Limitations Of Analysis Research Articles

Intelligent assistance system for people with visual impairments

Subject of the Research: The creation of an intelligent assistance system for people with visual impairments. Nowadays, the task of developing effective intelligent assistance systems that allow people with vision problems to achieve maximum independence is important and relevant, as existing systems have a number of drawbacks, such as limited autonomy, limited integration with other devices and systems, limited analysis of dynamic obstacles, and limited user feedback capabilities, which in most cases are restricted to voice guidance. Objective: The objective of this work is to create a generalized functional model of an intelligent assistance system for people with visual impairments, which has enhanced autonomy, integration with other devices and systems, the ability to analyze dynamic objects and predict their movement trajectory, and provide diverse feedback to the user. Tasks: To achieve the set objective, the following tasks were accomplished: a generalized functional model of the proposed intelligent assistance system for people with visual impairments was created; the functional dependencies of the components of the developed model were substantiated; a review of the basic modules of the proposed system model was conducted. Methods: The methods used include functional modeling methods and system analysis methods. Results: The following results were obtained: a functional model of an intelligent assistance system for people with visual impairments was proposed. This system surpasses existing analogs in a number of functional capabilities: detection of static and dynamic obstacles with prediction of dynamic obstacles’ movement trajectory, the ability to operate in various conditions (indoors, outdoors, in light or dark, in different weather conditions), support for integration with other systems and devices, and a high level of autonomy. Conclusions: The developed system model has enhanced autonomy, integration with other devices and systems, the ability to analyze dynamic objects and predict their movement trajectory, and provides diverse feedback to the user.

Simulating urban energy use under climate change scenarios and retrofit plans in coastal Texas

Rapid urbanization, climate change, and aging infrastructure pose significant challenges to achieving sustainability and resilience goals in urban building energy use. Although retrofitting offers a viable solution to mitigate building energy use, there has been limited analysis of its effects under various weather conditions associated with climate change in urban building energy use simulations. Moreover, certain parameters in energy simulations necessitate extensive auditing or survey work, which is often impractical. This research proposes a framework that integrates various datasets, including building footprints, Lidar data, property appraisals, and street view images, to conduct neighborhood-scale building energy use analysis using the Urban Modeling Interface (UMI), an Urban Building Energy Model (UBEM), in a coastal neighborhood in Galveston, Texas. Seven retrofit plans and three weather conditions are considered in the scenarios of building energy use. The results show that decreasing the U-value of building envelopes helps reduce energy use, while increasing the U-value leads to higher energy consumption in the Galveston neighborhood. This finding provides direction for coastal Texas cities, like Galveston, to update building standards and implement retrofit measures.

Defect‑engineered graphene nanoribbons based molecularly imprinting voltammetric sensor for sensitive determination of interleukin-6

As an important proinflammatory cytokine, interleukin-6 (IL-6) possesses great influence in the treatment of several diseases (e.g. acute pancreatitis and infections) and regulation of immune, thus it’s necessary to develop a simple and sensitive method for IL-6 detection. In this work, by preparing defect‑engineered graphene nanoribbons (EGr) as substrate material of sensor and using 1,2-diaminobenzene as the functional monomer, a simple and sensitive molecularly imprinted polymer (MIP) voltammetric sensor was successfully designed via simple electropolymerization method to detect IL-6. Because of the superior electrical property coupled with large surface area of EGr and high specific-recognition of MIP, the as-designed MIP sensor exhibits wide linearity (0.1–1000 pg mL−1) and low analytical limit for IL-6 (0.025 pg mL−1) via optimizing various influencing factors. In addition, the as-designed voltammetric MIP sensor exhibits superior repeatability and long-term stability as well as real application performances. The yielded acceptable performances based on the developed MIP sensor reveal it could realize simple and sensitive detection of IL-6 as a novel sensing platform.

