Economic Forecasts Using Many Noises

This paper addresses a key question in economic forecasting: does pure noise truly lack predictive power? Economists typically conduct variable selection to eliminate noises from predictors. Yet, we prove a compelling result that in most economic forecasts, the inclusion of noises in predictions yields greater benefits than its exclusion. Furthermore, if the total number of predictors is not sufficiently large, intentionally adding more noises yields superior forecast performance, outperforming benchmark predictors relying on dimension reduction. The intuition lies in economic predictive signals being densely distributed among regression coefficients, maintaining modest forecast bias while diversifying away overall variance, even when a significant proportion of predictors constitute pure noises. One of our empirical demonstrations shows that intentionally adding 300~6,000 pure noises to the Welch and Goyal (2008) dataset achieves a noteworthy 10% out-of-sample R square accuracy in forecasting the annual U.S. equity premium. The performance surpasses the majority of sophisticated machine learning models

SphereNet: Learning a Noise-Robust and General Descriptor for Point Cloud Registration

Point cloud registration is to estimate a transformation to align point clouds collected in different perspectives. In learning-based point cloud registration, a robust descriptor is vital for high-accuracy registration. However, most methods are susceptible to noise and have poor generalization ability on unseen datasets. Motivated by this, we introduce SphereNet to learn a noise-robust and unseen-general descriptor for point cloud registration. In our method, first, the spheroid generator builds a geometric domain based on spherical voxelization to encode initial features. Then, the spherical interpolation of the sphere is introduced to realize robustness against noise. Finally, a new spherical convolutional neural network with spherical integrity padding completes the extraction of descriptors, which reduces the loss of features and fully captures the geometric features. To evaluate our methods, a new benchmark 3DMatch-noise with strong noise is introduced. Extensive experiments are carried out on both indoor and outdoor datasets. Under high-intensity noise, SphereNet increases the feature matching recall by more than 25 percentage points on 3DMatch-noise. In addition, it sets a new state-of-the-art performance for the 3DMatch and 3DLoMatch benchmarks with 93.5\% and 75.6\% registration recall and also has the best generalization ability on unseen datasets.Comment: 15 pages, under review for IEEE Transactions on Circuits and Systems for Video Technolog

Agreeableness, Extraversion, Stressor and Physiological Stress Response

Based on the theoretical analysis, with first-hand data collection and using multiple regression models, this study explored the relationship between agreeableness, extraversion, stressor and stress response and figured out interactive effect of agreeableness, extraversion, and stressor on stress response. We draw on the following conclusions: (1) the interaction term of stressor (work) and agreeableness can negatively predict physiological stress response; (2) the interaction term of stressor (health) and agreeableness can negatively predict physiological stress response; (3) the interaction term of stressor (family) and agreeableness can negatively predict physiological stress response; (4) the interaction term of stressor (social) and agreeableness can negatively predict physiological stress response; (5) the interaction term of stressor (work) and extraversion can negatively predict physiological stress response; (6) the interaction term of stressor (health) and extraversion can negatively predict physiological stress response; (7) the interaction term of stressor (family) and extraversion can negatively predict physiological stress response; (8) the interaction term of stressor (social) and extraversion can negatively predict physiological stress response

Antidyskinetic Effects of MEK Inhibitor Are Associated with Multiple Neurochemical Alterations in the Striatum of Hemiparkinsonian Rats

L-DOPA-induced dyskinesia (LID) represents one of the major problems of the long-term therapy of patients with Parkinson's disease (PD). Although, the pathophysiologic mechanisms underlying LID are not completely understood, activation of the extracellular signal regulated kinase (ERK) is recognized to play a key role. ERK is phosphorylated by mitogen-activated protein kinase kinase (MEK), and thus MEK inhibitor can prevent ERK activation. Here the effect of the MEK inhibitor PD98059 on LID and the associated molecular changes were examined. Rats with unilateral 6-OHDA lesions of the nigrostriatal pathway received daily L-DOPA treatment for 3 weeks, and abnormal involuntary movements (AIMs) were assessed every other day. PD98059 was injected in the lateral ventricle daily for 12 days starting from day 10 of L-DOPA treatment. Striatal molecular markers of LID were analyzed together with gene regulation using microarray. The administration of PD98059 significantly reduced AIMs. In addition, ERK activation and other associated molecular changes including ΔFosB were reversed in rats treated with the MEK inhibitor. PD98059 induced significant up-regulation of 418 transcripts and down-regulation of 378 transcripts in the striatum. Tyrosine hydroxylase (Th) and aryl hydrocarbon receptor nuclear translocator (Arnt) genes were down-regulated in lesioned animals and up-regulated in L-DOPA-treated animals. Analysis of protein levels showed that PD98059 reduced the striatal TH. These results support the association of p-ERK1/2, ΔFosB, p-H3 to the regulation of TH and ARNT in the mechanisms of LID, and pinpoint other gene regulatory changes, thus providing clues for identifying new targets for LID therapy

CDBA: a novel multi-branch feature fusion model for EEG-based emotion recognition

