159370 research outputs found
Sort by
Spatiotemporal variability of PM2.5 infiltrations in higher education buildings: a multizone air-thermal co-simulation analysis
Get PDFExposure to outdoor-sourced PM2.5 infiltration poses significant health risks to occupants of Higher Education Institution (HEI) buildings. Unlike residential settings, HEI buildings are complex and heterogeneous, presenting unique challenges for comprehensive Indoor Air Quality (IAQ) assessment. This study addresses this gap by employing a novel high-resolution multizone air-thermal co-simulation approach to investigate PM2.5 infiltration dynamics across 2,729 zones from an HEI building stock, which is crucial when consistent long-term monitoring data is unavailable. Hourly time series data reveal significant spatial and temporal variations in indoor PM2.5 concentrations and air change rates (ACHINF), underscoring the necessity of room-level resolution for accurate assessment in HEI environments. Our results demonstrate a clear positive impact of improving building airtightness (Q50) on indoor PM2.5 levels. For instance, reducing Q50 from 13 m3/h/m2 (leaky) to 3 m3/h/m2 (well-sealed) significantly decreased zones exceeding the WHO 2005 guideline (10 μg/m3) from 82% to merely 1%. Crucially, the study also revealed that outdoor PM2.5 background concentrations in the study location already frequently exceeded WHO 2005 annual guidelines (e.g., an average of 17.04 μg/m3 during the heating season). Consequently, even with well-sealed buildings (Q50=3 m3/h/m2), a significant proportion (approaching 88%) of zones still exceeded the more stringent WHO 2021 guideline of 5 μg/m3. These findings underscore the critical interplay between building airtightness and ambient pollution levels in determining indoor air quality and highlight the limit of what airtightness alone can achieve in highly polluted outdoor environments
Arylsulfamates inhibit colonic Bacteroidota growth through a sulfatase-independent mechanism
Get PDFExcessive degradation of the colonic mucin layer by Bacteroides within the human gut microbiota drives inflammatory bowel disease (IBD) in mice. Bacterial carbohydrate sulfatases are key enzymes in gut colonization, and they are elevated in human IBD and correlate with disease severity. Selective inhibitors of carbohydrate sulfatases could function as sulfatase-selective drugs, allowing precise control of sulfatase activity while preserving these otherwise beneficial bacteria. Arylsulfamates are covalent inhibitors that target a catalytic formylglycine residue of steroid sulfatases, a residue that is also conserved in carbohydrate sulfatases. Here, we find that a library of aryl- and carbohydrate sulfamates is ineffective against carbohydrate sulfatases, yet can inhibit human gut microbiota (HGM) species grown on sulfated glycans. Leveraging thermal proteome profiling (TPP), we identify a lipid kinase as the target responsible for these effects. This work highlights the imperative for developing specific inhibitors targeting carbohydrate sulfatases and reveals the adverse effects that arylsulfamates have on Bacteroides species of the HGM
Understanding the substrate recognition and catalytic mechanism of methyl fucosidases from glycoside hydrolase family 139
Get PDFRhamnogalacturonan II is one of the most complex plant cell wall carbohydrates and is composed of 13 different sugars and 21 different glycosidic linkages. It is abundant in fruit and indulgence foods, such as chocolate and wine, making it common in the human diet. The human colonic commensal Bacteroides thetaiotaomicron expresses a consortium of 22 enzymes to metabolise rhamnogalacturonan II, some of which exclusively target sugars unique to rhamnogalacturonan II. Several of these enzyme families remain poorly described, and, consequently, our knowledge of rhamnogalacturonan II metabolism is limited. Chief among the poorly understood activities is glycoside hydrolase (GH) family 139, with targets α1,2-2O-methyl L-fucoside linkages, a sugar residue a sugar not found in any other plant cell wall complex glycans. Although the founding enzyme BT0984 was placed in the RG-II degradative pathway, no GH139 structure or catalytic blueprint had been available. We report the crystal structures of BT0984 and a second homologue, and reveal that the family operates with inverting stereochemistry. Using this data we undertook a mutagenic strategy, backed by molecular dynamics, to identify the important substrate binding and catalytic residues, mapping these residues throughout the GH139 family revealing the importance of the O2 methyl interaction of the substrate. We propose a catalytic mechanism that uses a non-canonical Asn as a catalytic base and shares similarity with L-fucosidases/L-galactosidases of family GH95
Data-Driven Predictive Modelling of Agile Projects Using Explainable Artificial Intelligence
Get PDFOne of the fundamental challenges in managing software and information technology projects is monitoring and predicting project status at the end of each sprint, release or project. Agile project management has emerged over the past two decades, significantly impacting project success. However, no comprehensive approach based on the features of this approach has been found in studies to monitor and predict the status of a sprint, release or project. This study aims to develop a data-driven approach for predicting the status of software projects based on agility features. For this purpose, 22 agility features were first identified to evaluate and predict the status of projects in four aspects: Endurance, Effectiveness, Efficiency, and Complexity. The findings indicate that the aspects of Effectiveness and Efficiency have the greatest impact on project success. Additionally, the results show that features related to team work, team capacity, experience and project objectives have the most significant impact on project success. An artificial neural network algorithm was then used, and a model was developed to predict project status, which was optimized using the Neural Architecture Search algorithm with a 93 percent accuracy rate. The neural network model was interpreted using the SHapley Additive exPlanations (SHAP) algorithm, and sensitivity analysis was performed on the important components. Finally, the behavior of the projects in each category was analyzed and evaluated using the Apriori algorithm
Robust constrained weighted least squares for in vivo human cardiac diffusion kurtosis imaging
Get PDFPurpose
Cardiac diffusion tensor imaging (cDTI) can investigate the microstructure of heart tissue.
