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Thyroid cancer is a common endocrine malignancy that poses considerable therapeutic challenges in treating anaplastic carcinoma and advanced aggressive disease. Immunotherapy has become a prominent strategy for cancer treatment, and has shown remarkable advancements in recent years. In this study, we utilized visualization and bibliometric tools to analyze publications on thyroid cancer immunotherapy from the Web of Science Core Collection (WoSCC). A total of 409 articles were included, with an annual increase in both publications and citations since 2016. China leads research efforts in this area, while the University of Texas System and UTMD Anderson Cancer Center rank first in publication output. The journal Thyroid has garnered the highest citations. Notable authors contributing to this field include Antonelli Alessandro, Fallahi Poupak, and Wang Yu. Current research hotspots include immune checkpoint inhibitors, combination therapies involving immunotherapy with targeted therapy, CAR-T cell therapy, and modulation of the tumor microenvironment, all of which underscore the evolving landscape and potential for innovative treatments in thyroid cancer.

The unsatisfactory energy density of the state-of-art batteries imposes constraints on the practical application of unmanned aerial vehicles (UAVs). Establishing a UAV airport network that integrates energy supply and information exchange functionalities represents an ideal solution for enabling synergistic UAV operations. However, devising efficient distribution protocols for these airports remains a challenge. By leveraging modeling and analysis of the energy density of existing UAV batteries, we can forecast the flight range and distances achievable by UAVs. Here, we propose a distribution protocol for UAV airport platforms aimed at enhancing distribution accuracy by the use of AI principles. Furthermore, considering the possibility of emergency UAV stop, we introduce an emergency stop system in conjunction with standard stopping procedures to optimize distribution efficiency and enhance UAV inspection safety. Moreover, existing UAV airports usually provide energy to UAVs without harnessing UAVs to facilitate interconnection and interoperability among different airports. This inefficiency leads to significant resource wastage in energy distribution. To address this, we introduce a shared energy network that allows different companies to operate according to energy distribution needs. This network not only supplies energy to UAVs but also employs UAVs for energy collection and transportation, facilitating energy trading, business collaboration, and data transmission among diverse organizations. By enabling ubiquitous energy trading, this study provides us an ideal strategy for the future construction of energy network with interconnection and interoperability, which can be extended to other applications calling for energy distribution.

Background The detection rate of thyroid nodules has witnessed a significant surge in recent years, triggering heightened public apprehension. Short video platforms such as TikTok and BiliBili have showed tremendous potential in the dissemination of health information. There is a plethora of videos about thyroid nodules on TikTok and BiliBili, but the quality and reliability of videos concerning thyroid nodules remains unknown. Methods On December 3 rd , 2023, the top 100 short videos related to thyroid nodules on BiliBili and TikTok were collected through a comprehensive search in Chinese. After extracting the basic information, the quality and reliability of each video was assessed by using the global quality score (GQS) and DISCERN score. Further, Spearman correlation analyses were applied to examine the correlation among video variables, GQS and DISCERN score. Results Compared to BiliBili, TikTok exhibits greater popularity, as evidenced by higher counts of likes ( P = 0.021), comments ( P = 0.008) and shares ( P = 0.017). The median (interquartile range) scores of GQS and DISCERN score were 3 (2–3) on TikTok while 2 (2–3) on BiliBili. Both reviewers exhibited good consistency in GQS and DISCERN score. Moreover, it was observed that the videos shared by thyroid specialists demonstrated higher scores both in GQS ( P = 0.014) and DISCERN score ( P = 0.022) than others on TikTok. Spearman correlation analysis revealed no significant correlation between video variables and the scores of GQS and DISCERN score. Conclusions The quality and reliability of thyroid nodules videos on BiliBili and TikTok were unsatisfactory. Notably, videos shared by thyroid specialists are more likely to exhibit superior quality and reliability. People should exercise caution when perusing short videos.

This study aims to obtain the effect of SO2 on the corrosion of Q235B low carbon steel in simulated coastal industrial atmospheres.The corrosion performance of the steel in the simulated atmospheres was studied using wet/dry cyclic corrosion test (CCT), electrochemical testing analysis, scanning electron microscopy, X-ray diffraction, and COMSOL finite element simulation methods. As a result the influence of SO2, the corrosion rate of Q235B steel increases rapidly in the initial stage of corrosion, and gradually decreases in the later stages, eventually stabilizing. There is a gradual increase in ferrite content on the surface of the steel during the initial stage of corrosion. In addition, the main components of the rust layer α-FeOOH and γ-FeOOH, and high concentrations of SO2 promote α-FeOOH formation and inhibit γ-FeOOH formation.

For Brain-Computer Interface (BCI) based on motor imagery (MI), the MI task is abstract and spontaneous, presenting challenges in measurement and control and resulting in a lower signal-to-noise ratio. The quality of the collected MI data significantly impacts the cross-subject calibration results. To address this challenge, we introduce a novel cross-subject calibration method based on passive tactile afferent stimulation, in which data induced by tactile stimulation is utilized to calibrate transfer learning models for cross-subject decoding. During the experiments, tactile stimulation was applied to either the left or right hand, with subjects only required to sense tactile stimulation. Data from these tactile tasks were used to train or fine-tune models and subsequently applied to decode pure MI data. We evaluated BCI performance using both the classical Common Spatial Pattern (CSP) combined with the Linear Discriminant Analysis (LDA) algorithm and a state-of-the-art deep transfer learning model. The results demonstrate that the proposed calibration method achieved decoding performance at an equivalent level to traditional MI calibration, with the added benefit of outperforming traditional MI calibration with fewer trials. The simplicity and effectiveness of the proposed cross-subject tactile calibration method make it valuable for practical applications of BCI, especially in clinical settings.

Polysomnography (PSG) recordings have been widely used for sleep staging in clinics, containing multiple modality signals (i.e., EEG and EOG). Recently, many studies have combined EEG and EOG modalities for sleep staging, since they are the most and the second most powerful modality for sleep staging among PSG recordings, respectively. However, EEG is complex to collect and sensitive to environment noise or other body activities, imbedding its use in clinical practice. Comparatively, EOG is much more easily to be obtained. In order to make full use of the powerful ability of EEG and the easy collection of EOG, we propose a novel framework to simplify multimodal sleep staging with a single EOG modality. It still performs well with only EOG modality in the absence of the EEG. Specifically, we first model the correlation between EEG and EOG, and then based on the correlation we generate multimodal features with time and frequency guided generators by adopting the idea of generative adversarial learning. We collected a real-world sleep dataset containing 67 recordings and used other four public datasets for evaluation. Compared with other existing sleep staging methods, our framework performs the best when solely using the EOG modality. Moreover, under our framework, EOG provides a comparable performance to EEG.

