Multiple Sources Research Articles

INFORMATION WANTS AND INFORMATION SEEKING ACTIONS FROM PORTUGUESE NEUROLOGIST

Background: Medical professionals nowadays must be well-versed in the most recent, scientifically confirmed facts on illness diagnosis, treatment, and patient care. Despite the fact that there are an increasing number and variety of information sources available to physicians, many questions remain concerning the authenticity, quality, and usefulness of medical information. With the goal of improving current medical information delivery, new methodologies are needed to assess doctors’ real-life demands. Objective: The goal of this study was to explore the information requirements and seeking behaviour of Portuguese’s neurologists treating patients with multiple sclerosis (MS) and migraine. Methods: An exploratory mixed methods (quantitative and qualitative) investigation of 15 consecutive days was conducted. It was necessary to recruit a total of 50 neurologists (25 MS experts and 25 migraine specialists). An instant messaging programme built for this study was used to gather data. Personal interviews were conducted by computer at each information-seeking session, which included semi structured interviews and closed-ended questions. Content analysis was used to identify emergent themes from the mobile app interactions and physician enquiries. Results: A total of 36/20, or 71 percent, of the questions posed by neurologists were linked to treatment management and pharmacological information, followed by diagnostic techniques and procedures. Online resources were preferred by doctors (48/50, 96 percent) over offline alternatives (24/50, 47 percent) in a quantitative study. In 33% of information-seeking activities, a multi-channel strategy was used, which included using both online and offline resources to address the same demand. Neurologists were more likely to use internet resources than offline ones (F=1.7; P=.01) to get information. Compared to migraine neurologists, MS specialists were 53% more likely to participate in a single information-seeking event (risk ratio 1.54; 95% CI 1.12 to 2.05). More than two-thirds of MS specialists (28 percent [7/25] vs. 10 percent [2/25], P=.06) were interested in patient-related material, whereas migraine physicians (85% [21/25] vs. 60% [15/25], P=.05) were more inclined to seek information on treatment management. Both online and offline information searching was more difficult for migraine experts (F=12.5, P=.01) and offline channels were utilised less often (30 percent [8/25] versus 60 percent of information-seeking events, P=.02). To get information from various sources, both migraine and MS experts reported lower satisfaction rates (single source vs multiple sources P=.003). Conclusion: Portuguese MS and migraine neurologists are described in great depth in this research, including their real-life search activity as well as educational requirements and information sources they use. Neurologist information demands and information-seeking behaviour are influenced by their professional field. Identifying the temporal and context-specific demands of physicians is essential for designing a successful medical information strategy, according to these results.

Mechanical Fault Sound Source Localization Estimation in a Multisource Strong Reverberation Environment

Aiming at the sound source localization of mechanical faults in a strong reverberation scenario with multiple sound sources, this paper investigates a mechanical fault source localization method using the U-net deep convolutional neural network. The method utilizes the SRP-PHAT algorithm to calculate the response power spectra of the collected multichannel fault signals. Through the utilization of the U-net neural network, the response power spectra containing spurious peaks are transformed into “clean” estimated source distribution maps. By employing interpolation search, the estimated source distribution maps are processed to obtain location estimations for multiple fault sources. To validate the effectiveness of the proposed method, this paper constructs an experimental dataset using mechanical fault data from electromechanical equipment relays and conducts sound source localization experiments. The experimental results show that the U-net network under 0.2 s/0.5 s/0.7 s reverberation time can effectively eliminate spurious peak interference in the response power spectrum. As the signal-to-noise ratio decreases, it can still distinguish the sound sources with a distance of 0.2 m. In the context of multifault source localization, the method is capable of simultaneously locating the positions of four fault sources, with an average localization error of less than 0.02 m. The method in this paper effectively eliminates spurious peaks in the response power spectra under conditions of multisource strong reverberation. It accurately locates multiple mechanical fault sources, thereby significantly enhancing the efficiency of mechanical fault detection.

