Motor learning and prediction in a variable environment

Traditional studies of motor learning and prediction have focused on how subjects perform a single task. Recent advances have been made in our understanding of motor learning and prediction by investigating the way we learn variable tasks, which change either predictably or unpredictably over time. Similarly, studies have examined how variability in our own movements affects motor learning.     

Explainable deep transfer learning model for disease risk prediction using high-dimensional genomic data

Building an accurate disease risk prediction model is an essential step in the modern quest for precision medicine. While high-dimensional genomic data provides valuable data resources for the investigations of disease risk, their huge amount of noise and complex relationships between predictors and outcomes have brought tremendous analytical challenges. Deep learning model is the state-of-the-art methods for many prediction tasks, and it is a promising framework for the analysis of genomic data. However, deep learning models generally suffer from the curse of dimensionality and the lack of biological interpretability, both of which have greatly limited their applications. In this work, we have developed a deep neural network (DNN) based prediction modeling framework. We first proposed a group-wise feature importance score for feature selection, where genes harboring genetic variants with both linear and non-linear effects are efficiently detected. We then designed an explainable transfer-learning based DNN method, which can directly incorporate information from feature selection and accurately capture complex predictive effects. The proposed DNN-framework is biologically interpretable, as it is built based on the selected predictive genes. It is also computationally efficient and can be applied to genome-wide data. Through extensive simulations and real data analyses, we have demonstrated that our proposed method can not only efficiently detect predictive features, but also accurately predict disease risk, as compared to many existing methods.     

An ANN-GA model based promoter prediction in Arabidopsis thaliana using tilling microarray data

Identification of promoter region is an important part of gene annotation. Identification of promoters in eukaryotes is important as promoters modulate various metabolic functions and cellular stress responses. In this work, a novel approach utilizing intensity values of tilling microarray data for a model eukaryotic plant Arabidopsis thaliana, was used to specify promoter region from non-promoter region. A feed-forward back propagation neural network model supported by genetic algorithm was employed to predict the class of data with a window size of 41. A dataset comprising of 2992 data vectors representing both promoter and non-promoter regions, chosen randomly from probe intensity vectors for whole genome of Arabidopsis thaliana generated through tilling microarray technique was used. The classifier model shows prediction accuracy of 69.73% and 65.36% on training and validation sets, respectively. Further, a concept of distance based class membership was used to validate reliability of classifier, which showed promising results. The study shows the usability of micro-array probe intensities to predict the promoter regions in eukaryotic genomes.     

Adaptive responses in eye-head-hand coordination following exposures to a virtual environment as a possible space flight analog

Virtual reality environments (VRs) offer unique training opportunities, particularly for training astronauts and preadapting them to the novel sensory conditions of microgravity. The purpose of the current research was to compare disturbances in eye-head-hand (EHH) sensorimotor coordination produced by repeated exposures to VR systems. In general, we observed significant increases in position errors for both horizontal and vertical targets. The largest decrements were observed immediately following exposure to VR and showed general recovery within 6 hours across each test session, but not across days. Subjects generally showed faster reaction times across days. These findings provide some direction for developing training schedules for VR users that facilitate adaptation and support the idea that VRs may serve as an analog for sensorimotor effects of spaceflight.     

最大熵模型在物种分布预测中的优化

最大熵模型在物种分布的预测研究中得到广泛应用,但未经优化的模型的预测结果可能存在严重的拟合偏差.本文汇总了最大熵模型在取样偏差修正、模型复杂性调整、物种分布判定阈值选择以及模型检验过程中的若干优化方法.在取样偏差的修正中,空间筛除法的修正效果最好,而背景限制法表现不佳.模型复杂性受建模变量的数量、函数模式和调控系数的影响.在样本量小于建模变量的数量时需进行变量筛选,筛选标准应侧重其生态学意义,而非变量间的相关性;函数模式对模型表现影响不大,在预测结果相近情况下应选择简单模型;建模时需要调整调控系数以控制过度拟合,一般最优模型调控系数高于默认值.判定物种出现阈值应遵从客观性、等效性和判别力3个原则,敏感度和特异性加和最大是良好的阈值判定标准.模型检验可分为不依赖阈值的检验和依赖阈值的检验,在不依赖阈值的模型评估方法中,基于信息标准选择的模型表现优于基于AUC或相关系数(COR)选择的模型;在基于阈值的模型评估方法中,真实技能统计能够兼顾模型遗漏误差和错判误差,不受假设缺失影响,且受物种流行度的影响较小.     

