[Predicting response to non-small cell lung cancer immunotherapy using pre-treatment contrast-enhanced CT texture-based classification].

Objective: To explore the value of pre-treatment contrast-enhanced computed tomography (CT)-based texture analysis in predicting response to non-small cell lung cancer (NSCLC) immunotherapy. Methods: From January to July 2018, a total of 51 lesions from 42 patients with advanced non-small cell lung cancer receiving immunotherapy at Shanghai Chest Hospital were selected in this retrospective study. Pre-treatment contrast-enhanced CT-based texture features were extracted by MaZda software. Ten optimal texture features were chosen based on three different methods: Fisher coefficient, mutual information measure (MI) and minimization of classification error probability combined average correlation coefficients(POE+ ACC), respectively. According to the efficacy of the first immunotherapy, 51 lesions were divided into non-progressive disease (non-PD, n=26) and progressive disease (PD, n=25). The differences were tested in each texture feature set between the two groups. The immunotherapy effects of target lesions were analyzed by principal component analysis(PCA), linear discriminant analysis (LDA) and nonlinear discriminant analysis (NDA). The sensitivity, specificity, accuracy, positive-predictive value (PPV) and negative-predictive value (NPV) were calculated. The area under the curve (AUC) was used to quantify the predictive accuracy of the three analysis models for each texture feature set and compare them with the actual classification results. Results: In all of three texture feature sets, the texture parameter differences of Perc.50%, Perc.90%, "S(5, 5)SumEntrp" and "S(4, 4)SumEntrp" were higher in PD group than those in non-PD group (all P<0.05). The classification result of texture feature set chosen by POE+ ACC and analyzed by NDA was identified as the best model (AUC=0.802, 95%CI: 0.674-0.930), and its sensitivity, specificity, accuracy, PPV and NPV were 72%, 88.5%, 80.4%, 85.7%, 76.7%, respectively. Conclusion: Pre-treatment contrast-enhanced CT-based texture characteristics of NSCLC may function as non-invasive biomarkers for the evaluation of response to immunotherapy.

Chiral topological excitons in the monolayer transition metal dichalcogenides.

We theoretically investigate the chiral topological excitons emerging in the monolayer transition metal dichalcogenides, where a bulk energy gap of valley excitons is opened up by a position dependent external magnetic field. We find two emerging chiral topological nontrivial excitons states, which exactly connects to the bulk topological properties, i.e., Chern number = 2. The dependence of the spectrum of the chiral topological excitons on the width of the magnetic field domain wall as well as the magnetic filed strength is numerically revealed. The chiral topological valley excitons are not only important to the excitonic transport due to prevention of the backscattering, but also give rise to the quantum coherent control in the optoelectronic applications.

Geometric decoherence of valley excitons in monolayer transition metal dichachogenides.

We study the effects of the Berry phases of the valley excitons in the monolayer transition metal dichalcogenides (TMDs) when the valley excitons are manipulated by an external terahertz field. We find that the decoherence of the valley degree of freedom of the valley excitons is spontaneously induced because of the different Berry phases of valley excitons accumulated along the opposite trajectories under the manipulation of the external field. It is called the geometric decoherence because it completely results from the geometric phases. The obvious phenomenon related to such spontaneous decoherence is the gradual decrement of the dipole moment matrix element of the valley exciton and consequently the decrement of the emitted signals after the valley excitons are recombined. Moreover, another effect due to the Berry phases is the giant Faraday rotation of the polarization of the emitted photons. Such imperfection of the valley degree of freedom is supposed to provide the potential limits of the valleytronics based on the TMDs optoelecronic devices.

Using multithreshold quadratic sigmoidal neurons to improve classification capability of multilayer perceptrons.

This letter proposes a new type of neurons called multithreshold quadratic sigmoidal neurons to improve the classification capability of multilayer neural networks. In cooperation with single-threshold quadratic sigmoidal neurons, the multithreshold quadratic sigmoidal neurons can be used to improve the classification capability of multilayer neural networks by a factor of four compared to committee machines and by a factor of two compared to the conventional sigmoidal multilayer perceptrons.

User adaptive handwriting recognition by self-growing probabilistic decision-based neural networks.

It is generally agreed that, for a given handwriting recognition task, a user dependent system usually outperforms a user independent system, as long as a sufficient amount of training data is available. When the amount of user training data is limited, however, such a performance gain is not guaranteed. One way to improve the performance is to make use of existing knowledge, contained in a rich multiuser data base, so that a minimum amount of training data is sufficient to initialize a model for the new user.We mainly address the user adaption issues for a handwriting recognition system. Based on self-growing probabilistic decision-based neural networks (SPDNNs), user adaptation of the parameters of SPDNN is formulated as incremental reinforced and antireinforced learning procedures, which are easily integrated into the batched training procedures of the SPDNN. In this study, we developed 1) an SPDNN based handwriting recognition system; 2) a two-stage recognition structure; and 3) a three-phase training methodology for a) a global coarse classifier (stage 1); b) a user independent hand written character recognizer (stage 2); and c) a user adaptation module on a personal computer. With training and testing on a 600-word commonly used Chinese character set, the recognition results indicate that the user adaptation module significantly improved the recognition accuracy. The average recognition rate increased from 44.2% to 82.4% in five adapting cycles, and the performance could finally increase up to 90.2% in ten adapting cycles.

Divide-and-conquer learning and modular perceptron networks.