Major Depression Disorder (MDD) is a common yet destructive mental disorder that affects millions of people worldwide. Making early and accurate diagnosis of it is very meaningful. Recently, EEG, a non-invasive technique of recording spontaneous electrical activity of brains, has been widely used for MDD diagnosis. However, there are still some challenges in data quality and data size of EEG: (1) A large amount of noise is inevitable during EEG collection, making it difficult to extract discriminative features from raw EEG; (2) It is difficult to recruit a large number of subjects to collect sufficient and diverse data for model training. Both of the challenges cause the overfitting problem, especially for deep learning methods. In this paper, we propose DiffMDD, a diffusion-based deep learning framework for MDD diagnosis using EEG. Specifically, we extract more noise-irrelevant features to improve the model’s robustness by designing the Forward Diffusion Noisy Training Module. Then we increase the size and diversity of data to help the model learn more generalized features by designing the Reverse Diffusion Data Augmentation Module. Finally, we re-train the classifier on the augmented dataset for MDD diagnosis. We conducted comprehensive experiments to test the overall performance and each module’s effectiveness. The framework was validated on two public MDD diagnosis datasets, achieving the state-of-the-art performance.

Abstract Emerging memories have been developed as new physical infrastructures for hosting neural networks owing to their low-power analog computing characteristics. However, accurately and efficiently programming devices in an analog-valued array is still largely limited by the intrinsic physical non-idealities of the devices, thus hampering their applications in in-situ training of neural networks. Here, we demonstrate a passive electrochemical memory (ECRAM) array with many important characteristics necessary for accurate analog programming. Different image patterns can be open-loop and serially programmed into our ECRAM array, achieving high programming accuracies without any feedback adjustments. The excellent open-loop analog programmability has led us to in-situ train a bilayer neural network and reached software-like classification accuracy of 99.4% to detect poisonous mushrooms. The training capability is further studied in simulation for large-scale neural networks such as VGG-8. Our results present a new solution for implementing learning functions in an artificial intelligence hardware using emerging memories.

This is an essay in the field of mineral analysis. The ore sample was digested with Hydrochloric acid、nitric acid、 hydrofluoric acid、 sulfuric acid in a closed polytetrafluoroethylene digestion tank. Hydrochloric acid and tartaric acid medium is used for extraction. The Lithium、Beryllium、niobium and tantalum in the prepared solution were determined by inductively coupled plasma atomic emission spectrometry (ICP-AES). The emission spectral intensity of Lithium、Beryllium、niobium and tantalum showed a good linear relationship with ρ(Li) and ρ(Be) in the range of 0~50 μg/mL、ρ(Nb2O5) and ρ(Ta2O5) in the range of 0~100 μg/mL. The correlation coefficients of the calibration curve for Lithium、Beryllium、niobium and tantalum were both 0.9999．The detection limits for Lithium、Beryllium、niobium and tantalum were 0.1 μg/g、0.5 μg/g、1.0 μg/g、0.1 μg/g, respectively．The proposed method was applied to the determination of the actual sample giving recovery rate of 94.6%~105.6% and the relative standard deviations (RSD n=11) of Lithium、Beryllium、niobium and tantalum are 1.08%~7.04%、0.65%~13.06%、2.59~7.32%、2.02%~7.60%, respectively．This method was applied to the analysis of the standard sample and the results were consistent with the certified values．

ObjectivesTo explore the preoperative high-risk clinical factors for contralateral medium-volume central lymph node metastasis (conMVCLNM) in unilateral papillary thyroid carcinoma (uPTC) and the indications for dissection of contralateral central lymph nodes (conCLN).MethodsClinical and pathological data of 204 uPTC patients who underwent thyroid surgery at the Hangzhou First People’s Hospital from September 2010 to October 2022 were collected. Univariate and multivariate logistic regression analyses were conducted to determine the independent risk factors for contralateral central lymph node metastasis (conCLNM) and conMVCLNM in uPTC patients based on the preoperative clinical data. Predictive models for conCLNM and conMVCLNM were constructed using logistic regression analyses and validated using receiver operating characteristic (ROC) curves, concordance index (C-index), calibration curves, and decision curve analysis (DCA).ResultsUnivariate and multivariate logistic regression analyses showed that gender (P < 0.001), age (P < 0.001), tumor diameter (P < 0.001), and multifocality (P = 0.008) were independent risk factors for conCLNM in uPTC patients. Gender(P= 0.026), age (P = 0.010), platelet-to-lymphocyte ratio (PLR) (P =0.003), and tumor diameter (P = 0.036) were independent risk factors for conMVCLNM in uPTC patients. A predictive model was established to assess the risk of conCLNM and conMVCLNM, with ROC curve areas of 0.836 and 0.845, respectively. The C-index, the calibration curve, and DCA demonstrated that the model had good diagnostic value.ConclusionGender, age, tumor diameter, and multifocality are high-risk factors for conCLNM in uPTC patients. Gender, age, tumor diameter, and PLR are high-risk factors for conMVCLNM in uPTC patients, and preventive conCLN dissection should be performed.

Abstract Significant progress has been made in image inpainting methods in recent years. However, they are incapable of producing inpainting results with reasonable structures, rich detail, and sharpness at the same time. In this paper, we propose the Pyramid-VAE-GAN network for image inpainting to address this limitation. Our network is built on a variational autoencoder (VAE) backbone that encodes high-level latent variables to represent complicated high-dimensional prior distributions of images. The prior assists in reconstructing reasonable structures when inpainting. We also adopt a pyramid structure in our model to maintain rich detail in low-level latent variables. To avoid the usual incompatibility of requiring both reasonable structures and rich detail, we propose a novel cross-layer latent variable transfer module. This transfers information about long-range structures contained in high-level latent variables to low-level latent variables representing more detailed information. We further use adversarial training to select the most reasonable results and to improve the sharpness of the images. Extensive experimental results on multiple datasets demonstrate the superiority of our method. Our code is available at https://github.com/thy960112/Pyramid-VAE-GAN .

Sea Surface Temperature (SST) is of great importance to study several major phenomena due to ocean interactions with other earth systems. Previous studies on SST based on statistical inference methods were less accurate for longer prediction lengths. A considerable number of studies in recent years involve machine learning for SST modeling. These models were able to mitigate this problem to some length by modeling SST patterns and trends. Sequence analysis by decomposition is used for SST forecasting in several studies. Ensemble Empirical Mode Decomposition (EEMD) has been proven in previous studies as a useful method for this. The application of EEMD in spatiotemporal modeling has been introduced as Multidimensional EEMD (MEEMD). The aim of this study is to employ fast MEEMD methods to decompose the SST spatiotemporal dataset and apply a Convolutional Long Short-Term Memory (ConvLSTM)-based model to model and forecast SST. The results show that the fast MEEMD method is capable of enhancing spatiotemporal SST modeling compared to the Linear Inverse Model (LIM) and ConvLSTM model without decomposition. The model was further validated by making predictions from April to May 2023 and comparing them to original SST values. There was a high consistency between predicted and real SST values.

