Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness

Representation multi-task learning (MTL) and transfer learning (TL) have achieved tremendous success in practice. However, the theoretical understanding of these methods is still lacking. Most existing theoretical works focus on cases where all tasks share the same representation, and claim that MTL and TL almost always improve performance. However, as the number of tasks grow, assuming all tasks share the same representation is unrealistic. Also, this does not always match empirical findings, which suggest that a shared representation may not necessarily improve single-task or target-only learning performance. In this paper, we aim to understand how to learn from tasks with \textit{similar but not exactly the same} linear representations, while dealing with outlier tasks. We propose two algorithms that are \textit{adaptive} to the similarity structure and \textit{robust} to outlier tasks under both MTL and TL settings. Our algorithms outperform single-task or target-only learning when representations across tasks are sufficiently similar and the fraction of outlier tasks is small. Furthermore, they always perform no worse than single-task learning or target-only learning, even when the representations are dissimilar. We provide information-theoretic lower bounds to show that our algorithms are nearly \textit{minimax} optimal in a large regime.Comment: 60 pages, 5 figure

Vertical-supercooling-controlled interfacial instability for a spreading liquid film

Thermal effect is essential to regulate the interfacial instabilities for diverse technology applications. Here we report the fingering instability at the propagation front for a spreading liquid film subjected to the supercooling at the vertical direction. We find the onset timescale of hydrodynamic instability is strongly correlated with that of the vertical solidification process. This correlation is further validated in a non-uniform geometry, demonstrating the capability of controlling fingering instability by structure design. Moreover, based on the experimental observations, we propose a physical mechanism by considering thermal Marangoni effect at the spreading front, and the predicted wavelength from the linear stability analysis agrees with experiments excellently. This work offers another valuable dimension by gating the vertical temperature to exploit the interfacial stabilities and steer liquid flow, consequently shedding light on the microfluidic cooling for electronics, and the advanced functional fibers and fabrics

Vertical Stress and Deformation Characteristics of Roadside Backfilling Body in Gob-Side Entry for Thick Coal Seams with Different Pre-Split Angles

Retained gob-side entry (RGE) is a significant improvement for fully-mechanized longwall mining. The environment of surrounding rock directly affects its stability. Roadside backfilling body (RBB), a man-made structure in RGE plays the most important role in successful application of the technology. In the field, however, the vertical deformation of RBB is large during the panel extraction, which leads to malfunction of the RGE. In order to solve the problem, roof pre-split is employed. According to geological conditions as well as the physical modeling of roof behavior and deformation of surrounding rock, the support resistance of RBB is calculated. The environment of surrounding rock, vertical stress and vertical deformation of the RBB in the RGE with different roof pre-split angles are analyzed using FLAC3D software. With the increase of roof pre-split angle, the vertical stresses both in the coal wall and RBB are minimum, and the vertical deformation of RBB also decreases from 110.51 mm to 6.1 mm. Therefore, based on the results of numerical modeling and field observation, roof pre-split angle of 90° is more beneficial to the maintenance of the RGE

HyFish: hydrological factor fusion for prediction of fishing effort distribution with VMS dataset

Predicting fishing effort distribution is crucial for guiding fisheries management in developing effective strategies and protecting marine ecosystems. This task requires a deep understanding of how various hydrological factors, such as water temperature, surface height, salinity, and currents influence fishing activities. However, there are significant challenges in designing the prediction model. Firstly, how hydrological factors affect fishing effort distributions remains unquantified. Secondly, the prediction model must effectively integrate the spatial and temporal dynamics of fishing behaviors, a task that shows analytical difficulties. In this study, we first quantify the correlation between hydrological factor fields and fishing effort distributions through spatiotemporal analysis. Building on the insights from this analysis, we develop a deep-learning model designed to forecast the daily distribution of fishing effort for the upcoming week. The proposed model incorporates residual networks to extract features from both the fishing effort distribution and the hydrological factor fields, thus addressing the spatial limits of fishing activity. It also employs Long Short-Term Memory (LSTM) networks to manage the temporal dynamics of fishing activity. Furthermore, an attention mechanism is included to capture the importance of various hydrological factors. We apply the approach to the VMS dataset from 1,899 trawling fishing vessels in the East China Sea from September 2015 to May 2017. The dataset from September 2015 to May 2016 is used for correlation analysis and training the prediction model, while the dataset from September 2016 to May 2017 is employed to evaluate the prediction accuracy. The prediction error ratio for each day of the upcoming week range is only 5.6% across all weeks from September 2016 to May 2017. HyFish, notable for its low prediction error ratio, will serve as a versatile tool in fisheries management for developing sustainable practices and in fisheries research for providing quantitative insights into fishing resource dynamics and assessing ecological risks related to fishing activities

