Prediction model

OBJECTIVE: To evaluate the oncologic safety and quality of life associated with the use of sentinel lymph node biopsy (SLNB) as compared to elective neck dissection (END) in patients with cT1/2N0 maxillary squamous cell carcinoma. METHODS: This study constituted a retrospective analysis of consecutively treated patients who underwent SLNB or END, with data collected prospectively. We analyzed the impact of the different neck procedures on regional control and disease-specific survival via the Cox model...

BACKGROUND: 7 Tesla (7T) apparent diffusion coefficient (ADC) maps derived from diffusion-weighted imaging (DWI) demonstrate improved image quality and spatial resolution over 3 Tesla (3T) ADC maps. However, 7T magnetic resonance imaging (MRI) currently suffers from limited clinical unavailability, higher cost, and increased susceptibility to artifacts. PURPOSE: To address these issues, we propose a hybrid CNN-transformer model to synthesize high-resolution 7T ADC maps from multimodal 3T MRI...

BACKGROUND: Real-time prediction of the remaining surgery duration (RSD) is important for optimal scheduling of resources in the operating room. METHODS: We focus on the intraoperative prediction of RSD from laparoscopic video. An extensive evaluation of seven common deep learning models, a proposed one based on the Transformer architecture (TransLocal) and four baseline approaches, is presented. The proposed pipeline includes a CNN-LSTM for feature extraction from salient regions within short video segments and a Transformer with local attention mechanisms...

BACKGROUND: The international flight network creates multiple routes by which pathogens can quickly spread across the globe. In the early stages of infectious disease outbreaks, analyses using flight passenger data to identify countries at risk of importing the pathogen are common and can help inform disease control efforts. A challenge faced in this modelling is that the latest aviation statistics (referred to as contemporary data) are typically not immediately available. Therefore, flight patterns from a previous year are often used (referred to as historical data)...

The efficient coding hypothesis posits that early sensory neurons transmit maximal information about sensory stimuli, given internal constraints. A central prediction of this theory is that neurons should preferentially encode stimuli that are most surprising. Previous studies suggest this may be the case in early visual areas, where many neurons respond strongly to rare or surprising stimuli. For example, previous research showed that when presented with a rhythmic sequence of full-field flashes, many retinal ganglion cells (RGCs) respond strongly at the instance the flash sequence stops, and when another flash would be expected...

Network inference is used to model transcriptional, signaling, and metabolic interactions among genes, proteins, and metabolites that identify biological pathways influencing disease pathogenesis. Advances in machine learning (ML)-based inference models exhibit the predictive capabilities of capturing latent patterns in genomic data. Such models are emerging as an alternative to the statistical models identifying causative factors driving complex diseases. We present CoVar, an ML-based framework that builds upon the properties of existing inference models, to find the central genes driving perturbed gene expression across biological states...

Humanoid robots that can autonomously operate in diverse environments have the potential to help address labor shortages in factories, assist elderly at home, and colonize new planets. Although classical controllers for humanoid robots have shown impressive results in a number of settings, they are challenging to generalize and adapt to new environments. Here, we present a fully learning-based approach for real-world humanoid locomotion. Our controller is a causal transformer that takes the history of proprioceptive observations and actions as input and predicts the next action...

This ecological study aimed to identify the factors with the greatest power to discriminate the proportion of oral and oropharyngeal cancer (OOC) records with time to treatment initiation (TTI) within 30 days of diagnosis in Brazilian municipalities. A descriptive analysis was performed on the variables grouped into five dimensions related to patient characteristics, access to health services, support for cancer diagnosis, human resources, and socioeconomic characteristics of 3,218 Brazilian municipalities that registered at least one case of OOC in 2019...

PURPOSE: To explore the feasibility and validity of machine learning models in determining causality in medical malpractice cases and to try to increase the scientificity and reliability of identification opinions. METHODS: We collected 13,245 written judgments from PKULAW.COM, a public database. 963 cases were included after the initial screening. 21 medical and ten patient factors were selected as characteristic variables by summarising previous literature and cases...

Central nervous system (CNS) tumors, due to their unique locations, pose a serious threat to human health and present challenges to modern medicine. These tumors exhibit notable epidemiological characteristics across various ethnicities, regions, and age groups. This study investigated the trend of disease burden of CNS tumors in China from 1990-2019 and predicted the incidence and death rate from 2020-2030. Employing data from the 2019 Global Burden of Disease (GBD) database, we utilized key indicators to scrutinize the disease burden associated with CNS tumors in China...