Objective: To evaluate the clinical effect of subcapsular saline injection (SCASI) after total thyroidectomy. Methods: A total of 77 patients who underwent total thyroidectomy in our hospital from January 2020 to December 2021 were selected and divided into the SCASI group (n = 43) and the non-SCASI group (n = 34). The general clinical data of the patients were collected, and serum parathyroid hormone (PTH) and serum calcium levels were determined preoperatively, on the 1st postoperative day, and at 1 and 6 months after the operation. These data were compared between groups. Results: There was no significant difference in postoperative complications between the two groups. The PTH and serum calcium levels in the SCASI group were significantly higher than those in the non-SCASI group on the 1st postoperative day (t = 2.340, 5.208, both P

Consciousness detection is important in diagnosis and treatment of disorders of consciousness (DOC). Recent studies have demonstrated that electroencephalography (EEG) signals contain effective information for consciousness state evaluation. We propose two novel EEG measures: the spatiotemporal correntropy and the neuromodulation intensity, to reflect the temporal-spatial complexity in brain signals for consciousness detection. Then, we build a pool of EEG measures with different spectral, complexity and connectivity features, and propose Consformer, a transformer network to learn an adaptive optimization of features for different subjects with the attention mechanism. Experiments are carried out using a large dataset of 280 resting-state EEG recordings of DOC patients. Consformer discriminates minimally conscious state (MCS) from vegetative state (VS) with an accuracy of 85.73% and an F1-score of 86.95%, which achieves the state-of-the-art performance.

Narcolepsy is a sleep disorder affecting millions of people worldwide and causes serious public health problems. It is hard for doctors to correctly and objectively diagnose narcolepsy. Polysomnography (PSG) recordings, a gold standard for sleep monitoring and quality measurement, can provide abundant and objective cues for the narcolepsy diagnosis. There have been some studies on automatic narcolepsy diagnosis using PSG recordings. However, the sleep stage information, an important cue for narcolepsy diagnosis, has not been fully utilized. For example, some studies have not considered the sleep stage information to diagnose narcolepsy. Although some studies consider the sleep stage information, the stages are manually scored by experts, which is time-consuming and subjective. And the framework using sleep stages scored automatically for narcolepsy diagnosis is designed in a two-phase learning manner, where sleep staging in the first phase and diagnosis in the second phase, causing cumulative error and degrading the performance. To address these challenges, we propose a novel end-to-end framework for automatic narcolepsy diagnosis using PSG recordings. In particular, adopting the idea of multi-task learning, we take the sleep staging as our auxiliary task, and then combine the sleep stage related features with narcolepsy related features for our primary task of narcolepsy diagnosis. We collected a dataset of PSG recordings from 77 participants and evaluated our framework on it. Both of the sleep stage features and the end-to-end fashion contribute to diagnosis performance. Moreover, we do a comprehensive analysis on the relationship between sleep stages and narcolepsy, correlation of different channels, predictive ability of different sensing data, and diagnosis results in subject level.

Although the water environment has certain self-purification capability, the natural balance is disrupted, leading to water quality deterioration when the discharge load of wastewater exceeds a certain threshold. This problem implies the urgency of evaluating marine environmental capacity as a necessary parameter for marine sustainable development of marine ecosystems. Through principal component analysis (PCA), clustering, and other methods, we analyzed the average concentration and fluxes of land-sourced pollutants and determined the pollution level around the Leizhou Peninsula. Combined with the Delft3D hydrodynamic numerical model, tidal hydrodynamic forces and pollutants migration and diffusion were calculated. Based on in-situ measured data, the model was validated. The sharing rate method was used to calculate the marine environmental capacity in Zhanjiang Bay and analyzed their impact on seawater eutrophication. The results showed that: (1) The average concentrations of chemical oxygen demand (COD), ammonia nitrogen (NH4+), total nitrogen (TN), and total phosphorus (TP) around Leizhou Peninsula were 22.56 mg/L, 0.69 mg/L, 6.69 mg/L, and 0.69 mg/L, respectively. (2) Six areas (Area A-F) can be divided into, based on the discharge of land-sourced pollutants into the sea area. According to the results of PCA, clustering, and other methods, the average concentration and fluxes of land-sourced pollutants in Area B (i.e. Zhanjiang Bay) were very high. (3) The environmental capacity of Zhanjiang Bay was calculated through Delft3D numerical simulation, and it was found that the COD and TN environmental capacity of 6 sewage outlets exceeded the standard, while the TP environmental capacity of 3 sewage outlets exceeded the standard. (4) According to the statistical research result, most of the Zhanjiang Bay waters has been restricted by nitrogen for over a decade. Therefore, we speculate that although TN environmental capacity exceeds the standard, its impact on eutrophication in Zhanjiang Bay is still limited to a certain extent.

Objective: Blood-oxygen-level-dependent functional MRI allows to investigte neural activities and connectivity. While the non-human primate plays an essential role in neuroscience research, multimodal methods combining functional MRI with other neuroimaging and neuromodulation enable us to understand the brain network at multiple scales. Approach: In this study, a tight-fitting helmet-shape receive array with a single transmit loop for anesthetized macaque brain MRI at 7T was fabricated with four openings constructed in the coil housing to accommodate multimodal devices, and the coil performance was quantitatively evaluated and compared to a commercial knee coil. In addition, experiments over three macaques with infrared neural stimulation (INS), focused ultrasound stimulation (FUS), and transcranial direct current stimulation (tDCS) were conducted. Main results: The RF coil showed higher transmit efficiency, comparable homogeneity, improved SNR and enlarged signal coverage over the macaque brain. Infrared neural stimulation was applied to the amygdala in deep brain region, and activations in stimulation sites and connected sites were detected, with the connectivity consistent with anatomical information. Focused ultrasound stimulation was applied to the left visual cortex, and activations were acquired along the ultrasound traveling path, with all time course curves consistent with pre-designed paradigms. The existence of transcranial direct current stimulation electrodes brought no interference to the RF system, as evidenced through high-resolution MPRAGE structure images. Significance: This pilot study reveals the feasibility for brain investigation at multiple spatiotemporal scales, which may advance our understanding in dynamic brain networks.

This paper aims to reveal the motion law and collision behaviors of shotcrete particle flow jets. A physical model of the jet flow field composed of a nozzle structure and jet area was constructed and meshes with various sizes were used to mesh the nozzle and jet area. With the basic contact parameters and contact model parameters of the particles set, the CFD-DEM-coupling simulation method was adopted to perform the numerical simulation of concrete-particle-flow-jet impingement. The variation laws of the continuous-phase velocity and pressure drop of the shotcrete, coarse-aggregate motion characteristics, and particle collision behavior under the interaction of the continuous and discrete phases were obtained. The results showed that the velocity field and pressure-drop field of the continuous phase had an ideal symmetry in the XY plane in the stable injection stage, the continuous-phase velocity gradually increased inside the nozzle and gradually decreased after entering the jet area, the continuous-phase pressure drop was the maximum at the nozzle inlet, and the pressure value at the nozzle outlet became atmospheric pressure. The central axis of the particle flow jet was displaced by 0.15 m in the negative direction of the Y-axis under the action of gravity, the diffusion angle of the small particles that exited the nozzle and entered the jet area was larger than that of the large particles, and the large-particle jets were more concentrated and easier to spray into the designated spraying areas. The particle flow reached a stable jet state about 0.3 s after the jet began, and the peak velocity of the 4 mm particles in the flow reached 25 m/s, while the peak velocity of the 12 mm particles was only 19 m/s. The acceleration time for particles of different sizes to reach the peak velocity also varied, and the large particles took longer to reach the maximum velocity: small particles reached their peak within 0.4 m–8 m of the jet area, and large particles reached their peak within 0.8 m–1.2 m of the jet area. The particle velocity peaked within 0.6 m–1 m of the jet area. Particle collision took three forms: particle collision with the inner wall of the nozzle, interparticle collision, and particle collision with the sprayed wall. The collision between the particles and the sprayed wall was the main form leading to the rebound of the wet shotcrete, and the rebound angle after particle collision was uncertain.

