A 41-year-old female patient was referred to the head and neck surgery department because of a large midline submandibular mass that was diagnostic for functioning thyroid tissue in a totally ectopic location.

As a composition-modulation strategy, NH4+-doped (NH4)xCs3-xCu2I5 (0≤x≤3) perovskites using NH4I as the dopant were successfully synthesized via eco-friendly ball milling method in this work. As the ammonium content increases, the lattice constants of (NH4)xCs3-xCu2I5 shrink and the grain sizes increase. The doping of NH4+ effectively passivates the lattice defects, suppresses the non-radiative recombination and tunes the energy band structure, resulting in better fluorescence properties. Among doped and undoped Cs3Cu2I5, (NH4)0.6Cs2.4Cu2I5 and (NH4)0.9Cs2.1Cu2I5 exhibit the highest fluorescence intensity at 450 nm, with an absolute 12.2% improvement in the photoluminescence quantum yield (PLQY) compared with that of pure Cs3Cu2I5. Finally, the UV-pumped deep-blue LEDs based on (NH4)xCs3-xCu2I5 phosphors were fabricated, showing improved performance and tunable emission spectra. These results demonstrate the potential of inorganic NH4+-doping strategy for improving the performance of lead-free perovskite optoelectronics.

In order to improve the accuracy of image-based transmission line defect detection, while reducing the computational complexity and the high demand on chip performance, an object detection framework is proposed, which aims to improve model performance without increasing the scale of the model and the amount of calculation. We first introduce an efficient feature fusion module to combine different-level semantic features in nonlinear transformations. It includes channel-level hierarchy features, linear projection and residual mappings to gather task-oriented features across different spatial locations and scales. Then a context information modelling module is proposed to extract features around the target objects, which further increases the detection accuracy. Finally, an Intersection-over-Union-based training examples sampling strategy is adopted to alleviate the class imbalance problem. Experiments on our dataset show that the proposed method, with a similar number of model parameters, has an accuracy improved by 8.1% compared to the baseline, and outperforms all the competitors in the area of transmission line defect detection.

The Nanling Mountains are an important mountain range and watershed in southcentral China. Because of the abundance in relictual plant species and geological stability throughout the Cenozoic, the mountains are considered a “museum” of subtropical biological diversity. With respect to birds, however, the roles of the Nanling Mountains in impeding the dispersal of the subtropical birds and, as a result, shaping their population and community structures have received little consideration. To examine these roles, we compiled and analyzed two datasets. (1) To test the mountains’ influence on gene flow, we undertook a population phylogeographic study comparing mitochondrial COI and Cytb DNA sequences of 5 sylvioid resident bird species of the mountains (Huet’s Fulvetta Alcippe hueti, Red-billed Leiothrix Leiothrix lutea, Greater Necklaced Laughingthrush Pterorhinus pectoralis, Indochinese Yuhina Staphida torqueola, and Mountain Bulbul Ixos mcclellandii). (2) To examine differentiated community development over the history of modern birds, we examined distributional data of all species of the Nanling region using public species occurrence records. For part (1), we sampled 327 individuals from 36 sites and conducted correlation analysis of genetic and geographic distances, taking into account the landscape of the mountains. We found that the mountains do not seriously impede gene flow among populations but influenced species differently. For part (2), comparative analysis of 446 species in 81 families indicated that family membership influenced the community composition of birds in Nanling region. Variation in family distributions is attributable to both environmental and evolutionary factors. Overall, we found that the Nanling Mountains are not currently a substantial barrier to gene flow among the species we studied but act as a corridor and refuge for these birds. However, analyses on higher ranked community data suggest the mountains acted as a barrier in older times, corresponding to the known diversification events in southeast Asian avifauna.