Macro and micro-scale material removal mechanisms during ECM/hybrid laser-ECM of a passivating multiphase NbC–Ni cermet

Electrochemical machining (ECM) is a non-contact and athermal machining process where the material removal is accomplished through controlled anodic dissolution of the workpiece governed by Faraday laws. ECM process has been hybridized with several other processes for improving material processing windows. Hybrid laser electrochemical machining (LECM) synergistically applies electrochemical and laser process energies with added benefits of escalated reaction kinetics leading to enhanced transpassive dissolution, weakening of passivation layer, process localisation and uniform dissolution. The laser energy acts as a localised and controllable heat source thereby offering multi-fold processing benefits. For alloys and cermets, a characteristic surficial fingerprint is the presence of inhomogeneous multiphase dissolution and sporadically distributed passivation layer, necessitating addition of aggressive reagents in electrolytes. LECM has the potential to addresses these challenges while processing in pH neutral electrolytes. Previous works have very limited analysis on the macro and micro removal mechanisms while processing relevant strategic materials and multitude of applications of LECM remain unexploited. Therefore, this work presents in-depth investigations into macro and micro-scale material removal mechanisms of ECM/LECM on sintered niobium carbide with nickel binder (NbC–Ni), which is a potential cobalt-free alternative to tungsten carbide. The results revealed new insights into the removal behaviour of the constituent phases which differed from the first principles and their interaction with the laser. During ECM, the Ni phase dissolved preferentially and influenced the surface pattern and particle breakout which was reduced with laser assistance. The surface evolution characteristics were also analysed based on the ridge-crevice pattern. Additionally, the weakening of passive layer was correlated with the pulse analysis that revealed quantitatively the different process regimes occurring during ECM and LECM. The grain level study revealed that orientation effects still exist during LECM and the grains with higher surface energy (FCC (001) vicinal planes) passivated more and dissolved less. Furthermore, the improvement in surface quality, overcut and reduction in particle breakout with LECM process makes it promising for machining newer recipes of metal carbides.

Environmental nanoplastics quantification by pyrolysis-gas chromatography–mass spectrometry in the Pearl River, China: First insights into spatiotemporal distributions, compositions, sources and risks

Nanoplastics (NPs, size <1000 nm) are ubiquitous plastic particles, potentially more abundant than microplastics in the environment; however, studies highlighting their distribution dynamics in freshwater are rare due to analytical limitations. Here, we investigated spatiotemporal levels of nine polymers of NPs in surface water samples (n = 30) from the full stretch of the Pearl River (sites, n = 15) using pyrolysis gas chromatography-mass spectrometry (Py-GC/MS). Six polymers were detected, including polystyrene (PS), polyvinyl chloride (PVC), nylon/polyamide 66 (PA66), polyester (PES), poly(methyl methacrylate) (PMMA) and polyethylene (PE), where three polymers showed high detection frequencies; PS (100 % in winter and summer), followed by PVC (73 % in winter and 87 % in summer) and PA66 (53 % in winter and 67 % in summer). The spatiotemporal distribution revealed the sites related to aquaculture (AQ) and shipping (SHP) showed higher NP levels than those of human settlement (HS) and wastewater treatment plants (WWTPs) (p = 0.004), and relatively high average levels of NPs in the urban sites compared to rural sites (p = 0.04), albeit showed no obvious seasonal differences (p = 0.78). For instance, the average PS levels in the Pearl River were in the following order: AQ 411.55 µg/L > SHP 81.75 µg/L > WWTP 56.66 µg/L > HS 47.75 µg/L in summer and HS 188.1 µg/L > SHP 103.55 µg/L > AQ 74.7 µg/L > WWTP 62.1 µg/L in winter. Source apportionment showed a higher contribution through domestic plastic waste emissions among urban sites, while rural sites showed an elevated contribution via aquaculture, agriculture, and surface run-off to the NP pollution. Risk assessment revealed that NPs at SHP and AQ sites posed a higher integrated risk in terms of pollution load index (PLI) than those at WWTP and HS sites. Regarding polymer hazard index (HI), 80 % of sampling sites in summer and 60 % of sampling sites in winter posed level III polymer risk, with PVC posing the highest risk. This study provides novel insights into the seasonal contamination and polymer risks of NP in the Pearl River, which will help to regulate the production and consumption of plastics in the region. Environmental ImplicationsThe contamination dynamics of field nanoplastics (NPs) in freshwater resources remain little understood, mainly attributed to analytical constraints. This study aims to highlight the spatiotemporal distribution of NPs in the Pearl River among various land use types, urban-rural comparison, seasonal comparison, their compositional profiles, potential sources, interaction with environmental factors, and ecological and polymer hazard assessments of investigated polymers in the full stretch of the Pearl River from Liuxi Reservoir to the Pearl River Delta (PRD) region. This study, with a comparatively large number of samples and NP polymers, will offer novel insights into the contamination profiles of nano-sized plastic particles in one of the important freshwater riverine systems in China.

