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DNA methylation is a process that can affect gene accessibility and therefore gene expression. In this study, a machine learning pipeline is proposed for the prediction of breast cancer and the identification of significant genes that contribute to the prediction. The current study utilized breast cancer methylation data from The Cancer Genome Atlas (TCGA), specifically the TCGA-BRCA dataset. Feature engineering techniques have been utilized to reduce data volume and make deep learning scalable. A comparative analysis of the proposed approach on Illumina 27K and 450K methylation data reveals that deep learning methodologies for cancer prediction can be coupled with feature selection models to enhance prediction accuracy. Prediction using 450K methylation markers can be accomplished in less than 13 s with an accuracy of 98.75%. Of the list of 685 genes in the feature selected 27K dataset, 578 were mapped to Ensemble Gene IDs. This reduced set was significantly (FDR < 0.05) enriched in five biological processes and one molecular function. Of the list of 1572 genes in the feature selected 450K data set, 1290 were mapped to Ensemble Gene IDs. This reduced set was significantly (FDR < 0.05) enriched in 95 biological processes and 17 molecular functions. Seven oncogene/tumor suppressor genes were common between the 27K and 450K feature selected gene sets. These genes were RTN4IP1, MYO18B, ANP32A, BRF1, SETBP1, NTRK1, and IGF2R. Our bioinformatics deep learning workflow, incorporating imputation and data balancing methods, is able to identify important methylation markers related to functionally important genes in breast cancer with high accuracy compared to deep learning or statistical models alone.

Pet ownership is widely studied with respect to human well-being but inconsistent findings underscore its unclear psychological mechanisms and effects. Here, we compare the well-being of pet owners and non-pet owners and investigate the roles of loneliness, living arrangement, and pet attachment in explaining the effects of pet ownership. The results indicated that pet owners experienced lower loneliness than non-pet owners when living alone, such that loneliness mediated the effects of pet ownership on well-being. Furthermore, the interpersonal substitution aspect of pet attachment was prominently associated to well-being, with loneliness completely mediating its negative effects. This study contributes to a more contextual and attachment-focused understanding of how pet ownership could implicate loneliness and well-being. It emphasizes the need to foster healthy pet attachments and to balance pet companionship with human social interactions.

This study evaluates the new Aqua MODIS Dark Target (DT) Collection 6 (C6) Aerosol Optical Depth (AOD) (MYD04_3K) retrieval algorithm at 3 km resolution over Asian countries that have recently experienced severe and increasing air pollution. Retrievals showed generally low accuracy compared with the AErosol RObotic NETwork (AERONET), with only 55% of retrievals within the expected error (EE). The uncertainty appears mainly due to systematic overestimation at both low and high AOD levels. This is attributed to under-prediction of surface reflectance, similar to, but more severe than, the C6 DT product at 10-km resolution. This is because MYD04_3K observes more noise in the surface reflectance computations, due to retention of some bright pixels in the retrieval window which would be discarded at 10 km. Greatest uncertainty was observed at urban sites, especially those dominated by coarse aerosols. Results suggest that the DT at 3 km is less reliable than MODIS C6 AOD products at 10 km.

Electron spins in silicon quantum dots are promising qubits due to their long coherence times, scalable fabrication, and potential for all-electrical control. However, charge noise in the host semiconductor presents a major obstacle to achieving high-fidelity single- and two-qubit gates in these devices. In this work, we measure the charge-noise spectrum of a Si/SiGe singlet-triplet qubit over nearly 12 decades in frequency using a combination of methods, including dynamically-decoupled exchange oscillations with up to 512 π pulses during the qubit evolution. The charge noise is colored across the entire frequency range of our measurements, although the spectral exponent changes with frequency. Moreover, the charge-noise spectrum inferred from conductance measurements of a proximal sensor quantum dot agrees with that inferred from coherent oscillations of the singlet-triplet qubit, suggesting that simple transport measurements can accurately characterize the charge noise over a wide frequency range in Si/SiGe quantum dots. Charge noise is a major limitation to achieving high-fidelity quantum gate performance with semiconductor qubits. Here the authors report the results of charge noise spectroscopy for electron spin qubits in silicon quantum dots, spanning nearly twelve decades in frequency.

Emerging infectious diseases such as Ebola Virus Disease (EVD), Nipah Virus Encephalitis and Lassa fever pose significant epidemic threats. Responses to emerging infectious disease outbreaks frequently occur in resource-constrained regions and under high pressure to quickly contain the outbreak prior to potential spread. As seen in the 2020 EVD outbreaks in the Democratic Republic of Congo and the current COVID-19 pandemic, there is a continued need to evaluate and address the ethical challenges that arise in the high stakes environment of an emerging infectious disease outbreak response. The research presented here provides analysis of the ethical challenges with regard to allocation of limited resources, particularly experimental therapeutics, using the 2013–2016 EVD outbreak in West Africa as a case study. In-depth semi-structured interviews were conducted with senior healthcare personnel (n = 16) from international humanitarian aid organizations intimately engaged in the 2013–2016 EVD outbreak response in West Africa. Interviews were recorded in private setting, transcribed, and iteratively coded using grounded theory methodology. A majority of respondents indicated a clear propensity to adopt an ethical framework of guiding principles for international responses to emerging infectious disease outbreaks. Respondents agreed that prioritization of frontline workers’ access to experimental therapeutics was warranted based on a principle of reciprocity. There was widespread acceptance of adaptive trial designs and greater trial transparency in providing access to experimental therapeutics. Many respondents also emphasized the importance of community engagement in limited resource allocation scheme design and culturally appropriate informed consent procedures. The study results inform a potential ethical framework of guiding principles based on the interview participants’ insights to be adopted by international response organizations and their healthcare workers in the face of allocating limited resources such as experimental therapeutics in future emerging infectious disease outbreaks to ease the moral burden of individual healthcare providers.

