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While the geodetic excitation χ(t) of polar motion p(t) is essential to improve our understanding of global mass redistributions and relative motions with respect to the terrestrial frame, the widely adopted method to derive χ(t) from p(t) has biases in both amplitude and phase responses. This study has developed a new simple but more accurate method based on the combination of the frequency- and time-domain Liouville's equation (FTLE). The FTLE method has been validated not only with 6-h sampled synthetic excitation series but also with daily and 6-h sampled polar motion measurements as well as χ(t) produced by the interactive webpage tool of the International Earth Rotation and Reference Systems Service (IERS). Numerical comparisons demonstrate that χ(t) derived from the FTLE method has superior performances in both the time and frequency domains with respect to that obtained from the widely adopted method or the IERS webpage tool, provided that the input p(t) series has a length around or more than 25 years, which presents no practical limitations since the necessary polar motion data are readily available. The FTLE code is provided in the form of MatLab function.

Primary immune thrombocytopenia (ITP) is an acquired autoimmune disorder characterized by the destruction of platelets. Although it was long believed that the critical role of autoantibodies in platelet destruction, primarily through the Fc-dependent platelet clearance pathway, recent findings indicate that the significance of the Fc-independent platelet clearance pathway mediated by hepatocytes, thus shedding light on a previously obscure aspect of ITP pathogenesis. Within this context, the desialylation of platelets has emerged as a pivotal biochemical marker. Consequently, targeting platelet desialylation emerges as a novel therapeutic strategy in the pathogenesis of ITP. Notably, prevailing research has largely focused on antiplatelet antibodies and the glycosylation-associated mechanisms of platelet clearance, while comprehensive analysis of platelet desialylation remains scant. In response, we retrospectively discuss the historical progression, inducing factors, generation process, and molecular regulatory mechanisms underlying platelet desialylation in ITP pathogenesis. By systematically evaluating the most recent research findings, we contribute to a comprehensive understanding of the intricate processes involved. Moreover, our manuscript delves into the potential application of desialylation regulatory strategies in ITP therapy, heralding novel therapeutic avenues. In conclusion, this manuscript not only fills a critical void in existing literature but also paves the way for future research by establishing a systematic theoretical framework. By inspiring new research ideas and offering insights into the development of new therapeutic strategies and targeted drugs, our study is poised to significantly advance the clinical management of ITP.

Thrombocytopenia caused by long-term radiotherapy and chemotherapy exists in cancer treatment. Previous research demonstrates that 5-Hydroxtrayptamine (5-HT) and its receptors induce the formation of megakaryocytes (MKs) and platelets. However, the relationships between 5-HT1A receptor (5-HTR1A) and MKs is unclear so far. We screened and investigated the mechanism of vilazodone as a 5-HTR1A partial agonist in promoting MK differentiation and evaluated its therapeutic effect in thrombocytopenia. We employed a drug screening model based on machine learning (ML) to screen the megakaryocytopoiesis activity of Vilazodone (VLZ). The effects of VLZ on megakaryocytopoiesis were verified in HEL and Meg-01 cells. Tg (itga2b: eGFP) zebrafish was performed to analyze the alterations in thrombopoiesis. Moreover, we established a thrombocytopenia mice model to investigate how VLZ administration accelerates platelet recovery and function. We carried out network pharmacology, Western blot, and immunofluorescence to demonstrate the potential targets and pathway of VLZ. VLZ has been predicted to have a potential biological action. Meanwhile, VLZ administration promotes MK differentiation and thrombopoiesis in cells and zebrafish models. Progressive experiments showed that VLZ has a potential therapeutic effect on radiation-induced thrombocytopenia in vivo. The network pharmacology and associated mechanism study indicated that SRC and MAPK signaling are both involved in the processes of megakaryopoiesis facilitated by VLZ. Furthermore, the expression of 5-HTR1A during megakaryocyte differentiation is closely related to the activation of SRC and MAPK. Our findings demonstrated that the expression of 5-HTR1A on MK, VLZ could bind to the 5-HTR1A receptor and further regulate the SRC/MAPK signaling pathway to facilitate megakaryocyte differentiation and platelet production, which provides new insights into the alternative therapeutic options for thrombocytopenia.