The true bottleneck of artificial intelligence (AI) is not access to the data, but rather labeling this data. We have tons of raw agriculture image data coming from various sources and manual labelling remains to be a crucial step to keep the data well organized which requires considerable amount of time, money, and labor. This process can be made more efficient if we can automatically label the raw data. We propose contrastive learning representations for agriculture images (AgCLR) model that uses self-supervised representation learning approach on unlabeled Encirca Notes data (customer generated field notes data), to learn the useful image feature representations from the real-world agriculture images. Contrastive learning is a self-supervised approach that enables model to learn attributes by contrasting samples against each other without the use of labels. AgCLR leverages the state-of-the-art SimCLRv2 framework to learn representations by maximizing the agreement between differently augmented views of same sample. We have incorporated critical enablers like mixed precision, multi-GPU distributed parallel computing, and use of Google Cloud's Tensor Processing Units (TPU) for optimizing the training process. We achieved 80.2% accuracy while classifying the test data. We further applied AgCLR to unrelated task to determine the alleys and rows in corn field videos for corn phenotyping and we observed two cluster formations for alleys and rows when plotted embeddings in a 3-dimensional space. We also developed a content-based image retrieval tool (pixel affinity) to identify similar images in our database and results were visually very promising.

The aim of this paper is to continue the investigation into the set of translation-invariant splitting Gibbs measures (TISGMs) for Ising model having the mixed spin (1,1/2) (shortly, (1,1/2)-MSIM) on a Cayley tree of arbitrary order. In our previous work [Akın and Mukhamedov, J. Stat. Mech. (2022) 053204], we provided a thorough explanation of the TISGMs, and studied the extremality of disordered phases using a Markov chain with a tree index on a semi-finite Cayley tree with order two. In this paper, we construct the TISGMs and tree-indexed Markov chains associated with to the model. Considering a tree-indexed Markov chain on a Cayley tree of any order, we clarify the extremality of the related disordered phases. By using the Kesten-Stigum condition (KSC), we investigate non-extremality of the disordered phases by means of the eigenvalues of the stochastic matrix associated with (1,1/2)-MSIM on a CT of order k≥2.

Atoms of confusion, or simply “atoms,” are pieces of code that lead to misunderstanding while being interpreted. Previous research has shown that the presence of atoms has an effect on code readability. Aside from simple misunderstanding in lab setting, atoms of confusion are common and meaningful in open source C and C++ projects, and are thus removed by bug-fix commits. However, due to syntactical differences between language paradigms, the prevalence of atoms may vary in projects written in other languages (e.g. Java), which is yet to be explored. In this study, the first step is taken towards investigating the prevalence of 12 different atoms in the 13 most popular open-source Java projects. The relationship between the presence of atoms and aspects of code maintainability is also studied. Results show that, atoms are 4.7 time more prevalent in Java projects compared to open source C/C++ projects based on occurrence per line. For a total of 1085223 atoms in our corpus, they occur once every 4.8 lines. Some atoms are very obscure (e.g. the Logic As Control Flow atom which occurs once in 440060 lines). Some atoms are frequently occurring (e.g. the Infix Operator Precedence atom which occurs once in 6.4 lines). Impact of the presence of atoms on code maintainability is also explored. Besides, correlation between atoms are investigated. Results indicate that object oriented metrics contribute less in atom prevalence, whereas fine grained code-metrics have relatively better association.

Anemia is one of the global public health challenges that particularly affect children and pregnant women. A study by WHO indicates that 42% of children below 6 years and 40% of pregnant women worldwide are anemic. This affects the world’s total population by 33%, due to the cause of iron deficiency. The non-invasive technique, such as the use of machine learning algorithms, is one of the methods used in the diagnosing or detection of clinical diseases, which anemia detection cannot be overlooked in recent days. In this study, machine learning algorithms were used to detect iron-deficiency anemia with the application of Naïve Bayes, CNN, SVM, k-NN, and Decision Tree. This enabled us to compare the conjunctiva of the eyes, the palpable palm, and the colour of the fingernail images to justify which of them has a higher accuracy for detecting anemia in children. The technique utilized in this study was categorized into three different stages: collecting of datasets (conjunctiva of the eyes, fingernails and the palpable palm images), preprocessing the images; image extraction, segmentation of the Region of Interest of the images, obtained each component of the CIE L*a*b* colour space (CIELAB). The models were then developed for the detection of anemia using various algorithms. The CNN had an accuracy of 99.12% in the detection of anemia, followed by the Naïve Bayes with an accuracy of 98.96%, while Decision Tree and k-NN had 98.29% and 98.92% accuracy respectively. However, the SVM had the least accuracy of 95.4% on the palpable palm. The performance of the models justifies that the non-invasive approach is an effective mechanism for anemia detection. Keywords: Iron deficiency, anemia, non-invasive, machine learning, data augmentation, algorithms, region of interest.

