Laser capture microscopy (LCM) coupled with global transcriptome profiling could enable precise analyses of cell populations without the need for tissue dissociation, but has so far required ...relatively large numbers of cells. Here we report a robust and highly efficient strategy for LCM coupled with full-length mRNA-sequencing (LCM-seq) developed for single-cell transcriptomics. Fixed cells are subjected to direct lysis without RNA extraction, which both simplifies the experimental procedures as well as lowers technical noise. We apply LCM-seq on neurons isolated from mouse tissues, human post-mortem tissues, and illustrate its utility down to single captured cells. Importantly, we demonstrate that LCM-seq can provide biological insight on highly similar neuronal populations, including motor neurons isolated from different levels of the mouse spinal cord, as well as human midbrain dopamine neurons of the substantia nigra compacta and the ventral tegmental area.
Despite evidence suggesting the utility of Epstein‐Barr virus (EBV) markers to stratify individuals with respect to nasopharyngeal carcinoma (NPC) risk in NPC high‐risk regions, no validated NPC risk ...prediction model exists. We aimed to validate an EBV‐based NPC risk score in an endemic population undergoing screening for NPC. This prospective study was embedded within an ongoing NPC screening trial in southern China initiated in 2008, with 51 235 adult participants. We assessed the score's discriminatory ability (area under the receiver‐operator‐characteristics curve, AUC). A new model incorporating the EBV score, sex and family history was developed using logistic regression and internally validated using cross‐validation. AUCs were compared. We also calculated absolute NPC risk combining the risk score with population incidence and competing mortality data. A total of 151 NPC cases were detected in 2008 to 2016. The EBV‐based score was highly discriminating, with AUC = 0.95 (95% CI = 0.93‐0.97). For 90% specificity, the score had 87.4% sensitivity (95% CI = 81.0‐92.3%). As specificity increased from 90% to 99%, the positive predictive value increased from 2.4% (95% CI = 1.9‐3.0%) to 12.5% (9.9‐15.5%). Correspondingly, the number of positive tests per detected NPC case decreased from 272 (95% CI = 255‐290) to 50 (41‐59). Combining the score with other risk factors (sex, first‐degree family history of NPC) did not improve AUC. Men aged 55 to 59 years with the highest risk profile had the highest 5‐year absolute NPC risk of 6.5%. We externally validated the discriminatory accuracy of a previously developed EBV score in a high‐risk population. Adding nonviral risk factors did not improve NPC prediction.
What's new?
Evidence suggests that markers of Epstein‐Barr virus (EBV) infection are useful in screening for nasopharyngeal carcinoma (NPC). Few prospective studies, however, have validated the performance of EBV‐based risk scores for NPC. In this prospective validation study with data for more than 51 000 participants, EBV‐based risk score based on the combination of IgA antibodies against viral capsid antigen and EBV nuclear antigen 1 was found to be highly discriminating for NPC over follow‐up periods lasting five years. The findings indicate that EBV‐based scores could be valuable risk‐prediction tools for early NPC diagnosis, leading to improved clinical outcomes.
Network slicing (NS) is an emerging technology in recent years, which enables network operators to slice network resources (e.g., bandwidth, power, spectrum, etc.) in different types of slices, so ...that it can adapt to different application scenarios of 5 g network: enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) and ultra-reliable and low-latency communications (URLLC). In order to allocate these sliced network resources more effectively to users with different needs, it is important that manage the allocation of network resources. Actually, in the practical network resource allocation problem, the resources of the base station (BS) are limited and the demand of each user for mobile services is different. To better deal with the resource allocation problem, more effective methods and algorithms have emerged in recent years, such as the bidding method, deep learning (DL) algorithm, ant colony algorithm (AG), and wolf colony algorithm (WPA). This paper proposes a two tier slicing resource allocation algorithm based on Deep Reinforcement Learning (DRL) and joint bidding in wireless access networks. The wireless virtual technology divides mobile operators into infrastructure providers (InPs) and mobile virtual network operators (MVNOs). This paper considers a single base station, multi-user shared aggregated bandwidth radio access network scenario and joins the MVNOs to fully utilize base station resources, and divides the resource allocation process into two tiers. The algorithm proposed in this paper takes into account both the utilization of base station (BS) resources and the service demand of mobile users (MUs). In the upper tier, each MVNO is treated as an agent and uses a combination of bidding and Deep Q network (DQN) allows the MVNO to get more resources from the base station. In the lower tier allocation process, each MVNO distributes the received resources to the users who are connected to it, which also uses the Dueling DQN method for iterative learning to find the optimal solution to the problem. The results show that in the upper tier, the total system utility function and revenue obtained by the proposed algorithm are about 5.4% higher than double DQN and about 2.6% higher than Dueling DQN; In the lower tier, the user service quality obtained by using the proposed algorithm is more stable, the system utility function and Se are about 0.5-2.7% higher than DQN and Double DQN, but the convergence is faster.