Automatically detecting the wild giant panda using deep learning with context and species distribution model

The giant panda is a flagship species in ecological conservation. The infrared camera trap is an effective tool for monitoring the giant panda. Images captured by infrared camera traps must be accurately recognized before further statistical analyses can be implemented. Previous research has demonstrated that spatiotemporal and positional contextual information and the species distribution model (SDM) can improve image detection accuracy, especially for difficult-to-see images. Difficult-to-see images include those in which individual animals are only partially observed and it is challenging for the model to detect those individuals. By utilizing the attention mechanism, we developed a unique method based on deep learning that incorporates object detection, contextual information, and the SDM to achieve better detection performance in difficult-to-see images. We obtained 1169 images of the wild giant panda and divided them into a training set and a test set in a 4:1 ratio. Model assessment metrics showed that our proposed model achieved an overall performance of 98.1% in mAP_0.5 and 82.9% in recall on difficult-to-see images. Our research demonstrated that the fine-grained multimodal-fusing method applied to monitoring giant pandas in the wild can better detect the difficult-to-see panda images to enhance the wildlife monitoring system.     

Improving prediction of chickpea wilt severity using machine learning coupled with model combination techniques under field conditions

Accurate estimation of disease severity in the field is a key to minimize the yield losses in agriculture. Existing disease severity assessment methods have poor accuracy under field conditions. To overcome this limitation, this study used thermal and visible imaging with machine learning (ML) and model combination (MC) techniques to estimate plant disease severity under field conditions. Field experiments were conducted during 2017–18, 2018–19 and 2021–22 to obtain RGB and thermal images of chickpea cultivars with different levels of wilt resistance grown in wilt sick plots. ML models were constructed using four different datasets created using the wilt severity and image derived indices. ML models were also combined using MC techniques to assess the best predictor of the disease severity. Results indicated that the Cubist was the best ML model, while the KNN model was the poorest predictor of chickpea wilt severity under field conditions. MC techniques improved the prediction accuracy of wilt severity over individual ML models. Combining ML models using the least absolute deviation technique gave the best predictions of wilt severity. The results obtained in the present study showed the MC techniques coupled with ML models improved the prediction accuracies of plant disease severity under field conditions.     

DCAMCP: A deep learning model based on capsule network and attention mechanism for molecular carcinogenicity prediction

The carcinogenicity of drugs can have a serious impact on human health, so carcinogenicity testing of new compounds is very necessary before being put on the market. Currently, many methods have been used to predict the carcinogenicity of compounds. However, most methods have limited predictive power and there is still much room for improvement. In this study, we construct a deep learning model based on capsule network and attention mechanism named DCAMCP to discriminate between carcinogenic and non-carcinogenic compounds. We train the DCAMCP on a dataset containing 1564 different compounds through their molecular fingerprints and molecular graph features. The trained model is validated by fivefold cross-validation and external validation. DCAMCP achieves an average accuracy (ACC) of 0.718 ± 0.009, sensitivity (SE) of 0.721 ± 0.006, specificity (SP) of 0.715 ± 0.014 and area under the receiver-operating characteristic curve (AUC) of 0.793 ± 0.012. Meanwhile, comparable results can be achieved on an external validation dataset containing 100 compounds, with an ACC of 0.750, SE of 0.778, SP of 0.727 and AUC of 0.811, which demonstrate the reliability of DCAMCP. The results indicate that our model has made progress in cancer risk assessment and could be used as an efficient tool in drug design.     

