ITV) of lung cancer patients, and thereby affect treatment quality, this study evaluates dose tolerance of tumor motion amplitude variations in ITV-based volumetric modulated arc therapy (VMAT). A motion-incorporated planning technique was employed to simulate treatment delivery of 10 lung cancer patients' clinical VMAT plans using original and three scaling-up (by 0.5, 1.0, and 2.0 cm) motion waveforms from single-breath four-dimensional computed tomography (4DCT) and multi-breath time-resolved 4D magnetic resonance imaging (TR-4DMRI). The planning tumor volume (PTV = ITV + 5 mm margin) dose coverage (PTV D95%) was evaluated. The repeated waveforms were used to move the isocenter in sync with the clinical leaf motion and gantry rotation. The continuous VMAT arcs were broken down into many static beam fields at the control points (2°-interval) and the composite plan represented the motion-incorporated VMAT plan. Eight motion-incorporated plans per patient were simulated and the plan with the native 4DCT waveform was used as a control. The first (D95% ≤ 95%) and second (D95% ≤ 90%) plan breaching points due to motion amplitude increase were identified and analyzed. The PTV D95% in the motion-incorporated plans was 99.4 ± 1.0% using 4DCT, closely agreeing with the corresponding ITV-based VMAT plan (PTV D95% = 100%). Tumor motion irregularities were observed in TR-4DMRI and triggered D95% ≤ 95% in one case. For small tumors, 4 mm extra motion triggered D95% ≤ 95%, and 6-8 mm triggered D95% ≤ 90%. For large tumors, 14 mm and 21 mm extra motions triggered the first and second breaching points, respectively. This study has demonstrated that PTV D95% breaching points may occur for small tumors during treatment delivery. Clinically, it is important to monitor and avoid systematic motion increase, including baseline drift, and large random motion spikes through threshold-based beam gating.

The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) around the world affects the normal lives of people all over the world. The computational methods can be used to accurately identify SARS-CoV-2 phosphorylation sites. In this paper, a new prediction model of SARS-CoV-2 phosphorylation sites, called DE-MHAIPs, is proposed. First, we use six feature extraction methods to extract protein sequence information from different perspectives. For the first time, we use a differential evolution (DE) algorithm to learn individual feature weights and fuse multi-information in a weighted combination. Next, Group LASSO is used to select a subset of good features. Then, the important protein information is given higher weight through multi-head attention. After that, the processed data is fed into long short-term memory network (LSTM) to further enhance model's ability to learn features. Finally, the data from LSTM are input into fully connected neural network (FCN) to predict SARS-CoV-2 phosphorylation sites. The AUC values of the S/T and Y datasets under 5-fold cross-validation reach 91.98% and 98.32%, respectively. The AUC values of the two datasets on the independent test set reach 91.72% and 97.78%, respectively. The experimental results show that the DE-MHAIPs method exhibits excellent predictive ability compared with other methods.

The metal-free synthesis of graphene on single-crystal silicon substrates, the most common commercial semiconductor, is of paramount significance for many technological applications. In this work, we report the growth of graphene directly on an upside-down placed, single-crystal silicon substrate using metal-free, ambient-pressure chemical vapor deposition. By controlling the growth temperature, in-plane propagation, edge-propagation, and core-propagation, the process of graphene growth on silicon can be identified. This process produces atomically flat monolayer or bilayer graphene domains, concave bilayer graphene domains, and bulging few-layer graphene domains. This work would be a significant step toward the synthesis of large-area and layer-controlled, high-quality graphene on single-crystal silicon substrates.

Biologically inert gases play important roles in the biological functionality of proteins. However, researchers lack a full understanding of the effects of these gases since they are very chemically stable only weakly absorbed by biological tissues. By combining X-ray fluorescence, particle sizing and molecular dynamics (MD) simulations, this work shows that the aggregation of these inert gases near the hydrophobic active cavity of pepsin should lead to protein deactivation. Micro X-ray fluorescence spectra show that a pepsin solution can contain a high concentration of Xe or Kr after gassing, and that the gas concentrations decrease quickly with degassing time. Biological activity experiments indicate a reversible deactivation of the protein during this gassing and degassing. Meanwhile, the nanoparticle size measurements reveal a higher number of "nanoparticles" in gas-containing pepsin solution, also supporting the possible interaction between inert gases and the protein. Further, MD simulations indicate that gas molecules can aggregate into a tiny bubble shape near the hydrophobic active cavity of pepsin, suggesting a mechanism for reducing their biological function.

The study of protein self-interactions (SIPs) can not only reveal the function of proteins at the molecular level, but is also crucial to understand activities such as growth, development, differentiation, and apoptosis, providing an important theoretical basis for exploring the mechanism of major diseases. With the rapid advances in biotechnology, a large number of SIPs have been discovered. However, due to the long period and high cost inherent to biological experiments, the gap between the identification of SIPs and the accumulation of data is growing. Therefore, fast and accurate computational methods are needed to effectively predict SIPs. In this study, we designed a new method, NLPEI, for predicting SIPs based on natural language understanding theory and evolutionary information. Specifically, we first understand the protein sequence as natural language and use natural language processing algorithms to extract its features. Then, we use the Position-Specific Scoring Matrix (PSSM) to represent the evolutionary information of the protein and extract its features through the Stacked Auto-Encoder (SAE) algorithm of deep learning. Finally, we fuse the natural language features of proteins with evolutionary features and make accurate predictions by Extreme Learning Machine (ELM) classifier. In the SIPs gold standard data sets of human and yeast, NLPEI achieved 94.19% and 91.29% prediction accuracy. Compared with different classifier models, different feature models, and other existing methods, NLPEI obtained the best results. These experimental results indicated that NLPEI is an effective tool for predicting SIPs and can provide reliable candidates for biological experiments.

