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Reservoirs are globally important sources of greenhouse gases, but the magnitude of their emissions is highly uncertain. Here, we present data for 146 reservoirs from two surveys of reservoir methane and carbon dioxide emissions, one at the regional scale in the midwestern United States and one at the national scale in the United States, plus data from two hand-picked sites in Washington and Puerto Rico. At all reservoirs, ebullitive and diffusive emissions and basic physicochemistry were measured at 15-55 locations during one 22 to 64-h period during the summers of 2016-2023, with four reservoirs revisited a second time. Contemporaneous water chemistry measurements were made at one or two locations in each reservoir. The dataset consists of two geospatial files and seven CSV files containing greenhouse gas emissions, water chemistry, morphology, and other relevant data. To date, these data comprise the largest multi-reservoir emissions dataset assembled using consistent measurement methods.

The web offers an abundance of health-related information, but quality and trustworthiness are often lacking. To address this, the tala-med search engine was introduced as a non-commercial platform for German evidence-based health information, drawing on hand-picked, quality-assessed sources. An early prototype was positively evaluated, but technical limitations hindered further development. The system was therefore re-engineered using open, modular technologies and a scalable Apache Nutch-based crawler. Key components include an index-optimized ETL pipeline with boilerplate removal, de-duplication, and metadata enrichment, as well as content integration via a WordPress-based CMS. The platform is deployed across two virtual servers to separate crawling from the user-facing service, ensuring stability and scalability. The resulting system demonstrates a modular and maintainable architecture that supports scalable ingestion and flexible content management. The public instance of the platform is accessible at https://suche.tala-med.info. Future work will address automated index evaluation and fine-grained metadata enrichment to support filter-based search and domain adaptation.

Enhanced recovery after surgery (ERAS) for brain tumor surgery holds promise for improving outcomes. However, its lack of standardization has led to financial and logistic barriers, hindering its widespread adoption and optimization. This study seeks to identify ERAS variables across the preoperative, intraoperative, and postoperative phases associated with shorter hospital length of stay (LOS), decreased postoperative pain, and reduced costs. A comprehensive literature search was performed to include MEDLINE, Embase, Cochrane Library, and Scopus from January 1, 2020, to December 31, 2023. Eligible studies were selected based on predefined inclusion/exclusion criteria. Additional references were hand-picked by the authors when appropriate. Statistical methods were used to identify variables linked to improved outcomes, such as LOS, cost, and pain control. Of the 278 abstracts, 50 met eligibility criteria, leading to 16 final full-text articles for the study, covering 2296 patients. Variables significantly reducing LOS on multivariable regression analysis included preoperative social assessment for discharge planning (&#x3b2; = -3.95, CI: -5.45 to -2.45, P < .001), nutritional counseling and preoperative optimization (&#x3b2; = -1.90, CI: -2.98 to -0.81, P < .01), intraoperative prophylactic antibiotics (&#x3b2; = -2.41, CI: -4.98 to 0.15, P < .06), extubation in the operating room (&#x3b2; = -4.39, CI: -8.36 to -0.42, P = .033), postoperative patient education on discharge instructions (&#x3b2; = -3.9, CI: -6.24 to -1.56, P < .01), opiate-sparing analgesia (&#x3b2; = -2.8, CI: -4.93 to 0.67, P = .017), and early mobility (&#x3b2; = -1.95, CI: -3.86 to -0.04, P = .046). Multivariable logistic regression demonstrated that ERAS protocols with a local/regional anesthetic block during the intraoperative period significantly predicted a pain benefit (odds ratio = 28, CI: 1.95 to 181.2, P = .03). Based on the results from the present analysis, the authors synthesize an ERAS protocol for patients undergoing brain tumor surgery with key elements that demonstrated a significant influence on the measured outcomes.