Bandpass filters play a core role in ECoG signal processing. Commonly used frequency bands such as alpha, beta, and gamma bands can reflect the normal rhythm of the brain. However, the universally predefined bands might not be optimal for a specific task. Especially the gamma band usually covers a wide frequency span (i.e., 30–200 Hz) which can be too coarse to capture features that appear in narrow bands. An ideal option is to find the optimal frequency bands for specific tasks in real-time and dynamically. To tackle this problem, we propose an adaptive band filter that selects the useful frequency band in a data-driven way. Specifically, we leverage the phase-amplitude coupling (PAC) of the coupled working mechanism of synchronizing neuron and pyramidal neurons in neuronal oscillations, in which the phase of slower oscillations modulates the amplitude of faster ones, to help locate the fine frequency bands from the gamma range, in a task-specific and individual-specific way. Thus, the information can be more precisely extracted from ECoG signals to improve neural decoding performance. Based on this, an end-to-end decoder (PACNet) is proposed to construct a neural decoding application with adaptive filter banks in a uniform framework. Experiments show that PACNet can improve neural decoding performance universally with different tasks.

Designing compact computing hardware and systems is highly desired for resource-restricted edge computing applications. Utilizing the rich dynamics in a physical device for computing is a unique approach in creating complex functionalities with miniaturized footprint. In this work, we developed a dynamical electrochemical memristor from a static memristor by replacing the gate material. The dynamical device possessed short-term fading dynamics and exhibited distinct frequency-dependent responses to varying input signals, enabling its use as a single device-based frequency classifier. Simulation showed that the device responses to different frequency components in a mixed-frequency signal were additive with nonlinear attenuation at higher frequency, providing a guideline in designing the system to process complex signals. We used a rate-coding scheme to convert real world auditory recordings into fixed amplitude spike trains to decouple amplitude-based information and frequency-based information and was able to demonstrate auditory classification of different animals. The work provides a new building block for temporal information processing.

Abstract Background Medical information has rapidly increased on the internet and has become one of the main targets of search engine use. However, medical information on the internet is subject to the problems of quality and accessibility, so ordinary users are unable to obtain answers to their medical questions conveniently. As a solution, researchers build medical question answering (QA) systems. However, research on medical QA in the Chinese language lags behind work on English-based systems. This lag is mainly due to the difficulty of constructing a high-quality knowledge base and the underutilization of medical corpora in the Chinese language. Results This study developed an end-to-end solution to implement a medical QA system for the Chinese language with low cost and time. First, we created a high-quality medical knowledge graph from hospital data (electronic health/medical records) in a nearly automatic manner that trained a supervised model based on data labeled using bootstrapping techniques. Then, we designed a QA system based on a memory-based neural network and attention mechanism. Finally, we trained the system to generate answers from the knowledge base and a QA corpus on the internet. Conclusions Bootstrapping and deep neural network techniques can construct a knowledge graph from electronic health/medical records with satisfactory precision and coverage. Our proposed context bridge mechanisms perform training with a variety of language features. Our QA system can achieve state-of-the-art quality in answering medical questions with constrained topics. As we evaluated, complex Chinese language processing techniques, such as segmentation and parsing, were not necessary for practice and complex architectures were not necessary to build the QA system. Lastly, we created an application using our method for internet QA usage.

This study aimed to evaluate the effects of Bacillus subtilis or Lentilactobacillus buchneri on the fermentation quality, aerobic stability, and bacterial and fungal communities of whole plant corn silage during aerobic exposure. Whole plant corn was harvested at the wax maturity stage, which chopped to a length of approximately 1 cm, and treated with the following: distilled sterile water control, 2.0 × 105 CFU/g of Lentilactobacillus buchneri (LB) or 2.0 × 105 CFU/g of Bacillus subtilis (BS) for 42 days silage. Then, the samples were exposed to air (23–28°C) after opening and sampled at 0, 18 and 60 h, to investigate fermentation quality, bacterial and fungal communities, and aerobic stability. Inoculation with LB or BS increased the pH value, acetic acid, and ammonia nitrogen content of silage (P < 0.05), but it was still far below the threshold of inferior silage, the yield of ethanol was reduced (P < 0.05), and satisfactory fermentation quality was achieved. With the extension of the aerobic exposure time, inoculation with LB or BS prolonged the aerobic stabilization time of silage, attenuated the trend of pH increase during aerobic exposure, and increased the residues of lactic acid and acetic acid. The bacterial and fungal alpha diversity indices gradually declined, and the relative abundance of Basidiomycota and Kazachstania gradually increased. The relative abundance of Weissella and unclassified_f_Enterobacteria was higher and the relative abundance of Kazachstania was lower after inoculation with BS compared to the CK group. According to the correlation analysis, Bacillus and Kazachstania are bacteria and fungi that are more closely related to aerobic spoilage and inoculation with LB or BS could inhibit spoilage. The FUNGuild predictive analysis indicated that the higher relative abundance of fungal parasite-undefined saprotroph in the LB or BS groups at AS2, may account for its good aerobic stability. In conclusion, silage inoculated with LB or BS had better fermentation quality and improved aerobic stability by effectively inhibiting the microorganisms that induce aerobic spoilage.

Thyroid cancer can be divided into two types according to its cellular origin, i.e., malignant tumors originating from thyroid cells and cancers that metastasize to the thyroid from other sites, the latter of which are, clinically rare. This article reports the diagnosis and treatment of a rectal neuroendocrine neoplasm metastasis to the thyroid. No similar cases have been reported before. This case suggests that when evaluating thyroid tumors, clinicians should not only carefully identify the clinical features of the tumor but also pay special attention to the patient’s history of tumors, especially neuroendocrine neoplasms. For definite secondary thyroid malignancies, neck surgery is feasible if the thyroid is the only site of metastasis; otherwise, the subsequent diagnosis and treatment plan should be determined after a comprehensive evaluation of the primary tumor and patient’s general condition.

This study aims to investigate the effects of adding Lactobacillus buchneri (LB), Lactobacillus brevis (LBR) and Bacillus subtilis (BS) on the fermentation quality, nitrate degradation and bacterial community of sorghum-sudangrass silage. The results showed that the addition of LB significantly increased the pH and acetic acid content (p

Biometric features, e.g., fingerprints, the iris, and the face, have been widely used to authenticate individuals. However, most biometrics are not cancellable, i.e., once these biometric features are cloned or stolen, they cannot be replaced easily. Unlike traditional biometrics, brain biometrics are extremely difficult to clone or forge due to the natural randomness across different individuals, which makes them an ideal option for identity authentication. Most existing brain biometrics are based on electroencephalogram (EEG), which is usually demonstrated unstable performance due to the low signal-to-noise ratio (SNR). For the first time, we propose the use of intracortical brain signals, which have higher resolution and SNR, to realize the construction of the high-performance brain biometrics. Specifically, we put forward a novel brain-based key generation approach called multidimensional Gaussian fitted bit allocation (MGFBA). The proposed MGFBA method extracts keys from the local field potential of ten rats with high reliability and high entropy. We found that with the proposed MGFBA, the average effective key length of the brain biometrics was 938 bits, while achieving high authentication accuracy of 88.1% at a false acceptance rate of 1.9%, which is significantly improved compared to conventional EEG-based approaches. In addition, the proposed MGFBA-based keys can be conveniently revoked using different motor behaviors with high entropy. Experimental results demonstrate the potential of using intracortical brain signals for reliable authentication and other security applications.

