A species’ demographic history provides important context to contemporary population genetics and a possible insight into past responses to climate change. An individual’s genome provides a window into the evolutionary history of contemporary populations. Pairwise Sequentially Markovian Coalescent (PSMC) analysis uses information from a single genome to derive fluctuations in effective population size change over the last ~5 million years. Here we apply PSMC analysis to two European nightjar (Caprimulgus europaeus) genomes, sampled in Northwest and Southern Europe, with the aim of revealing the demographic history of nightjar in Europe. We successfully reconstructed effective population size over the last 5 million years for two contemporary nightjar populations. Our analysis shows that nightjar are responsive to global climate change, with effective population size broadly increasing under stable warm periods and decreasing during cooler spans and prolonged glacial periods. PSMC analysis on the pseudo-diploid combination of the two genomes revealed fluctuations in gene flow between the populations over time, with gene flow ceasing by the last-glacial maximum. This pattern of differentiation is in line with the species utilising different refugia during glacial maxima. We suggest that nightjar in Europe may show latitudinal (East-West) genetic structuring as a result of reduced gene flow between different glacial refugia. Finally, our results suggest that migratory behaviour in nightjar likely evolved prior to the last-glacial maximum, with long-distance migration seemingly persisting throughout the Pleistocene. However, further genetic structure analysis of nightjar from known breeding sites across the species’ contemporary range is needed to fully understand the extent and origins of range-wide differentiation in the species.

The original article proposed a sampling method for detecting continuous contamination sources, a method that does not require elaborate water quality data or real-time sensors. The method has practical merit for many water utilities but the authors' work has some limitations not discussed in the article that we comment on here. First, the authors use terms like "real-world system" to refer to a study done with a model, which may mislead readers. Second, the method is advertised as simple but actually requires some advanced tools and steps that are not fully described and that may not be available to practitioners. Third, determination of flow directions is not always explicit and usually requires a hydraulic model, which the authors happened to have, but may not be available for every system. Fourth, the authors do not adequately describe the hydraulic model; their figures show networks that differ from the provided models and may have differing lengths or flow directions that affect the analysis. Finally, the study data are not provided as per the Data Availability Statement, but doing so could help overcome these limitations and make the method more useful.

Materials and Methods

Real-time object detection is a very challenging task, as it requires both high accuracy and high speed. One-stage object detectors such as YOLO models are very fast but they are also less accurate than two-stage object detectors such as Faster R-CNN. However, Faster R-CNN is not as fast as the YOLO models. In this study, we propose an ensemble approach to real-time object detection that combines the strengths of YOLOv5 and Faster R-CNN. We first use YOLOv5 to quickly generate a set of object proposals. We then use Faster R-CNN to refine these proposals and produce more accurate object detection results. To further improve the accuracy of our object detection results, we propose a cascade refinement network that uses dynamic fine-tuning. The cascade refinement network uses Kullback-Leibler divergence to dynamically adjust the weights of the Faster R-CNN model based on the confidence scores of the YOLOv5 object proposals. We evaluated our proposed approach on the novel dataset collected in Uganda with other State-of-the-art approaches which include RetinaNet, Cascade R-CNN, Single-Shot MultiBox Detector (SSD), and Region-based Convolutional Neural Network (R-CNN). Experimental results revealed that the proposed ensemble model outperformed both base models with an average precision of 0.96, which is significantly higher than the average precision of 0.91 for YOLOv5 and 0.90 for Faster R-CNN. The ensemble model was also able to achieve real-time inference speeds, processing frames at a rate of 25 frames per second, the same speed achieved by YOLOv5, faster than the speed of 15 frames per second by Faster R-CNN. The results also revealed that the proposed ensemble model is comparable to other state-of-the-art object detection models. Our proposed approach can be used to improve the accuracy and speed of real-time object detection in a variety of applications.

It’s urgent to develop benzocyclobutene (BCB)-based polymers with low curing temperatures for temperature-sensitive applications such as liquid crystal display (LCD) and flexible electronics. Herein, the effect of substituents on the ring-opening behavior of BCB derivatives was investigated. The ring-opening activation energy barriers (ΔGA) of BCB derivatives with one or two substituents on the four-membered alkyl ring were systematically calculated using the B3LYP function. Both mono- and di-substituted BCBs adopted the conrotatory ring-opening process, obeying the Woodward-Hoffmann’s Rules upon heating. The mono-/di-substituted BCBs ex-hibited 8.2 – 69% lower ΔGA compared with BCB, attributed to the electronic effects of the substituents. Disubstituted BCBs with both electron-donating and electron-withdrawing groups, e.g., 1-NH2-8-NO2-BCB, demonstrated the lowest ΔGA. In addition, BCB derivatives with amide/ester/acyloxy group modified on C1 position were synthesized as model molecules, and their ring-opening temperature can be decreased by 20 °C compared to the unsubstituted one, also consistent with our calculation results. This work combined the theoretical calculation method with experimental results to provide valuable insights into the design and synthesis of BCB derivatives and next-generation BCB functional packaging materials with low ring-opening temperatures.