Neuroscience has long relied on macaque studies to infer human brain function, yet identifying functionally corresponding brain regions across species and measurement modalities remains a fundamental challenge. This is especially true for the higher-order cortex, where functional interpretations are constrained by narrow hypotheses and anatomical landmarks are often nonhomologous. We present a data-driven approach for mapping functional correspondence across species using rich, naturalistic stimuli. By directly comparing macaque electrophysiology with human fMRI responses to 700 natural scenes, we identify fine-grained alignment based on response pattern similarity, without relying on predefined tuning concepts or hand-picked stimuli. As a test case, we examine the ventral face patch system, a well-studied but contested domain in cross-species alignment. Our approach resolves a long-standing ambiguity by supporting a correspondence between macaque ML and human FFA and between AL and more anterior temporal cortex in humans. This result is consistent with full-brain anatomical warping but inconsistent with prior studies limited by narrow functional hypotheses. These findings show that natural image-evoked response patterns provide a robust foundation for cross-species functional alignment, supporting scalable comparisons as large-scale primate recordings become more widespread.

Evolutionary game theory (EGT) has been pivotal in the study of cooperation, offering formal models that account for how cooperation may arise in groups of selfish, but simple agents. This is done by inspecting the complex dynamics arising from simple interactions between a few strategies in a large population. As such, the strategies at stake are typically hand-picked by the modeler, resulting in a system with many more individuals in the population than strategies available to them. In the presence of noise and with multiple equilibria, the choice of strategies can considerably alter the emergent dynamics. As a result, model outcomes may not be robust to how the strategy set is chosen, sometimes misrepresenting the conditions required for cooperation to emerge. We propose three principles that can lead to a more systematic choice of the strategies in EGT models of cooperation. These are the inclusion of all computationally equivalent strategies; explicit microeconomic models of interactions, and a connection between stylized facts and model assumptions. Further, we argue that new methods arising in AI may offer a promising path toward richer models. These richer models can push the field of cooperation forward together with the principles described above. At the same time, AI may benefit from connecting to the more abstract models of EGT. We provide and discuss examples to substantiate these claims.

Road infrastructure has short pavement lifespans and subgrade instability, requiring costly maintenance. This study looks at using high-elevation pine needles as a sustainable geotextile to improve construction and management. Benefits of this approach include the use of a readily available natural resource, cost effectiveness, and a lower environmental impact than more conventional materials like asphalt. The geotextiles are made using the natural fibers intrinsic qualities and are made from hand-picked pine needles that are chosen according to temperature, soil type, and altitude. Durability is increased by the moisture and UV resistance provided by the natural pine resin. In the production process, the pine needles are braided into geotextiles that adhere to ASTM D6381 and IS 15869-2020 standards. The incorporation of these geotextiles into the road structure improves water retention, load distribution, and ground stability. Environmental compatibility tests, durability studies, and mechanical testing are all part of comprehensive characterization. A validated numerical model was created to forecast performance and analyze soil-geotextile interactions. Research indicates that these geotextiles have the ability to increase the lifespan of roads and bridges. As a sustainable substitute for asphalt in road pavements, there is potential for widespread adoption in the future. The following UN Sustainable Development Goals (SDGs) are directly impacted by this research: SDG 9, SDG 11, SDG 12, and SDG 13. In order to produce infrastructure that benefits society and the environment, our research promotes sustainable road construction and maintenance.

Shade tolerance is a key trait for cultivars in inter/relay-cropped soybeans in maize fields. Our previous genome-wide association study (GWAS) results on southern China soybean germplasm revealed that the shade tolerance was conferred by a complex of genes with multiple alleles. To complete our understanding of the shade tolerance gene system, GWAS with gene-allele sequences as markers (designated GASM-RTM-GWAS) was conducted in a recombinant inbred line (RIL) population between two extreme parents using the shade tolerance index (STI) and relative pith cell length (RCL) as indicators. Altogether, 211 genes, comprising 99 and 119 genes (seven shared) for STI and RCL, respectively, were identified and then annotated into a similar set of five biological categories. Furthermore, transcriptome analysis detected 7837 differentially expressed genes (DEGs), indicating plentiful DEGs involved in the expression of regulatory/causal GWAS genes. Protein-protein interaction (PPI) analysis and gene functional analysis for both GWAS genes and DEGs showed a group of interrelated causal genes and a group of interrelated DEGs; the former were included in the latter and their functions were interconnected as a gene network. For further understanding of the response of soybean to shade stress in a sequential connection, six chronological gene modules were grouped as signal activation and transport, signal-transduction, signal amplification, gene expression, regulated metabolites, and material transport. From the modules, 12 key genes were selected as entry points for further analysis. Our study provides an overview of the shade tolerance gene network as a new insight into a complex-trait genetic system, rather than the usual way of starting from a hand-picked single gene.

