Field Study of an Oil Tank on Stone Column Ground

Two SPTs and two CPTs for ground before and after stabilization, three static loading tests for the stabilized ground with stone columns were performed. It was found that stone columns strengthened the resistance to seismic liquefaction of a saturated clayey silt layer and increased the ground bearing capacity two times. From a water preloading test of an oil tank on the improved ground it was obtained that stone columns not only decreased the total and differential settlement of the tank but also speeded the consolidation rate of the ground. Stone columns also reduced the initial excess pore water pressures developed in the improved ground in comparison with those in the unimproved one

First Characterization of Sphingomyeline Phosphodiesterase Expression in the Bumblebee, Bombus lantschouensis

  The bumblebee (Bombus lantschouensis Vogt) is an important pollinator of wild plants. Sphingomyelin phosphodiesterase (SMPD) is a hydrolase that plays a major role in sphingolipid metabolism reactions. We report the preparation and characterization of a polyclonal antibody for bumblebee SMPD. We then use the polyclonal antiserum to detect the SMPD protein at different development stages and in different tissues. Our results showed that a 1228bp fragment homologous with the B. terrestris SMPD gene was successfully amplified. The molecular weight of the fusion protein was about 70 kDa by SDS-PAGE. An effective polyclonal antibody against SMPD was also obtained from mice and found to have a higher specificity for bumblebee SMPD. Western blotting detection showed that SMPD was expressed at a high level in queen ovaries, although expression was lower in the midgut and venom gland. SMPD expression decreased from the egg stage until the pdd stage. We interpret our results as showing that the development of an effective polyclonal antiserum for the SMPD protein of a bumblebee, which provides a tool for exploring the function of the SMPD gene. In addition, the work has confirmed that SMPD should be considered as an important enzyme during bumblebee egg and larval stages

Integrative analysis based on survival associated co-expression gene modules for predicting Neuroblastoma patients' survival time

BACKGROUND: More than 90% of neuroblastoma patients are cured in the low-risk group while only less than 50% for those with high-risk disease can be cured. Since the high-risk patients still have poor outcomes, we need more accurate stratification to establish an individualized precise treatment plan for the patients to improve the long-term survival rate. RESULTS: We focus on extracting features and providing a workflow to improve survival prediction for neuroblastoma patients. With a workflow for gene co-expression network (GCN) mining in microarray and RNA-Seq datasets, we extracted molecular features from each co-expressed module and summarized them into eigengenes. Then we adopted the lasso-regularized Cox proportional hazards model to select the most informative eigengene features regarding association to the risk of metastasis. Nine eigengenes were selected which show strong association with patient survival prognosis. All of the nine corresponding gene modules also have highly enriched biological functions or cytoband locations. Three of them are unique modules to RNA-Seq data, which complement the modules from microarray data in terms of survival prognosis. We then merged all eigengenes from these unique modules and used an integrative method called Similarity Network Fusion to test the prognostic power of these eigengenes for prognosis. The prognostic accuracies are significantly improved as compared to using all eigengenes, and a subgroup of patients with very poor survival rate was identified. CONCLUSIONS: We first compared GCNs mined from microarray and RNA-seq data. We discovered that each data modality yields unique GCNs, which are enriched with clear biological functions. Then we do module unique analysis and use lasso-cox model to select survival-associated eigengenes. Integration of unique and survival-associated eigengenes from both data types provides complementary information that leads to more accurate survival prognosis

Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis

In cancer, both histopathologic images and genomic signatures are used for diagnosis, prognosis, and subtyping. However, combining histopathologic images with genomic data for predicting prognosis, as well as the relationships between them, has rarely been explored. In this study, we present an integrative genomics framework for constructing a prognostic model for clear cell renal cell carcinoma. We used patient data from The Cancer Genome Atlas (n = 410), extracting hundreds of cellular morphologic features from digitized whole-slide images and eigengenes from functional genomics data to predict patient outcome. The risk index generated by our model correlated strongly with survival, outperforming predictions based on considering morphologic features or eigengenes separately. The predicted risk index also effectively stratified patients in early-stage (stage I and stage II) tumors, whereas no significant survival difference was observed using staging alone. The prognostic value of our model was independent of other known clinical and molecular prognostic factors for patients with clear cell renal cell carcinoma. Overall, this workflow and the shared software code provide building blocks for applying similar approaches in other cancers

Parameter-Efficient Conformers via Sharing Sparsely-Gated Experts for End-to-End Speech Recognition