基于MaxEnt模型预测黄脊竹蝗在中国的适生区

为探明黄脊竹蝗Ceracris kiangsu在我国的潜在适生区,做好早期虫情监测.本研究根据267个黄脊竹蝗物种分布点,结合气候数据,采用最大熵(Maxent)模型和ArcGIS预测黄脊竹蝗在我国的适生区分布.结果表明:影响黄脊竹蝗适生区分布的主要环境变量为最干月降水量和最冷月最低温,次要环境变量为湿季降水量、最热月最高温、降水量季节性变异系数和温度年较差.预测的黄脊竹蝗高适生区、中适生区、低适生区分别占全国陆地总面积的3.0％、5.6％和10.3％,适生区主要分布在江淮流域、长江中下游地区、华南及西南等地.模型预测结果与实际调查一致性较高,能够反映真实分布情况,对科学开展黄脊竹蝗防控具有较高参考价值.     

极端酸性矿山环境微生物基于CRISPR系统的适应性免疫机制

【目的】通过对酸性矿山环境中嗜酸硫杆菌属(Acidithiobacillus)、脱硫弧菌属(Desulfovibrio)、钩端螺旋菌属(Leptospirillum)、硫化杆菌属(Sulfobacillus)、酸原体属(Acidiplasma)和铁质菌属(Ferroplasma)的100株冶金微生物基因组中CRISPR-Cas系统的结构特征和同源关系进行生物信息学分析,在基因组水平上解析冶金微生物基于CRISPR系统对极端环境的适应性免疫机制。【方法】从NCBI网站下载基因组序列,采用CRISPR Finder定位基因组中潜在的CRISPR簇。分析CRISPR系统的组成结构与功能:利用Clustal Omega对重复序列(repeat)分类;将间隔序列(spacer)分别与nr数据库、质粒数据库和病毒数据库比对,获得注释信息;根据Cas蛋白的种类和同源性对酸性矿山环境微生物的CRISPR-Cas系统分型。【结果】在100株冶金微生物基因组中共鉴定出415个CRISPR簇,在176个c CRISPR簇中共有80种不同的重复序列和4147条间隔序列。对重复序列分类,发现12类重复序列均能形成典型的RNA二级结构,Cluster10中的重复序列在冶金微生物中最具有代表性。间隔序列注释结果表明,这些微生物曾遭受来自细菌质粒与病毒的攻击,并通过不同的防御机制抵抗外源核酸序列的入侵。冶金微生物细菌的大部分CRISPR-Cas系统属于I-C和I-E亚类型,而古菌的CRISPR-Cas系统多为I-D亚类型,两者基于CRISPR-Cas系统的进化过程中存在显著差异。【结论】酸性矿山环境微生物的CRISPR结构可能采用不同免疫机制介导外源核酸序列与Cas蛋白的相互作用,为进一步揭示极端环境微生物的适应性进化机理奠定了基础。     

基于GAM模型的延河流域主要草地物种空间分布及其与环境的关系

对延河流域145个样地的主要草地物种空间分布和以GIS空间分析获取的该区12种主要环境因子之间的关系进行了定量研究,同时应用广义可加模型（GAM）分析了单物种的地理分布与环境梯度的关系。结果表明：影响延河流域典型草地物种百里香（Thymus mongolicus）空间分布的主要环境因子包括年均降雨、年均温度、蒸发量,其次是温度季节比,再次是坡位与坡度;主要草地物种与环境的关系非常密切,每个物种都存在自己特定的环境梯度空间中,并以不同的方式在空间上响应不同的环境因子;在流域尺度上利用GAM模型进行单物种和环境关系研究是可行的,能较好地描述物种空间分布和环境因子之间的关系。     

SWIFT: A deep learning approach to prediction of hypoxemic events in critically-Ill patients using SpO2 waveform prediction

Hypoxemia is a significant driver of mortality and poor clinical outcomes in conditions such as brain injury and cardiac arrest in critically ill patients, including COVID-19 patients. Given the host of negative clinical outcomes attributed to hypoxemia, identifying patients likely to experience hypoxemia would offer valuable opportunities for early and thus more effective intervention. We present SWIFT (SpO₂ Waveform ICU Forecasting Technique), a deep learning model that predicts blood oxygen saturation (SpO₂) waveforms 5 and 30 minutes in the future using only prior SpO₂ values as inputs. When tested on novel data, SWIFT predicts more than 80% and 60% of hypoxemic events in critically ill and COVID-19 patients, respectively. SWIFT also predicts SpO₂ waveforms with average MSE below .0007. SWIFT predicts both occurrence and magnitude of potential hypoxemic events 30 minutes in the future, allowing it to be used to inform clinical interventions, patient triaging, and optimal resource allocation. SWIFT may be used in clinical decision support systems to inform the management of critically ill patients during the COVID-19 pandemic and beyond.     

