The field of domain adaptation, particularly in cross-domain sentiment classification, leverages labeled data from a source domain alongside unlabeled or sparsely labeled data from a target domain. The objective is to enhance performance in the target domain by mitigating the discrepancies in data distributions. Traditional methods in this area have focused on identifying and differentiating between pivotal sentiment words (shared across domains) and domain-specific sentiment words. In our work, we introduce a novel framework called Sentiment Domain Adapter (SDA), which incorporates a Category Attention Network (CAN) alongside a Convolutional Neural Network (CNN). This approach treats pivotal and domain-specific words as part of a collective category of attributes, which SDA learns to discern automatically, thereby enhancing domain adaptation. Additionally, SDA seeks to provide interpretative insights by learning these category attributes. Our model's optimization targets three main goals: (1) supervised classification accuracy, (2) minimizing the disparity in category feature distribution, and (3) maintaining domain invariance. Evaluations on three benchmark datasets for sentiment analysis affirm that SDA surpasses several established baselines.

Many fundamental molecular techniques (PCR, Microarray, Southern and northern hybridization, siRNA, CRISPR/Cas9 etc.) developed so far shows errors. I wish to highlight these molecular techniques are developed on basis of Watson-Crick DNA model, ignoring the concept of parallel stranded DNA. Through this opinion article, I wish to highlight specificity and accuracy of these molecular techniques can be enhanced by considering both parallel and anti parallel hybridization of DNA. Hopefully my views will also solve issue of irreproducibility in life science research.

Off-grid electrification planning increasingly recognizes the importance of productive use of electricity (PUE) to promote community value creation and (financial) project sustainability. To ensure a sustainable and efficient integration in the community and energy system, PUE assets must carefully be evaluated to match both the community needs and the residential electricity demand patterns. We propose a novel methodology interlinking qualitative interviews, statistical analysis, and energy system modelling to optimize decision-making for PUE integration in off-grid energy systems in rural Madagascar by aligning relevant PUE effectively with anticipated residential electricity demand patterns based on socio-economic determinants of the community. We find that a possible contribution of the PUE to reducing the electricity costs depends signifi-cantly on three factors: 1. the residential electricity consumption patterns that are influenced by the socio-economic composition of the community, 2. the degree of flexibility of i) PUE assets and ii) operational preferences of the PUE user, and 3. the capacity of community members to finance and operate PUE assets. Our study demonstrates that significant cost reductions for PUE-integrated off-grid energy systems can be achieved by applying our proposed methodology. When matching PUE and residential consumption patterns, the integration of PUE assets in residential community energy system can reduce the financial risk of operators, provided the PUE enterprise operates reliably and sustainably. We highlight that the consideration of local value chains and co-creation approaches are essential to ensure the energy system is addressing the community’s needs, creates value for the community, enhances the project´s financial sustainability, and is achieving the overall objectives of decentralized energy system planning.

Human induced climate change led to the recognition of Community-Based Adaptation (CBA) as one of the remedies in building adaptive capacity and resilience of communities. It recognizes the resources and the skills as well as the agency of communities in shaping the optimal strategy. CBA interventions are vital in addressing community needs and contextual specificities, enable participatory and inclusive engagement and thereby facilitate more profound adaptation impacts. Agent-based modelling (ABM) harbors the potential to be the ideal tool to support developing of tailored adaptation strategies while considering capacities, resources, skills, priorities, needs and the cultural context of local communities. ABM allows to capture the complex relationships among various components within community decision making on CBA actions, the CBA actions themselves and the environment. There exist plenty of tools, however they provide for varying degrees of freedom in agent definition, sensitivity analysis, scalability, experiment design and output analysis to name a few. A set of required key criteria for CBA modelling is developed and all available ABM modelling and simulation tools are assessed for their suitability for CBA. NetLogo emerges as the most fitting tool for incorporating and handling the special features of CBA. It is closely followed by GAMA and Envision. The article provides insights and guidance to researchers and practitioners in selecting ABM tools aligned with the specific requirements of their CBA simulations and projects.

