Cybernetic Principles in Psychophysiology: Their Significance and Conclusions for Palliative Care.

Palliative care is dedicated to terminally ill patients with advanced disease, regardless of diagnosis, under the overarching premise of optimizing quality of life. This narrative review examines the extent to which principles of cybernetics and psychophysiology underlie this approach. Psychophysiology researches the physiological equivalents of psychological states and traits such as activation and individual reactivity, the interoception and the personal characteristics. Cybernetics specifies these principles, which are possible by understanding terms such as "psychophysiology" or "cybernetics" or "self-organization/autopoiesis". The meaning of these terms for palliative care can also be elucidated in relation to the terms "biofeedback", "consciousness", "pain", and "anxiety". The common themes of cybernetics and psychophysiology are environment, subjectivity, personality characteristics, the difference between time scale separation in cybernetic systems, and real-time procedures in environment and rhythm. These lead to special therapies based on psychophysiology, such as consciousness training. The concepts of quality of life, causality, the biopsychosocial model, therapy, and autonomy are examined as palliative care concepts. The equivalents can be described from the perspective of cybernetics. For some palliative care-related terms, cybernetic thinking is already present (quality of life, autonomy, symptom control), while for others, it is not (biopsychosocial). Cybernetic terms (complexity, stability, identity, rhythm) are still used to a lesser extent in palliative care. Terms like genetic basis are common in cybernetics and psychophysiology to explain the identity of the subject in transition. Identity, on the other hand, is the basis of the concept of dignity in palliative care. Psychophysiology investigates disturbances like pain and psychological illnesses, which are also present in palliative care. Psychophysiology, cybernetics, and palliative care have subjectivity and resources in common. Therapies based on cybernetic principles of psychophysiology can also be used for symptom control in palliative care in the oncology setting.

Detecting tipping points of complex diseases by network information entropy.

The progression of complex diseases often involves abrupt and non-linear changes characterized by sudden shifts that trigger critical transformations. Identifying these critical states or tipping points is crucial for understanding disease progression and developing effective interventions. To address this challenge, we have developed a model-free method named Network Information Entropy of Edges (NIEE). Leveraging dynamic network biomarkers, sample-specific networks, and information entropy theories, NIEE can detect critical states or tipping points in diverse data types, including bulk, single-sample expression data. By applying NIEE to real disease datasets, we successfully identified critical predisease stages and tipping points before disease onset. Our findings underscore NIEE's potential to enhance comprehension of complex disease development.

Tipping point analysis for the between-arm correlation in an arm-based evidence synthesis.

Systematic reviews and meta-analyses are essential tools in contemporary evidence-based medicine, synthesizing evidence from various sources to better inform clinical decision-making. However, the conclusions from different meta-analyses on the same topic can be discrepant, which has raised concerns about their reliability. One reason is that the result of a meta-analysis is sensitive to factors such as study inclusion/exclusion criteria and model assumptions. The arm-based meta-analysis model is growing in importance due to its advantage of including single-arm studies and historical controls with estimation efficiency and its flexibility in drawing conclusions with both marginal and conditional effect measures. Despite its benefits, the inference may heavily depend on the heterogeneity parameters that reflect design and model assumptions. This article aims to evaluate the robustness of meta-analyses using the arm-based model within a Bayesian framework. Specifically, we develop a tipping point analysis of the between-arm correlation parameter to assess the robustness of meta-analysis results. Additionally, we introduce some visualization tools to intuitively display its impact on meta-analysis results. We demonstrate the application of these tools in three real-world meta-analyses, one of which includes single-arm studies.

CPMI: comprehensive neighborhood-based perturbed mutual information for identifying critical states of complex biological processes.