Multi-label (ML) protein subcellular localization (SCL) is an indispensable way to study protein function. It can locate a certain protein (such as the human transmembrane protein that promotes the invasion of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)) or expression product at a specific location in a cell, which can provide a reference for clinical treatment of diseases such as coronavirus disease 2019 (COVID-19).

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

Bacteria-associated infections and thrombosis, particularly catheter-related bloodstream infections and catheter-related thrombosis, are life-threatening complications. Herein, we utilize a concise assembly of heparin sodium with organosilicon quaternary ammonium surfactant to fabricate a multifunctional coating complex. In contrast to conventional one-time coatings, the complex attaches to medical devices with arbitrary shapes and compositions through a facile dipping process and further forms robust coatings to treat catheter-related bloodstream infections and thrombosis simultaneously. Through their robustness and adaptively dissociation, coatings not only exhibit good stability under extreme conditions but also significantly reduce thrombus adhesion by 60%, and shows broad-spectrum antibacterial activity ( > 97%) in vitro and in vivo. Furthermore, an ex vivo rabbit model verifies that the coated catheter has the potential to prevent catheter-related bacteremia during implantation. This substrate-independent and portable long-lasting multifunctional coating can be employed to meet the increasing clinical demands for combating catheter-related bloodstream infections and thrombosis.

Cell migration is orchestrated by a complicated mechanochemical system. However, few cell migration models take into account the coupling between the biochemical network and mechanical factors. Here, we construct a mechanochemical cell migration model to study the cell tension effect on cell migration. Our model incorporates the interactions between Rac-GTP, Rac-GDP, F-actin, myosin, and cell tension, and it is very convenient in capturing the change of cell shape by taking the phase field approach. This model captures the characteristic features of cell polarization, cell shape change, and cell migration modes. It shows that cell tension inhibits migration ability monotonically when cells are applied with persistent external stimuli. On the other hand, if random internal noise is significant, the regulation of cell tension exerts a nonmonotonic effect on cell migration. Because the increase of cell tension hinders the formation of multiple protrusions, migration ability could be maximized at intermediate cell tension under random internal noise. These model predictions are consistent with our single-cell experiments and other experimental results.

Melanoma is the most aggressive type of skin cancer with a high incidence and low survival rate. More than half of melanomas present the activating BRAF mutations, along which V600E mutant represents 70%-90%. Vemurafenib (Vem) is an FDA-approved small-molecule kinase inhibitor that selectively targets activated BRAF V600E and inhibits its activity. However, the majority of patients treated with Vem develop acquired resistance. Hence, this study aims to explore a new treatment strategy to overcome the Vem resistance. Here, we found that a potential anticancer drug norcantharidin (NCTD) displayed a more significant proliferation inhibitory effect against Vem-resistant melanoma cells (A375R) than the parental melanoma cells (A375), which promised to be a therapeutic agent against BRAF V600E-mutated and acquired Vem-resistant melanoma. The metabolomics analysis showed that NCTD could, especially reverse the upregulation of pentose phosphate pathway and lipogenesis resulting from the Vem resistance. In addition, the transcriptomic analysis showed a dramatical downregulation in genes related to lipid metabolism and mammalian target of the rapamycin (mTOR) signaling pathway in A375R cells, but not in A375 cells, upon NCTD treatment. Moreover, NCTD upregulated butyrophilin (BTN) family genes, which played important roles in modulating T-cell response. Consistently, we found that Vem resistance led to an obvious elevation of the p-mTOR expression, which could be remarkably reduced by NCTD treatment. Taken together, NCTD may serve as a promising therapeutic option to resolve the problem of Vem resistance and to improve patient outcomes by combining with immunomodulatory therapy.

Interfraction motion during cervical cancer radiotherapy is substantial in some patients, minimal in others. Non-adaptive plans may miss the target and/or unnecessarily irradiate normal tissue. Adaptive radiotherapy leads to superior dose-volume metrics but is resource-intensive. The aim of this study was to predict target motion, enabling patient selection and efficient resource allocation.

The COVID-19 has now spread all over the world and causes a huge burden for public health and world economy. Drug repositioning has become a promising treatment strategy in COVID-19 crisis because it can shorten drug development process, reduce pharmaceutical costs and reposition approval drugs. Existing computational methods only focus on single information, such as drug and virus similarity or drug-virus network feature, which is not sufficient to predict potential drugs. In this paper, a sequence combined attentive network embedding model SANE is proposed for identifying drugs based on sequence features and network features. On the one hand, drug SMILES and virus sequence features are extracted by encoder-decoder in SANE as node initial embedding in drug-virus network. On the other hand, SANE obtains fields for each node by attention-based Depth-First-Search (DFS) to reduce noises and improve efficiency in representation learning and adopts a bottom-up aggregation strategy to learn node network representation from selected fields. Finally, a forward neural network is used for classifying. Experiment results show that SANE has achieved the performance with 81.98% accuracy and 0.8961 AUC value and outperformed state-of-the-art baselines. Further case study on COVID-19 indicates that SANE has a strong predictive ability since 25 of the top 40 (62.5%) drugs are verified by valuable dataset and literatures. Therefore, SANE is powerful to reposition drugs for COVID-19 and provides a new perspective for drug repositioning.