The short-term prediction of single well production can provide direct data support for timely guiding the optimization and adjustment of oil well production parameters and studying and judging oil well production conditions. In view of the coupling effect of complex factors on the daily output of a single well, a short-term prediction method based on a multi-agent hybrid model is proposed, and a short-term prediction process of single well output is constructed. First, CEEMDAN method is used to decompose and reconstruct the original data set, and the sliding window method is used to compose the data set with the obtained components...

Collective motion is ubiquitous in nature; groups of animals, such as fish, birds, and ungulates appear to move as a whole, exhibiting a rich behavioral repertoire that ranges from directed movement to milling to disordered swarming. Typically, such macroscopic patterns arise from decentralized, local interactions among constituent components (e.g., individual fish in a school). Preeminent models of this process describe individuals as self-propelled particles, subject to self-generated motion and "social forces" such as short-range repulsion and long-range attraction or alignment...

OBJECTIVES: Carpal tunnel syndrome (CTS) stands as the most prevalent upper extremity entrapment neuropathy, with a multifaceted etiology encompassing various risk factors. This study aimed to investigate whether anthropometric measurements of the hand, grip strength, and pinch strength could serve as predictive indicators for CTS through machine learning techniques. METHODS: Enrollment encompassed patients exhibiting CTS symptoms (n = 56) and asymptomatic healthy controls (n = 56), with confirmation via electrophysiological assessments...

The aim of the paper is to provide an ex-post assessment of the impact of the COVID-19 pandemic on food insecurity in the EU-27 countries expressed by physical and economic food access. We analysed trade and price effects, together with food insecurity and malnutrition indicators. Actual levels of the indicators were compared with their pre-pandemic magnitudes and/or with counterfactual levels derived from predictive models. We also aimed to compare the objective statistics with the subjective consumers' perception of their households' food security...

Crime remains a crucial concern regarding ensuring a safe and secure environment for the public. Numerous efforts have been made to predict crime, emphasizing the importance of employing deep learning approaches for precise predictions. However, sufficient crime data and resources for training state-of-the-art deep learning-based crime prediction systems pose a challenge. To address this issue, this study adopts the transfer learning paradigm. Moreover, this study fine-tunes state-of-the-art statistical and deep learning methods, including Simple Moving Averages (SMA), Weighted Moving Averages (WMA), Exponential Moving Averages (EMA), Long Short Term Memory (LSTM), Bi-directional Long Short Term Memory (BiLSTMs), and Convolutional Neural Networks and Long Short Term Memory (CNN-LSTM) for crime prediction...

Nuclear forward scattering (NFS) is a synchrotron-based technique relying on the recoil-free nuclear resonance effect similar to Mössbauer spectroscopy. In this work, we introduce NFS for in situ and operando measurements during electrocatalytic reactions. The technique enables faster data acquisition and better discrimination of certain iron sites in comparison to Mössbauer spectroscopy. It is directly accessible at various synchrotrons to a broad community of researchers and is applicable to multiple metal isotopes...

The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality...

Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accurately and efficiently identify these binding sites from sequences becomes essential. However, current methods mostly rely on expensive multiple sequence alignments or experimental protein structures, limiting their genome-scale applications. Besides, these methods haven't fully explored the geometry of the protein structures...

Conventional semen parameters have long been considered fundamental in male fertility analyses. However, doubts have been raised regarding the clinical utility of the assessment of spermatozoa (sperm) DNA damage. In this retrospective study, we investigated the potential correlation between conventional semen parameters and semen DNA fragmentation (SDF) assessed as sperm DNA damage, in 11,339 semen samples collected between January 2019 and June 2022. We observed significant negative correlations between the DNA fragmentation index (DFI) and sperm viability (correlation coefficient [ r ] = -0...

BACKGROUND: Previous studies have demonstrated that incorporating a polygenic risk score (PRS) to existing risk prediction models for breast cancer improves model fit, but to determine its clinical utility the impact on risk categorisation needs to be established. We add a PRS to two well-established models and quantify the difference in classification using the net reclassification improvement (NRI). METHODS: We analysed data from 126,490 post-menopausal women of "White British" ancestry, aged 40-69 years at baseline from the UK Biobank prospective cohort...