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PurposeThe coronavirus disease 2019 (COVID-19) pandemic unveiled resilience deficits in supply chains. Scholars and practitioners aim to identify supply chain resilience (SCRES) measures suitable for this unique disruption; however, empirical evidence on a pandemic's specific characteristics, resulting challenges, and suitable countermeasures is scarce.Design/methodology/approachA single-case study on an automotive supply chain network (ASCN), including eight nodes, was conducted. Based on current research and interviews with 35 experts, characteristic pandemic challenges for the ASCN were identified. Moreover, promising SCRES measures were determined along the most prominent SCRES levers. The findings lead to five central propositions and advance organizational information processing theory in the context of SCRES.FindingsThis study’s results confirm unique pandemic characteristics along the supply chain disruption's duration, severity, propagation, and volatility. The resulting unprecedented challenges made the ASCN apply novel SCRES measures, particularly regarding collaboration and risk management culture. However, well-known visibility and flexibility strategies were also suitable. Overall, agility and collaboration measures showed the highest capacity to address characteristic pandemic challenges. A lack of preparedness impeded some measures' application, calling for enhanced proactive risk management following the pandemic.Originality/valueThis paper addresses several research calls by providing in-depth empirical evidence on hitherto conceptually researched pandemic characteristics, challenges, and suitable SCRES measures from a network perspective. The study uncovers the different perceptions of individual tiers, emphasizing the need to analyze supply chain disruptions from multiple angles.

[...]for every person in Europe, more than two metric tons of materials such as metals, cement, plastics, and wood are used per year—the equivalent of about 90 bags of cement. McKinsey hosted a discussion at the 2024 annual meeting of the World Economic Forum in Davos, Switzerland, on “Transitioning from trials to triumphs in building materials circularity.” According to McKinsey analysis, there is a revenue opportunity of more than $1 trillion in Europe alone in 2050—sixfold growth from 2021.

The fabrication of nanostructures in a bottom-up approach from specific molecular precursors offers the opportunity to create tailored materials for applications in nanoelectronics. However, the formation of defect-free two-dimensional (2D) covalent networks remains a challenge, which makes it difficult to unveil their electronic structure. Here we report on the hierarchical on-surface synthesis of nearly defect-free 2D covalent architectures with carbonyl-functionalized pores on Au(111), which is investigated by low-temperature scanning tunnelling microscopy in combination with density functional theory calculations. The carbonyl-bridged triphenylamine precursors form six-membered macrocycles and one-dimensional (1D) chains as intermediates in an Ullmann-type coupling reaction that are subsequently interlinked to 2D networks. The electronic band gap is narrowed when going from the monomer to 1D and 2D surface-confined π -conjugated organic polymers comprising the same building block. The significant drop of the electronic gap from the monomer to the polymer confirms an efficient conjugation along the triphenylamine units within the nanostructures. On-surface synthesis is an excellent tool for the controlled synthesis of tailored nanomaterials. Here, the authors hierarchically synthesize covalent structures on Au(111) through Ullmann-type coupling of triphenylamines, forming macrocycles, 1D chains, and 2D networks, allowing them to study the effect of dimensionality on electronic band gap.

The use of antibodies against tumour necrosis factor (TNF) has markedly improved the treatment of Crohn's disease, but only certain patients respond to therapy. Here, Raja Atreya and colleagues have developed an approach using topical fluorescent antibodies to TNF and confocal laser endomicroscopy to evaluate the expression of transmembrane TNF (mTNF) in the intestinal mucosa of patients with active Crohn's disease in order to identify patients likely to respond to subsequent treatment with the anti-TNF therapy, adalimumab. As antibodies to tumor necrosis factor (TNF) suppress immune responses in Crohn's disease by binding to membrane-bound TNF (mTNF), we created a fluorescent antibody for molecular mTNF imaging in this disease. Topical antibody administration in 25 patients with Crohn's disease led to detection of intestinal mTNF + immune cells during confocal laser endomicroscopy. Patients with high numbers of mTNF + cells showed significantly higher short-term response rates (92%) at week 12 upon subsequent anti-TNF therapy as compared to patients with low amounts of mTNF + cells (15%). This clinical response in the former patients was sustained over a follow-up period of 1 year and was associated with mucosal healing observed in follow-up endoscopy. These data indicate that molecular imaging with fluorescent antibodies has the potential to predict therapeutic responses to biological treatment and can be used for personalized medicine in Crohn's disease and autoimmune or inflammatory disorders.

The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas.

