Further remarks on convergence of decomposition method

The decomposition method solves a wide class of nonlinear functional equations. This method uses a series solution with rapid convergence. This paper is intended as a useful review and clarification of related issues.     

Accelerating the weighted histogram analysis method by direct inversion in the iterative subspace

The weighted histogram analysis method (WHAM) for free energy calculations is a valuable tool to produce free energy differences with the minimal errors. Given multiple simulations, WHAM obtains from the distribution overlaps the optimal statistical estimator of the density of states, from which the free energy differences can be computed. The WHAM equations are often solved by an iterative procedure. In this work, we use a well-known linear algebra algorithm which allows for more rapid convergence to the solution. We find that the computational complexity of the iterative solution to WHAM, and the closely-related multiple Bennett acceptance ratio method can be improved using the method of direct inversion in the iterative subspace. We give examples from a lattice model, a simple liquid and an aqueous protein solution.     

Simulating vestibular compensation using recurrent back-propagation

Vestibular compensation is simulated as learning in a dynamic neural network model of the horizontal vestibulo-ocular reflex (VOR). The bilateral, three-layered VOR model consists of nonlinear units representing horizontal canal afferents, vestibular nuclei (VN) neurons and eye muscle motoneurons. Dynamic processing takes place via commissural connections that link the VN bilaterally. The intact network is trained, using recurrent back-propagation, to produce the VOR with velocity storage integration. Compensation is simulated by removing vestibular afferent input from one side and retraining the network. The time course of simulated compensation matches that observed experimentally. The behavior of model VN neurons in the compensated network also matches real data, but only if connections at the motoneurons, as well as at the VN, are allowed to be plastic. The dynamic properties of real VN neurons in compensated and normal animals are found to differ when tested with sinusoidal but not with step stimuli. The model reproduces these conflicting data, and suggests that the disagreement may be due to VN neuron nonlinearity.     

BP神经网络优化微生物浸矿工艺

以低品位黄铜矿溶液为原料,浸矿制备Cu2＋能有效提高低品位黄铜矿的利用价值。基于浸矿过程中存在多因素影响的现象,通过正交试验与神经网络分析方法,对浸矿条件（接种量、矿石品位、Fe2＋添加量及浸矿溶液pH）实行优化。结果表明：在正交试验组中最佳试验结果为浸矿产128．753mg／LCu^2＋;BP神经网络优化后的最佳实验组合为微生物接种量12％、矿石品位0．3％、添加Fe^2＋24g/L及浸矿溶液pH1．7,该条件下验证试验产Cu^2＋ 141．352mg／L,通过正交试验及神经网络优化提高了微生物浸出低品位黄铜矿酸性溶液Cu^2＋的产量。     

Predicting siRNA activity based on back-propagation neural network

RNA interference (RNAi) is a phenomenon of gene silence induced by a double-stranded RNA (dsRNA) homologous to a target gene. RNAi can be used to identify the function of genes or to knock down the targeted genes. In RNAi technology, 19 bp double-stranded short interfering RNAs (siRNA) with characteristic 39 overhangs are usually used. The effects of siRNAs are quite varied due to the different choices in the sites of target mRNA. Moreover, there are many factors influencing siRNA activity and these factors are usually nonlinear. To find the motif features and the effect on siRNA activity, we carried out a feature extraction on some published experimental data and used these features to train a back-propagation neural network (BP NN). Then, we used the trained BP NN to predict siRNA activity. __________ Translated from Acta Biophysica Sinica, 2006, 22(6): 429–434 [译自: 生物物理学报]     

Predicting siRNA activity based on back-propagation neural network

RNA interference (RNAi) is a phenomenon of gene silence induced by a double-stranded RNA (dsRNA) homologous to a target gene.RNAi can be used to identify the function of genes or to knock down the targeted genes.In RNAi technology,19 bp double-stranded short interfering RNAs (siRNA) with characteristic 3' overhangs are usually used.The effects of siRNAs are quite varied due to the different choices in the sites of target mRNA.Moreover,there are many factors influencing siRNA activity and these factors are usually nonlinear.To find the motif features and the effect on siRNA activity,we carried out a feature extraction on some published experimental data and used these features to train a backpropagation neural network (BP NN).Then,we used the trained BP NN to predict siRNA activity.     