The extent of access to energy of displaced populations in settlements and camps in Africa is largely 14 unknown. A multitude of contextual factors, such as the location and the characteristics of housing, 15 the legal status, the socio-cultural background, and the availability of humanitarian and public ser- 16 vices impact the living conditions and the needed energy services. Limitations in accessing energy 17 services have direct, multilayered, and far-reaching implications, including impacts on health, nu- 18 trition, education, protection, and livelihood. The objective of this article is to contribute to a more 19 comprehensive overview of the current state of energy access in displacement contexts on the Afri- 20 can continent by identifying and utilizing existing data. After a screening of the vast and various 21 available information, setting up of a database, consolidating the gathered data as well as assessing 22 the quality through a quality assessment method, the currently available information is visualized 23 and discussed. Considerable differences in the access to electricity for displaced persons across the 24 countries are found. For both electricity and clean cooking, the availability for displaced persons 25 ranges from nearly no access at all up to an access rate of 100%. More strikingly, the results also 26 show that besides South Africa and countries in the Maghreb region, the access to both clean cook- 27 ing and electricity for displaced persons is very low. At the same time, the fragmented data availa- 28 bility, the poor data quality, and the questionable expediency of available data allows neither solid 29 theoretical conclusions nor planning of effective practical implementation measures. Novel inter- 30 disciplinary research, conceptual frameworks and indicators are needed for the purpose of compa- 31 rability and consistency. Future research has the potential to more comprehensively capture the 32 current state of access to energy in displacements contexts and in a next step to examine how energy 33 is interwoven in the lives of displaced people to derive a set of more detailed context-sensitive en- 34 ergy indicators. It is essential that the concerned persons, the displaced persons themselves are in- 35 cluded in the research in a meaningful way.

Intelligent fault diagnosis techniques have replaced the time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning model can improve the accuracy of intelligent fault diagnosis with the help of its multilayer nonlinear mapping ability. This paper has proposed a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in first convolutional layer for extracting feature and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform state of the art DNN model which is based on frequency features under different working load and noisy environment.

The log data generated during the operation of a software system contains information about the system, and using logs for anomaly detection can detect system failures in a timely manner. Most of the existing log anomaly detection methods are specific to a particular system, have cold start problems, and are sensitive to updates in log format. In this paper, we propose a log anomaly detection method LogBD based on pre-training model and domain adaptation, which uses the pre-training model BERT to learn the semantic information of logs, and can solve the problems caused by multiple meaning words and log statement updates. An anomaly detection method LogBD based on distance to determine anomalies is constructed based on domain adaptation, using TCN networks to extract common features of different system logs and map them to the same hypersphere space. Finally, experiments are conducted on two publicly available datasets to evaluate the method, and the experimental results show that the method can better solve the log instability problem and has some improvement in the cross-system log anomaly detection effect.

As the development of low-altitude economies and aerial countermeasures continues, the safety of unmanned aerial vehicles becomes increasingly critical, making the emitter identification in remote sensing practices more essential. Effective recognition of Radio Frequency (RF) signal attributes is a prerequisite for identifying emitters. However, due to the diverse wireless communication environments, RF signals often face challenges from complex and time-varying wireless channel conditions. These challenges lead to difficulties in data collection and annotation, as well as disparities in data distribution across different communication scenarios. To address this issue, this paper proposes a Progressive Maximum Similarity-based Unsupervised Domain Adaptation (PMS-UDA) method for RF signal attribute recognition. Initially, we introduce a noise perturbation consistency optimization method to enhance the robustness of the PMS-UDA method under low signal-to-noise conditions. Subsequently, a progressive label alignment training method is proposed, combining sample-level maximum correlation with distribution-level maximum similarity optimization techniques to enhance the similarity of cross-domain features. Finally, domain adversarial optimization method is employed to extract domain-independent features, reducing the impact of channel scenario. Experimental results demonstrate that the PMS-UDA method achieves superior recognition performance in automatic modulation recognition and RF fingerprint identification tasks, as well as cross both ground-to-ground and air-to-ground scenarios, compared to baseline methods.

According to current forecasts, global heating is likely to exceed 2,8°C by the end of the century. This makes substantial adaptation measures necessary to secure a broad basis for livelihoods provision and the conservation of biodiversity. While the implementation of top-down and technocratic adaptation efforts is the majority, related adaptation shortcomings of socio-economic and ecological nature are becoming more and more apparent. Community-based adaptation (CBA) with its participatory, inclusive and needs-based bottom-up approach offers a promising and powerful alternative. This article uses a semi-systematic literature review approach to screen the current literature and identify core issues of CBA. Linking communality, locality, multidimensionality, power imbalances, transformative potential, localisation, the triad of adaptation metrics and adaptation with nature to corresponding potential actions for practical implementations provides a more holistic conceptualisation and broadens the horizons for further learnings, research and improved application.

