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We develop a general optical model and describe the absolute radiometric calibration of the readings provided by two widely-used night sky brightness sensors based on irradiance-to-frequency conversion. The calibration involves the precise determination of the overall spectral sensitivity of the devices and also the constant G relating the output frequency of the light-to-frequency converter chip to the actual band-weighted and field-of-view averaged spectral radiance incident on the detector (brightness). From these parameters, we show how to define a rigorous astronomical absolute photometric system in which the sensor measurements can be reported in units of magnitudes per square arcsecond with precise physical meaning.

Biometrics is the process of measuring and analysing human characteristics to verify a given person’s identity. Most real-world applications rely on unique human traits such as fingerprints or iris. However, among these unique human characteristics for biometrics, the use of Electroencephalogram (EEG) stands out given its high inter-subject variability. Recent advances in Deep Learning and a deeper understanding of EEG processing methods have led to the development of models that accurately discriminate unique individuals. However, it is still uncertain how much EEG data is required to train such models. This work aims at determining the minimal amount of training data required to develop a robust EEG-based biometric model (+95% and +99% testing accuracies) from a subject for a task-dependent task. This goal is achieved by performing and analysing 11780 combinations of training sizes, by employing various neural network-based learning techniques of increasing complexity, and feature extraction methods on the affective EEG-based DEAP dataset. Findings suggest that if Power Spectral Density or Wavelet Energy features are extracted from the artefact-free EEG signal, 1 and 3 seconds of data per subject is enough to achieve +95% and +99% accuracy, respectively. These findings contributes to the body of knowledge by paving a way for the application of EEG to real-world ecological biometric applications and by demonstrating methods to learn the minimal amount of data required for such applications.

The widespread consensus argues that the emergence of abstract concepts in the human brain, such as a “table”, requires complex, perfectly orchestrated interaction of myriads of neurons. However, this is not what converging experimental evidence suggests. Single neurons, the so-called concept cells (CCs), may be responsible for complex tasks performed by humans. This finding, with deep implications for neuroscience and theory of neural networks, has no solid theoretical grounds so far. Our recent advances in stochastic separability of highdimensional data have provided the basis to validate the existence of CCs. Here, starting from a few first principles, we layout biophysical foundations showing that CCs are not only possible but highly likely in brain structures such as the hippocampus. Three fundamental conditions, fulfilled by the human brain, ensure high cognitive functionality of single cells: a hierarchical feedforward organization of large laminar neuronal strata, a suprathreshold number of synaptic entries to principal neurons in the strata, and a magnitude of synaptic plasticity adequate for each neuronal stratum. We illustrate the approach on a simple example of acquiring “musical memory” and show how the concept of musical notes can emerge.

Transposable Elements (TEs) are ubiquitous genetic elements with the ability to move within a genome. TEs contribute to a large fraction of the repetitive elements of a genome, and because of their nature, they are not routinely analyzed in RNA-Seq gene expression studies. Amyotrophic Lateral Sclerosis (ALS) is a lethal neurodegenerative disease, and a well-accepted model for its study is the mouse harboring the human SOD1 G93A mutant. In this model, landmark stages of the disease can be recapitulated at specific time points, making possible to understand changes in gene expression across time. While there are several works reporting TE activity in ALS models, they have not explored their activity through the disease progression. Moreover, they have done it at the expense of losing their locus of expression. Depending on their genomic location, TEs can regulate genes in cis and in trans, making locus-specific analysis of TEs of importance in order to understand their role in modulating gene expression. Particularly, the locus-specific role of TEs in ALS has not been fully elucidated. In this work, we analyzed publicly available RNA-Seq datasets of the SOD1 G93A mouse model, to understand the locus-specific role of TEs. We show that TEs become up-regulated at the early stages of the disease, and via statistical associations, we speculate that they can regulate several genes, which in turn might be contributing to the genetic dysfunction observed in ALS.

Mercury (Hg(II)) has been classified as a pollutant and its removal from aqueous sources is considered a priority for public health as well as ecosystem protection policies. Oxidized graphenes have attracted vast interest in water purification and wastewater treatment. In this report, a partially reduced graphene oxide is proposed as a pristine adsorbent material for Hg(II) removal. The proposed material exhibits a high saturation Hg(II) uptake capacity of 110.21 mg g −1 , and can effectively reduce the Hg(II) concentration from 150 mg L −1 to concentrations smaller than 40 mg L −1 , with an efficiency of about 75% within 20 min. The adsorption of Hg(II) on reduced graphene oxide shows a mixed physisorption–chemisorption process. Density functional theory calculations confirm that Hg atom adsorbs preferentially on clean zones rather than locations containing oxygen functional groups. The present work, therefore, presents new findings for Hg(II) adsorbent materials based on partially reduced graphene oxide, providing a new perspective for removing Hg(II).

