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Instability of high polygenic risk classification and mitigation by integrative scoring
Predictive analysis of vitiligo treatment drugs using degree and neighborhood degree-based topological descriptors
Atypical hippocampal excitatory neurons express and govern object memory
Abstract Classically, pyramidal cells of the hippocampus are viewed as flexibly representing spatial and non-spatial information. Recent work has illustrated distinct types of hippocampal excitatory neurons, suggesting that hippocampal representations and functions may be constrained and interpreted by these underlying cell-type identities. In mice, here we reveal a non-pyramidal excitatory neuron type — the “ovoid” neuron — that is spatially adjacent to subiculum pyramidal cells but differs in gene expression, electrophysiology, morphology, and connectivity. Functionally, novel object encounters drive sustained ovoid neuron activity, whereas familiar objects fail to drive activity even months after single-trial learning. Silencing ovoid neurons prevents non-spatial object learning but leaves spatial learning intact, and activating ovoid neurons toggles novel-object seeking to familiar-object seeking. Such function is doubly dissociable from pyramidal neurons, wherein manipulation of pyramidal cells affects spatial assays but not non-spatial learning. Ovoid neurons of the subiculum thus illustrate selective cell-type-specific control of non-spatial memory and behavioral preference.
Fossil specimens of the autotrophic protist Mallomonas asmundiae bearing cysts and attached scales from an Eocene locality
Nuclear position controls the activity of cortical actomyosin networks powering simultaneous morphogenetic events
Author Correction: Superiority of high sensitivity cardiac troponin I over NT‑proBNP and adiponectin for 7‑year mortality in stable patients receiving haemodialysis
In-depth analysis of 17,115 rice transcriptomes reveals extensive viral diversity in rice plants
Publisher Correction: Innovative MIM diplexer with neural network enhanced refractive index detection for advanced photonic applications
High-throughput screening identifies Aurora kinase B as a critical therapeutic target for Merkel cell carcinoma
Abstract Merkel cell carcinoma (MCC) is a rare, aggressive skin cancer. Most MCCs contain Merkel cell polyomavirus (virus-positive MCC; VP-MCC), and the remaining are virus-negative (VN-MCC). Immune checkpoint inhibitors are the first-line treatment for metastatic MCC, but durable responses are achieved in less than 50% of patients. To identify new treatments, we screen ~4,000 compounds for their ability to reduce MCC viability and demonstrate that VP-MCC and VN-MCC exhibit distinct response profiles. Aurora kinase inhibitors selectively reduce VP-MCC viability, with RNAi screening independently identifying AURKB as an essential gene for MCC survival, especially in VP-MCC. AZD2811, a selective AURKB inhibitor, induces mitotic dysregulation and apoptosis in MCC cells, with greater efficacy in VP-MCC. In mice, AZD2811 nanoparticles inhibit tumor growth and increase survival in both VP-MCC and VN-MCC xenograft models. Overall, our unbiased screens identify AURKB as a promising therapeutic target and AZD2811NP as a potential treatment for MCC.
ANFIS algorithm for mapping computational data of water reservoir homogenization with air bubble flows
Abstract Air as an inert gas is usually applied for homogenization and mixing liquids. In the current research, we study a 3-D bubble column reactor (BCR) filled with water by using an Artificial intelligence algorithm (AI) and CFD. We used one of the adaptive networks and fuzzy inference systems (ANFIS) to study fluid flow and see its effect on the accuracy of the AI. Therefore, the Gaussian membership function was used to have a prediction in the 3-D BCR. Also, the grid partition system was used to cluster the data. The number of membership functions increases in the training process of the AI system, from 2 to 5. The influence of input numbers on AI data prediction is analyzed. The four inputs in the training process included air velocity and pressure, as well as the x-direction and z-direction. Finally, air vorticity was considered as the output parameter of the study in the predictions. Correlations were developed to predict the air vorticity in each node using x and z direction, air velocity, and pressure. The results showed the AI accuracy increased by the rise of membership and input numbers. The AI intelligence level was found by five memberships and four inputs. The AI and CFD were in suitable agreement (regression number around 1). The developed correlations could simplify the calculation of air vorticity instead of using the complicated and time-consuming CFD simulation. As far as the authors know, there are no studies that have developed correlations to find the air vorticity in bubble column reactors.
Multi-stage phase transformation pathways in MAX phases
Steering drilling wellbore trajectory prediction based on the NOA-LSTM-FCNN method
Galloping Bubbles
Abstract Despite centuries of investigation, bubbles continue to unveil intriguing dynamics relevant to a multitude of practical applications, including industrial, biological, geophysical, and medical settings. Here we introduce bubbles that spontaneously start to ‘gallop’ along horizontal surfaces inside a vertically-vibrated fluid chamber, self-propelled by a resonant interaction between their shape oscillation modes. These active bubbles exhibit distinct trajectory regimes, including rectilinear, orbital, and run-and-tumble motions, which can be tuned dynamically via the external forcing. Through periodic body deformations, galloping bubbles swim leveraging inertial forces rather than vortex shedding, enabling them to maneuver even when viscous traction is not viable. The galloping symmetry breaking provides a robust self-propulsion mechanism, arising in bubbles whether separated from the wall by a liquid film or directly attached to it, and is captured by a minimal oscillator model, highlighting its universality. Through proof-of-concept demonstrations, we showcase the technological potential of the galloping locomotion for applications involving bubble generation and removal, transport and sorting, navigating complex fluid networks, and surface cleaning. The rich dynamics of galloping bubbles suggest exciting opportunities in heat transfer, microfluidic transport, probing and cleaning, bubble-based computing, soft robotics, and active matter.
Bioinformatics and experimental insights into F2RL1 as a key biomarker in cervical cancer diagnosis and prognosis
Author Correction: Interformer: an interaction-aware model for protein-ligand docking and affinity prediction
Serological and molecular detection of Toxoplasma Gondii among cancer patients in Sohag, Upper Egypt: a case-control study
Abstract Toxoplasma gondii (T. gondii) affects around 30% of humans worldwide. Recently, it has emerged as a significant opportunistic pathogen to immunocompromised patients. Data available is still lacking about toxoplasmosis in cancer patients in Egypt. This study aimed to reveal the current trend of T. gondii in cancer patients in Sohag, Egypt. Sera from 50 cancer patients and 50 healthy controls were screened for Toxoplasma IgG and IgM. Further, buffy coats from both groups were used for detection of T. gondii B1 and RE genes via conventional and nested PCR, respectively. The overall seroprevalence of T. gondii IgG was high (58%). IgG and IgM were detected in 30% and 9% cancer patients, respectively. Patients with solid cancers exhibited a greater IgG seropositivity compared to those with hematologic tumors (77.27% and 46.43%, respectively) (P = 0.03). Concerning the molecular results, only 4 (9%) were positive regarding both PCR assays. In conclusion, T. gondii is highly prevalent in cancer patients in Sohag, Egypt. PCR is strongly recommended to complement serology to diagnose acute or reactivated toxoplasmosis in cancer patients. B1 PCR was found to be equivalent to RE PCR. Nevertheless, thorough large-scale research must be implemented.