Two-tiered Approach Research Articles

Estimating the likelihood of epilepsy from clinically noncontributory electroencephalograms using computational analysis: A retrospective, multisite case-control study.

This study was undertaken to validate a set of candidate biomarkers of seizure susceptibility in a retrospective, multisite case-control study, and to determine the robustness of these biomarkers derived from routinely collected electroencephalography (EEG) within a large cohort (both epilepsy and common alternative conditions such as nonepileptic attack disorder). The database consisted of 814 EEG recordings from 648 subjects, collected from eight National Health Service sites across the UK. Clinically noncontributory EEG recordings were identified by an experienced clinical scientist (N = 281; 152 alternative conditions, 129 epilepsy). Eight computational markers (spectral [n = 2], network-based [n = 4], and model-based [n = 2]) were calculated within each recording. Ensemble-based classifiers were developed using a two-tier cross-validation approach. We used standard regression methods to assess whether potential confounding variables (e.g., age, gender, treatment status, comorbidity) impacted model performance. We found levels of balanced accuracy of 68% across the cohort with clinically noncontributory normal EEGs (sensitivity =61%, specificity =75%, positive predictive value =55%, negative predictive value =79%, diagnostic odds ratio =4.64, area under receiver operated characteristics curve =.72). Group level analysis found no evidence suggesting any of the potential confounding variables significantly impacted the overall performance. These results provide evidence that the set of biomarkers could provide additional value to clinical decision-making, providing the foundation for a decision support tool that could reduce diagnostic delay and misdiagnosis rates. Future work should therefore assess the change in diagnostic yield and time to diagnosis when utilizing these biomarkers in carefully designed prospective studies.

Wind turbine fault detection and identification using a two-tier machine learning framework

A proactive approach is essential to optimize wind turbine maintenance and minimize downtime. By utilizing advanced data analysis techniques on the existing Supervisory Control and Data Acquisition (SCADA) system data, valuable insights can be gained into wind turbine performance without incurring high costs. This allows for early fault detection and predictive maintenance, ensuring that unscheduled or reactive maintenance is minimized and revenue loss is mitigated. In this study, data from a wind turbine SCADA system in the southeast of Ireland were collected, preprocessed, and analyzed using statistical and visualization techniques to uncover hidden patterns related to five fault types within the system. The paper introduces a conditional function designed to test two given scenarios. The first scenario employs a two-tier approach involving fault detection followed by fault identification. Initially, faulty samples are detected in the first tier and then passed to the second tier, which is trained to diagnose the specific fault type for each sample. In contrast, the second scenario involves a simpler solution referred to as naive, which treats fault types and normal cases together in the same dataset and trains a model to distinguish between normal samples and those related to specific fault types. Machine learning models, particularly robust classifiers, were tested in both scenarios. Thirteen classifiers were included, ranging from tree-based to traditional classifiers, neural networks, and ensemble learners. Additionally, an averaging feature importance technique was employed to select the most impactful features on the model decisions as a starting point. A comparison of the results reveals that the proposed two-tier approach is more accurate and less time-consuming, achieving 95% accuracy in separating faulty from normal samples and approximately 91% in diagnosing each fault type. Furthermore, ensemble learners, particularly bagging and stacking, demonstrated superior fault detection and identification performance. The performance of the classifiers was validated using t-SNE and explainable AI techniques, confirming that the impactful features align with the findings and that the proposed two-tier solution outperforms the naive solution. These results strongly indicate that the proposed solution is accurate, independent, and less complex compared to existing solutions.

Depression assessment using integrated multi-featured EEG bands deep neural network models: Leveraging ensemble learning techniques

Mental Status Assessment (MSA) holds significant importance in psychiatry. In recent years, several studies have leveraged Electroencephalogram (EEG) technology to gauge an individual's mental state or level of depression. This study introduces a novel multi-tier ensemble learning approach to integrate multiple EEG bands for conducting mental state or depression assessments. Initially, the EEG signal is divided into eight sub-bands, and then a Long Short-Term Memory (LSTM)-based Deep Neural Network (DNN) model is trained for each band. Subsequently, the integration of multi-band EEG frequency models and the evaluation of mental state or depression level are facilitated through a two-tier ensemble learning approach based on Multiple Linear Regression (MLR). The authors conducted numerous experiments to validate the performance of the proposed method under different evaluation metrics. For clarity and conciseness, the research employs the simplest commercialized one-channel EEG sensor, positioned at FP1, to collect data from 57 subjects (49 depressed and 18 healthy subjects). The obtained results, including an accuracy of 0.897, F1-score of 0.921, precision of 0.935, negative predictive value of 0.829, recall of 0.908, specificity of 0.875, and AUC of 0.8917, provide evidence of the superior performance of the proposed method compared to other ensemble learning techniques. This method not only proves effective but also holds the potential to significantly enhance the accuracy of depression assessment.