At sufficiently high b-values, additional information on microstructure can be observed, but the data
require a representation such as diffusion kurtosis imaging (DKI). cDTI is prone to image corruption,
which is usually treated with shot-rejection but which can be handled more generally with robust estimation. Unconstrained fitting allows DKI parameters to violate necessary constraints on signal behaviour,
causing errors in diffusion and kurtosis measures.
Methods
We developed robust constrained weighted
least squares (RCWLS) specifically for DKI. Using in vivo cardiac DKI data from 11 healthy volunteers collected with a Connectom scanner up to b-value 1350 s/mm²
, we compared fitting techniques
with/without robustness and with/without constraints.
Results
Constraints, but not robustness, made
a significant difference on all measures. Robust fitting corrected large errors for some subjects. RCWLS
was the only technique that showed radial kurtosis to be larger than axial kurtosis for all subjects,
which is expected in myocardium due to increased restrictions to diffusion perpendicular to the primary myocyte direction. For b = 1350 s/mm²
, RCWLS gave the following measures across subjects:
mean diffusivity (MD) 1.68 ± 0.050 ×10‾³mm²
/s, fractional anisotropy (FA) 0.30 ± 0.013, mean kurtosis
(MK) 0.36 ± 0.027, axial kurtosis (AK) 0.26 ± 0.027, radial kurtosis (RK) 0.42 ± 0.040, and RK/AK
1.65 ± 0.19.
Conclusion
Fitting techniques utilizing both robust estimation and convexity constraints,
such as RCWLS, are essential to obtain robust and feasible diffusion and kurtosis measures from in vivo
cardiac DKI
From Workplace-Based to Work-Related Violence:Reframing HRM Research and Practice in the Era of Growing Tensions
Get PDFViolence at work has traditionally been conceptualized in human resource management (HRM) as workplace-based violence—an episodic, interpersonal issue occurring within bounded organizational settings. This perspective article adopts the term work-related violence as a more expansive and timely framing, encompassing physical, psychological, and symbolic harm related to work but occurring across dispersed geographies, identities, relationships, and organizational arrangements. It contends that prevailing HRM frameworks remain ill-equipped to address these fragmented and often unacknowledged harms, particularly as work becomes increasingly hybrid, precarious, and digitally mediated. Drawing on interdisciplinary scholarship, we advance a multilevel and multistakeholder analytical framework that theorizes violence as relational and spatially unbounded, embedded across micro (identity and employees' lived experience, and psychological factors), meso (organizational culture, HRM systems and silencing mechanisms), and macro (regulatory, ideological, and institutional) levels. The framework further identifies underexplored domains of violence within HRM, including employee-perpetrated violence, ideologically motivated aggression, and the critical role of community-based interventions in mitigating harm. In doing so, the article contributes to HRM theory by problematizing the spatial and behavioral assumptions underpinning conventional approaches to workplace violence. We argue for a broadened research and practice agenda that expands the field's analytical and operational capacity, calling for the development of HRM models that are structurally, institutionally, and ideologically attuned to violence emerging from inequality, institutional complicity, and the broader political economies of contemporary work
Hybrid Renewable Energy Systems in Türkiye:A Multi-Scenario Assessment of Demand, Metering, and Carbon Tax Policies
Get PDFThe accelerating demand for low-carbon energy solutions highlights the critical role of hybrid renewable energy systems (HRES) in achieving decarbonization, energy security, and economic resilience. This study offers a comprehensive techno-economic and environmental evaluation of HRES integrating photovoltaic, wind, and battery storage technologies across Türkiye’s diverse climatic regions and sectoral demand profiles (residential, commercial, industrial). Utilizing 48 scenario-based simulations via HOMER Pro, the analysis incorporates real-world policy instruments including carbon taxation, net metering (NM), and net billing (NB). A novel sensitivity analysis reveals that under NB, battery integration becomes economically viable at carbon prices above 40–50 /tCO2. Additionally, a 40 $/tCO2 carbon tax increases the renewable energy fraction by 32% and reduces the levelized cost of energy (LCOE) by 11.75%. Commercial users in wind-rich regions benefit most under NM, achieving up to 22% lower net present cost (NPC). While NM maximizes renewable deployment and short-term returns, NB ensures greater economic discipline and supports battery investments under high tariff and carbon scenarios. Expanded performance indicators, including self-supply rate, self-consumption rate, and energy exchange rate, provide operational insights beyond conventional metrics. The findings offer region-specific and policy-aware recommendations, suggesting that hybrid models combining NM and NB, supported by moderate carbon pricing and targeted incentives, can optimize system performance while ensuring affordability and equity in Türkiye’s energy transition. This framework offers strategic guidance for regulators, investors, and planners in emerging economies
The routine use of a digital tool for the tumor cell fraction quantification in molecular pathology: an international validation of QuANTUM
Get PDFObjective. The absolute and relative quantification of tumor cell fraction (TCF) in tissue samples for molecular pathology testing is time-consuming and poorly reproducible.
