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A customized bed based stand alone array of optically pumped magnetometers for fetal magnetocardiography measurements
Sequence processing with quantum-inspired tensor networks
Abstract We introduce efficient tensor network models for sequence processing motivated by correspondence to probabilistic graphical models, interpretability and resource compression. Inductive bias is introduced via network architecture as motivated by correlation and compositional structure in the data. We create expressive networks utilising tensors that are both complex and unitary. As such they may be represented by parameterised quantum circuits and describe physical processes. The relevant inductive biases result in networks with logarithmic treewidth which is paramount for avoiding trainability issues in these spaces. For the same reason, they are also efficiently contractable or ‘quantum-inspired’. We demonstrate experimental results for the task of binary classification of bioinformatics and natural language, characterised by long-range correlations and often equipped with syntactic information. This work provides a scalable route for experimentation on the role of tensor structure and syntactic priors in NLP. Since these models map operationally to the qubits of a quantum processor, unbiased sampling equates to taking measurements on the quantum state encoding the learnt probability distribution. We demonstrate implementation on Quantinuum’s H2-1 trapped-ion quantum processor, showing the potential of near-term quantum devices.
Predicting MBTI personality of YouTube users
Towards a World Wide Web powered by generative AI
Abstract Generative Artificial Intelligence (AI) is a cutting-edge technology capable of producing text, images, and various media content leveraging generative models and user prompts. Between 2022 and 2023, generative AI surged in popularity with a plethora of applications spanning from AI-powered movies to chatbots. This paper investigates the potential of generative AI within the realm of the World Wide Web, specifically focusing on image generation. Web developers already harness generative AI to help craft text and images, while Web browsers might use it in the future to locally generate images for tasks such as repairing broken webpages, conserving bandwidth, and enhancing privacy. To explore this research area, this paper developed WebDiffusion, a tool that allows to simulate a Web powered by stable diffusion, a popular text-to-image model, from both a client and server perspective. Such a tool is the first of its kind, paving the way towards a futuristic world wide web where web images can be created using generative AI. WebDiffusion further supports crowdsourcing of user opinions, which is used to evaluate the quality and accuracy of 409 AI-generated images sourced from 60 webpages. Our findings suggest that generative AI is already capable of producing pertinent and high-quality Web images, even without requiring Web designers to manually input prompts, just by leveraging contextual information available within the webpages. However, direct in-browser image generation remains a challenge, as only highly powerful GPUs, such as the A40 and A100, can (partially) compete with classic image downloads. Nevertheless, this approach could be valuable for a subset of the images, for example, when fixing broken webpages or handling highly private content.
Inverted U-shape association between urine equol levels and cancer: a national population-based cross-sectional study
Effect of die designed geometrical parameters on riveting quality of self-piercing riveting joints in 5052 aluminium alloy
Comparative analysis of the mitochondrial genomes of the soft-shelled turtles Palea steindachneri and Pelodiscus axenaria and phylogenetic implications for Trionychia
Abstract Soft-shelled turtles, or Trionychia, are an enigmatic and fascinating group due to their specific morphological features and ecological adaptations. Based on mitochondrial DNA (mtDNA) and/or nuclear markers, previous studies showed the incongruent phylogenetic topologies within Trionychia (e.g., the Palea and its closely related species). In order to resolve the equivocal relationships and obtain some “genome-level” common evolutionary characters of soft-shelled turtles, in this study, we assembled and annotated the complete mitochondrial genomes of Palea steindachneri and Pelodiscus axenaria, both naturally distributed in Asia. The sizes of the two mitochondrial genomes were 16,811 bp and 17,143 bp, respectively. Typical vertebrate animal mtDNA features were observed, such as the usual gene components and arrangements (37 genes with a non-coding control region) and the A + T biased nucleotide compositions on the light strand (61.5% and 62.7%, respectively). All conserved blocks common to the vertebrates control region except for the extended terminal associated sequences (ETAS2) were found in the two soft-shelled turtles. The ω ratio averaged over all sites of each protein-coding gene (PCG) was below 1, which indicated purifying selection at the gene-wide level. However, a positive selection site at the 350-codon position in the cytb gene was detected, as estimated by Bayes empirical Bayes (BEB) analysis. Compared with the gene subsets, the mitogenomes provided the most robust phylogenetic resolution. The monophyly of the clades Amydona, Gigantaesuarochelys, and Apalonia was well supported. Topology discrepancies were observed among different datasets (e.g., the positions of Lissemys and Palea), reflecting the heterogeneous phylogenetic signals in the soft-shelled turtle mitogenomes. Precise date estimation based on Bayesian relaxed clock analyses indicated that the crown group age of extant Trionychia was approximately 115.84 Ma (95% HPD: 91.33–142.18 Ma). Paleoclimate changes, especially the Eocene − Oligocene transition, could be responsible for the speciation in these groups. Our results reiterated the necessity and effectiveness of incorporating entire mitochondrial genomes to delineate phylogenetic relationships in chelonian phylogeny studies.