Depressive and anxiety symptoms are prevalent among patients with various clinical conditions, resulting in diminished emotional well-being and impaired daily functioning. The neural mechanisms underlying these symptoms, particularly across different disorders, remain unclear, limiting the effectiveness of conventional treatments. Therefore, it is crucial to elucidate the neural underpinnings of depressive and anxiety symptoms and investigate novel, effective treatments across clinical conditions. Transcranial direct current stimulation (tDCS) is a neuromodulatory technique that can help understand the neural underpinnings of symptoms and facilitate the development of interventions, addressing the two research gaps at both neural and clinical levels. Thus, this systematic review and meta-analysis aims to evaluate the existing evidence regarding the therapeutic efficacy of tDCS in reducing depressive and anxiety symptoms among individuals with diverse clinical diagnoses. This review evaluated evidence from fifty-six randomized, sham-controlled trials that administered repeated tDCS sessions with a parallel design, applying a three-level meta-analytic model. tDCS targeting the left dorsolateral prefrontal cortex (DLPFC) at 2-mA intensity demonstrates moderate efficacy in alleviating depressive symptoms, identifying the left DLPFC as a transdiagnostic neural mechanism of depressive symptoms across clinical conditions. In comparison, the findings on anxiety symptoms demonstrate greater heterogeneity. tDCS over the left DLPFC is effective in reducing depressive symptoms and shows promising effects in alleviating anxiety symptoms among individuals with diverse diagnoses. These findings enhance our understanding of the neuropsychological basis of depressive and anxiety symptoms, laying the groundwork for the development of more effective tDCS interventions applicable across clinical conditions.

The neurobiological underpinnings of Autism Spectrum Disorder (ASD) are diverse and likely multifactorial. One possible mechanism is increased oxidative stress leading to altered neurodevelopment and brain function. However, this hypothesis has mostly been tested in post-mortem studies. So far, available in vivo studies in autistic individuals have reported no differences in glutathione (GSH) levels in frontal, occipital, and subcortical regions. However, these studies were limited by the technically challenging quantification of GSH, the main brain antioxidant molecule. This study aimed to overcome previous studies' limitations by using a GSH-tailored spectroscopy sequence and optimised quantification methodology to provide clarity on GSH levels in autistic adults. We used spectral editing proton-magnetic resonance spectroscopy (1H-MRS) combined with linear combination model fitting to quantify GSH in the dorsomedial prefrontal cortex (DMPFC) and medial occipital cortex (mOCC) of autistic and non-autistic adults (male and female). We compared GSH levels between groups. We also examined correlations between GSH and current autism symptoms, measured using the Autism Quotient (AQ). Data were available from 31 adult autistic participants (24 males, 7 females) and 40 non-autistic participants (21 males, 16 females); the largest sample to date. The GSH levels did not differ between groups in either region. No correlations with AQ were observed. GSH levels as measured using 1H-MRS are unaltered in the DMPFC and mOCC regions of autistic adults, suggesting that oxidative stress in these cortical regions is not a marked neurobiological signature of ASD.

Cryptographic key exchange protocols traditionally rely on computational conjectures such as the hardness of prime factorization 1 to provide security against eavesdropping attacks. Remarkably, quantum key distribution protocols such as the Bennett–Brassard scheme 2 provide information-theoretic security against such attacks, a much stronger form of security unreachable by classical means. However, quantum protocols realized so far are subject to a new class of attacks exploiting a mismatch between the quantum states or measurements implemented and their theoretical modelling, as demonstrated in numerous experiments 3 – 6 . Here we present the experimental realization of a complete quantum key distribution protocol immune to these vulnerabilities, following Ekert’s pioneering proposal 7 to use entanglement to bound an adversary’s information from Bell’s theorem 8 . By combining theoretical developments with an improved optical fibre link generating entanglement between two trapped-ion qubits, we obtain 95,628 key bits with device-independent security 9 – 12 from 1.5 million Bell pairs created during eight hours of run time. We take steps to ensure that information on the measurement results is inaccessible to an eavesdropper. These measurements are performed without space-like separation. Our result shows that provably secure cryptography under general assumptions is possible with real-world devices, and paves the way for further quantum information applications based on the device-independence principle. This study demonstrates the experimental realization of a complete protocol for quantum key distribution using entangled trapped strontium ions with device-independent quantum security guarantees.

Climate change will be a powerful stressor on ecosystems and biodiversity in the second half of the 21 st century. In this study, we used the satellite-derived Normalized Difference Vegetation Index (NDVI) to examine a 34-year trend along with the response of vegetation to climate indicators surrounding the world’s largest megacity: the Pearl River Delta (PRD) of China. An overall increasing trend is observed in vegetation productivity metrics over the study period 1982 to 2015. Increase in winter productivity in both natural ecosystems and croplands is more related to increasing temperatures (r = 0.5–0.78), than to changes in rainfall. For growing season productivity, negative correlations with temperature were observed in cropland regions, and some forests in the northern part of PRD region, suggesting high-temperature stress on crop production and forest vegetation. However, increased winter and spring temperatures provide higher opportunities for cropping in winter. During the decade 1995–2004, vegetation productivity metrics showed a reversal in the upward trend. The geographical and biological complexity of the region under significant climatic and development impacts suggests causative factors would be synergistic. These include our observed decrease in sunshine hours, increasing cloud cover associated with atmospheric aerosols from industrial and urban development, direct pollution effects on plant growth, and exceedance of high temperature growth thresholds.