Greenhouse gas emissions and nutrient use efficiency assessment of six New York organic dairies.

Improving nutrient use efficiency and reducing greenhouse gas (GHG) emissions are important environmental priorities for organic-certified dairy operations. The objectives of this research were to quantify annual nutrient use and GHG emissions in 6 organic New York dairy farms. Farm-gate nutrient mass balances (NMB) were estimated with the Cornell NMB calculator. Whole-farm GHG emissions were estimated using Cool Farm Tool (CFT) and COMET. Farm-gate NMBs were low, ranging from -6.5 to 19 kg N ha-1 for N1 (without legume N fixation), 26 to 71 kg N ha-1 for N2 (including N fixation), -2.4 to 8.2 kg P ha-1 for P, and 1.1 to 19.8 kg K ha-1 for K. Additional nutrient imports, coupled with nutrient management planning, adequate legume stands and diet balancing may help improve P balances, and ensure no N deficiencies in the system. Estimates of annual GHG emission intensity ranged from 0.98 to 2.10 kg CO2-eq per kg of fat and protein corrected milk (FPCM) estimated by CFT, and from 0.69 to 2.48 kg CO2-eq kg FPCM-1 estimated by COMET. Enteric fermentation, feed production and fuel and energy use represented the largest sources of GHGs. For farms with liquid manure storages, manure management was also a significant source. Estimates of soil carbon (C) stock changes from CFT were in agreement or smaller than previous studies, and estimates from COMET were in agreement or greater. Variability and uncertainty in the results for soil C stock change indicate more research and new protocols are needed. Impact of individual management changes on GHG emissions intensity were small, ranging from -8 to +7% in CFT, and -8% to +8% in COMET. The management changes that resulted in the largest reductions in GHG emissions intensity included increasing individual cow productivity and milk to total feed ratio, and implementation of manure treatment systems.

Ageing, Cognitive Decline, and Effects of Physical Exercise: Complexities, and Considerations from Animal Models.

In our ageing global population, the cognitive decline associated with dementia and neurodegenerative diseases represents a major healthcare problem. To date, there are no effective treatments for age-related cognitive impairment, thus preventative strategies are urgently required. Physical exercise is gaining traction as a non-pharmacological approach to promote brain health. Adult hippocampal neurogenesis (AHN), a unique form of brain plasticity which is necessary for certain cognitive functions declines with age and is enhanced in response to exercise. Accumulating evidence from research in rodents suggests that physical exercise has beneficial effects on cognition through its proneurogenic capabilities. Given ethical and technical limitations in human studies, preclinical research in rodents is crucial for a better understanding of such exercise-induced brain and behavioural changes. In this review, exercise paradigms used in preclinical research are compared. We provide an overview of the effects of different exercise paradigms on age-related cognitive decline from middle-age until older-age. We discuss the relationship between the age-related decrease in AHN and the potential impact of exercise on mitigating this decline. We highlight the emerging literature on the impact of exercise on gut microbiota during ageing and consider the role of the gut-brain axis as a future possible strategy to optimize exercise-enhanced cognitive function. Finally, we propose a guideline for designing optimal exercise protocols in rodent studies, which would inform clinical research and contribute to developing preventative strategies for age-related cognitive decline.

Hippo-PKCζ-NFκB signaling axis: A druggable modulator of chondrocyte responses to mechanical stress.

Recent studies have implicated a crucial role of Hippo signaling in cell fate determination by biomechanical signals. Here we show that mechanical loading triggers the activation of a Hippo-PKCζ-NFκB pathway in chondrocytes, resulting in the expression of NFκB target genes associated with inflammation and matrix degradation. Mechanistically, mechanical loading activates an atypical PKC, PKCζ, which phosphorylates NFκB p65 at Serine 536, stimulating its transcriptional activation. This mechanosensitive activation of PKCζ and NFκB p65 is impeded in cells with gene deletion or chemical inhibition of Hippo core kinases LATS1/2, signifying an essential role of Hippo signaling in this mechanotransduction. A PKC inhibitor AEB-071 or PKCζ knockdown prevents p65 Serine 536 phosphorylation. Our study uncovers that the interplay of the Hippo signaling, PKCζ, and NFκB in response to mechanical loading serves as a therapeutic target for knee osteoarthritis and other conditions resulting from mechanical overloading or Hippo signaling deficiencies.

