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Xin Yang,1,2,* Zhikuan Xia,1,* Yunlong Fan,1 Yitong Xie,1 Ge Ge,1 Dexiu Lang,3 Junhong Ao,1 Danxia Yue,1 Jiamin Wu,1 Tong Chen,1 Yuekun Zou,1 Mingwang Zhang,4 Rongya Yang1,2 1Department of Dermatology, The Seventh Medical Center of PLA General Hospital, Beijing, People’s Republic of China; 2Department of Dermatology, Yanbian University Hospital, Yanji, People’s Republic of China; 3Department of Dermatology, XingYi People’s Hospital, Xingyi, People’s Republic of China; 4Department of Dermatology, Southwest Hospital, Army Medical University, Chongqing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Mingwang Zhang; Rongya Yang, Email mingwangzhang2@163.com; yangrya@sina.comBackground: Solar lentigines (SLs), serving as a prevalent characteristic of skin photoaging, present as cutaneous aberrant pigmentation. However, the underlying pathogenesis remains unclear and there is a dearth of reliable diagnostic biomarkers.Objective: The aim of this study was to identify diagnostic biomarkers for SLs and reveal its immunological features.Methods: In this study, gene expression profiling datasets (GSE192564 and GSE192565) of SLs were obtained from the GEO database. The GSE192564 was used as the training group for screening of differentially expressed genes (DEGs) and subsequent depth analysis. Gene set enrichment analysis (GSEA) was employed to explore the biological states associated with SLs. The weighted gene co-expression network analysis (WGCNA) was employed to identify the significant modules and hub genes. Then, the feature genes were further screened by the overlapping of hub genes and up-regulated differential genes. Subsequently, an artificial neural network was constructed for identifying SLs samples. The GSE192565 was used as the test group for validation of feature genes expression level and the model’s classification performance. Furthermore, we conducted immune cell infiltration analysis to reveal the immune infiltration landscape of SLs.Results: The 9 feature genes were identified as diagnostic biomarkers for SLs in this study. And an artificial neural network based on diagnostic biomarkers was successfully constructed for identification of SLs. GSEA highlighted potential role of immune system in pathogenesis of SLs. SLs samples had a higher proportion of several immune cells, including activated CD8 T cell, dendritic cell, myeloid-derived suppressor cell and so on. And diagnostic biomarkers exhibited a strong relationship with the infiltration of most immune cells.Conclusion: Our study identified diagnostic biomarkers for SLs and explored its immunological features, enhancing the comprehension of its pathogenesis.Keywords: solar lentigines, photoaging, diagnostic biomarkers, immune infiltration, artificial neural network

Context. Photometric redshifts for galaxies hosting an accreting supermassive black hole in their center, known as active galactic nuclei (AGNs), are notoriously challenging. At present, they are most optimally computed via spectral energy distribution (SED) fittings, assuming that deep photometry for many wavelengths is available. However, for AGNs detected from all-sky surveys, the photometry is limited and provided by a range of instruments and studies. This makes the task of homogenizing the data challenging, presenting a dramatic drawback for the millions of AGNs that wide surveys such as SRG/eROSITA are poised to detect. Aims. This work aims to compute reliable photometric redshifts for X-ray-detected AGNs using only one dataset that covers a large area: the tenth data release of the Imaging Legacy Survey (LS10) for DESI. LS10 provides deep grizW1-W4 forced photometry within various apertures over the footprint of the eROSITA-DE survey, which avoids issues related to the cross-calibration of surveys. Methods. We present the results from CIRCLEZ, a machine-learning algorithm based on a fully connected neural network. CIRCLEZ is built on a training sample of 14 000 X-ray-detected AGNs and utilizes multi-aperture photometry, mapping the light distribution of the sources. Results. The accuracy (σNMAD) and the fraction of outliers (η) reached in a test sample of 2913 AGNs are equal to 0.067 and 11.6%, respectively. The results are comparable to (or even better than) what was previously obtained for the same field, but with much less effort in this instance. We further tested the stability of the results by computing the photometric redshifts for the sources detected in CSC2 and Chandra-COSMOS Legacy, reaching a comparable accuracy as in eFEDS when limiting the magnitude of the counterparts to the depth of LS10. Conclusions. The method can be applied to fainter samples of AGNs using deeper optical data from future surveys (for example, LSST, Euclid), granting LS10-like information on the light distribution beyond the morphological type. Along with this paper, we have released an updated version of the photometric redshifts (including errors and probability distribution functions) for eROSITA/eFEDS. [ABSTRACT FROM AUTHOR]

With the 50th year mark since the launch of Drug Metabolism and Disposition journal, the field of drug metabolism and bioactivation has advanced exponentially in the past decades (Guengerich 2023). This has, in a major part, been due to the continued advances across the whole spectrum of applied technologies in hardware, software, machine learning (ML), and artificial intelligence (AI). LC–MS platforms continue to evolve to support key applications in the field, and automation is also improving the accuracy, precision, and throughput of these supporting assays. In addition, sample generation and processing is being aided by increased diversity and quality of reagents and bio-matrices so that what is being analyzed is more relevant and translatable. The application of in silico platforms (applied software, ML, and AI) is also making great strides, and in tandem with the more traditional approaches mentioned previously, is significantly advancing our understanding of bioactivation pathways and how these play a role in toxicity. All of this continues to allow the area of bioactivation to evolve in parallel with associated fields to help bring novel or improved medicines to patients with urgent or unmet needs. Shuai Wang and Cyrus Khojasteh, on behalf of the authors. [ABSTRACT FROM AUTHOR]