The Hobby–Eberly Dark Energy Experiment (HETDEX) is an untargeted ∼540 deg2 spectroscopic survey of Lyα emission in the 1.9 < z < 3.5 Universe. In surface brightness, this survey reaches 1σ Lyα sensitivities of ∼2–5 × 10−18 erg s−1 cm −2arcsec−2 , allowing large samples of extended Lyα nebulae (LAN) to be studied. We selected a sample of 70,691 Lyα-emitting galaxies (LAEs) with an emission-line signal-to-noise ratio greater than 6 and modeled the Lyα emission as a point-source component with an optional exponential envelope. Half (∼47.5%) of the LAE sample (33,612 objects) exhibits significant extended emission and is best fit by the two-component model. The fraction of resolved sources increases with Lyα flux and luminosity. Their isophotal areas range from 10 to 130 arcsec2 (median 15 arcsec2), with integrated Lyα fluxes from 6 to 2000 × 10−17 erg s−1 cm−2 (median 20 × 10−17 erg s−1 cm−2). Comparison between point-spread function-weighted and isophotal flux measurements shows that the HETDEX pipeline underestimates the total Lyα flux by ∼30% on average, reflecting the substantial halo contribution in extended sources. Approximately 420 LANs are found per deg2 over 79.5 deg2 of noncontiguous sky. About 12% of resolved sources show active galactic nuclei signatures and are bright in Lyα and continuum. The remaining 88% span a wide range of morphologies and often lack continuum counterparts. Exponential scale lengths show no strong correlation with Lyα flux or luminosity (median 11.6 ± 1.9 kpc). Only 2.9% of the full S/N > 6 LAE population with ancillary data have radio counterparts, but 64% of those are found to be extended, with the radio fraction increasing with Lyα size. We present a catalog of all modeled sources, with their positions, redshifts, luminosities, and structural parameters for over 70,000 LAEs consisting of 33,000 spatially extended LAN. The catalog can be found at https://hetdex.org/data-results/ and in the online version of this paper.

Purpose This paper aims to establish a standardized, quick, reliable and cost-efficient method of quality control (QC) in metal powder bed fusion (PBFM) based on process monitoring data. Design/methodology/approach Based on destructive testing results that emerged from a statistical investigation on powder bed fusion process exceeding reproducibility of mechanical properties, it was investigated if the generated monitoring data from a concept laser machine allows reliable deductions on resulting mechanical properties of the manufactured specimens. Findings The application of machine learning on generated melt pool images, under-recognition of destructive testing results, enables enhanced pattern recognition. The generated computational model successfully classified 9,280 unseen layer images by 98.9 per cent accuracy. This finding offers an automated approach to quality control within PBFM. Originality/value To the authors knowledge, it is the first time that machine learning has been applied for the purpose of QC in additive manufacturing. The ability of deep convolutional neural networks to recognize patterns, which are imperceptible to the human eye, shows high potential to facilitate activities of QC and to minimize QC-related costs and throughput times. The achieved processing speed for image analyses also points a way for future developments of self-corrective PBFM systems.

The Hobby–Eberly Telescope Dark Energy Experiment (HETDEX) is designed to detect and measure the redshifts of more than 1 million Lyα emitting galaxies (LAEs) 1.88 < z < 3.52. In addition to its cosmological measurements, these data enable studies of Lyα spectral profiles and the underlying radiative transfer. Using the roughly half a million LAEs in the HETDEX Data Release 3, we stack various subsets to obtain the typical Lyα profile for the z ∼ 2–3 epoch and to understand their physical properties. We find clear absorption wings around Lyα emission, which extend ∼2000 km s−1 both redward and blueward of the central line. Using far-UV spectra of nearby (0.002 < z < 0.182) LAEs in the COS Legacy Archive Spectroscopic Survey treasury and optical/near-IR spectra of 2.8 < z < 6.7 LAEs in the Multi Unit Spectroscopic-Wide survey, we observe absorption profiles in both redshift regimes. Dividing the sample by volume density shows that the troughs increase in higher-density regions. This trend suggests that the depth of the absorption is dependent on the local density of objects near the LAE, a geometry that is similar to damped Lyα systems. Simple simulations of Lyα radiative transfer can produce similar troughs due to absorption of light from background sources by H i gas surrounding the LAEs.

The SARS-CoV-2 pandemic has highlighted the need to better define in-hospital transmissions, a need that extends to all other common infectious diseases encountered in clinical settings. To evaluate how whole viral genome sequencing can contribute to deciphering nosocomial SARS-CoV-2 transmission 926 SARS-CoV-2 viral genomes from 622 staff members and patients were collected between February 2020 and January 2021 at a university hospital in Munich, Germany, and analysed along with the place of work, duration of hospital stay, and ward transfers. Bioinformatically defined transmission clusters inferred from viral genome sequencing were compared to those inferred from interview-based contact tracing. An additional dataset collected at the same time at another university hospital in the same city was used to account for multiple independent introductions. Clustering analysis of 619 viral genomes generated 19 clusters ranging from 3 to 31 individuals. Sequencing-based transmission clusters showed little overlap with those based on contact tracing data. The viral genomes were significantly more closely related to each other than comparable genomes collected simultaneously at other hospitals in the same city (n = 829), suggesting nosocomial transmission. Longitudinal sampling from individual patients suggested possible cross-infection events during the hospital stay in 19.2% of individuals (14 of 73 individuals). Clustering analysis of SARS-CoV-2 whole genome sequences can reveal cryptic transmission events missed by classical, interview-based contact tracing, helping to decipher in-hospital transmissions. These results, in line with other studies, advocate for viral genome sequencing as a pathogen transmission surveillance tool in hospitals.