Design and optimization of joint iterative detection and decoding receiver for uplink polar coded SCMA system

SCMA and polar coding are possible candidates for 5G systems. In this paper, we firstly propose the joint iterative detection and decoding (JIDD) receiver for the uplink polar coded sparse code multiple access (PC-SCMA) system. Then, the EXIT chart is used to investigate the performance of the JIDD receiver. Additionally, we optimize the system design and polar code construction based on the EXIT chart analysis. The proposed receiver integrates the factor graph of SCMA detector and polar soft-output decoder into a joint factor graph, which enables the exchange of messages between SCMA detector and polar decoder iteratively. Simulation results demonstrate that the JIDD receiver has better BER performance and lower complexity than the separate scheme. Specifically, when polar code length N=256 and code rate R=1/2 , JIDD outperforms the separate scheme 4.8 and 6 dB over AWGN channel and Rayleigh fading channel, respectively. It also shows that, under 150% system loading, the JIDD receiver only has 0.3 dB performance loss compared to the single user uplink PC-SCMA over AWGN channel and 0.6 dB performance loss over Rayleigh fading channel

Pressure-Controlled Motion of Single Polymers through Solid-State Nanopores

Voltage-biased solid-state nanopores are well established in their ability to detect and characterize single polymers, such as DNA, in electrolytes. The addition of a pressure gradient across the nanopore yields a second molecular driving force that provides new freedom for studying molecules in nanopores. In this work, we show that opposing pressure and voltage bias enables nanopores to detect and resolve very short DNA molecules, as well as to detect near-neutral polymers.Engineering and Applied SciencesPhysic

BPTF promotes tumor growth and predicts poor prognosis in lung adenocarcinomas.

BPTF, a subunit of NURF, is well known to be involved in the development of eukaryotic cell, but little is known about its roles in cancers, especially in non-small-cell lung cancer (NSCLC). Here we showed that BPTF was specifically overexpressed in NSCLC cell lines and lung adenocarcinoma tissues. Knockdown of BPTF by siRNA significantly inhibited cell proliferation, induced cell apoptosis and arrested cell cycle progress from G1 to S phase. We also found that BPTF knockdown downregulated the expression of the phosphorylated Erk1/2, PI3K and Akt proteins and induced the cleavage of caspase-8, caspase-7 and PARP proteins, thereby inhibiting the MAPK and PI3K/AKT signaling and activating apoptotic pathway. BPTF knockdown by siRNA also upregulated the cell cycle inhibitors such as p21 and p18 but inhibited the expression of cyclin D, phospho-Rb and phospho-cdc2 in lung cancer cells. Moreover, BPTF knockdown by its specific shRNA inhibited lung cancer growth in vivo in the xenografts of A549 cells accompanied by the suppression of VEGF, p-Erk and p-Akt expression. Immunohistochemical assay for tumor tissue microarrays of lung tumor tissues showed that BPTF overexpression predicted a poor prognosis in the patients with lung adenocarcinomas. Therefore, our data indicate that BPTF plays an essential role in cell growth and survival by targeting multiply signaling pathways in human lung cancers

Gap-filling using machine learning : implementations and applications in remote sensing

Gap-filling is an important preprocessing step in remote sensing applications because it enables successful sensor-based studies by greatly recovering the Earth’s surface records lost due to sensor failures and cloud cover. To date, a great number of methods have been proposed to reconstruct missing data in remote sensing images, but methods that deliver satisfactory performance in handling large-area gaps over heterogeneous landscapes are scant. To address this problem, this thesis proposes two methods—Missing Observations Prediction based on Spectral-Temporal Metrics (MOPSTM) and Spectral and Temporal Information for Missing Data Reconstruction (STIMDR)—that are capable of recovering small and large-area gaps in Landsat time series. Machine learning algorithms are used to implement MOPSTM and STIMDR. MOPSTM applies the k-Nearest Neighbors (k-NN) regression to the target image (i.e. image that is to be reconstructed) and spectral-temporal metrics (STMs, e.g. statistical quantiles) derived from a 1-year Landsat time series. Improved from MOPSTM, STIMDR achieves more powerful performance by employing an effective mechanic that excludes dissimilar data in a longer time series (e.g., changes in land cover). The proposed methods are compared site-to-site with six state-of-the-art gap-filling methods including three temporal interpolation methods and three hybrid methods. With higher accuracy in four study sites located in Kenya, Finland, Germany, and China, MOPSTM and STIMDR have indicated more robust performance than other methods, with STIMDR yielding higher accuracy than MOPSTM. Although gap-filling methods are proposed with increasing frequency, their necessity and effects are rarely evaluated, so this has become an unsolved research gap. This thesis addresses this research gap using land use and land cover (LULC) classification and tree canopy cover (TCC) modelling with the assistance of machine learning algorithms. Random forest algorithm is used to examine whether gap-filled images outperform non-gap-filled (or actual) images in LULC and TCC applications. The results indicate that (i) gap-filled images achieve no worse performance in LULC classification than the actual image, and (ii) gap-filled predictors derived from the Landsat time series deliver better performance on average than non-gap-filled predictors in TCC modelling. Therefore, we conclude that gap-filling has positive effects on LULC classification and TCC modelling, which justifies its inclusion in image preprocessing workflows.-

