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\"In numerous civilizations throughout world history city walls were an indispensable part of every city. In China they can be traced back to the 21th century BC as fortified symbols of power and manifestation of the Middle Kingdom. In the course of the country's long history several thousand have been erected, varying enormously in form, length, construction technology, functionality and significance. These city walls represent a unique heritage and a central identification factor from which to gain access to the self-image of Chinese culture. After years of decay and ignorance, it was only a few decades ago that they were discovered as cultural monuments and the securing and restoration work began. The city walls recorded in the statistics today, of which a selection is presented in this book by new and historic photos, range from wall ruins in the ground via about 150 with a length of more than one kilometer to the famous fortification of Nanjing, which still has more than 20 kilometers standing.\" -- amazon

Accumulating experimental studies have indicated that lncRNAs play important roles in various critical biological process and their alterations and dysregulations have been associated with many important complex diseases. Developing effective computational models to predict potential disease-lncRNA association could benefit not only the understanding of disease mechanism at lncRNA level, but also the detection of disease biomarkers for disease diagnosis, treatment, prognosis and prevention. However, known experimentally confirmed disease-lncRNA associations are still very limited. In this study, a novel model of HyperGeometric distribution for LncRNA-Disease Association inference (HGLDA) was developed to predict lncRNA-disease associations by integrating miRNA-disease associations and lncRNA-miRNA interactions. Although HGLDA didn’t rely on any known disease-lncRNA associations, it still obtained an AUC of 0.7621 in the leave-one-out cross validation. Furthermore, 19 predicted associations for breast cancer, lung cancer and colorectal cancer were verified by biological experimental studies. Furthermore, the model of LncRNA Functional Similarity Calculation based on the information of MiRNA (LFSCM) was developed to calculate lncRNA functional similarity on a large scale by integrating disease semantic similarity, miRNA-disease associations and miRNA-lncRNA interactions. It is anticipated that HGLDA and LFSCM could be effective biological tools for biomedical research.

The discovery of topological quantum states marks a new chapter in both condensed matter physics and materials sciences. By analogy to spin electronic system, topological concepts have been extended into phonons, boosting the birth of topological phononics (TPs). Here, we present a high-throughput screening and data-driven approach to compute and evaluate TPs among over 10,000 real materials. We have discovered 5014 TP materials and grouped them into two main classes of Weyl and nodal-line (ring) TPs. We have clarified the physical mechanism for the occurrence of single Weyl, high degenerate Weyl, individual nodal-line (ring), nodal-link, nodal-chain, and nodal-net TPs in various materials and their mutual correlations. Among the phononic systems, we have predicted the hourglass nodal net TPs in TeO 3 , as well as the clean and single type-I Weyl TPs between the acoustic and optical branches in half-Heusler LiCaAs. In addition, we found that different types of TPs can coexist in many materials (such as ScZn). Their potential applications and experimental detections have been discussed. This work substantially increases the amount of TP materials, which enables an in-depth investigation of their structure-property relations and opens new avenues for future device design related to TPs. Topological phononic (TP) materials are attracting wide attentions and it is more difficult to seek TP materials compared to electronic materials. Here, the authors present a high-throughput screening and data-driven approach to discover 5014 TP materials and further clarify the mechanism for the occurrence of various TPs.

We study the Pcs(4459)0 recently observed by LHCb using the method of QCD sum rules. Our results support its interpretation as the D¯∗Ξc hadronic molecular state of either JP=1/2- or 3/2-. Within the hadronic molecular picture, the three LHCb experiments observing Pc and Pcs states (Aaij et al., Phys Rev Lett 115:072001, 2015; Aaij et al., Phys Rev Lett 122:222001, 2019; Aaij et al., arXiv:2012.10380 [hep-ex], 2012) can be well understood as a whole. This strongly supports the existence of hadronic molecules, whose studies can significantly improve our understanding on the construction of the subatomic world. To verify this picture, we propose to further investigate the Pcs(4459)0 to examine whether it can be separated into two states, and to search for the D¯Ξc molecular state of JP=1/2-.

