An HR-MAS MR Metabolomics Study on Breast Tissues Obtained with Core Needle Biopsy

BACKGROUND: Much research has been devoted to the development of new breast cancer diagnostic measures, including those involving high-resolution magic angle spinning (HR-MAS) magnetic resonance (MR) spectroscopic techniques. Previous HR-MAS MR results have been obtained from post-surgery samples, which limits their direct clinical applicability. METHODOLOGY/PRINCIPAL FINDINGS: In the present study, we performed HR-MAS MR spectroscopic studies on 31 breast tissue samples (13 cancer and 18 non-cancer) obtained by percutaneous core needle biopsy. We showed that cancer and non-cancer samples can be discriminated very well with Orthogonal Projections to Latent Structure-Discriminant Analysis (OPLS-DA) multivariate model on the MR spectra. A subsequent blind test showed 69% sensitivity and 94% specificity in the prediction of the cancer status. A spectral analysis showed that in cancer cells, taurine- and choline-containing compounds are elevated. Our approach, additionally, could predict the progesterone receptor statuses of the cancer patients. CONCLUSIONS/SIGNIFICANCE: HR-MAS MR metabolomics on intact breast tissues obtained by core needle biopsy may have a potential to be used as a complement to the current diagnostic and prognostic measures for breast cancers

DYNAMIC CONTROL OF DNA PRECURSOR SYNTHESIS IN EARLY EMBRYOS

Animal embryogenesis starts with multiple rounds of nuclear divisions. During and shortly after these divisions, the zygotic genome is activated, the body plan of the organism is established, and gastrulation initiates the formation of tissues and organs. For these events to occur, the embryo needs to generate energy and provide metabolic precursors for biosynthesis. In this thesis, we used quantitative mass spectrometry and genetic manipulation techniques to examine how the early Drosophila melanogaster embryo controls the synthesis of DNA precursors. Early Drosophila embryos undergo 13 rapid and synchronous nuclear division cycles within two hours of fertilization. This exponential increase in the number of nuclei requires massive amounts of deoxynucleoside triphosphates (dNTPs). Surprisingly, despite the breakneck speed at which Drosophila embryos synthesize DNA, maternally deposited dNTPs can generate less than half of the genomes needed to reach gastrulation. The rest of the dNTPs are synthesized “on the go''. The rate-limiting enzyme of dNTP synthesis, ribonucleotide reductase (RNR), is inhibited by endogenous levels of dATP present at fertilization and is activated as dATP is depleted via DNA polymerization. In the absence of inhibition by dATP, dNTP levels increase dramatically and induce embryonic lethality with particularly severe structural defects in the anterior regions. In conclusion, this thesis demonstrated that dNTP synthesis in early Drosophila embryos is controlled mainly through a single feedback inhibition loop at the end of the dNTP production pathway. In the process, we have also found that misregulation of RNR activity suprisingly confers tissue specific defects. Going forward, this thesis establishes Drosophila development as a platform for mechanistic and quantitative studies of dNTP metabolism

A Patch-Based Light Convolutional Neural Network for Land-Cover Mapping Using Landsat-8 Images

This study proposes a light convolutional neural network (LCNN) well-fitted for medium-resolution (30-m) land-cover classification. The LCNN attains high accuracy without overfitting, even with a small number of training samples, and has lower computational costs due to its much lighter design compared to typical convolutional neural networks for high-resolution or hyperspectral image classification tasks. The performance of the LCNN was compared to that of a deep convolutional neural network, support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF). SVM, KNN, and RF were tested with both patch-based and pixel-based systems. Three 30 km × 30 km test sites of the Level II National Land Cover Database were used for reference maps to embrace a wide range of land-cover types, and a single-date Landsat-8 image was used for each test site. To evaluate the performance of the LCNN according to the sample sizes, we varied the sample size to include 20, 40, 80, 160, and 320 samples per class. The proposed LCNN achieved the highest accuracy in 13 out of 15 cases (i.e., at three test sites with five different sample sizes), and the LCNN with a patch size of three produced the highest overall accuracy of 61.94% from 10 repetitions, followed by SVM (61.51%) and RF (61.15%) with a patch size of three. Also, the statistical significance of the differences between LCNN and the other classifiers was reported. Moreover, by introducing the heterogeneity value (from 0 to 8) representing the complexity of the map, we demonstrated the advantage of patch-based LCNN over pixel-based classifiers, particularly at moderately heterogeneous pixels (from 1 to 4), with respect to accuracy (LCNN is 5.5% and 6.3% more accurate for a training sample size of 20 and 320 samples per class, respectively). Finally, the computation times of the classifiers were calculated, and the LCNN was confirmed to have an advantage in large-area mapping

CompCertM: CompCert with C-assembly linking and lightweight modular verification

Supporting multi-language linking such as linking C and handwritten assembly modules in the verified compiler CompCert requires a more compositional verification technique than that used in CompCert just supporting separate compilation. The two extensions, CompCertX and Compositional CompCert, supporting multi-language linking take different approaches. The former simplifies the problem by imposing restrictions that the source modules should have no mutual dependence and be verified against certain well-behaved specifications. On the other hand, the latter develops a new verification technique that directly solves the problem but at the expense of significantly increasing the verification cost. In this paper, we develop a novel lightweight verification technique, called RUSC (Refinement Under Self-related Contexts), and demonstrate how RUSC can solve the problem without any restrictions but still with low verification overhead. For this, we develop CompCertM, a full extension of the latest version of CompCert supporting multi-language linking. Moreover, we demonstrate the power of RUSC as a program verification technique by modularly verifying interesting programs consisting of C and handwritten assembly against their mathematical specifications.Y

Energy budget of Drosophila embryogenesis

Eggs of oviparous animals must be prepared to develop rapidly and robustly until hatching. The balance between sugars, fats, and other macromolecules must therefore be carefully considered when loading the egg with nutrients. Clearly, packing too much or too little fuel would lead to suboptimal conditions for development. While many studies have measured the overall energy utilization of embryos, little is known of the identity of the molecular-level processes that contribute to the energy budget in the first place [1]. Here, we introduce Drosophila embryos as a platform to study the energy budget of embryogenesis. We demonstrate through three orthogonal measurements - respiration, calorimetry, and biochemical assays - that Drosophila melanogaster embryogenesis utilizes 10 mJ of energy generated by the oxidation of the maternal glycogen and triacylglycerol (TAG) stores (Figure 1). Normalized for mass, this is comparable to the resting metabolic rates of insects [2]. Interestingly, alongside data from earlier studies, our results imply that protein, RNA, and DNA polymerization require less than 10% of the total ATPs produced in the early embryo

Who will drive electric vehicles, olivine or spinel?

Lithium iron phosphate olivine (LFP) and lithium manganese oxide spinel (LMO) are competitive and complementary to each other as cathode materials for lithium ion batteries, especially for use in hybrid electric vehicles and electric vehicles. Interest in these materials, due to their low cost and high safety, has pushed research and development forward and toward high performance in terms of rate capability and capacity retention or cyclability at a high temperature of around 60 degrees C. From the view point of basic properties, LFP shows a higher gravimetric capacity while LMO has better conductivities, both electrically and ionically. According to our comparison experiments, depending on the material properties and operational potential window, LFP was favored for fast charging while LMO led to better discharge performances. Capacity fading at high temperatures due to metal dissolution was revealed to be the most problematic issue of LFP and LMO-based cells for electric vehicles (EVs), with thicker electrodes, in the case of no additives in the electrolyte and no coating to prevent metal dissolution on cathode materials. Various strategies to enhance the properties of LFP and LMO are ready for the realization of EVs in the near future.close15314