Sample size for detecting differentially expressed genes in microarray experiments

BACKGROUND: Microarray experiments are often performed with a small number of biological replicates, resulting in low statistical power for detecting differentially expressed genes and concomitant high false positive rates. While increasing sample size can increase statistical power and decrease error rates, with too many samples, valuable resources are not used efficiently. The issue of how many replicates are required in a typical experimental system needs to be addressed. Of particular interest is the difference in required sample sizes for similar experiments in inbred vs. outbred populations (e.g. mouse and rat vs. human). RESULTS: We hypothesize that if all other factors (assay protocol, microarray platform, data pre-processing) were equal, fewer individuals would be needed for the same statistical power using inbred animals as opposed to unrelated human subjects, as genetic effects on gene expression will be removed in the inbred populations. We apply the same normalization algorithm and estimate the variance of gene expression for a variety of cDNA data sets (humans, inbred mice and rats) comparing two conditions. Using one sample, paired sample or two independent sample t-tests, we calculate the sample sizes required to detect a 1.5-, 2-, and 4-fold changes in expression level as a function of false positive rate, power and percentage of genes that have a standard deviation below a given percentile. CONCLUSIONS: Factors that affect power and sample size calculations include variability of the population, the desired detectable differences, the power to detect the differences, and an acceptable error rate. In addition, experimental design, technical variability and data pre-processing play a role in the power of the statistical tests in microarrays. We show that the number of samples required for detecting a 2-fold change with 90% probability and a p-value of 0.01 in humans is much larger than the number of samples commonly used in present day studies, and that far fewer individuals are needed for the same statistical power when using inbred animals rather than unrelated human subjects

Session: FF4-1 ENGINEERED COLOR APPEARANCE WITH DIGITAL APPROACH

Abstract For the past ten years, digital color standard, color evaluation, as well as color communication were proved to be effective way to help manufactures and retailers achieve the most consistent and reliable products with designated color. These will guarantee the color integrity of the standard and significantly reduce the time from conception to production. However, appearance is more than color. Many a time, designer asked manufactures to produce color on different substrate but failed to achieve exact match, as it is quite difficult to have real match on different substrate. In this discussion, some advanced image technologies in the field of color and appearance will be introduced to improve color quality in industrial manufacture. Descriptions and key specifications of engineered digital color appearances are defined

Imaging-based amplitude laser beam shaping for material processing by 2D reflectivity tuning of a spatial light modulator

We have demonstrated an imaging-based amplitude laser-beam-shaping technique for material processing by 2D reflectivity tuning of a spatial light modulator. Intensity masks with 256 gray levels were designed to shape the input laser beam in the outline profile and inside intensity distribution. Squared and circular flattop beam shapes were obtained at the diffractive near-field and then reconstructed at an image plane of a

Ultrafast laser beam shaping for material processing at imaging plane by geometric masks using a spatial light modulator

We have demonstrated an original ultrafast laser beam shaping technique for material processing using a spatial light modulator (SLM). Complicated and time-consuming diffraction far-field phase hologram calculations based on Fourier transformations are avoided, while simple and direct geometric masks are used to shape the incident beam at diffraction near-field. Various beam intensity shapes, such as square, triangle, ring and star, are obtained and then reconstructed at the imaging plane of an f-theta lens. The size of the shaped beam is approximately 20 µm, which is comparable to the beam waist at the focal plane. A polished stainless steel sample is machined by the shaped beam at the imaging plane. The shape of the ablation footprint well matches the beam shape

STUDY ON EXTRACTION PROCESS OF TANNINS FROM SEMEN CUSCUTAE AND THEIR ANTI-PAPILLOMA ACTIVITY

The objective of this paper was to study the extraction methods of tannin constituents from Semen Cuscutae and their anti-papilloma effects. Single factor test and orthogonal design methods were used to determine the optimal extraction method; the mouse skin papilloma model induced by DMBA/croton oil was established, which was a classic two-stage carcinogenesis model being used to observe and evaluate the anti-carcinogenic effects of tannins extracted from Semen Cuscutae in different stages. The optimal extraction method of Semen Cuscutae was a 20-fold volume of solvent, a temperature of 50 oC, three times of extraction, with 20 min each, skin papilloma experiment revealed that the number of bearing tumors gradually reduced, and the inhibition rate gradually increased with the increase of dose, in the high-dose group, its inhibition rate reached 70.2%. Tannin extract from Semen Cuscutae has an obvious inhibitory effect on skin papilloma development

