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Glioblastoma (GBM) is a highly aggressive brain tumor characterized by pronounced invasiveness, rapid progression, frequent recurrence, and poor clinical prognosis. Current treatment strategies remain inadequate due to the lack of effective molecular targets, underscoring the urgent need to identify novel therapeutic avenues. In this study, we employed weighted gene co-expression network analysis and meta-analysis, incorporating clinical immunotherapy datasets, to identify ten candidate genes associated with GBM initiation, progression, prognosis, and response to immunotherapy. Multi-omics analyses across glioma and pan-cancer datasets revealed that these genes play pivotal roles in cancer biology. Phospholipase Cb4 (PLCB4) showed a negative correlation with tumor grade in clinical samples, suggesting its potential role as a tumor suppressor. Evidence indicated that PLCB4 expression is modulated by Wnt signaling, and its overexpression may activate the calcium ion signaling pathway. Notably, is strongly associated with aberrant tumor proliferation, making it a compelling therapeutic target. Through structure-based virtual screening, five small molecules with high predicted affinity for were identified as potential drug candidates. This study's integrative approach-combining target identification, pathway inference, and in silico drug screening-offers a promising framework for rational drug development in GBM. The findings may reduce unnecessary experimental screening and medical costs, and represent a significant step toward improving therapeutic outcomes and prognosis for GBM patients.

We study competition among contests in a general model that allows for an arbitrary and heterogeneous space of contest design, where the goal of the contest designers is to maximize the contestants' sum of efforts. Our main result shows that optimal contests in the monopolistic setting (i.e., those that maximize the sum of efforts in a model with a single contest) form an equilibrium in the model with competition among contests. Under a very natural assumption these contests are in fact dominant, and the equilibria that they form are unique. Moreover, equilibria with the optimal contests are Pareto-optimal even in cases where other equilibria emerge. In many natural cases, they also maximize the social welfare.

We study graph data augmentation by mixup, which has been used successfully on images. A key operation of mixup is to compute a convex combination of a pair of inputs. This operation is straightforward for grid-like data, such as images, but challenging for graph data. The key difficulty lies in the fact that different graphs typically have different numbers of nodes, and thus there lacks a node-level correspondence between graphs. In this work, we propose S-Mixup, a simple yet effective mixup method for graph classification by soft alignments. Specifically, given a pair of graphs, we explicitly obtain node-level correspondence via computing a soft assignment matrix to match the nodes between two graphs. Based on the soft assignments, we transform the adjacency and node feature matrices of one graph, so that the transformed graph is aligned with the other graph. In this way, any pair of graphs can be mixed directly to generate an augmented graph. We conduct systematic experiments to show that S-Mixup can improve the performance and generalization of graph neural networks (GNNs) on various graph classification tasks. In addition, we show that S-Mixup can increase the robustness of GNNs against noisy labels.

We study competition among contests in a general model that allows for an arbitrary and heterogeneous space of contest design, where the goal of the contest designers is to maximize the contestants' sum of efforts. Our main result shows that optimal contests in the monopolistic setting (i.e., those that maximize the sum of efforts in a model with a single contest) form an equilibrium in the model with competition among contests. Under a very natural assumption these contests are in fact dominant, and the equilibria that they form are unique. Moreover, equilibria with the optimal contests are Pareto-optimal even in cases where other equilibria emerge. In many natural cases, they also maximize the social welfare.

In the robotic industry, specular and textureless metallic components are ubiquitous. The 6D pose estimation of such objects with only a monocular RGB camera is difficult because of the absence of rich texture features. Furthermore, the appearance of specularity heavily depends on the camera viewpoint and environmental light conditions making traditional methods, like template matching, fail. In the last 30 years, pose estimation of the specular object has been a consistent challenge, and most related works require massive knowledge modeling effort for light setups, environment, or the object surface. On the other hand, recent works exhibit the feasibility of 6D pose estimation on a monocular camera with convolutional neural networks(CNNs) however they mostly use opaque objects for evaluation. This paper provides a data-driven solution to estimate the 6D pose of specular objects for grasping them, proposes a cost function for handling symmetry, and demonstrates experimental results showing the system's feasibility.