Biometrics, e.g., fingerprints, the iris, and the face, have been widely used to authenticate individuals. However, most biometrics are not cancellable, i.e., once these traditional biometrics are cloned or stolen, they cannot be replaced easily. Unlike traditional biometrics, brain biometrics are extremely difficult to clone or forge due to the natural randomness across different individuals, which makes them an ideal option for identity authentication. Most existing brain biometrics are based on an electroencephalogram (EEG), which typically demonstrates unstable performance due to the low signal-to-noise ratio (SNR). Thus, in this paper, we propose the use of intracortical brain signals, which have higher resolution and SNR, to realize the construction of a high-performance brain biometric. Significantly, this is the first study to investigate the features of intracortical brain signals for identification. Specifically, several features based on local field potential are computed for identification, and their performance is compared with different machine learning algorithms. The results show that frequency domain features and time-frequency domain features are excellent for intra-day and inter-day identification. Furthermore, the energy features perform best among all features with 98% intra-day and 93% inter-day identification accuracy, which demonstrates the great potential of intracraial brain signals to be biometrics. This paper may serve as a guidance for future intracranial brain researches and the development of more reliable and high-performance brain biometrics.

Dissolved organic matter (DOM) serves as the most active and sensitive organic component in the bay, and its biogeochemical characteristics and reactivity are affected by the properties of terrestrial and marine substances significantly. In this study, in order to study the distribution and characteristics of DOM in a semi-closed bay, 34 water samples from 19 stations were collected from Zhanjiang Bay and analyzed for δ13C of dissolved inorganic carbon (DIC) and fluorescent components of DOM. The results showed that there were many sources of organic matter in the bay, including soil input, algae input, and sewage input. Influenced by freshwater input, DOM in the bay decreased from the upper bay to the outer bay. The organic matter in the bay displayed two characteristics, where the northern bay is composed of terrigenous organic matter mainly with high humus, while the southern bay is more inclined to marine sources with a high biological index (BIX) and low humification index (HIX). The correlation between organic matter with different characteristics and environmental parameters such as salinity, pH, and chlorophyll a was analyzed. The discrepancy may be caused by the weak turbulent mixing in the semi-closed bay.

The intestinal microbiota regulate the brain function of the host through the production of a myriad of metabolites and are associated with various neurological diseases. Understanding the intestinal microbiome of patients with prolonged disorders of consciousness (DoC) is important for the evaluation and treatment of the disease. To investigate the differences in the intestinal microbiome and short-chain fatty acids (SCFAs) among patients in a vegetative state (VS), a minimally conscious state (MCS), and emerged from MCS (EMCS), as well as the influence of antibiotics on these patients, 16S ribosomal RNA (16S rRNA) sequencing and targeted lipidomics were performed on fecal samples from patients; in addition, analysis of the electroencephalogram (EEG) signals was performed to evaluate the brain function of these patients. The results showed that the intestinal microbiome of the three groups differed greatly, and some microbial communities showed a reduced production of SCFAs in VS patients compared to the other two groups. Moreover, reduced microbial communities and five major SCFAs, along with attenuated brain functional connectivity, were observed in MCS patients who were treated with antibiotics compared to those who did not receive antibiotic treatment, but not in the other pairwise comparisons. Finally, three genus-level microbiota—Faecailbacterium, Enterococcus, and Methanobrevibacter—were considered as potential biomarkers to distinguish MCS from VS patients, with high accuracy both in the discovery and validation cohorts. Together, our findings improved the understanding of patients with prolonged DoC from the intestinal microbiome perspective and provided a new reference for the exploration of therapeutic targets.

Tropical storms (TS) are important drivers of short-term changes and affects the coastal and marine environment. Based on in situ observational data from four locations in the coastal area of the northern Beibu Gulf and satellite data, we analyzed the changes in temperature, salinity, and turbidity during the transit of TS “Wipha” in 2019 and assessed the environmental factors controlling chlorophyll a concenteation (Chl-a) increases in the coastal area. Our results showed that in the coastal area, the growth of phytoplankton after the TS was mainly controlled by the nutrient and light availability. The increased input of freshwater by TS, including direct inputs from rainfall and increased river discharge, reduced the salinity. The decrease in salinity may indicate an increased input of nutrient-rich freshwater at all four stations (nutrients input: S1>S2>S3>S4). Nutrient concentration at S1, S2, and S3 implied by salinity was high, but that at S4 was limited. The shorter recovery time of turbidity after TS indicated the faster improvement of light conditions in this area [recovery time: S4 (2 days)< S1 (3 days)< S3 (5 days)< S2 (10 days)]. The high turbidity associated with poor light penetration was an important factor limiting phytoplankton growth at station 2, with a slow recovery of the turbidity to pre-TS levels. The rapid recovery of the turbidity to the pre-TS levels at S1, S3, and S4 suggested good light conditions soon after the TS, and probably led to a significant increase in Chl-a after the TS ([Chl-a]: S1>S3>S4). The less of an increase of Chl-a at S4 was not only related to nutrient restriction but also related to weak mixing of the water column, while the least significant decrease in the SST at S4 implied that the enhancing mixing after TS was limited.

Macrophages originating from the yolk sac or bone marrow play essential roles in tissue homeostasis and disease. Bone marrow-derived monocytes differentiate into Ly6Chi and Ly6Clo macrophages according to the differential expression of the surface marker protein Ly6C. Ly6Chi and Ly6Clo cells possess diverse functions and transcriptional profiles and can accelerate the disease process or support tissue repair and reconstruction. In this review, we discuss the basic biology of Ly6Chi and Ly6Clo macrophages, including their origin, differentiation, and phenotypic switching, and the diverse functions of Ly6Chi and Ly6Clo macrophages in homeostasis and disease, including in injury, chronic inflammation, wound repair, autoimmune disease, and cancer. Furthermore, we clarify the differences between Ly6Chi and Ly6Clo macrophages and their connections with traditional M1 and M2 macrophages. We also summarize the limitations and perspectives for Ly6Chi and Ly6Clo macrophages. Overall, continued efforts to understand these cells may provide therapeutic approaches for disease treatment.