Author Correction: An isothermal calorimetry assay for determining steady state kinetic and Ensitrelvir inhibition parameters for SARS-CoV-2 3CL-protease
Methodology of murine lung cancer mimics clinical lung adenocarcinoma progression and metastasis
Abstract Lung cancer is the leading cause of cancer-related deaths, of which adenocarcinoma is the most common subtype. Despite this, lung adenocarcinoma and its metastasis are poorly understood, due to difficulties in feasibly recapitulating disease progression and predicting clinical benefits of therapy. We outline a methodology to develop immunogenic orthotopic lung adenocarcinoma mouse models, by injecting cell-specific cre viruses into the lung of a genetically engineered mouse, which mirrors cancer progression defined by the International Association for the Study of Lung Cancer. Evaluation of different cre virus/concentrations models demonstrate remarkable consistency in cancer initiation and metastasis, allowing for high throughput, while showing differences in timing and severity, offering greater flexibility when selecting models. Histological and immune profiles reflect clinical observations suggesting similar mechanisms are recapitulated and preliminary data show resultant tumors to be responsive to clinical treatments. We present a clinically relevant, next-generation murine model for studying lung adenocarcinoma.
Integrating physiological and anatomical insights to unveil the mechanism of coloration in Prunus sibirica
Identification of FZD7 as a potential ferroptosis-related diagnostic gene in endometriosis by bioinformatics analysis
Software defect prediction based on residual/shuffle network optimized by upgraded fish migration optimization algorithm
High throughput screening identifies potential inhibitors targeting trimethoprim resistant DfrA1 protein in Klebsiella pneumoniae and Escherichia coli
The role of advanced glycation end products between thyroid function and diabetic nephropathy and metabolic disorders
Higher relevance of mechanical determinants for short-distance performance and metabolic determinants for middle-distance performance in female adolescent swimmers at national level
Abstract The study investigated associations of metabolic, anthropometric, and neuromuscular parameters with 50 to 400 m front crawl performance. Competition performances of 24 female swimmers (14.9 ± 1.3 years) were recorded and metabolic determinants (maximal oxygen uptake and lactate accumulation [ċLamax], cost of swimming [C], and lactate threshold 1 [LT1] using 200 m all-out, 20 s sprint, 500 m submaximal, and 3 min incremental test, respectively), anthropometry and dryland strength (squat and bench press 1 repetition maximum [1RMSQ/1RMBP] and mean propulsive power [MPPSQ/MPPBP]) were assessed. 1RMSQ (61.9 ± 13.3 kg) and MPPBP (207 ± 45 W) correlated significantly with 50 (1.84 ± 0.07 m∙s−1) and 100 m performance (1.68 ± 0.06 m∙s−1) (r ≥ 0.45) and ċLamax (0.35 ± 0.12 mmol·L−1·s−1) and body mass (60.1 ± 7.0 kg) with 50 and 100 m, respectively (r ≥ 0.44). Only LT1 (1.23 ± 0.04 m∙s−1) correlated significantly with 200 (1.52 ± 0.05 m∙s−1) and 400 m performance (1.43 ± 0.06 m∙s−1) (r ≥ 0.56). Multiple regression explained 33–35% and 61–86% of the variance in short- and middle-distance performance based on 1RMSQ and arm span and LT1, C, and fat percentage, respectively. Based on the analyses, mechanical determinants are more predictive of short- and metabolic determinants of middle-distance performance.
Relationships of the serum levels of toxic trace elements in pregnant women versus exposure to second-hand smoke
The time-sensitive nature of microRNA expression after neonatal hypoxic-ischemic encephalopathy
Author Correction: A hybrid in silico/in-cell controller that handles process-model mismatches using intracellular biosensing
Experimental and numerical investigation to optimise liquid desiccant system for advanced air conditioning
Abstract This study aims to experimentally demonstrate a liquid desiccant systems effectiveness by using thermo-chemical fluid, such as aqueous solution of calcium chloride. This study evaluated the effect of operating temperatures on air properties (temperature, relative humidity, and moisture content) and system effectiveness by varying air flow rates. The system’s functionality was influenced by the operational temperature and air flow rate, and the dehumidification effectiveness was higher at low operating temperatures and low airflow rates. An ANN metamodel-based control strategy is also proposed for implementation in hybrid thermo-chemical networks with the help of system performance data and real-time data. The suggested ANN model’s results were validated using a variety of measuring techniques, including the RMSE, MAPE, correlation (R), and coefficient of determination (R2). The proposed ANN analysis achieved an excellent correlation between predicted and experimentally measured data.