Crank-Nicolson method is a finite difference scheme used to solve the heat and other parabolic partial differential equations. In order to solve the Burgers’ equation which is parabolic partial differential equations, an invariant numerical scheme, based on Crank-Nicolson method and discrete symmetries of Burgers’ equation, is established here. A comparison of the proposed numerical scheme with the Crank-Nicolson method and the exact solution is also presented. It is clearly observed that the convergence and performance of the modified Crank-Nicolson is better than that of the Crank-Nicolson method.

The purpose of this paper is to construct generating functions in terms of hypergeometric function and logarithm function for finite and infinite sums involving higher powers of inverse binomial coefficients. These generating functions provide a novel way of examining higher powers of inverse binomial coefficients from the perspective of these sums, assessing how several of these sums and these coefficients related to each other. A unique relation between the Euler-Frobenius polynomial and B-spline associated with exponential Euler spline is reported. Moreover, with the aid of derivative operator and functional equations for generating functions, many new computational formulas involving the special finite sums of higher powers of (inverse) binomial coefficients, the Bernoulli polynomials and numbers, Euler polynomials and numbers, the Stirling numbers, the harmonic numbers, and finite sums are derived. Moreover, A few recurrence relations containing these particular finite sums are given. Using these recurence relations, we give a solution of the problem which was given by Charalambides7, Exercise 30, p. 273. We give calculations algorithms for these finite sums. Applying these algorithms and Wolfram Mathematica 12.0, we give some plots and many values of these polynomials and finite sums.

Background: Currently, there is a lack of systematic research on the prognostic variables of metastatic cervical cancer (mCEC) among young and middle-aged females. Therefore, this research is purposed to identify the independent prognostic markers in mCEC patients (< 50 years of age) and to develop the nomograms applicable to predict their prognosis. Materials and Methods: The data on patients pathologically proven to have cervical cancer between 2004 and 2015 were collected from the American detection, epidemiology, and results (SEER) database. Then, univariate and multivariate cox analyses were conducted to determine independent prognostic variables. On this basis, nomograms were constructed to predict the 12-, 24-, and 36-month overall survival (OS) of mCEC patients aged under 50. The model was evaluated for predictive accuracy, discriminant ability, and clinical value through the receiver operating characteristic curve, the calibration curve and the decision-making curve analysis. Additionally, a survival calculator based on nomogram and network was developed. Results: A total of 616 mCEC patients aged under 50 were involved in the research. They were divided into a training set (419) and a validation set (176), at a ratio of 7:3. The independent predictive markers include race, tumour size, T stage, chemotherapy, radiotherapy, and surgery. According ot the analysis of the calibration curve, receiver operating characteristic curve, and decision curve plotted for both the training and validation cohorts, the NOMOGRAMS was highly discriminative and applicable clinically. The AUC value at 12, 24, and 36 months was 0.741, 0.739, and 0.731 in the training set, and 0.760, 0.695, and 0.696 in the validation set, respectively. The nomograms proposed in this paper can be accessed via the link: [https://shubei11.shinyapps.io/Youngcervicalnomogram/.](https://shubei11.shinyapps.io/Youngcervicalnomogram/.) In addition, a method novel risk categorization was developed to categorise all patients into three groups depending on their prognosis. Conclusions: In this study, a unique web-based nomogram is developed to estimate the risk of OS among the young and middle-aged mCEC patients (< 50), which would be beneficial for clinicians to personalise both survival evaluation and therapeutic treatment for patients.

Cyclophosphamide (CP) impacts several types of normal and healthy tissues, especially reproductive organ. The current work sought to investigate the preventive role of the green tea ingredient epigallocatechin-3-gallate (EGCG) against CP-induced testicular damage in mice. Thirty mature male mice were randomly allocated to three groups (n=10). Group 1 functioned as the control. Group 2 was administered Mod with CP (50mg/kg/d) for five days. Group 3 received CP (50mg/kg/d) for 5 days and EGCG (80mg/kg/d) for the following 30 days. The findings demonstrated that EGCG enhanced testicular mass and indix. Evaluation of Histology and Transmission Electron microscopy analysis revealed that EGCG treatment mitigated CP-induced injury to testicular structures such spermatogonial cells and seminiferous tubules. A proteomics investigation of testis revealed that EGCG administration substantially affected the quantity of 863 proteins. GO and KEGG analysis revealed that these proteins were mostly found throughout development cell grouth and PI3K-Akt signaling pathway. The PPI network analysis and RT-PCR results showed that Cdk1, Kif11, and Aurka may be the targets for EGCG. This is the first study to indicate the preventive role of EGCG on CP-induced testicular damage, and EGCG might be a natural active ingredient for tolerance to the testis toxicity generated by CP.