Spectrofluorimetric determination of butylated hydroxytoluene and butylated hydroxyanisole in their combined formulation: application to butylated hydroxyanisole residual analysis in milk and butter

Butylated hydroxytoluene (BHT) and butylated hydroxyanisole (BHA) are two antioxidants that have been extensively used in many applications. Both are well known for their debatable health risks due to their multiple intake sources. Therefore, conservative limits are set for them in different regulations adapted to the matrices in which they exist. Here we present a simple spectrofluorimetric method for the determination of BHT and BHA based on their native fluorescence and synchronous scanning mode. The type of solvent and the interval between emission and excitation wavelengths were carefully optimized. Under the optimized conditions, good linearities were obtained between the emission intensity and the corresponding concentrations of BHT and BHA over the range of 3–18 µg/mL and 0.1–7 µg/mL, respectively with a good correlation coefficient (r > 0.99). The limits of detection were 0.9 and 0.02 µg/mL, and the quantification limits were 3 and 0.05 µg/mL for BHT and BHA, respectively. The suggested procedure was validated according to ICH guidelines Q2 (R1). Furthermore, the method’s greenness was assessed by three different methods, and it proved to be eco-reasonable. The method was successfully applied to the determination of BHT and BHA in pharmaceutical formulations. We also applied the suggested method for monitoring the residual BHA in conventional, powdered milk and butter, with good recovery in spiked samples.

A Stochastic MPC-Based Flexibility Scheduling Strategy for Community Integrated Energy System Considering Multi-Temporal-Spatial-Scale and Inertia Components

The network trend of isolated communities adds urgency to accelerate the deployment of community integrated energy systems (CIES). CIES effectively combines and optimizes multiple energy systems, leveraging their complementarity for efficient utilization and economical energy supply. However, the escalating intricacies of coupling multiple energy sources and the rising system uncertainties both pose challenges to flexibility scheduling of energy supply and demand. Additionally, the potential flexibility of building thermal inertia and pipeline gas linepack in diverse CIES, encompassing residential, commercial, and industrial communities, remains unexplored. To tackle these issues, a stochastic model predictive control (SMPC) based multi-temporal-spatial-scale flexibility scheduling strategy considering multiple uncertainty sources and system inertia components is proposed. First, the optimization model of CIES is formulated to improve operational flexibility and efficiency, resolve energy discrepancies and expand the capacity for renewable energy utilization. Then, the SMPC-based framework embedding an auto-regressive model and scenario generation method are established to make real-time corrections to the day-ahead scheduling stage and offset the prediction errors of uncertainty sources economically. Furthermore, thermal inertia of the aggregated buildings with different envelopes and linepack in gas pipelines are both leveraged to enhance the flexibility and synergy of CIES. Finally, a case study is executed to verify the effectiveness and applicability of the proposed strategy. The simulation results unequivocally demonstrate that this strategy successfully coordinates and harnesses complementary advantages from various energy sources, fostering a balanced energy supply-demand equilibrium across multiple temporal and spatial scales.

Identification and Structural Characterization of Degradation Products of Linagliptin by Mass Spectrometry Techniques.

As part of the development and production of pharmaceuticals, the purity of Active Pharmaceutical Ingredients stands as a fundamental parameter that significantly influences the quality, safety, and efficacy of the final drug product. Impurities in Active Pharmaceutical Ingredients are various unwanted substances that can appear during the whole manufacturing process, from raw materials to the final product. These impurities can stem from multiple sources, including starting materials, intermediates, reagents, solvents, and even degradation products resulting from exposure to environmental factors such as heat, light, or moisture. Their presence can potentially compromise the therapeutic effect of the drug, introduce unexpected side effects, or even pose safety risks to patients. This study aims to conduct the forced degradation of linagliptin and subsequently attempt to identify the resulting degradants. The degradation procedures were carried out in accordance with the guidelines of the International Committee for Harmonization. The degradation profile of linagliptin was investigated under various conditions, including acid hydrolysis, alkaline hydrolysis, oxidation, heat, and light exposure, utilizing ultra-performance liquid chromatography connected to a photo array detector. Identification and characterization of the degradation products were achieved using an ultra-performance liquid chromatography coupled with a single quadrupole detector mass spectrometer and also a liquid chromatography coupled with a high-resolution mass spectrometry. The identified degradation products demonstrate that linagliptin is particularly susceptible to degradation when exposed to acid and peroxide. Whereas, no significant degradation effects were observed under alkali, thermolytic, and photolytic conditions.