Highly precise community science annotations of video camera‐trapped fauna in challenging environments

AbstractAs camera trapping grows in popularity and application, some analytical limitations persist including processing time and accuracy of data annotation. Typically images are recorded by camera traps although videos are becoming increasingly collected even though they require much more time for annotation. To overcome limitations with image annotation, camera trap studies are increasingly linked to community science (CS) platforms. Here, we extend previous work on CS image annotations to camera trap videos from a challenging environment; a dense tropical forest with low visibility and high occlusion due to thick canopy cover and bushy undergrowth at the camera level. Using the CS platform Chimp&amp;See, established for classification of 599 956 video clips from tropical Africa, we assess annotation precision and accuracy by comparing classification of 13 531 1‐min video clips by a professional ecologist (PE) with output from 1744 registered, as well as unregistered, Chimp&amp;See community scientists. We considered 29 classification categories, including 17 species and 12 higher‐level categories, in which phenotypically similar species were grouped. Overall, annotation precision was 95.4%, which increased to 98.2% when aggregating similar species groups together. Our findings demonstrate the competence of community scientists working with camera trap videos from even challenging environments and hold great promise for future studies on animal behaviour, species interaction dynamics and population monitoring.

Sequencing approaches to identify distal jejunum microbial community composition and function in broiler chickens fed anti-interleukin-10 during coccidiosis and necrotic enteritis challenge

Strategies to counteract interleukin (IL)-10-mediated immune evasion by Eimeria spp. during coccidiosis- like anti-IL-10 antibodies- may protect broiler chicken health and reduce incidence of secondary necrotic enteritis (Clostridium perfringens) via undetermined mechanisms. Objectives were to use sequencing techniques to evaluate jejunal microbial community composition and function in anti-IL-10-fed broilers during coccidiosis and necrotic enteritis. On d0, Ross 308 chicks were placed in 32 cages (15 chicks/ cage) for a 25-d study and randomly assigned to diets ± 0.03% anti-IL-10. Six chicks/ diet were euthanized for distal jejunum content and tissue collection on d 14 (baseline) before inoculating the remainder with saline or 15,000 E. maxima oocysts (M6 strain). Half the chicks challenged with E. maxima were challenged with C. perfringens (1×108 colony forming units) on d 18 and 19. Follow-up samples (6 chicks/treatment) were collected at 7 and 11 d postinoculation (pi) for the E. maxima-only group, or 3 and 7 dpi for the E. maxima + C. perfringens group with 3/7 dpi being designated as peak and 7/11dpi as postpeak challenge. DNA was extracted from digesta for microbiota composition analysis (16S rRNA gene sequencing) while RNA was extracted from tissue to evaluate the metatranscriptome (RNA sequencing). Alpha diversity and genus relative abundances were analyzed using the diet or challenge main effects with associated interactions (SAS 9.4; P ≤ 0.05). No baseline microbial changes were associated with dietary anti-IL-10. At peak challenge, a diet main effect reduced observed species 36.7% in chicks fed anti-IL-10 vs. control; however, the challenge effect reduced observed species and Shannon diversity 51.2-58.3% and 33.0 to 35.5%, respectively, in chicks challenged with E. maxima ± C. perfringens compared to their unchallenged counterparts (P ≤ 0.05). Low sequencing depth limited metatranscriptomic analysis of jejunal microbial function via RNA sequencing. This study demonstrates that challenge impacted the broiler distal jejunum microbiota more than anti-IL-10 while future research to characterize the microbial metatranscriptome may benefit from investigating other intestinal compartments.