Background: A photometric stereo method aims to recover the surface normal of a 3D object observed under varying light directions. It is an ill-defined problem because the general reflectance properties of the surface are unknown. Methods: This paper reviews existing data-driven methods, with a focus on their technical insights into the photometric stereo problem. We divide these methods into two categories, per-pixel and all-pixel, according to how they process an image. We discuss the differences and relationships between these methods from the perspective of inputs, networks, and data, which are key factors in designing a deep learning approach. Results: We demonstrate the performance of the models using a popular benchmark dataset. Conclusions: Data-driven photometric stereo methods have shown that they possess a superior performance advantage over traditional methods. However, these methods suffer from various limitations, such as limited generalization capability. Finally, this study suggests directions for future research.

Ambient temperature and nitrogen (N) fertilizer are two of the most important factors that affect rice grain quality. However, less information has been available on the interactive effect of N fertilizer and ambient temperature on grain quality under stressful high temperature (HT). In this article, the effects of panicle N fertilizer, ambient temperature, and their interaction on starch composition, particle size distribution of starch granules, starch physicochemical properties, and storage protein accumulation in milled grains were investigated to clarify the potential role of panicle N fertilizer topdressing in regulating rice grain quality under stressful HT by using a two-factor experiment of three N levels in combination with two temperature regimes. Results showed that appropriate application of panicle N fertilizer could attenuate the adverse effect of HT during grain filling on milling quality and chalky occurrence to some extent, particularly for the effective alleviation of HT-induced decrease in milling quality. However, the topdressing of panicle N fertilizer tended to enhance starch gelatinization enthalpy (ΔH) and its setback viscosity in HT-ripening grains, with the simultaneous decrements in the number and surface area proportions of smaller starch granules under the higher N fertilizer in combination with HT exposure. The effects of higher nitrogen fertilizer and HT exposure on total protein content and gluten composition of grains were additively increased. Hence, the topdressing of panicle N fertilizer exacerbated HT-induced deterioration in cooking and eating quality, rather than alleviating the negative impact of HT exposure on the palatability of cooked rice.

Long noncoding RNAs (lncRNAs) play a significant role in cancer biology. This study aimed to determine the roles of lncRNAs in establishing the differences in clinical features between patients with papillary thyroid carcinoma (PTC) without Hashimoto’s thyroiditis (HT) and patients with PTC and HT. In the present study, we detected the differentially expressed lncRNAs between tumor tissues of patients with PTC with or without HT through lncRNA microarrays. The data were verified and analyzed through qRT-PCR, cell viability, cell cycle and bioinformatics analyses. We found that 1031 lncRNAs and 1338 mRNAs were abnormally expressed in 5 tissue samples of PTC complicated with HT [PTC/HT (+)] compared with 5 samples of PTC without HT [PTC/HT (-)]. Gene Ontology and pathway analyses of the mRNAs suggested that several biological processes and pathways, particularly immune system processes, were induced in the PTC/HT (+) tissues. Twenty lncRNAs were verified in 31 PTC/HT (+) and 64 PTC/HT (-) specimens by qRT-PCR, and the results were consistent with the microarray data. Specifically, ENST00000452578, a downregulated lncRNA in PTC/HT(+), was negatively correlated with the tumor size. Cell viability assays revealed that ENST00000452578 could inhibit cell proliferation. Our results indicate that lncRNAs and mRNAs play an important role in establishing the different clinical characteristics between patients with PTC/HT(+) and patients with PTC/HT(-), and might provide new insights from the perspective of RNA for obtaining a further understanding of the clinical features related to PTC with HT.

This study aimed to evaluate the fermentation quality, bacterial community, and nitrate content of sorghum-sudangrass silage with two ensiling densities [550 kg fresh weight (FW)/m3 (low density, LD) and 650 kg FW/m3 (high density, HD)] stored at two temperatures [10°C (low temperature, LT) and 25°C (normal temperature, NT)] for 60 days. The fermentation parameters, microbial counts, bacterial community, nutritional composition, and nitrate and nitrite levels were assessed. The pH and ammonia nitrogen (N) in all silages were below 4.0 and 80 g/kg total N, respectively. Compared with LT treatments, NT treatments had lower pH and lactic acid (LA) bacteria and yeasts counts and contained higher LA and LA/acetic acid (LA/AA) (p < 0.05). The LT-LD contained more ammonia–N than LT-HD (p < 0.05) and had higher nitrate and lower nitrate degradation than other treatments (p < 0.05). Lactobacillus was the most dominant genus with all treatments (57.2–66.9%). The LA, LA/AA, and abundances of Pantoea, Pseudomonas, and Enterobacter in the silage negatively correlated with nitrate concentration and positively correlated with nitrate degradation (p < 0.05). Moreover, pH and ammonia–N were positively correlated with nitrate concentration and negatively correlated with nitrate degradation (p < 0.05). Overall, all silage had satisfactory fermentation quality, and the silage with HD and NT had better fermentation quality and higher nitrate degradation. The bacterial communities in all silages were dominated by Lactobacillus. The nitrate degradation during the fermentation process might be related to the fermentation quality and the activity of Pantoea, Pseudomonas, and Enterobacter in silage.

The method of atmospheric corrosion protection of metals has always been a research hot spot at home and abroad. In this paper, superhydrophobic coating is prepared on the surface of galvanized steel by chemical etching using 1H,1H,2H,2H-perfluorodecyltriethoxysilane (PFDTES), graphene oxide (GO), anhydrous ethanol and water-based varnish as the main raw materials. The constitution of the superhydrophobic coating surface and the corrosion resistance of the steel are studied by electrochemical testing, X-ray diffraction (XRD), scanning electron microscopy (SEM), X-ray energy dispersive spectroscopy (EDS) and atomic force microscopy (AFM). Meanwhile, COMSOL software is used for the simulation of experiment. The results show that the surface of the superhydrophobic coating is composed of micro–nano sized papillary bulges, which play an important role in the improvement of metal corrosion resistance. The superhydrophobic coating effectively improves the alternating current impedance of the electrode and reduces the corrosion rate of the substrate. In addition, the results calculated by COMSOL software consist of the experimental results.

Although previous studies have recorded that tropical cyclones cause a significant increase in chlorophyll a concentration (Chl-a), most of these results were only based on surface Chl-a observed by satellite data. Using satellite, reanalysis and model data, this study investigated the response of the upper ocean and sea surface Chl-a to three different levels of tropical cyclones in the South China Sea. In our results, the severe tropical storm (STS) did not cause an increase in surface Chl-a or depth-integrated Chl-a in the short term (i.e., ~2 days); the typhoon (TY) increased the surface Chl-a from 0.12 mg·m−3 to 0.15 mg·m−3 in the short term, but the depth-integrated Chl-a did not increase significantly; the super typhoon (STY) caused the surface Chl-a to increase from 0.15 mg·m−3 to 0.37 mg·m−3 in the short term, and also increased the depth-integrated Chl-a from 40.41 mg·m−2 to 42.59 mg·m−2. These results suggest that the increase in the surface Chl-a after TY and STY were primarily caused by physical processes (e.g., vertical mixing). However, the increase in the depth-integrated Chl-a of STY may be due to the entrainment of both nutrients and phytoplankton through upwelling and turbulent mixing under the influence of STY.

