Data-driven interpretable analysis for polysaccharide yield prediction

Cornstalks show promise as a raw material for polysaccharide production through xylanase. Rapid and accurate prediction of polysaccharide yield can facilitate process optimization, eliminating the need for extensive experimentation in actual production to refine reaction conditions, thereby saving time and costs. However, the intricate interplay of enzymatic factors poses challenges in predicting and optimizing polysaccharide yield accurately. Here, we introduce an innovative data-driven approach leveraging multiple artificial intelligence techniques to enhance polysaccharide production. We propose a machine learning framework to identify highly accurate polysaccharide yield prediction modeling methods and uncover optimal enzymatic parameter combinations. Notably, Random Forest (RF) and eXtreme Gradient Boost (XGB) demonstrate robust performance, achieving prediction accuracies of 93.0% and 95.6%, respectively, while an independently developed deep neural network (DNN) model achieves 91.1% accuracy. A feature importance analysis of XGB reveals the enzyme solution volume's dominant role (43.7%), followed by time (20.7%), substrate concentration (15%), temperature (15%), and pH (5.6%). Further interpretability analysis unveils complex parameter interactions and potential optimization strategies. This data-driven approach, incorporating machine learning, deep learning, and interpretable analysis, offers a viable pathway for polysaccharide yield prediction and the potential recovery of various agricultural residues.

Cost dynamics of onshore wind energy in the context of China's carbon neutrality target

Wind energy has become one of the most important measures for China to achieve its carbon neutrality goal. The spatial and temporal evolvement of economic competitiveness for wind energy becomes an important concern in shaping the decarbonization pathway in China. There has been an urgent need in power system planning to model the future dynamics of cost decline and supply potential for wind power in the context of carbon neutrality until 2060. Existing studies often fail to capture the rapid decline in the cost of wind power generation in recent years, and the prediction of wind power cost decline is more conservative than the reality. This study constructs an integrated model to evaluate the cost-competitiveness and grid parity potential of China's onshore wind electricity at fine spatial resolution with updated parameters. Results indicate that the total onshore wind potential amounts to 54.0 PWh. The average levelized cost of wind power is expected to decline from CNY 0.39 kWh-1 in 2020 to CNY 0.30 and CNY 0.21 kWh-1 in 2030 and 2060. 28.3%, 67.6%, and 97.6% of the technical potentials hold power costs lower than coal power in 2020, 2030, and 2060.

The 2022 report of synergetic roadmap on carbon neutrality and clean air for China: Accelerating transition in key sectors

China is now confronting the intertwined challenges of air pollution and climate change. Given the high synergies between air pollution abatement and climate change mitigation, the Chinese government is actively promoting synergetic control of these two issues. The Synergetic Roadmap project was launched in 2021 to track and analyze the progress of synergetic control in China by developing and monitoring key indicators. The Synergetic Roadmap 2022 report is the first annual update, featuring 20 indicators across five aspects: synergetic governance system and practices, progress in structural transition, air pollution and associated weather-climate interactions, sources, sinks, and mitigation pathway of atmospheric composition, and health impacts and benefits of coordinated control. Compared to the comprehensive review presented in the 2021 report, the Synergetic Roadmap 2022 report places particular emphasis on progress in 2021 with highlights on actions in key sectors and the relevant milestones. These milestones include the proportion of non-fossil power generation capacity surpassing coal-fired capacity for the first time, a decline in the production of crude steel and cement after years of growth, and the surging penetration of electric vehicles. Additionally, in 2022, China issued the first national policy that synergizes abatements of pollution and carbon emissions, marking a new era for China's pollution-carbon co-control. These changes highlight China's efforts to reshape its energy, economic, and transportation structures to meet the demand for synergetic control and sustainable development. Consequently, the country has witnessed a slowdown in carbon emission growth, improved air quality, and increased health benefits in recent years.

Knowledge-guided machine learning reveals pivotal drivers for gas-to-particle conversion of atmospheric nitrate

Particulate nitrate, a key component of fine particles, forms through the intricate gas-to-particle conversion process. This process is regulated by the gas-to-particle conversion coefficient of nitrate (ε(NO3-)). The mechanism between ε(NO3-) and its drivers is highly complex and nonlinear, and can be characterized by machine learning methods. However, conventional machine learning often yields results that lack clear physical meaning and may even contradict established physical/chemical mechanisms due to the influence of ambient factors. It urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact of ε(NO3-). Here we introduce a supervised machine learning approach-the multilevel nested random forest guided by theory approaches. Our approach robustly identifies NH4+, SO42-, and temperature as pivotal drivers for ε(NO3-). Notably, substantial disparities exist between the outcomes of traditional random forest analysis and the anticipated actual results. Furthermore, our approach underscores the significance of NH4+ during both daytime (30%) and nighttime (40%) periods, while appropriately downplaying the influence of some less relevant drivers in comparison to conventional random forest analysis. This research underscores the transformative potential of integrating domain knowledge with machine learning in atmospheric studies.