Modelers typically produce different system dynamics models for the same problem, depending on each modeler’s perspective, leading to reduced stakeholder confidence. Validation of system dynamics can increase stakeholder confidence. This research proposes the use of Fuzzy-set Qualitative Comparative Analysis (fsQCA), based on the set theory method, as a method to validate causal relationships between entities in Causal Loop Diagram (CLD) models. The problem of mobile network operators in Indonesia with small sample data characteristics is used as a case study to demonstrate the use of fsQCA. The fsQCA method is used after the CLD model is built in the system dynamics methodology. The fsQCA method is used to test causal relationships between entities in the CLD that need to be validated. The results can be used to improve the previously created CLD model. The Fuzzy-set Qualitative Comparative Analysis (fsQCA) method, which combines QCA with fuzzy set theory that allows partial membership, can be used to test whether or not there is a causal relationship between entities in the CLD model. The fsQCA method can be used to test causal relationships between entities in the CLD model with small sample data and increase stakeholder confidence in the CLD model.

Various optically active polymers are known to afford sophisticated chirality-related functionalities, i.e. asymmetric catalysis, chiroptical switching and memory in UV-vis-NIR region, chromatographic separation of enantiomers, and sensors for molecular chirality. Recently, material researchers are received much attention to design chiral supramolecular architectures from achiral polymers upon intermolecular interactions with help of greener biosources. The present article reports an instantaneous generation of ambidextrous supramolecules revealing light-driven chiroptical switching/memory in UV-vis region when achiral azobenzene-containing vinylpolymers are non-covalently interacted with alkyl ester derivatives of natural cellulose and D-/L-glucose. It was recognized that the semi-synthetic biomaterials efficiently work as chirality-inducing scaffoldings to several achiral and optically inactive molecules, oligomers, and polymers. Our successful results shed light on a new approach of how inexpensive poly-/mono-saccharide derivatives can afford supramolecular chiroptical systems with the azobenzene pendant polymer as aggregates in suspension and liquid-crystalline films with minimal energy, time, and cost.

Nine people with cystic fibrosis (pwCF) were found to have isolated elevations in serum total bilirubin after starting elexacaftor/tezacaftor/ivacaftor (ETI) that were associated with Gilbert’s Syndrome. In longitudinal examination, total bilirubin levels increased substantially after initiation of ETI without elevations in liver transaminases in those with this syndrome. Because elevated bilirubin levels in Gilbert’s Syndrome are benign, ETI was able to be continued in these individuals. Genetic testing for this relatively common syndrome should be strongly considered for pwCF experiencing isolated hyperbilirubinemia after starting ETI, since appropriate diagnosis may help pwCF avoid unnecessary interruption in this therapy with significant health benefits in CF.

This is an editorial on the article by Drs. Hnaini et al. in this month’s issue of Pediatric Pulmonology reporting respiratory data in patients with Duchernne muscular dystrophy from a large, real-world database– The Canadian Neuromuscular Disease Registry (CNDR).

Fungal melanin contributes to the survival and virulence of pathogenic fungi, such as Fonsecaea pedrosoi, which is responsible for causing chromoblastomycosis. The objective of this study was to employ Fourier transform infrared spectroscopy (FTIR) to predict the melanin content of F. pedrosoi. The melanin content, in percentage, was previously determined using gravimetry for twenty-six clinical isolates. Quintuplicate spectra of each isolate were obtained using attenuated total reflection (ATR) within the range of 4000 to 650 cm-1. To predict the melanin content, modeling was performed using partial least squares regression (PLS) in the region 1800 – 750 cm-1. Two models were tested: PLS and successive projections algorithms for interval selection in partial least squares (iSPA-PLS). The best modeling results were achieved using iSPA-PLS with one factor. The calibration set exhibited a determination coefficient (R²) of 0.9745 and a root mean square error of cross-validation (RMSECV) of 0.0977. In the prediction set, the R² value was 0.9711, and the root mean square error of prediction (RMSEP) was 0.0999. Modeling with FTIR and multivariate calibration provides a valuable means of predicting fungal melanin content, which is simpler and more robust, thereby contributing to the advancement of this field of study.

