Tremor is an involuntary, rhythmic movement disorder. Despite its prevalence, the underlying pathophysiology remains poorly understood, and effective treatment options are limited. Animal models are ...essential in enhancing our understanding of the mechanisms of tremorogenesis and developing new therapeutic interventions. Although tremor is amenable to measurement by automated systems, visual observation is still the most prevalent method for recording tremor in animal studies. This review gives a brief summary of two behavioral methods that enable quantitative measurement of forelimb tremor (the press-while-licking task) and whole-body tremor (the force-plate actometer) in rodents. These methods utilize force transducer and computing technologies to generate high-resolution force-time waveforms for automated detection and characterization of tremor. The focus will be on the sensitive, precise, and quantitative measurement of tremors induced in rodents by low-dose pharmacological agents, brain lesion, physical training, and genetic mutations. The methods reviewed here provide new tools that can facilitate preclinical assessment of treatment strategies for tremor.
Learning depth from a single image, as an important issue in scene understanding, has attracted a lot of attention in the past decade. The accuracy of the depth estimation has been improved from ...conditional Markov random fields, non-parametric methods, to deep convolutional neural networks most recently. However, there exist inherent ambiguities in recovering 3D from a single 2D image. In this paper, we first prove the ambiguity between the focal length and monocular depth learning and verify the result using experiments, showing that the focal length has a great influence on accurate depth recovery. In order to learn monocular depth by embedding the focal length, we propose a method to generate synthetic varying-focal-length data set from fixed-focal-length data sets, and a simple and effective method is implemented to fill the holes in the newly generated images. For the sake of accurate depth recovery, we propose a novel deep neural network to infer depth through effectively fusing the middle-level information on the fixed-focal-length data set, which outperforms the state-of-the-art methods built on pre-trained VGG. Furthermore, the newly generated varying-focal-length data set is taken as input to the proposed network in both learning and inference phases. Extensive experiments on the fixed- and varying-focal-length data sets demonstrate that the learned monocular depth with embedded focal length is significantly improved compared to that without embedding the focal length information.
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Apigenin is a dietary flavonoid with known antioxidant and antitumor effects against several types of cancers by promoting cell death and inducing cell cycle arrest. Apigenin also ...regulates a variety of intracellular signal transduction pathways during apoptosis or autophagy. However, the precise mechanism underlying the anticancer effects of apigenin in liver cancer remains poorly understood. In this study, we demonstrated that apigenin has anticancer activity against hepatocellular carcinoma cells. Apigenin inhibited the cell growth and induced cell death in a dose- and time-dependent manner in HepG2 cells. We found that apigenin treatment increased the expression of LC3-II and the number of GFP-LC3 puncta. Moreover, inhibition of autophagy with 3-MA and Atg5 gene silencing strengthened apigenin-induced proliferation inhibition and apoptosis. Our data has indicated that apigenin-induced autophagy has a protective effect against cell death. Additionally, apigenin induced apoptosis and autophagy through inhibition of PI3K/Akt/mTOR pathway. Most importantly, in vivo data showed that administration of apigenin decreased tumor growth and autophagy inhibition by 3-MA significantly enhanced the anticancer effect of apigenin. Collectively, our results reveal that apigenin inhibits cell proliferation and induces autophagy via suppressing the PI3K/Akt/mTOR pathway. Our results also suggest combination of autophagy inhibitors and apigenin would be a potential chemotherapeutic strategy against hepatocellular carcinoma.
In the past decades, the iridium-catalyzed C–H bond borylation and other newly discovered catalytic borylation reactions have received extensive research interests and developed into a practical ...approach for functionalization of C−H bonds and therefore an effective and versatile tool in synthesis of novel organic materials, natural products and fine chemicals. The advances of this booming field include significant improvements of the venerable iridium-catalyzed borylation and development of other transition-metal, especially the first-row transition-metal catalyzed borylation. More recently, a metal-free catalytic borylation system has been disclosed. These new methodologies has dramatically expanded the substate scope, increased the reaction efficiency, lowered the cost, and more importantly, provided previously unknown complementary chemical and regioselectivity. During these discoveries, novel catalyst design concepts and/or principles have been generated. In this context, this review aims to detail the recent evolution in the exciting research direction, focusing on the discovery and development of new reactivity, new selectivity.
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With the development of deep learning techniques in the field of remote sensing change detection, many change detection algorithms based on convolutional neural networks (CNNs) and nonlocal ...self-attention (NLSA) mechanisms have been widely used and have obtained good detection accuracy. However, these methods mainly extract semantic features on images from different periods without taking into account the temporal dependence between these features. This will lead to more “pseudo-change” in complex scenes. In this paper, we propose a network architecture named UVACD for bitemporal image change detection. The network combines a CNNs extraction backbone for extracting high-level semantic information with a visual transformer. Here, visual transformer constructs change intensity tokens to complete the temporal information interaction and suppress irrelevant information weights to help extract more distinguishable change features. Our network is validated and tested on both the LEVIR-CD and WHU datasets. For the LEVIR-CD dataset, we achieve an intersection over union (IoU) of 0.8398 and an F1 score of 0.9130. For the WHU dataset, we achieve an IoU of 0.8664 and an F1 score of 0.9284. The experimental results show that the proposed method outperforms some previous state of the art change detection methods.