Roots of quince (Cydonia oblonga Mill.) exhibiting lesions/darkening were collected from private property in Sheffield, England (53&#xb0;23'23.1"N 1&#xb0;30'53.4"W), along with rhizospheric soil. Nematode extraction revealed the presence of Pratylenchus specimens (60 nematodes per g of root; 100 nematodes/cm3 of soil). No above-ground symptoms were evident. Females were slender with the labial region slightly offset from the body and comprising three annuli (Figure 1A and 1B), measuring 505.6 &#xb5;m (427.0 - 580.5), a = 22.5 (19.1 - 25.1), b= 6.85 (5.3 - 10.0), c= 17.3 (9.4 - 27.0), and V% = 81.6 (78.9 - 83.0) (n = 10). Basal knobs were rounded but frequently found as slightly indented/cupped in some individuals (1B and 1C). The tail was rounded and smooth (Figure 1E). Males were common and slightly smaller than females (Figure 1D), measuring 482 &#xb5;m (477.81 - 489.22), a = 25.9 (24.6 - 27.3), b= 7.3 (6.2 - 9.5), c= 15.2 (14.0 - 15.0) (n = 4). These characteristics are consistent with Pratylenchus penetrans (Loof, 1991; Castillo and Vovlas, 2007). To validate morphological observations, molecular identification was carried out using 28S rDNA D2A/D3B primers (Gamboa-Cort&#xe9;s et al., 2023). DNA was extracted from hand-picked individuals according to Sonawala et al. (2024). PCR was carried out using 5 &#xb5;L of the digested sample with KOD Xtreme polymerase (Sigma Aldrich) using an initial denaturation at 94&#x2da;C for 2 min; 40 cycles of 98&#x2da;C for 10 s, 55&#x2da;C for 30 s and 68&#x2da;C for 1 min). Purified PCR products (Monarch Gel Extraction Kit) were sequenced, compared to those publicly available for Pratylenchus spp. and Xiphinema index, and aligned and trimmed using Clustal Omega (Madeira et al., 2024) on Geneious (2025.0.3) using default settings. Model selection (TPM3+G4) and phylogenetic tree inference (SH-aLRT, 1000 bootstraps (Minh et al., 2020)) were performed on the aligned sequences using IQ-TREE 2 version 2.2.0 (Figure 2A). Phylogenetic analysis corroborated the morphological identification of P. penetrans (Figure 2A). Nematodes were multiplied and maintained on maize (Zea mays L.) 'B73' and tomato (Solanum lycopersicum L.) 'Moneymaker' in growth chambers (light/dark cycle of 16h/8h at 30&#xb0;C/21&#xb0;C, respectively). To assess the host status of quince, five plants of C. oblonga (acquired commercially), and an equal number of Maize 'B73', were inoculated with 1,200 individuals. The inoculum was administered into two holes (2 cm depth) close to the stem and partially filled with vermiculite to avoid heat stress. Plants were assessed 90 days post-inoculation, as previously reported for sufficient reproduction of Pratylenchus brachyurus on Eucalyptus spp. (Souza and Inomoto, 2021). For quince and maize, similar reproductive values (2.3 and 2.2) and nematodes per gram of root (410 and 426) were found, respectively. Quince-infested roots were lesioned and decaying (Figure 2B), and reduced in weight by 31% compared to non-inoculated controls (7.8 g and 11.3 g, respectively, p<0.05, Student's t-test). Taken together, these data demonstrate that P. penetrans successfully reproduces on quince, and induces significant damage to host roots, indicating both host status and potential economic impact. The presence of P. penetrans should be considered when establishing quince orchards, as trees can become a source of inoculum over-season and/or to fast-growing crops (e.g., wheat or maize) grown near them. To our knowledge, this is the first report of P. penetrans on quince in England.