The BRCA mutation spectrum among breast and ovarian cancers in India: highlighting the need to screen BRCA1 185delAG among South Indians.

Mutations in BRCA1 and BRCA2 significantly elevate the risk of developing breast and ovarian cancer. Limited data exists regarding the prevalence ofBRCA mutations, and optimal, cost-effective testing strategies in developing countries like India. This study aimed to evaluate the utility of a Next Generation Sequencing (NGS) panel for BRCA1/2 mutation testing among women diagnosed with, or at risk of developing hereditary breast and ovarian cancers. We also aimed to identify population specific BRCA1/2 mutation hotspots, to enable the development of more affordable testing strategies. We identified 921 patients with breast and ovarian cancer who underwent mutation testing. The target enrichment wasfollowed by targeted NGS in 772 patients and an allele-specific PCR (ASPCR) based genotyping for BRCA1:c.68_69delAG (or 185delAG), was carried out in 149 patients. We identified 188 (20.4%) patients with BRCA1/2 variants: 118 (62.8%) withpathogenic/likely pathogenic and 70 (37.2%)with VUS. The 185delAG was identified as a recurrent mutation in the Southern Indian population, accounting for 24.6% of the pathogenic variants. In addition, a family history of breast, ovary, pancreas, or prostate (BOPP) cancer was found to be associated with an increased risk of identifying a deleterious BRCA1/2 variant [OR = 2.11 (95% CI 1.45-3.07) p ≤ 0.001]. These results suggest that TargetedNGS is a sensitive and specific strategy for BRCA testing. For Southern Indian patients, a two-tiered approach can be considered: Initial screening with ASPCR for BRCA1185delAG followed by NGS for those testing negative. Expanding the gene panel and identifying other population-specific mutation hot spots is a promising area with potential for improvements in testing and treatment strategies.

Guidelines for assistive technology service provision – A scoping review

Purpose: Despite the high unmet need for effective AT provision, multiple service delivery models across different countries, and a shortage of personnel trained in this field, no widely useable and accepted Assistive Technology (AT) service provision guidelines currently exist. This review aims to provide an overview of the literature regarding AT service provision guidelines to inform the development of globally useable AT provision guidance, aligned with contemporary global initiatives to improve access to AT. Materials and Methods: The rapid scoping review method used a two-tiered approach to identifying relevant publications: (1) systematic search of academic databases (Medline, CINAHL, SCOPUS, and Google Scholar); (2) consultation with international AT organisations and experts. The search was conducted in March 2023 with no date limitations. Analysis was guided by the TIDE-funded HEART research on quality AT provision and service delivery processes in Europe, as well as the WHO-GATE 5 P framework for strengthening access to AT. Results: 35 publications were identified from various countries, and directed at differing assistive products, personnel, and provision contexts. No established guidelines for AT service provision currently exist. However, despite the variety in contexts, the range of assistive products and the range of stakeholders to whom guidelines are directed, several key service delivery steps were identified that may form part of such guidelines. Conclusions: This review offers a strong starting point for developing guidance for AT provision to meet global needs. Careful consideration of vocabulary, process, and application to the diversity of assistive products is recommended in systematizing globally applicable guidance.

Integration of rapid bioburden testing into production quality management systems and process control.