BackgroundIdentification of patients at risk for type 2 diabetes mellitus (T2DM) can not only prevent complications and reduce suffering but also ease the health care burden. While routine physical examination can provide useful information for diagnosis, manual exploration of routine physical examination records is not feasible due to the high prevalence of T2DM.ObjectivesWe aim to build interpretable machine learning models for T2DM diagnosis and uncover important diagnostic indicators from physical examination, including age- and sex-related indicators.MethodsIn this study, we present three weighted diversity density (WDD)-based algorithms for T2DM screening that use physical examination indicators, the algorithms are highly transparent and interpretable, two of which are missing value tolerant algorithms.PatientsRegarding the dataset, we collected 43 physical examination indicator data from 11,071 cases of T2DM patients and 126,622 healthy controls at the Affiliated Hospital of Southwest Medical University. After data processing, we used a data matrix containing 16004 EHRs and 43 clinical indicators for modelling.ResultsThe indicators were ranked according to their model weights, and the top 25% of indicators were found to be directly or indirectly related to T2DM. We further investigated the clinical characteristics of different age and sex groups, and found that the algorithms can detect relevant indicators specific to these groups. The algorithms performed well in T2DM screening, with the highest area under the receiver operating characteristic curve (AUC) reaching 0.9185.ConclusionThis work utilized the interpretable WDD-based algorithms to construct T2DM diagnostic models based on physical examination indicators. By modeling data grouped by age and sex, we identified several predictive markers related to age and sex, uncovering characteristic differences among various groups of T2DM patients.

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(1) Background: Radiation-induced thrombocytopenia (RIT) often occurs in cancer patients undergoing radiation therapy, which can result in morbidity and even death. However, a notable deficiency exists in the availability of specific drugs designed for the treatment of RIT. (2) Methods: In our pursuit of new drugs for RIT treatment, we employed three deep learning (DL) algorithms: convolutional neural network (CNN), deep neural network (DNN), and a hybrid neural network that combines the computational characteristics of the two. These algorithms construct computational models that can screen compounds for drug activity by utilizing the distinct physicochemical properties of the molecules. The best model underwent testing using a set of 10 drugs endorsed by the US Food and Drug Administration (FDA) specifically for the treatment of thrombocytopenia. (3) Results: The Hybrid CNN+DNN (HCD) model demonstrated the most effective predictive performance on the test dataset, achieving an accuracy of 98.3% and a precision of 97.0%. Both metrics surpassed the performance of the other models, and the model predicted that seven FDA drugs would exhibit activity. Isochlorogenic acid A, identified through screening the Chinese Pharmacopoeia Natural Product Library, was subsequently subjected to experimental verification. The results indicated a substantial enhancement in the differentiation and maturation of megakaryocytes (MKs), along with a notable increase in platelet production. (4) Conclusions: This underscores the potential therapeutic efficacy of isochlorogenic acid A in addressing RIT.

Autophagy is a primary mechanism for maintaining cellular homeostasis. The synergistic actions of autophagy-related (ATG) proteins strictly regulate the whole autophagic process. Therefore, accurate identification of ATGs is a first and critical step to reveal the molecular mechanism underlying the regulation of autophagy. Current computational methods can predict ATGs from primary protein sequences, but owing to the limitations of algorithms, significant room for improvement still exists. In this research, we propose EnsembleDL-ATG, an ensemble deep learning framework that aggregates multiple deep learning models to predict ATGs from protein sequence and evolutionary information. We first evaluated the performance of individual networks for various feature descriptors to identify the most promising models. Then, we explored all possible combinations of independent models to select the most effective ensemble architecture. The final framework was built and maintained by an organization of four different deep learning models. Experimental results show that our proposed method achieves a prediction accuracy of 94.5 % and MCC of 0.890, which are nearly 4 % and 0.08 higher than ATGPred-FL, respectively. Overall, EnsembleDL-ATG is the first ATG machine learning predictor based on ensemble deep learning. The benchmark data and code utilized in this study can be accessed for free at https://github.com/jingry/autoBioSeqpy/tree/2.0/examples/EnsembleDL-ATG.

Abstract Synthetic nanoparticles with surface bioconjugation are promising platforms for targeted therapy, but their simple biological functionalization is still a challenging task against the complex intercellular environment. Once synthetic nanoparticles enter the body, they are phagocytosed by immune cells by the immune system. Recently, the cell membrane camouflage strategy has emerged as a novel therapeutic tactic to overcome these issues by utilizing the fundamental properties of natural cells. Macrophage, a type of immune system cells, plays critical roles in various diseases, including cancer, atherosclerosis, rheumatoid arthritis, infection and inflammation, due to the recognition and engulfment function of removing substances and pathogens. Macrophage membranes inherit the surface protein profiles and biointerfacing properties of source cells. Therefore, the macrophage membrane cloaking can protect synthetic nanoparticles from phagocytosis by the immune cells. Meanwhile, the macrophage membrane can make use of the natural correspondence to accurately recognize antigens and target inflamed tissue or tumor sites. In this review, we have summarized the advances in the fabrication, characterization and homing capacity of macrophage membrane cloaking nanoparticles in various diseases, including cancers, immune diseases, cardiovascular diseases, central nervous system diseases, and microbial infections. Although macrophage membrane-camouflaged nanoparticles are currently in the fetal stage of development, there is huge potential and challenge to explore the conversion mode in the clinic.

