The Relationship Between Human Behavior Pattern and Urban Street Space

As the most important and largest public space in urban built-up areas, street space is the most exciting and exciting part for people in the city, which is closely related to people's behavior patterns.Streets are full of vitality because of people's participation, and successful street Spaces can effectively promote the public life of urban residents.Therefore, it is of great significance to study the relationship between human behavior pattern and urban street space.After studying the common behavior patterns of people in street space, this paper analyzes the mutual influence between them, and finally puts forward the existing problems and improvement suggestions of urban street space under the current background

Epigenetic Regulation of Prostate Cancer

Prostate cancer is (PCa) the second leading cause of cancer death in males in the United State, with 174,650 new cases and 31,620 deaths estimated in 2019. It has been documented that epigenetic deregulation such as histone modification and DNA methylation contributes to PCa initiation and progression. EZH2 (enhancer of zeste homolog 2), the catalytic subunit of the Polycomb Repressive Complex (PRC2) responsible for H3K27me3 and gene repression, has been identified as a promising target in PCa. In addition, overexpression of other epigenetic regulators such as DNA methyltransferases (DNMT) is also observed in PCa. These epigenetic regulators undergo extensive post-translational modifications, in particular, phosphorylation. AKT, CDKs, PLK1, PKA, ATR and DNA-PK are the established kinases responsible for phosphorylation of various epigenetic regulators

Multi-target QSAR modelling in the analysis and design of HIV-HCV co-inhibitors: an in-silico study

<p>Abstract</p> <p>Background</p> <p>HIV and HCV infections have become the leading global public-health threats. Even more remarkable, HIV-HCV co-infection is rapidly emerging as a major cause of morbidity and mortality throughout the world, due to the common rapid mutation characteristics of the two viruses as well as their similar complex influence to immunology system. Although considerable progresses have been made on the study of the infection of HIV and HCV respectively, few researches have been conducted on the investigation of the molecular mechanism of their co-infection and designing of the multi-target co-inhibitors for the two viruses simultaneously.</p> <p>Results</p> <p>In our study, a multi-target Quantitative Structure-Activity Relationship (QSAR) study of the inhibitors for HIV-HCV co-infection were addressed with an in-silico machine learning technique, i.e. multi-task learning, to help to guide the co-inhibitor design. Firstly, an integrated dataset with 3 HIV inhibitor subsets targeted on protease, integrase and reverse transcriptase respectively, together with another 6 subsets of 2 HCV inhibitors targeted on NS3 serine protease and NS5B polymerase respectively were compiled. Secondly, an efficient multi-target QSAR modelling of HIV-HCV co-inhibitors was performed by applying an accelerated gradient method based multi-task learning on the whole 9 datasets. Furthermore, by solving the <it>L</it>-1-infinity regularized optimization, the Drug-like index features for compound description were ranked according to their joint importance in multi-target QSAR modelling of HIV and HCV. Finally, a drug structure-activity simulation for investigating the relationships between compound structures and binding affinities was presented based on our multiple target analysis, which is then providing several novel clues for the design of multi-target HIV-HCV co-inhibitors with increasing likelihood of successful therapies on HIV, HCV and HIV-HCV co-infection.</p> <p>Conclusions</p> <p>The framework presented in our study provided an efficient way to identify and design inhibitors that simultaneously and selectively bind to multiple targets from multiple viruses with high affinity, and will definitely shed new lights on the future work of inhibitor synthesis for multi-target HIV, HCV, and HIV-HCV co-infection treatments.</p

A new protein-ligand binding sites prediction method based on the integration of protein sequence conservation information

<p>Abstract</p> <p>Background</p> <p>Prediction of protein-ligand binding sites is an important issue for protein function annotation and structure-based drug design. Nowadays, although many computational methods for ligand-binding prediction have been developed, there is still a demanding to improve the prediction accuracy and efficiency. In addition, most of these methods are purely geometry-based, if the prediction methods improvement could be succeeded by integrating physicochemical or sequence properties of protein-ligand binding, it may also be more helpful to address the biological question in such studies.</p> <p>Results</p> <p>In our study, in order to investigate the contribution of sequence conservation in binding sites prediction and to make up the insufficiencies in purely geometry based methods, a simple yet efficient protein-binding sites prediction algorithm is presented, based on the geometry-based cavity identification integrated with sequence conservation information. Our method was compared with the other three classical tools: PocketPicker, SURFNET, and PASS, and evaluated on an existing comprehensive dataset of 210 non-redundant protein-ligand complexes. The results demonstrate that our approach correctly predicted the binding sites in 59% and 75% of cases among the TOP1 candidates and TOP3 candidates in the ranking list, respectively, which performs better than those of SURFNET and PASS, and achieves generally a slight better performance with PocketPicker.</p> <p>Conclusions</p> <p>Our work has successfully indicated the importance of the sequence conservation information in binding sites prediction as well as provided a more accurate way for binding sites identification.</p