Structural batteries have emerged as a promising alternative to address the limitations of conventional batteries, with the potential to integrate energy storage into stationary constructions or mobile vehicles/planes. Developing multifunctional composites is effective to realize the structural plus concept, which can reduce the inert weight and improve the performance of the energy storage beyond the material level (e.g., cell- or system-level). Specifically, multifunctional composites in structural batteries can work as both a functional composite electrode to store charges and a structural composite to bear mechanical loads. However, they suffer from the trade-off between mechanical properties and energy storage performance due to the scientific challenges of unstable interfaces and the lack of viable manufacturing approaches. In this review, we first introduce recent research developments of electrodes, electrolytes, separators, and interface engineering specific to structure plus composites for structure batteries, and then summarize the mechanical and electrochemical characterizations. We discuss in detail the reinforced multifunctional composites for structure batteries, the exploration of multifunctionalities on different composite structures and battery configurations, and then conclude with a perspective on future opportunities. The knowledge synthesized in this review contributes to the advancement of this field and facilitates the realization of efficient and durable energy storage systems integrated into structural components.

RETINAL HAMARTOMAS AT DIFFERENT STAGES IN A PATIENT WITH TUBEROUS SCLEROSIS - OCT-SS DESCRIPTION

Case Report

We obtain a complete classification of scalar nth order ordinary differential equations for all subalgebras of vector fields in the real plane. While softwares like Maple can compute invariants of a given order; our results are for a general n. The n=1 ,2 ,3 cases are well-known in the literature. Further, it is known that there are three types of nth order equations depending upon the point symmetry algebra they possess, viz. first-order equations which admit an infinite dimensional Lie algebra of point symmetries, second-order equations possessing the maximum eight point symmetries and higher-order, n≥3, admitting the maximum n+4 dimensional point symmetry algebra. We show that scalar nth order equations for n>5 do not admit maximally an n+3 dimensional real Lie algebra of point symmetries. Moreover, we prove that for n>4 equations can admit two types of n+2 dimensional real Lie algebra of point symmetries: one type resulting in nonlinear equations which are not linearizable via a point transformation and the second type yielding linearizable (via point transformation) equations. Furthermore, we present the types of maximal real n dimensional and higher than n dimensional point symmetry algebras admissible for equations of order n≥4 and their canonical forms. The types of lower dimensional point symmetry algebras which can be admitted are shown and the equations are constructible as well. We state the relevant results in tabular form and in theorems.

This work aims to prove new results in an M v b - metric space for a noncontinuous single-valued self-map. As a result, we extend, generalize, and unify various fixed-point conclusions for a single-valued map and come up with examples to exhibit the theoretical conclusions. Further, we solve a mathematical model of the spread of specific infectious diseases as an application of one of the conclusions. In the sequel, we explain the significance of M v b - metric space because the underlying map is not necessarily continuous even at a fixed point in M v b - metric space thereby adding a new answer to the question concerning to continuity at a fixed point posed by Rhoades'. Consequently, we may conclude that the results via M v b - metric are very inspiring, and underlying contraction via M v b - metric does not compel the single-valued self-map to be continuous even at the fixed point. Our research is greatly inspired by the exciting possibilities of using noncontinuous maps to solve real-world nonlinear problems.

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Case Report: A Novel Homozygous LRBA Mutation in a Child with Episodes of Recurrent Infection and Hypogammaglobulinemia

In this paper, an event-triggered nearly optimal tracking control method is investigated for a class of uncertain nonlinear systems by integrating adaptive dynamic programming (ADP) and integral sliding mode (ISM) control. By introducing a neural network (NN) adaptive term, the designed ISM-based discontinuous control law is employed to eliminate the influence of the uncertainties and obtain the tracking error system constructed from the sliding mode dynamics, as well as relax the known upper-bounded condition of uncertainties. In order to guarantee the stability of tracking error system and improve the control performance, under the ADP technique, a critic NN is applied to approximate the optimal value function for solving the event-triggered Hamilton-Jacobi-Bellman equation and the event-triggered nearly optimal feedback control is obtained. The feedback control law is updated and transmitted to plant only when events occur, thus both the communication and the computational resources can be saved. Furthermore, the stability of tracking error is proven thanks to Lyapunov’s direct method. Finally, we provide two simulation examples to validate the developed control scheme.

Natural language processing (NLP) is one among most researched topic Today. Usage of Neural Net model and other Machine learning improved model are gaining traction for solving complex single/Multi feature classification problem. Very often it has been observed that user utterances have many topics/intents, which carefully have to be identified and also do right entity mapping. In this direction various researches have been conducted and with time NLP models are well trained and coded. The goal of the paper is to improve the existing systems. We have identified a gap where “alarming situations” are being late handled either or it is being ignored. Therefore we are designing a system to improve performance as Better Accuracy, Finetuning of existing Model, Reduce Time complexity by invoking Asynchronous Method for parallel processing of tasks.