Introduction: The top priority in drug development is to identify novel and effective drug targets. In vitro assays are frequently used for this purpose; however, traditional experimental approaches are insufficient for large-scale exploration of novel drug targets, as they are expensive, time-consuming and laborious. Therefore, computational methods have emerged in recent decades as an alternative to aid experimental drug discovery studies by developing sophisticated predictive models to estimate unknown drugs/compounds and their targets. The recent success of deep learning (DL) techniques in machine learning and artificial intelligence has further attracted a great deal of attention in the biomedicine field, including computational drug discovery. Objectives: This study focuses on the practical applications of deep learning algorithms for predicting druggable proteins and proposes a powerful predictor for fast and accurate identification of potential drug targets. Methods: Using a gold-standard dataset, we explored several typical protein features and different deep learning algorithms and evaluated their performance in a comprehensive way. We provide an overview of the entire experimental process, including protein features and descriptors, neural network architectures, libraries and toolkits for deep learning modelling, performance evaluation metrics, model interpretation and visualization. Results: Experimental results show that the hybrid model (architecture: CNN-RNN (BiLSTM) + DNN; feature: dictionary encoding + DC_TC_CTD) performed better than the other models on the benchmark dataset. This hybrid model was able to achieve 90.0% accuracy and 0.800 MCC on the test dataset and 84.8% and 0.703 on a nonredundant independent test dataset, which is comparable to those of existing methods. Conclusion: We developed the first deep learning-based classifier for fast and accurate identification of potential druggable proteins. We hope that this study will be helpful for future researchers who would like to use deep learning techniques to develop relevant predictive models.

Abstract Background Neuroblastoma (NBL) is the most common extra-cranial solid tumour in childhood, with prognosis ranging from spontaneous remission to high risk for rapid and fatal progression. Despite existing therapy approaches, the 5-year event-free survival (EFS) for patients with advanced NBL remains below 30%, emphasizing urgent necessary for novel therapeutic strategies. Studies have shown that epigenetic disorders play an essential role in the pathogenesis of NBL. However, the function and mechanism of N7-methylguanosine (m7G) methyltransferase in NBL remains unknown. Methods The expression levels of m7G tRNA methyltransferase Methyltransferase-like 1 (METTL1) were analyzed by querying the Gene Expression Omnibus (GEO) database and further confirmed by immunohistochemistry (IHC) assay. Kaplan-Meier, univariate and multivariate cox hazard analysis were performed to reveal the prognostic role of METTL1. Cell function assays were performed to evaluate how METTL1 works in proliferation, apoptosis and migration in cell lines and xenograft mouse models. The role of METTL1 on mRNA translation activity of NBL cells was measured using puromycin intake assay and polysome profiling assay. The m7G modified tRNAs were identified by tRNA reduction and cleavage sequencing (TRAC-seq). Ribosome nascent-chain complex-bound mRNA sequencing (RNC-seq) was utilized to identify the variation of gene translation efficiency (TE). Analyzed the codon frequency decoded by m7G tRNA to clarify the translation regulation and mechanism of m7G modification in NBL. Results This study found that METTL1 were significantly up-regulated in advanced NBL, which acted as an independent risk factor and predicted poor prognosis. Further in NBL cell lines and BALB/c-nu female mice, we found METTL1 played a crucial role in promoting NBL progression. Furthermore, m7G profiling and translation analysis revealed downregulation of METTL1 would inhibit puromycin intake efficiency of NBL cells, indicating that METTL1 did count crucially in regulation of NBL cell translation. With all tRNAs with m7G modification identified in NBL cells, knockdown of METTL1 would significantly reduce the levels of both m7G modification and m7G tRNAs expressions. Result of RNC-seq shew there were 339 overlapped genes with impaired translation in NBL cells upon METTL1 knockdown. Further analysis revealed these genes contained higher frequency of codons decoded by m7G-modified tRNAs and were enriched in oncogenic pathways. Conclusion This study revealed the critical role and mechanism of METTL1-mediated tRNA m7G modification in regulating NBL progression, providing new insights for developing therapeutic approaches for NBL patients.

Post-transcriptionally RNA modifications, also known as the epitranscriptome, play crucial roles in the regulation of gene expression during development. Recently, deep learning (DL) has been employed for RNA modification site prediction and has shown promising results. However, due to the lack of relevant studies, it is unclear which DL architecture is best suited for some pyrimidine modifications, such as 5-methyluridine (m5U). To fill this knowledge gap, we first performed a comparative evaluation of various commonly used DL models for epigenetic studies with the help of autoBioSeqpy. We identified optimal architectural variations for m5U site classification, optimizing the layer depth and neuron width. Second, we used this knowledge to develop Deepm5U, an improved convolutional-recurrent neural network that accurately predicts m5U sites from RNA sequences. We successfully applied Deepm5U to transcriptomewide m5U profiling data across different sequencing technologies and cell types. Third, we showed that the techniques for interpreting deep neural networks, including LayerUMAP and DeepSHAP, can provide important insights into the internal operation and behavior of models. Overall, we offered practical guidance for the development, benchmark, and analysis of deep learning models when designing new algorithms for RNA modifications.

