Synergistic and Antagonistic Drug Combinations Depend on Network Topology

Drug combinations may exhibit synergistic or antagonistic effects. Rational design of synergistic drug combinations remains a challenge despite active experimental and computational efforts. Because drugs manifest their action via their targets, the effects of drug combinations should depend on the interaction of their targets in a network manner. We therefore modeled the effects of drug combinations along with their targets interacting in a network, trying to elucidate the relationships between the network topology involving drug targets and drug combination effects. We used three-node enzymatic networks with various topologies and parameters to study two-drug combinations. These networks can be simplifications of more complex networks involving drug targets, or closely connected target networks themselves. We found that the effects of most of the combinations were not sensitive to parameter variation, indicating that drug combinational effects largely depend on network topology. We then identified and analyzed consistent synergistic or antagonistic drug combination motifs. Synergistic motifs encompass a diverse range of patterns, including both serial and parallel combinations, while antagonistic combinations are relatively less common and homogenous, mostly composed of a positive feedback loop and a downstream link. Overall our study indicated that designing novel synergistic drug combinations based on network topology could be promising, and the motifs we identified could be a useful catalog for rational drug combination design in enzymatic systems

Utjecaj procesa očvršćivanja na ostatno naprezanje u modulu solarne ćelije

Panels using solar power require high reliability, and the residual stress in the solar panel has an important effect on its reliability and lifetime. The finite element method was adopted to simulate the impacts of the rectangular solar panel encapsulation process parameters, such as the elastic modulus, the thickness of adhesive, and the curing temperature on the residual stress in the solar cell module. The results show that the residual stress in the solar cell module increases linearly with the increase in these three factors. The residual strain is consistent with that of the stress. The generation mechanism and distribution evolution of stress are discussed in detail. Both the thickness and the elastic modulus of the silicone rubber have significant impact on the residual stress. However, the influence of the curing temperature is less observable.Solarni paneli trebaju biti iznimno pouzdani, a na pouzdanost i životni vijek znatno utječe ostatno naprezanje. Za simulaciju utjecaja parametara izrade četvrtastog solarnog panela, kao što su modul elastičnosti, debljina ljepila i temperatura očvršćivanja, na ostatno naprezanje u solarnom modulu primijenjena je metoda konačnih elemenata. Rezultati pokazuju da ostatno naprezanje linearno raste s porastom tih triju faktora. Rezidualna deformacija slijedi rezidualno naprezanje. Detaljno su razmatrani mehanizam nastanka i tijek raspodjele naprezanja. Na ostatno naprezanje značajan je utjecaj debljine i modula elastičnosti silikonske gume, no manje je uočljivo djelovanje temperature očvršćivanja

Identification and Functional Analysis of ThADH1 and ThADH4 Genes Involved in Tolerance to Waterlogging Stress in Taxodium hybrid ‘Zhongshanshan 406’

The Taxodium hybrid ‘Zhongshanshan 406’ (T. hybrid ‘Zhongshanshan 406’) [Taxodium mucronatum Tenore × Taxodium distichum (L.). Rich] has an outstanding advantage in flooding tolerance and thus has been widely used in wetland afforestation in China. Alcohol dehydrogenase genes (ADHs) played key roles in ethanol metabolism to maintain energy supply for plants in low-oxygen conditions. Two ADH genes were isolated and characterized—ThADH1 and ThADH4 (GenBank ID: AWL83216 and AWL83217—basing on the transcriptome data of T. hybrid ‘Zhongshanshan 406’ grown under waterlogging stress. Then the functions of these two genes were investigated through transient expression and overexpression. The results showed that the ThADH1 and ThADH4 proteins both fall under ADH III subfamily. ThADH1 was localized in the cytoplasm and nucleus, whereas ThADH4 was only localized in the cytoplasm. The expression of the two genes was stimulated by waterlogging and the expression level in roots was significantly higher than those in stems and leaves. The respective overexpression of ThADH1 and ThADH4 in Populus caused the opposite phenotype, while waterlogging tolerance of the two transgenic Populus significantly improved. Collectively, these results indicated that genes ThADH1 and ThADH4 were involved in the tolerance and adaptation to anaerobic conditions in T. hybrid ‘Zhongshanshan 406’

Prediction of Drug-Likeness Using Deep Autoencoder Neural Networks

Due to diverse reasons, most drug candidates cannot eventually become marketed drugs. Developing reliable computational methods for prediction of drug-likeness of candidate compounds is of vital importance to improve the success rate of drug discovery and development. In this study, we used a fully connected neural networks (FNN) to construct drug-likeness classification models with deep autoencoder to initialize model parameters. We collected datasets of drugs (represented by ZINC World Drug), bioactive molecules (represented by MDDR and WDI), and common molecules (represented by ZINC All Purchasable and ACD). Compounds were encoded with MOLD2 two-dimensional structure descriptors. The classification accuracies of drug-like/non-drug-like model are 91.04% on WDI/ACD databases, and 91.20% on MDDR/ZINC, respectively. The performance of the models outperforms previously reported models. In addition, we develop a drug/non-drug-like model (ZINC World Drug vs. ZINC All Purchasable), which distinguishes drugs and common compounds, with a classification accuracy of 96.99%. Our work shows that by using high-latitude molecular descriptors, we can apply deep learning technology to establish state-of-the-art drug-likeness prediction models

