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High-strength titanium alloy TC21 is often used to make aircraft load-bearing components in the aviation field. However, due to vibration, load-bearing components often suffer from fretting fatigue (FF), which greatly reduces their actual service life. Nowadays, laser shock peening (LSP) is being recognized as an effective strengthening process for many materials. Therefore, in this study, the effectiveness of LSP on the strengthening of high-strength titanium alloy TC21 is studied, in terms of surface integrity and FF properties. TC21 fretting specimens and tests are designed. Before fretting tests, the specimens are processed by LSP with varying laser pulse energies, number of shocks and overlap rate, and their surface integrity and FF properties are analyzed. It is found that the greater the laser pulse energy and number of shocks, the higher the surface hardness and compressive residual stress, the more the grain refinement, the greater the improvement in FF life. Under 32J-50%-T1, the FF life is improved by 191.3% compared with BM. But the overlap rate has little effect on surface integrity and FF life. Meanwhile, there forms amorphous structures, which tended to be transformed into nanograins during fretting process. This transformation is helpful for prolonging FF life due to its action of energy absorption. This study reveals the strengthening mechanism of LSP on TC21 and its effect on the FF properties of TC21 specimens.

5-formylcytidine (f5C) is a unique post-transcriptional RNA modification found in mRNA and tRNA at the wobble site, playing a crucial role in mitochondrial protein synthesis and potentially contributing to the regulation of translation. Recent studies have unveiled that the f5C modifications may drive mitochondrial mRNA translation to power cancer metastasis. Accurate identification of f5C sites is essential for further unraveling their molecular functions and regulatory mechanisms, but there are currently no computational methods available for predicting their locations. In this study, we introduce an innovative ensemble approach, successfully enabling the computational recognition of Saccharomyces cerevisiae f5C. We conducted a comprehensive model selection process that involved multiple basic machine learning and deep learning algorithms such as recurrent neural networks, convolutional neural networks and Transformer-based models. Initially trained only on sequence information, these individual models achieved an AUROC ranging from 0.7104 to 0.7492. Through the integration of 32 novel domain-derived genomic features, the performance of individual models has significantly improved to an AUROC between 0.7309 and 0.8076. To further enhance accuracy and robustness, we then constructed the ensembles of these individual models with different combinations. The best performance attained by our ensemble models reached an AUROC of 0.8391. Shapley additive explanations were conducted to explain the significant contributions of genomic features, providing insights into the putative distribution of f5C across various topological regions and potentially paving the way for revealing their functional relevance within distinct genomic contexts. A freely accessible web server that allows real-time analysis of user-uploaded sites can be accessed at: www.rnamd.org/Resf5C-Pred.

BackgroundMany clinics and health systems implemented telemedicine appointment services out of necessity due to the COVID-19 pandemic. ObjectiveOur objective was to evaluate patient and general provider satisfaction with neurology telemedicine implementation at an urban academic medical center. MethodsPatients who had completed 1 or more teleneurology visits from April 1 to December 31, 2020, were asked to complete a survey regarding their demographic information and satisfaction with teleneurology visits. Providers of all specialties within the same hospital system were given a different survey to gather their experiences of providing telemedicine care. ResultsOf the estimated 1500 patients who had completed a teleneurology visit within the given timeframe, 117 (7.8%) consented to complete the survey. Of these 117 respondents, most appointments were regarding epilepsy (n=59, 50.4%), followed by multiple sclerosis (n=33, 28.2%) and neuroimmunology (n=7, 6%). Overall, 74.4% (n=87) of patients rated their experience as 8 out of 10 or higher, with 10 being the highest satisfaction. Furthermore, 75.2% (n=88) of patients reported missing an appointment in the previous year due to transportation issues and thought telemedicine was more convenient instead. A significant relationship between racial or ethnic group and comfort sharing private information was found (P

Abstract Background N6-methyladenosine (m6A) is the most prevalent post-transcriptional modification in eukaryotic cells that plays a crucial role in regulating various biological processes, and dysregulation of m6A status is involved in multiple human diseases including cancer contexts. A number of prediction frameworks have been proposed for high-accuracy identification of putative m6A sites, however, none have targeted for direct prediction of tissue-conserved m6A modified residues from non-conserved ones at base-resolution level. Results We report here m6A-TCPred, a computational tool for predicting tissue-conserved m6A residues using m6A profiling data from 23 human tissues. By taking advantage of the traditional sequence-based characteristics and additional genome-derived information, m6A-TCPred successfully captured distinct patterns between potentially tissue-conserved m6A modifications and non-conserved ones, with an average AUROC of 0.871 and 0.879 tested on cross-validation and independent datasets, respectively. Conclusion Our results have been integrated into an online platform: a database holding 268,115 high confidence m6A sites with their conserved information across 23 human tissues; and a web server to predict the conserved status of user-provided m6A collections. The web interface of m6A-TCPred is freely accessible at: www.rnamd.org/m6ATCPred .