A Comparative Study to Analyze the Validity of Well Water for Some Areas of Eastern and Western Diyala Governorate

General Background: Groundwater is a critical resource for many regions, particularly in arid and semi-arid areas, where surface water is scarce. Specific Background: In Diyala Governorate, Iraq, reliance on groundwater is high, especially in the northeastern and western regions, which use it extensively for domestic and agricultural purposes. Knowledge Gap: Despite its importance, there is limited comprehensive analysis comparing the quality and suitability of groundwater across different areas within the governorate. Aims: This study aims to analyze the chemical and physical properties of groundwater from selected wells in the northeastern (Khanaqin and Saadia) and western (Khalis and Dali Abbas) regions of Diyala Governorate to determine its suitability for use. Results: The analysis revealed a significant increase in nitrate concentrations, particularly in the western regions, with a value of 26.3 mg/L, and higher nitrite levels were also observed. The pH levels indicated a tendency toward acidity in the northeastern regions (7.1-7.2), whereas the western regions showed values closer to the acidity standard (7.05-7.45). The dissolved salts were notably lower in Khalis (575 mg/L). Novelty: The study uniquely highlights the variation in groundwater quality across different regions of the governorate, revealing critical discrepancies in water composition and well depth that have not been previously documented. Implications: These findings underscore the need for region-specific water management strategies in Diyala Governorate, particularly in the eastern regions where groundwater characteristics align more closely with international standards, making them more suitable for sustainable groundwater investment and utilization. Highlights: Significant regional differences in groundwater quality were observed. Higher nitrate/nitrite concentrations found in western regions. Eastern regions' groundwater closer to international standards. Keywords: Groundwater quality, Diyala Governorate, nitrate concentration, pH levels, water suitability

Playing pin-the-tail-on-the-protein in extracellular vesicle (EV) proteomics.

Extracellular vesicles (EVs) are anucleate particles enclosed by a lipid bilayer that are released from cells via exocytosis or direct budding from the plasma membrane. They contain an array of important molecular cargo such as proteins, nucleic acids, and lipids, and can transfer these cargoes to recipient cells as a means of intercellular communication. One of the overarching paradigms in the field of EV research is that EV cargo should reflect the biological state of the cell of origin. The true relationship or extent of this correlation is confounded by many factors, including the numerous ways one can isolate or enrich EVs, overlap in the biophysical properties of different classes of EVs, and analytical limitations. This presents a challenge to research aimed at detecting low-abundant EV-encapsulated nucleic acids or proteins in biofluids for biomarker research and underpins technical obstacles in the confident assessment of the proteomic landscape of EVs that may be affected by sample-type specific or disease-associated proteoforms. Improving our understanding of EV biogenesis, cargo loading, and developments in top-down proteomics may guide us towards advanced approaches for selective EV and molecular cargo enrichment, which could aid EV diagnostics and therapeutics research.

Multi-scale Segformer: image segmentation algorithm for electric vehicles in fire accident

ABSTRACT Electric vehicles (EVs) have played a significant role in sustainability, and EVs fire accidents have raised doubts in recent years. To solve the mobile analytical equipment limitation in EVs fire accident and help staff receive prompt results at on-spot inspection, we provide a lightweight but accurate Transformer that can ideally adapt to the mobile environment. First, we built on the simple Segformer and extended it to aggregate the representations of amorphous objects, such as fire traces, in image recognition. Second, we used shunt-based self-attention (SSA) to enhance the model for capturing multi-scale contextual information and help distinguish the deformed level of EVs after combustion. Third, we redesigned a simple multi-level information aggregation (MIA) decoder to obtain the relationship between pixels in the channel dimensions by a weighted aggregation. Furthermore, to foster image trace recognition, we put forwards and evaluated the accuracy of models on electric vehicle fire traces (EVFTrace), a dataset of images of burnt EVs. On EVFTrace, the mean intersection over union (mIoU) achieves 72.24%. The float point operations (Flops) and parameters (Params) achieve 114.83 G and 89.5 M. Our model shows excellent efficiency and accuracy for burnt EVs segmentation tasks.