The inherent dependency of deep learning models to labeled data is a well-known problem and one of the barriers that slows down the integration of such methods into different fields of applied sciences and engineering, in which experimental and numerical methods can easily generate a colossal amount of unlabeled data. This paper proposes an unsupervised domain adaptation methodology that mimics the peer review process to label new observations in a different domain from the training set. The approach evaluates the validity of a hypothesis using domain knowledge acquired from the training set through a similarity analysis, exploring the projected feature space to examine the class centroid shifts. The methodology is tested on a binary classification problem, where synthetic images of cubes and cylinders in different orientations are generated. The methodology improves the accuracy of the object classifier from 60% to around 90% in the case of a domain shift in physical feature space without human labeling.

Due to its intriguing applications across a wide range of industries, nanotechnology is generating a lot of interest globally. Titanium dioxide (TiO2) nanoparticles are one type of nanoparticle (NP) that is commonly utilised in everyday use and can be produced using a variety of physical, chemical, and environmentally friendly techniques. Green synthesis is an economical, environmentally benign, and non-toxic method of synthesising NPs. Under normal circumstances, the application of TiO2 greatly enhanced the plants' shoot length, leaf area, and root dry weight. These growth-promoting factors coincided with elevated proline, soluble sugar, and chlorophyll B levels as well as improved antioxidant enzyme activity. Thus, higher levels of proline and other metabolites played a role in osmo-protection, while increased antioxidant enzyme activities helped to explain the observed decrease in hydrogen peroxide and malondialdehyde contents. Taken together, these factors significantly improved plant growth under salinity. TiO2 nanoparticles have demonstrated efficacy in treating contaminated water and have been shown to positively impact plant physiology, particularly in response to abiotic stresses. However, the reaction to these nanoparticles varies depending on characteristics such as metal species, type, size, shape, dose, duration of exposure, and other variables. One of the most popular nanomaterials in customer goods, agricultural, and energy industries is TiO2 NPs. Therefore, harm to ecosystems and living things will unavoidably result from high demand and widespread usage. Gaining more knowledge about the toxicity of TiO2 NP to living things could help with risk assessments and safe handling procedures for these nanoparticles. The synthesis of titanium oxide, its many uses, the toxicity of titanium dioxide nanoparticles, and their mode of toxicity are all summarised in this paper.

This study investigates the capabilities of high-resolution Sentinel-2 observations and deep learning (DL) segmentation models, specifically convolutional neural networks (CNNs), for accurate mapping of small and fragmented agricultural burn areas in Punjab, India. Initially, the model was trained using ICNF burn area data from Portugal to capture large fire and burn area delineation, achieving moderate accuracy. Subsequent fine-tuning using annotated data from Punjab improved the model’s ability to detect small burn patches, demonstrating higher accuracy and precision than the baseline Normalised Burn Ratio (NBR) Index method. On-ground validation using buffer zone analysis and crop field images confirms the DL approach effectiveness. Cloud interference and temporal gaps in satellite data posed some challenges. Despite these limitations, the study highlights the methodological advancements and potential of using DL models for small burn area detection in agricultural settings. The model achieved overall testing accuracy of 0.64 in Dice score assessment. This report presents a promising approach for improved mapping of the burn area, which might engender better estimation of emissions and the management practices in regions with little available ground truth data.

German noise action plans aim to reduce negative health outcomes from noise exposure and identify quiet areas free of noise pollution. Quiet area identification in German noise action plans is based primarily on noise mapping and spatial analysis and not empirical or qualitative data about acoustic environments, thus leaving a gap in the understanding of the quality of formally recognized quiet areas in noise action plans. This work presents a comparative empirical case study in Dortmund, Germany, with the aim to describe the diurnal dB(A) and biophonic properties of quiet areas versus noise 'hot spots'. Sound observations were collected (n=282,764) in five different natural or recreational land use patch types larger than four acres within 33 proposed quiet areas in Dortmund (n=70) and 23 noise hot spots between April 27^th, 2022 and March 2^nd, 2023. We find that quiet areas are on average more than 20 dB(A) quieter than noise hot spots almost every hour of the day. Forests, managed tree stands, cemeteries, and agriculture diel patterns are dominated by dawn dusk chorus in spring and summer, whereas sport and recreation as well as noise hot spots are dominated by traffic and human noise. A novel composite biophony mapping procedure is presented that finds distinct temporal distribution of biophony in forested and agriculture peri-urban locations positively associated with patch size, distance away from LDEN > 55, proximity to water, and the number of vegetation layers in the plant community. Anthrophony distribution dominates urban land uses in all hours of the day, but expands during day and evening and contracts at night and in dusk hours. The procedures presented here illustrate how qualitative information regarding quiet areas can be integrated into German noise action planning.