The CoQ oxidoreductase FSP1 acts parallel to GPX4 to inhibit ferroptosis.

Ferroptosis is a form of regulated cell death that is caused by the iron-dependent peroxidation of lipids1,2. The glutathione-dependent lipid hydroperoxidase glutathione peroxidase 4 (GPX4) prevents ferroptosis by converting lipid hydroperoxides into non-toxic lipid alcohols3,4. Ferroptosis has previously been implicated in the cell death that underlies several degenerative conditions2, and induction of ferroptosis by the inhibition of GPX4 has emerged as a therapeutic strategy to trigger cancer cell death5. However, sensitivity to GPX4 inhibitors varies greatly across cancer cell lines6, which suggests that additional factors govern resistance to ferroptosis. Here, using a synthetic lethal CRISPR-Cas9 screen, we identify ferroptosis suppressor protein 1 (FSP1) (previously known as apoptosis-inducing factor mitochondrial 2 (AIFM2)) as a potent ferroptosis-resistance factor. Our data indicate that myristoylation recruits FSP1 to the plasma membrane where it functions as an oxidoreductase that reduces coenzyme Q10 (CoQ) (also known as ubiquinone-10), which acts as a lipophilic radical-trapping antioxidant that halts the propagation of lipid peroxides. We further find that FSP1 expression positively correlates with ferroptosis resistance across hundreds of cancer cell lines, and that FSP1 mediates resistance to ferroptosis in lung cancer cells in culture and in mouse tumour xenografts. Thus, our data identify FSP1 as a key component of a non-mitochondrial CoQ antioxidant system that acts in parallel to the canonical glutathione-based GPX4 pathway. These findings define a ferroptosis suppression pathway and indicate that pharmacological inhibition of FSP1 may provide an effective strategy to sensitize cancer cells to ferroptosis-inducing chemotherapeutic agents

A method for predicting large-area missing observations in Landsat time series using spectral-temporal metrics

Combined with increasing computing ability, the free and open access to Landsat archive has enabled the changes on the Earth’s surface to be monitored for almost 50 years. However, due to missing observations that result from clouds, cloud shadows, and scan line corrector failure, the Landsat data record is neither a continuous nor consistent time series. We present a new gap-filling method, Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM), which uses spectral-temporal metrics computed from Landsat one-year time series and the k-Nearest Neighbor (k-NN) regression. Herein, we demonstrate the performance of MOPSTM by using five, nearly cloud-free, full scene Landsat images from Kenya, Finland, Germany, the USA, and China. Cloud masks from the images with extensive cloud cover were used to simulate large-area gaps, with the highest value we tested being 92% of missing data. The gap-filling accuracy was assessed quantitatively considering all five sites and different land use/land cover types, and the MOPSTM algorithm performed better than the spectral angle-mapper based spatiotemporal similarity (SAMSTS) gap-filling algorithm. The mean RMSE values of MOPSTM were 0.010, 0.012, 0.025, 0.012, and 0.018 for the five sites, while those of SAMSTS were 0.011, 0.017, 0.038, 0.014, and 0.023, respectively. Furthermore, MOPSTM had mean coefficient of determination (R2) values of 0.90, 0.86, 0.78, 0.92, and 0.89, which were higher than those for SAMSTS (0.84, 0.75, 0.55, 0.89, and 0.83). The performance of MOPSTM was not considerably affected by image gap sizes as images ranging from gap sizes of 51% of the image all the way to 92% of the image yielded similar gap-filling accuracy. Also, MOPSTM does not require local parametertuning except for the k values in the k-NN regression, and it can make a gap-free image from any acquisition date. MOPSTM provides a new spectral-temporal approach to generate the gap-free imagery for typical Landsat applications, such as land use, land cover, and forest monitoring.Peer reviewe