Assessing the Stability of Photon-Counting CT: Insights from a Two-Year Longitudinal Study.

Background: Among the advancements in computed tomography (CT) technology, photon-counting computed tomography (PCCT) stands out as a significant innovation, providing superior spectral imaging capabilities while simultaneously reducing radiation exposure. Its long-term stability is important for clinical care, especially longitudinal studies, but is currently unknown. Purpose: This study sets out to comprehensively analyze the long-term stability of a first-generation clinical PCCT scanner. Materials and Methods: Over a two-year period, from November 2021 to November 2023, we conducted weekly identical experiments utilizing the same multi-energy CT protocol. These experiments included various tissue-mimicking inserts to rigorously assess the stability of Hounsfield Units (HU) and image noise in Virtual Monochromatic Images (VMIs) and iodine density maps. Throughout this period, notable software and hardware modifications were meticulously recorded. Each week, VMIs and iodine density maps were reconstructed and analyzed to evaluate quantitative stability over time. Results: Spectral results consistently demonstrated the quantitative stability of PCCT. VMIs exhibited stable HU values, such as variation in relative error for VMI 70 keV measuring 0.11% and 0.30% for single-source and dual-source modes, respectively. Similarly, noise levels remained stable with slight fluctuations linked to software changes for VMI 40 and 70 keV that corresponded to changes of 8 and 1 HU, respectively. Furthermore, iodine density quantification maintained stability and showed significant improvement with software and hardware changes, especially in dual-source mode with nominal errors decreasing from 1.44 to 0.03 mg/mL. Conclusion: This study provides the first long-term reproducibility assessment of quantitative PCCT imaging, highlighting its potential for the clinical arena. This study indicates its long-term utility in diagnostic radiology, especially for longitudinal studies.

Hybrid spectral CT system with clinical rapid kVp-switching x-ray tube and dual-layer detector for improved iodine quantification.

Spectral computed tomography (CT) is a powerful diagnostic tool offering quantitative material decomposition results that enhance clinical imaging by providing physiologic and functional insights. Iodine, a widely used contrast agent, improves visualization in various clinical contexts. However, accurately detecting low-concentration iodine presents challenges in spectral CT systems, particularly crucial for conditions like pancreatic cancer assessment. In this study, we present preliminary results from our hybrid spectral CT instrumentation which includes clinical-grade hardware (rapid kVp-switching x-ray tube, dual-layer detector). This combination expands spectral datasets from two to four channels, wherein we hypothesize improved quantification accuracy for low-dose and low-iodine concentration cases. We modulate the system duty cycle to evaluate its impact on quantification noise and bias. We evaluate iodine quantification performance by comparing two hybrid weighting strategies alongside rapid kVp-switching. This evaluation is performed with a polyamide phantom containing seven iodine inserts ranging from 0.5 to 20 mg/mL. In comparison to alternative methodologies, the maximum separation configuration, incorporating data from both the 80 kVp, low photon energy detector layer and the 140 kVp, high photon energy detector layer produces spectral images containing low quantitative noise and bias. This study presents initial evaluations on a hybrid spectral CT system, leveraging clinical hardware to demonstrate the potential for enhanced precision and sensitivity in spectral imaging. This research holds promise for advancing spectral CT imaging performance across diverse clinical scenarios.

Common use herbicides increase wetland greenhouse gas emissions.

Wetlands play a disproportionate role in the global climate as major sources and sinks of greenhouse gases. Herbicides are the most heavily used agrochemicals and are frequently detected in aquatic ecosystems, with glyphosate and 2,4-Dichlorophenoxyacetic acid (2,4-D), representing the two most commonly used worldwide. In recent years, these herbicides are being used in mixtures to combat herbicide-tolerant noxious weeds. While it is well documented that herbicide use for agriculture is expected to increase, their indirect effects on wetland greenhouse gas dynamics are virtually unknown. To fill this knowledge gap, we conducted a factorial microcosm experiment using low, medium, and high concentrations of glyphosate or 2,4-D, individually and in combination to investigate their effects on wetland methane, carbon dioxide, and nitrous oxide fluxes. We predicted that mixed herbicide treatments would have a synergistic effect on greenhouse gases compared to individual herbicides. Our results showed that carbon dioxide flux rates and cumulative emissions significantly increased from both individual and mixed herbicide treatments, whereas methane and nitrous oxide dynamics were less affected. This study suggests that extensive use of glyphosate and 2,4-D may increase carbon dioxide emissions from wetlands, which could have implications for climate change.