We present the first systematic follow-up of Planck Sunyaev–Zeldovich effect (SZE) selected candidates down to signal-to-noise (S/N) of 3 over the 5000 deg2 covered by the Dark Energy Survey. Using the MCMF cluster confirmation algorithm, we identify optical counterparts, determine photometric redshifts, and richnesses and assign a parameter, f cont, that reflects the probability that each SZE-optical pairing represents a random superposition of physically unassociated systems rather than a real cluster. The new PSZ-MCMF cluster catalogue consists of 853 MCMF confirmed clusters and has a purity of 90 per cent. We present the properties of subsamples of the PSZ-MCMF catalogue that have purities ranging from 90 per cent to 97.5 per cent, depending on the adopted f cont threshold. Halo mass estimates M 500, redshifts, richnesses, and optical centres are presented for all PSZ-MCMF clusters. The PSZ-MCMF catalogue adds 589 previously unknown Planck identified clusters over the DES footprint and provides redshifts for an additional 50 previously published Planck -selected clusters with S/N>4.5. Using the subsample with spectroscopic redshifts, we demonstrate excellent cluster photo- z performance with an RMS scatter in Δ z /(1 + z) of 0.47 per cent. Our MCMF based analysis allows us to infer the contamination fraction of the initial S/N>3 Planck -selected candidate list, which is ∼50 per cent. We present a method of estimating the completeness of the PSZ-MCMF cluster sample. In comparison to the previously published Planck cluster catalogues, this new S/N>3 MCMF confirmed cluster catalogue populates the lower mass regime at all redshifts and includes clusters up to z ∼1.3. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to present the transdisciplinary case study (TCS) as a learning framework based on what we call functional socio-cultural constructivism and project-based learning (PBL). In doing so, the paper attempts to illustrate the applicability of TCS to learn competencies and skills necessary to research problems of sustainable development. Design/methodology/approach: TCS is considered a learning framework based on the principle of self-regulated learning; i.e. students must actively deal with the requirements as well as plan and execute their project work within their own worldviews and goals. TCS methods are essential as we tackle complex real-world problems. Findings: The paper discusses challenges and obstacles of such an approach and present lessons learned since 1994, on both the viewpoints of students and of teachers. It conclude that case study learning is a demanding task, especially in a transdisciplinary context where more challenges emerge than in PBL, since goals of teachers, case agents, and students have to be balanced. Practical implications: TCS or courses like it are important for universities at the present time. Under present budget restrictions and a wide-ranging mistrust of society toward universities, there is a necessity for a new contract between society and research: students should learn to take over responsibility in societal contexts and be able to communicate beyond the "ivory tower." Originality/value: The learning goals of TCS differ from the goals of most university courses. They are more comprehensive and include complex problem solving, societal context, and group processes. The ambitious goal is that students become enabled to tackle complex, real-world problems. (Contains 5 figures, 1 table, and 2 notes.)

The various Euclid imaging surveys will become a reference for studies of galaxy morphology by delivering imaging over an unprecedented area of 15 000 square degrees with high spatial resolution. In order to understand the capabilities of measuring morphologies from Euclid-detected galaxies and to help implement measurements in the pipeline of the Organisational Unit MER of the Euclid Science Ground Segment, we have conducted the Euclid Morphology Challenge, which we present in two papers. While the companion paper focusses on the analysis of photometry, this paper assesses the accuracy of the parametric galaxy morphology measurements in imaging predicted from within the Euclid Wide Survey. We evaluate the performance of five state-of-the-art surface-brightness-fitting codes, DeepLeGATo, Galapagos-2, Morfometryka, ProFit and SourceXtractor++, on a sample of about 1.5 million simulated galaxies (350 000 above 5σ) resembling reduced observations with the Euclid VIS and NIR instruments. The simulations include analytic Sérsic profiles with one and two components, as well as more realistic galaxies generated with neural networks. We find that, despite some code-specific differences, all methods tend to achieve reliable structural measurements (< 10% scatter on ideal Sérsic simulations) down to an apparent magnitude of about IE = 23 in one component and IE = 21 in two components, which correspond to a signal-to-noise ratio of approximately 1 and 5, respectively. We also show that when tested on non-analytic profiles, the results are typically degraded by a factor of 3, driven by systematics. We conclude that the official Euclid Data Releases will deliver robust structural parameters for at least 400 million galaxies in the Euclid Wide Survey by the end of the mission. We find that a key factor for explaining the different behaviour of the codes at the faint end is the set of adopted priors for the various structural parameters. [ABSTRACT FROM AUTHOR]

Evening exposure to electric light can acutely suppress melatonin levels and adversely affect subsequent sleep. We conducted a systematic review with meta-analysis investigating the influence of evening illuminance levels on polysomnographically (PSG)-assessed sleep. We also explored how melanopsin (expressed in melanopic equivalent daylight illuminance (EDI) affects human sleep features. We included polysomnographic laboratory sleep studies with healthy humans for effects of illuminance and exposure duration, for pre-sleep exposures between 6:00 p.m. to 1:00 a.m. From 440 identified articles, 114 met eligibility criteria for screening, and 21 also reported type of light source/spectral characteristics, with 12 identified as eligible for review. Meta-analysis showed evening light affects sleep latency, sleep efficiency and slow wave sleep, with overall effect sizes (95% confidence interval) of 0.69 (−0.50; 1.88), 0.34 (−0.13; 0.82) and −0.61 (−1.85; 0.62), respectively. Estimated melanopic EDI in the range of 100–1000 lx yielded clear dose–response relationships for sleep latency and sleep efficiency, but not for slow wave sleep. Whilst illuminance and duration indicated no apparent effects for a single evening light exposure on PSG-assessed sleep latency, sleep efficiency and slow wave sleep, we observed evidence for a relationship between light exposure and sleep effects based on melanopic EDI. Hence, melanopic EDI may provide a robust predictor of non-visual responses on human sleep. [ABSTRACT FROM AUTHOR]