Accelerating the Original Profile Kernel

One of the most accurate multi-class protein classification systems continues to be the profile-based SVM kernel introduced by the Leslie group. Unfortunately, its CPU requirements render it too slow for practical applications of large-scale classification tasks. Here, we introduce several software improvements that enable significant acceleration. Using various non-redundant data sets, we demonstrate that our new implementation reaches a maximal speed-up as high as 14-fold for calculating the same kernel matrix. Some predictions are over 200 times faster and render the kernel as possibly the top contender in a low ratio of speed/performance. Additionally, we explain how to parallelize various computations and provide an integrative program that reduces creating a production-quality classifier to a single program call. The new implementation is available as a Debian package under a free academic license and does not depend on commercial software. For non-Debian based distributions, the source package ships with a traditional Makefile-based installer. Download and installation instructions can be found at https://rostlab.org/owiki/index.php/Fast_Profile_Kernel. Bugs and other issues may be reported at https://rostlab.org/bugzilla3/enter_bug.cgi?product=fastprofkernel.     

Deterministic convergence of chaos injection-based gradient method for training feedforward neural networks

It has been shown that, by adding a chaotic sequence to the weight update during the training of neural networks, the chaos injection-based gradient method (CIBGM) is superior to the standard backpropagation algorithm. This paper presents the theoretical convergence analysis of CIBGM for training feedforward neural networks. We consider both the case of batch learning as well as the case of online learning. Under mild conditions, we prove the weak convergence, i.e., the training error tends to a constant and the gradient of the error function tends to zero. Moreover, the strong convergence of CIBGM is also obtained with the help of an extra condition. The theoretical results are substantiated by a simulation example.     

Accelerating the reconstruction of genome-scale metabolic networks

Background  

The genomic information of a species allows for the genome-scale reconstruction of its metabolic capacity. Such a metabolic reconstruction gives support to metabolic engineering, but also to integrative bioinformatics and visualization. Sequence-based automatic reconstructions require extensive manual curation, which can be very time-consuming. Therefore, we present a method to accelerate the time-consuming process of network reconstruction for a query species. The method exploits the availability of well-curated metabolic networks and uses high-resolution predictions of gene equivalency between species, allowing the transfer of gene-reaction associations from curated networks.     

Normalized difference vegetation index prediction based on the delta downscaling method and back-propagation artificial neural network under climate change in the Sanjiangyuan region,China

Predicting the response of vegetation to climate change through mathematical methods is an important way to understand ecosystem condition changes in ecologically vulnerable regions. We took the Sanjiangyuan region, one of the most sensitive areas to climate change, as the study area to construct a simpler calculation and higher resolution (suitable for regional scale study) nonlinear method to predict the normalized difference vegetation index (NDVI) under climate change by combining the delta downscaling method and backpropagation artificial neural network. We first used the delta downscaling method to downscale the coarse-resolution climate element data of the Coupled Model Intercomparison Project (Phase 6) (CMIP6) to 0.08333° (regional scale). By analysing the relationship between NDVI and climate elements, we found that NDVI has the highest correlation with annual total precipitation, annual mean temperature, variation range of precipitation and temperature, etc. Then, we used these impact factors to train the back propagation artificial neural network (BP-ANN) and predict the NDVI in 2030 and 2060 under the SSP1–2.6 scenario and SSP5–8.5 scenario. The simulated results show that the BP-ANN can be used to construct the nonlinear relationship between NDVI and the impact factors on different scales. In the future, NDVI will increase under both the SSP1–2.6 scenario and the SSP5–8.5 scenario. The western part of the study area has the highest altitude, the ecosystem is more vulnerable, and the changes will be the most intense. This study is expected to provide a reference for understanding the impact of climate change on vegetation in national parks in plateaus and to provide a simpler NDVI prediction method for the evaluation of environmental quality under the impact of climate change with NDVI as one of the parameters.     