Amyloid precursor protein (APP) has an important function in the generation of Alzheimer's disease (AD). In our previous study, miR‑193b was found to be downregulated in the hippocampi of 9‑month‑old ...APP/PS1 double‑transgenic mice using microRNA (miR) array. In the present study, bioinformatic analyses showed that miR‑193b was a miR that was predicted to potentially target the 3'‑untranslated region (UTR) of APP. Subsequently, the function of miR‑193b on APP was studied. The levels of miR‑193b, exosomal miR‑193b, Aβ, tau, p‑tau, HCY and APOE in samples from APP/PS1 double‑transgenic mice, mild cognitive impairment (MCI) and dementia of Alzheimer‑type (DAT) patients, were measured. The results indicated that overexpression of miR‑193b could repress the mRNA and protein expression of APP. The miR‑193b inhibitor oligonucleotide induced upregulation of APP. Binding sites of miR‑193b in the 3'‑UTR of APP were identified by luciferase assay. MCI and DAT patients had lower exosomal miR‑193b, but not total miR‑193b, in the blood as compared with the controls. DAT patients had lower exosomal miR‑193b levels in blood as compared with the MCI group. A decreased exosomal miR‑193b expression level was additionally observed in the cerebral spinal fluid (CSF) of DAT patients. Negative correlations were found between exosomal miR‑193b and Aβ42 in the CSF of DAT patients. In conclusion, these findings showed that miR‑193b may function in the development of AD and exosomal miR‑193b has potential as a novel, non-invasive, blood‑based biomarker of MCI and DAT patients.
Stretchable conductors are the basic units of advanced flexible electronic devices, such as skin‐like sensors, stretchable batteries and soft actuators. Current fabrication strategies are mainly ...focused on the stretchability of the conductor with less emphasis on the huge mismatch of the conductive material and polymeric substrate, which results in stability issues during long‐term use. Thermal‐radiation‐assisted metal encapsulation is reported to construct an interlocking layer between polydimethylsiloxane (PDMS) and gold by employing a semipolymerized PDMS substrate to encapsulate the gold clusters/atoms during thermal deposition. The stability of the stretchable conductor is significantly enhanced based on the interlocking effect of metal and polymer, with high interfacial adhesion (>2 MPa) and cyclic stability (>10 000 cycles). Also, the conductor exhibits superior properties such as high stretchability (>130%) and large active surface area (>5:1 effective surface area/geometrical area). It is noted that this method can be easily used to fabricate such a stretchable conductor in a wafer‐scale format through a one‐step process. As a proof of concept, both long‐term implantation in an animal model to monitor intramuscular electric signals and on human skin for detection of biosignals are demonstrated. This design approach brings about a new perspective on the exploration of stretchable conductors for biomedical applications.
Thermal‐radiation‐assisted metal encapsulation is used to prepare large‐scale high‐performance stretchable conductors that possess high stretchability, stability and adhesion and large surface area. They are used to simultaneously monitor electromyography and skin deformation and implanted to detect intramuscular signals. This study offers a new path for highly stable stretchable conductors and related biointerface applications.
The Buckley-James method for the classical accelerated failure time model has been extended to accommodate heteroscedastic survival data in two ways. The first is the weighted least squares method Yu ...et al. Weighted least-squares method for right-censored data in accelerated failure time model. Biometrics. 2013;69:358-365, which estimates the heteroscedasticity nonparametrically, while the second is the local Buckley-James method Pang et al. Local Buckley-James estimation for heteroscedastic accelerated failure time model. Stat Sin. 2015;25:863-877, which uses local Kaplan-Meier method to estimate the heteroscedasticity. However, no comparisons have been done for these two methods. Furthermore, there is no hypothesis testing procedure for this heteroscedastic accelerated failure time model. This paper is then aimed to fill these two gaps to compare the two methods theoretically and numerically with extensive simulation studies. In addition, we propose a class of hypothesis tests for the parameters to provide a complete procedure for analysing heteroscedastic survival data. Two real data examples are used for practical illustration of the comparison and the new proposed tests.
Due to the hidden nature and complexity of resistance spot welding weld nugget formation, how to avoid the time-consuming and money-consuming problem of traditional defect diagnosis methods and ...accurately grasp the weld nugget status is still an urgent problem. In this paper, an improved GAN model is proposed to solve the corresponding problem by combining the weld nugget defects with the dynamic resistance curve. Aiming at the problem that traditional GAN algorithms are prone to pattern collapse, this paper utilizes a variational autoencoder integrated with a channel attention mechanism as the generator part of the generative adversarial network, which helps the model pay better attention to the high-weight part of the defective sample data and combines the encoding and decoding processes to highlight defective features, thus reconstructing the defective samples with higher quality. Convolutional neural networks are then utilized to identify the features of the generated samples and diagnose the type of weldment defects. The test results show that the proposed scheme is highly reliable and the model outperforms other schemes in diagnosing welded nugget defects under the same conditions, avoiding undesirable effects such as underfitting. The validation of the actual dataset shows that, compared with other diagnostic methods that generally have an accuracy rate of less than 75%, the accuracy of the weld nugget defects diagnosis of this paper's method reaches more than 94%, which is a positive impetus to the development of auto body welding diagnosis.