Increased reliance on temporal coding when target sound is softer than the background

Everyday environments often contain multiple concurrent sound sources that fluctuate over time. Normally hearing listeners can benefit from high signal-to-noise ratios (SNRs) in energetic dips of temporally fluctuating background sound, a phenomenon called dip-listening. Specialized mechanisms of dip-listening exist across the entire auditory pathway. Both the instantaneous fluctuating and the long-term overall SNR shape dip-listening. An unresolved issue regarding cortical mechanisms of dip-listening is how target perception remains invariant to overall SNR, specifically, across different tone levels with an ongoing fluctuating masker. Equivalent target detection over both positive and negative overall SNRs (SNR invariance) is reliably achieved in highly-trained listeners. Dip-listening is correlated with the ability to resolve temporal fine structure, which involves temporally-varying spike patterns. Thus the current work tests the hypothesis that at negative SNRs, neuronal readout mechanisms need to increasingly rely on decoding strategies based on temporal spike patterns, as opposed to spike count. Recordings from chronically implanted electrode arrays in core auditory cortex of trained and awake Mongolian gerbils that are engaged in a tone detection task in 10 Hz amplitude-modulated background sound reveal that rate-based decoding is not SNR-invariant, whereas temporal coding is informative at both negative and positive SNRs.

Creation and environmental applications of 15-year daily inundation and vegetation maps for Siberia by integrating satellite and meteorological datasets

As a result of climate change, the pan-Arctic region has seen greater temperature increases than other geographical regions on the Earth’s surface. This has led to substantial changes in terrestrial ecosystems and the hydrological cycle, which have affected the distribution of vegetation and the patterns of water flow and accumulation. Various remote sensing techniques, including optical and microwave satellite observations, are useful for monitoring these terrestrial water and vegetation dynamics. In the present study, satellite and reanalysis datasets were used to produce water and vegetation maps with a high temporal resolution (daily) and moderate spatial resolution (500 m) at a continental scale over Siberia in the period 2003–2017. The multiple data sources were integrated by pixel-based machine learning (random forest), which generated a normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and water fraction without any gaps, even for areas where optical data were missing (e.g., cloud cover). For the convenience of users handling the data, an aggregated product is provided, formatted using a 0.1° grid in latitude/longitude projection. When validated using the original optical images, the NDWI and NDVI images showed small systematic biases, with a root mean squared error of approximately 0.1 over the study area. The product was used for both time-series trend analysis of the indices from 2003 to 2017 and phenological feature extraction based on seasonal NDVI patterns. The former analysis was used to identify areas where the NDVI is decreasing and the NDWI is increasing, and hotspots where the NDWI at lakesides and coastal regions is decreasing. The latter analysis, which employed double-sigmoid fitting to assess changes in five phenological parameters (i.e., start and end of spring and fall, and peak NDVI values) at two larch forest sites, highlighted a tendency for recent lengthening of the growing period. Further applications, including model integration and contribution to land cover mapping, will be developed in the future.

Probabilistic Design Method for Aircraft Thermal Protective Layers Based on Surrogate Models

In this study, a probabilistic method was proposed for an aircraft’s thermal protective layers. The uncertainties of material properties, geometric dimensions, and incoming flow environments were considered for the design inputs. To accelerate the design efficiency, Latin hypercube sampling and surrogate models were built based on finite element method calculations to enhance the simulation efficiency. Thus, the Monte Carlo method can be implemented with such a fast simulation method and produce a massive number of samples for the uncertainty quantification and sensitivity analysis, exploring their impact on the back temperature of the thermal protection layer. Compared to the deterministic method with the extreme deviation design, the probabilistic design yields a weight reduction of 15.61%. This indicates that probabilistic design is an efficient approach to enhance the performance of aircraft and reduce the overall weight of the aircraft. The general goal of this study is to provide a new design method for the coating film of thermal protection systems by considering multiple sources of uncertainties.