Parkinson’s disease (PD) is the second most common neurodegenerative disease. The characteristic feature of PD is the progressive degeneration of the dopaminergic (DAergic) neurons in the substantia ...nigra (SN). DAergic neurons in the SN accumulate black and insoluble membrane structures known as neuromelanin during aging. The oxidation of dopamine (DA) to form neuromelanin generates many o-quinones, including DA o-quinones, aminochrome, and 5,6-indolequinone. The focus of this review is to discuss the role of DA oxidation in association with PD. The oxidation of DA produces oxidative products, inducing mitochondrial dysfunction, impaired protein degradation, α-synuclein aggregation into neurotoxic oligomers, and oxidative stress, in vitro. Recent studies have demonstrated that the DA content is critical for both DJ-1 knockout and A53T α-synuclein transgenic mice to develop PD pathological features, providing evidence for DA action in PD pathogenesis in vivo. The effects of L-DOPA, as the most effective anti-PD drug, are also briefly discussed.
Deep learning has achieved exciting results in face recognition; however, the accuracy is still unsatisfying for occluded faces. To improve the robustness for occluded faces, this paper proposes a ...novel deep dictionary representation-based classification scheme, where a convolutional neural network is employed as the feature extractor and followed by a dictionary to linearly code the extracted deep features. The dictionary is composed by a gallery part consisting of the deep features of the training samples and an auxiliary part consisting of the mapping vectors acquired from the subjects either inside or outside the training set and associated with the occlusion patterns of the testing face samples. A squared Euclidean norm is used to regularize the coding coefficients. The proposed scheme is computationally efficient and is robust to large contiguous occlusion. In addition, the proposed scheme is generic for both the occluded and non-occluded face images and works with a single training sample per subject. The extensive experimental evaluations demonstrate the superior performance of the proposed approach over other state-of-the-art algorithms.
Tumor-associated neutrophils (TAN) have been reported in a variety of malignancies. We conducted an up-to-date meta-analysis to evaluate the prognostic role of TAN in cancer.
Pubmed, Embase and web ...of science databases were searched for studies published up to April 2013. Pooled hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) were calculated. The impact of neutrophils localization and primary antibody were also assessed.
A total of 3946 patients with various solid tumors from 20 studies were included. High density of intratumoral neutrophils were independently associated with unfavorable survival; the pooled HRs were 1.68 (95%CI: 1.36-2.07, I2 = 55.8%, p<0.001) for recurrence-free survival (RFS)/disease-free survival (DFS), 3.36 (95%CI: 2.08-5.42, I2 = 0%, p<0.001) for cancer-specific survival (CSS) and 1.66 (95%CI: 1.37-2.01, I2 = 70.5%, p<0.001) for overall survival (OS). Peritumoral and stromal neutrophils were not statistically significantly associated with survival. When grouped by primary antibody, the pooled HRs were 1.80 (95%CI: 1.47-2.22, I2 = 67.7%, p<0.001) for CD66b, and 1.44 (95%CI: 0.90-2.30, I2 = 45.9%, p = 0.125) for CD15, suggesting that CD66b positive TAN might have a better prognostic value than CD15.
High levels of intratumoral neutrophils are associated with unfavorable recurrence-free, cancer-specific and overall survival.
An iridium-catalyzed ortho C–H borylation reaction directed by cyclic dithioacetal moiety is disclosed. A series of borylation products were obtained in moderate to good yields under mild conditions ...in exclusive mono- and ortho-regioselectivity. Thus, the 1,3-dithiane or 1,3-dithiolane group serves as a remarkable effective directing group for C–H borylation without any ligand assistance. The further transformations of the borylation products are also carried out to change boryl group to other functional groups.
Colorectal cancer is the third most common cancer diagnosed in both men and women in the United States. Most colorectal cancers start as a growth on the inner lining of the colon or rectum, called ...'polyp'. Not all polyps are cancerous, but some can develop into cancer. Early detection and recognition of the type of polyps is critical to prevent cancer and change outcomes. However, visual classification of polyps is challenging due to varying illumination conditions of endoscopy, variant texture, appearance, and overlapping morphology between polyps. More importantly, evaluation of polyp patterns by gastroenterologists is subjective leading to a poor agreement among observers. Deep convolutional neural networks have proven very successful in object classification across various object categories. In this work, we compare the performance of the state-of-the-art general object classification models for polyp classification. We trained a total of six CNN models end-to-end using a dataset of 157 video sequences composed of two types of polyps: hyperplastic and adenomatous. Our results demonstrate that the state-of-the-art CNN models can successfully classify polyps with an accuracy comparable or better than reported among gastroenterologists. The results of this study can guide future research in polyp classification.
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