The move to integrated continuous bioprocessing (ICB), while providing a means for process intensification, can put added strain on process analytics when conventional methods are used. For instance, traditional microbial methods provide minimal value to ICB processes given that the time required for data to become available is much longer than a typical full cycle of the manufacturing process. Although rapid microbial detection has been in discussion for over 30 years, it is still not routinely deployed in commercial biopharmaceutical manufacturing. One contributing factor is the ability to integrate this technology into a process control strategy and existing quality systems. An understanding of the capability of microbial detection technology available today can be leveraged to implement a control strategy for bioburden monitoring in real time for process intermediates. One key tenet of this proposed control strategy is the use of a "two-tiered approach" wherein a fast (but possibly less sensitive) test is used to monitor the process and trigger further action for a second, longer duration test which is used to confirm and quantify the presence of bioburden and identify the organism. This approach, presented here alongside several case studies for microbial monitoring, can have broader application for other process analytical technologies where fit for purpose methods could be employed to establish process control alongside real time continuous processes.

Utilizing BERT for entity relationship extraction in Chinese medical texts

The Chinese medical sector has been somewhat lacking in knowledge graphs, a deficiency this study aims to address. By leveraging the prowess of the BERT pre-training model, a two-tier approach has been innovated that utilizes separate pre-trained encoders for both entity and relational models. These models are intricately linked: the output from the entity model seamlessly flows into the relational one, making it possible to adeptly extract entity relationships from Chinese medical texts. This research is anchored in the CMeIE dataset, sourced from the esteemed CHIP (China Health Information Processing) conference. This dataset stands as a recognized benchmark in evaluating Chinese medical texts. By harnessing this data, the methods have been rigorously tested and validated. The promising experimental results underscore the effectiveness of the approach in distilling relationships from Chinese medical literature. The implications of this research are profound. Beyond just enriching the Chinese medical domain, the boundaries of NLP technology are also being pushed. Potential applications are manifold: from constructing comprehensive Chinese medical knowledge graphs to assisting in early-stage medical diagnoses. This innovative approach not only addresses an existing gap but also sets the stage for future advancements in medical NLP.

Streaking and self-care planning: the influence of integrating a well-being initiative in one teacher education program

ABSTRACT Concerns about teacher burnout, compassion fatigue, and retaining early career professionals have prompted teacher education programs to seek out ways to promote mental and emotional health during pre-service teacher training. This two-year, educational design study explored pre-service teachers’ experiences with the implementation of two school-based mental health interventions: the intentional introduction of self-care planning during field experience practicums, and the promotion of ‘streaking’, a health promotion event, to encourage pre-service teachers to implement a daily individual mental health intervention. Data were collected at several different points over a two-year period through surveys and individual interviews. The data analysis indicated that this two-tiered approach holds promise as a health promotion activity, however other intentional and educational actions were needed to ensure that pre-service teachers had the resources and tools required to support their mental and emotional health during their practicum courses. A further finding suggested that the term ‘self-care’ was widely misunderstood by study participants and work is needed to help pre-service teachers understand the importance of self-care during their practicum as a prevention strategy for well-being once they begin their teaching career.

Can Startups Disrupt the Gender Pay Gap? Disruption of Economic Exclusion A Two-Tiered Approach,

La disruption entrepreneuriale pourrait-il mitiger l’écart de rémunération entre les hommes et les femmes. Des recherches récentes sur les détails de l’écart salariale démontrent son caractère persistant, ainsi que ses nuances au niveau de l’industrie. Alors que les données européennes indiquent que les femmes de l’UE gagnent nettement moins par heure que les hommes, les données sectorielles révèlent une extrême hétérogénéité de l’écart de rémunération dans les États membres de l’UE et de l’EEE. Cette hétérogénéité est particulièrement prononcée dans des secteurs comme la finance, qui se caractérisent par des structures de marché très concentrées ainsi que par de grandes entreprises déjà solidement implantées. Alors que les start-ups et l’investissement en capital-risque deviennent des sujets d’intérêt majeurs dans les écosystèmes d’entreprises, des travaux de recherche ont révélé des écarts significatifs entre les hommes et les femmes dans la sélection et la valorisation des start-ups, ainsi que à la tête des entreprises. En utilisant une base de données originale sur les investissements de capital risque dans l’UE et l’EEE, et une approche empirique à deux niveaux reposant à la fois sur le MCO ainsi que sur l’apprentissage automatique, cette étude examine l’impact des valorisations et de la concentration entrepreneuriale sur l’écart de rémunération au niveau sectoriel dans l’UE. Les résultats indiquent que c’est l’émergence des start-ups à forte valorisation qui sont capables de perturber la structure du marché au niveau sectoriel permettent à la fois de mitiger les écarts de rémunération et de réduire le retranchement par des insiders au niveau sectoriel, ce qui mesure l’exclusion économique en général. Cette étude contribue à la compréhension du rôle joué par la disruption des startups pour mitiger l’exclusion économique basée sur le genre.