The research field of artificial intelligence (AI) in medicine and especially in gastroenterology is rapidly progressing with the first AI tools entering routine clinical practice, for example, in colorectal cancer screening. Contrast-enhanced ultrasound (CEUS) is a highly reliable, low-risk, and low-cost diagnostic modality for the examination of the liver. However, doctors need many years of training and experience to master this technique and, despite all efforts to standardize CEUS, it is often believed to contain significant interrater variability. As has been shown for endoscopy, AI holds promise to support examiners at all training levels in their decision-making and efficiency. In this systematic review, we analyzed and compared original research studies applying AI methods to CEUS examinations of the liver published between January 2010 and February 2024. We performed a structured literature search on PubMed, Web of Science, and IEEE. Two independent reviewers screened the articles and subsequently extracted relevant methodological features, e.g., cohort size, validation process, machine learning algorithm used, and indicative performance measures from the included articles. We included 41 studies with most applying AI methods for classification tasks related to focal liver lesions. These included distinguishing benign versus malignant or classifying the entity itself, while a few studies tried to classify tumor grading, microvascular invasion status, or response to transcatheter arterial chemoembolization directly from CEUS. Some articles tried to segment or detect focal liver lesions, while others aimed to predict survival and recurrence after ablation. The majority (25/41) of studies used hand-picked and/or annotated images as data input to their models. We observed mostly good to high reported model performances with accuracies ranging between 58.6% and 98.9%, while noticing a general lack of external validation. Even though multiple proof-of-concept studies for the application of AI methods to CEUS examinations of the liver exist and report high performance, more prospective, externally validated, and multicenter research is needed to bring such algorithms from desk to bedside.

Self-supervised magnetic resonance imaging (MRI) reconstruction methods train deep learning networks without the need for fully sampled reference data. One such approach, self-supervised via data under-sampling (SSDU), partitions under-sampled k-space into two disjoint sets, with a neural network mapping between them. However, SSDU and its variants rely on heuristic k-space partitioning, which may lead to suboptimal performance and necessitates new partitioning schemes when initial under-sampling patterns change. In this work, we propose a novel approach to learn optimal k-space partitioning by modeling a probability distribution which we use for partitioning. Specifically, we employ the LOUPE framework to learn an optimal partitioning probability distribution. Furthermore, we introduce a weighted dual-domain self-supervised loss function that incorporates both k-space and image-space loss terms. Evaluations on the fastMRI dataset demonstrate that our dual-domain learned partitioning method outperforms existing partitioning strategies and adapts to new sampling patterns without requiring hand-picked partitioning methods.Clinical Relevance- Typical clinical MRI protocols are under-sampled and reconstructed using parallel imaging. Self-supervised reconstruction can train directly on under-sampled clinical data, eliminating the need for separately acquired fully sampled datasets.

Emerging research on 2D cadmium chalcogenide nanoplatelets (NPLs) has garnered significant interest due to their large absorption cross-section, narrow emission band, fast photoluminescence decay, high optical gain, and significant giant oscillator strength transitions, which render them highly suitable for optoelectronic devices. This Perspective highlights their ultrafast decay dynamics and their applications in photodetectors, nonlinear properties, electrocatalysis, and photocatalysis. A deeper understanding of excited-state charge carrier dynamics is crucial for optoelectronic applications, particularly in photodetectors, nonlinear properties, and photocatalytic systems, where charge separation and transfer are essential. The in-depth knowledge of photophysical phenomena in semiconductor nanoplatelets is an increasingly valuable hand-picked topic within the broader materials research community.

Affective states are reflected in the facial expressions of all mammals. Facial behaviors linked to pain have attracted most of the attention so far in non-human animals, leading to the development of numerous instruments for evaluating pain through facial expressions for various animal species. Nevertheless, manual facial expression analysis is susceptible to subjectivity and bias, is labor-intensive and often necessitates specialized expertise and training. This challenge has spurred a growing body of research into automated pain recognition, which has been explored for multiple species, including cats. In our previous studies, we have presented and studied artificial intelligence (AI) pipelines for automated pain recognition in cats using 48 facial landmarks grounded in cats' facial musculature, as well as an automated detector of these landmarks. However, so far automated recognition of pain in cats used solely static information obtained from hand-picked single images of good quality. This study takes a significant step forward in fully automated pain detection applications by presenting an end-to-end AI pipeline that requires no manual efforts in the selection of suitable images or their landmark annotation. By working with video rather than still images, this new pipeline approach also optimises the temporal dimension of visual information capture in a way that is not practical to preform manually. The presented pipeline reaches over 70% and 66% accuracy respectively in two different cat pain datasets, outperforming previous automated landmark-based approaches using single frames under similar conditions, indicating that dynamics matter in cat pain recognition. We further define metrics for measuring different dimensions of deficiencies in datasets with animal pain faces, and investigate their impact on the performance of the presented pain recognition AI pipeline.