Cancer is a complex and multifaceted group of diseases characterized by uncontrolled cell growth that leads to the formation of malignant tumors. Recent studies suggest that N6-methyladenosine (m6A) RNA methylation plays pivotal roles in cancer pathology by influencing various cellular processes. However, the degree to which these mechanisms are shared across different cancer types remains unclear. In this study, we analyze an expansive array of 167 m6A epitranscriptome profiles covering 12 distinct cancer types and their originating normal tissues. We trained 12 distinct, cancer type-specific interpretable deep cross network models, which successfully distinguish between specific pairs of normal and cancer m6A contexts using integrated information from both the sequences and curated genomic knowledge. Interestingly, cross-cancer type testing indicated the existence of shared genomic patterns across various cancers at the epitranscriptome level. A pan-cancer model was subsequently developed to identify these shared patterns that could not be observed in a single cancer type. Our analysis uncovered, for the first time, a common epitranscriptome signature shared across multiple cancer types, particularly associated with RNA hybridization process and aberrant splicing. This highlights the importance of a comprehensive understanding of the pan-cancer epitranscriptome and holding potential implications in the development of RNA methylation-based therapeutics for various cancers.

As the most pervasive epigenetic marker present on mRNAs and long non-coding RNAs (lncRNAs), N6-methyladenosine (m6A) RNA methylation has been shown to participate in essential biological processes. Recent studies have revealed the distinct patterns of m6A methylome across human tissues, and a major challenge remains in elucidating the tissue-specific presence and circuitry of m6A methylation. We present here a comprehensive online platform, m6A-TSHub, for unveiling the context-specific m6A methylation and genetic mutations that potentially regulate m6A epigenetic mark. m6A-TSHub consists of four core components, including (1) m6A-TSDB, a comprehensive database of 184,554 functionally annotated m6A sites derived from 23 human tissues and 499,369 m6A sites from 25 tumor conditions, respectively; (2) m6A-TSFinder, a web server for high-accuracy prediction of m6A methylation sites within a specific tissue from RNA sequences, which was constructed using multi-instance deep neural networks with gated attention; (3) m6A-TSVar, a web server for assessing the impact of genetic variants on tissue-specific m6A RNA modifications; and (4) m6A-CAVar, a database of 587,983 The Cancer Genome Atlas (TCGA) cancer mutations (derived from 27 cancer types) that were predicted to affect m6A modifications in the primary tissue of cancers. The database should make a useful resource for studying the m6A methylome and the genetic factors of epitranscriptome disturbance in a specific tissue (or cancer type). m6A-TSHub is accessible at www.xjtlu.edu.cn/biologicalsciences/m6ats.

Three-dimensional (3D) photoacoustic imaging (PAI) can provide rich information content and has gained increasingly more attention in various biomedical applications. However, current 3D PAI methods either involves pointwise scanning of the 3D volume using a single-element transducer, which can be time-consuming, or requires an array of transducers, which is known to be complex and expensive. By utilizing a 3D encoder and compressed sensing techniques, we develop a new imaging modality that is capable of single-shot 3D PAI using a single-element transducer. The proposed method is validated with phantom study, which demonstrates single-shot 3D imaging of different objects and 3D tracking of a moving object. After one-time calibration, while the system could perform single-shot 3D imaging for different objects, the calibration could remain effective over 7 days, which is highly beneficial for practical translation. Overall, the experimental results showcase the potential of this technique for both scientific research and clinical applications.

5-Methyluridine (m5U) is one of the most common post-transcriptional RNA modifications, which is involved in a variety of important biological processes and disease development. The precise identification of the m5U sites allows for a better understanding of the biological processes of RNA and contributes to the discovery of new RNA functional and therapeutic targets. Here, we present m5U-GEPred, a prediction framework, to combine sequence characteristics and graph embedding-based information for m5U identification. The graph embedding approach was introduced to extract the global information of training data that complemented the local information represented by conventional sequence features, thereby enhancing the prediction performance of m5U identification. m5U-GEPred outperformed the state-of-the-art m5U predictors built on two independent species, with an average AUROC of 0.984 and 0.985 tested on human and yeast transcriptomes, respectively. To further validate the performance of our newly proposed framework, the experimentally validated m5U sites identified from Oxford Nanopore Technology (ONT) were collected as independent testing data, and in this project, m5U-GEPred achieved reasonable prediction performance with ACC of 91.84%. We hope that m5U-GEPred should make a useful computational alternative for m5U identification.

Aiming at the problem that the prediction performance of remote operation state of hydro-turbine needs to be improved, this paper uses whale optimization algorithm (WOA) to optimize random forest (RF) . It combines it with Adaboost algorithm to propose the prediction model in this paper. Firstly, the signal of hydro-turbine is analyzed by variational modal decomposition (VMD), and the penalty factor and the number of IMF components K of VMD are optimized by fruit fly optimization algorithm (FOA). The arrangement entropy of intrinsic mode function (IMF) components, kurtosis, mean value of original signal are calculated, and the input eigenvector of hydro-turbine state prediction model is constructed; Secondly, the number of split attribute sets and the optimal number of decision trees of RF are optimized by WOA, and multiple WOA-RF models are iteratively trained by Adaboost algorithm to construct WOA-RF-Adaboost state prediction model. The prediction effect of the proposed model and the traditional model is evaluated by correct rate and confusion matrix. The results show that the prediction accuracy of WOA-RF-Adaboost model proposed in this paper is 99.2%, it has good state prediction performance.