Data-driven systematic analysis of waterborne viruses and health risks during the wastewater reclamation process

Waterborne viral epidemics are a major threat to public health. Increasing interest in wastewater reclamation highlights the importance of understanding the health risks associated with potential microbial hazards, particularly for reused water in direct contact with humans. This study focused on identifying viral epidemic patterns in municipal wastewater reused for recreational applications based on long-term, spatially explicit global literature data during 2000-2021, and modelled human health risks from multiple exposure pathways using a well-established quantitative microbial risk assessment methodology. Global median viral loads in municipal wastewater ranged from 7.92 × 104 to 1.4 × 106 GC L-1 in the following ascending order: human adenovirus (HAdV), norovirus (NoV) GII, enterovirus (EV), NoV GI, rotavirus (RV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Following secondary or tertiary wastewater treatment, NoV GI, NoV GII, EV, and RV showed a relatively higher and more stable log reduction value with medians all above 0.8 (84%), whereas SARS-CoV-2 and HAdV showed a relatively lower reduction, with medians ranging from 0.33 (53%) to 0.55 (72%). A subsequent disinfection process effectively enhanced viral removal to over 0.89-log (87%). The predicted event probability of virus-related gastrointestinal illness and acute febrile respiratory illnesses in reclaimed recreational water exceeded the World Health Organization recommended recreational risk benchmark (5% and 1.9%, respectively). Overall, our results provided insights on health risks associated with reusing wastewater for recreational purposes and highlighted the need for establishing a regulatory framework ensuring the safety management of reclaimed waters.

H2 mediated mixed culture microbial electrosynthesis for high titer acetate production from CO2

Microbial electrosynthesis (MES) converts CO2 into value-added products such as volatile fatty acids (VFAs) with minimal energy use, but low production titer has limited scale-up and commercialization. Mediated electron transfer via H2 on the MES cathode has shown a higher conversion rate than the direct biofilm-based approach, as it is tunable via cathode potential control and accelerates electrosynthesis from CO2. Here we report high acetate titers can be achieved via improved in situ H2 supply by nickel foam decorated carbon felt cathode in mixed community MES systems. Acetate concentration of 12.5 g L-1 was observed in 14 days with nickel-carbon cathode at a poised potential of -0.89 V (vs. standard hydrogen electrode, SHE), which was much higher than cathodes using stainless steel (5.2 g L-1) or carbon felt alone (1.7 g L-1) with the same projected surface area. A higher acetate concentration of 16.0 g L-1 in the cathode was achieved over long-term operation for 32 days, but crossover was observed in batch operation, as additional acetate (5.8 g L-1) was also found in the abiotic anode chamber. We observed the low Faradaic efficiencies in acetate production, attributed to partial H2 utilization for electrosynthesis. The selective acetate production with high titer demonstrated in this study shows the H2-mediated electron transfer with common cathode materials carries good promise in MES development.

Four buildings and a flush: Lessons from degraded water quality and recommendations on building water management

A reduction in building occupancy can lead to stagnant water in plumbing, and the potential consequences for water quality have gained increasing attention. To investigate this, a study was conducted during the COVID-19 pandemic, focusing on water quality in four institutional buildings. Two of these buildings were old (>58 years) and large (>19,000 m2), while the other two were new (>13 years) and small (<11,000 m2). The study revealed significant decreases in water usage in the small buildings, whereas usage remained unchanged in the large buildings. Initial analysis found that residual chlorine was rarely detectable in cold/drinking water samples. Furthermore, the pH, dissolved oxygen, total organic carbon, and total cell count levels in the first draw of cold water samples were similar across all buildings. However, the ranges of heavy metal concentrations in large buildings were greater than observed in small buildings. Copper (Cu), lead (Pb), and manganese (Mn) sporadically exceeded drinking water limits at cold water fixtures, with maximum concentrations of 2.7 mg Cu L-1, 45.4 μg Pb L-1, 1.9 mg Mn L-1. Flushing the plumbing for 5 min resulted in detectable residual at fixtures in three buildings, but even after 125 min of flushing in largest and oldest building, no residual chlorine was detected at the fixture closest to the building's point of entry. During the pandemic, the building owner conducted fixture flushing, where one to a few fixtures were operated per visit in buildings with hundreds of fixtures and multiple floors. However, further research is needed to understand the fundamental processes that control faucet water quality from the service line to the faucet. In the absence of this knowledge, building owners should create and use as-built drawings to develop flushing plans and conduct periodic water testing.