Deep learning approaches are state-of-the-art for semantic segmentation of medical images, but unlike many deep learning applications, medical segmentation is characterized by small amounts of annotated training data. Thus, while mainstream deep learning approaches focus on performance in domains with large training sets, researchers in the medical imaging field must apply new methods in creative ways to meet the more constrained requirements of medical datasets. We propose a framework for incrementally fine-tuning a multi-class segmentation of a high-resolution multiplex (multi-channel) immuno-flourescence image of a rat brain section, using a minimal amount of labelling from a human expert. Our framework begins with a modified Swin-UNet architecture that treats each biomarker in the multiplex image separately and learns an initial "global" segmentation (pre-training). This is followed by incremental learning and refinement of each class using a very limited amount of additional labeled data provided by a human expert for each region and its surroundings. This incremental learning utilizes the multi-class weights as an initialization and uses the additional labels to steer the network and optimize it for each region in the image. In this way, an expert can identify errors in the multi-class segmentation and rapidly correct them by supplying the model with additional annotations hand-picked from the region. In addition to increasing the speed of annotation and reducing the amount of labelling, we show that our proposed method outperforms a traditional multi-class segmentation by a large margin.

Fresh blueberries are delicate, hand-picked, packaged, and refrigerated fruits vulnerable to spoilage and contamination. Cold atmospheric plasma (CAP) is a promising antimicrobial technology; therefore, this study evaluated the CAP treatment effect on acid-tolerant Listeria innocua and Listeria monocytogenes and evaluated changes in the quality of the treated fruit. Samples were spot-inoculated with pH 5.5 and 6.0 acid-adapted Listeria species. Samples were treated with gliding arc CAP for 15, 30, 45, and 60 s and evaluated after 0, 1, 4, 7, and 11 days of storage at 4 &#xb0;C and 90% humidity for the following quality parameters: total aerobic counts, yeast and molds, texture, color, soluble solids, pH, and titratable acidity. CAP treatments of 30 s and over demonstrated significant reductions in pathogens under both the resistant strain and pH conditions. Sixty-second CAP achieved a 0.54 Log CFU g-1 reduction in L. monocytogenes (pH 5.5) and 0.28 Log CFU g-1 for L. monocytogenes (pH 6.0). Yeast and mold counts on day 0 showed statistically significant reductions after 30, 45, and 60 s CAP with an average 2.34 Log CFU g-1 reduction when compared to non-CAP treated samples. Quality parameters did not show major significant differences among CAP treatments during shelf life. CAP is an effective antimicrobial treatment that does not significantly affect fruit quality.

More than 670 million people have been infected by COVID-19. This case series reports 8 of 55 cases in a broader study of COVID-positive clients who sought homeopathic care for symptoms. Existing studies of homeopathy and COVID-19 have sometimes failed to employ the underpinning theoretical framework of homeopathy-the genus epidemicus. Special focus has been placed on standout symptoms not often reported in conventional medical outlets, known among homeopaths as "strange, rare and peculiar" (SRP) symptoms. The Homeopathy Help Network (HHN) team of practitioners noted SRP symptoms across dozens of cases and studied how they shifted collectively as different variants of the virus emerged. COVID-positive individuals self-selected for individualized care for their symptoms using homeopathy. They received tele-health consultations and individualized homeopathy interventions in an out-patient homeopathy clinical setting. Clients were seen by individual professional homeopathy practitioners and students under supervision working at the HHN in the United States. Cases for the series were hand-picked with the aim of being an average representation of the more than 4,000 COVID-positive cases seen by members of the HHN. Cases in the full compendium are grouped according to a predominant case feature: Multiple remedies, Posology, Time ill, Single remedy resolution, Hospitalization and, in this case series, SRP symptoms. SRP symptoms included: continually on the verge of unconsciousness; dark green stools; very low pulse alternating with tachycardia; sensation of strong or burning chemical smells; sensation of inhaling water through the nose; recurring electric shock sensations in head or extremities; yellow-green stools. Collective SRP symptoms from the pandemic provided the opportunity to study the hallmark features of COVID-19 in depth. The importance of these symptoms highlights the applicability of Hahnemannian principles and good case-taking practices.