Vibration-assisted micro milling is a promising technique for fabricating engineered mi-cro-scaled surface textures. This paper presents a novel approach for theoretical modeling of three-dimensional (3D) surface textures produced by vibration-assisted micro milling. The proposed model considers the effects of tool edge geometry, minimum uncut chip thickness (MUCT), and material elastic recovery. The surface texture formation under different machining parameters is simulated and analyzed through mathematical modeling. Two typical surface morphologies can be generated: wave-type and fish scale-type textures, depending on the phase difference between tool paths. A 2-degrees-of-freedom (2-DOF) vibration stage is also developed to provide vibration along the feed and cross-feed directions during micro-milling process. Micro-milling experiments on copper were carried out to verify the ability to fabricate controlled surface textures using the vibration stage. The simulated and experimentally generated surfaces show good agreement in geometry and dimensions. This work provides an accurate analytical model for vibration-assisted micro-milling surface generation and demonstrates its feasibility for efficient, flexible texturing.

Interferon (IFN) is a cell-secreted cytokine possessing biological activities including antiviral functioning, immune regulation, and others. Interferon-alpha (IFN-α) mainly derives from plasmacytoid dendritic cells, which activate natural killer cells and regulate immune responses. IFN-α responds to the primary antiviral mechanism in the innate immune system, which can effectively cure acute infectious diseases. Pseudorabies (PR) is an acute infectious disease caused by pseudorabies virus (PRV). The clinical symptoms of PRV are as follows: reproductive dysfunction among pregnant sows and high mortality rates among piglets. These pose a severe threat to the swine industry. Related studies show that IFN-α has broad applications in preventing and treating viral diseases. Therefore, a PRV mouse model using artificial infection was established in this study to explore the pathogenic effect of IFN-α on PRV. We designed a sequence with IFN-α4 (M28623, Genbank) and cloned it on the lentiviral vector. CHO-K1 cells were infected and identified using WB and RT-PCR; a CHO-K1 cell line with a stable expression of the recombinant protein PoIFN-α was successfully constructed. H&E staining and virus titer detection were used to investigate the recombinant protein PoIFN-α’s effect on PR in BALB/c mice. The results show that the PoIFN-α has a preventive and therapeutic impact on PR. In conclusion, the recombinant protein can alleviate symptoms and reduce the replication of PRV in vivo.

Abstract An optical rotation bio-sensor based on the photonic spin Hall effect was established and applied to detecting the concentration varieties of chiral molecules. The optical rotation, introduced by sample solutions, was exploited to modulate the postselected polarization of a weak measurement system. Much work has been done in the case of glucose and fructose. However, little attention has been paid for biomolecules, such as proteins and amino acids. With this modulation, the optical rotation can be determined through the direction and spin accumulation of light spots, thus mirroring the concentration of solutions. A resolution of 2×10−4 degree was achieved.

Abstract The ability to quantify optical properties (i.e., absorption and scattering) of strongly turbid media has major implications on the characterization of biological tissues, fluid fields, and many others. However, there are few methods that can provide wide-field quantification of optical properties, and none is able to perform quantitative optical property imaging with high-speed (e.g., kilohertz) capabilities. Here we develop a new imaging modality termed halftone spatial frequency domain imaging (halftone-SFDI), which is approximately two orders of magnitude faster than the state-of-the-art, and provides kilohertz high-speed, label-free, non-contact, wide-field quantification for the optical properties of strongly turbid media. This method utilizes halftone binary patterned illumination to target the spatial frequency response of turbid media, which is then mapped to optical properties using model-based analysis. We validate the halftone-SFDI on an array of phantoms with a wide range of optical properties as well as in vivo human tissue. We demonstrate with an in vivo rat brain cortex imaging study, and show that halftone-SFDI can longitudinally monitor the absolute concentration as well as spatial distribution of functional chromophores in tissue. We also show that halftone-SFDI can spatially map dual-wavelength optical properties of a highly dynamic flow field at kilohertz speed. Together, these results highlight the potential of halftone-SFDI to enable new capabilities in fundamental research and translational studies including brain science and fluid dynamics.

Measurement(s) leaf area index Technology Type(s) destructive sampling method Sample Characteristic - Environment area of cropland Sample Characteristic - Location China Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.15124524

Epidermal electronics, an emerging interdisciplinary field, is advancing the development of flexible devices that can seamlessly integrate with the skin. These devices, especially Electric Double Layer (EDL)-based sensors, overcome the limitations of conventional electronic devices, offering high sensitivity, rapid response, and excellent stability. Especially, Electric Double Layer (EDL)-based epidermal sensors show great potential in the application of wearable electronics to detect biological signals due to their high sensitivity, fast response, and excellent stability. The advantages can be attributed to the biocompatibility of the materials, the flexibility of the devices, and the large capacitance due to the EDL effect. Furthermore, we discuss the potential of EDL epidermal electronics as wearable sensors for health monitoring and wound healing. These devices can analyze various biofluids, offering real-time feedback on parameters like pH, temperature, glucose, lactate, and oxygen levels, which aids in accurate diagnosis and effective treatment. Beyond healthcare, we explore the role of EDL epidermal electronics in human-machine interaction, particularly their application in prosthetics and pressure-sensing robots. By mimicking the flexibility and sensitivity of human skin, these devices enhance the functionality and user experience of these systems. This review summarizes the latest advancements in EDL-based epidermal electronic devices, offering a perspective for future research in this rapidly evolving field.