Novel two-tiered screening approach identifies synergistic combinations of natural compounds for prostate cancer prevention and treatment.

Prostate cancer (PCa) is the second most common cancer type among American men and it is estimated that in 2023, 34,700 men will die from PCa. Since it can take a considerable amount of time for the disease to progress to clinically evident cancer, there is ample opportunity for effective chemopreventive strategies to be applied for the successful management of PCa progression. In the current study, we have developed a two-tiered metabolomics-based screen to identify synergistic combinations of phytochemicals for PCa chemoprevention. This involves an initial screen for ATP depletion in PCa cells followed by a targeted screen for blocking glutamine uptake in the same cells. One of the phytochemical combinations (enoxolone [ENO] + silibinin [SIL]), identified via this screen, was examined for effects on PCa cell survival, oncogenic signaling and tumor growth in vivo. This combination was found to synergistically reduce cell survival, colony formation and cell cycle progression of PCa cell lines to a greater extent than either agent alone. The combination of ENO and SIL also synergistically reduced tumor growth when administered ad libitum through the diet in a HMVP2 allograft PCa tumor model. Treatment with the combination also significantly reduced STAT3 and mTORC1 signaling pathways in mouse and human PCa cells while significantly reducing levels of critical cell cycle regulatory proteins, contributing to the synergistic inhibition of tumor growth observed. Collectively, the current results demonstrate a novel approach to identifying synergistic combinations of phytochemicals for chemoprevention of PCa and possibly other cancers.

Optimizing external advisory committee meetings of Clinical and Translational Science Awards through focused pre-review.

External advisory committees (EACs) are critical peer-review meetings that drive improvement at Clinical and Translational Science Award Program Hubs. Despite their ubiquity, evaluations of EAC optimization and effective implementation remain scarce. We present a two-tiered approach to optimizing EAC meetings through (1) in-depth, topically focused "pre-review" meetings comprised of external topic experts and at least one standing "full-board" EAC member, followed by (2) a traditional "full-board" EAC meeting. This approach allowed pre-review discussion of program-focused topics and specific recommendations, later delivered to the full-board for review and direction. To evaluate this approach, we interviewed 18 people who planned, administered, or attended pre-review and/or full-board meetings, including internal Hub staff, external topic experts, and standing EAC members. Thematic analysis was used to explore planning, implementation, and value of our two-tiered approach versus the traditional single full-board approach. Interviewees preferred the two-tiered approach, noting benefits including additional time to reflect, shared identification of strengths and challenges, and discussion of solutions to share later with the full-board. Those who attended pre-review meetings described building "transformational," rather than "transactional," relationships with invitees through more discussion and inter-hub sharing. That increased sharing invited more exploration, discussion, and planning of next steps toward innovation.

“Китайские горки”