Methods. Here we report the results of an international survey on non-small cell lung cancer (NSCLC), validating the Qupath Analysis of Nuclei from Tumor to Uniform Molecular tests (QuANTUM) automated computational pipeline for TCF quantification.
Results. The TCF obtained with QuANTUM is reliable, as demonstrated by the comparison with the manual counting of cells (ground truth, GT) in cell blocks, small biopsies and surgical specimens (overall correlation of 0.89). The visual evaluation of QuANTUMprocessed images increased the pathologists’ agreement with GT and QuANTUM of +0.16, +0.21, +0.09 and +0.17, +0.29, +0.21 across the three sample types, respectively. An overall increase in cases classified as containing ≥100 tumor cells for all sample types was noted after QuANTUM (from 75 cases, 63% to 96 cases, 80% among cell blocks, p = 0.003).
Conclusions. QuANTUM is an easy-to-use and reliable tool for the TCF assessment and its employment significantly modifies the visual estimation by pathologists, improving the assessment of NSCLC cases for molecular analysi
Synthetic G-quadruplex components for predictable, precise two-level control of mammalian recombinant protein expression
Get PDFControl of mammalian recombinant protein expression underpins the in vitro manufacture and in vivo performance of all biopharmaceutical products. However, routine optimization of protein expression levels in these applications is hampered by a paucity of genetic elements that function predictably across varying molecular formats and host cell contexts. Herein, we describe synthetic genetic components that are specifically built to simplify bioindustrial expression cassette design processes. Synthetic G-quadruplex elements with varying sequence feature compositions were systematically designed to exhibit a wide range of regulatory activities and inserted into identified optimal positions within a standardized, bioindustry compatible core promoter-5′UTR control unit. The resulting library tuned protein production rates over two orders of magnitude, where DNA and RNA G-quadruplexes could be deployed individually or in combination to achieve synergistic two-level regulatory control. We demonstrate these components can predictably and precisely tailor protein expression levels in (i) varying gene therapy and biomanufacturing cell hosts and (ii) both plasmid DNA and synthetic messenger RNA contexts. As an exemplar use case, a vector design platform was created to facilitate rapid optimization of polypeptide expression ratios for difficult-to-express multichain products. Permitting simple, predictable titration of recombinant protein expression, this technology should prove useful for gene therapy and biopharmaceutical manufacturing applications
A multiscale framework for simulating landslide runout and impact with barriers
Get PDFThis study proposes a multiscale framework for simulating landslide runout and its impact on mitigation structures, particularly focusing on debris flows. The proposed methodology combines depth-averaged (DA) models for basin-scale flow propagation with three-dimensional (3D) models for detailed impact analysis. This multiscale approach effectively balances computational efficiency with the need for detailed simulation in critical areas, such as the barrier near-field. Validation is conducted using both laboratory-scale experiments and a real-world case study in the Italian Alps. The results demonstrate the capability of the framework to accurately replicate flow dynamics, including run-up, velocity profiles, and flow lamination operated by barriers. To our knowledge, this represents the first validated application of a DA-3D multiscale framework for debris flow-barrier interactions at the site scale. The study also introduces a novel 3D adaptation of the widely used Voellmy rheology, which allows consistent cross-scale parameterisation. This framework enables simulating debris flows at the site scale with a precision previously confined to the laboratory scale
Data sources:
Access Repository Dashboard
Do you manage Open Research Online? Become a CORE Member to access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard! 👇