Knockdown of lncRNA-PANDAR suppresses the proliferation, cell cycle and promotes apoptosis in thyroid cancer cells

Long non-coding RNAs (lncRNAs) have been found to show important regulatory roles in various human cancers. Lnc-RNA PANDAR is a novel identified lncRNA that was previously reported to show abnormal expression pattern in various cancers. However, little is known of its expression and biological function in thyroid cancer. Here, we used the quantitative real-time PCR (qRT-PCR) to determine the expression of PANDAR in 64 thyroid cancer tissues. We found that expression of PANDAR was up-regulated in thyroid cancer tissues compared with adjacent non-tumor tissues. Functional assays in vitro demonstrated that knockdown of PANDAR could inhibit proliferation, cell cycle progression, induces the apoptosis, inhibit invasion of thyroid cancer cells. Thus, our study provides evidence that PANDAR may function as a potential target for treatment for patients with thyroid cancer

The Study on Fractal Characteristics of Television Audience Ratings Based on R/S Analysis Method

According to the nonlinear distribution of Television Audience ratings data, a fractal dimension study on average daily television audience ratings of three TV stations based on R/S analysis method. Results show that the Hurst index of time series is significantly greater than 0.5 and there is a trend of long-term memory; All the time series is significantly different in the non-periodic cycles length and offer explanation from the perspective of three Television stations’ characteristics of development. This method can help television managers realize the viewing situation, master the change rule and help advertisers make a scientific decision

Corticosteroids for the prevention of bronchopulmonary dysplasia in preterm infants: a network meta-analysis

Objective: To determine the comparative efficacy and safety of corticosteroids in the prevention of bronchopulmonary dysplasia (BPD) in preterm infants.  Study design: We systematically searched PubMed, EMBASE and the Cochrane Library. Two reviewers independently selected randomised controlled trials (RCTs) of postnatal corticosteroids in preterm infants. A Bayesian network meta-analysis and subgroup analyses were performed.  Results: We included 47 RCTs with 6747 participants. The use of dexamethasone at either high dose or low dose decreased the risk of BPD (OR 0.29, 95% credible interval (CrI) 0.14 to 0.52; OR 0.58, 95% CrI 0.39 to 0.76, respectively). High-dose dexamethasone was more effective than hydrocortisone, beclomethasone and low-dose dexamethasone. Early and long-term dexamethasone at either high dose or low dose decreased the risk of BPD (OR 0.11, 95% CrI 0.02 to 0.4; OR 0.37, 95% CrI 0.16 to 0.67, respectively). There were no statistically significant differences in the risk of cerebral palsy (CP) between different corticosteroids. However, high-dose and long-term dexamethasone ranked lower than placebo and other regimens in terms of CP. Subgroup analyses indicated budesonide was associated with a decreased risk of BPD in extremely preterm and extremely low birthweight infants (OR 0.60, 95% CrI 0.36 to 0.93).  Conclusions: Dexamethasone can reduce the risk of BPD in preterm infants. Of the different dexamethasone regimens, aggressive initiation seems beneficial, while a combination of high-dose and long-term use should be avoided because of the possible adverse neurodevelopmental outcome. Dexamethasone and inhaled corticosteroids need to be further evaluated in large-scale RCTs with long-term follow-ups

Deep Feature Screening: Feature Selection for Ultra High-Dimensional Data via Deep Neural Networks

The applications of traditional statistical feature selection methods to high-dimension, low sample-size data often struggle and encounter challenging problems, such as overfitting, curse of dimensionality, computational infeasibility, and strong model assumption. In this paper, we propose a novel two-step nonparametric approach called Deep Feature Screening (DeepFS) that can overcome these problems and identify significant features with high precision for ultra high-dimensional, low-sample-size data. This approach first extracts a low-dimensional representation of input data and then applies feature screening based on multivariate rank distance correlation recently developed by Deb and Sen (2021). This approach combines the strengths of both deep neural networks and feature screening, and thereby has the following appealing features in addition to its ability of handling ultra high-dimensional data with small number of samples: (1) it is model free and distribution free; (2) it can be used for both supervised and unsupervised feature selection; and (3) it is capable of recovering the original input data. The superiority of DeepFS is demonstrated via extensive simulation studies and real data analyses

AMG: Automated Efficient Approximate Multiplier Generator for FPGAs via Bayesian Optimization

Approximate computing is a promising approach to reduce the power, delay, and area in hardware design for many error-resilient applications such as machine learning (ML) and digital signal processing (DSP) systems, in which multipliers usually are key arithmetic units. Due to the underlying architectural differences between ASICs and FPGAs, existing ASIC-based approximate multipliers do not offer symmetrical gains when they are implemented by FPGA resources. In this paper, we propose AMG, an open-source automated approximate multiplier generator for FPGAs driven by Bayesian optimization (BO) with parallel evaluation. The proposed method simplifies the exact half adders (HAs) for the initial partial product (PP) compression in a multiplier while preserving coarse-grained additions for the following accumulation. The generated multipliers can be effectively mapped to lookup tables (LUTs) and carry chains provided by modern FPGAs, reducing hardware costs with acceptable errors. Compared with 1167 multipliers from previous works, our generated multipliers can form a Pareto front with 28.70%-38.47% improvements in terms of the product of hardware cost and error on average. All source codes, reproduced multipliers, and our generated multipliers are available at https://github.com/phyzhenli/AMG.Comment: 7 pages, 2023 IEEE International Conference on Field-Programmable Technology (ICFPT

Multi-view Inverse Rendering for Large-scale Real-world Indoor Scenes

We present a multi-view inverse rendering method for large-scale real-world indoor scenes that reconstructs global illumination and physically-reasonable SVBRDFs. Unlike previous representations, where the global illumination of large scenes is simplified as multiple environment maps, we propose a compact representation called Texture-based Lighting (TBL). It consists of 3D meshs and HDR textures, and efficiently models direct and infinite-bounce indirect lighting of the entire large scene. Based on TBL, we further propose a hybrid lighting representation with precomputed irradiance, which significantly improves the efficiency and alleviate the rendering noise in the material optimization. To physically disentangle the ambiguity between materials, we propose a three-stage material optimization strategy based on the priors of semantic segmentation and room segmentation. Extensive experiments show that the proposed method outperforms the state-of-the-arts quantitatively and qualitatively, and enables physically-reasonable mixed-reality applications such as material editing, editable novel view synthesis and relighting. The project page is at https://lzleejean.github.io/TexIR.Comment: The project page is at: https://lzleejean.github.io/TexI