The C5.0 decision model is used to study the distribution of vegetation under global climate conditions, providing a theoretical basis for vegetation conservation and coping with the effects of global climate change on vegetation. Based on websites such as WorldClim, CHELSA, and global vegetation distribution datasets, and using ArcGis for meteorological and vegetation data extraction. Subsequently, global meteorological and vegetation data were integrated into SPSS Modeler to build C5.0 prediction model, and model analysis was performed using accuracy, confidence, scatter plot, K-Means, and substituted into future climate data for vegetation prediction, and finally a map of current and future predicted vegetation types was drawn and overlaid with the global administrative map to analyze the global vegetation distribution. The results showed that the accuracy of the C5.0 model prediction was 69.67%, 68.23%, and 72.59% for the training set, test set, and validation set, respectively, which had high accuracy and thus had some reference significance for the study of global vegetation distribution.

The impact of climate warming on biodiversity loss is exacerbated not only by changes in mean but also by spatio-temporal variability in temperature. Access to refugia can mitigate the impact of thermal fluctuations amongst species. The effectiveness of refugia during periods of adverse warming scenarios, i.e., seasonal fluctuations, hotter-than-average summers, and warmer-than-average winters remains largely unexplored. Here, we study a bio-energetic consumer-resource model and identify the mixed success of refugia in maintaining species persistence and stability, depending on the amplitude of fluctuations, diverse warming scenarios, and species body size. Whilst refugia withhold otherwise inevitable extinction at high amplitude fluctuations in all the warming scenarios, at lower amplitudes, they may not provide similar benefits. This arises due to non-monotone thermal responses of their foraging efforts and monotonically increasing metabolic requirements. The qualitative difference among thermal responses leads to more energy losses rather than gains at low amplitudes. We find that refugia are most beneficial during hotter summers and least during typical seasonal fluctuations. Our results also suggest that refugia can be more favourable to species in temperate and Mediterranean regions, unlike those inhabiting tropical regions. We also consider an extreme heat wave event and observe that small-bodied species can counteract their negative effects by seeking refuge at low amplitudes. Overall, our work hints at selective adaptation to refugia - conditioned on the aggregated effect of thermal conditions of the local habitat and species body size - as a mechanism for biodiversity maintenance.

Methicillin-resistant Staphylococcus aureus (MRSA) is a superbug that often causes serious inflammation-related injuries. Host immune defense against MRSA infection and MRSA immune evasion strategies are the main mechanisms of these injuries. Conventional drugs, such as antibiotics, optimized antibiotics and vaccines, can treat MRSA infections, but their use is limited because of drug-resistance and impairment of immunity. Traditional medicine (TM) therapies, presently used to address these unmet clinical needs and treat injuries, are regarded as a potential solution to combat MRSA infection. This review systematically summarizes the immune mechanisms of MRSA infection, analyzes the efficacy characteristics and corresponding mechanisms of conventional and TM therapies in treating injuries, and discusses the potential advantages of combined therapy. Furthermore, several appropriate immune responses-related conditions that could be treated with the combination therapy were summarized, and new perspectives on the clinical and basic research on this combination therapy were proposed. This review lays a foundation for the development of anti-MRSA therapies and provides novel strategies and methods for the treatment of inflammation-related injuries caused by multiple drug-resistant bacteria.

Melatonin (5-methoxy-acetyl tryptamine) sleep-inducing hormone, and the pineal gland produces it in response to the circadian clock of darkness. In the body, MT1 and MT2 receptors are mostly found, having an orthosteric pocket and ligand binding determinants. Melatonin acts by binding on melatonin receptors, intracellular proteins, and orphan nuclear receptors. It inhibits adenyl cyclase and activates phospholipase C, resulting in gene expression and an intracellular alteration environment. Melatonin signaling pathways are also associated with other intracellular signaling pathways, i.e., cAMP/PKA and MAPK/ERK pathways. Relative expression of different proteins depends on the coupling profile of G protein, accounting pharmacology of the melatonin receptor bias system, and mediates action in a Gi-dependent manner. It shows antioxidant, antitumor, antiproliferative and neuroprotective activity. Different types of melatonin agonists have been synthesized for the treatment of sleeping disorders. Researchers have developed therapeutics that target melatonin signaling, which could benefit a wide range of medical conditions. This review focuses on melatonin receptors, pharmacology, and signaling cascades; it aims to provide basic mechanical aspects of the receptor’s pharmacology, melatonin functions in cancer and neurodegenerative diseases, and any treatments and drugs designed for these diseases. This will allow a basic comparison between the receptors in question, highlighting any parallels and differences that may exist and providing fundamental knowledge about these receptors to future researchers.