Athlete’s heart is characterised by structural, functional, and electrical remodelling. During an exercise session, the heart increases the use of oxygen, which is associated with decreased vascular resistance in the lungs’ circulation. Left ventricular hypertrophy is an echocardiographic modification especially met in endurance athlete’s heart. Moreover, left atrial dilatation represents another important characteristic of endurance athletes. Electrical modulation in the athletic heart induces neurological modifications leading to sinus arrhythmia, bradycardia, early repolarisation, and first-grade atrioventricular (AV) blocks. Athletes' nutritional status involves the assimilation of macronutrients such as proteins, carbohydrates, and fats. Depending on the duration of physical exercise, ATP can be obtained in aerobic or anaerobic conditions. This systematic review aims to find correlations between different evaluative methods of cardiac adaptation and present the metabolism in performance athletes.

The process of oxygenic photosynthesis is primarily driven by two multiprotein complexes known as photosystem II (PSII) and photosystem I (PSI). PSII facilitates the light–induced reactions of water–splitting and plastoquinone reduction, while PSI functions as the light–driven plastocyanin-ferredoxin oxidoreductase. In contrast to the highly conserved structure of PSII among all oxygen–evolving photosynthetic organisms, the structures of PSI exhibit remarkable variations, especially for photosynthetic organisms that grow in special environments. In this review, we make a concise overview of the recent investigations of PSI from photosynthetic microorganisms including prokaryotic cyanobacteria and eukaryotic algae from the perspective of structural biology. All known PSI complexes contain a highly conserved heterodimeric core; however, their pigment compositions and peripheral light-harvesting proteins are substantially flexible. This structural plasticity of PSI reveals the dynamic adaptation to environmental changes for photosynthetic organisms.

Abstract: The VSI represents a significant advancement in the assessment of gastrointestinal-specific anxiety among patients with Irritable Bowel Syndrome (IBS) and other Chronic Inflammatory Bowel Diseases (IBD). However, an Italian version of the instrument is not yet available for the reference population. This study utilized a national sample of 500 individuals, including healthy controls and patients with Ulcerative Colitis and Crohn's Disease, to test the validity and reliability of the Italian VSI. Using back-translation methodology to ensure translation fidelity, the research applied a questionnaire and the VSI through an online format to 500 participants. Data analysis revealed that the Italian VSI had excellent psychometric properties, demonstrating high internal consistency (Cronbach’s α = 0.95) and construct validity. The scale proved sensitive in detecting significant differences in visceral sensitivity among groups, highlighting its utility as a clinical and research assessment tool. Specifically, the Italian VSI exhibited a unidimensional factorial structure and maintained a strong correlation with other measures of anxiety and the severity of gastrointestinal symptoms, confirming its role in enhancing the understanding and management of IBS and IBD in Italy.

This study aimed to learn disturbance information to effectively control multiple small unmanned surface vehicles (USVs) for marine research purposes at sea. The learned disturbance information was used to investigate the control of multiple USV swarms based on the leading–following formation technique. Numerous studies have already been conducted on swarm control using drones in the air; however, there has been a paucity of studies on swarm control in the sea. Contrary to swarm control in aviation, the fluid density of the sea is high, and the environment is constantly changing; thus, a robot system would be significantly affected by disturbances. This ultimately influences the maintenance of swarm formations and the accomplishment of missions [1,2]. Therefore, it is difficult to control numerous moving objects while maintaining a formation at sea by using the existing aerial drone swarm control technology. To solve this problem, we learned a long short-term memory (LSTM) model with time-series marine environmental data to predict maritime disturbances. Thereafter, the model was applied to a mission-based position control algorithm of each USV constituting the swarm to design a marine unmanned floating vessel swarm control system that can be applied to various marine environments. Specifically, to effectively control numerous small USVs for marine research purposes, ocean currents are learned using the LSTM algorithm, and the predicted ocean currents are used to generate a swarm USV control system for USV formations. In this study, a control system model of several small USVs equipped with two rear thrusters and a front lateral thruster was designed. The LSTM algorithm was learned using the previous ocean current data, and the velocity of the following ocean currents was predicted. The predictions were then used as a system disturbance to determine the thrust of the controller. To measure ocean currents in the sea when each USV moves, the velocity, azimuth, and position (latitude, longitude) data from the GPS mounted on the USV were used to measure the velocity and direction of the hull's movement. Further, the flow rate was measured using a flow rate sensor on a small USV. The USV movement and position were controlled using an artificial neural network-PID (ANN-PID) controller. This study entailed a comparative analysis of the designed USV model results and those generated by the simulator, including the behavior control rule of the USV swarm and the path of the actual USV swarm at sea. It was verified that the effectiveness of the USV mathematical model and behavior control rules. By comparing the movement path of the swarm USV with and without the disturbance learning algorithm and ANN-PID control algorithm applied to the designed simulator, the position error and maintenance performance of the swarm formation were analyzed, and the application results were compared.