In Ukraine, China continues to adhere to the policy of “neutrality benevolent towards Russia”, remaining “above the fray” and at the same time looking for its place in the conflict as a “mediator” that does not condemn Russia, but does not recognize the transfer of part of Ukrainian territories to Russia. However, in Chinese society and the expert community controlled by the Communist Party of China (CPC), the attitude towards the Ukrainian crisis is changing. If at the initial stage of Russia’s Special Military Operation (SMO) in Ukraine, pro-Russian sentiments prevailed in the context of the Taiwan problem: “Russia is doing well!”, “And we need it!” Today, the angle is different: “neither for Russia, nor against”, with the addition that “the situation in Ukraine interferes with the normalization of China’s relations with the West and, above all, with the EU”. At the same time, Beijing is still interested in Russia as the main factor of “pressure on the United States” in the Sino-American confrontation. China’s two-tiered approach to Ukraine and Russia was manifested in Beijing’s reaction to the “Wagner” PMC mutiny. The official authorities limited themselves to a short statement by the Chinese Foreign Ministry: “what happened is an internal affair of Russia”. At the same time, the situation around “Wagner” caused active discussions in Chinese social networks. Their main topics were the following: how stable the situation inside Russia is, how what happened can affect Sino-Russian relations, and the development of the SMO in Ukraine. In the past three months, Beijing has stepped up its foreign policy. High-level contacts were organized with the U.S. and the EU, conciliatory proposals were made to Japan and South Korea. However, this has not yet led to an improvement in relations, although, according to Beijing’s expectations, it may lay the foundations for normalization in the future. In other areas, progress was achieved where the PRC directed its financial resources – Latin America, Africa, Central Asia, Pakistan, Afghanistan, etc. Relations deteriorated with those countries which improved their ties with the United States – India and the Philippines. Despite some personnel changes the internal political situation remains stable. Xi Jinping’ promotion as China’s “chief ideologue” in the “new era” continues. Without exception, all CPC members study Xi’s writings on “socialism with Chinese characteristics in a new era” every day. At the end of June, Xi talked at a group study of the Party’s Central Committee members about “sinified Marxism”, the importance of combining “Marxism and Chinese civilization”. China’s main problems are in the area of the economy. According to foreign experts, China’s GDP growth rate is too low. Chinese analysts, on the contrary, believe that economic growth rates turned out to be moderately high compared to a low base in 2022. The PRC is starting to react more aggressively to U.S. sanctions. A set of laws has been formed that impose restrictions on foreign companies within China. At the same time, there is a belief in the country that foreign businesses interested in the Chinese market will themselves bypass U.S. restrictions.

Assessing Clinical Relevance of Gastric Cancer Cell Lines as Tumor Models by Identifying Key Genes and miRNAs involved in EMT Metastasis Pathway

Cancer cell lines are in-vitro cancer cell models that continue to divide and proliferate under controlled laboratory conditions. They are used in medical research to study tumor pathology and for testing therapeutics efficacy. Despite their extensive application, significant differences in genetic and transcriptomic expression exist between cell lines and tumor cells. The purpose of this study was to identify the relevance of gastric cancer cell lines as suitable models for gastric tumor research. Using a two-tiered bioinformatics approach- a comparison between the gastric tumor and the gastric cancer cell line was performed using functional genomics data available in the literature to identify the common Differentially Expressed Genes (DEGs). The study revealed differential expression of a total 380 genes common in both gastric cancer cell lines and tumor samples. Interestingly, four of these genes were identified as the Epithelial Mesenchymal Transition (EMT) markers, involved in the accelerated metastasis, of which two genes with discrete over-expression patterns, namely- ITGB1 and TGFB1 were selected for further analysis. Their survival analysis revealed their key role in gastric cancer severity, evident by the regressed survival rate of gastric cancer patients upon their overexpression. A total of nine tumor-suppressor miRNAs relevant to ITGB1 and TGFB1 were also identified. In conclusion, our findings indicate that inspite of various genetic and transcriptomic deviations, the tumor and cell line exhibit comparable expression patterns. The data obtained would be useful to implore the druggability of these targets with a goal of improved translational rates.

Machine Learning-Based Optimization of Wind-PV Solution for Grid Demand

This research addresses the imperative need for innovative approaches to harness renewable resources while ensuring grid stability amidst global electricity demand. The proposed optimization methodology, centered on machine learning, harmonizes the intermittent output of Wind-PV systems with dynamic grid requirements. Leveraging historical meteorological data, energy trends, and real-time demand, the study employs ensemble learning, specifically the CNN (Convolutional Neural Network) algorithm, for accurate forecasting of PMSG-based (Permanent Magnet Synchronous Generator) Wind-PV system output. The two-tiered optimization approach involves short-term prediction using the CNN model and real-time correction through a genetic algorithm, enhancing the Capacity Utilization factor from 73% to 92% and reducing mean absolute error from 8% to 4.5%. This method not only anticipates energy fluctuations efficiently but also contributes to grid stability and decreased reliance on nonrenewable sources. Comparative analyses demonstrate the superiority of the machine learning-based approach in terms of accuracy and adaptability over traditional control methods.