The role of functional vitamin D deficiency and low vitamin D reservoirs in relation to cardiovascular health and mortality.

The role of vitamin D deficiency in cardiovascular disease (CVD) is controversial. Inherent biological and analytical limitations compromise the specificity of widely used 25-hydroxyvitamin D [25(OH)D] cut-offs. Simultaneous determination of 25(OH)D and 24,25-dihydroxyvitamin D [24,25(OH)2D] permits a functional assessment of vitamin D metabolism. The present study compared the associations of functional vitamin D deficiency and low vitamin D reservoirs with CVD mortality and CVD burden. 25(OH)D, 24,25(OH)2D, the degree of coronary obstruction on angiography, high-sensitive cardiac troponin T (hs-cTnT), N-terminal brain natriuretic peptide (NT-proBNP), and 10-year CVD mortality were obtained from 2,456 participants of the LURIC (Ludwigshafen Risk and Cardiovascular Health) study. Neither low 25(OH)D concentrations nor functional vitamin D deficiency were associated with the number of atherosclerotic coronary arteries or the degree of coronary obstruction. Over a median follow-up of 9.9 years, 454 participants died (23.6 %) due to CVD. CVD mortality was doubled in individuals with 25(OH)D concentrations below the widely used cut-off for deficiency of <50 nmol/L [20 ng/mL] (21.6 vs. 11.5 %). In individuals with and without functional vitamin D deficiency, CVD mortality was 25.0 and 16.7 %, respectively. NT-proBNP and heart failure prevalence were also higher in vitamin D deficient individuals. Vitamin D deficient individuals have markedly higher CVD mortality, but only marginally higher hs-cTnT concentrations. A higher prevalence of heart failure and higher NT-proBNP concentrations suggest a link between vitamin D deficiency and cardiac function. The traditional and metabolic assessment of vitamin D status showed comparable associations for the different parameters of cardiac health.

Americium-241 in peat bogs as a global marker of the beginning of the Anthropocene: examples from Europe and North America.

Fallout radionuclides, potential global indicators of the Anthropocene, raise questions about their preservation in bogs. This study evaluates the reliability of &lt;sup&gt;241&lt;/sup&gt;Am records in acidic, ombrotrophic peat cores. The &lt;sup&gt;241&lt;/sup&gt;Am source is the radioactive decay of the short-lived &lt;sup&gt;241&lt;/sup&gt;Pu (half-life 14.29 years) present in fallout from atmospheric nuclear weapons testing. We assessed 35 peat cores from 30 European and North American sites, collected over three decades for trace metal deposition studies. Each core underwent independent &lt;sup&gt;210&lt;/sup&gt;Pb dating. We excluded 10 cores due to significant &lt;sup&gt;241&lt;/sup&gt;Am peak quantification uncertainty. Of the remaining 25 cores, 22 displayed a single &lt;sup&gt;241&lt;/sup&gt;Am peak and 3 exhibited two peaks with similar activities. Among the 22 single-peak cores, 18 were within 10 years of AD 1963, with 10 within 5 years. For 10 of these 22 cores, &lt;sup&gt;14&lt;/sup&gt;C dates from the atmospheric bomb pulse curve were compared, showing ≤ 10 years of difference, with 7 cores ≤ 5 years from the &lt;sup&gt;210&lt;/sup&gt;Pb CRS model AD 1963. Factors contributing to age discrepancies included analytical limitations in &lt;sup&gt;241&lt;/sup&gt;Am measurements, peat accumulation rates affecting &lt;sup&gt;241&lt;/sup&gt;Am activities, post-depositional migration of &lt;sup&gt;241&lt;/sup&gt;Pu and/or &lt;sup&gt;241&lt;/sup&gt;Am, uncertainties in &lt;sup&gt;210&lt;/sup&gt;Pb age-modelling, and limited &lt;sup&gt;14&lt;/sup&gt;C samples. The utility of &lt;sup&gt;241&lt;/sup&gt;Am as an Anthropocene marker depends on the accepted uncertainty level in defining this epoch. The detection of &lt;sup&gt;241&lt;/sup&gt;Am in 35 cores, validated in 22 cores, suggests a hemispheric Anthropocene onset. The bomb pulse curve of &lt;sup&gt;14&lt;/sup&gt;C in bogs could serve as an alternative marker. Common &lt;sup&gt;137&lt;/sup&gt;Cs post-depositional migration in ombrotrophic peatlands contrasts with its immobility in minerotrophic peatlands, suggesting the latter may also retain nuclear weapons test fallout, including &lt;sup&gt;241&lt;/sup&gt;Am.