In order to streamline and summarize the status quo of human–computer interaction (HCI) design research in minimally invasive surgery robots, and to inspire and promote in-depth design research in related fields, this study utilizes literature research methods, inductive summarizing methods, and comparative analysis methods to analyze and organize the usage scenarios, users, interaction content and form, and relevant design methods of minimally invasive surgery robots, with the purpose of arriving at a review. Through a summary method, this study will obtain outcomes such as design requirements, interaction information classification, and the advantages and disadvantages of different interaction forms, and then make predictions of future trends in this field. Research findings show that the HCI design in the relevant field display a highly intelligent, human-centered, and multimodal development trend through the application of cutting-edge technology, taking full account of work efficiency and user needs. However, meanwhile, there are problems such as the absence of guidance by a systematic user knowledge framework and incomplete design evaluation factors, which need to be supplemented and improved by researchers in related fields in the future.

Abstract Speed and enhancement are the two most important metrics for anti-scattering light focusing by wavefront shaping (WS), which requires a spatial light modulator with a large number of modulation modes and a fast speed of response. Among the commercial modulators, the digital-micromirror device (DMD) is the sole solution providing millions of modulation modes and a pattern rate higher than 20 kHz. Thus, it has the potential to accelerate the process of anti-scattering light focusing with a high enhancement. Nevertheless, modulating light in a binary mode by the DMD restricts both the speed and enhancement seriously. Here, we propose a multi-pixel encoded DMD-based WS method by combining multiple micromirrors into a single modulation unit to overcome the drawbacks of binary modulation. In addition, to efficiently optimize the wavefront, we adopted separable natural evolution strategies (SNES), which could carry out a global search against a noisy environment. Compared with the state-of-the-art DMD-based WS method, the proposed method increased the speed of optimization and enhancement of focus by a factor of 179 and 16, respectively. In our demonstration, we achieved 10 foci with homogeneous brightness at a high speed and formed W- and S-shape patterns against the scattering medium. The experimental results suggest that the proposed method will pave a new avenue for WS in the applications of biomedical imaging, photon therapy, optogenetics, dynamic holographic display, etc.

RNA modifications appear to play a role in determining RNA structure and function. Here, the authors develop a deep learning model that predicts the location of 12 RNA modifications using primary sequence, and show that several modifications are associated, which suggests dependencies between them.

Abstract In order to reveal the relationship between steam turbine operating state and energy characteristics, the energy characteristics and frequency band distribution of steam turbine tail pipe pressure pulsation acquisition are studied. Wavelet analysis is used to remove the noise of steam turbine pressure pulsation signal, the noiseless signal is transformed by short‐time Fourier transform, and the energy histogram and energy proportion diagram of each frequency band are given. The traditional image continuation method is improved, the cubic Hermite interpolation method is used to replace the traditional cubic spline interpolation method, and then the traditional Hilbert–Huang transform (HHT) method is improved, which overcomes the endpoint effect problems, and reduces the overshoot and undershoot phenomenon in curve fitting to a certain extent. Using wavelet transform, empirical mode decomposition and improved HHT to study the distribution diagram, energy proportion diagram, and frequency band energy of pressure pulsation signal with frequency and time. The results show that with the increase of internal cavitation, the energy ratio in low‐frequency band decreases, the signal energy in high‐frequency band increases, the spectrum widens, and tends to the state of continuous spectrum.

Abstract Background Recent studies have confirmed that N7-methylguanosine (m7G) modification plays an important role in regulating various biological processes and has associations with multiple diseases. Wet-lab experiments are cost and time ineffective for the identification of disease-associated m7G sites. To date, tens of thousands of m7G sites have been identified by high-throughput sequencing approaches and the information is publicly available in bioinformatics databases, which can be leveraged to predict potential disease-associated m7G sites using a computational perspective. Thus, computational methods for m7G-disease association prediction are urgently needed, but none are currently available at present. Results To fill this gap, we collected association information between m7G sites and diseases, genomic information of m7G sites, and phenotypic information of diseases from different databases to build an m7G-disease association dataset. To infer potential disease-associated m7G sites, we then proposed a heterogeneous network-based model, m7G Sites and Diseases Associations Inference (m7GDisAI) model. m7GDisAI predicts the potential disease-associated m7G sites by applying a matrix decomposition method on heterogeneous networks which integrate comprehensive similarity information of m7G sites and diseases. To evaluate the prediction performance, 10 runs of tenfold cross validation were first conducted, and m7GDisAI got the highest AUC of 0.740(± 0.0024). Then global and local leave-one-out cross validation (LOOCV) experiments were implemented to evaluate the model’s accuracy in global and local situations respectively. AUC of 0.769 was achieved in global LOOCV, while 0.635 in local LOOCV. A case study was finally conducted to identify the most promising ovarian cancer-related m7G sites for further functional analysis. Gene Ontology (GO) enrichment analysis was performed to explore the complex associations between host gene of m7G sites and GO terms. The results showed that m7GDisAI identified disease-associated m7G sites and their host genes are consistently related to the pathogenesis of ovarian cancer, which may provide some clues for pathogenesis of diseases. Conclusion The m7GDisAI web server can be accessed at http://180.208.58.66/m7GDisAI/ , which provides a user-friendly interface to query disease associated m7G. The list of top 20 m7G sites predicted to be associted with 177 diseases can be achieved. Furthermore, detailed information about specific m7G sites and diseases are also shown.

