SHAPNN: Shapley Value Regularized Tabular Neural Network

We present SHAPNN, a novel deep tabular data modeling architecture designed for supervised learning. Our approach leverages Shapley values, a well-established technique for explaining black-box models. Our neural network is trained using standard backward propagation optimization methods, and is regularized with realtime estimated Shapley values. Our method offers several advantages, including the ability to provide valid explanations with no computational overhead for data instances and datasets. Additionally, prediction with explanation serves as a regularizer, which improves the model's performance. Moreover, the regularized prediction enhances the model's capability for continual learning. We evaluate our method on various publicly available datasets and compare it with state-of-the-art deep neural network models, demonstrating the superior performance of SHAPNN in terms of AUROC, transparency, as well as robustness to streaming data.Comment: 9 pages, 8 figure

Differential gene expression in an elite hybrid rice cultivar (Oryza sativa, L) and its parental lines based on SAGE data

<p>Abstract</p> <p>Background</p> <p>It was proposed that differentially-expressed genes, aside from genetic variations affecting protein processing and functioning, between hybrid and its parents provide essential candidates for studying heterosis or hybrid vigor. Based our serial analysis of gene expression (SAGE) data from an elite Chinese super-hybrid rice (<it>LYP9</it>) and its parental cultivars (<it>93-11 </it>and <it>PA64s</it>) in three major tissue types (leaves, roots and panicles) at different developmental stages, we analyzed the transcriptome and looked for candidate genes related to rice heterosis.</p> <p>Results</p> <p>By using an improved strategy of tag-to-gene mapping and two recently annotated genome assemblies (<it>93-11 and PA64s</it>), we identified 10,268 additional high-quality tags, reaching a grand total of 20,595 together with our previous result. We further detected 8.5% and 5.9% physically-mapped genes that are differentially-expressed among the triad (in at least one of the three stages) with <it>P</it>-values less than 0.05 and 0.01, respectively. These genes distributed in 12 major gene expression patterns; among them, 406 up-regulated and 469 down-regulated genes (<it>P </it>< 0.05) were observed. Functional annotations on the identified genes highlighted the conclusion that up-regulated genes (some of them are known enzymes) in hybrid are mostly related to enhancing carbon assimilation in leaves and roots. In addition, we detected a group of up-regulated genes related to male sterility and 442 down-regulated genes related to signal transduction and protein processing, which may be responsible for rice heterosis.</p> <p>Conclusion</p> <p>We improved tag-to-gene mapping strategy by combining information from transcript sequences and rice genome annotation, and obtained a more comprehensive view on genes that related to rice heterosis. The candidates for heterosis-related genes among different genotypes provided new avenue for exploring the molecular mechanism underlying heterosis.</p

The Impact of Social Context on Preschoolers’ Flexibility

The current study investigates whether social interaction without communication between partners may influence preschoolers’ flexibility. Fifty-three 5 year old Singaporean children were randomly assigned to three conditions of a block sorting task (Fawcett & Garton, 2005): playing individually, cooperating with another player, and competing against another player. To control for individual differences, before the block sorting task children were given four cognitive tasks testing vocabulary, short-term memory, and executive function, as well as two affective scales on mood and motivation. Separate one-way Analysis of Variance (ANOVA) showed that although they performed the same on the cognitive tasks and the affective measures, children in the competition condition sorted blocks along significantly more dimensions compared to children in the individual condition. These results suggest that preschoolers’ flexibility is sensitive to social contexts

Error-aware Quantization through Noise Tempering

Quantization has become a predominant approach for model compression, enabling deployment of large models trained on GPUs onto smaller form-factor devices for inference. Quantization-aware training (QAT) optimizes model parameters with respect to the end task while simulating quantization error, leading to better performance than post-training quantization. Approximation of gradients through the non-differentiable quantization operator is typically achieved using the straight-through estimator (STE) or additive noise. However, STE-based methods suffer from instability due to biased gradients, whereas existing noise-based methods cannot reduce the resulting variance. In this work, we incorporate exponentially decaying quantization-error-aware noise together with a learnable scale of task loss gradient to approximate the effect of a quantization operator. We show this method combines gradient scale and quantization noise in a better optimized way, providing finer-grained estimation of gradients at each weight and activation layer's quantizer bin size. Our controlled noise also contains an implicit curvature term that could encourage flatter minima, which we show is indeed the case in our experiments. Experiments training ResNet architectures on the CIFAR-10, CIFAR-100 and ImageNet benchmarks show that our method obtains state-of-the-art top-1 classification accuracy for uniform (non mixed-precision) quantization, out-performing previous methods by 0.5-1.2% absolute

β-Glucan Oligosaccharide Enhances CD8+ T Cells Immune Response Induced by a DNA Vaccine Encoding Hepatitis B Virus Core Antigen

DNA vaccination can induce specific CD8+ T cell immune response, but the response level is low in large mammals and human beings. Coadministration of an adjuvant can optimize protective immunity elicited by a DNA vaccine. In this study, we investigated the effect of a synthetic glucohexaose (β-glu6), an analogue of Lentinan basic unit, on specific CD8+ T cell response induced by a DNA vaccine encoding HBcAg (pB144) in mice. We found that β-glu6 promoted the recruitment and maturation of dendritic cells, enhanced the activation of CD8+ and CD4+ T cells and increased the number of specific CD8+/IFN-γ+ T cells in lymphoid and nonlymphoid tissues in mice immunized by pB144. Immunization with pB144 and β-glu6 increased the anti-HBc IgG and IgG2a antibody titer. These results demonstrate that β-glu6 can enhance the virus-specific CTL and Th1 responses induced by DNA vaccine, suggesting β-glu6 as a candidate adjuvant in DNA vaccination

Strategies targeting endoplasmic reticulum stress to improve Parkinson’s disease

Parkinson’s disease (PD) is a common neurodegenerative disorder with motor symptoms, which is caused by the progressive death of dopaminergic (DA) neurons in the substantia nigra pars compacta (SNpc). Accumulating evidence shows that endoplasmic reticulum (ER) stress occurring in the SNpc DA neurons is an early event in the development of PD. ER stress triggers the activation of unfolded protein response (UPR) to reduce stress and restore ER function. However, excessive and continuous ER stress and UPR exacerbate the risk of DA neuron death through crosstalk with other PD events. Thus, ER stress is considered a promising therapeutic target for the treatment of PD. Various strategies targeting ER stress through the modulation of UPR signaling, the increase of ER’s protein folding ability, and the enhancement of protein degradation are developed to alleviate neuronal death in PD models. In this review, we summarize the pathological role of ER stress in PD and update the strategies targeting ER stress to improve ER protein homeostasis and PD-related events