ANTI-INFLAMMATORY AND ANALGESIC ACTIVITY OF R.A.P. (RADIX ANGELICAE PUBESCENTIS) ETHANOL EXTRACTS

The objective of this paper was to study the anti-inflammatory and analgesic effects of Radix Angelicae Pubescentis (R.A.P) ethanol extracts. Three classic anti-inflammatory models and two analgesic models were used in this research. In anti-inflammatory tests, all the extracts have a certain inhibition on the acute inflammation induced by xylene, however, 60% ethanol extract significantly inhibited the inflammation in the three models. In analgesic experiment, compared with the blank control group, the comparisons between R.A.P. groups and control group had significant difference (p﹤0.01). The incubation period in mouse writhing test or the tail-curl immersion tests could be extended greatly

Transmission of H7N9 influenza virus in mice by different infective routes.

BackgroundOn 19 February 2013, the first patient infected with a novel influenza A H7N9 virus from an avian source showed symptoms of sickness. More than 349 laboratory-confirmed cases and 109 deaths have been reported in mainland China since then. Laboratory-confirmed, human-to-human H7N9 virus transmission has not been documented between individuals having close contact; however, this transmission route could not be excluded for three families. To control the spread of the avian influenza H7N9 virus, we must better understand its pathogenesis, transmissibility, and transmission routes in mammals. Studies have shown that this particular virus is transmitted by aerosols among ferrets.MethodsTo study potential transmission routes in animals with direct or close contact to other animals, we investigated these factors in a murine model.ResultsViable H7N9 avian influenza virus was detected in the upper and lower respiratory tracts, intestine, and brain of model mice. The virus was transmissible between mice in close contact, with a higher concentration of virus found in pharyngeal and ocular secretions, and feces. All these biological materials were contagious for naïve mice.ConclusionsOur results suggest that the possible transmission routes for the H7N9 influenza virus were through mucosal secretions and feces

SFNet: Faster and Accurate Semantic Segmentation via Semantic Flow

In this paper, we focus on exploring effective methods for faster and accurate semantic segmentation. A common practice to improve the performance is to attain high-resolution feature maps with strong semantic representation. Two strategies are widely used: atrous convolutions and feature pyramid fusion, while both are either computationally intensive or ineffective. Inspired by the Optical Flow for motion alignment between adjacent video frames, we propose a Flow Alignment Module (FAM) to learn \textit{Semantic Flow} between feature maps of adjacent levels and broadcast high-level features to high-resolution features effectively and efficiently. Furthermore, integrating our FAM to a standard feature pyramid structure exhibits superior performance over other real-time methods, even on lightweight backbone networks, such as ResNet-18 and DFNet. Then to further speed up the inference procedure, we also present a novel Gated Dual Flow Alignment Module to directly align high-resolution feature maps and low-resolution feature maps where we term the improved version network as SFNet-Lite. Extensive experiments are conducted on several challenging datasets, where results show the effectiveness of both SFNet and SFNet-Lite. In particular, when using Cityscapes test set, the SFNet-Lite series achieve 80.1 mIoU while running at 60 FPS using ResNet-18 backbone and 78.8 mIoU while running at 120 FPS using STDC backbone on RTX-3090. Moreover, we unify four challenging driving datasets into one large dataset, which we named Unified Driving Segmentation (UDS) dataset. It contains diverse domain and style information. We benchmark several representative works on UDS. Both SFNet and SFNet-Lite still achieve the best speed and accuracy trade-off on UDS, which serves as a strong baseline in such a challenging setting. The code and models are publicly available at https://github.com/lxtGH/SFSegNets.Comment: IJCV-2023; Extension of Previous work arXiv:2002.1012

Learning with Noisy labels via Self-supervised Adversarial Noisy Masking

Collecting large-scale datasets is crucial for training deep models, annotating the data, however, inevitably yields noisy labels, which poses challenges to deep learning algorithms. Previous efforts tend to mitigate this problem via identifying and removing noisy samples or correcting their labels according to the statistical properties (e.g., loss values) among training samples. In this paper, we aim to tackle this problem from a new perspective, delving into the deep feature maps, we empirically find that models trained with clean and mislabeled samples manifest distinguishable activation feature distributions. From this observation, a novel robust training approach termed adversarial noisy masking is proposed. The idea is to regularize deep features with a label quality guided masking scheme, which adaptively modulates the input data and label simultaneously, preventing the model to overfit noisy samples. Further, an auxiliary task is designed to reconstruct input data, it naturally provides noise-free self-supervised signals to reinforce the generalization ability of deep models. The proposed method is simple and flexible, it is tested on both synthetic and real-world noisy datasets, where significant improvements are achieved over previous state-of-the-art methods