The accuracy assessment of the global leaf area index (LAI) products is an indispensable step before applications. Four popular LAI products, namely MCD15A2H, GLASS, GEOV2, and GLOBMAP, were assessed over croplands in China using field measurements. All of these four products were validated with upscaled reference LAI maps. The validation results revealed uncertainties in these products for crops with RMSE ranging from 0.49 to 1.37 over the validation sites in Beijing, in Zhoukou and Jiaozuo counties in Henan province, Youyi farm in Heilongjiang province, and in Longkang farm in Anhui province. GEOV2 gave the highest accuracy (R2 = 0.85, RMSE = 0.49, relative bias = −7.2%) compared to MCD15A2H (R2 = 0.54, RMSE = 0.91, and relative bias = −24.4%), GLASS (R2 = 0.80, RMSE = 0.73, and relative bias = −23.3%), and GLOBMAP (R2 = 0.25, RMSE = 1.37, and relative bias = −55.4%). The LAI products overestimated over the croplands with dark soil-backgrounds in Youyi farm while they underestimated over other validation sites. The seasonal variation of these products was assessed with continuous in situ measurements at Daman Station, Gansu province. All of these four products showed good temporal consistency at Daman Station, while GEOV2 showed the highest accuracy (R2 = 0.92, RMSE = 0.41, relative bias = −10.3%). The influence of scaling effect on the products’ accuracy was also investigated, and the scaling differences of these LAI products contributes overestimation at coarse-resolution for croplands with dark soil-backgrounds, while underestimation at coarse-resolution in other validation sties.

As one of the widely occurring RNA modifications, 5-methyluridine (m5U) has recently been shown to play critical roles in various biological functions and disease pathogenesis, such as under stress response and during breast cancer development. Precise identification of m5U sites on RNA is vital for the understanding of the regulatory mechanisms of RNA life. We present here m5UPred, the first web server for in silico identification of m5U sites from the primary sequences of RNA. Built upon the support vector machine (SVM) algorithm and the biochemical encoding scheme, m5UPred achieved reasonable prediction performance with the area under the receiver operating characteristic curve (AUC) greater than 0.954 by 5-fold cross-validation and independent testing datasets. To critically test and validate the performance of our newly proposed predictor, the experimentally validated m5U sites were further separated by high-throughput sequencing techniques (miCLIP-Seq and FICC-Seq) and cell types (HEK293 and HAP1). When tested on cross-technique and cross-cell-type validation using independent datasets, m5UPred achieved an average AUC of 0.922 and 0.926 under mature mRNA mode, respectively, showing reasonable accuracy and reliability. The m5UPred web server is freely accessible now and it should make a useful tool for the researchers who are interested in m5U RNA modification.

The Permian Tethys subduction-accretionary complex (SAC) in the Qinghai-Tibet Plateau (QTP) is distributed in six geotectonic provinces, which are known as the Zongwulong-Ganjia, the Western Kunlun, the Eastern Kunlun, the Ganzi-Litang, the Xijinwulan-Jinshajiang-Ailaoshan and the Longmucuo-Shuanghu-Nujiang from north to south. The SAC is a set of mélange generated by subduction of oceanic crust at trenches, which consists of relics from mid-ocean ridge, abyssal plains, oceanic island-seamounts, intra-oceanic arcs and trench turbidites. In this paper, we apply the ocean plate stratigraphy (OPS) theory to reconstruct the OPS sequence of the Permian SAC in the QTP. We divided the Permian SAC in the QTP into two types, i.e., convergent zone and accretionary zone according to their tectonic environments. Six relics from mid-ocean ridge, five relics from abyssal plain, five relics from seamount, two relics from intra-oceanic arc, two relics from fore-arc trench, and seven relics from continental fore-arc trench were recognized and then the OPS sequences from different tectonic environments were reconstructed. The spatial-temporal distributions and evolution of ocean basins during the Permian represented by six SACs are discussed.

Post-transcriptional RNA modification occurs on all types of RNA and plays a vital role in regulating every aspect of RNA function. Thanks to the development of high-throughput sequencing technologies, transcriptome-wide profiling of RNA modifications has been made possible. With the accumulation of a large number of high-throughput datasets, bioinformatics approaches have become increasing critical for unraveling the epitranscriptome. We review here the recent progress in bioinformatics approaches for deciphering the epitranscriptomes, including epitranscriptome data analysis techniques, RNA modification databases, disease-association inference, general functional annotation, and studies on RNA modification site prediction. We also discuss the limitations of existing approaches and offer some future perspectives.

The upper Oligocene–lower Miocene Gangdese conglomerate is deposited along the Yarlung-Zangbo suture zone, which extends 1,500 km from west to east and is located in the core area of the India–Eurasia plate collision zone. The Gangdese conglomerate records richly uplift and denudation histories of the Lhasa terrane and Tethyan Himalaya on both sides of the suture zone, thus revealing the growth process of the southern Tibetan Plateau during the Late Oligocene–Early Miocene. In this study, we documented the detailed sedimentology and chronology of the Gangdese conglomerate. The Gangdese conglomerate is dominated by conglomerate and sandstone, with minor volumes of siltstone and tuff deposited in an alluvial fan and fluvial system. Based on sedimentology and structural relationships, we suggest that the Gangdese conglomerate was deposited in an extensional tectonic environment in the early period and an extrusion tectonic environment in the late period, which was controlled by Indian slab shearing and breakoff during the Late Oligocene–Miocene. According to the new magnetostratigraphy and detrital zircon U-Pb dating, the main depositional age of the Gangdese conglomerate was likely the Late Oligocene–Early Miocene (26–18 Ma), and it trended younger from west to east. Moreover, paleocurrent data from the Gangdese conglomerate showed westward axial sediment transport; thus, we inferred that a westward axial palaeo-Yarlung-Zangbo River occurred along the whole Yarlung-Zangbo suture zone during the Late Oligocene–Early Miocene, and its flow was opposite to that of the current Yarlung-Zangbo River.