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm.

Objective. Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels.Method. The lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different size extension rings to mimic a small- and medium-sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error, structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image.Results.DLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25%-83% in the small phantom and by 50%-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR.Conclusion. DLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose, which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments.

How Active Are Our Patients in the First 6 Weeks Following Total Knee Arthroplasty?

BACKGROUND: Patient activity after total knee arthroplasty (TKA) surgery has been estimated through patient-reported outcome measures. The use of data from an implanted sensor that transmits daily gait activity provides a more objective, complete recovery trajectory. METHODS: In this retrospective analysis of 794 patients who received a TKA with sensors in the tibial extension between October 4, 2021, and January 13, 2023, the average age of the patients was 64 years, and the cohort was 54.9% women. During the 6-week postoperative period, 90.3% of patients transmitted data. Patient activity in terms of qualified step count, cadence, walking speed, stride length, functional tibial range of motion (ROM), and functional knee ROM were compared at 1 week, 3 weeks, and 6 weeks postoperatively. RESULTS: All gait parameters increased in the first 6 weeks postsurgery: qualified step count increased 733%, cadence increased 22%, walking speed increased 50%, stride length increased 17%, tibial ROM increased 19%, and functional knee ROM increased 14%. There were statistically significant differences at both postoperative periods (P = .029, P < .001, and P < .001 at 3 and 6 weeks, respectively) in step counts for different body mass index (BMI) categories, with qualified step counts decreasing with increasing BMI. Patients under 65 years tended to have a higher qualified step count than those 65 and older at all time points, but these differences were not statistically significant. Men had significantly higher step counts than women (P < .001 at 1, 3, and 6 weeks). CONCLUSIONS: Initial results with an implanted sensor that collects data during activities of daily living confirm that 90% of patients transmit objective gait metrics daily after TKA surgery. Those results differ by sex and BMI. LEVEL OF EVIDENCE: III Retrospective Cohort Study.

Corrigendum to "Validation of a convolutional neural network that reliably identifies electromyographic compound motor action potentials following train-of-four stimulation: an algorithm development experimental study" [BJA Open 8 (2023) 100236].

[This corrects the article DOI: 10.1016/j.bjao.2023.100236.].

Unusual combinations of neurodegenerative pathologies with chronic traumatic encephalopathy (CTE) complicates clinical prediction of CTE.

BACKGROUND AND PURPOSE: Chronic traumatic encephalopathy (CTE) has gained widespread attention due to its association with multiple concussions and contact sports. However, CTE remains a postmortem diagnosis, and the link between clinical symptoms and CTE pathology is poorly understood. This study aimed to investigate the presence of copathologies and their impact on symptoms in former contact sports athletes. METHODS: This was a retrospective case series design of 12 consecutive cases of former contact sports athletes referred for autopsy. Analyses are descriptive and include clinical history as well as the pathological findings of the autopsied brains. RESULTS: All participants had a history of multiple concussions, and all but one had documented progressive cognitive, psychiatric, and/or motor symptoms. The results showed that 11 of the 12 participants had evidence of CTE in the brain, but also other copathologies, including different combinations of tauopathies, and other rare entities. CONCLUSIONS: The heterogeneity of symptoms after repetitive head injuries and the diverse pathological combinations accompanying CTE complicate the prediction of CTE in clinical practice. It is prudent to consider the possibility of multiple copathologies when clinically assessing patients with repetitive head injuries, especially as they age, and attributing neurological or cognitive symptoms solely to presumptive CTE in elderly patients should be discouraged.

Reproducible spectral CT thermometry with liver-mimicking phantoms for image-guided thermal ablation.