ObjectiveOur goal was to investigate the correlation between papillary thyroid carcinoma (PTC) characteristics on ultrasonography and metastases of lymph nodes posterior to the right recurrent laryngeal nerve (LN-prRLN). There is still no good method for clinicians to judge whether a patient needs LN-prRLN resection before surgery, and we also wanted to establish a new scoring system to determine whether patients with papillary thyroid carcinoma require LN-prRLN resection before surgery.Patients and MethodsThere were 482 patients with right or bilateral PTC who underwent thyroid gland resection from December 2015 to December 2017 recruited as study subjects. The relationship between the PTC characteristics on ultrasonography and the metastases of LN-prRLN was analyzed by univariate and logistic regression analyses. Based on the risk factors identified in univariate and logistic regression analysis, a nomogram-based LN-prRLN prediction model was established.ResultLN-prRLN were removed from all patients, of which 79 had LN-prRLN metastasis, with a metastasis rate of 16.39%. Multivariate logistic regression analysis revealed that LN-prRLN metastasis was closely related to sex, age, blood supply, larger tumors (> 1 cm) and capsular invasion. A risk prediction model has been established and fully verified. The calibration curve used to evaluate the nomogram shows that the consistency index was 0.75 ± 0.065.ConclusionPreoperative clinical data, such as sex, age, abundant blood supply, larger tumor (> 1 cm) and capsular invasion, are positively correlated with LN-prRLN metastasis. Our scoring system can help surgeons non-invasively determine which patients should undergo LN-prRLN resection before surgery. We recommend that LN-prRLN resection should be performed when the score is above 103.1.

A Spiking Neural Network (SNN) can be trained indirectly by first training an Artificial Neural Network (ANN) with the conventional backpropagation algorithm, then converting it into an equivalent SNN. To reduce the computational cost of the resulting SNN as measured by the number of spikes, we present Logarithmic Temporal Coding (LTC), where the number of spikes used to encode an activation grows logarithmically with the activation value; and the accompanying Exponentiate-and-Fire (EF) neuron model, which only involves efficient bit-shift and addition operations. Moreover, we improve the training process of ANN to compensate for approximation errors due to LTC. Experimental results indicate that the resulting SNN achieves competitive performance in terms of classification accuracy at significantly lower computational cost than related work.

Novelty search, which was inspired by the nature that evolves creatures with diversity, has shown great potential in solving reinforcement learning (RL) tasks with sparse and deceptive rewards. However, most of the existing novelty search methods evolve the populations through hybrization and mutation, which is inefficient in diverging populations. In this paper, we propose a method which incorporates deep RL with novelty search to improve the efficiency of diverging the populations for novelty search. We first propose a strategy that improves the novelty of individuals generated by genetic algorithm using reinforcement learning. Based on this strategy, we propose a framework that incorporates deep RL with novelty search, and then derive an algorithm to improve the search efficiency of the novelty search for continuous control tasks. Our experimental results show that our method can improve the search efficiency of novelty search and can also provide a competitive performance compared to some of the existing novelty search methods. The implementation of our method is available at: https://github.com/shilx001/NoveltySearch_Improvement.

Abstract Background The objective of this study was to summarize clinical features and PRRT2 mutations of paediatric paroxysmal kinesigenic dyskinesia (PKD) patients and observe the tolerability and effects of morning draughts of oxcarbazepine. Methods Twenty patients diagnosed with PKD at Children’s Hospital of Fudan University between January 2011 and December 2015 were enrolled. These patients’ medical records were reviewed. Peripheral venous blood was obtained from all enrolled patients, and polymerase chain reaction (PCR) and Sanger sequencing were used to sequence proline-rich transmembrane protein 2 (PRRT2) gene mutations. Clinical features of PKD patients with and without PRRT2 mutations were compared. All enrolled patients were treated with morning draughts of oxcarbazepine (OXC). The starting dose was 5 mg/kg·d, and the dose was increased by 5 mg/kg·d each week until attacks stopped. Effective doses and adverse effects were recorded. Results For all enrolled patients, dyskinesia was triggered by sudden movement. Dyskinetic movement usually involved the limbs and was bilateral; the majority of enrolled patients exhibited both dystonia and choreoathetosis. We identified PRRT2 mutations in 5 patients, including 4 familial patients and 1 sporadic patient. All 20 patients took low doses of OXC (5–20 mg/kg·d) as draughts in the morning, and dyskinesia attacks stopped in 19 patients. Conclusions Paediatric PKD patients have various phenotypes. PRRT2 mutations are common in familial cases. OXC taken as morning draughts can be a treatment option for paediatric PKD patients.

Based on known cDNAs of rice starch synthase isoforms, we constructed dsRNA interference vectors for starch synthase I (SSI) to produce transgenic plants containing starch with a moderately high amylose content. We investigated the effect of SSI suppression on grain quality traits, starch biosynthesis, and amylopectin chain distribution in rice plants exposed to two different temperature regimes. The activities and transcripts of BEs, DBEs, and other SS isoforms were further investigated to clarify the effect of SSI suppression on these key enzymes and their specific isoforms under different temperature treatments. Suppression of SSI by RNAi altered grain starch component and amylopectin chain distribution, but it exerted only a slight effect on total starch content (%) and accumulation amount (mg kernel−1) and on starch granule morphology and particle size distribution. Under normal temperature (NT), insignificant differences in kernel weight, chalky kernel proportion, chalky degree, and starch granule morphology between SSI-RNAi line and its wild type (WT) were observed. However, amylose content (AC) level and granule-bound starch synthase (GBSS) activity in rice endosperms were markedly increased by SSI-RNAi suppression. The chalky kernel proportion and chalky degree of SSI-RNAi lines were significantly higher than those of WT under high temperature (HT) exposure at filling stage. Inhibition of SSI by RNAi affected amylopectin chain distribution and raised starch gelatinization temperature (GT) in two ways: directly from the SSI deficiency itself and indirectly by reducing BEIIb amounts in an SSI-deficient background. The deficiency of SSI expression led to an alteration in the susceptibility of grain chalkiness occurrence and starch gelatinization temperature to HT exposure, owing to a pleiotropic effect of SSI deficiency on the expression of other genes associated with starch biosynthesis. Keywords: Rice, Starch synthase I, RNA interference, Grain quality, Amylopectin, High temperature

Abstract Background The utilization of Chlorella for the dual goals of biofuel production and wastewater nutrient removal is highly attractive. Moreover, this technology combined with flue gas (rich in CO2) cleaning is considered to be an effective way of improving biofuel production. However, the sterilization of wastewater is an energy-consuming step. This study aimed to comprehensively evaluate a cost-effective method of culturing Chlorella pyrenoidosa in unsterilized piggery wastewater for biofuel production by sparging air or simulated flue gas, including algal biomass production, lipid production, nutrient removal rate and the mutual effects between algae and other microbes. Results The average biomass productivity of C. pyrenoidosa reached 0.11 g L−1 day−1/0.15 g L−1 day−1 and the average lipid productivity reached 19.3 mg L−1 day−1/30.0 mg L−1 day−1 when sparging air or simulated flue gas, respectively. This method achieved fairish nutrient removal efficiency with respect to chemical oxygen demand (43.9%/55.1% when sparging air and simulated flue gas, respectively), ammonia (98.7%/100% when sparging air and simulated flue gas, respectively), total nitrogen (38.6%/51.9% when sparging air or simulated flue gas, respectively) and total phosphorus (42.8%/60.5% when sparging air or simulated flue gas, respectively). Culturing C. pyrenoidosa strongly influenced the microbial community in piggery wastewater. In particular, culturing C. pyrenoidosa enriched the abundance of the obligate parasite Vampirovibrionales, which can result in the death of Chlorella. Conclusion The study provided a comprehensive evaluation of culturing C. pyrenoidosa in unsterilized piggery wastewater for biofuel production. The results indicated that this cost-effective method is feasible but has considerable room for improving. More importantly, this study elucidated the mutual effects between algae and other microbes. In particular, a detrimental effect of the obligate parasite Vampirovibrionales on algal biomass and lipid production was found.