Development of a G-band broadband continuous wave (CW) traveling wave tube (TWT) for wireless communications is described in this paper. This device provides the saturation output power over 8 W and the saturation gain over 30.5 dB with a bandwidth of 27 GHz. The maximum output power is 16 W and the bandwidth of 10 W output power is 23 GHz. The 3 dB bandwidth is greater than 12.3% of fc (center frequency). The gain ripple is less than 10 dB in band. A pencil beam of 50 mA and 20 kV is used and a transmission ratio over 93% is realized. The intercept power of the beam is less than 70 W and the TWT is conduction cooled through mounting plate and air fan, which makes the device capable of operating in continuous wave mode. A Pierce’s electron gun and periodic permanent magnets are employed. Chemical vapor deposition diamond disc is used in the input and output radio frequency (RF) windows to minimize the loss and voltage standing wave ratios of the traveling wave tube. Double stages deeply depressed collector is used for improving the total efficiency of the device, which can be over 5.5% in band. The weight of the device is 2.5 kg, and the packaged size is 330 mm × 70 mm × 70 mm.

Stable isotope analysis is pivotal for investigating the paleodiet and paleoecology of ancient mammals. Recently, a ∼9-Myr-old Hipparion fauna was discovered at an elevation of ∼2,200 m above sea level (a.s.l.) in the Xunhua Basin on the northeastern Tibetan Plateau. These fossils can provide key evidence for the ecosystem structure and regional paleoenvironmental reconstruction. Here we present carbon and oxygen isotopic compositions of 56 bulk and 85 serial tooth enamel samples from this fauna, which includes Hipparion platyodus Selve, Chilotherium sp. and Gazella gaudryi Schlosser. The enamel−δ13°C values display a wide range of variation (−11.4 to −5.0‰), with a mean value of −8.9 ± 1.0‰, indicating that the fauna consumed mainly C3 plants. However, the heaviest δ13C value of Hipparion (−5.0‰) suggests at least some consumption of C4 plants. Combined with pollen records, we infer that the Hipparion fauna in the Xunhua Basin was living in forest-grassland setting at ∼9 Ma. The reconstructed paleo-meteoric δ18O values of the Xunhua Basin at ∼9 Ma are lower than the mean annual δ18O of present-day precipitation in this region, suggesting a wetter climate or stronger monsoonal influence than today. Hipparion fauna in the Xunhua Basin yield significantly higher mean values of δ13C and δ18O than contemporary fossils in the Linxia Basin to the east, which is attributed to rapid uplift of the Jishi Shan, blocking water vapor transport by the East Asian Summer Monsoon and making the climate of the Xunhua Basin relatively drier.

Spatial frequency domain imaging (SFDI) is a powerful, label-free imaging technique capable of the wide-field quantitative mapping of tissue optical properties and, subsequently, chromophore concentrations. While SFDI hardware acquisition methods have advanced towards video-rate, the inverse problem (i.e., the mapping of acquired diffuse reflectance to optical properties) has remained a bottleneck for real-time data processing and visualization. Deep learning methods are adept at fitting nonlinear patterns, and may be ideal for rapidly solving the SFDI inverse problem. While current deep neural networks (DNN) are growing increasingly larger and more complex (e.g., with millions of parameters or more), our study shows that it can also be beneficial to move in the other direction, i.e., make DNNs that are smaller and simpler. Here, we propose an ultracompact, two-layer, fully connected DNN structure (each layer with four and two neurons, respectively) for ultrafast optical property extractions, which is 30×–600× faster than current methods with a similar or improved accuracy, allowing for an inversion time of 5.5 ms for 696 × 520 pixels. We further demonstrated the proposed inverse model in numerical simulations, and comprehensive phantom characterization, as well as offering in vivo measurements of dynamic physiological processes. We further demonstrated that the computation time could achieve another 200× improvement with a GPU device. This deep learning structure will help to enable fast and accurate real-time SFDI measurements, which are crucial for pre-clinical, clinical, and industrial applications.

N6-methyladenosine (m6A) is the most abundant post-transcriptional modification in mRNA, and regulates critical biological functions via m6A reader proteins that bind to m6A-containing transcripts. There exist multiple m6A reader proteins in the human genome, but their respective binding specificity and functional relevance under different biological contexts are not yet fully understood due to the limitation of experimental approaches. An in silico study was devised to unveil the target specificity and regulatory functions of different m6A readers. We established a support vector machine-based computational framework to predict the epitranscriptome-wide targets of six m6A reader proteins (YTHDF1-3, YTHDC1-2, and EIF3A) based on 58 genomic features as well as the conventional sequence-derived features. Our model achieved an average AUC of 0.981 and 0.893 under the full-transcript and mature mRNA model, respectively, marking a substantial improvement in accuracy compared to the sequence encoding schemes tested. Additionally, the distinct biological characteristics of each individual m6A reader were explored via the distribution, conservation, Gene Ontology enrichment, cellular components and molecular functions of their target m6A sites. A web server was constructed for predicting the putative binding readers of m6A sites to serve the research community, and is freely accessible at: http://m6areader.rnamd.com.