Parent Perceptions of Participating in a Parent-Mediated Program for Young Autistic Children

Parents may be well placed to deliver therapeutic support to their own autistic preschool children. Parent-implemented Early Start Denver Model (P-ESDM) is one well-researched program for coaching parents to use strategies in daily routines to support child development. This qualitative study examined the perceptions of parents of autistic preschool children who participated in a two-tiered 20-week P-ESDM program. This program was novel in that it involved group delivery of P-ESDM as well as a two-tiered approach to support provision. Semi-structured interviews were conducted with the nine parents of seven autistic preschool boys who participated in the program. Data were analyzed using reflexive thematic analysis. Four key themes and 13 subthemes were developed. The themes centered on (a) program accessibility and participation, (b) experience of the two-tiered coaching process, (c) commitment and perseverance, and (d) outcomes. Parents perceived the program to be feasible, acceptable, and effective and gave recommendations for improving future tiered P-ESDM supports. The mixed perceptions from parents suggest the need for flexible, individualized support. Understanding the reported strengths and barriers of this program could allow future programs to better meet the needs of diverse families.

A new method of image encryption using advanced encryption Standard (AES) for network security

With the rapid increase in the use of technology, images have become a major source of sharing personal, confidential and official information and there is a dire need to protect this secret data. Image encryption plays major role in the security of images and there are many techniques developed for this purpose. Chaos based image encryption has now become most applicable and beneficial technique for image encryption. The purpose of this paper is to highlight a new method of image encryption with the use of advanced encryption standard (AES) and chaotic maps. This technique is composed of substitution and permutation phases. AES is found to be most secure cipher until now against different kinds of attacks. The round keys are generated by AES using key expansion algorithm. The sensitivity of this technique is that it is dependent on initial values and input image. S-boxes in AES introduce non-linearity, confusion, and an avalanche effect, enhancing security and resistance to cryptographic attacks by substituting bytes in the encryption process. The combination of AES and chaotic maps in encryption schemes provides a two-tiered approach to enhance security. AES offers a strong and well-established encryption method, while chaotic maps introduce randomness and complexity, making it more difficult for attackers to decipher encrypted data. This combination is often used to achieve a higher level of encryption security in various applications, including data transmission and storage. Different kinds of analysis and tests are performed on the technique which includes information entropy, number of pixel change rate (NPCR), and peak signal-to-noise ratio (PSNR), unified average changing intensity (UACI) and histogram correlation of adjacent pixels. The results of these tests show that this technique is secure and resistant towards attacks.

Revolutionizing Urban Mobility: IoT-Enhanced Autonomous Parking Solutions with Transfer Learning for Smart Cities.

Smart cities have emerged as a specialized domain encompassing various technologies, transitioning from civil engineering to technology-driven solutions. The accelerated development of technologies, such as the Internet of Things (IoT), software-defined networks (SDN), 5G, artificial intelligence, cognitive science, and analytics, has played a crucial role in providing solutions for smart cities. Smart cities heavily rely on devices, ad hoc networks, and cloud computing to integrate and streamline various activities towards common goals. However, the complexity arising from multiple cloud service providers offering myriad services necessitates a stable and coherent platform for sustainable operations. The Smart City Operational Platform Ecology (SCOPE) model has been developed to address the growing demands, and incorporates machine learning, cognitive correlates, ecosystem management, and security. SCOPE provides an ecosystem that establishes a balance for achieving sustainability and progress. In the context of smart cities, Internet of Things (IoT) devices play a significant role in enabling automation and data capture. This research paper focuses on a specific module of SCOPE, which deals with data processing and learning mechanisms for object identification in smart cities. Specifically, it presents a car parking system that utilizes smart identification techniques to identify vacant slots. The learning controller in SCOPE employs a two-tier approach, and utilizes two different models, namely Alex Net and YOLO, to ensure procedural stability and improvement.

Immunoblot Criteria for Diagnosis of Lyme Disease: A Comparison of CDC Criteria to Alternative Interpretive Approaches.