Interleukins, a diverse family of cytokines produced by various cells, play crucial roles in immune responses, immunoregulation, and a wide range of physiological and pathological processes. In the context of megakaryopoiesis, thrombopoiesis, and platelet function, interleukins have emerged as key regulators, exerting significant influence on the development, maturation, and activity of megakaryocytes (MKs) and platelets. While the therapeutic potential of interleukins in platelet-related diseases has been recognized for decades, their clinical application has been hindered by limitations in basic research and challenges in drug development. Recent advancements in understanding the molecular mechanisms of interleukins and their interactions with MKs and platelets, coupled with breakthroughs in cytokine engineering, have revitalized the field of interleukin-based therapeutics. These breakthroughs have paved the way for the development of more effective and specific interleukin-based therapies for the treatment of platelet disorders. This review provides a comprehensive overview of the effects of interleukins on megakaryopoiesis, thrombopoiesis, and platelet function. It highlights the potential clinical applications of interleukins in regulating megakaryopoiesis and platelet function and discusses the latest bioengineering technologies that could improve the pharmacokinetic properties of interleukins. By synthesizing the current knowledge in this field, this review aims to provide valuable insights for future research into the clinical application of interleukins in platelet-related diseases.

Thrombocytopenia is a thrombopoietin (TPO)-related disorder with very limited treatment options, and can be lifethreatening. There are major problems with typical thrombopoietic agents targeting TPO signaling, so it is urgent to discover a novel TPO-independent mechanism involving thrombopoiesis and potential druggable targets. We developed a drug screening model by the multi-grained cascade forest (gcForest) algorithm and found that 3,8-di-O-methylellagic acid 2- O-glucoside (DMAG) (10, 20 and 40 μM) promoted megakaryocyte differentiation in vitro. Subsequent investigations revealed that DMAG (40 mM) activated ERK1/2, HIF-1b and NF-E2. Inhibition of ERK1/2 blocked megakaryocyte differentiation and attenuated the upregulation of HIF-1b and NF-E2 induced by DMAG. Megakaryocyte differentiation induced by DMAG was inhibited via knockdown of NF-E2. In vivo studies showed that DMAG (5 mg/kg) accelerated platelet recovery and megakaryocyte differentiation in mice with thrombocytopenia. The platelet count of the DMAG-treated group recovered to almost 72% and 96% of the count in the control group at day 10 and 14, respectively. The platelet counts in the DMAG-treated group were almost 1.5- and 1.3-fold higher compared with those of the irradiated group at day 10 and 14, respectively. Moreover, DMAG (10, 25 and 50 mM) stimulated thrombopoiesis in zebrafish. DMAG (5 mg/kg) could also increase platelet levels in c-MPL knockout (c-MPL-/-) mice. In summary, we established a drug screening model through gcForest and demonstrated that DMAG promotes megakaryocyte differentiation via the ERK/HIF1/NF-E2 pathway which, importantly, is independent of the classical TPO/c-MPL pathway. The present study may provide new insights into drug discovery for thrombopoiesis and TPO-independent regulation of thrombopoiesis, as well as a promising avenue for thrombocytopenia treatment.

Summary: Despite the impressive success of deep learning techniques in various types of classification and prediction tasks, interpreting these models and explaining their predictions are still major challenges. In this article, we present an easy-to-use command line tool capable of visualizing and analyzing alternative representations of biological observations learned by deep learning models. This new tool, namely, layerUMAP, integrates autoBioSeqpy software and the UMAP library to address learned high-level representations. An important advantage of the tool is that it provides an interactive option that enables users to visualize the outputs of hidden layers along the depth of the model. We use two different classes of examples to illustrate the potential power of layerUMAP, and the results demonstrate that layerUMAP can provide insightful visual feedback about models and further guide us to develop better models.

Cancer is one of the most dangerous diseases to human health. The accurate prediction of anticancer peptides (ACPs) would be valuable for the development and design of novel anticancer agents. Current deep neural network models have obtained state-of-the-art prediction accuracy for the ACP classification task. However, based on existing studies, it remains unclear which deep learning architecture achieves the best performance. Thus, in this study, we first present a systematic exploration of three important deep learning architectures: convolutional, recurrent, and convolutional-recurrent networks for distinguishing ACPs from non-ACPs. We find that the recurrent neural network with bidirectional long short-term memory cells is superior to other architectures. By utilizing the proposed model, we implement a sequence-based deep learning tool (DeepACP) to accurately predict the likelihood of a peptide exhibiting anticancer activity. The results indicate that DeepACP outperforms several existing methods and can be used as an effective tool for the prediction of anticancer peptides. Furthermore, we visualize and understand the deep learning model. We hope that our strategy can be extended to identify other types of peptides and may provide more assistance to the development of proteomics and new drugs.