Surveillance-image-based outdoor air quality monitoring

Air pollution threatens human health, necessitating effective and convenient air quality monitoring. Recently, there has been a growing interest in using camera images for air quality estimation. However, a major challenge has been nighttime detection due to the limited visibility of nighttime images. Here we present a hybrid deep learning model, capitalizing on the temporal continuity of air quality changes for estimating outdoor air quality from surveillance images. Our model, which integrates a convolutional neural network (CNN) and long short-term memory (LSTM), adeptly captures spatial-temporal image features, enabling air quality estimation at any time of day, including PM2.5 and PM10 concentrations, as well as the air quality index (AQI). Compared to independent CNN networks that solely extract spatial features, our model demonstrates superior accuracy on self-constructed datasets with R2 = 0.94 and RMSE = 5.11 μg m-3 for PM2.5, R2 = 0.92 and RMSE = 7.30 μg m-3 for PM10, and R2 = 0.94 and RMSE = 5.38 for AQI. Furthermore, our model excels in daytime air quality estimation and enhances nighttime predictions, elevating overall accuracy. Validation across diverse image datasets and comparative analyses underscore the applicability and superiority of our model, reaffirming its applicability and superiority for air quality monitoring.

An Artificial Intelligence Enabled System for Retinal Nerve Fiber Layer Thickness Damage Severity Staging

Purpose

To develop an objective glaucoma damage severity classification system based on OCT-derived retinal nerve fiber layer (RNFL) thickness measurements.

Design

Algorithm development for RNFL damage severity classification based on multicenter OCT data.

Subjects and Participants

A total of 6561 circumpapillary RNFL profiles from 2269 eyes of 1171 subjects to develop models, and 2505 RNFL profiles from 1099 eyes of 900 subjects to validate models.

Methods

We developed an unsupervised k-means model to identify clusters of eyes with similar RNFL thickness profiles. We annotated the clusters based on their respective global RNFL thickness. We computed the optimal global RNFL thickness thresholds that discriminated different severity levels based on Bayes' minimum error principle. We validated the proposed pipeline based on an independent validation dataset with 2505 RNFL profiles from 1099 eyes of 900 subjects.

Main Outcome Measures

Accuracy, area under the receiver operating characteristic curve, and confusion matrix.

Results

The k-means clustering discovered 4 clusters with 1382, 1613, 1727, and 1839 samples with mean (standard deviation) global RNFL thickness of 58.3 (8.9) μm, 78.9 (6.7) μm, 87.7 (8.2) μm, and 101.5 (7.9) μm. The Bayes' minimum error classifier identified optimal global RNFL values of > 95 μ m , 86 to 95 μ m , 70 to 85 μ m , and < 70 μ m for discriminating normal eyes and eyes at the early, moderate, and advanced stages of RNFL thickness loss, respectively. About 4% of normal eyes and 98% of eyes with advanced RNFL loss had either global, or ≥ 1 quadrant, RNFL thickness outside of normal limits provided by the OCT instrument.

Conclusions

Unsupervised machine learning discovered that the optimal RNFL thresholds for separating normal eyes and eyes with early, moderate, and advanced RNFL loss were 95 μ m , 85 μm, and 70 μ m , respectively. This RNFL loss classification system is unbiased as there was no preassumption or human expert intervention in the development process. Additionally, it is objective, easy to use, and consistent, which may augment glaucoma research and day-to-day clinical practice.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Early Sign of Retinal Neovascularization Evolution in Diabetic Retinopathy: A Longitudinal OCT Angiography Study

Purpose

To assess whether the combination of en face OCT and OCT angiography (OCTA) can capture observable, but subtle, structural changes that precede clinically evident retinal neovascularization (RNV) in eyes with diabetic retinopathy (DR).

Design

Retrospective, longitudinal study.