The current Centers for Disease Control and Prevention (CDC) interpretive criteria for serodiagnosis of Lyme disease (LD) involve a two-tiered approach, consisting of a first-tier EIA, IFA, or chemiluminescent assay, followed by confirmation of positive or equivocal results by either immunoblot or a second-tier EIA. To increase overall sensitivity, single-tier alternative immunoblot assays have been proposed, often utilizing antigens from multiple Borrelia burgdorferi strains or genospecies in a single immunoblot; including OspA and OspB in their antigen panel; requiring fewer positive bands than permitted by current CDC criteria; and reporting equivocal results. Published reports concerning alternative immunoblot assays have used relatively small numbers of LD patients and controls to evaluate novel multi-antigen assays and interpretive criteria. We compared the two most commonly used alternative immunoblot interpretive criteria (labeled A and B) to CDC criteria using data from multiple FDA-cleared IgG and IgM immunoblot test kits. These single-tier alternative interpretive criteria, applied to both IgG and IgM immunoblots, demonstrated significantly more false-positive or equivocal results in healthy controls than two-tiered CDC criteria (12.4% and 35.0% for Criteria A and B, respectively, versus 1.0% for CDC criteria). Due to limited standardization and high false-positive rates, the presently evaluated single-tier alternative immunoblot interpretive criteria appear inferior to CDC two-tiered criteria.

Restoration of linear disturbances from oil-and-gas exploration in boreal landscapes: How can network models help?

In western Canada, decades of oil-and-gas exploration have fragmented boreal landscapes with a dense network of linear forest disturbances (seismic lines). These seismic lines are implicated in the decline in wildlife populations that are adapted to function in unfragmented forest landscapes. In particular, anthropogenic disturbances have led to a decline of woodland caribou populations due to increasing predator access to core caribou habitat. Restoration of seismic lines aims to reduce the landscape fragmentation and stop the decline of caribou populations. However, planning restoration in complex landscapes can be challenging because it must account for a multitude of diverse aspects.To assist with restoration planning, we present a spatial network optimization approach that selects restoration locations in a fragmented landscape while addressing key environmental and logistical constraints. We applied the model to develop restoration scenarios in the Redrock-Prairie Creek caribou range in northwestern Alberta, Canada, which includes a combination of caribou habitat and active oil-and-gas and timber extraction areas.Our study applies network optimization at two distinct scales to address both the broad-scale restoration policy planning and project-level constraints at the level of individual forest sites. We first delineated a contiguous set of coarse-scale regions where restoration is most cost-effective and used this solution to solve a fine-scale network optimization model that addresses environmental and logistical planning constraints at the level of forest patches. Our two-tiered approach helps address the challenges of fine-scale spatial optimization of restoration activities. An additional coarse-scale optimization step finds a feasible starting solution for the fine-scale restoration problem, which serves to reduce the time to find an optimal solution. The added coarse-scale spatial constraints also make the fine-scale restoration solution align with the coarse-scale landscape features, which helps address the broad-scale restoration policies. The approach is generalizable and applicable to assist restoration planning in other regions fragmented by oil-and-gas activities.

Investigating the Relationship of Outdoor Heat Stress upon Indoor Thermal Comfort and Qualitative Sleep Evaluation: The Case of Ankara

The necessity of exploring the relationship between sleep quality and the thermal environment has amplified regarding increasing heat stress risk on the human body due to climate change, particularly in vulnerable uninsulated buildings in Ankara. Within this scope, this study investigated occupants’ sleep quality and human thermal comfort in insulated and uninsulated buildings under three local extreme heat event thresholds: (1) typical summer days (TSD25), (2) very hot days (VHD33), and lastly, (3) heat wave events (HWE31). Within a two-tiered approach to thermal comfort evaluations, the human thermal comfort of occupants was identified through the calculation of physiologically equivalent temperature (PET) from the climatic data of local meteorological stations. The psychological thermal comfort and sleep quality of participants were evaluated by questionnaires during each heat event. The results of this study demonstrated that the physiological thermal load of the participants was highest during VHD33s, given that both outdoor and indoor PET values presented their highest values within VHD33 events. Furthermore, the outdoor PET values reached extreme heat stress based on physiological stress grades with 43.5 °C, which indicated the exacerbated vulnerability of Ankara during extreme heat events. The PET values were consistently higher in uninsulated buildings than in insulated buildings. Also, most of the mean psychological thermal comfort votes and sleep quality votes were better in uninsulated buildings than in insulated ones during TSD25s and HWE31s, while it was the opposite within extreme conditions of VHD33s. The outputs of this study contribute to interdisciplinary efforts to attenuate the existing and impending risks of climate change on human life by defining the influence of increasing outdoor heat stress on indoor spaces, thermal comfort, and the sleep quality of occupants.