Moustaq Karim Khan Rony1*, Mst. Rina Parvin21Master of Public Health, Bangladesh Open University, Dhaka, Bangladesh2 Major at Bangladesh Army, Combined Military Hospital, Dhaka, BangladeshCorresponding Author: Moustaq Karim Khan Rony; 1Master of Public Health, Bangladesh Open University, Dhaka, Bangladesh; Email: mkkrony@yahoo.com; Cell: +880-1987106275Keywords: Health outcomes, Planning, Management, Bangladesh.Bangladesh is a developing nation with a rapidly expanding population and healthcare infrastructure. Despite tremendous advances in recent years, Bangladesh still faces severe health difficulties, including high maternal and child mortality rates, malnutrition, and the burden of communicable and non-communicable diseases 1. This is why improving health outcomes in Bangladesh is dependent on the planning and administration of the health care system. Health planning is creating healthcare goals and objectives and identifying the necessary resources. Health management includes allocating resources, monitoring and evaluating services, and developing policies and programs. For the effective delivery of healthcare services in Bangladesh, both health planning and administration are required.Access to quality healthcare services is one of Bangladesh’s most important methods for improving health outcomes2. This can be accomplished through expanding primary healthcare services, bolstering community-based health programs, and establishing referral mechanisms between primary and secondary care. Improving access to healthcare services will lessen the burden of preventable diseases and boost the detection and management of conditions at an earlier stage. The improvement of the quality of healthcare services3is a second strategy. This can be accomplished by boosting the availability of vital drugs and supplies and enhancing the physical infrastructure of healthcare institutions. Quality improvement programs should be led by evidence-based approaches, responsive to the local people’s requirements, and culturally appropriate. Increasing the use of technology in the healthcare sector4 is the third strategy for improving health outcomes in Bangladesh. This involves the introduction of telemedicine, mobile health services, and electronic medical records and health information systems. These technologies can enhance the effectiveness of the healthcare system and expand access to healthcare services in underprivileged and remote locations. Educating and training more medical professionals is another tactic for better health in Bangladesh5. Among these include expanding access to continuing education and professional development options for healthcare workers, training and educating healthcare workers better, and increasing the number of healthcare workers.In addition to these initiatives, it is essential to address the socioeconomic determinants of health that contribute to Bangladesh’s low health outcomes6. This involves combating poverty, enhancing access to clean water and sanitation, supporting education and gender equality, and minimizing environmental degradation. By addressing the underlying social determinants of health, it is possible to create a society that is healthier, more equal, and more capable of achieving improved health outcomes. Finally, effective health planning and management also require the participation of key stakeholders, such as the government, healthcare providers, and community members. Collaborative approaches that include various stakeholders can ensure that health policies and programs are planned and executed in a way that is sensitive to local needs and considers the local context and culture.In conclusion, improving health outcomes in Bangladesh will require a holistic approach that addresses the concerns of the healthcare system and the socioeconomic factors that influence health. Adequate health planning and management, increasing access to excellent healthcare services, the application of technology, and the participation of key stakeholders are all crucial measures for improving health outcomes in Bangladesh. Bangladesh can establish a robust and effective healthcare system that ensures all individuals have access to the treatment they require to live healthy and productive lives with the sustained dedication and investment of the government, healthcare providers, and the community.

To enable lower-cost building materials, a free-swinging bifacial vertical solar photovoltaic (PV) rack has been proposed, which complies with Canadian building codes and is the lowest-capital-cost agrivoltaics rack. The wind force applied to the free-swinging PV, however, causes it to have a non-90° tilt angle and no energy performance model accurately describes such a system. To provide a simulation model for the free-swinging PV, where wind speed and direction govern the array tilt angle, this study builds upon the open-source System Advisor Model (SAM) using Python. After the SAM python model is validated, a view factor model is used to calculate front and back irradiations. The findings reveal that free-swinging PV generates 12% more energy than vertical fixed-tilt PV. Free-swinging PV offers a levelized racking cost, which is 30% lower than that of other agrivoltaics racks including commercial fixed-tilt metal, optimized fixed-tilt wood, and seasonally-adjusted wood PV racking.