Separation of full, empty, and partial adeno-associated virus capsids via anion-exchange chromatography with continuous recycling and accumulation

The field of recombinant adeno-associated virus (rAAV) gene therapy has attracted increasing attention over decades. Within the ongoing challenges of rAAV manufacturing, the co-production of impurities, such as empty and partial capsids containing no or truncated transgenes, poses a significant challenge. Due to their potential impact on drug efficacy and clinical safety, it is imperative to conduct comprehensive monitoring and characterization of these impurities prior to the release of the final gene therapy product. Nevertheless, existing analytical techniques encounter notable limitations, encompassing low throughput, long turnaround times, high sample consumption, and/or complicated data analysis. Chromatography-based analytical methods are recognized for their current Good Manufacturing Practice (cGMP) alignment, high repeatability, reproducibility, low limit of detection, and rapid turnaround times. Despite these advantages, current anion exchange high pressure liquid chromatography (AEX-HPLC) methods struggle with baseline separation of partial capsids from full and empty capsids, resulting in inaccurate full-to-empty capsid ratio, as partial capsids are obscured within peaks corresponding to empty and full capsids. In this study, we present a unique analytical AEX method designed to characterize not only empty and full capsids but also partial capsids. This method utilizes continuous N-Rich chromatography with recycling between two identical AEX columns for the accumulation and isolation of partial capsids. The development process is comprehensively discussed, covering the preparation of reference materials representing full (rAAV-LacZ), partial (rAAV-GFP), and empty (rAAV-empty) capsids, N-rich method development, fraction analysis, determination of fluorescence response factors between capsid variants, and validation through comparison with other comparative techniques.

Accelerating the Transition to sustainable energy: An intelligent decision support system for generation expansion planning with renewables

Sustainable energy planning for power generation with renewables requires a multidimensional approach since it directly impacts several sectors such as the national grid, the environment, and the economy. This paper suggests an intelligent decision based on a combination of the Analytical Hierarchy Process and Artificial Neural Networks to assist policymakers in evaluating future renewable energy investments for power generation. A case study of Bahrain's power system is used to illustrate the usefulness of the proposed planning approach and also to discuss its efficiency. The Analytical Hierarchy Process model outcomes revealed that wind turbines are the most appropriate technology for Bahrain with 32.5 % priority, followed by PV and CSP with 32.2 % and 16.3 %, respectively. Then, the Artificial Neural Networks model was structured based on the generated scenarios from the Analytical Hierarchy Process model, and it reached its best performance after 384 cycles. The integrated approach overcomes some of the Analytical Hierarchy Process's limitations and provides an intelligent tool for advanced assessment of a sustainable power system.

Ligand Screening of Membrane Proteins Embedded in Nanodiscs: How to Manage Non-Specific Interactions in Weak Affinity Chromatography?