DNA N4-methylcytosine (4mC) is a pivotal epigenetic modification that plays an essential role in DNA replication, repair, expression and differentiation. To gain insight into the biological functions of 4mC, it is critical to identify their modification sites in the genomics. Recently, deep learning has become increasingly popular in recent years and frequently employed for the 4mC site identification. However, a systematic analysis of how to build predictive models using deep learning techniques is still lacking. In this work, we first summarized all existing deep learning-based predictors and systematically analyzed their models, features and datasets, etc. Then, using a typical standard dataset with three species (A. thaliana, C. elegans, and D. melanogaster), we assessed the contribution of different model architectures, encoding methods and the attention mechanism in establishing a deep learning-based model for the 4mC site prediction. After a series of optimizations, convolutional-recurrent neural network architecture using the one-hot encoding and attention mechanism achieved the best overall prediction performance. Extensive comparison experiments were conducted based on the same dataset. This work will be helpful for researchers who would like to build the 4mC prediction models using deep learning in the future.

Geophysical excitations of length-of-day (LOD) variations is of great significance in understanding changes in the Earth's spin rate and interactions between geophysical fluids and the solid Earth, as well as validating the reliability of atmospheric, oceanic and hydrological models. In this study seasonal excitations of LOD variation during 06.30,1987–06.30,2017 are investigated using both harmonic and inharmonic analyses. We examined the agreements between the IERS EOP 14 C04 ΔLOD series and that from the previous version EOP08C04, and analyzed contributions of atmospheric, oceanic, hydrological and sea level angular momenta to seasonal excitations of LOD variations on the bases of the ESMGFZ products. We found that the sea level angular momentum plays an important role in global mass conservation and can bring better agreements between the geophysical and geodetic excitations. Keywords: LOD variation, Geophysical excitations, Sea level angular momentum, Wavelet analysis

Platelets are the second most abundant blood component after red blood cells and can participate in a variety of physiological and pathological functions. Beyond its traditional role in hemostasis and thrombosis, it also plays an indispensable role in inflammatory diseases. However, thrombocytopenia is a common hematologic problem in the clinic, and it presents a proportional relationship with the fatality of many diseases. Therefore, the prevention and treatment of thrombocytopenia is of great importance. The expression of Toll-like receptors (TLRs) is one of the most relevant characteristics of thrombopoiesis and the platelet inflammatory function. We know that the TLR family is found on the surface or inside almost all cells, where they perform many immune functions. Of those, TLR2 and TLR4 are the main stress-inducing members and play an integral role in inflammatory diseases and platelet production and function. Therefore, the aim of this review is to present and discuss the relationship between platelets, inflammation and the TLR family and extend recent research on the influence of the TLR2 and TLR4 pathways and the regulation of platelet production and function. Reviewing the interaction between TLRs and platelets in inflammation may be a research direction or program for the treatment of thrombocytopenia-related and inflammatory-related diseases.

Abstract Background CRISPR-Cpf1 has recently been reported as another RNA-guided endonuclease of class 2 CRISPR-Cas system, which expands the molecular biology toolkit for genome editing. However, most of the online tools and applications to date have been developed primarily for the Cas9. There are a limited number of tools available for the Cpf1. Results We present DeepCpf1, a deep convolution neural networks (CNN) approach to predict Cpf1 guide RNAs on-target activity and off-target effects using their matched and mismatched DNA sequences. Trained on published data sets, DeepCpf1 is superior to other machine learning algorithms and reliably predicts the most efficient and less off-target effects guide RNAs for a given gene. Combined with a permutation importance analysis, the key features of guide RNA sequences are identified, which determine the activity and specificity of genome editing. Conclusions DeepCpf1 can significantly improve the accuracy of Cpf1-based genome editing and facilitates the generation of optimized guide RNAs libraries.

Abstract Wound healing is the physiologic response to a disruption in normal skin architecture and requires both spatial and temporal coordination of multiple cell types and cytokines. This complex process is prone to dysregulation secondary to local and systemic factors such as ischemia and diabetes that frequently lead to chronic wounds. Chronic wounds such as diabetic foot ulcers are epidemic with great cost to the healthcare system as they heal poorly and recur frequently, creating an urgent need for new and advanced therapies. Stem cell therapy is emerging as a potential treatment for chronic wounds, and adult-derived stem cells are currently employed in several commercially available products; however, stem cell therapy is limited by the need for invasive harvesting techniques, immunogenicity, and limited cell survival in vivo. Induced pluripotent stem cells (iPSC) are an exciting cell type with enhanced therapeutic and translational potential. iPSC are derived from adult cells by in vitro induction of pluripotency, obviating the ethical dilemmas surrounding the use of embryonic stem cells; they are harvested non-invasively and can be transplanted autologously, reducing immune rejection; and iPSC are the only cell type capable of being differentiated into all of the cell types in healthy skin. This review focuses on the use of iPSC in animal models of wound healing including limb ischemia, as well as their limitations and methods aimed at improving iPSC safety profile in an effort to hasten translation to human studies.