Pseudouridine (Ψ) is the first discovered and the most prevalent posttranscriptional modification, which has been widely studied during the past decades. Pseudouridine was observed in almost all kinds of RNAs and shown to have important biological functions. Currently, the time-consuming and high-cost procedures of experimental approaches limit its uses in real-life Ψ site detection. Alternatively, by taking advantage of the explosive growth of Ψ sequencing data, the computational methods may provide a more cost-effective avenue. To date, the existing mouse Ψ site predictors were all developed based on sequence-derived features, and their performance can be further improved by adding the domain knowledge derived feature. Therefore, it is highly desirable to propose a genomic feature-based computational method to increase the accuracy and efficiency of the identification of Ψ RNA modification in the mouse transcriptome. In our study, a predictive framework PSI-MOUSE was built. Besides the conventional sequence-based features, PSI-MOUSE first introduced 38 additional genomic features derived from the mouse genome, which achieved a satisfactory improvement in the prediction performance, compared with other existing models. Moreover, PSI-MOUSE also features in automatically annotating the putative Ψ sites with diverse types of posttranscriptional regulations (RNA-binding protein [RBP]-binding regions, miRNA-RNA interactions, and splicing sites), which can serve as a useful research tool for the study of Ψ RNA modification in the mouse genome. Finally, 3282 experimentally validated mouse Ψ sites were also collected in a database with customized query functions. For the convenience of academic users, a website was built to provide a user-friendly interface for the query and analysis on the database. The website is freely accessible at www.xjtlu.edu.cn/biologicalsciences/psimouse and http://psimouse.rnamd.com . We introduced the genome-derived features to mouse for the first time, and we achieved a good performance in mouse Ψ site prediction. Compared with the existing state-of-art methods, our newly developed approach PSI-MOUSE obtained a substantial improvement in prediction accuracy, marking the reliable contributions of genomic features for the prediction of RNA modifications in a species other than human.

Known as the “fifth RNA nucleotide”, pseudouridine (Ψ or psi) is the first-discovered and most abundant RNA modification occurring at the Uridine site, and it plays a prominent role in a number of biological processes. Thousands of Ψ sites have been identified within different biological contexts thanks to the advancement in high-throughput sequencing technology; nevertheless, the transcriptome-wide distribution, biomolecular functions, regulatory mechanisms, and disease relevance of pseudouridylation are largely elusive. We report here a web server—PIANO—for pseudouridine site (Ψ) identification and functional annotation. PIANO was built upon a high-accuracy predictor that takes advantage of both conventional sequence features and 42 additional genomic features. When tested on six independent datasets generated from four independent Ψ-profiling technologies (Ψ-seq, RBS-seq, Pseudo-seq, and CeU-seq) as benchmarks, PIANO achieved an average AUC of 0.955 and 0.838 under the full transcript and mature mRNA models, respectively, marking a substantial improvement in accuracy compared to the existing in silico Ψ-site prediction methods, i.e., PPUS (0.713 and 0.707), iRNA-PseU (0.713 and 0.712), and PseUI (0.634 and 0.652). Besides, PIANO web server systematically annotates the predicted Ψ sites with post-transcriptional regulatory mechanisms (miRNA-targets, RBP-binding regions, and splicing sites) in its prediction report to help the users explore potential machinery of Ψ. Moreover, a concise query interface was also built for 4,303 known Ψ sites, which is currently the largest collection of experimentally validated human Ψ sites. The PIANO website is freely accessible at: http://piano.rnamd.com.

Recent studies have revealed that the RNA N6-methyladenosine (m6A) modification plays a critical role in a variety of biological processes and associated with multiple diseases including cancers. Till this day, transcriptome-wide m6A RNA methylation sites have been identified by high-throughput sequencing technique combined with computational methods, and the information is publicly available in a few bioinformatics databases; however, the association between individual m6A sites and various diseases are still largely unknown. There are yet computational approaches developed for investigating potential association between individual m6A sites and diseases, which represents a major challenge in the epitranscriptome analysis. Thus, to infer the disease-related m6A sites, we implemented a novel multi-layer heterogeneous network-based approach, which incorporates the associations among diseases, genes and m6A RNA methylation sites from gene expression, RNA methylation and disease similarities data with the Random Walk with Restart (RWR) algorithm. To evaluate the performance of the proposed approach, a ten-fold cross validation is performed, in which our approach achieved a reasonable good performance (overall AUC: 0.827, average AUC 0.867), higher than a hypergeometric test-based approach (overall AUC: 0.7333 and average AUC: 0.723) and a random predictor (overall AUC: 0.550 and average AUC: 0.486). Additionally, we show that a number of predicted cancer-associated m6A sites are supported by existing literatures, suggesting that the proposed approach can effectively uncover the underlying epitranscriptome circuits of disease mechanisms. An online database DRUM, which stands for disease-associated ribonucleic acid methylation, was built to support the query of disease-associated RNA m6A methylation sites, and is freely available at: www.xjtlu.edu.cn/biologicalsciences/drum.

