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Urinary proteomics is emerging as a potent tool for detecting sensitive and non-invasive biomarkers. At present, the comparability of urinary proteomics data across diverse liquid chromatography−mass spectrometry (LC-MS) platforms remains an area that requires investigation. In this study, we conduct a comprehensive evaluation of urinary proteome across multiple LC-MS platforms. To systematically analyze and assess the quality of large-scale urinary proteomics data, we develop a comprehensive quality control (QC) system named MSCohort, which extracted 81 metrics for individual experiment and the whole cohort quality evaluation. Additionally, we present a standard operating procedure (SOP) for high-throughput urinary proteome analysis based on MSCohort QC system. Our study involves 20 LC-MS platforms and reveals that, when combined with a comprehensive QC system and a unified SOP, the data generated by data-independent acquisition (DIA) workflow in urine QC samples exhibit high robustness, sensitivity, and reproducibility across multiple LC-MS platforms. Furthermore, we apply this SOP to hybrid benchmarking samples and clinical colorectal cancer (CRC) urinary proteome including 527 experiments. Across three different LC-MS platforms, the analyses report high quantitative reproducibility and consistent disease patterns. This work lays the groundwork for large-scale clinical urinary proteomics studies spanning multiple platforms, paving the way for precision medicine research. Urinary proteomics offers sensitive, non-invasive biomarkers, but cross-platform LC-MS data comparability requires investigation. Here, the authors evaluate 20 LC-MS platforms, developing MSCohort, a QC system with 81 metrics, and a unified SOP, paving the way for precision medicine research. [ABSTRACT FROM AUTHOR]

Nanoporous copper (np-Cu) has attracted much more attention due to its lower cost compared to other noble metals and high functionality in practical use. Herein, Al100−xCux(x = 13–88 at.%) precursor films with thicknesses of 0.16–1.1 μm were fabricated by varying magnetron co-sputtering parameters. Subsequently, utilizing a one-step dealloying strategy, a series of np-Cu films with ligament sizes ranging from 11.4–19.0 nm were synthesized. The effects of precursor composition and substrate temperature on the microstructure of np-Cu films were investigated. As the atomic ratio of Cu increases from 15 to 34, the np-Cu film detached from the substrate gradually transforms into a bi-continuous ligament-channel structure that is well bonded to the substrate. Furthermore, the novel bi-layer hierarchical np-Cu films were successfully prepared based on single-layer nanoporous films. Our findings not only contribute to the systematic understanding of the modification of the morphology and structure of np-Cu films but also offer a valuable framework for the design and fabrication of other non-noble nanoporous metals with tailored properties. [ABSTRACT FROM AUTHOR]

Ferroelectric photoelectric devices show promising applications in artificial vision systems. However, this type of devices used in artificial vision systems exhibits low photoresponsivity and this limited cyclability of photocurrents, which hinders the progress of artificial vision systems. In this study, an artificial vision system is constructed with high photoresponsivity, fast photoresponse speed, and high read/write speed based on the Pd/PbZr0.4Ti0.6O3/La0.3Sr0.7MnO3/LaAlO3 ferroelectric photomemristor. The high optical response (18.86 nanoamperes) and read/write speeds (50 nanoseconds) of the device significantly improve the robustness and speed of the neuromorphic visual system while reducing power consumption for recognition. The artificial vision system developed in this study provides excellent real‐time image processing capabilities, including edge detection and classification with 100% accuracy for musical key patterns, and it is expected to offer great potential for the development of high‐performance self‐powered artificial vision systems. [ABSTRACT FROM AUTHOR]

Massive medical data are indispensable for training diagnostic models to provide high-quality health monitoring services. The methods for sharing data in existing works involve securely and essentially copying data but often overlook the integration and efficiency of data storage, exchange and application. In this paper, we propose a Secure Decentralized Trading Alliance (SDTA) to encompass the entire process holistically. With monetary incentives, we formulate a chain-net structure for recording data digests and authentic transactions, thereby transforming data sharing into data trading without duplicating data storage. Data privacy is promised by encryption. To manage and employ encrypted medical data, users can update and search their encrypted data using an index and keywords, subsequently retrieving data within the SDTA framework. It is realized by a novel dynamic searchable symmetric encryption (SSE) with an |$l$| -level access strategy, which confines users to data pertinent solely to them, thus circumventing unnecessary data leakage. We scrutinize the storage efficiency and prove the fairness and security of SDTA. Finally, we generate datasets of varying sizes, where the time required to search for a single keyword is approximately 0.04 s with 1 000 000 (keyword, identifier) pairs, showing it quite acceptable. [ABSTRACT FROM AUTHOR]

Retrieval-augmented generation (RAG) adds a simple but powerful feature to chatbots, the ability to upload files just-in-time. Chatbots are trained on large quantities of public data. The ability to upload files just-in-time makes it possible to reduce hallucinations by filling in gaps in the knowledge base that go beyond the public training data such as private data and recent events. For example, in a customer service scenario, with RAG, we can upload your private bill and then the bot can discuss questions about your bill as opposed to generic FAQ questions about bills in general. This tutorial will show how to upload files and generate responses to prompts; see https://github.com/kwchurch/RAG for multiple solutions based on tools from OpenAI, LangChain, HuggingFace transformers and VecML. [ABSTRACT FROM AUTHOR]

The intrinsic variability of memristor switching behavior can be used as a natural source of randomness, this variability is valuable for safe applications in hardware, such as the true random number generator (TRNG). However, the speed of TRNG is still be further improved. Here, we propose a reliable Ag/SiNx/n-Si volatile memristor, which exhibits a typical threshold switching device with stable repeat ability and fast switching speed. This volatile-memristor-based TRNG is combined with nonlinear feedback shift register (NFSR) to form a new type of high-speed dual output TRNG. Interestingly, the bit generation rate reaches a high speed of 112 kb/s. In addition, this new TRNG passed all 15 National Institute of Standards and Technology (NIST) randomness tests without post-processing steps, proving its performance as a hardware security application. This work shows that the SiNx-based volatile memristor can realize TRNG and has great potential in hardware network security. [ABSTRACT FROM AUTHOR]

Neuromorphic computing aims to achieve artificial intelligence by mimicking the mechanisms of biological neurons and synapses that make up the human brain. However, the possibility of using one reconfigurable memristor as both artificial neuron and synapse still requires intensive research in detail. In this work, Ag/SrTiO3(STO)/Pt memristor with low operating voltage is manufactured and reconfigurable as both neuron and synapse for neuromorphic computing chip. By modulating the compliance current, two types of resistance switching, volatile and nonvolatile, can be obtained in amorphous STO thin film. This is attributed to the manipulation of the Ag conductive filament. Furthermore, through regulating electrical pulses and designing bionic circuits, the neuronal functions of leaky integrate and fire, as well as synaptic biomimicry with spike-timing-dependent plasticity and paired-pulse facilitation neural regulation, are successfully realized. This study shows that the reconfigurable devices based on STO thin film are promising for the application of neuromorphic computing systems. [ABSTRACT FROM AUTHOR]