Participants

Patients with DR that had at least 2 visits.

Methods

We obtained wide-field OCTA scans of 1 eye from each participant and generated en face OCT, en face OCTA, and cross-sectional OCTA. We identified eyes with RNV sprouts, defined as epiretinal hyperreflective materials on en face OCT with flow signals breaching the internal limiting membrane on the cross-sectional OCTA without recognizable RNV on en face OCTA and RNV fronds, defined as recognizable abnormal vascular structures on the en face OCTA. We examined the corresponding location from follow-up or previous visits for the presence or progression of the RNV.

Main Outcome Measures

The characteristics and longitudinal observation of early signs of RNV.

Results

From 71 eyes, we identified RNV in 20 eyes with the combination of OCT and OCTA, of which 13 (65%) were photographically graded as proliferative DR, 6 (30%) severe nonproliferative DR, and 1 (5%) moderate nonproliferative diabetic retinopathy. From these eyes, we identified 38 RNV sprouts and 26 RNV fronds at the baseline. Thirty-four RNVs (53%) originated from veins, 24 (38%) were from intraretinal microabnormalities, and 6 (9%) were from a nondilated capillary bed. At the final visit, 53 RNV sprouts and 30 RNV fronds were detected. Ten eyes (50%) showed progression, defined as having a new RNV lesion or the development of an RNV frond from an RNV sprout. Four (11%) RNV sprouts developed into RNV fronds with a mean interval of 7.0 months. Nineteen new RNV sprouts developed during the follow-up, whereas no new RNV frond was observed outside an identified RNV sprout. The eyes with progression were of younger age (P = 0.014) and tended to be treatment naive (P = 0.07) compared with eyes without progression.

Conclusions

Longitudinal observation demonstrated that a combination of en face OCT and cross-sectional OCTA can identify an earlier form of RNV before it can be recognized on en face OCTA.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

On the specificity of figurative language comprehension impairment in schizophrenia and its relation to cognitive skills but not psychopathological symptoms - Study on metaphor, humor and irony

People with schizophrenia have difficulty understanding figurative expressions, such as metaphors, humor or irony. The present study investigated the specificity of figurative language impairment in schizophrenia and its relation with cognitive and psychotic symptoms. It included 54 schizophrenia and 54 age and sex-matched healthy subjects who performed a cognitive screening (ACE-III) and figurative language comprehension task consisting of 60 short stories with three types of endings: a figurative one and its literal and an absurd (meaningless) counterparts. Each figurative domain - metaphor, humor, irony - was split into two sub-domains, i.e., conventional and novel metaphors, intended-to-be-funny and social-norm-violation jokes, simple irony and critical sarcasm, respectively. The main findings are: i) in schizophrenia, figurative language deficit manifests itself in each domain; ii) the most pronounced subdomain-specific impairment has been found for novel vs conventional metaphors and irony vs sarcasm; iii) altered figurative language comprehension was related to diminished cognitive abilities but not to psychopathology symptoms (PANSS) or other clinical characteristics. This may suggest that figurative language impairment, as a specific part of communication deficit, may be regarded as an essential characteristic of schizophrenia, related to primary cognitive deficits but independent of psychopathology.

Automated Recognition of Visual Acuity Measurements in Ophthalmology Clinical Notes Using Deep Learning

Purpose

Visual acuity (VA) is a critical component of the eye examination but is often only documented in electronic health records (EHRs) as unstructured free-text notes, making it challenging to use in research. This study aimed to improve on existing rule-based algorithms by developing and evaluating deep learning models to perform named entity recognition of different types of VA measurements and their lateralities from free-text ophthalmology notes: VA for each of the right and left eyes, with and without glasses correction, and with and without pinhole.

Design

Cross-sectional study.

Subjects

A total of 319 756 clinical notes with documented VA measurements from approximately 90 000 patients were included.

Methods

The notes were split into train, validation, and test sets. Bidirectional Encoder Representations from Transformers (BERT) models were fine-tuned to identify VA measurements from the progress notes and included BERT models pretrained on biomedical literature (BioBERT), critical care EHR notes (ClinicalBERT), both (BlueBERT), and a lighter version of BERT with 40% fewer parameters (DistilBERT). A baseline rule-based algorithm was created to recognize the same VA entities to compare against BERT models.

Main Outcome Measures

Model performance was evaluated on a held-out test set using microaveraged precision, recall, and F1 score for all entities.