BACKGROUND: There exists a critical transition or tipping point during the complex biological process. Such critical transition is usually accompanied by the catastrophic consequences. Therefore, hunting for the tipping point or critical state is of significant importance to prevent or delay the occurrence of catastrophic consequences. However, predicting critical state based on the high-dimensional small sample data is a difficult problem, especially for single-cell expression data. RESULTS: In this study, we propose the comprehensive neighbourhood-based perturbed mutual information (CPMI) method to detect the critical states of complex biological processes. The CPMI method takes into account the relationship between genes and neighbours, so as to reduce the noise and enhance the robustness. This method is applied to a simulated dataset and six real datasets, including an influenza dataset, two single-cell expression datasets and three bulk datasets. The method can not only successfully detect the tipping points, but also identify their dynamic network biomarkers (DNBs). In addition, the discovery of transcription factors (TFs) which can regulate DNB genes and nondifferential 'dark genes' validates the effectiveness of our method. The numerical simulation verifies that the CPMI method is robust under different noise strengths and is superior to the existing methods on identifying the critical states. CONCLUSIONS: In conclusion, we propose a robust computational method, i.e., CPMI, which is applicable in both the bulk and single cell datasets. The CPMI method holds great potential in providing the early warning signals for complex biological processes and enabling early disease diagnosis.

Unveiling Microbial Nitrogen Metabolism in Rivers using a Machine Learning Approach.

Microbial nitrogen metabolism is a complicated and key process in mediating environmental pollution and greenhouse gas emissions in rivers. However, the interactive drivers of microbial nitrogen metabolism in rivers have not been identified. Here, we analyze the microbial nitrogen metabolism patterns in 105 rivers in China driven by 26 environmental and socioeconomic factors using an interpretable causal machine learning (ICML) framework. ICML better recognizes the complex relationships between factors and microbial nitrogen metabolism than traditional linear regression models. Furthermore, tipping points and concentration windows were proposed to precisely regulate microbial nitrogen metabolism. For example, concentrations of dissolved organic carbon (DOC) below tipping points of 6.2 and 4.2 mg/L easily reduce bacterial denitrification and nitrification, respectively. The concentration windows for NO3--N (15.9-18.0 mg/L) and DOC (9.1-10.8 mg/L) enabled the highest abundance of denitrifying bacteria on a national scale. The integration of ICML models and field data clarifies the important drivers of microbial nitrogen metabolism, supporting the precise regulation of nitrogen pollution and river ecological management.

Whole genomes show contrasting trends of population size changes and genomic diversity for an Amazonian endemic passerine over the late quaternary.

The "Amazon tipping point" is a global change scenario resulting in replacement of upland terra-firme forests by large-scale "savannization" of mostly southern and eastern Amazon. Reduced rainfall accompanying the Last Glacial Maximum (LGM) has been proposed to have acted as such a tipping point in the past, with the prediction that terra-firme inhabiting species should have experienced reductions in population size as drier habitats expanded. Here, we use whole-genomes of an Amazonian endemic organism (Scale-backed antbirds - Willisornis spp.) sampled from nine populations across the region to test this historical demography scenario. Populations from southeastern Amazonia and close to the Amazon-Cerrado ecotone exhibited a wide range of demographic patterns, while most of those from northern and western Amazonia experienced uniform expansions between 400 kya and 80-60 kya, with gradual declines toward 20 kya. Southeastern populations of Willisornis were the last to diversify and showed smaller heterozygosity and higher runs of homozygosity values than western and northern populations. These patterns support historical population declines throughout the Amazon that affected more strongly lineages in the southern and eastern areas, where historical "tipping point" conditions existed due to the widespread replacement of humid forest by drier and open vegetation during the LGM.

The tipping point in medial opening wedge high tibial osteotomy relates to the shape of the proximal tibia more than to lower limb alignment correction.