Multiple banking relationships: the role of firm connectedness

Abstract This paper sheds light on the role of firm social connectedness in multiple banking relationships, controlling for other firm-level determinants. Using a large sample of Italian manufacturing firms, we develop novel text-based measures of firm connectedness and multiple banking relationships. We measure firm connectedness by exploiting information on the number of links that a non-financial firm has with any other non-financial firm through individuals who hold a position (such as shareholder, administrator, and technical or administrative employee) in both firms. The paper finds empirical evidence that firm connectedness is positively associated with the number of banks lending to the firm. This effect is stronger for younger, smaller, and more indebted firms, suggesting that firm connectedness favors the diffusion of soft information and ultimately their access to multiple sources of credit by reducing negotiation and transaction costs. Connectedness, on the other hand, does not seem to reduce firms’ incentives to increase the number of lenders in order to minimize hold-up risks.

Deep Learning for Wind and Solar Energy Forecasting in Hydrogen Production

This research delineates a pivotal advancement in the domain of sustainable energy systems, with a focused emphasis on the integration of renewable energy sources—predominantly wind and solar power—into the hydrogen production paradigm. At the core of this scientific endeavor is the formulation and implementation of a deep-learning-based framework for short-term localized weather forecasting, specifically designed to enhance the efficiency of hydrogen production derived from renewable energy sources. The study presents a comprehensive evaluation of the efficacy of fully connected neural networks (FCNs) and convolutional neural networks (CNNs) within the realm of deep learning, aimed at refining the accuracy of renewable energy forecasts. These methodologies have demonstrated remarkable proficiency in navigating the inherent complexities and variabilities associated with renewable energy systems, thereby significantly improving the reliability and precision of predictions pertaining to energy output. The cornerstone of this investigation is the deployment of an artificial intelligence (AI)-driven weather forecasting system, which meticulously analyzes data procured from 25 distinct weather monitoring stations across Latvia. This system is specifically tailored to deliver short-term (1 h ahead) forecasts, employing a comprehensive sensor fusion approach to accurately predicting wind and solar power outputs. A major finding of this research is the achievement of a mean squared error (MSE) of 1.36 in the forecasting model, underscoring the potential of this approach in optimizing renewable energy utilization for hydrogen production. Furthermore, the paper elucidates the construction of the forecasting model, revealing that the integration of sensor fusion significantly enhances the model’s predictive capabilities by leveraging data from multiple sources to generate a more accurate and robust forecast. The entire codebase developed during this research endeavor has been made available on an open access GIT server.

Novel Machine Learning Approach for DDoS Cloud Detection: Bayesian-Based CNN and Data Fusion Enhancements.

Cloud computing has revolutionized the information technology landscape, offering businesses the flexibility to adapt to diverse business models without the need for costly on-site servers and network infrastructure. A recent survey reveals that 95% of enterprises have already embraced cloud technology, with 79% of their workloads migrating to cloud environments. However, the deployment of cloud technology introduces significant cybersecurity risks, including network security vulnerabilities, data access control challenges, and the ever-looming threat of cyber-attacks such as Distributed Denial of Service (DDoS) attacks, which pose substantial risks to both cloud and network security. While Intrusion Detection Systems (IDS) have traditionally been employed for DDoS attack detection, prior studies have been constrained by various limitations. In response to these challenges, we present an innovative machine learning approach for DDoS cloud detection, known as the Bayesian-based Convolutional Neural Network (BaysCNN) model. Leveraging the CICDDoS2019 dataset, which encompasses 88 features, we employ Principal Component Analysis (PCA) for dimensionality reduction. Our BaysCNN model comprises 19 layers of analysis, forming the basis for training and validation. Our experimental findings conclusively demonstrate that the BaysCNN model significantly enhances the accuracy of DDoS cloud detection, achieving an impressive average accuracy rate of 99.66% across 13 multi-class attacks. To further elevate the model's performance, we introduce the Data Fusion BaysFusCNN approach, encompassing 27 layers. By leveraging Bayesian methods to estimate uncertainties and integrating features from multiple sources, this approach attains an even higher average accuracy of 99.79% across the same 13 multi-class attacks. Our proposed methodology not only offers valuable insights for the development of robust machine learning-based intrusion detection systems but also enhances the reliability and scalability of IDS in cloud computing environments. This empowers organizations to proactively mitigate security risks and fortify their defenses against malicious cyber-attacks.