This work introduces a new architecture for a time-domain feedback-based beamformer designed to meet real-time processing demands. The primary goal of this design is to locate reflective sources by estimating their direction of arrival (DOA) and signal range. Incorporating a feedback mechanism in this architecture is a unique aspect that refines localization precision. We conduct an in-depth analysis highlighting their respective benefits and limitations to compare the effectiveness of time-domain feedback beamforming against conventional time-domain methods. Our evaluation of the proposed architecture, based on critical performance indicators such as peak-to-sidelobe ratio, mainlobe width, and directivity factor, demonstrates its ability to improve beamformer effectiveness significantly.

Prompt removal of misfolded membrane proteins and misassembled membrane protein complexes is essential for membrane homeostasis. However, the elimination of these toxic proteins from the hydrophobic membrane environment has high energetic barriers. Transmembrane FtsH is the only known ATP-dependent protease responsible for this task, unlike other well-studied soluble ATP-dependent proteases. The mechanisms by which FtsH recognizes, unfolds, translocates, and proteolyzes its substrates remain unclear. Here, we report the crystal structures of the Thermotoga maritima FtsH periplasmic domain (PD) in an associative trimeric state at a 1.5-1.95 Å resolution. We also describe the pH-dependent oligomerization states of the isolated PD using dynamic light scattering. These observations help us understand how FtsH recognizes membrane-anchored misfolded proteins.

The agricultural industry relies on physical fences to manage livestock. However, physical fences present some practical, financial, and ecological problems, that partly may be solved by virtual fencing. However, it is necessary for the livestock to be adapted to virtual fencing. This study aims to identify if adaptation to virtual fence can be improved through social facilitation between experienced cattle and the remaining herd. Moreover, which of the two different learning processes, a gradual or instant introduction to virtual fence is the most effective in associating warnings with impulses. Three pastures containing Angus (Bos taurus) will be used to examine these investigations; A, B and C. All investigations are conducted on the Danish Island of Fanø. Three methods of learning process were used, one gradual and two instant introductions to the virtual fence. The investigations had varying amounts of experienced cattle. Impulses and warnings were recorded during a learning period and the following observation period. To quantify if the cattle adapt to the warnings, impulses per warning are used. There was no evidence of improvement with the presence of experienced cattle for any pastures, indicating no social facilitation. Gradual introduction was found to be more beneficial than instant introduction. More standardized tests are necessary to discover the full effect of experienced cattle.

Domain generalization has led to remarkable achievements in Hyperspectral Image (HSI) classification. Inspired by contrastive language-image pre-training (CLIP), the language-aware domain generalization method has been explored to learn cross-domain-invariant representation. However, existing methods face some challenges: 1) The weak capacity to extract long-range contextual information and inter-class correlation. 2) Due to the inadequacies of the large-scale pre-training for HSI data, the spatial-spectral features of HSI and linguistic features can not be straightforwardly alignment. To address the above problems, a novel network has been proposed with a CLIP framework, which consists of an image encoder, based on an encoder-only transformer to obtain the global contextual information and inter-class correlation, a frozen text encoder, and a cross-attention mechanism, named Linguistic-Interact-with-Visual Engager (LIVE), enhances the interaction between two modalities. Extensive experiments demonstrating superior performance over state-of-the-art methods in HSI Domain Generalization with a CLIP framework.