The fraction of absorbed photosynthetically active radiation by vegetation (FAPAR) is a key variable in describing the light absorption ability of the vegetation canopy. Most global FAPAR products, such as MCD15A2H and GEOV1, correspond to FAPAR under black-sky conditions at the satellite overpass time only. In this paper, we aim to produce both the global white-sky and black-sky FAPAR products based on the moderate resolution imaging spectroradiometer (MODIS) visible (VIS) albedo, leaf area index (LAI), and clumping index (CI) products. Firstly, a non-linear spectral mixture model (NSM) was designed to retrieve the soil visible (VIS) albedo. The global soil VIS albedo and its dynamics were successfully mapped at a resolution of 500 m using the MCD43A3 VIS albedo product and the MCD15A2H LAI product. Secondly, a method based on the energy balance residual (EBR) principle was presented to retrieve the white-sky and black-sky FAPAR using the MODIS broadband VIS albedo (white-sky and black-sky) product (MCD43A3), the LAI product (MCD15A2H) and CI products. Finally, the two EBR FAPAR products were compared with the MCD15A2H and Geoland2/BioPar version 1 (GEOV1) black-sky FAPAR products. A comparison of the results indicates that these FAPAR products show similar spatial and seasonal patterns. Direct validation using FAPAR observations from the Validation of Land European Remote sensing Instrument (VALERI) project demonstrates that the EBR black-sky FAPAR product was more accurate and had a lower bias (R2 = 0.917, RMSE = 0.088, and bias = −2.8 %) than MCD15A2H (R2 = 0.901, RMSE = 0.096, and bias = 7.6 % ) and GEOV1 (R2 = 0.868, RMSE = 0.105, and bias = 6.1%).

Vegetation variable retrieval from reflectance data is typically grouped into three categories: the statistical–empirical category, the physical category and the hybrid category (physical models applied to statistical models). Based on the similarities between the spectra of leaves in the optical domain, the leaf reflectance spectra can be linearly modelled using a very limited number of principal components (PCs) if the PCA (principal component analysis) transformation is carried out at the sample dimension. In this paper, we present a novel data-driven approach that uses the PCA transformation to reconstruct leaf reflectance spectra and also to retrieve leaf biochemical contents. First, the PCA transformation was carried out on a training dataset simulated by the PROSPECT-5 model. The results showed that the leaf reflectance spectra can be accurately reconstructed using only a few leading PCs, as the ten leading PCs contained 99.999% of the total information in the 3636 training samples. The spectral error between the simulated or measured reflectance and the reconstructed spectra was also investigated using the simulated and measured datasets (ANGERS and LOPEX’93). The mean root mean squared error (RMSE) values varied from 5.56 × 10−5 to 6.18 × 10−3, which is about 3–10 times more accurate than the PROSPECT simulation method for measured datasets. Secondly, the relationship between PCs and leaf biochemical components was investigated, and we found that the PCs are closely related to the leaf biochemical components and to the reflectance spectra. Only when the weighting coefficient of the most sensitive PC was employed to retrieve the leaf biochemical contents, the coefficients of determination for the PCA data-driven model were 0.69, 0.99, 0.94 and 0.68 for the specific leaf weight (SLW), equivalent water thickness (EWT), chlorophyll content (Cab) and carotenoid content (Car), respectively. Finally, statistical models for the retrieval of leaf biochemical contents were developed based on the weighting coefficients of the sensitive PCs, and the PCA data-driven models were validated and compared to the traditional VI-based and physically-based approaches for the retrieval of leaf properties. The results show that the PCA method shows similar or better performance in the estimation of leaf biochemical contents. Therefore, the PCA method provides a new and accurate data-driven method for reconstructing leaf reflectance spectra and also for retrieving leaf biochemical contents.

Previous genome-wide expression studies have highlighted distinct gene expression patterns in inflammatory bowel disease (IBD) compared to control samples, but the interpretation of these studies has been limited by sample heterogeneity with respect to disease phenotype, disease activity, and anatomic sites. To further improve molecular classification of inflammatory bowel disease phenotypes we focused on a single anatomic site, the disease unaffected proximal ileal margin of resected ileum, and three phenotypes that were unlikely to overlap: ileal Crohn's disease (ileal CD), ulcerative colitis (UC), and control patients without IBD. Whole human genome (Agilent) expression profiling was conducted on two independent sets of disease-unaffected ileal samples collected from the proximal margin of resected ileum. Set 1 (47 ileal CD, 27 UC, and 25 Control non-IBD patients) was used as the training set and Set 2 was subsequently collected as an independent test set (10 ileal CD, 10 UC, and 10 control non-IBD patients). We compared the 17 gene signatures selected by four different feature-selection methods to distinguish ileal CD phenotype with non-CD phenotype. The four methods yielded different but overlapping solutions that were highly discriminating. All four of these methods selected FOLH1 as a common feature. This gene is an established biomarker for prostate cancer, but has not previously been associated with Crohn's disease. Immunohistochemical staining confirmed increased expression of FOLH1 in the ileal epithelium. These results provide evidence for convergent molecular abnormalities in the macroscopically disease unaffected proximal margin of resected ileum from ileal CD subjects.