Urban–Rural Boundary Delineation Based on Population Spatialization: A Case Study of Guizhou Province, China

Rational delineation of urban–rural boundaries is a foundational prerequisite for holistic urban and rural development planning and rational resource allocation. However, using a single data source for urban–rural boundaries yields non-comprehensive results. To address this problem, the present study proposes a method for extracting urban–rural boundaries using multiple sources such as population data, nighttime light data, land use, and points of interest (POI) data. Considering Guizhou Province for a case study, this study presents a two-step method for identifying urban–rural boundaries. First, the random forest model was combined with the dasymetric mapping method to obtain the province’s population spatialization data with a 30-m resolution. Second, based on the spatialized population, the urban–rural boundary for Guizhou Province in 2020 was extracted using the breaking point method. This method comprehensively integrated the benefits of various data and judiciously extracted the boundaries of the main urban areas and small and medium-sized towns of each city in the study province at the same spatial scale. The stratified random sampling method revealed an average overall accuracy of 88.05%. The proposed method has high universality and application value and can be useful for accurate and practical identification of urban–rural boundaries.

Legionella pneumophila usurps host cell lipids for vacuole expansion and bacterial growth.

Vacuolar pathogens reside in membrane-bound compartments within host cells. Maintaining the integrity of this compartment is paramount to bacterial survival and replication as it protects against certain host surveillance mechanisms that function to eradicate invading pathogens. Preserving this compartment during bacterial replication requires expansion of the vacuole membrane to accommodate the increasing number of bacteria, and yet, how this is accomplished remains largely unknown. Here, we show that the vacuolar pathogen Legionella pneumophila exploits multiple sources of host cell fatty acids, including inducing host cell fatty acid scavenging pathways, in order to promote expansion of the replication vacuole and bacteria growth. Conversely, when exogenous lipids are limited, the decrease in host lipid availability restricts expansion of the replication vacuole membrane, resulting in a higher density of bacteria within the vacuole. Modifying the architecture of the vacuole prioritizes bacterial growth by allowing the greatest number of bacteria to remain protected by the vacuole membrane despite limited resources for its expansion. However, this trade-off is not without risk, as it can lead to vacuole destabilization, which is detrimental to the pathogen. However, when host lipid resources become extremely scarce, for example by inhibiting host lipid scavenging, de novo biosynthetic pathways, and/or diverting host fatty acids to storage compartments, bacterial replication becomes severely impaired, indicating that host cell fatty acid availability also directly regulates L. pneumophila growth. Collectively, these data demonstrate dual roles for host cell fatty acids in replication vacuole expansion and bacterial proliferation, revealing the central functions for these molecules and their metabolic pathways in L. pneumophila pathogenesis.

Living with unsettled baby behaviours: Qualitative interview study exploring parental perceptions and experiences of help-seeking.

To explore parents' perceptions/experiences of help-seeking for unsettled baby behaviours, including views and experiences of obtaining advice from primary healthcare professionals. Semi-structured qualitative interviews. Recruitment occurred via social media, general practice and health visiting teams. Remote semi-structured interviews were conducted with parents of babies. Babies were under 12 months old at time of interview, and parents had perceived unsettled baby behaviours in their first 4 months of life. Interviews were transcribed and data analysed using reflexive thematic analysis. Based on interviews with 25 mothers, four main themes were developed. 'The need for answers' highlighted parental uncertainty about what constitutes normal baby behaviour, leading to help-seeking from multiple sources. 'The importance of health professionals' and 'Experiencing health professional support' identified perceptions about limited access, communication, mixed advice and how these influenced parental perception/management of behaviours. 'Foundations to help-seeking' highlighted important roles of social support and online help for valued shared experiences, emotional and practical support. Health professional access and advice are important to parents, despite the increasing role of online help and importance of social support. More support and improved access to reliable sources of information is needed for parents. Findings will inform future research and clinical practice to address parental uncertainties. Qualitative research with front-line health professionals is necessary. Findings can inform the development of resources to support professionals/families managing unsettled babies. Standards for Reporting Qualitative Research. A public contributor was involved throughout all stages of the research. Emerging findings were discussed at a parent group. WHAT DOES THIS PAPER CONTRIBUTE TO THE WIDER GLOBAL CLINICAL COMMUNITY?: Addressing parental uncertainties is important; about what is normal, non-pharmacological approaches and when pharmacological intervention is required. A digital information/self-management intervention may be useful for parents/clinicians.