Comprehensive molecular and cellular phenotyping of human islets can enable deep mechanistic insights for diabetes research. We established the Human Islet Data Analysis and Sharing (HI-DAS) consortium to advance goals in accessibility, usability, and integration of data from human islets isolated from donors with and without diabetes at the Alberta Diabetes Institute (ADI) IsletCore. Here we introduce HumanIslets.com, an open resource for the research community. This platform, which presently includes data on 547 human islet donors, allows users to access linked datasets describing molecular profiles, islet function and donor phenotypes, and to perform various statistical and functional analyses at the donor, islet and single-cell levels. As an example of the analytic capacity of this resource we show a dissociation between cell culture effects on transcript and protein expression, and an approach to correct for exocrine contamination found in hand-picked islets. Finally, we provide an example workflow and visualization that highlights links between type 2 diabetes status, SERCA3b Ca2+-ATPase levels at the transcript and protein level, insulin secretion and islet cell phenotypes. HumanIslets.com provides a growing and adaptable set of resources and tools to support the metabolism and diabetes research community.

During the first years of COVID-19 pandemic, X-ray structures of the coronavirus drug targets were acquired at an unprecedented rate, giving hundreds of PDB depositions in less than a year. The main protease (Mpro) of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) is the primary validated target of direct-acting antivirals. The selection of the optimal ensemble of structures of Mpro for the docking-driven virtual screening campaign was thus non-trivial and required a systematic and automated approach. Here we report a semi-automated active site RMSD based procedure of ensemble selection from the SARS-CoV-2 Mpro crystallographic data and virtual screening of its inhibitors. The procedure was compared with other approaches to ensemble selection and validated with the help of hand-picked and peer-reviewed activity-annotated libraries. Prospective virtual screening of non-covalent Mpro inhibitors resulted in a new chemotype of thienopyrimidinone derivatives with experimentally confirmed enzyme inhibition.

Fungicide resistance in foliar fungal pathogens is an increasing challenge to crop production. Yield impacts due to loss of fungicide efficacy may be reduced through effective surveillance and appropriate management intervention. For stubble-borne pathogens, off-season crop residues may be used to monitor fungicide resistance to inform pre-planting decisions; however, appropriate sampling strategies and support sizes for crop residues have not previously been considered. Here, we used Pyrenophora teres f. teres (Ptt) with resistance to demethylase inhibitor fungicides as a model system to assess spatial dependency and to compare the effects of different sampling strategies and support sizes on pathogen density (Ptt DNA concentration) and the frequency of fungicide resistance mutation. The results showed that sampling strategies (hand-picked versus raked) did not affect estimates of pathogen density or fungicide resistance frequency; however, sample variances were lower from raked samples. The effects of differing sample support size, as the size of the collection area (1.2, 8.6, or 60 m2), on fungicide resistance frequency were not evident (P > 0.05). However, measures of pathogen density increased with area size (P < 0.05); the 60 m2 area yielded the highest Ptt DNA concentration and produced the lowest number of pathogen-absent samples. Sample variances for pathogen density and fungicide resistance frequency were generally homogeneous between area sizes. The pattern of pathogen density was spatially independent; however, spatial dependency was identified for fungicide resistance frequency, with a range of 110 m, in one of the two fields surveyed. Collectively, the results inform designs for monitoring of fungicide resistance in stubble-borne pathogens.

As water treatment technology has improved, the amount of available process data has substantially increased, making real-time, data-driven fault detection a reality. One shortcoming of the fault detection literature is that methods are usually evaluated by comparing their performance on hand-picked, short-term case studies, which yields no insight into long-term performance. In this work, we first evaluate multiple statistical and machine learning approaches for detrending process data. Then, we evaluate the performance of a PCA-based fault detection approach, applied to the detrended data, to monitor influent water quality, filtrate quality, and membrane fouling of an ultrafiltration membrane system for indirect potable reuse. Based on two short case studies, the adaptive lasso detrending method is selected, and the performance of the multivariate approach is evaluated over more than a year. The method is tested for different sets of three critical tuning parameters, and we find that for long-term, autonomous monitoring to be successful, these parameters should be carefully evaluated. However, in comparison with industry standards of simpler, univariate monitoring or daily pressure decay tests, multivariate monitoring produces substantial benefits in long-term testing.