EEG-based emotion recognition through artificial intelligence is one of the major areas of biomedical and machine learning, which plays a key role in understanding brain activity and developing decision-making systems. However, the traditional EEG-based emotion recognition is a single feature input mode, which cannot obtain multiple feature information, and cannot meet the requirements of intelligent and high real-time brain computer interface. And because the EEG signal is nonlinear, the traditional methods of time domain or frequency domain are not suitable. In this paper, a CNN-DSC-Bi-LSTM-Attention (CDBA) model based on EEG signals for automatic emotion recognition is presented, which contains three feature-extracted channels. The normalized EEG signals are used as an input, the feature of which is extracted by multi-branching and then concatenated, and each channel feature weight is assigned through the attention mechanism layer. Finally, Softmax was used to classify EEG signals. To evaluate the performance of the proposed CDBA model, experiments were performed on SEED and DREAMER datasets, separately. The validation experimental results show that the proposed CDBA model is effective in classifying EEG emotions. For triple-category (positive, neutral and negative) and four-category (happiness, sadness, fear and neutrality), the classification accuracies were respectively 99.44% and 99.99% on SEED datasets. For five classification (Valence 1—Valence 5) on DREAMER datasets, the accuracy is 84.49%. To further verify and evaluate the model accuracy and credibility, the multi-classification experiments based on ten-fold cross-validation were conducted, the elevation indexes of which are all higher than other models. The results show that the multi-branch feature fusion deep learning model based on attention mechanism has strong fitting and generalization ability and can solve nonlinear modeling problems, so it is an effective emotion recognition method. Therefore, it is helpful to the diagnosis and treatment of nervous system diseases, and it is expected to be applied to emotion-based brain computer interface systems

Electrical switching of ferro-rotational order in nano-thick 1T-TaS2_2 crystals

Hysteretic switching of domain states is a salient character of all ferroic materials and the foundation for their multifunctional applications. Ferro-rotational order is emerging as a new type of ferroic order featuring structural rotations, but its controlled switching remains elusive due to its invariance under both time reversal and spatial inversion. Here, we demonstrate electrical switching of ferro-rotational domain states in nanometer-thick 1T-TaS$_2$ crystals in its charge-density-wave phases. Cooling from the high-symmetry phase to the ferro-rotational phase under an external electric field induces domain state switching and domain wall formation, realized in a simple two-terminal configuration using a volt-scale voltage. Although the electric field does not couple with the order due to symmetry mismatch, it drives domain wall propagation to give rise to reversible, durable, and nonvolatile isothermal state switching at room temperature. These results pave the path for manipulation of the ferro-rotational order and its nanoelectronic applications

Slug down-regulation by RNA interference inhibits invasion growth in human esophageal squamous cell carcinoma

<p>Abstract</p> <p>Background</p> <p>Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive carcinomas of the gastrointestinal tract. We assessed the relevance of Slug in measuring the invasive potential of ESCC cells <it>in vitro </it>and <it>in vivo </it>in immunodeficient mice.</p> <p>Methods</p> <p>We utilized RNA interference to knockdown Slug gene expression, and effects on survival and invasive carcinoma were evaluated using a Boyden chamber transwell assay <it>in vitro</it>. We evaluated the effect of Slug siRNA-transfection and Slug cDNA-transfection on E-cadherin and Bcl-2 expression in ESCC cells. A pseudometastatic model of ESCC in immunodeficient mice was used to assess the effects of Slug siRNA transfection on tumor metastasis development.</p> <p>Results</p> <p>The EC109 cell line was transfected with Slug-siRNA to knockdown Slug expression. The TE13 cell line was transfected with Slug-cDNA to increase Slug expression. EC109 and TE13 cell lines were tested for the expression of apoptosis-related genes bcl-2 and metastasis-related gene E-cadherin identified previously as Slug targets. Bcl-2 expression was increased and E-cadherin was decreased in Slug siRNA-transfected EC109 cells. Bcl-2 expression was increased and E-cadherin was decreased in Slug cDNA-transfected TE13 cells. Invasion of Slug siRNA-transfected EC109 cells was reduced and apoptosis was increased whereas invasion was greater in Slug cDNA-transfected cells. Animals injected with Slug siRNA-transfected EC109 cells exhihited fewer seeded nodes and demonstrated more apoptosis.</p> <p>Conclusions</p> <p>Slug down-regulation promotes cell apoptosis and decreases invasion capability <it>in vitro </it>and <it>in vivo</it>. Slug inhibition may represent a novel strategy for treatment of metastatic ESCC.</p

Stereotaxical Infusion of Rotenone: A Reliable Rodent Model for Parkinson's Disease

A clinically-related animal model of Parkinson's disease (PD) may enable the elucidation of the etiology of the disease and assist the development of medications. However, none of the current neurotoxin-based models recapitulates the main clinical features of the disease or the pathological hallmarks, such as dopamine (DA) neuron specificity of degeneration and Lewy body formation, which limits the use of these models in PD research. To overcome these limitations, we developed a rat model by stereotaxically (ST) infusing small doses of the mitochondrial complex-I inhibitor, rotenone, into two brain sites: the right ventral tegmental area and the substantia nigra. Four weeks after ST rotenone administration, tyrosine hydroxylase (TH) immunoreactivity in the infusion side decreased by 43.7%, in contrast to a 75.8% decrease observed in rats treated systemically with rotenone (SYS). The rotenone infusion also reduced the DA content, the glutathione and superoxide dismutase activities, and induced alpha-synuclein expression, when compared to the contralateral side. This ST model displays neither peripheral toxicity or mortality and has a high success rate. This rotenone-based ST model thus recapitulates the slow and specific loss of DA neurons and better mimics the clinical features of idiopathic PD, representing a reliable and more clinically-related model for PD research