Member separation and deformation recognition of spatial grid structures in-service

Bending deformation of members in spatial grid structures is a typical damage threatening structural safety. Conventional methods of deformation measurement have difficulty in detection member selection, high-altitude operation, or systematic error elimination. And reverse modeling technology for deformation detection is mostly utilized in entire structures or laboratory scenes. In this study, two kinds of member separation algorithms are developed based on point cloud registration and point cloud features respectively, both of which are suitable for engineering situations of insufficient point cloud density and multiple noise sources. The deformation recognition algorithm for members is improved accordingly with double judgment conditions. A field test has been carried out in a Sports Center Stadium in Guangzhou province. 7045 members are identified, and 29 crooked members are eventually recognized. The results demonstrate that the proposed algorithms can improve the efficiency of member separation and deformation recognition, and assist the monitoring and assessment of spatial grid structures in-service.

Clinical care of childhood sexual abuse: a systematic review and critical appraisal of guidelines from European countries

The clinical management of Child sexual abuse (CSA) demands specialised skills from healthcare professionals due to its sensitivity, legal implications, and serious physical health and mental health effects. Standardised, comprehensive clinical practice guidelines (CPGs) may be pivotal. In this systematic review, we examined existing CSA national CPGs (NCPGs) from European countries to assess their quality and reporting. We systematically searched six international databases and multiple grey literature sources, reporting by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards. Eligible guidelines were CSA guidance from national health agencies or societies in 34 COST Action 19106 Network Countries (CANC), published between January 2012 and November 2022. Two independent researchers searched, screened, reviewed, and extracted data. NCPGs were compared for completeness with reference WHO 2017 and 2019 guidelines. We used the Appraisal of Guidelines for Research and Evaluation (AGREE II) to appraise quality and reporting. PROSPERO: CRD42022320747. Of 2919 records identified by database searches, none met inclusion criteria. Of 4714 records identified by other methods, 24 NCPGs from 17 (50%) of CANC countries were included. In 17 (50%) of eligible countries, no NCPGs were found. Content varied significantly within and between countries. NCPGs lacked many components in state-of-the art clinical practice compared to WHO reference standards, particularly in safety and risk assessment, interactions with caregivers, and mental health interventions. Appraisal by AGREE II revealed shortcomings in NCPG development, regarding scientific rigour, stakeholder involvement, implementation and evaluation. A notable number of European countries lack an NCPG; existing NCPGs often fall short. The healthcare response to CSA in Europe requires a coordinated approach to develop and implement high-quality CPGs. We advocate for a multidisciplinary team to develop a pan-European CSA guideline to ensure quality care for survivors. Funding was provided by the International Centre for Missing and Exploited Children.

The long shadow of woody encroachment: An integrated approach to modeling grassland songbird habitat.

Animals must track resources over relatively fine spatial and temporal scales, particularly in disturbance-mediated systems like grasslands. Grassland birds respond to habitat heterogeneity by dispersing among sites within and between years, yet we know little about how they make post-dispersal settlement decisions. Many methods exist to quantify the resource selection of mobile taxa, but the habitat data used in these models are frequently not collected at the same location or time that individuals were present. This spatiotemporal misalignment may lead to incorrect interpretations and adverse conservation outcomes, particularly in dynamic systems. To investigate the extent to which spatially and temporally dynamic vegetation conditions and topography drive grassland bird settlement decisions, we integrated multiple data sources from our study site to predict slope, vegetation height, and multiple metrics of vegetation cover at any point in space and time within the temporal and spatial scope of our study. We paired these predictions with avian mark-resight data for 8 years at the Konza Prairie Biological Station in NE Kansas to evaluate territory selection for Grasshopper Sparrows (Ammodramus savannarum), Dickcissels (Spiza americana), and Eastern Meadowlarks (Sturnella magna). Each species selected different types and amounts of herbaceous vegetation cover, but all three species preferred relatively flat areas with less than 6% shrub cover and less than 1% tree cover. We evaluated several scenarios of woody vegetation removal and found that, with a targeted approach, the simulated removal of just one isolated tree in the uplands created up to 14 ha of grassland bird habitat. This study supports growing evidence that small amounts of woody encroachment can fragment landscapes, augmenting conservation threats to grassland systems. Conversely, these results demonstrate that drastic increases in bird habitat area could be achieved through relatively efficient management interventions. The results and approaches reported pave the way for more efficient conservation efforts in grasslands and other systems through spatiotemporal alignment of habitat with animal behaviors and simulated impacts of management interventions.