Quantitative electrical analysis of sub-cells is crucial for the designing of high-efficiency GaAs multi-junction laser power converters (MJLPCs). Spectral response (SR) measurement is the main mean of characterizing the electrical performance of sub-cell. However, all the sub-cells in GaAs MJLPCs have the same SR range, resulting that finding suitable bias lights to make the sub-cells current-limited is difficult for the SR measurement of GaAs MJLPCs. Here, we report a method of determining bias lights for the SR measurement of GaAs MJLPCs based on the variation rules of simulated short-circuit current densities with wavelengths in the sub-cells of GaAs MJLPCs. The method of determining bias lights is applicable to a variety of GaAs MJLPCs, including in original state, at high temperature or after high-energy particles irradiation. With the method of determining bias lights, the measured external quantum efficiency (EQE) curves of the 1st cell, the 2nd cell and the 3rd cell in a GaAs triple-junction LPC were successfully obtained. The work is anticipated to provide guidelines for further developments of high-efficiency and high-reliability III-V MJLPCs.

Objective: The purpose of this study was to explore the optimal time of laparoscopic cystectomy for unilateral ovarian endometrioma patients and to evaluate the influence on the ovarian reserve. Design: Prospective randomized controlled study. Setting: University hospital Population or Sample：88 patients received their first identified diagnosis of ovarian endometrioma by ultrasound (>4 cm) and were given oral contraceptive pills (OCP, drospirenone and ethinylestradiol) for one cycle before laparoscopy. Methods：Randomly divided into two groups: laparoscopy at Late luteal phase (group LLP) (n=44): Termination of OCP for 2 days; and laparoscopy at Early follicular phase (group EFP) (n=44): Day 3 after menstruation. Main Outcome Measure(s): Serum Anti-Müllerian hormone (AMH) and Leukocyte esterase (LE) levels of endometrioma wall were measured. Assessment of ovarian reserve damage based on alterations in the serum AMH levels after unilateral ovarian endometrioma surgery. Result(s): Preoperative serum AMH levels of both group decreased from preoperative to post-operative 1 week and post-operative 6 months, while difference values of group EFP were larger than those of group LLP at post-operative 1 week and post-operative 6 months respectively(1.87±0.97 vs 1.31±0.93, P＜0.01; 1.91±1.06 vs 1.54±0.93, P＜0.01)；the mean rates of post-operative serum AMH decline were 37.92% and 46.34% in group EFP respectively, which were significantly higher than those of group LLP (25.83% vs 31.43%，P＜0.01). Ovarian endometrioma wall AMH of group LLP was significantly lower than that of group EFP ([22.86±3.74] vs [31.02±5.23], P＜0.01)；While ovarian endometrioma LE concentration of group LLP was significantly higher than that group EFP([482.83±115.88] vs [371.68±84.49], P＜0.01). And significant negative correlation between leukocyte esterase and AMH concentration in the cyst wall of ovarian endometrioma (r=-0.564，P＜0.01). Conclusion(s): The optimal time for laparoscopic cystectomy for patients with first identified unilateral ovarian endometrioma is late luteal phase, which reduces ovarian tissue loss and preserves ovarian reserve effectively and safely.

Rapid population aging has prompted most emerging economies to consider introducing long-term care insurance (LTCI) as part of a comprehensive social health protection scheme. We examine discrete choice experiment (DCE) evidence from LTCI and evaluate several relevant attributes, including the elimination period, maximum monthly benefit, out-of-pocket rate and annual premium. The study uses a mix logit model to elicit respondents’ preferences and willingness to pay (WTP) for these attributes of LTCI and uses physical health status to assess heterogeneity in responses to insurance choice. We found that most respondents would consider purchasing LTCI, with respondents most preferring the following attributes: 1) a out-of-pocket rate of 25%, 2) a maximum monthly benefit level of 2000 CNY (about 296 USD), and 3) three months elimination period. The study can provide a reference for further adjustments to the existing scheme, increasing residents’ willingness to participate in insurance and promoting the sustainable development of long-term care insurance.

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94 respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines were shown to recall the physiological dimers with significantly higher accuracy than the non-physiological set, lending support for the pertinence of our benchmark dataset. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.