PURPOSE: The tipping point (TP) of the knee joint is the centre of rotation of the joint in the coronal plane. This study aimed to define the TP in medial opening wedge high tibial osteotomy (MOWHTO). METHODS: Data from 154 consecutive patients with varus knee malalignment, who underwent MOWHTO between 2017 and 2021, was retrospectively reviewed. The degree of preoperative osteoarthritis (OA), using the Kellgren-Lawrence (KL) grading system, was recorded. Long-leg standing radiographs were used to record the alignment parameters, including the hip-knee-ankle angle (HKA), the mechanical lateral distal femoral angle (mLDFA), the medial proximal tibial angle (MPTA), the joint line convergence angle (JLCA) and the joint line obliquity (JLO) angle. Postoperative Tegner activity scores, Western Ontario and McMaster University Scores and patients' satisfaction were recorded. To define the TP, the relationship of all variables to Δ JLCA (absolute difference between preoperative to postoperative JLCA values) was analysed. Linear regression was employed for Δ JLCA to preoperative JLCA and postoperative and Δ MPTA (absolute difference between preoperative and postoperative values). K-means clustering was used to partition observations into clusters, in which each observation belongs to the cluster with the nearest mean serving as a prototype of the cluster, and analysed if there was any specific threshold influencing Δ JLCA. After defining the TP, further subanalysis of the TP based on the preoperative KL OA grade and analysis of variance of this TP to the KL OA grade was performed. RESULTS: A total of 154 patients (77.9% males and 22.1% females) were included. The mean age was 48.2 ± 11 years, and the mean body mass index was 27.1 ± 4 kg/m2. Preoperatively, 26 (16.9%) patients had KL grade IV OA. The mean preoperative and postoperative JLCA and the significance of their relation to Δ JLCA were 2.6° ± 1.8° (p < 0.0001) and 1.9° ± 1.8° (p = 0.6), respectively. The mean Δ JLCA was 1.4° ± 1.5°. The mean pre- and postoperative MPTA and the significance of their relation to Δ JLCA were 84.6 ± 2.2 (p = 0.005) and 91.8 ± 2.5 (p < 0.0007), respectively. The mean Δ MPTA was 7.2 ± 2.3 (p = 0.3). The mean preoperative and postoperative HKA and the significance of their relation to Δ JLCA were 174.6 ± 2.5 (p = 0.2) and 181.9 ± 2.4 (p = 0.7), respectively. The overall linear regression for Δ JLCA was statistically significant for preoperative JLCA (R2 = 0.3, p < 0.0001) and postoperative MPTA (R2 = 0.09, p = 0.0001) and statistically insignificant for Δ MPTA (R2 = 0.01, p = 0.2) and postoperative HKA (R2 = 0.04, p = 0.7). MPTA > 91.5° was the optimal threshold dividing this series data set between substantial and nonsignificant Δ JLCA. CONCLUSION: In this study, the main predictive factors for intra-articular correction (Δ JLCA) after MOWHTO were the preoperative value of JLCA and the postoperative value of MPTA. A value of 92° for postoperative MPTA is potentially the optimal threshold to predict intra-articular correction. LEVEL OF EVIDENCE: Level IV, retrospective cohort study.

Advanced hemodynamics for prognostication in heart failure: the pursuit of the patient-specific tipping point.

Background: Objective tools to define the optimal time for referral for advanced therapies and to help guide escalation and de-escalation of support can improve management decisions and outcomes for patients with advanced heart failure. The current parameters have variable prognostic potential depending on the patient population being studied and often have arbitrary thresholds. Methods: Here, a mathematical and physiological framework to define the patient-specific tipping point of myocardial energetics is defined. A novel hemodynamic parameter known as the myocardial performance score (MPS), a marker of power and efficiency, is introduced that allows for the objective assessment of the physiological tipping point. The performance of the MPS and other advanced hemodynamic parameters including aortic pulsatility index (API) and cardiac power output (CPO) in predicting myocardial energetics and the overall myocardial performance was evaluated using a validated computer simulation model of heart failure (Harvi) as well as a proof-of-concept clinical validation using a cohort of the Society for Cardiovascular Angiography and Interventions (SCAI) Stage C cardiogenic shock patients. Results: Approximately 1010 discrete heart failure scenarios were modeled. API strongly correlated with the left ventricular coupling ratio (R2 = 0.81) and the strength of association became even stronger under loaded conditions where pulmonary capillary wedge pressure (PCWP) was >20 mmHg (R2 = 0.94). Under loaded conditions, there is a strong logarithmic relationship between MPS and mechanical efficiency (R2 = 0.93) with a precipitous rise in potential energy (PE) and drop in mechanical efficiency with an MPS <0.5. An MPS <0.5 was able to predict a CPO <0.6 W and coupling ratio of <0.7 with sensitivity (Sn) of 87%, specificity (Sp) of 91%, positive predictive value of 81%, and negative predictive value of 94%. In a cohort of 224 patients with SCAI Stage C shock requiring milrinone initiation, a baseline MPS score of <0.5 was associated with a 35% event rate of the composite endpoint of death, left ventricular assist device, or transplant at 30 days compared with 3% for those with an MPS >1 (p < 0.001). Patients who were able to augment their MPS to >1 after milrinone infusion had a lower event rate than those with insufficient reserve (40% vs. 16%, p = 0.01). Conclusions: The MPS, which defines the patient-specific power-to-efficiency ratio and is inversely proportional to PE, represents an objective assessment of the myocardial energetic state of a patient and can be used to define the physiological tipping point for patients with advanced heart failure.