Impact of smoking on the incidence and post-operative complications of total knee arthroplasty: A systematic review and meta-analysis of cohort studies

Osteoarthritis and rheumatoid arthritis are the most ubiquitous joint disorders which cause tremendous loss of life quality and impose an economic burden on society. At present, the treatment options for these two diseases comprise non-operative and surgical treatments, amongst those total knee arthroplasties (TKA). Various studies have recognized smoking as a significant risk factor for postoperative complications. Therefore, the purpose of this study was to examine the impact of smoking on the incidence and postoperative complications after a total knee arthroplasty by a systematic review and meta-analysis. The research was performed using PUBMED, Cochrane Library and EMBASE, extracting data from thirteen suitable studies and incorporating 2,109,482 patients. Cohort studies evaluating the impact of smoking on TKA with sufficient data were included for the study, and cohort studies without a proper control group and complete data were excluded. A fixed-effects or random-effects model was used to measure the pooled risk ratio (RR) or hazard ratio (HR) with 95% confidence interval (CI). Compared to non-smokers, smokers had a significantly lower incidence of TKA (p<0.01). However, smokers had a higher incidence of total complications (p=0.01), surgical complications (p<0.01), pneumonia (p<0.01) and revision surgery (p=0.01). No significant difference in the risk of blood transfusion (p=0.42), deep vein thrombosis (p=0.31), pulmonary embolism (p=0.34), urinary tract infection (p=0.46) or mortality (p=0.39) was found between smokers and non-smokers. In conclusion, the study indicated that tobacco has two diametrically opposite effects on TKA patients: 1. Tobacco increases the incidence of surgical complications, pneumonia and revision after TKA; 2. It decreases the overall risk of being a candidate for TKA

A Bidirectional Label Propagation Based Computational Model for Potential Microbe-Disease Association Prediction

A growing number of clinical observations have indicated that microbes are involved in a variety of important human diseases. It is obvious that in-depth investigation of correlations between microbes and diseases will benefit the prevention, early diagnosis, and prognosis of diseases greatly. Hence, in this paper, based on known microbe-disease associations, a prediction model called NBLPIHMDA was proposed to infer potential microbe-disease associations. Specifically, two kinds of networks including the disease similarity network and the microbe similarity network were first constructed based on the Gaussian interaction profile kernel similarity. The bidirectional label propagation was then applied on these two kinds of networks to predict potential microbe-disease associations. We applied NBLPIHMDA on Human Microbe-Disease Association database (HMDAD), and compared it with 3 other recent published methods including LRLSHMDA, BiRWMP, and KATZHMDA based on the leave-one-out cross validation and 5-fold cross validation, respectively. As a result, the area under the receiver operating characteristic curves (AUCs) achieved by NBLPIHMDA were 0.8777 and 0.8958 ± 0.0027, respectively, outperforming the compared methods. In addition, in case studies of asthma, colorectal carcinoma, and Chronic obstructive pulmonary disease, simulation results illustrated that there are 10, 10, and 8 out of the top 10 predicted microbes having been confirmed by published documentary evidences, which further demonstrated that NBLPIHMDA is promising in predicting novel associations between diseases and microbes as well

Genomic signatures underlying the oogenesis of the ectoparasitic mite Varroa destructor on its new host Apis mellifera

Introduction Host shifts of parasites can have devastating effects on novel hosts. One remarkable example is that of the ectoparasitic mite Varroa destructor, which has shifted hosts from Eastern honey bees (Apis cerana) to Western honey bees (Apis mellifera) and posed a major global threat to apiculture and wild honey bees. Objectives and methods To uncover the mechanisms underlying this rare successful host shift, we conducted a whole-genome analysis of host-shifted and nonshifted V. destructor mites and a cross-fostering infestation experiment. Results We found that oogenesis was upregulated in host-shifted mites on the new host A. mellifera relative to nonshifted mites. The transcriptomes of the host-shifted and nonshifted mites significantly differed as early as 1 h post-infestation of the new hosts. The differentially expressed genes were associated with nine genes carrying nonsynonymous high-FST SNPs, including mGluR2-like, Lamb2-like and Vitellogenin 6-like, which were also differentially expressed, and eIF4G, CG5800, Dap160 and Sas10, which were located in the center of the networks regulating the differentially expressed genes based on protein–protein interaction analysis. Conclusions The annotated functions of these genes were all associated with oogenesis. These genes appear to be the key genetic determinants of the oogenesis of host-shifted mites on the new host. Further study of these candidate genes will help elucidate the key mechanism underlying the success of host shifts of V. destructor

A Novel Human Microbe-Disease Association Prediction Method Based on the Bidirectional Weighted Network