A novel modular perceptron network (MPN) and divide-and-conquer learning (DCL) schemes for the design of modular neural networks are proposed. When a training process in a multilayer perceptron falls into a local minimum or stalls in a flat region, the proposed DCL scheme is applied to divide the current training data region into two easier to be learned regions. The learning process continues when a self-growing perceptron network and its initial weight estimation are constructed for one of the newly partitioned regions. Another partitioned region will resume the training process on the original perceptron network. Data region partitioning, weight estimating and learning are iteratively repeated until all the training data are completely learned by the MPN. We evaluated and compared the proposed MPN with several representative neural networks on the two-spirals problem and real-world dataset. The MPN achieved better weight learning performance by requiring much less data presentations during the network training phases, and better generalization performance, and less processing time during the retrieving phase.

On the classification capability of a dynamic threshold neural network.

This paper proposes a new type of neural network called the Dynamic Threshold Neural Network (DTNN) which is theoretically and experimentally superior to a conventional sigmoidal multilayer neural network in classification capability. Given a training set containing 4k + 1 patterns in Rn, to successfully learn this training set, the upper bound on the number of free parameters for a DTNN is (k + 1)(n + 2) + 2(k + 1), while the upper bound for a sigmoidal network is 2k(n + 1) + (2k + 1). We also derive a learning algorithm for the DTNN in a similar way to the derivation of the backprop learning algorithm. In simulations on learning the Two-Spirals problems, our DTNN with 30 neurons in one hidden layer takes only 3200 epochs on average to successfully learn the whole training set, while the single-hidden-layer feedforward sigmoidal neural networks have never been reported to successfully learn the given training set even though more hidden neurons are used.

A fuzzy neural network for knowledge learning.

This paper presents a fuzzy neural network for learning the knowledge of a fuzzy logic rule-based system. The network contains five layers: an Input Layer, Membership-function Layer, AND Layer, OR Layer, and Defuzzification Layer. We propose a backpropagation-like learning algorithm to train this neural network to acquire the fuzzy rules and to fine-tune the knowledge on the parameters of AND and OR nodes. Compared with methods other than the gradient descent search, the proposed learning process acquires more precise knowledge. In addition, the functions of the AND and OR nodes in the network are formulated with the minimum or maximum operations, respectively. Therefore, the adjustments of the learnable weights (parameters) can be focused on the dominant terms related to the (minimum/maximum) operations. The convergence time for the proposed learning algorithm is much faster than that for conventional backpropagation algorithms. In summary, the learnable weights (parameters) of the network are adjusted very quickly to obtain precise knowledge. Simulation results show that in learning the truck backer-upper problem, our network completes the training procedure in only several dozen epochs with an error rate of less than 1%.

Recognition of handwritten similar Chinese characters by self-growing probabilistic decision-based neural network.

Recognition of similar (confusion) characters is a difficult problem in optical character recognition (OCR). In this paper, we introduce a neural network solution that is capable of modeling minor differences among similar characters, and is robust to various personal handwriting styles. The Self-growing Probabilistic Decision-based Neural Network (SPDNN) is a probabilistic type neural network, which adopts a hierarchical network structure with nonlinear basis functions and a competitive credit-assignment scheme. Based on the SPDNN model, we have constructed a three-stage recognition system. First, a coarse classifier determines a character to be input to one of the pre-defined subclasses partitioned from a large character set, such as Chinese mixed with alphanumerics. Then a character recognizer determines the input image which best matches the reference character in the subclass. Lastly, the third module is a similar character recognizer, which can further enhance the recognition accuracy among similar or confusing characters. The prototype system has demonstrated a successful application of SPDNN to similar handwritten Chinese recognition for the public database CCL/HCCR1 (5401 characters x200 samples). Regarding performance, experiments on the CCL/HCCR1 database produced 90.12% recognition accuracy with no rejection, and 94.11% accuracy with 6.7% rejection, respectively. This recognition accuracy represents about 4% improvement on the previously announced performance. As to processing speed, processing before recognition (including image preprocessing, segmentation, and feature extraction) requires about one second for an A4 size character image, and recognition consumes approximately 0.27 second per character on a Pentium-100 based personal computer, without use of any hardware accelerator or co-processor.

Androgen receptor inclusions acquire GRP78/BiP to ameliorate androgen-induced protein misfolding stress in embryonic stem cells.

Commitment of differentiating embryonic stem cells (ESCs) toward the various lineages is influenced by many factors, including androgens. However, the mechanisms underlying proteotoxic stress conferred by androgen receptor (AR) actions on embryonic cell fate remains unclear. Here we show that mouse ESCs display stress-related cellular phenotypes in response to androgens during early phase of differentiation. Androgen induced a significant increase in the percentage of ESCs and embryoid bodies with the intranuclear and juxtanuclear AR inclusions, which were colocalized with the E3 ubiquitin ligase, C terminus of Hsc70-interacting protein. Caspase-3 activity corresponded with AR expression, was enhanced in cells engaged more differentiation phenotypes. Androgen-mediated accumulation of AR aggregates exacerbated endoplasmic reticulum (ER) stress and rendered ESCs susceptible to apoptosis. Increasing expression levels of the ER chaperones, GRP78/BiP and GRP94, as well as ER stress markers, such as ATF6, phosphorylated PERK, GADD153/CHOP and spliced XBP-1 mRNA, were dramatically elevated in ESCs overexpressing AR. We found that androgen induced GRP78/BiP to dissociate from ATF6, and act as an AR-interacting protein, which was recruited into AR inclusions in ESCs. GRP78/BiP was also colocalized with AR inclusions in the cells of spinal bulbar muscular atrophy transgenic mouse model. Overexpression of GRP78/BiP suppressed ubiquitination of AR aggregates and ameliorated the misfolded AR-mediated cytopathology in ESCs, whereas knockdown of GRP78/BiP increased the accumulation of AR aggregates and significantly higher levels of caspase-3 activity and cell apoptosis. These results generate novel insight into how ESCs respond to stress induced by misfolded AR proteins and identify GRP78/BiP as a novel regulator of the AR protein quality control.