mNFE: microbiome network flow entropy for detecting pre-disease states of type 1 diabetes.

In the development of Type 1 diabetes (T1D), there are critical states just before drastic changes, and identifying these pre-disease states may predict T1D or provide crucial early-warning signals. Unlike gene expression data, gut microbiome data can be collected noninvasively from stool samples. Gut microbiome sequencing data contain different levels of phylogenetic information that can be utilized to detect the tipping point or critical state in a reliable manner, thereby providing accurate and effective early-warning signals. However, it is still difficult to detect the critical state of T1D based on gut microbiome data due to generally non-significant differences between healthy and critical states. To address this problem, we proposed a new method - microbiome network flow entropy (mNFE) based on a single sample from each individual - for detecting the critical state before seroconversion and abrupt transitions of T1D at various taxonomic levels. The numerical simulation validated the robustness of mNFE under different noise levels. Furthermore, based on real datasets, mNFE successfully identified the critical states and their dynamic network biomarkers (DNBs) at different taxonomic levels. In addition, we found some high-frequency species, which are closely related to the unique clinical characteristics of autoantibodies at the four levels, and identified some non-differential 'dark species' play important roles during the T1D progression. mNFE can robustly and effectively detect the pre-disease states at various taxonomic levels and identify the corresponding DNBs with only a single sample for each individual. Therefore, our mNFE method provides a new approach not only for T1D pre-disease diagnosis or preventative treatment but also for preventative medicine of other diseases by gut microbiome.

Maintenance of different life stages of old-growth forest trees in deforested tropical landscapes.

Tropical tree species are increasingly being pushed to inhabit deforested landscapes. The habitat amount hypothesis posits that, in remaining forest patches, species diversity in equal-sized samples decreases with decreasing forest cover in the surrounding landscape. We tested this prediction by taking into account three important factors that can affect species responses to forest loss. First, forest loss effects can be linear (proportional) or nonlinear, as there can be threshold values of forest loss beyond which species extirpation may be accelerated. Second, such effects are usually scale dependent and may go unnoticed if assessed at suboptimal scales. Finally, species extirpation may take decades to become evident, so the effects of forest loss can be undetected when assessing long-lived organisms, like adult old-growth forest trees. Here, we evaluated the linear and nonlinear effects of landscape forest loss across different spatial scales on site-scale abundance and diversity of old-growth forest trees, separately for four plant-life stages (seeds, saplings, juveniles, and adults) in two rainforest regions with different levels of deforestation. We expected stronger (and negative) forest loss effects on early plant-life stages, especially in the region with the highest deforestation. Surprisingly, in 13 of 16 study cases (2 responses × 4 life stages × 2 regions), null models showed higher empirical support than linear and nonlinear models at any scale. Therefore, the species richness and abundance of local tree assemblages seem to be weakly affected by landscape-scale forest loss independently of the spatial scale, life stage, and region. Yet, as expected, the predictive power of forest cover was relatively lower in the least deforested region. Our findings suggest that landscape-scale forest loss is poorly related to site-scale processes, such as seed dispersal and seedling recruitment, or, at least, such effects are too small to shape the abundance and diversity of tree assemblages within forest patches. Therefore, our findings do not support the most important prediction of the habitat amount hypothesis but imply that, on a per-area basis, a unit of habitat (forest) in a highly deforested landscape has a conservation value similar to that of a more forested one, particularly in moderately deforested rainforests.

Spatio-temporal coupling analysis and tipping points detection of China's coastal integrated land-human activity-ocean system.