The heterogeneous pathogenesis and treatment response of non-small cell lung cancer (NSCLC) has led clinical treatment decisions to be guided by NSCLC subtypes, with lung adenocarcinoma and lung squamous cell carcinoma being the most common subtypes. While histology-based subtyping remains challenging, NSCLC subtypes were found to be distinct at the transcriptomic level. However, unlike genomic alterations, gene expression is generally not assessed in clinical routine. Since subtyping of NSCLC has remained elusive using mutational data, we aimed at developing a neural network model that simultaneously learns from adenocarcinoma and squamous cell carcinoma samples of other tissue types and is regularized using a neural network model trained from gene expression data. While substructures of the expression-based manifold were captured in the mutation-based manifold, NSCLC classification accuracy did not significantly improve. However, performance was increased when rejecting inconclusive samples using an ensemble-based approach capturing prediction uncertainty. Importantly, SHAP analysis of misclassified samples identified co-occurring mutations indicative of both NSCLC subtypes, questioning the current NSCLC subtype classification to adequately represent inherent mutational heterogeneity. Since our model captures mutational patterns linked to clinical heterogeneity, we anticipate it to be suited as foundational model of genomic data for clinically relevant prognostic or predictive downstream tasks.

Little is known about the impact of control group therapy on clinical benefit scales such as American Society of Clinical Oncology Value Framework (ASCO-VF), European Society for Medical Oncology Magnitude Clinical Benefit Scale (ESMO-MCBS), National Comprehensive Cancer Network (NCCN) Evidence Blocks and ASCO Cancer Research Committee (ASCO-CRC). We searched Drugs@FDA to identify cancer drugs approved between January 2012 and December 2021 based on randomized trials (RCTs). Definition of substantial clinical benefit was based on recommendations for each scale. Associations between characteristics of control group therapy and clinical benefit were explored using logistic regression. RCTs with a control group of active treatment plus placebo were associated with significantly lower odds of substantial benefit with ESMO-MCBS (OR 0.27, P = 0.003) and ASCO-VF (OR 0.30, P = 0.008) but not with NCCN Evidence Blocks or ASCO-CRC. This effect was attenuated and lost statistical significance without adjustment for quality of life (QoL) and/or toxicity (ESMO-MCBS OR 0.50, P = 0.17; ASCO-VF OR 0.49, P = 0.11). Clinical benefit scales can be sensitive to control group therapy. RCTs with substantial overlap between experimental and control therapy showed lower magnitude of clinical benefit using ESMO-MCBS and ASCO-VF scales; possibly due to differences in the weighting of QoL and toxicity between different frameworks.

Mutations in FUS cause amyotrophic lateral sclerosis (ALS), including some of the most aggressive, juvenile-onset forms of the disease. FUS loss-of-function and toxic gain-of-function mechanisms have been proposed to explain how mutant FUS leads to motor neuron degeneration, but neither has been firmly established in the pathogenesis of ALS. Here we characterize a series of transgenic FUS mouse lines that manifest progressive, mutant-dependent motor neuron degeneration preceded by early, structural and functional abnormalities at the neuromuscular junction. A novel, conditional FUS knockout mutant reveals that postnatal elimination of FUS has no effect on motor neuron survival or function. Moreover, endogenous FUS does not contribute to the onset of the ALS phenotype induced by mutant FUS. These findings demonstrate that FUS-dependent motor degeneration is not due to loss of FUS function, but to the gain of toxic properties conferred by ALS mutations. The mechanism by which FUS mutations cause familial ALS remains unclear. Here, the authors use mouse transgenic models to show that a toxic gain-of-function underlies motor neuron degeneration, and that the toxicity of mutant FUS does not depend on a loss or excess of FUS activity.

Numerous visualisation methods have been proposed, including Origin-Destination maps to represent movement patterns gathered from social media; however, visual clutter remains a persistent issue due to complex data dimensionality. Besides, most Origin-Destination maps fail to illustrate the temporal dimension of social network phenomena within the geographical environment. To tackle this issue, we propose the visualisation method for geo-located Facebook social-media data while emphasising the time aspect. Based on the citizen-generated data for the European Union (EU), we estimated the EU citizens' residing or travelling across the EU member states as a means of current and previous destinations to reveal the extent of the hypothetical human migration. The proposed methodology consists of Origin-Destination maps implemented within the time geography framework as a model to support the process of analysis for decision-making. The generated visualisation allows comprehension of the scale of human movement distribution internally within the EU from a space-time perspective.

Pollen is bees’ primary source of proteins. Using pollen substitutes could reduce colony losses in areas with limited floral resources. In this study, we compared the effects of pollen substitutes made from intact proteins versus free amino acids on bee survival, as well as the levels of vitellogenin (vg), major royal jelly protein 1 (mrjp1), and deformed wing virus (DWV). Our study revealed no significant differences in vg mRNA levels between bees fed intact protein and free amino acid diets. However, mrjp1 mRNA levels were higher in bees fed free amino acid diets, suggesting that the availability of amino acids affects the expression of this nutritionally regulated gene differently. Thus, the combined expression of vg and mrjp1 could be used to assess the nutritional value of different pollen substitutes. Our results also indicate that bees fed diets rich in free amino acids exhibited high levels of DWV and increased mortality during the first week, even though they showed a high expression of genes related to good nutrition. In contrast, bees fed an intact protein diet showed the lowest DWV levels during the first two weeks, although viral infection increased afterward. These results suggest that ingested free amino acids are quickly absorbed in the intestine, transported in the hemolymph, and taken up by cells, where they can facilitate viral replication. On the other hand, ingested intact proteins may undergo slower digestion and gradual release of amino acids in the hemolymph, which may not trigger an overt DWV infection. Possible interactions among Varroa mites, DWV infection, and nutrition are also discussed.