Motivated by diffraction experiments on the $\left(2\sqrt{3}\times \sqrt{3}\right)\mathrm{R}{30}^{\circ }$ reconstructed Si(111) due to deposition of rare earth elements (Dy, Tb) and silicide formation we analyse the splitting and non-splitting of superstructure spots. For this purpose, we model diffraction patterns for one dimensional structures generated by the binary surface technique and use supercell models to keep the analysis simple. Diffraction pattern are calculated in the framework of the kinematical diffraction theory and they are analyzed as a function of the domains and domain boundaries. Basic properties of the diffraction pattern are analyzed for model systems of a two-fold and a three-fold periodicity. The rules derived from these calculations are applied to the "real-world" system of Si(111)-($\left(2\sqrt{3}\times \sqrt{3}\right)\mathrm{R}{30}^{\circ }$)-RESi$2_{}$ (RE = Dy or Tb). Depending on the combination of domains and domain boundaries of different types a plethora of different features are observed in the diffraction patterns. These are analyzed to determine the sizes of both domain boundaries and domains from experimentally observed splitting of specific superstructure spots.

In this research paper, design and development of pyramidal horn antenna for J-band application is reported. It is particularly designed for 17 dB gain and half beam width about 25 degrees at 6.93 GHz. Horn aperture, horn axial length and distance from the throat of the antenna to aperture are the main design constraints which are calculated and used for the antenna design and simulation. Beam width in E-plane and H-plane horn is calculated and it is 19.18 dB and 22.86 dB respectively. The reported antenna design shows good performance for J-band in radiometry, satellite, and radar applications.

Structural health monitoring has been studied by a number of researchers as well as various industries to keep up with the increasing demand for preventive maintenance routines. This work presents a novel method for reconstruct prompt, informed strain/stress responses at the hot spots of the structures based on strain measurements at remote locations. The structural responses measured from usage monitoring system at available locations are decomposed into modal responses using empirical mode decomposition. Transformation equations based on finite element modeling are derived to extrapolate the modal responses from the measured locations to critical locations where direct sensor measurements are not available. Then, two numerical examples (a two-span beam and a 19956-degree of freedom simplified airfoil) are used to demonstrate the overall reconstruction method. Finally, the present work investigates the effectiveness and accuracy of the method through a set of experiments conducted on an aluminium alloy cantilever beam commonly used in air vehicle and spacecraft. The experiments collect the vibration strain signals of the beam via optical fiber sensors. Reconstruction results are compared with theoretical solutions and a detailed error analysis is also provided.

Lactococcus lactis is a potential bacterial cell factory to develop delivery systems for vaccines and therapeutic proteins. Large progress has been made in applications using engineered L. lactis against, e.g., inflammatory bowel disease and cervical cancer, but improvement of secretion and cell anchoring efficacy is still desirable. A double-labelling method based on biarsenical hairpin binding and nickel-polyhistidine affinity was used for visualization of protein trafficking and quantification of targeted proteins on the cell surface and in the cytoplasm. To investigate the importance of mature domain targeting signals (MTSs), we generated truncated constructs encoding 126, 66, and 26 amino acid residues from the N-terminus of the basic membrane protein A (BmpA) and fused those with the gene for the human papillomavirus serotype 16 (HPV16) E7 oncoprotein. Overexpression of fusion proteins was observed to come at a cost of cell proliferation. L. lactis cells produced and displayed the shortest fusion protein only with difficulty, suggesting that the entire absence of a homologous sequence containing MTSs significantly impedes export and surface anchoring of fusion proteins. With 40 amino acids following the signal peptide and containing one MTS, effective translocation was possible. Mutations of MTSs towards increased hydrophobicity resulted in an increase of secreted and surface-displayed fusion protein, suggesting the potential to design rationally improved constructs.

Despite the increasing prevalence of pet ownership in Chinese societies, standardized tools to assess grief from pet loss remain lacking. Research predominantly focuses on Western populations, creating a gap in understanding pet bereavement in Chinese cultural settings. This study aimed to adapt and validate the Pet Bereavement Questionnaire (PBQ-C) for a Chinese context to create a culturally appropriate assessment tool. A total of 245 participants with companion animal loss experiences were recruited through the university of the research team. They were invited to complete an online survey including the Chinese PBQ, the Depression subscale of the Depression Anxiety Stress Scales (DASS-21), and the Inventory of Complicated Grief (ICG). Both Exploratory Factor Analysis and Confirmatory Factor Analysis were conducted to examine the psychometric properties of the PBQ-C and the findings supported a three-factor structure—grief, anger, and guilt—aligned with the original PBQ, with three items were reassigned to different factors. Despite these adjustments, the PBQ-C showed strong internal consistency, split-half reliability, and concurrent validity. The validated PBQ-C provides a culturally sensitive tool for assessing pet bereavement in Chinese society that can promote research and counselling support for this under-researched and under-recognized type of loss of human-animal relationships.