The coastal zone is typically highly developed and its ocean environment is vastly exposed to the onshore activities. Land-based pollution, as the "metabolite" of terrestrial human activities, significantly impacts the ocean environment. Although numerous studies have investigated these effects, few have quantified the interactions among land-human activity-ocean across both spatial and temporal scales. In this study, we have developed a land-human activity-ocean systemic framework integrating the coupling coordination degree model and tipping point to quantify the spatiotemporal dynamic interaction mechanism among the land-based pollution, human activities, and ocean environment in China from 2001 to 2020. Our findings revealed that the overall coupling coordination degree of the China's coastal zone increased by 36.9 % over last two decades. Furthermore, the effect of human activities on China's coastal environment remained within acceptable thresholds, as no universal tipping points for coastal pollution or ocean environment has been found over the 20-year period. Notably, the lag time for algal blooms, the key indicator of ocean environment health, was found to be 0-3 years in response to the land economic development and 0-4 years in response to land-based pollution. Based on the differences in spatiotemporal interactions among land-human activity-ocean system, we employed cluster analysis to categorize China's coastal provinces into four types and to develop appropriate management measures. Quantifying the interaction mechanism within the land-human activity-ocean system could aid decision-makers in creating sustainable coastal development strategies. This enables efficient use of land and ocean resources, supports coastal conservation and restoration efforts, and fosters effective management recommendations to enhance coastal sustainability and resilience.

Imputation methods for informative censoring in survival analysis with time dependent covariates.

Cox proportional hazards model has been an established model for survival analysis. The flexibility of incorporating time dependent covariates has made the analysis more suitable in many clinical trials when the time dependent covariates may be predictive factors for the events. Subjects are censored for various reasons, but they are usually nonnormatively censored in the analysis. Methods for informative censoring are not well studied for settings with time dependent covariates. In this paper, we propose a few methods for informative censoring in survival analysis by Cox model with time dependent covariates, including tipping point method and Reference Based Imputation (Jump to Reference and Copy Reference). The implementation of these methods by multiple imputation is described and illustrated with two data examples.

Fish microplastic ingestion may induce tipping points of aquatic ecosystems.

Microplastics can be ingested by a wide range of aquatic animals. Extensive studies have demonstrated that microplastic ingestion-albeit often not lethal-can affect a range of species life-history traits. However, it remains unclear how the sublethal effects of microplastics on individual levels scale up to influence ecosystem-level dynamics through cascading trophic interactions. Here we employ a well-studied, empirically fed three-species trophic chain model, which was parameterized to mimic a common type of aquatic ecosystems to examine how microplastic ingestion by fish on an intermediate trophic level can produce cascading effects on the species at both upper and lower trophic levels. We show that gradually increasing microplastics in the ingested substances of planktivorous fish may cause population structure effects such as skewed size distributions (i.e. reduced average body length vs. increased maximal body size), and induce abrupt declines in fish biomass and reproduction. Our model analysis demonstrates that these abrupt changes correspond to an ecosystem-level tipping point, crossing which difficult-to-reverse ecosystem degradation can happen. Importantly, microplastic pollution may interact with other anthropogenic stressors to reduce safe operating space of aquatic ecosystems. Our work contributes to better understanding complex effects of microplastic pollution and anticipating tipping points of aquatic ecosystems in a changing world. It also calls attention to an emerging threat that novel microplastic contaminants may lead to unexpected and abrupt degradation of aquatic ecosystems, and invites systematic studies on the ecosystem-level consequences of microplastic exposure.

Analysis of the communication mechanism for a hospital handling public health emergencies from the perspective of"Tipping Point"theory / 现代医院

In April 2022,an epidemic occurred in Gongshu District,Hangzhou,where Zhejiang Cancer Hospital is loca-ted.And the hospital was confronted with the most extensive,the most unusual and the most urgent public health emergency crisis since its establishment.Taking patients as the core and proceeding from the public perspective,an authoritative voice of a hospital was conveyed in a timely and accurate manner,which demonstrated the hospital's resolution and executive power in the crisis.Based on Malcolm Gladwell's"tipping point"theory,the paper reveals the situation of online public opinion caused by the sudden epidemic and its response,discusses the hospital's communication practices in handling public health emergencies and provides hospitals with practical reference for brand promotion and operation.

Rate-induced tipping in complex high-dimensional ecological networks.