Objectives. Evaluate the reproducibility, temperature tolerance, and radiation dose requirements of spectral CT thermometry in tissue-mimicking phantoms to establish its utility for non-invasive temperature monitoring of thermal ablations.Methods. Three liver mimicking phantoms embedded with temperature sensors were individually scanned with a dual-layer spectral CT at different radiation dose levels during heating (35 °C-80 °C). Physical density maps were reconstructed from spectral results using varying reconstruction parameters. Thermal volumetric expansion was then measured at each temperature sensor every 5 °C in order to establish a correlation between physical density and temperature. Linear regressions were applied based on thermal volumetric expansion for each phantom, and coefficient of variation for fit parameters was calculated to characterize reproducibility of spectral CT thermometry. Additionally, temperature tolerance was determined to evaluate effects of acquisition and reconstruction parameters. The resulting minimum radiation dose to meet the clinical temperature accuracy requirement was determined for each slice thickness with and without additional denoising.Results. Thermal volumetric expansion was robustly replicated in all three phantoms, with a correlation coefficient variation of only 0.43%. Similarly, the coefficient of variation for the slope and intercept were 9.6% and 0.08%, respectively, indicating reproducibility of the spectral CT thermometry. Temperature tolerance ranged from 2 °C to 23 °C, decreasing with increased radiation dose, slice thickness, and iterative reconstruction level. To meet the clinical requirement for temperature tolerance, the minimum required radiation dose ranged from 20, 30, and 57 mGy for slice thickness of 2, 3, and 5 mm, respectively, but was reduced to 2 mGy with additional denoising.Conclusions. Spectral CT thermometry demonstrated reproducibility across three liver-mimicking phantoms and illustrated the clinical requirement for temperature tolerance can be met for different slice thicknesses. The reproducibility and temperature accuracy of spectral CT thermometry enable its clinical application for non-invasive temperature monitoring of thermal ablation.

Qualitative Ascriptions of Autistic Behavior by Non-Autistic College Students.

In studies that assess perceptions of autistic people by non-autistic people, researchers often ask participants to review vignettes depicting fictional autistic characters. However, few studies have investigated whether non-autistic peers accurately identify these hypothetical individuals as being on the autism spectrum. Accurately ascribing autism as a cause of depicted behaviors likely influences perceptions about autistic peers. In this study, 469 college students (Mage = 18.62; 79.3% female) ascribed cause(s) of an autistic peers' behaviors as depicted in a written vignette. We reviewed and categorized open-ended responses into 16 categories. Non-autistic college students primarily attributed an autistic vignette character's behavior to non-autistic origins. The most commonly ascribed causes of behavior were: attention-deficit/hyperactivity disorder (55.4%), inattention symptoms (20.9%), autism (12.8%), generalized anxiety disorder (11.7%), hyperactivity (11.3%), an unspecified diagnosis (10.7%), an environmental influence (9.6), anxiety or insecurity (8.3%), irritability or anger or annoyance (6.0%), social anxiety disorder (5.3%), and learning disorder (5.1%). Additional ascribed causes include other mental health diagnoses; environmental stressors; and cognitive, emotional, behavioral, biological, or personality characteristics/etiologies. Non-autistic young adults may not always recognize their autistic peers as autistic, which may affect acceptance and inclusion. Future anti-stigma interventions should assess the impact of helping non-autistic peers to accurately identify and better understand behaviors associated with autism. Additionally, autism-focused researchers using vignettes should assess participants' awareness of the character as autistic and interpret their findings with this in mind.

Motivations and Barriers for Women Orthopaedic Surgeons Considering Arthroplasty Fellowship.

BACKGROUND: Arthroplasty is one of the least gender-diverse orthopaedic subspecialties. While previous studies have looked at factors influencing fellowship choices for women, few studies have attempted to understand the decision for or against arthroplasty specifically. Working to better understand fellowship choice is a critical step in the process of increasing women recruitment. METHODS: An anonymous survey was distributed using REDCap to women orthopaedic surgeons and trainees through listservs, social media groups, and residency programs. Surgeons who had decided on a specific subspecialty or already completed fellowship were included. Responses were obtained from 164 surgeons (72 arthroplasty surgeons, 92 other subspecialties). Chi-squared and Fisher's Exact tests were then performed. RESULTS: The most important factor for those who chose arthroplasty was enjoyment of the surgeries. The biggest concerns from those in the arthroplasty group about the field were work-life balance, ability to become pregnant and/or have a healthy pregnancy, and sex bias from referring physicians. Of those who ultimately chose another subspecialty, 30.4% considered arthroplasty "a little" and 8.7% considered it "strongly." The most important dissuaders for the group that considered arthroplasty were concerns about "boy's club" culture, concerns about the physicality of the surgeries, and a lack of mentors. CONCLUSION: While the decision to choose a career path is multifactorial, our hope is that through the identification of modifiable factors we can increase women representation in arthroplasty. Increasing mentorship, implementing practical solutions to improve work-life balance, supporting healthy pregnancies, and mitigating the physical demands of surgery could help address current disparities.