A document by Patrick Thompson. Click on the document to view its contents.

Background: A quasi-experimental study investigated a pharmacist-led intervention aimed at deprescribing among patients with type 2 diabetes at risk of hypoglycaemia. Objective: To evaluate the process of implementing the intervention in primary care in order to understand variations in implementation and outcomes. Methods: Mixed-methods study based on the Grant-framework, with 10 domains, including recruitment of patients, delivery of the intervention, and response of pharmacists and patients. Data collected were: administrative logs, semi-structured observations of patient consultations (n=8), interviews with pharmacists (n=16), and patient-reported experience measure (PREM) questionnaires (n=66; response 73%). Results: Ninety patients from 14 pharmacies were included. Although the selection of patients based on high hypoglycaemia-risk was considered useful, pharmacists experienced barriers to proposing deprescribing in patients with recent medication changes, patients without current health problems or hypoglycaemic events, and patients treated in secondary care. The consultation aid and deprescribing tool provided were evaluated positively by the pharmacists. The majority of patients were satisfied with the service and information that the pharmacists provided. Conclusion: Pharmacists and patients were positive about the intervention. Both groups valued the consultation on deprescribing, supported by tools. To optimise the effect, improvements can be made to patient selection and local agreements on proactive deprescribing.

A document by Mehul Mahrishi. Click on the document to view its contents.

To date (2/5/2023), nearly three years after the official declaration of the COVID-19 pandemic, a total of 754, 018, 841 cases and 6,817,478 deaths have been officially reported to the world health organization (WHO) ([https://covid19.who.int/](https://covid19.who.int/)). During this time, the global populace has witnessed the devastating effects of this pandemic, which can undoubtedly be considered the sanitary event of the century. In my opinion, understanding the mechanisms associated with the evolution of SARS-CoV-2 is the most important aspect of understanding the course of this pandemic. In this respect, the millions of viral sequences generated during the pandemic (1) have significantly aided in the tracking of the pandemic, resulting in the identification of multiple variants of concern.

(Sceloporus undulatus) is a female-larger, sexually-size dimorphic lizard species that is heavily parasitized by chigger mites (Eutrombicula alfreddugesi). In particular, the intensity of mite parasitism is higher in male than in female juveniles during the period of time when sex differences in growth rate lead to the development of sexual size dimorphism (SSD). Sex-biased differences in fitness costs of parasitism have been documented in other species. We tested whether a sex-biased growth cost of parasitism contributes to the development of SSD in S. undulatus. We measured growth and mite loads in two cohorts of unmanipulated, field-active yearlings by conducting descriptive mark-recapture studies during the activity seasons of 2016 and 2019. Yearling males had consistently higher mid-summer mite loads and consistently lower growth rates than females. However, we found that growth rate and body condition were independent of mite load in both sexes. Furthermore, growth rates and mite loads were higher in 2019 than in 2016. Our findings suggest that juveniles of S. undulatus are highly tolerant of chigger mites and that any costs imposed by mites must be at the expense of functions other than growth. We conclude that sex-biased mite ectoparasitism does not contribute to sex differences in growth rate and the development of SSD.

The integration of ecosystem processes over large spatial extents is critical to predicting whether and how global changes may impact biodiversity and ecosystem functions. Yet, there remains an important gap in meta-ecosystem models to predict multiple functions (e.g., carbon sequestration, elemental cycling, trophic efficiency) across ecosystem types (e.g., terrestrial-aquatic, benthic-pelagic). We derive a flexible meta-ecosystem model to predict ecosystem functions at landscape extents by integrating the spatial dimension of natural systems as spatial networks of different habitat types connected by cross-ecosystem flows of materials and organisms. We partition the physical connectedness of ecosystems from the spatial flow rates of materials and organisms, allowing the representation of all types of connectivity across ecosystem boundaries as well as the interaction(s) between them. Through simulating a forest-lake-stream meta-ecosystem, our model illustrated that even if spatial flows induced significant local losses of nutrients, differences in local ecosystem efficiencies could lead to increased secondary production at regional scale. This emergent result, which we dub the ‘cross-ecosystem efficiency hypothesis’, emphasizes the importance of integrating ecosystem diversity and complementarity in meta-ecosystem models to generate empirically testable hypotheses for ecosystem functions.