In an ecosystem, environmental changes as a result of natural and human processes can cause some key parameters of the system to change with time. Depending on how fast such a parameter changes, a tipping point can occur. Existing works on rate-induced tipping, or R-tipping, offered a theoretical way to study this phenomenon but from a local dynamical point of view, revealing, e.g., the existence of a critical rate for some specific initial condition above which a tipping point will occur. As ecosystems are subject to constant disturbances and can drift away from their equilibrium point, it is necessary to study R-tipping from a global perspective in terms of the initial conditions in the entire relevant phase space region. In particular, we introduce the notion of the probability of R-tipping defined for initial conditions taken from the whole relevant phase space. Using a number of real-world, complex mutualistic networks as a paradigm, we find a scaling law between this probability and the rate of parameter change and provide a geometric theory to explain the law. The real-world implication is that even a slow parameter change can lead to a system collapse with catastrophic consequences. In fact, to mitigate the environmental changes by merely slowing down the parameter drift may not always be effective: Only when the rate of parameter change is reduced to practically zero would the tipping be avoided. Our global dynamics approach offers a more complete and physically meaningful way to understand the important phenomenon of R-tipping.

An immune memory-structured SIS epidemiological model for hyperdiverse pathogens.

A hyperdiverse class of pathogens of humans and wildlife, including the malaria parasite Plasmodium falciparum, relies on multigene families to encode antigenic variation. As a result, high (asymptomatic) prevalence is observed despite high immunity in local populations under high-transmission settings. The vast diversity of "strains" and genes encoding this variation challenges the application of established models for the population dynamics of such infectious diseases. Agent-based models have been formulated to address theory on strain coexistence and structure, but their complexity can limit application to gain insights into population dynamics. Motivated by P. falciparum malaria, we develop an alternative formulation in the form of a structured susceptible-infected-susceptible population model in continuous time, where individuals are classified not only by age, as is standard, but also by the diversity of parasites they have been exposed to and retain in their specific immune memory. We analyze the population dynamics and bifurcation structure of this system of partial-differential equations, showing the existence of alternative steady states and an associated tipping point with transmission intensity. We attribute the critical transition to the positive feedback between parasite genetic diversity and force of infection. Basins of attraction show that intervention must drastically reduce diversity to prevent a rebound to high infection levels. Results emphasize the importance of explicitly considering pathogen diversity and associated specific immune memory in the population dynamics of hyperdiverse epidemiological systems. This statement is discussed in a more general context for ecological competition systems with hyperdiverse trait spaces.

Differential reaction norms to ocean acidification in two oyster species from contrasting habitats.

Ocean acidification (OA), a consequence of the increase in anthropogenic emissions of carbon dioxide, causes major changes in the chemistry of carbonates in the ocean with deleterious effects on calcifying organisms. The pH/PCO2 range to which species are exposed in nature is important to consider when interpreting the response of coastal organisms to OA. In this context, emerging approaches, which assess the reaction norms of organisms to a wide pH gradient, are improving our understanding of tolerance thresholds and acclimation potential to OA. In this study, we deciphered the reaction norms of two oyster species living in contrasting habitats: the intertidal oyster Crassostrea gigas and the subtidal flat oyster Ostrea edulis, which are two economically and ecologically valuable species in temperate ecosystems. Six-month-old oysters of each species were exposed in common garden tanks for 48 days to a pH gradient ranging from 7.7 to 6.4 (total scale). Both species were tolerant down to a pH of 6.6 with high plasticity in fitness-related traits such as survival and growth. However, oysters underwent remodelling of membrane fatty acids to cope with decreasing pH along with shell bleaching impairing shell integrity and consequently animal fitness. Finally, our work revealed species-specific physiological responses and highlights that intertidal C. gigas seem to have a better acclimation potential to rapid and extreme OA changes than O. edulis. Overall, our study provides important data about the phenotypic plasticity and its limits in two oyster species, which is essential for assessing the challenges posed to marine organisms by OA.

Earthquake alerting based on spatial geodetic data by spatiotemporal information transformation learning.