When recognizing conditions such as mechanical faults, the operating data samples each have a label indicating the sample type, whether normal or a known fault category. However, the class label set of the test data is the same as that in the training set, which means that samples having an unexpected or unknown type are simply incorporated into existing classes rather than being assigned a new label for inclusion in another class. To address this problem, we propose a method called the emerging new labels using a sparse auto-encoder (SAE) method. After extracting features using the traditional SAE, it constructs a distribution from the training sample and identifies those samples that do not fit into known categories. The latter receive new labels and are used for retraining the model, enabling it to identify the new condition. The simulation and gearbox fault diagnosis experiments confirm that our proposed diagnostic model effectively identifies new fault types in samples, improving the diagnostic model’s general application.

Due to the conservation of global angular momentum, polar motion (PM) is dominated by global mass redistributions and relative motions in the atmosphere, oceans and land water at seasonal time scales. Thus, accurately measured PM data can be used to validate the general circulation models (GCMs) for the atmosphere, oceans and land water. This study aims to analyze geophysical excitations and observed excitations obtained from PM observations from both the harmonic and wavelet analysis perspectives, in order to refine our understanding of the geophysical excitation of PM. The geophysical excitations are derived from two sets of GCMs and a monthly gravity model combining satellite gravity data and some GCM outputs using the PM theory for an Earth model with frequency-dependent responses, while the observed excitation is obtained from the PM data using the frequency-domain Liouville’s equation. Our results show that wavelet analysis can reveal the time-varying nature of all excitations and identify when changes happen and how strong they are, while harmonic analysis can only show the average amplitudes and phases. In particular, the monthly gravity model can correct the mismodeled GCM outputs, while the Earth’s frequency-dependent responses provide us with a better understanding of atmosphere–ocean–land water–solid Earth interactions.

Gram-negative bacteria can deliver secreted proteins (also known as secreted effectors) directly into host cells through type III secretion system (T3SS), type IV secretion system (T4SS), and type VI secretion system (T6SS) and cause various diseases. These secreted effectors are heavily involved in the interactions between bacteria and host cells, so their identification is crucial for the discovery and development of novel anti-bacterial drugs. It is currently challenging to accurately distinguish type III secreted effectors (T3SEs) and type IV secreted effectors (T4SEs) because neither T3SEs nor T4SEs contain N-terminal signal peptides, and some of these effectors have similar evolutionary conserved profiles and sequence motifs. To address this challenge, we develop a deep learning (DL) approach called DeepT3_4 to correctly classify T3SEs and T4SEs. We generate amino-acid character dictionary and sequence-based features extracted from effector proteins and subsequently implement these features into a hybrid model that integrates recurrent neural networks (RNNs) and deep neural networks (DNNs). After training the model, the hybrid neural network classifies secreted effectors into two different classes with an accuracy, F-value, and recall of over 80.0%. Our approach stands for the first DL approach for the classification of T3SEs and T4SEs, providing a promising supplementary tool for further secretome studies.

Time-dependent loading deformations of the Earth’s surface, due to nontidal changes in the atmosphere, ocean, land water/ice, etc., contribute significantly to the seasonal and secular Global Positioning System (GPS) site displacements, especially for the up component. While loading deformations derived from general circulation model (GCM) outputs are usually used to correct loading signals in the GPS site displacements, this study aims to provide a loading correction model based on the multiple-data combined monthly gravity products LDCmgm90. We have adopted GPS measurements from 249 IGS reference frame stations and 3 different GCM-based loading models to test the reliability of the LDCmgm90 model. Compared to the GCM-based models, the LDCmgm90 loading correction is more effective in attenuating seasonal (especially annual) loading signals and can bring more significant improvements to most stations for both the data-trend-removed and the data-trend-retained cases. Thus, we have validated the LDCmgm90 model from the loading aspect and proved it to be a reliable loading-correction model for GPS displacements. The relatively better secular loading signals provided by the LDCmgm90 loading model may provide us a chance to study the long-term, nonloading signals in GPS data.

Abstract RNA and protein interactions play crucial roles in multiple biological processes, while these interactions are significantly influenced by the structures and sequences of protein and RNA molecules. In this study, we first performed an analysis of RNA-protein interacting complexes, and identified interface properties of sequences and structures, which reveal the diverse nature of the binding sites. With the observations, we built a three-step prediction model, namely RPI-Bind, for the identification of RNA-protein binding regions using the sequences and structures of both proteins and RNAs. The three steps include 1) the prediction of RNA binding regions on protein, 2) the prediction of protein binding regions on RNA, and 3) the prediction of interacting regions on both RNA and protein simultaneously, with the results from steps 1) and 2). Compared with existing methods, most of which employ only sequences, our model significantly improves the prediction accuracy at each of the three steps. Especially, our model outperforms the catRAPID by >20% at the 3rd step. All of these results indicate the importance of structures in RNA-protein interactions, and suggest that the RPI-Bind model is a powerful theoretical framework for studying RNA-protein interactions.