Miniaturized weak affinity chromatography is emerging as an interesting alternative to conventional biophysical tools for performing fragment-screening studies in the context of fragment-based drug discovery. In order to push back the analytical limits, it is necessary not only to control non-specific interactions with chromatographic support, but also to adapt this methodology by comparing the results obtained on an affinity column to a control column. The work presented in this study focused on fragment screening that targets a model membrane protein, the adenosine A2A receptor, embedded in nanodiscs (NDs) as biomimetic membranes. By studying the retention behavior of test fragment mixtures on supports modified with different types of NDs, we were able to determine the contribution of ND-related non-specific interactions, in particular the electrostatic effect of anionic phospholipids and the hydrophobic effect of neutral phospholipids. Different strategies for the preparation of control columns (empty NDs, orthosteric site blocking) were investigated and are presented for the first time. With these two types of control columns, the screening enabled the identification of two new fragments of AA2AR, which were confirmed by competition experiments and whose Kd values, estimated directly during the screening or after the competition experiments in frontal mode, were in good agreement.

A Method for Determining Incorporation Depth in Core-Shell UiO-66 Nanoparticles Synthesized Via Postsynthetic Exchange.

Postsynthetic exchange (PSE) is a key technique for integrating sensitive linkers into metal-organic frameworks (MOFs). Despite its importance, investigations into linker distributions have primarily focused on micrometer-sized crystals due to the analytical limitations, leaving nanoparticles less explored, although they are commonly synthesized and used in applications. In particular, the emergence of core-shell nanostructures via PSE has shown potential for applications in CO2 adsorption and selective catalysis. This study addresses this gap by investigating the formation of core-shell structures on nanoparticles under diffusion-controlled PSE conditions. By analyzing volume-to-surface ratios and conducting time-dependent experiments, we confirmed that these conditions facilitate the development of core-shell architectures. We also developed a straightforward method to calculate the minimum incorporation depth using basic parameters such as particle size and the total amount of incorporated linker. The accuracy of our approach was validated against data obtained from transmission electron microscopy coupled with energy-dispersive X-ray spectroscopy. These findings enhance the understanding of PSE in MOF nanoparticles and open up promising avenues for developing advanced MOF core-shell structures for various applications.

Evidence of RNA polymerase III recruitment and transcription at protein-coding gene promoters.

RNA polymerase (Pol) I, II, and III are most commonly described as having distinct roles in synthesizing ribosomal RNA (rRNA), messenger RNA (mRNA), and specific small noncoding (nc)RNAs, respectively. This delineation of transcriptional responsibilities is not definitive, however, as evidenced by instances of Pol II recruitment to genes conventionally transcribed by Pol III, including the co-transcription of RPPH1 - the catalytic RNA component of RNase P. A comprehensive understanding of the interplay between RNA polymerase complexes remains lacking, however, due to limited comparative analyses for all three enzymes. To address this gap, we applied a uniform framework for quantifying global Pol I, II, and III occupancies that integrates currently available human RNA polymerase ChIP-seq datasets. Occupancy maps are combined with a comprehensive multi-class promoter set that includes protein-coding genes, noncoding genes, and repetitive elements. While our genomic survey appropriately identifies recruitment of Pol I, II, and III to canonical target genes, we unexpectedly discover widespread recruitment of the Pol III machinery to promoters of specific protein-coding genes, supported by colocalization patterns observed for several Pol III-specific subunits. We show that Pol III-occupied Pol II promoters are enriched for small, nascent RNA reads terminating in a run of 4 Ts, a unique hallmark of Pol III transcription termination and evidence of active Pol III activity at these sites. Pol III disruption differentially modulates the expression of Pol III-occupied coding genes, which are functionally enriched for ribosomal proteins and genes broadly linked to unfavorable outcomes in cancer. Our map also identifies additional, currently unannotated genomic elements occupied by Pol III with clear signatures of nascent RNA species that are sensitive to disruption of La (SSB) - a Pol III-related RNA chaperone protein. These findings reshape our current understanding of the interplay between Pols II and III and identify potentially novel small ncRNAs with broad implications for gene regulatory paradigms and RNA biology.