The search for new two-dimensional monolayers with diverse electronic properties has attracted growing interest in recent years. Here, we present an approach to construct MA 2 Z 4 monolayers with a septuple-atomic-layer structure, that is, intercalating a MoS 2 -type monolayer MZ 2 into an InSe-type monolayer A 2 Z 2 . We illustrate this unique strategy by means of first-principles calculations, which not only reproduce the structures of MoSi 2 N 4 and MnBi 2 Te 4 that were already experimentally synthesized, but also predict 72 compounds that are thermodynamically and dynamically stable. Such an intercalated architecture significantly reconstructs the band structures of the constituents MZ 2 and A 2 Z 2 , leading to diverse electronic properties for MA 2 Z 4 , which can be classified according to the total number of valence electrons. The systems with 32 and 34 valence electrons are mostly semiconductors. Whereas, those with 33 valence electrons can be nonmagnetic metals or ferromagnetic semiconductors. In particular, we find that, among the predicted compounds, (Ca,Sr)Ga 2 Te 4 are topologically nontrivial by both the standard density functional theory and hybrid functional calculations. While VSi 2 P 4 is a ferromagnetic semiconductor and TaSi 2 N 4 is a type-I Ising superconductor. Moreover, WSi 2 P 4 is a direct gap semiconductor with peculiar spin-valley properties, which are robust against interlayer interactions. Our study thus provides an effective way of designing septuple-atomic-layer MA 2 Z 4 with unusual electronic properties to draw immediate experimental interest. The discovery of a new two-dimensional van der Waals layered MoSi 2 N 4 material inspires many attentions. Here, the authors report intercalation strategies to explore a much wider range of MA 2 Z 4 family and predict amount of materials accessible to experimental verifications with emergent topological, magnetic or Ising superconductivity properties.

Predicting novel microRNA (miRNA)-disease associations is clinically significant due to miRNAs' potential roles of diagnostic biomarkers and therapeutic targets for various human diseases. Previous studies have demonstrated the viability of utilizing different types of biological data to computationally infer new disease-related miRNAs. Yet researchers face the challenge of how to effectively integrate diverse datasets and make reliable predictions. In this study, we presented a computational model named Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction (LRSSLMDA), which projected miRNAs/diseases' statistical feature profile and graph theoretical feature profile to a common subspace. It used Laplacian regularization to preserve the local structures of the training data and a L1-norm constraint to select important miRNA/disease features for prediction. The strength of dimensionality reduction enabled the model to be easily extended to much higher dimensional datasets than those exploited in this study. Experimental results showed that LRSSLMDA outperformed ten previous models: the AUC of 0.9178 in global leave-one-out cross validation (LOOCV) and the AUC of 0.8418 in local LOOCV indicated the model's superior prediction accuracy; and the average AUC of 0.9181+/-0.0004 in 5-fold cross validation justified its accuracy and stability. In addition, three types of case studies further demonstrated its predictive power. Potential miRNAs related to Colon Neoplasms, Lymphoma, Kidney Neoplasms, Esophageal Neoplasms and Breast Neoplasms were predicted by LRSSLMDA. Respectively, 98%, 88%, 96%, 98% and 98% out of the top 50 predictions were validated by experimental evidences. Therefore, we conclude that LRSSLMDA would be a valuable computational tool for miRNA-disease association prediction.

Quercetin is a polyphenol with multiple biological activities, and results of our preliminary study showed that it could shorten the immobility time of mice in the forced swimming test and tail suspending test. The aim of this study was to investigate its effects on the behavioral performance of lipopolysaccharide (LPS)-challenged rats and explore the potential mechanism. The results showed that intragastrical administration of quercetin (40 mg/kg) could improve the bodyweight gain of LPS-challenged rats, increase the saccharin preference index in the saccharin preference test and the novel arm preference index in the Y-maze, and decrease the immobility time in the FST. However, it showed no significant effect on the performance of LPS-challenged rats in the Morris water maze and the plasma concentrations of nesfatin-1, C-reactive protein (CRP), and IL-6. Results of western blot showed that the expression levels of BDNF, Copine 6, p-TrkB, and the triggering receptors expressed on myeloid cells (TREM) 1 were decreased in both the hippocampus and the prefrontal cortex (PFC) of LPS-challenged rats, while the expression of TREM2 was increased. The protein expression of synapsin-1 was decreased in the hippocampus without significant changes in the PFC. These imbalance protein expressions could be balanced by treatment with quercetin. The results suggested that quercetin could alleviate LPS-induced depression-like behaviors and impairment of learning and memory in rats, the mechanism of which might be involved with regulating the BDNF-related imbalance expression of Copine 6 and TREM1/2 in the hippocampus and the PFC.