While transformers and their variant conformers show promising performance in speech recognition, the parameterized property leads to much memory cost during training and inference. Some works use cross-layer weight-sharing to reduce the parameters of the model. However, the inevitable loss of capacity harms the model performance. To address this issue, this paper proposes a parameter-efficient conformer via sharing sparsely-gated experts. Specifically, we use sparsely-gated mixture-of-experts (MoE) to extend the capacity of a conformer block without increasing computation. Then, the parameters of the grouped conformer blocks are shared so that the number of parameters is reduced. Next, to ensure the shared blocks with the flexibility of adapting representations at different levels, we design the MoE routers and normalization individually. Moreover, we use knowledge distillation to further improve the performance. Experimental results show that the proposed model achieves competitive performance with 1/3 of the parameters of the encoder, compared with the full-parameter model.Comment: accepted in INTERSPEECH 202

Growing Aligned Carbon Nanotubes for Interconnections in ICs

A process for growing multiwalled carbon nanotubes anchored at specified locations and aligned along specified directions has been invented. Typically, one would grow a number of the nanotubes oriented perpendicularly to a silicon integrated-circuit (IC) substrate, starting from (and anchored on) patterned catalytic spots on the substrate. Such arrays of perpendicular carbon nanotubes could be used as electrical interconnections between levels of multilevel ICs. The process (see Figure 1) begins with the formation of a layer, a few hundred nanometers thick, of a compatible electrically insulating material (e.g., SiO(x) or Si(y)N(z) on the silicon substrate. A patterned film of a suitable electrical conductor (Al, Mo, Cr, Ti, Ta, Pt, Ir, or doped Si), having a thickness between 1 nm and 2 m, is deposited on the insulating layer to form the IC conductor pattern. Next, a catalytic material (usually, Ni, Fe, or Co) is deposited to a thickness between 1 and 30 nm on the spots from which it is desired to grow carbon nanotubes. The carbon nanotubes are grown by plasma-enhanced chemical vapor deposition (PECVD). Unlike the matted and tangled carbon nanotubes grown by thermal CVD, the carbon nanotubes grown by PECVD are perpendicular and freestanding because an electric field perpendicular to the substrate is used in PECVD. Next, the free space between the carbon nanotubes is filled with SiO2 by means of CVD from tetraethylorthosilicate (TEOS), thereby forming an array of carbon nanotubes embedded in SiO2. Chemical mechanical polishing (CMP) is then performed to remove excess SiO2 and form a flat-top surface in which the outer ends of the carbon nanotubes are exposed. Optionally, depending on the application, metal lines to connect selected ends of carbon nanotubes may be deposited on the top surface. The top part of Figure 2 is a scanning electron micrograph (SEM) of carbon nanotubes grown, as described above, on catalytic spots of about 100 nm diameter patterned by electron-beam lithography. These and other nanotubes were found to have lengths ranging from 2 to 10 m and diameters ranging from 30 to 200 nm, the exact values of length depending on growth times and conditions and the exact values of diameter depending on the diameters and thicknesses of the catalyst spots. The bottom part of Figure 2 is an SEM of an embedded array of carbon nanotubes after CMP

Historical Refugia and Isolation by Distance of the Mud Snail, Bullacta exarata (Philippi, 1849) in the Northwestern Pacific Ocean

Many phylogeographic studies on marine organisms in the Northwestern Pacific have supported for the biogeographic hypotheses that isolation in the marginal seas of this region during the Pleistocene glaciation lower sea level led to population genetic divergence, and thus population expansion was a common phenomenon when the sea level rebounded. However, most of these studies were based on maternally inherited mitochondrial DNA markers with limited sample sites and therefore, were unable to reveal detailed pictures encompassing paternal line information covering of the entire range. In this study, we used the mitochondrial cytochrome c oxidase subunit I (COI) and nine nuclear microsatellite loci to investigate the phylogeography of the mud snail, Bullacta exarata (Philippi, 1849), a species endemic to the Northwestern Pacific. We sampled 14 natural populations spanning across 3800 km of the Chinese coastline, essentially covering most of the species distribution range. COI analysis identified a total of 149 haplotypes separated into two distinct groups with nine mutation steps, revealing a prominent phylogeographic structure. Nuclear microsatellite data also demonstrated a similar but weaker genetic structure. The estimated time to the most recent common ancestor between the two COI haplogroups is at ∼0.89 Ma, indicating that B. exarata populations survived the Pleistocene glaciation in the Sea of Japan and the Okinawa Trough, two marginal seas around the species range. The consistent significant patterns of isolation by distance of both COI and microsatellites suggests that limited mobility of adults and short planktonic stage of larvae may have played an important role in promoting or maintaining the genetic differentiation of B. exarata. Results from population demographic analyses support population expansion late in the Pleistocene era