Exploring the potential of representation and transfer learning for anatomical neuroimaging: Application to psychiatry

The perspective of personalized medicine for brain disorders requires efficient learning models for anatomical neuroimaging-based prediction of clinical conditions. There is now a consensus on the benefit of deep learning (DL) in addressing many medical imaging tasks, such as image segmentation. However, for single-subject prediction problems, recent studies yielded contradictory results when comparing DL with Standard Machine Learning (SML) on top of classical feature extraction. Most existing comparative studies were limited in predicting phenotypes of little clinical interest, such as sex and age, and using a single dataset. Moreover, they conducted a limited analysis of the employed image pre-processing and feature selection strategies. This paper extensively compares DL and SML prediction capacity on five multi-site problems, including three increasingly complex clinical applications in psychiatry namely schizophrenia, bipolar disorder, and Autism Spectrum Disorder (ASD) diagnosis. To compensate for the relative scarcity of neuroimaging data on these clinical datasets, we also evaluate three pre-training strategies for transfer learning from brain imaging of the general healthy population: self-supervised learning, generative modeling and supervised learning with age. Overall, we find similar performance between randomly initialized DL and SML for the three clinical tasks and a similar scaling trend for sex prediction. This was replicated on an external dataset. We also show highly correlated discriminative brain regions between DL and linear ML models in all problems. Nonetheless, we demonstrate that self-supervised pre-training on large-scale healthy population imaging datasets (N≈10k), along with Deep Ensemble, allows DL to learn robust and transferable representations to smaller-scale clinical datasets (N≤1k). It largely outperforms SML on 2 out of 3 clinical tasks both in internal and external test sets. These findings suggest that the improvement of DL over SML in anatomical neuroimaging mainly comes from its capacity to learn meaningful and useful abstract representations of the brain anatomy, and it sheds light on the potential of transfer learning for personalized medicine in psychiatry

Application of C(t)-integral Solutions in Extending ASME BPVC Section Xi Division 2 Code Case N-934 for Transient Creep Crack Growth

Abstract Creep crack growth is a phenomenon which arises in damaged metallics structures under combined primary and secondary loads in the creep regime. The High Temperature Flaw Evaluation Code Committee of ASME BPVC is evaluating methods in extending Code Case N-934 to capture transient creep crack growth. This paper provides context and basic examples on the selected approach to transient creep crack growth methods based on the C(t)-integral. The basis for selected C(t)-integral solution as analytical method is established, including an overview of its derivation. Practical assessments of a crack growing under creep conditions in a realistic component are conducted to illustrate the analytical approach. Additional considerations in the application of the analytical methods and limitations are discussed.

Developing a residential occupancy schedule generator based on smart thermostat data

Occupancy patterns play a major role in residential buildings’ energy demand. This role is essential to be represented realistically in urban-scale energy simulations with a focus on matching the supply of renewable energy to demand. However, the lack of large-scale datasets that represent the seasonality and dynamic nature of occupancy, especially within the residential sector limited such analysis. Recently, the fast adoption of smart thermostats, featuring passive infrared sensors for motion detection, in residential buildings has allowed for extracting more representative occupancy information for different applications. To this end, this study introduces an open-source Python package to generate large-scale hourly occupancy profiles for residential buildings based on smart thermostat readings. The package takes advantage of the Donate Your Data (DYD) dataset by Ecobee to develop a rule-based framework that addresses the limitations of relying on motion-detection data to represent the whole-building occupancy. The framework was applied to over 8000 Canadian households as a case study. The generated profiles for these buildings are validated by comparing them with residential occupancy profiles generated from the Canadian Time Use Survey (TUS). The results showed that both profiles were statistically similar with a 3 % difference in the aggregated daily occupied hours. Finally, the diversity of the generated profiles before and after the COVID-19 pandemic is investigated to demonstrate the usefulness of the tool. The results proved the potential of the developed package to generate realistic and diverse occupancy schedules for the residential sector.