Results

On the human-annotated subset, BlueBERT achieved the best microaveraged F1 score (F1 = 0.92), followed by ClinicalBERT (F1 = 0.91), DistilBERT (F1 = 0.90), BioBERT (F1 = 0.84), and the baseline model (F1 = 0.83). Common errors included labeling VA in sections outside of the examination portion of the note, difficulties labeling current VA alongside a series of past VAs, and missing nonnumeric VAs.

Conclusions

This study demonstrates that deep learning models are capable of identifying VA measurements from free-text ophthalmology notes with high precision and recall, achieving significant performance improvements over a rule-based algorithm. The ability to recognize VA from free-text notes would enable a more detailed characterization of ophthalmology patient cohorts and enhance the development of models to predict ophthalmology outcomes.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Underestimated activity-based microplastic intake under scenario-specific exposures

Despite increasing alarms over the health impacts of microplastics (MPs) due to their detection in human organs and feces, precise exposure evaluations remain scarce. To comprehend their risks, there is a distinct need to prioritize quantitive estimates in MP exposome, particularly at the environmentally-realistic level. Here we used a method rooted in real-world MP measurements and activity patterns to determine the daily intake of MPs through inhalation and from ground dust/soil ingestion. We found that nearly 80% of this intake comes from residential sectors, with activity intensity and behavioral types significantly affecting the human MP burden. The data showed a peak in MP exposure for those aged 18-64. When compared to dietary MP intake sources like seafood, salt, and water, we identified a previously underestimated exposure from inhalation and dust/soil ingestion, emphasizing the need for more realistic evaluations that incorporate activity factors. This discovery raises questions about the accuracy of past studies and underscores MP's potential health risks. Moreover, our time-based simulations revealed increased MP intake during the COVID-19 lockdown due to more surface dust ingestion, shedding light on how global health crises may inadvertently elevate MP exposure risks.

Gradeability and Reproducibility of Geographic Atrophy Measurement in GATHER-1, a Phase II/III Randomized Interventional Trial

Objective

To validate GATHER-1 inclusion criteria and the study's primary anatomic end point by assessing the reproducibility of geographic atrophy (GA) measurements and factors that affect reproducibility.

Design

Post hoc analysis of phase II/III clinical trial.

Subjects

All 286 participants included in the GATHER-1 study.

Methods

For each subject, blue-light fundus autofluorescence (FAF), color fundus photographs, fluorescein angiograms, and OCT scans were obtained on the study eye and fellow eye. Geographic atrophy area and other lesion characteristics were independently graded by 2 experienced primary readers. If the 2 readers differed on gradeability, GA area (> 10%) or other lesion characteristics, the image was graded by an arbitrator whose measurement or characterization was the final grade.

Main Outcome Measures

The main outcome measures were gradeability and reproducibility of FAF imaging data. Imaging data included lesion area, confluence of GA with peripapillary atrophy (PPA), whether GA involved the foveal centerpoint, and type of hyperautofluorescence pattern.

Results

A total of 2004 images (1002 visits, 286 participants) were analyzed. Gradeability (90.5%) and interreader gradeability concordance (90.2%) were high across all visits. Study eye images were more gradable compared with fellow-eye images. A greater proportion of smaller lesions required arbitration, but interreader reproducibility was consistently high for all images. There was no difference in gradeability, gradeability concordance, or lesion-area concordance for images with PPA-confluent GA compared with those with nonconfluent PPA. Foveal centerpoint-involving lesions had lower gradeability and lesion-area concordance. Images with diffuse patterns of hyperautofluorescence had better gradeability and gradeability concordance than those with nondiffuse patterns but had no difference in lesion-area or lesion-area concordance.

Conclusions

There is high gradeability and excellent reproducibility measures across all images. These data support the validity of conclusions from GATHER-1 and the chosen inclusion criteria and end point.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Sampling efficiency of a polyurethane foam air sampler: Effect of temperature