Training back-propagation neural networks to define and detect DNA-binding sites

A three layered back-propagation neural network was trained to recognize E. coli promoters of the 17 base spacing class. To this end, the network was presented with 39 promoter sequences and derivatives of those sequences as positive inputs; 60% A + T random sequences and sequences containing 2 promoter-down point mutations were used as negative inputs. The entire promoter sequence of 58 bases, approximately -50 to +8, was entered as input. The network was asked to associate an output of 1.0 with promoter sequence input and 0.0 with non-promoter input. Generally, after 100,000 input cycles, the network was virtually perfect in classifying the training set. A trained network was about 80% effective in recognizing 'new' promoters which were not in the training set, with a false positive rate below 0.1%. Network searches on pBR322 and on the lambda genome were also performed. Overall the results were somewhat better than the best rule-based procedures. The trained network can be analyzed both for its choice of base and relative weighting, positive and negative, at each position of the sequence. This method, which requires only appropriate input/output training pairs, can be used to define and search for any DNA regulatory sequence for which there are sufficient exemplars.     

Inferring species membership using DNA sequences with back-propagation neural networks

DNA barcoding as a method for species identification is rapidly increasing in popularity. However, there are still relatively few rigorous methodological tests of DNA barcoding. Current distance-based methods are frequently criticized for treating the nearest neighbor as the closest relative via a raw similarity score, lacking an objective set of criteria to delineate taxa, or for being incongruent with classical character-based taxonomy. Here, we propose an artificial intelligence-based approach - inferring species membership via DNA barcoding with back-propagation neural networks (named BP-based species identification) - as a new advance to the spectrum of available methods. We demonstrate the value of this approach with simulated data sets representing different levels of sequence variation under coalescent simulations with various evolutionary models, as well as with two empirical data sets of COI sequences from East Asian ground beetles (Carabidae) and Costa Rican skipper butterflies. With a 630-to 690-bp fragment of the COI gene, we identified 97.50% of 80 unknown sequences of ground beetles, 95.63%, 96.10%, and 100% of 275, 205, and 9 unknown sequences of the neotropical skipper butterfly to their correct species, respectively. Our simulation studies indicate that the success rates of species identification depend on the divergence of sequences, the length of sequences, and the number of reference sequences. Particularly in cases involving incomplete lineage sorting, this new BP-based method appears to be superior to commonly used methods for DNA-based species identification.     

Identification and quantification of prosthetic mitral regurgitation by flow convergence method using transthoracic approach

Ultrasound is the preferred imaging modality in diagnosis of vascular complications following cardiac catheterization and intervention. In some cases, however, bleeding surrounding the femoral vessels, may severely distort the color Doppler images, making detection of venous complications especially difficult. This report refers to such a case where post-catheterization haematoma was suspected to cause an obstruction of the femoral vein. Spectral Doppler recordings of blood flow in the common femoral vein, up-stream, distal to the hemorrhagic area, confirmed the diagnosis of obstruction by demonstrating changes in the venous flow pattern in the common femoral vein, consistent with venous hypertension. Due to the poor quality of the ultrasound images, the exact cause of the obstruction had to be established by another imaging modality, not affected by haemorrhages. CT showed that the common femoral vein was compressed at the puncture site by surrounding haemorrhages. Thus, when bleeding due to cardiac catheterization is associated with possible venous obstruction and findings by color Doppler are equivocal due to degradation of the color-Doppler image, detection of venous hypertension by spectral Doppler, performed distal to the bleeding area, strongly supports the presence of venous obstruction where the exact cause may be established by CT.     

Accelerating Mountain Forest Dynamics in the Alps

Ecosystems - Climate change alters forest development pathways, with consequences for ecosystem services and biodiversity. As the rate of warming increases, ecosystem change is expected to...