Induced pluripotent stem cells (iPSC) represent an innovative, somatic cell-derived, easily obtained and renewable stem cell source without considerable ethical issues. iPSC and their derived cells may have enhanced therapeutic and translational potential compared with other stem cells. We previously showed that human iPSC-derived smooth muscle cells (hiPSC-SMC) promote angiogenesis and wound healing. Accordingly, we hypothesized that hiPSC-SMC may be a novel treatment for human patients with chronic limb-threatening ischemia who have no standard options for therapy. We determined the angiogenic potential of hiPSC-SMC in a murine hindlimb ischemia model. hiPSC-SMC were injected intramuscularly into nude mice after creation of hindlimb ischemia. Functional outcomes and perfusion were measured using standardized scores, laser Doppler imaging, microCT, histology and immunofluorescence. Functional outcomes and blood flow were improved in hiPSC-SMC-treated mice compared with controls (Tarlov score, p < 0.05; Faber score, p < 0.05; flow, p = 0.054). hiPSC-SMC-treated mice showed fewer gastrocnemius fibers (p < 0.0001), increased fiber area (p < 0.0001), and enhanced capillary density (p < 0.01); microCT showed more arterioles (p < 0.05) and increased numbers of M2-type macrophages (p < 0.0001). Vascular endothelial growth factor (VEGF) expression in ischemic limbs was significantly elevated with hiPSC-SMC treatment (p < 0.05), and inhibition of VEGFR-2 with SU5416 was associated with fewer capillaries in hiPSC-SMC-treated limbs (p < 0.0001). hiPSC-SMC promote VEGF-mediated angiogenesis, leading to improved hindlimb ischemia. Stem cell therapy using iPSC-derived cells may represent a novel and potentially translatable therapy for limb-threatening ischemia.

There is an urgent need for an efficient approach to obtain a large-scale and renewable source of functional human vascular smooth muscle cells (VSMCs) to establish robust, patient-specific tissue model systems for studying the pathogenesis of vascular disease, and for developing novel therapeutic interventions. Here, we have derived a large quantity of highly enriched functional VSMCs from human induced pluripotent stem cells (hiPSC-VSMCs). Furthermore, we have engineered 3D tissue rings from hiPSC-VSMCs using a facile one-step cellular self-assembly approach. The tissue rings are mechanically robust and can be used for vascular tissue engineering and disease modeling of supravalvular aortic stenosis syndrome. Our method may serve as a model system, extendable to study other vascular proliferative diseases for drug screening. Thus, this report describes an exciting platform technology with broad utility for manufacturing cell-based tissues and materials for various biomedical applications.

In addition to studies of sea level change and mantle rheology, reliable Glacial Isostatic Adjustment (GIA) models are necessary as a background model to correct the widely used Gravity Recovery and Climate Experiment (GRACE) monthly gravity solutions to determine subsecular, nonviscous variations. Based on spherical harmonic analyses, we developed a method using degree-dependent weighting to assimilate the Global Positioning System (GPS) derived crustal uplift rates into GIA model predictions, in which the good global pattern of GIA model predictions and better local resolution of GPS solutions are both retained. Some systematic errors in global GPS uplift rates were also corrected during the spherical harmonic analyses. Further, we used the refined GIA uplift rates to infer the GIA-induced rates of Stokes coefficients (complete to degree/order 120) relying on the accurate relationship between GIA vertical surface deformation and gravitational potential changes. The results show notable improvements relative to GIA model outputs, and may serve as a GIA-correction model for GRACE time-variable gravity data.

Summary: Background: Evidence from cohort studies in North America and Europe indicates that long-term exposure to fine particulate matter (PM2·5) is associated with an increased mortality risk. However, this association has rarely been quantified at higher ambient concentrations. We estimated the hazard ratio (HR) for all-cause mortality from long-term exposure to PM2·5 in a well established Chinese cohort of older adults. Methods: The Chinese Longitudinal Healthy Longevity Survey (CLHLS) is a prospective cohort study of men and women aged 65 years and older enrolled in 2008 and followed up through 2014 for mortality events. We studied individuals for whom residential locations were available in 2008 for linkage to 1 km grids of PM2·5 concentrations, derived from satellite remote sensing. Cox proportional hazards models were used to estimate the effect of long-term exposure to PM2·5 on all-cause mortality, controlling for age, sex, smoking status, drinking status, physical activity, body-mass index, household income, marital status, and education. We then used our results to estimate premature mortality related to PM2·5 exposure in the population aged 65 years and older in China in 2010. Findings: 13 344 individuals in the CLHLS cohort had data for all timepoints, yielding follow-up data for 49 440 person-years. In a 3-year window, these individuals were exposed to a median PM2·5 concentration of 50·7 μg/m3 (range 6·7–113·3). The overall HR for a 10 μg/m3 increase in this value was 1·08 (95% CI 1·06–1·09). In stratified analyses, HRs were higher in rural than in urban locations, in southern versus northern regions, and with exposure to lower versus higher PM2·5 concentrations. Based on the overall HR, we estimated that 1 765 820 people aged 65 years and older in China in 2010 had premature mortality related to PM2·5 exposure. Interpretation: Long-term exposure to PM2·5 is associated with an increased risk of all-cause mortality among adults aged 65 years and older in China, but the magnitude of the risk declines as the concentration of PM2·5 increases. Funding: National Natural Science Foundation of China, National High-Level Talents Special Support Plan of China for Young Talents, US National Aeronautics and Space Administration, and the Columbia University Global Policy Initiative.