Deep learning methods have been shown to be effective in representing ground-state wave functions of quantum many-body systems. Existing methods use convolutional neural networks (CNNs) for square lattices due to their image-like structures. For non-square lattices, existing method uses graph neural network (GNN) in which structure information is not precisely captured, thereby requiring additional hand-crafted sublattice encoding. In this work, we propose lattice convolutions in which a set of proposed operations are used to convert non-square lattices into grid-like augmented lattices on which regular convolution can be applied. Based on the proposed lattice convolutions, we design lattice convolutional networks (LCN) that use self-gating and attention mechanisms. Experimental results show that our method achieves performance on par or better than existing methods on spin 1/2 \\(J_1\\)-\\(J_2\\) Heisenberg model over the square, honeycomb, triangular, and kagome lattices while without using hand-crafted encoding.

The design of permissioned blockchains places an access control requirement for members to read, access, and write information over the blockchains. In this paper, we study a hierarchical scenario to include three types of participants: providers, collectors, and governors. To be specific, providers forward transactions, collected from terminals, to collectors; collectors upload received transactions to governors after verifying and labeling them; and governors validate a part of received labeled transactions, pack valid ones into a block, and append a new block on the ledger. Collectors in the hierarchical model play a crucial role in the design: they have connections with both providers and governors, and are responsible for collecting, verifying, and uploading transactions. However, collectors are rational and some of them may behave maliciously (not necessarily for their own benefits). In this paper, we introduce a reputation protocol as a measure of the reliability of collectors in the permissioned blockchain environment. Its objective is to encourage collectors to behave truthfully and, in addition, to reduce the verification cost. The verification cost on provider \\(p\\) is defined as the total number of invalid transactions provided by \\(p\\) and checked by governors. Through theoretical analysis, our protocol with the reputation mechanism has a significant improvement in efficiency. Specifically, the verification loss that governors suffer is proved to be asymptotically \\(O(\\sqrt{T_{total}})\\) (\\(T_{total}\\), representing the number of transactions verified by governors and provided by \\(p\\)), as long as there exists at least one collector who behaves well. At last, two typical cases where our model can be well applied are also demonstrated.

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science.

Topological photonic states, inspired by robust chiral edge states in topological insulators, have recently been demonstrated in a few photonic systems, including an array of coupled on-chip ring resonators at communication wavelengths. However, the intrinsic difference between electrons and photons determines that the ‘topological protection’ in time-reversal-invariant photonic systems does not share the same robustness as its counterpart in electronic topological insulators. Here in a designer surface plasmon platform consisting of tunable metallic sub-wavelength structures, we construct photonic topological edge states and probe their robustness against a variety of defect classes, including some common time-reversal-invariant photonic defects that can break the topological protection, but do not exist in electronic topological insulators. This is also an experimental realization of anomalous Floquet topological edge states, whose topological phase cannot be predicted by the usual Chern number topological invariants. The limits of topological protection in photonic systems remain unclear. Here, Gao et al . construct photonic topological edge states and probe their robustness against a variety of defect classes, including some common time-reversal-invariant photonic defects that can break the topological protection.

Esophageal squamous cell carcinoma (ESCC) ranks fourth among cancer-related deaths in China due to the lack of actionable molecules. We performed whole-exome and T-cell receptor (TCR) repertoire sequencing on multi-regional tumors, normal tissues and blood samples from 39 ESCC patients. The data revealed 12.8% of ERBB4 mutations at patient level and functional study supported its oncogenic role. 18% of patients with early BRCA1 /2 variants were associated with high-level contribution of signature 3, which was validated in an independent large cohort ( n  = 508). Furthermore, knockdown of BRCA1 /2 dramatically increased sensitivity to cisplatin in ESCC cells. 5% of patients harbored focal high-level amplification of CD274 that led to massive expression of PD-L1, and might be more sensitive to immune checkpoint blockade. Finally, we found a tight correlation between genomic and TCR repertoire intra-tumor heterogeneity (ITH). Collectively, we reveal high-level ITH in ESCC, identify several potential actionable targets and may provide novel insight into ESCC treatment. Esophageal squamous cell carcinoma (ESCC) is highly prevalent in China. Here, the authors carry out multi-region sampling of Chinese ESCC samples, and find recurrent ERBB4 mutations, BRCA1/2 variants, and amplification of CD274 ; together with high levels of genomic and T-cell receptor heterogeneity.