Pleistocene drainage reorganization driven by the isostatic response to deep incision into the northeastern Tibetan Plateau

Pleistocene drainage basin integration led to progressive excavation of Tertiary-Quaternary sedimentary basins along the Yellow River in the northeastern Tibetan Plateau. Cosmogenic burial dating of ancestral river deposits and basin fill from two key watershed divides confirms a fluvial connection between basins at 0.5–1.2 Ma, prior to excavation by the Yellow River. Preservation of the relict depositional surface that represents the maximum height of basin fill allows reconstruction of the volume of eroded material across a broad region. We quantify the isostatic response to this erosional unloading using a two-dimensional (not one-dimensional) flexural model. Calculated maximum vertical displacements for different effective elastic thicknesses vary from ~160 m to ~260 m near the Pleistocene spillway from the Qinghai paleo-lake. We suggest that the isostatic response to fluvial excavation along the Yellow River defeated local tributaries, isolated Lake Qinghai, and led to the development of an internally drained basin in the past 0.5–1.2 Ma

Multiplexed Quantum Dot Labeling of Activated c-Met Signaling in Castration-Resistant Human Prostate Cancer

The potential application of multiplexed quantum dot labeling (MQDL) for cancer detection and prognosis and monitoring therapeutic responses has attracted the interests of bioengineers, pathologists and cancer biologists. Many published studies claim that MQDL is effective for cancer biomarker detection and useful in cancer diagnosis and prognosis, these studies have not been standardized against quantitative biochemical and molecular determinations. In the present study, we used a molecularly characterized human prostate cancer cell model exhibiting activated c-Met signaling with epithelial to mesenchymal transition (EMT) and lethal metastatic progression to bone and soft tissues as the gold standard, and compared the c-Met cell signaling network in this model, in clinical human prostate cancer tissue specimens and in a castration-resistant human prostate cancer xenograft model. We observed c-Met signaling network activation, manifested by increased phosphorylated c-Met in all three. The downstream survival signaling network was mediated by NF-κB and Mcl-1 and EMT was driven by receptor activator of NF-κB ligand (RANKL), at the single cell level in clinical prostate cancer specimens and the xenograft model. Results were confirmed by real-time RT-PCR and western blots in a human prostate cancer cell model. MQDL is a powerful tool for assessing biomarker expression and it offers molecular insights into cancer progression at both the cell and tissue level with high degree of sensitivity

An efficient algorithm for energy harvesting in IIoT based on machine learning and swarm intelligence

The Internet of Things (IoT) is a network of smart gadgets that are connected through the Internet, including computers, cameras, smart sensors, and mobile phones. Recent developments in the industrial IoT (IIoT) have enabled a wide range of applications, from small businesses to smart cities, which have become indispensable to many facets of human existence. In a system with a few devices, the short lifespan of conventional batteries, which raises maintenance costs, necessitates more replacements and has a negative environmental impact, does not present a problem. However, in networks with millions or even billions of devices, it poses a serious problem. The rapid expansion of the IoT paradigm is threatened by these battery restrictions, thus academics and businesses are now interested in prolonging the lifespan of IoT devices while retaining optimal performance. Resource management is an important aspect of IIoT because it's scarce and limited. Therefore, this paper proposed an efficient algorithm based on federated learning. Firstly, the optimization problem is decomposed into various sub-problems. Then, the particle swarm optimization algorithm is deployed to solve the energy budget. Finally, a communication resource is optimized by an iterative matching algorithm. Simulation results show that the proposed algorithm has better performance as compared with existing algorithms