•Provides guidance for diagnosis in the presence of small samples of weld nugget defects•Proposes a method for diagnosing weld defect types that incorporates the characteristics of the spot-welding process.•An improved generative adversarial network is used to reconstruct weld nugget samples with defect characteristics.•Performance comparison demonstrates the effectiveness of the proposed method in diagnosing weld nugget defect types.
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•DMPNN exhibited a good performance to predict the logBCFs of target bisphenols.•DMPNN predicted the estrogen receptor (ER) binding of target bisphenols.•Estrogenic effects of ...bisphenols correlated well with their logBCF and ER binding.•Gender difference was noted in bioconcentration and estrogenicity of bisphenols.
The bioconcentration factor (BCF) is a key parameter for bioavailability assessment of environmental pollutants in regulatory frameworks. The comparative toxicology and mechanism of action of congeners are also of concern. However, there are limitations to acquire them by conducting field and laboratory experiments while machinelearning is emerging as a promising predictive tool to fill the gap. In this study, the Direct Message Passing Neural Network (DMPNN) was applied to predict logBCFs of bisphenol A (BPA) and its four analogues (bisphenol AF (BPAF), bisphenol B (BPB), bisphenol F (BPF) and bisphenol S (BPS)). For the test set, the Pearson correlation coefficient (PCC) and mean square error (MSE) were 0.85 and 0.52 respectively, suggesting a good predictive performance. The predicted logBCFs values by the DMPNN ranging from 0.35 (BPS) to 2.14 (BPAF) coincided well with those by the classical EPI Suite (BCFBAF model). Besides, estrogen receptor α (ERα) bioactivity of these bisphenols was also predicted well by the DMPNN, with a probability of 97.0 % (BPB) to 99.7 % (BPAF), which was validated by the extent of vitellogenin (VTG) induction in male zebrafish as a biomarker except BPS. Thus, with little need for expert knowledge, DMPNN is confirmed to be a useful tool to accurately predict logBCF and screen for estrogenic activity from molecular structures. Moreover, a gender difference was noted in the changes of three endpoints (logBCF, ER binding affinity and VTG levels), the rank order of which was BPAF > BPB > BPA > BPF > BPS consistently, and abnormal amino acid metabolism is featured as an omics signature of abnormal hormone protein expression.
Objectives
To examine the cognitive and neural effects of vision‐based speed‐of‐processing (VSOP) training in older adults with amnestic mild cognitive impairment (aMCI) and contrast those effects ...with an active control (mental leisure activities (MLA)).
Design
Randomized single‐blind controlled pilot trial.
Setting
Academic medical center.
Participants
Individuals with aMCI (N = 21).
Intervention
Six‐week computerized VSOP training.
Measurements
Multiple cognitive processing measures, instrumental activities of daily living (IADLs), and two resting state neural networks regulating cognitive processing: central executive network (CEN) and default mode network (DMN).
Results
VSOP training led to significantly greater improvements in trained (processing speed and attention: F1,19 = 6.61, partial η2 = 0.26, P = .02) and untrained (working memory: F1,19 = 7.33, partial η2 = 0.28, P = .01; IADLs: F1,19 = 5.16, partial η2 = 0.21, P = .03) cognitive domains than MLA and protective maintenance in DMN (F1, 9 = 14.63, partial η2 = 0.62, P = .004). VSOP training, but not MLA, resulted in a significant improvement in CEN connectivity (Z = −2.37, P = .02).
Conclusion
Target and transfer effects of VSOP training were identified, and links between VSOP training and two neural networks associated with aMCI were found. These findings highlight the potential of VSOP training to slow cognitive decline in individuals with aMCI. Further delineation of mechanisms underlying VSOP‐induced plasticity is necessary to understand in which populations and under what conditions such training may be most effective.
In the censored data exploration, the classical linear regression model which assumes normally distributed random errors is perhaps one of the commonly used frameworks. However, practical studies ...have often criticized the classical linear regression model because of its sensitivity to departure from the normality and partial nonlinearity. This paper proposes to solve these potential issues simultaneously in the context of the partial linear regression model by assuming that the random errors follow a scale-mixture of normal (SMN) family of distributions. The postulated method allows us to model data with great flexibility, accommodating heavy tails and outliers. By implementing the B-spline approximation and using the convenient hierarchical representation of the SMN distributions, a computationally analytical EM-type algorithm is developed for obtaining maximum likelihood (ML) parameter estimates. Various simulation studies are conducted to investigate the finite sample properties, as well as the robustness of the model in dealing with the heavy tails distributed datasets. Real-world data examples are finally analyzed for illustrating the usefulness of the proposed methodology.
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