The stationary distribution of an asymmetrical model of selection in a random environment

The stationary distribution for the asymmetrical form of the SAS-CFF model of selection in a random environment is presented. Also presented are the conditions for the stable coexistence of K alleles. These conditions are the same as the conditions obtained from the classical constant-fitness model with the formal substitution of geometric mean fitnesses for the constant fitnesses of the classical model. Two examples are explored. In the “equally spaced” example, increases in the degree of asymmetry raise the homozygosity, which is accompanied by loss of alleles from the population. In the “best allele” example, increases in the degree of asymmetry raise the homozygosity without the loss of alleles. In both cases the frequency spectra are altered by the changes in the degree of asymmetry.     

OPSA: an optimized prediction based scheduling approach for scientific applications in cloud environment

Cluster Computing - Cloud computing has attracted scientists to deploy scientific applications by offering services such as Infrastructure-as-a-service (IaaS), Software-as-a-service (SaaS), and...     

Fuzzy neural network based prediction model applied in primary component analysis

To solve the low accuracy and bad robustness problems in traditional water quality prediction method, this paper put forward a primary component analysis (PCA)–fuzzy neural network (FNN)–DEBP based prediction model of dissolved oxygen (DO) in aquaculture water quality. This model used PCA to extract the PC of aquaculture ecological indexes, then reduced the input vector dimension of the model, and utilized differential evolutionary algorithm to optimize the weight parameter of FNN, in order to automatically obtain the optimum parameters and build nonlinear prediction model of DO in aquaculture water quality. The model was applied in a predictive analysis on the water quality data online monitored from December 1st 2015 to December 8th 2015 in a Penaeus orientalis culture pond. The testing results show that this model has obtained a good predictive effect. Compared to BP-FNN model, in PCA–FNN–DEBP model, the absolute error of 95.8% test samples is less than 20%, and the maximum error is 0.22 mg/L, both of which are superior than BP-FNN prediction method. Due to rapid computation speed and high prediction accuracy, PCA–FNN–DEBP algorithm can provide strategic basis for the regulation and management of water quality in P. orientalis culture.     

A polynomial based model for cell fate prediction in human diseases

Background

Cell fate regulation directly affects tissue homeostasis and human health. Research on cell fate decision sheds light on key regulators, facilitates understanding the mechanisms, and suggests novel strategies to treat human diseases that are related to abnormal cell development.

Results

In this study, we proposed a polynomial based model to predict cell fate. This model was derived from Taylor series. As a case study, gene expression data of pancreatic cells were adopted to test and verify the model. As numerous features (genes) are available, we employed two kinds of feature selection methods, i.e. correlation based and apoptosis pathway based. Then polynomials of different degrees were used to refine the cell fate prediction function. 10-fold cross-validation was carried out to evaluate the performance of our model. In addition, we analyzed the stability of the resultant cell fate prediction model by evaluating the ranges of the parameters, as well as assessing the variances of the predicted values at randomly selected points. Results show that, within both the two considered gene selection methods, the prediction accuracies of polynomials of different degrees show little differences. Interestingly, the linear polynomial (degree 1 polynomial) is more stable than others. When comparing the linear polynomials based on the two gene selection methods, it shows that although the accuracy of the linear polynomial that uses correlation analysis outcomes is a little higher (achieves 86.62%), the one within genes of the apoptosis pathway is much more stable.

Conclusions

Considering both the prediction accuracy and the stability of polynomial models of different degrees, the linear model is a preferred choice for cell fate prediction with gene expression data of pancreatic cells. The presented cell fate prediction model can be extended to other cells, which may be important for basic research as well as clinical study of cell development related diseases.