Effective monitoring of atmospheric concentrations is vital for assessing the Stockholm Convention's effectiveness on persistent organic pollutants (POPs). This task, particularly challenging in polar regions due to low air concentrations and temperature fluctuations, requires robust sampling techniques. Furthermore, the influence of temperature on the sampling efficiency of polyurethane foam discs remains unclear. Here we employ a flow-through sampling (FTS) column coupled with an active pump to collect air samples at varying temperatures. We delved into breakthrough profiles of key pollutants, such as polycyclic aromatic hydrocarbons (PAHs), polychlorobiphenyls (PCBs), and organochlorine pesticides (OCPs), and examined the temperature-dependent behaviors of the theoretical plate number (N) and breakthrough volume (VB) using frontal chromatography theory. Our findings reveal a significant relationship between temperature dependence coefficients (KTN, KTV) and compound volatility, with decreasing values as volatility increases. While distinct trends are noted for PAHs, PCBs, and OCPs in KTN, KTV values exhibit similar patterns across all chemicals. Moreover, we establish a binary linear correlation between log (VB/m3), 1/(T/K), and N, simplifying breakthrough level estimation by enabling easy conversion between N and VB. Finally, an empirical linear solvation energy relationship incorporating a temperature term is developed, yielding satisfactory results for N at various temperatures. This approach holds the potential to rectify temperature-related effects and loss rates in historical data from long-term monitoring networks, benefiting polar and remote regions.

Trends in Research Payments for Diabetic Macular Edema from 2015 to 2021

Purpose

To evaluate characteristics of research payments for diabetic macular edema (DME) studies and correlations to current management trends.

Design

Retrospective cross-sectional study.

Subjects

Research payments for DME.

Methods

Studies with keywords of "diabetic macular edema" or "DME" in the title were extracted from the Centers of Medicare & Medicaid Services Open Payments database from 2015 to 2021. Recipients, payors, and payment amounts were identified. Industry funding was compared with public research funding by the National Eye Institute (NEI).

Main Outcome Measures

Trends and total value of industry and public fundings for DME from 2015 to 2021.

Results

From 2015 to 2021, 451 beneficiaries received 6062 industry payments for a total of $120 148 997.41 for DME-related research. The total value of industry funding increased from $8 225 859.08 in 2015 to $50 092 778.45 in 2021. Of the 6062 industry payments, 5367 (88.5%) were reported by male recipients compared with 695 (11.5%) female beneficiaries. Payments to female recipients increased from 60 (7.1%) in 2015 to 335 (13.7%) in 2021. In comparison, public funding for DME-related research from the NEI was comprised of $18 863 266.00 to 17 principal investigators from 2015 to 2021. The total value of public funding increased from $973 590.00 in 2015 to $3 354 376.00 in 2021. Of 59 public research payments, 46 (78.0%) were reported by male recipients and 13 (22.0%) by female recipients. Payments to female recipients increased from 1 (25.0%) in 2015 to 3 (30.0%) in 2021. The most highly invested product by industry were anti-VEGF agents, accounting for $89 955 595.20 (74.9%) of total payment value.

Conclusions

There was an increase in both industry and public-sponsored funding for DME-related research from 2015 to 2021. There seemed to be a possible discrepancy in both industry and public funding based on sex for DME studies during the study period.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Keratoconus Detection-based on Dynamic Corneal Deformation Videos Using Deep Learning

Objective

To assess the performance of convolutional neural networks (CNNs) for automated detection of keratoconus (KC) in standalone Scheimpflug-based dynamic corneal deformation videos.

Design

Retrospective cohort study.

Participants

We retrospectively analyzed datasets with records of 734 nonconsecutive, refractive surgery candidates, and patients with unilateral or bilateral KC.

Methods

We first developed a video preprocessing pipeline to translate dynamic corneal deformation videos into 3-dimensional pseudoimage representations and then trained a CNN to directly identify KC from pseudoimages. We calculated the model's KC probability score cut-off and evaluated the performance by subjective and objective accuracy metrics using 2 independent datasets.

Main Outcome Measures

Area under the receiver operating characteristics curve (AUC), accuracy, specificity, sensitivity, and KC probability score.

Results

The model accuracy on the test subset was 0.89 with AUC of 0.94. Based on the external validation dataset, the AUC and accuracy of the CNN model for detecting KC were 0.93 and 0.88, respectively.

Conclusions

Our deep learning-based approach was highly sensitive and specific in separating normal from keratoconic eyes using dynamic corneal deformation videos at levels that may prove useful in clinical practice.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Corneal Stress Distribution Evolves from Thickness-Driven in Normal Corneas to Curvature-Driven with Progression in Keratoconus

Purpose

To introduce the novel parameter of Corneal Contribution to Stress (CCS) and compare stress distribution patterns between keratoconus (KCN) and normal corneas.

Design

Prospective, observational, cross-sectional study.

Participants

The study included 66 eyes of 40 subjects diagnosed with KCN and 155 left eyes from 155 normal control (NRL) subjects.