Abstract Background Genome-wide association studies (GWAS) of diseases and traits have found associations to gene regions but not the functional SNP or the gene mediating the effect. Difference in gene regulatory signals can be detected using chromatin immunoprecipitation and next-gen sequencing (ChIP-seq) of transcription factors or histone modifications by aligning reads to known polymorphisms in individual genomes. The aim was to identify such regulatory elements in the human liver to understand the genetics behind type 2 diabetes and metabolic diseases. Methods The genome of liver tissue was sequenced using 10X Genomics technology to call polymorphic positions. Using ChIP-seq for two histone modifications, H3K4me3 and H3K27ac, and the transcription factor CTCF, and our established bioinformatics pipeline, we detected sites with significant difference in signal between the alleles. Results We detected 2329 allele-specific SNPs (AS-SNPs) including 25 associated to GWAS SNPs linked to liver biology, e.g., 4 AS-SNPs at two type 2 diabetes loci. Two hundred ninety-two AS-SNPs were associated to liver gene expression in GTEx, and 134 AS-SNPs were located on 166 candidate functional motifs and most of them in EGR1-binding sites. Conclusions This study provides a valuable collection of candidate liver regulatory elements for further experimental validation.

Abstract Video summarization aims to provide a compact video representation while preserving the essential activities of the original video. Most existing video summarization approaches relay on identifying important frames and optimizing target energy by a global optimum solution. But global optimum may fail to express continuous action or realistically validate how human beings perceive a story. In this paper, we present a bottom-up approach named clip growing for video summarization, which allows users to customize the quality of the video summaries. The proposed approach firstly uses clustering to oversegment video frames into video clips based on their similarity and proximity. Simultaneously, the importance of frames and clips is evaluated from their corresponding dissimilarity and representativeness. Then, video clips and frames are gradually selected according to their energy rank, until reaching the target length. Experimental results on SumMe dataset show that our algorithm can produce promising results compared to existing algorithms. Several video summarizations results are presented in supplementary material.

The surfaces of real objects can visually appear to be glossy, matte, or anywhere in between, but essentially, they display varying degrees of diffuse and specular reflectance. Diffuse and specular reflectance provides different clues for light estimation. However, few methods simultaneously consider the contributions of diffuse and specular reflectance for light estimation. To this end, we propose ReDDLE-Net, which performs Reflectance Decomposition for Directional Light Estimation. The primary idea is to take advantage of diffuse and specular clues and adaptively balance the contributions of estimated diffuse and specular components for light estimation. Our method achieves a superior performance advantage over state-of-the-art directional light estimation methods on the DiLiGenT benchmark. Meanwhile, the proposed ReDDLE-Net can be combined with existing calibrated photometric stereo methods to handle uncalibrated photometric stereo tasks and achieve state-of-the-art performance.

Autism spectrum disorder (ASD) is a heterogeneous neuropsychiatric disorder with a complex genetic background. Analysis of altered molecular processes in ASD patients requires linear and nonlinear methods that provide interpretable solutions. Interpretable machine learning provides legible models that allow explaining biological mechanisms and support analysis of clinical subgroups. In this work, we investigated several case-control studies of gene expression measurements of ASD individuals. We constructed a rule-based learning model from three independent datasets that we further visualized as a nonlinear gene-gene co-predictive network. To find dissimilarities between ASD subtypes, we scrutinized a topological structure of the network and estimated a centrality distance. Our analysis revealed that autism is the most severe subtype of ASD, while pervasive developmental disorder-not otherwise specified and Asperger syndrome are closely related and milder ASD subtypes. Furthermore, we analyzed the most important ASD-related features that were described in terms of gene co-predictors. Among others, we found a strong co-predictive mechanism between EMC4 and TMEM30A, which may suggest a co-regulation between these genes. The present study demonstrates the potential of applying interpretable machine learning in bioinformatics analyses. Although the proposed methodology was designed for transcriptomics data, it can be applied to other omics disciplines.

Abstract There is an urgent need for enhancing climate change resilience in rice production. Here, we report a reflective algal mulching‐based culture practice to attain a simple and low‐cost biological method for enhancement of climate change resilience. The dense mulching of Rhizoclonium sp. in field water highly reflected sunlight in the visible and infrared regions, thus significantly decreasing the root zone temperature, and significantly and simultaneously mitigated CO2, CH4, and N2O emissions through gas retention and removal in the field water layer, as well as reducing gas production. The algae could also enhance rice growth and soil fertility.

Recommendation systems and computing advertisements are of great value for commercial applications. Click-through rate (CTR) prediction is a critical issue because the prediction accuracy affects the user experience and the revenue of merchants and platforms. Feature engineering is usually used to improve the click-through rate prediction; however, it heavily relies on user experience. It is difficult to construct a feature combination that can describe the complex patterns implied in the data. This paper combines the traditional feature combination methods and the deep neural networks to automate the feature combinations to improve the accuracy of the click-through rate prediction. We propose a mechanism named Field-aware Neural Factorization Machine (FNFM). This paper can have strong second-order feature interactive learning ability, such as Field-aware Factorization Machine; on this basis, a deep neural network is used for higher order feature combination learning. This experiment shows that the model has stronger expression ability than previous deep learning feature combination models, such as the DeepFM, DCN, and NFM.

The prevalence of smartphones equipped with various sensors enables the pervasive capture of users' location data. WiFi scan lists on one smartphone, i.e., scan results of the network in a range, can roughly indicate the physical location of the phone in a time period. Considering the close relationship between location and daily life, users' lifestyle can be inferred from their WiFi scan lists. Given the issue of user privacy, in this paper, we explore anonymized WiFi scan lists to discover users' lifestyle. Individual lifestyle about mobility and important places of home and workplaces is discovered, respectively, based on the stay places extracted from anonymized WiFi scan lists and the reconstructed mobility trajectories. We first learn the lifestyle about mobility by detecting activity areas from mobility trajectories and introducing two metrics of activeness and diversity to measure individual mobility. Then, we discover the lifestyle about the home and workplaces identified from anonymized WiFi scan lists, such as stay duration at home, activeness of going outside at night, and working hours on weekdays and weekends. The experiments were conducted on a real-world large-scale dataset, which contains records of smartphone usage of more than 17 000 volunteering participants. Our work is a promising step toward automatically discover people's lifestyle from anonymized smartphone data.