A position-aware transformer for image captioning

Image captioning aims to generate a corresponding description of an image. In recent years, neural encoder-decoder models have been the dominant approaches, in which the Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) are used to translate an image into a natural language description. Among these approaches, the visual attention mechanisms are widely used to enable deeper image understanding through fine-grained analysis and even multiple steps of reasoning. However, most conventional visual attention mechanisms are based on high-level image features, ignoring the effects of other image features, and giving insufficient consideration to the relative positions between image features. In this work, we propose a Position-Aware Transformer model with image-feature attention and position-aware attention mechanisms for the above problems. The image-feature attention firstly extracts multi-level features by using Feature Pyramid Network (FPN), then utilizes the scaled-dot-product to fuse these features, which enables our model to detect objects of different scales in the image more effectively without increasing parameters. In the position-aware attention mechanism, the relative positions between image features are obtained at first, afterwards the relative positions are incorporated into the original image features to generate captions more accurately. Experiments are carried out on the MSCOCO dataset and our approach achieves competitive BLEU-4, METEOR, ROUGE-L, CIDEr scores compared with some state-of-the-art approaches, demonstrating the effectiveness of our approach

Pressure induced superconductivity in WB2 and ReB2 through modifying the B layers

The recent discovery of superconductivity up to 32 K in the pressurized MoB2 reignites the interests in exploring high-Tc superconductors in transition-metal diborides. Inspired by that work, we turn our attention to the 5d transition-metal diborides. Here we systematically investigate the responses of both structural and physical properties of WB2 and ReB2 to external pressure, which possess different types of boron layers. Similar to MoB2, the pressure-induced superconductivity was also observed in WB2 above 60 GPa with a maximum Tc of 15 K at 100 GPa, while no superconductivity was detected in ReB2 in this pressure range. Interestingly, the structures at ambient pressure for both WB2 and ReB2 persist to high pressure without structural phase transitions. Theoretical calculations suggest that the ratio of flat boron layers in this class of transition-metal diborides may be crucial for the appearance of high Tc. The combined theoretical and experimental results highlight the effect of geometry of boron layers on superconductivity and shed light on the exploration of novel high-Tc superconductors in borides.Comment: 17 pages,5 figure

The Nearest Neutron Star Candidate in a Binary Revealed by Optical Time-domain Surveys

Recent studies have revealed the global deposition on Earth of radioactive elements (e.g., $^{60}$Fe) resulting from the metal-enriched ejecta of nearby (within $\sim 100$ pc) supernova explosions. The majority of neutron stars in our Solar neighborhood remain to be discovered. Here we report the discovery of the nearest ($127.7 \pm 0.3$ pc) neutron star candidate in the single-lined spectroscopic binary LAMOST J235456.76+335625.7 (hereafter J2354). Utilizing the multi-epoch spectra and high-cadence periodic light curves, we measure the mass of the visible star ($M_{\rm vis}=0.70\pm 0.05\ M_{\odot}$) and determine the mass function of the invisible object $f(M)=0.525 \pm 0.004\ M_{\odot}$, i.e., the mass of the unseen compact object is $M_{\rm inv} \geq 1.26 \pm 0.03\ M_{\odot}$. The excess UV emission due to a hot supramassive white dwarf is absent. Hence, it is likely that J2354 harbors a neutron star. J2354 is X-ray dim (the$0.1$--$2.4$keV luminosity$<10^{30}\ {\rm erg\ s^{-1}}$) since it is not detected in the ROSAT all-sky surveys in X-ray. One-hour exceptionally sensitive radio follow-up observations with FAST, the largest single-dish radio telescope, failed to reveal any radio pulsating signals (the potential pulse power at$1.4$GHz is$<6.8\times 10^{23}\ {\rm erg\ s^{-1}}$). Hence, the neutron star candidate in J2354 can only be discovered via our time-resolved observations. The alternative scenario involving a nearby supramassive cold white dwarf cannot be fully excluded. Our discovery demonstrates a promising way to unveil the missing population of backyard inactive neutron stars or supramassive cold white dwarfs in binaries by exploring the optical time domain, thereby facilitating understanding of the supernovae explosion and metal-enrichment history in our Solar neighborhood.Comment: 35 pages, 8 figures, to be submitte