This review article explores the innovative application of software sensors for monitoring and controlling biosystems, emphasizing their advantage in managing the complexities of various biological processes. Biosystems—from cellular interactions to ecological dynamics—are characterized by intrinsic nonlinearity, temporal variability, and uncertainty, posing significant challenges for traditional monitoring approaches. A critical challenge highlighted is that what is typically measurable may not align with what needs to be monitored. Software sensors bridge this gap by combining hardware sensor data with advanced computational modelling techniques to indirectly infer hard-to-measure target variables, such as stress level, animal and human health indicators, and chemical soil properties. The article outlines advancements in sensor technologies and their integration into model-based monitoring and control systems, leveraging the capabilities of Internet of Things (IoT) devices, wearables, remote sensing, and smart sensors. It provides an overview of common methodologies for designing software sensors, focusing on the modelling process. The discussion contrasts hypothetico-deductive (mechanistic) models with inductive (data-driven) models, illustrating the trade-offs between model accuracy and interpretability. Specific case studies are presented, showcasing software sensor applications such as the use of Kalman filter in greenhouse control, remote detection of soil organic matter, and sound recognition algorithms for early detection of respiratory infections in animals. Key challenges in designing software sensors, including the complexity of biological systems, inherent temporal and individual variabilities, and the trade-offs between model simplicity and predictive performance, are also discussed. This article positions software sensor as remarkable tool for advancing biosystem management, driving forward the potential for sustainable practices in various sectors such as agriculture, healthcare, and environmental monitoring.

Virus taxonomy uses a Linnaean-like subsumption hierarchy to classify viruses into taxonomic units at species and higher rank levels. Virus species are considered monophyletic groups of mobile genetic elements (MGEs) often delimited by phylogenetic analysis of aligned genomic or metagenomic sequences. Taxonomic units are assumed independent organizational, functional and evolutionary units that follow a ‘natural’ historical rationale. Here, I use phylogenomic and other arguments to show that viruses are not self-standing genetically-driven biological systems. Instead, they are crucial components of holobionts, units of biological organization that dynamically integrate the genetics, epigenetic, physiological and functional properties of their co-evolving members. Remarkably, phylogenomic analyses show viruses share protein domain and loop structures with cells throughout history via massive processes of reticulate evolution, helping spread evolutionary innovations across a wider taxonomic spectrum. Thus, viruses are not merely MGEs or microbes. Instead, their genomes and proteomes conduct cellularly integrated processes akin to those cataloged by the GO Consortium. This prompts the generation of compositional hierarchies that replace the ‘is-a-kind-of’ by a ‘is-a-part-of’ logic to better describe the mereology of integrated cellular and viral makeup. My analysis demands a new paradigm that integrates virus taxonomy into a modern evolutionarily centered taxonomy of organisms.

Background: The electroretinogram is a clinical test used to assess the function of the photoreceptors and retinal circuits of various cells in the eye, with the recorded waveform being the result of the summated response of neural generators across the retina. Methods: The present investigation involved an analysis of the electroretinogram waveform in both the time and time-frequency domain through the utilization of the discrete wavelet transform and continuous wavelet transform techniques. The primary aim of this study was to monitor and evaluate the effects of treatment in a New Zealand rabbit model of endophthalmitis via electroretinogram waveform analysis and to compare these with normal human electroretinograms Results: The wavelet scalograms were analyzed using various mother wavelets, including the Daubechies, Ricker, Wavelet Biorthogonal 3.1 (bior3.1), Morlet, Haar, and Gaussian wavelets. Distinctive variances were identified in the wavelet scalograms between rabbit and human electroretinograms. The wavelet scalograms in the rabbit model of endophthalmitis showed recovery with treatment in parallel with the time -domain features. Conclusions: The study compared Adult, Child, and Rabbit electroretinogram responses using DWT and CWT, finding that Adult signals had higher power than Child signals, and Rabbit signals showed differences in a-wave and b-wave depending on the type of response tested, while Haar Wavelet was found to be superior in visualizing frequency components in electrophysiological signals in following the treatment of endophthalmitis and may give additional outcome measures for the management of retinal disease.