Alerting for imminent earthquakes is particularly challenging due to the high nonlinearity and nonstationarity of geodynamical phenomena. In this study, based on spatiotemporal information (STI) transformation for high-dimensional real-time data, we developed a model-free framework, i.e., real-time spatiotemporal information transformation learning (RSIT), for extending the nonlinear and nonstationary time series. Specifically, by transforming high-dimensional information of the global navigation satellite system into one-dimensional dynamics via the STI strategy, RSIT efficiently utilizes two criteria of the transformed one-dimensional dynamics, i.e., unpredictability and instability. Such two criteria contemporaneously signal a potential critical transition of the geodynamical system, thereby providing early-warning signals of possible upcoming earthquakes. RSIT explores both the spatial and temporal dynamics of real-world data on the basis of a solid theoretical background in nonlinear dynamics and delay-embedding theory. The effectiveness of RSIT was demonstrated on geodynamical data of recent earthquakes from a number of regions across at least 4 y and through further comparison with existing methods.

Pacific oysters do not compensate growth retardation following extreme acidification events.

Ocean acidification caused by anthropogenic carbon dioxide emissions alters the growth of marine calcifiers. Although the immediate effects of acidification from global ocean models have been well studied on calcifiers, their recovery capacity over a wide range of pH has never been evaluated. This aspect is crucial because acidification events that arise in coastal areas can far exceed global ocean predictions. However, such acidification events could occur transiently, allowing for recovery periods during which the effects on growth would be compensated, maintained or amplified. Here we evaluated the recovery capacity of a model calcifier, the Pacific oyster Crassostrea gigas. We exposed juveniles to 15 pH conditions between 6.4 and 7.8 for 14 days. Oyster growth was retarded below pH 7.1 while shells were corroded at pH 6.5. We then placed the oysters under ambient pH > 7.8 for 42 days. Growth retardation persisted at pH levels below pH 7.1 even after the stress was removed. However, despite persistent retardation, growth has resumed rapidly suggesting that the oysters can recover from extreme acidification. Yet we found that the differences in individual weight between pH conditions below 7.1 increased over time, and thus the growth retardation cannot be compensated and may affect the fitness of the bivalves.

Patient-reported outcome tools and baseline scores vary by country and region for arthroscopic repair of massive rotator cuff tears: a systematic review.

Background: Different patient-reported outcome (PRO) tools are used in patients with arthroscopic rotator cuff repair (ARCR) which complicates outcome comparisons. The purpose of this systematic review was to compare PRO usage and baseline scores across world regions and countries in patients with ARCR of massive rotator cuff tears (MRCT). Methods: A systematic review was performed on ARCR for MRCT. The search was conducted from September to November of 2022 using the MEDLINE database for articles published in the last 15 years. Thirty-seven articles were included after initial screening and full-text review. In each article, PRO usage, baseline scores, and country of origin were collected. PRO usage was reported in percentages and baseline scores were normalized for each region to facilitate comparisons. Normalization was performed using the PRO means from each article. These averages were converted to fractions using the worst and best possible scores. These were combined into a single numerical value, expressed as a decimal from 0 to 1, using the total sample size for each tool per region. Values closer to 0 represent worse functional outcomes. Results: Thirty-two percent (n = 12) of articles were from Asia, 43.2% (n = 16) from Europe, 5.4% (n = 2) from the Middle East, and 18.9% (n = 7) from North America. The most commonly reported PRO tools were American Shoulder and Elbow Surgeons (ASES) in 19 papers, Constant-Murley Score (CMS) in 26 papers, Visual Analog Scale for pain (VAS) in 19 papers, and University of California in Los Angeles (UCLA) in 11 papers. ASES was reported in 51% of articles with 63% being from Asia (n = 12) compared to 21% from North America (n = 4). CMS was reported in 70% of studies with 58% being from Europe. Upon normalization, the preoperative score ranged from 0.30 to 0.44. Europe (0.39), and North America (0.40) showed similar scores. The lowest and highest scores were seen in the Middle East (0.3) and Asia (0.44) respectively. Conclusion: There is no standardized method to report outcomes in patients undergoing ARCR for MRCT. Great variation in usage exists in PROs which complicates data comparison between world regions. With normalization, baseline scores where similar among Asia, North America, and Europe, and lowest in the Middle East.