The survival of human beings is inseparable from microbes. More and more studies have proved that microbes can affect human physiological processes in various aspects and are closely related to some human diseases. In this paper, based on known microbe-disease associations, a bidirectional weighted network was constructed by integrating the schemes of normalized Gaussian interactions and bidirectional recommendations firstly. And then, based on the newly constructed bidirectional network, a computational model called BWNMHMDA was developed to predict potential relationships between microbes and diseases. Finally, in order to evaluate the superiority of the new prediction model BWNMHMDA, the framework of LOOCV and 5-fold cross validation were implemented, and simulation results indicated that BWNMHMDA could achieve reliable AUCs of 0.9127 and 0.8967 ± 0.0027 in these two different frameworks respectively, which is outperformed some state-of-the-art methods. Moreover, case studies of asthma, colorectal carcinoma, and chronic obstructive pulmonary disease were implemented to further estimate the performance of BWNMHMDA. Experimental results showed that there are 10, 9, and 8 out of the top 10 predicted microbes having been confirmed by related literature in these three kinds of case studies separately, which also demonstrated that our new model BWNMHMDA could achieve satisfying prediction performance

Accuracy of narrow band imaging for detecting the malignant transformation of oral potentially malignant disorders: A systematic review and meta-analysis

ObjectiveOral potentially malignant disorders (OPMDs) are a spectrum of diseases that harbor the potential of malignant transformation and developing into oral squamous cell carcinoma (OSCC). Narrow band imaging (NBI) has been clinically utilized for the adjuvant diagnosis of OPMD and OSCC. This study aimed to comprehensively evaluate the diagnostic accuracy of NBI for malignant transformations of OPMD by applying the intraepithelial papillary capillary loop (IPCL) classification approach.MethodsStudies reporting the diagnostic validity of NBI in the detection of OPMD/OSCC were selected. Four databases were searched and 11 articles were included in the meta-analysis. We performed four subgroup analyses by defining IPCL I/II as negative diagnostic results and no/mild dysplasia as negative pathological outcome. Pooled data were analyzed using random-effects models. Meta-regression analysis was performed to explore heterogeneity.ResultsAfter pooled analysis of the four subgroups, we found that subgroup 1, defining IPCL II and above as a clinically positive result, demonstrated the most optimal overall diagnostic accuracy for the malignant transformation of OPMDs, with a sensitivity and specificity of NBI of 0.87 (95% confidence interval (CI) [0.67, 0.96], p &lt; 0.001) and 0.83 [95% CI (0.56, 0.95), p &lt; 0.001], respectively; while the other 3 subgroups displayed relatively low sensitivity or specificity.ConclusionsNBI is a promising and non-invasive adjunctive tool for identifying malignant transformations of OPMDs. The IPCL grading is currently a sound criterion for the clinical application of NBI. After excluding potentially false positive results, these oral lesions classified as IPCL II or above are suggested to undergo biopsy for early and accurate diagnosis as well as management

Population pharmacokinetics of nalbuphine in patients undergoing general anesthesia surgery

Purpose: The aim of this study was to build a population pharmacokinetics (PopPK) model of nalbuphine and to estimate the suitability of bodyweight or fixed dosage regimen.Method: Adult patients who were undergoing general anesthetic surgery using nalbuphine for induction of anesthesia were included. Plasma concentrations and covariates information were analyzed by non-linear mixed-effects modeling approach. Goodness-of-fit (GOF), non-parametric bootstrap, visual predictive check (VPC) and external evaluation were applied for the final PopPK model evaluation. Monte Carlo simulation was conducted to assess impact of covariates and dosage regimens on the plasma concentration to nalbuphine.Results: 47 patients aged 21–78 years with a body weight of 48–86 kg were included in the study. Among them, liver resection accounted for 14.8%, cholecystectomy for 12.8%, pancreatic resection for 36.2% and other surgeries for 36.2%. 353 samples from 27 patients were enrolled in model building group; 100 samples from 20 patients were enrolled in external validation group. The results of model evaluation showed that the pharmacokinetics of nalbuphine was adequately described by a two-compartment model. The hourly net fluid volume infused (HNF) was identified as a significant covariate about the intercompartmental clearance (Q) of nalbuphine with objective function value (OFV) decreasing by 9.643 (p &lt; 0.005, df = 1). Simulation results demonstrated no need to adjust dosage based on HNF, and the biases of two dosage methods were less than 6%. The fixed dosage regimen had lower PK variability than the bodyweight regimen.Conclusion: A two-compartment PopPK model adequately described the concentration profile of nalbuphine intravenous injection for anesthesia induction. While HNF can affect the Q of nalbuphine, the magnitude of the effect was limited. Dosage adjustment based on HNF was not recommended. Furthermore, fixed dosage regimen might be better than body weight dosage regimen