Practical Guide to Measuring Wetland Carbon Pools and Fluxes.

Wetlands cover a small portion of the world, but have disproportionate influence on global carbon (C) sequestration, carbon dioxide and methane emissions, and aquatic C fluxes. However, the underlying biogeochemical processes that affect wetland C pools and fluxes are complex and dynamic, making measurements of wetland C challenging. Over decades of research, many observational, experimental, and analytical approaches have been developed to understand and quantify pools and fluxes of wetland C. Sampling approaches range in their representation of wetland C from short to long timeframes and local to landscape spatial scales. This review summarizes common and cutting-edge methodological approaches for quantifying wetland C pools and fluxes. We first define each of the major C pools and fluxes and provide rationale for their importance to wetland C dynamics. For each approach, we clarify what component of wetland C is measured and its spatial and temporal representativeness and constraints. We describe practical considerations for each approach, such as where and when an approach is typically used, who can conduct the measurements (expertise, training requirements), and how approaches are conducted, including considerations on equipment complexity and costs. Finally, we review key covariates and ancillary measurements that enhance the interpretation of findings and facilitate model development. The protocols that we describe to measure soil, water, vegetation, and gases are also relevant for related disciplines such as ecology. Improved quality and consistency of data collection and reporting across studies will help reduce global uncertainties and develop management strategies to use wetlands as nature-based climate solutions. Supplementary Information: The online version contains supplementary material available at 10.1007/s13157-023-01722-2.

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm.

Objective: Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels. Approach: The lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different sized extension rings to mimic a small and medium sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error (RMSE), structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image. Main Results: DLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25-83% in the small phantom and by 50-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR with a non-anatomical physics phantom. Significance: DLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments.

Chronic exposure to environmental temperature attenuates the thermal sensitivity of salmonids.

Metabolism, the biological processing of energy and materials, scales predictably with temperature and body size. Temperature effects on metabolism are normally studied via acute exposures, which overlooks the capacity for organisms to moderate their metabolism following chronic exposure to warming. Here, we conduct respirometry assays in situ and after transplanting salmonid fish among different streams to disentangle the effects of chronic and acute thermal exposure. We find a clear temperature dependence of metabolism for the transplants, but not the in-situ assays, indicating that chronic exposure to warming can attenuate salmonid thermal sensitivity. A bioenergetic model accurately captures the presence of fish in warmer streams when accounting for chronic exposure, whereas it incorrectly predicts their local extinction with warming when incorporating the acute temperature dependence of metabolism. This highlights the need to incorporate the potential for thermal acclimation or adaptation when forecasting the consequences of global warming on ecosystems.

Validation of a convolutional neural network that reliably identifies electromyographic compound motor action potentials following train-of-four stimulation: an algorithm development experimental study.

Background: International guidelines recommend quantitative neuromuscular monitoring when administering neuromuscular blocking agents. The train-of-four count is important for determining the depth of block and appropriate reversal agents and doses. However, identifying valid compound motor action potentials (cMAPs) during surgery can be challenging because of low-amplitude signals and an inability to observe motor responses. A convolutional neural network (CNN) to classify cMAPs as valid or not might improve the accuracy of such determinations. Methods: We modified a high-accuracy CNN originally developed to identify handwritten numbers. For training, we used digitised electromyograph waveforms (TetraGraph) from a previous study of 29 patients and tuned the model parameters using leave-one-out cross-validation. External validation used a dataset of 19 patients from another study with the same neuromuscular block monitor but with different patient, surgical, and protocol characteristics. All patients underwent ulnar nerve stimulation at the wrist and the surface electromyogram was recorded from the adductor pollicis muscle. Results: The tuned CNN performed highly on the validation dataset, with an accuracy of 0.9997 (99% confidence interval 0.9994-0.9999) and F1 score=0.9998. Performance was equally good for classifying the four individual responses in the train-of-four sequence. The calibration plot showed excellent agreement between the predicted probabilities and the actual prevalence of valid cMAPs. Ten-fold cross-validation using all data showed similar high performance. Conclusions: The CNN distinguished valid cMAPs from artifacts after ulnar nerve stimulation at the wrist with >99.5% accuracy. Incorporation of such a process within quantitative electromyographic neuromuscular block monitors is feasible.