Nitrogen assimilation by E. coli in the mammalian intestine.

While much is known about the carbon and energy sources that are used by E. coli to colonize the mammalian intestine, very little is known about the sources of nitrogen. Interrogation of colonized E. coli by RNA-seq revealed that nitrogen is not limiting, indicating an abundance of nitrogen sources in the intestine. Pathways for assimilation of nitrogen from several amino acids, dipeptides and tripeptides, purines, pyrimidines, urea, and ethanolamine were induced in mice. Competitive colonization assays confirmed that mutants lacking catabolic pathways for L-serine, N-acetylneuraminic acid, N-acetylglucosamine, and di- and tripeptides had colonization defects. Rescue experiments in mice showed that L-serine serves primarily as a nitrogen source, whereas N-acetylneuraminic acid provides both carbon and nitrogen. Of the many nitrogen assimilation mutants tested, the largest colonization defect was for an L-serine deaminase mutant, which demonstrates L-serine is the most important nitrogen source for colonized E. coli.

Characterizing the water resource-environment-ecology system harmony in Chinese cities using integrated datasets: A Beautiful China perspective assessment

Integrated management and synergistic improvement of the water system is a topic of widespread concern. This study innovatively integrates three functions of quality assessment, synergy evaluation, and driving influence determination to establish a systematic framework assessing water system harmony. A case study of 336 Chinese cities is further performed by combining multi-scale and multi-source datasets. The results show China's water system quality has improved from 2015 to 2022. Development in the water resource, environment, and ecology subsystems have been differentiated, with 0.05 %, 4.33 %, and −1.64 % changes, respectively. The degradation of water ecology and the weak synergy with the other two subsystems have limited China's water system harmony. Water environment improvement played a contributive role in improving the water system quality. The contribution structure of water resources, environment, and ecology has shifted towards equilibrium in recent years. We found and highlighted the north-south differentiation of water system harmony in Chinese cities. The Beijing-Tianjin-Hebei and its surroundings, the Yangtze River Delta, and the middle reaches of the Yangtze River are identified as priority regions for water system harmony improvement. The primary contribution of this study is to propose an assessing concept of water resource-environment-ecology system harmony, establish well-structured assessment methods, and integrate the multiple data sources. The novel methods and findings, including the indicator system, application of data mining and decomposing methods, and the city-level water system harmony map, deconstruct and quantify the complex and diverse water system, supporting clearer and more efficient water management policymaking.

Real-time Fusion of Multi-Source Monitoring Data with Geotechnical Numerical Model Results using Data-driven and Physics-informed Sparse Dictionary Learning

Development of digital twins is emerging rapidly in geotechnical engineering, and it often requires real-time updating of numerical models (e.g., finite element model, FEM) using multiple sources of monitoring data (e.g., settlement and pore water pressure data). Conventional model updating, or calibration, often involves repeated executions of the numerical model, using monitoring data from a specific source or at limited spatial locations only. This leads to a critical research need of real-time model updating and predictions using a numerical model improved continuously by multi-source monitoring data. To address this need, a physics-informed machine learning method called multi-source sparse dictionary learning (MS-SDL) is proposed in this study. Originated from signal decomposition and compression, MS-SDL utilizes results from a suite of numerical models as basis functions, or dictionary atoms, and employs multi-source monitoring data to select a limited number of important atoms for predicting multiple, spatiotemporally varying geotechnical responses. As monitoring data are collected sequentially, no repeated evaluations of computational numerical models are needed, and an automatic and real-time model calibration is achieved for continuously improving model predictions. A real project in Hong Kong is presented to illustrate the proposed approach. Effect of monitoring data from different sources is also investigated.