Methods

Tomography was obtained to calculate the newly proposed CCS, defined according to the hoop stress formula without intraocular pressure, R/2t, where R is the radius of curvature and t is the thickness. CCS maps were calculated from pachymetry and tangential curvature maps. Custom software identified the 2-mm-diameter zones of greatest curvature (Cspot-max), thinnest pachymetry (Pach-min), greatest stress (CCSmax), and lowest stress (CCSmin). Stress difference (CCSdiff) was calculated as CCSmax - CCSmin. Distances between Cspot-max vs. Pach-min, vs. CCSmax, and vs. CCSmin, as well as between Pach-min vs. CCSmax and vs. CCSmin, were calculated. t tests were performed between cohorts, and paired t tests were performed within cohorts. Univariate linear regression analyses were performed between parameters and distances. The significance threshold was P < 0.05.

Main Outcome Measures

Corneal stress parameters, corneal features of maximum curvature, minimum thickness, and distances between corneal stress parameters and corneal features.

Results

CCSmax was significantly closer to Pach-min (0.79 ± 0.92) and Cspot-max (2.04 ± 0.85) than CCSmin (3.17 ± 0.38, 2.73 ± 1.53, respectively) in NRL, P < 0.0001, whereas CCSmin was significantly closer to Cspot-max (1.35 ± 1.43) than CCSmax (2.52 ± 0.72) in KCN, P < 0.0001. Cspot-max (severity) was significantly related to CCSdiff in KCN (P < 0.0001; R2 = 0.5882) with a weak relationship in NRL (P < 0.0080, R2 = 0.0451). Cspot-max was significantly related to the distance from Pach-min to CCSmax (P < 0.0001; R2 = 0.3737) without significance in NRL (P = 0.8011).

Conclusions

Corneal stress is driven by thickness in NRL, with greatest stress at thinnest pachymetry and greatest curvature. However, maximum stress moves away from thinnest pachymetry with progression in KCN, and minimum stress is associated with maximum curvature. Severity in KCN is significantly related to greater difference between maximum and minimum stress, consistent with the biomechanical cycle of decompensation.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Neighborhood garden's age shapes phyllosphere microbiota associated with respiratory diseases in cold seasons

Neighborhood gardens serve as sensitive sites for human microbial encounters, with phyllosphere microbes directly impacting our respiratory health. Yet, our understanding remains limited on how factors like season, garden age, and land use shape the risk of respiratory diseases (RDs) tied to these garden microbes. Here we examined the microbial communities within the phyllosphere of 72 neighborhood gardens across Shanghai, spanning different seasons (warm and cold), garden ages (old and young), and locales (urban and rural). We found a reduced microbial diversity during the cold season, except for Gammaproteobacteria which exhibited an inverse trend. While land use influenced the microbial composition, urban and rural gardens had strikingly similar microbial profiles. Alarmingly, young gardens in the cold season hosted a substantial proportion of RDs-associated species, pointing towards increased respiratory inflammation risks. In essence, while newer gardens during colder periods show a decline in microbial diversity, they have an increased presence of RDs-associated microbes, potentially escalating respiratory disease prevalence. This underscores the pivotal role the garden age plays in enhancing both urban microbial diversity and respiratory health.

Dynamic Stimulation Aberrometry: Objectively Measured Accommodation and Pupil Dynamics after Phakic Iris-Fixated Intraocular Lens Implantation

Purpose

Anterior iris-claw phakic intraocular lens (pIOL) implantation is a treatment option for refractive, ametropic patients. However, the postoperative accommodative ability has not been systematically researched. Dynamic stimulation aberrometry allows the objective and dynamical measurement of accommodation by observing ocular aberrations during the accommodation process. We investigated the dynamic accommodative ability after pIOL implantation compared with a healthy age- and gender-matched control group.

Design

Clinical, comparative case-control study.

Subjects

We included patients aged 18-50 years that either underwent pIOL implantation > 1 month ago or served as a healthy, phakic control group.

Methods

The accommodative ability and pupil dynamics of both groups were investigated using dynamic stimulation aberrometry. The method allows the analysis of dynamic parameters during accommodation, such as the accommodation speed. A 1:1 propensity score matching was conducted based on the patients' age and gender.

Main Outcome Measures

Parameters of objective accommodation, such as accommodative amplitude and pupil dynamic during accommodation.