Fluid‐rock dissolution occurs ubiquitously in geological systems. Surface‐volume scaling is central to predicting overall dissolution rate R involved in modeling dissolution processes. Previous works focused on single‐phase environments but overlooked the multiphase‐flow effect. Here, through limestone‐based microfluidics experiments, we establish a fundamental link between dissolution regimes and scaling laws. In regime I (uniform), the scaling is consistent with classic law, and a satisfactory prediction of R can be obtained. However, the scaling for regime II (localized) deviates significantly from classic law. The underlying mechanism is that the reaction‐induced gas phase forms a layer, acting as a barrier that hinders contact between the acid and rock. Consequently, the error between measurement and prediction continuously amplifies as dissolution proceeds; the predictability is poor. We propose a theoretical model that describes the regime transition, exhibiting excellent agreement with experimental results. This work offers guidance on the usage of scaling law in multiphase flow environments. Plain Language Summary Fluid‐rock dissolution is ubiquitous in natural and engineered systems, including karst formation, geological carbon sequestration, and acid stimulation. Recent developed method for CO2 sequestration relies on mineralization, which transforms CO2 into carbonate minerals through geochemical reactions involving dissolution. The precise modeling of dissolution processes at the continuum‐scale is dependent on the estimation of the overall dissolution rate using surface‐volume scaling laws. This important scaling law is always established in a single‐phase system. Here, through limestone‐based microfluidics experiments, we find that the scaling is significantly affected by the dissolution regime in a multiphase flow environment. When the injection rate is lower, and the geometry is more homogeneous, the dissolution regime adheres to classic law. On the other hand, when the flow is stronger and the heterogeneity exhibits, the dissolution scaling significantly diverges. Our discovery indicates that a layer of CO2 gas attaches to the uneven surface, causing a shielding effect on the dissolution and resulting in a notable deviation. Through establishing a theoretical model for the regime transition, this work offers guidance on the usage of scaling law across various dissolution scenarios. The newly developed scaling can enhance dissolution modeling precision in multiphase flow‐dissolution systems such as geologic carbon sequestration. Key Points We observe two regimes, and the scaling in regime II deviates significantly from classic law, with a poor predictability of dissolution rate We identify a barrier effect in real rock samples that inhibits the contact of acid and rock for the deviation of scaling in regime II We propose a theoretical model for regime transition that offers guidance on the usage of scaling law in multiphase environments

Salt precipitation is a crucial process occurring during CO2 injection into saline aquifers. It significantly alters the porous space, leading to reduced permeability and impaired injectivity. While the dynamics of precipitation have been studied within porous media, our understanding of precipitation patterns and permeability evolution within rough fractures remains inadequate. Here, we conduct flow‐visualization experiments on salt precipitation, wherein dry air invades brine‐filled rough fractures under various flow rate conditions. Our observations reveal that the precipitation pattern shifts from ex situ precipitation to homogeneous form as the flow rate (capillary number Ca) increases. Through real‐time imaging of the salt precipitation process, we determine that ex situ precipitation is due to capillary‐driven backflow. This backflow phenomenon occurs when previously precipitated salt, acting as a hydrophilic porous medium, attracts the brine flow backward. As a result, precipitation occurs at a location different from the original site. We further show that the impact of capillary‐driven backflow is significant at low flow rates and is gradually suppressed as the flow rate increases. We provide a theoretical estimation for the critical Ca for the occurrence of capillary‐driven backflow. As Ca is smaller than this critical value, backflow‐precipitation positive feedback causes fracture voids to become completely clogged, thereby leading to a more substantial permeability reduction. In contrast, a homogeneous precipitation pattern tends to only partially clog the fracture voids, causing a relatively smaller permeability reduction. This study enhances our understanding of the role of capillary‐driven backflow in controlling salt precipitation and permeability reduction in fractures. Plain Language Summary Injecting CO2 into underground water layers (saline aquifers) is one way to tackle climate change by storing it away from the air. However, this process can lead to salt formation within the rock fractures, especially near the injection well, which can block the flow pathways and make it more challenging to inject additional CO2. Our research focuses on how salt forms within the rock fractures when we introduce dry air into areas filled with salty water, at different flow rates. We discover that at slower flow rate, the salt forms in patches due to a process where the salt already formed pulls more water toward it, leading to blockages. At higher flow rates, this doesn’t happen, and the salt is distributed more uniformly, causing less blockage. We identify a specific flow rate at which the transition between these two types of salt formation occurs. Understanding this can help us better manage CO2 injection strategies and make it more effective by minimizing the risk of blockages. This work is important for enhancing how we store CO2 underground, an important strategy in reducing its levels in the atmosphere and fighting global warming. Key Points We show that precipitation pattern shifts from ex situ to homogeneous form and ex situ precipitation is due to capillary‐driven backflow We verify that capillary‐driven backflow occurs when previously precipitated salt, as a hydrophilic porous medium, draws brine flow back We quantify that capillary‐driven backflow causes voids to be completely clogged, leading to a more substantial permeability reductions