Objective To examine the extent to which effective interventions to prevent unintentional child injury are reflected in the laws and regulations of China. Methods We focused on the six common causes of fatal child injuries – drowning, road traffic injury, falls, poisoning, burns and suffocation. We investigated 27 interventions recommended by the United Nations Children’s Fund, the World Health Organization or the European Child Safety Alliance. We searched China National Knowledge Infrastructure and Lawyee for Chinese legislations using keywords and synonyms for the 27 interventions. We reviewed the identified legislations for statements specifying the responsible implementation department. Findings Seven national laws, nine regulations of the State Council and 46 departmental regulations were found to relate to at least one of the interventions. Although seven of the 27 internationally recommended interventions were covered by Chinese laws, 10 were not covered by any current Chinese law or regulation. None of the interventions against drowning and falls that we investigated was covered by national laws. The implementation responsibilities for effective interventions were either not specified or were assigned to multiple governmental departments in 11 or 20 legislative documents, respectively. Conclusion In Chinese laws and regulations, interventions proven to prevent major causes of unintentional child injuries are underrepresented and the associated implementation responsibilities are often poorly defined. China should include all such interventions in laws and regulations, and assign implementation responsibility for each to a single department of the national government.

The periodic impulse characteristics caused by rolling bearing damage are weak in the incipient failure stage. Thus, these characteristics are always drowned out by background noise and other harmonic interference. A novel approach based on multi-resolution singular value decomposition (MRSVD) is proposed in order to extract the periodic impulse characteristics for incipient fault detection. With the MRSVD method, the vibration signal is first decomposed to obtain a group of approximate signals and detailed signals with different resolutions. The first detail signal is mainly composed of noise and the last approximate signal is mainly composed of harmonic interference. Combined with the kurtosis index, the hidden periodic impulse signal will be extracted from the detail signals (in addition to the first detail signal). Thus, the incipient fault detection of a rolling bearing can be fulfilled according to the envelope demodulation spectrum of the extracted periodic impulse signal. The effectiveness of the proposed method has been demonstrated with both simulation and experimental analyses.

Order tracking has become one of the most effective methods for fault detection of rotating machinery under the time-varying shaft speed conditions. The transient phase estimation is very important for order tracking, especially when the tachometer installation is not convenient. The transient phase is usually obtained by integrating the instantaneous frequency (IF), so the IF estimation has attracted a great deal of concerns. This article describes a new IF estimation method based on the joint application of the synchrosqueezing transform (SST) and the multiscale chirplet path pursuit (MSCPP) method. The SST method as its high frequency resolution merits is used to estimate the frequency parameters for the parameter settings of the MSCPP method, that will resolve the high computation problem of the MSCPP method to a certain degree, so as to extensively use the high accuracy of the MSCPP method in IF estimation. The order spectrum based on the estimated IF can provide the demodulation information for the bearing fault diagnosis. The performance of the proposed method has been validated by both simulation and experimental data.

BackgroundType III secretion systems (T3SSs) are central to the pathogenesis and specifically deliver their secreted substrates (type III secreted proteins, T3SPs) into host cells. Since T3SPs play a crucial role in pathogen-host interactions, identifying them is crucial to our understanding of the pathogenic mechanisms of T3SSs. This study reports a novel and effective method for identifying the distinctive residues which are conserved different from other SPs for T3SPs prediction. Moreover, the importance of several sequence features was evaluated and further, a promising prediction model was constructed.ResultsBased on the conservation profiles constructed by a position-specific scoring matrix (PSSM), 52 distinctive residues were identified. To our knowledge, this is the first attempt to identify the distinct residues of T3SPs. Of the 52 distinct residues, the first 30 amino acid residues are all included, which is consistent with previous studies reporting that the secretion signal generally occurs within the first 30 residue positions. However, the remaining 22 positions span residues 30-100 were also proven by our method to contain important signal information for T3SP secretion because the translocation of many effectors also depends on the chaperone-binding residues that follow the secretion signal. For further feature optimisation and compression, permutation importance analysis was conducted to select 62 optimal sequence features. A prediction model across 16 species was developed using random forest to classify T3SPs and non-T3 SPs, with high receiver operating curve of 0.93 in the 10-fold cross validation and an accuracy of 94.29% for the test set. Moreover, when performing on a common independent dataset, the results demonstrate that our method outperforms all the others published to date. Finally, the novel, experimentally confirmed T3 effectors were used to further demonstrate the model's correct application. The model and all data used in this paper are freely available at http://cic.scu.edu.cn/bioinformatics/T3SPs.zip.