Results

Fifty-eight healthy, phakic eyes < 50 years of age and 21 eyes after pIOL implantation to correct myopia (pIOL, Verisyse, AMO, Inc) were enrolled. Patients that underwent anterior pIOL implantation were examined on average 24 ± 18 months after surgery. After matching, the mean age of both groups was not significantly different (35 ± 8 vs. 34 ± 8 years). No significant difference in dynamic parameters of accommodation or the accommodative amplitude (2.8 ± 1.4 and 2.9 ± 1.4 diopters [D] for pIOL and control group, P = 0.82) were seen. Maximum and minimum pupil sizes were not significantly different. The change in pupil size during deaccommodation was significantly faster in patients after pIOL implantation (P < 0.001).

Conclusions

Dynamic stimulation aberrometry allowed the objective, dynamic, measurement of wavefronts in subjects with accommodative amplitudes up to 7 D. Phakic intraocular lens implantation does not impair the accommodative ability. It alters pupil dynamics during deaccommodation.

Financial Disclosures

Proprietary or commercial disclosure may be found after the references.

Sedimentary records of contaminant inputs in Frobisher Bay, Nunavut

Contaminants, such as polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), heavy metals, and per and polyfluoroalkyl substances (PFASs), primarily reach the Arctic through long-range atmospheric and oceanic transport. However, local sources within the Arctic also contribute to the levels observed in the environment, including legacy sources and new sources that arise from activities associated with increasing commercial and industrial development. The City of Iqaluit in Frobisher Bay, Nunavut (Canada), has seen rapid population growth and associated development during recent decades yet remains a site of interest for ocean protection, where Inuit continue to harvest country food. In the present study, seven dated marine sediment cores collected in Koojesse Inlet near Iqaluit, and from sites in inner and outer Frobisher Bay, respectively, were analyzed for total mercury (THg), major and trace elements, PAHs, PCBs, and PFASs. The sedimentary record in Koojesse Inlet shows a period of Aroclor 1260-like PCB input concurrent with military site presence in the 1950-60s, followed by decades of input of pyrogenic PAHs, averaging about ten times background levels. Near-surface sediments in Koojesse Inlet also show evidence of transient local-source inputs of THg and PFASs, and recycling or continued slow release of PCBs from legacy land-based sources. Differences in PFAS congener composition clearly distinguish the local sources from long-range transport. Outside Koojesse Inlet but still in inner Frobisher Bay, 9.2 km from Iqaluit, sediments showed evidence of both local source (PCB) and long-range transport. In outer Frobisher Bay, an up-core increase in THg and PFASs in sediments may be explained by ongoing inputs of these contaminants from long-range transport. The context for ocean protection and country food harvesting in this region of the Arctic clearly involves both local sources and long-range transport, with past human activities leaving a long legacy insofar as levels of persistent organic pollutants are concerned.

Persistent organic pollutants in global surface soils: Distributions and fractionations

The distribution and fractionation of persistent organic pollutants (POPs) in different matrices refer to how these pollutants are dispersed and separated within various environmental compartments. This is a significant study area as it helps us understand the transport efficiencies and long-range transport potentials of POPs to enter remote areas, particularly polar regions. This study provides a comprehensive review of the progress in understanding the distribution and fractionation of POPs. We focus on the contributions of four intermedia processes (dry and wet depositions for gaseous and particulate POPs) and determine their transfer between air and soil. These processes are controlled by their partitioning between gaseous and particulate phases in the atmosphere. The distribution patterns and fractionations can be categorized into primary and secondary types. Equations are developed to quantificationally study the primary and secondary distributions and fractionations of POPs. The analysis results suggest that the transfer of low molecular weight (LMW) POPs from air to soil is mainly through gas diffusion and particle deposition, whereas high molecular weight (HMW) POPs are mainly via particle deposition. HMW-POPs tend to be trapped near the source, whereas LMW-POPs are more prone to undergo long-range atmospheric transport. This crucial distinction elucidates the primary reason behind their temperature-independent primary fractionation. However, the secondary distribution and fractionation can only be observed along a temperature gradient, such as latitudinal or altitudinal transects. An animation is produced by a one-dimensional transport model to simulate conceptively the transport of CB-28 and CB-180, revealing the similarities and differences between the primary and secondary distributions and fractionations. We suggest that the decreasing temperature trend along latitudes is not the major reason for POPs to be fractionated into the polar ecosystems, but drives the longer-term accumulation of POPs in cold climates or polar cold trapping.