High-Throughput Screening of Transition Metal Single-Atom Catalysts for Nitrogen Reduction Reaction

The discovery of metals as catalytic centers for nitrogen reduction reactions has stimulated great enthusiasm for single-atom catalysts. However, the poor activity and low selectivity of available SACs are far away from the industrial requirement. Through the high throughout first principles calculations, the doping engineering can effectively regulate the NRR performance of b-Sb monolayer. Especially, the origin of activated N2 is revealed from the perspective of the electronic structure of the active center. Among the 24 transition metal dopants, Re@Sb and Tc@Sb showed the best NRR catalytic performance with a low limiting potential. The Re@Sb and Tc@Sb also could significantly inhibit HER and achieve a high theoretical Faradaic efficiency of 100%. Our findings not only accelerate discovery of catalysts for ammonia synthesis but also contribute to further elucidate the structure-performance correlations

2.8–1.7 Ga history of the Jiao-Liao-Ji Belt of the North China Craton from the geochronology and geochemistry of mafic Liaohe meta-igneous rocks

Highlights • Lithospheric mantle stabilization under Jiao-Liao-Ji Belt at 2.8Ga (SmNd isochron) • Liaohe mafic meta-igneous rocks formed in active continental margin subduction zone • Emplacement of Liaohe mafic igneous rocks at ~2.2 Ga (LuHf isochron) • Amphibolite retrograde metamorphism from exhumation at 1824 ± 19 Ma (PbPb isochron) • Cooling of terrane to ~500 °C at 1671 ± 58 Ma (RbSr isochron) Abstract The assembly and long-term evolution of the Eastern Block of the North China Craton are poorly constrained. Here we use bulk rock geochronological and geochemical data from mafic meta-igneous rocks (hornblendites, amphibolites and a metagabbro) of the Liaohe Group to reconstruct the Neoarchean to Paleoproterozoic history of the Jiao-Liao-Ji Belt, located between the Longgang and Nangrim blocks that together form the Eastern Block of the North China Craton. The mafic/ultramafic meta-igneous rocks have intrusive or tectonic contacts with the Liaoji granitic rocks (~2.2–2.0 Ga), which form the basement of the Jiao-Liao-Ji Belt. The major and trace element data indicate that the protoliths had calc-alkaline composition and formed along an active continental margin subduction zone. The mafic rocks form a whole-rock 176Lu/177Hf isochron with an age of 2.25 ± 0.31 Ga, overlapping with UPb zircon ages for mafic and granitic rocks from the Jiao-Liao-Ji Belt and consistent with being the emplacement age of the mafic protoliths along the active continental margin. In contrast, the whole-rock 147Sm/144Nd isochron age of 2.83 ± 0.18 Ga is likely to reflect the average age of the lithospheric mantle source from which the mafic/ultramafic protoliths were extracted. Together with geological evidence, we propose that the southwestern portion of the Longgang Block was an active continental margin since at least the early Paleoproteorozic. Literature age data from metamorphic zircons show that peak granulite metamorphism took place at ~1.96–1.88 Ga, resulting from the collisional event that fused the Longgang and Nangrim blocks into the Eastern Block of the North China Craton. Our bulk-rock 207Pb/206Pb age of 1824 ± 19 Ma and our 87Rb/86Sr age of 1671 ± 58 Ma reflect retrograde (cooling) stages during the exhumation of the Jiao-Liao-Ji Belt after the orogenesis

Training Socially Aligned Language Models on Simulated Social Interactions

Social alignment in AI systems aims to ensure that these models behave according to established societal values. However, unlike humans, who derive consensus on value judgments through social interaction, current language models (LMs) are trained to rigidly replicate their training corpus in isolation, leading to subpar generalization in unfamiliar scenarios and vulnerability to adversarial attacks. This work presents a novel training paradigm that permits LMs to learn from simulated social interactions. In comparison to existing methodologies, our approach is considerably more scalable and efficient, demonstrating superior performance in alignment benchmarks and human evaluations. This paradigm shift in the training of LMs brings us a step closer to developing AI systems that can robustly and accurately reflect societal norms and values.Comment: Code, data, and models can be downloaded via https://github.com/agi-templar/Stable-Alignmen