The aim of this study is to assess of rare earth elements (REE) content in hair samples in children, living in Lovozero village, near REE mining site, and possible effects of REEs on the prevalence of nervous system diseases in the Lovozersky district (Murmansk region, Kola Peninsula). School-age children (53 persons) were recruited for analysis of REE content in their hair samples. REEs (Y, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu) content was estimated by inductively coupled plasma mass spectrometry (ICP-MS). Analysis of REE content in the hair of children living in the Russia, Kazakhstan and China indicates REEs intake from environment. A possible contribution of REEs to the nervous system disorders is supported by the link between REE content in the hair samples of children, living near REE mining (China) and by manifesting cognitive disorders in these children. It also turn out that highest prevalence of the nervous system diseases in children 15-17 years old manifested in the Lovozersky district then in other area in the Murmansk region. The paper discusses the possible contribution of REEs to the prevalence of episodic paroxysmal disorders (G40-G47), cerebral palsy (G80-G83), epilepsy and status epilepticus (G40-G41).

Gravity forward modeling as a basic tool has been wildly used to topography correction and 3D density inversion. The source region is usually discretized into tesseroid (i.e., spherical prism) to consider the influence of the curvature of planets in global or large-scale problems. Traditional gravity forward modeling methods in spherical coordinates, including the Taylor expansion and Gaussian-Legendre quadrature, are all based on spatial domain, which are mostly low computational efficiency. This study proposes a high-efficient forward modeling method of gravitational fields in spherical harmonic domain, in which the gravity anomalies and gradient tensors can be expressed as spherical harmonic synthesis form of spherical harmonic coefficients of 3D density distribution. A homogeneous spherical shell model has been used to test its effectiveness compared with traditional spatial domain methods. It demonstrates that the computational efficiency of the proposed spherical harmonic domain method has improved by four orders of magnitude and with a similar level of computational accuracy compared with the optimized 3D GLQ method. Finally, the proposed forward method is applied to topography correction of the Moon. The results show that the gravity response of the topography by our method is close to that of the optimized 3D GLQ method, and is also consistent with previous results.

This paper is a part of the broader research on “Adaptation of Laws in Science” and explores the limitations and challenges involved in adapting mathematical concepts to describe natural reality in physics. Specifically, the analysis focuses on two major examples: Euler's exponential laws and spacetime dilation derived from Lorentz transformations. By examining these concepts, the paper emphasizes that some complex laws are not universally applicable for deeper studies of nature, as they originate from mathematics and are correct only for specific parts of nature to which they have been adapted. These complex laws are particularly unsuitable where there is limited information available for adaptation. Therefore, without appropriate adaptations and a deep understanding of the physical context under investigation, the direct use of complex laws can lead to incorrect results. It is essential to avoid excessive generalization of these laws, once they become part of physics, unless a corresponding adaptation to the research context can be made. Furthermore, this paper supports the correctness of relativity theories by invoking that, like Euler's exponential growth theory, they were initially mathematical concepts. However, the nature of mathematics coincided with that of the phenomena in our universe, although through certain adaptations.

L-gulonolactone oxidase enzyme (GULO) catalyzes the last step of L-ascorbic acid (vitamin C) biosynthesis. This enzymatic activity is lost in primates. The full-length rat GULO has been previously produced in plants and demonstrated to be active. In this study, we compare activity of two variants of GULO produced in Escheriachia coli cells, full-length rat GULO (fGULO) and its C-terminal catalytic domain (cGULO). The expression and purification of the recombinant proteins were optimised and their biological activity was confirmed by two methods, the GULO activity assay in the protein extracts and the ‘in-gel’ staining for GULO activity. Both variants of recombinant GULO were biologically active in both assays. However, cGULO is more promising than fGULO for ascorbic acid production because it is more efficiently produced by bacteria.

The BET family of proteins is characterized by the presence of two tandem bromodomains and an extra-terminal domain. The mammalian BET family of proteins comprises Brd2, Brd3, Brd4, and Brdt, which are encoded by paralogous genes that may have been generated by repeated duplication of an ancestral gene during evolution. Bromodomains that can specifically bind acetylated lysine residues in histones serve as chromatin-targeting modules that decipher the histone acetylation code. BET proteins play a crucial role in regulating gene transcription through epigenetic interactions between bromodomains and acetylated histones during cellular proliferation and differentiation processes. On the other hand, BET proteins have been reported to mediate latent viral infection in host cells and be involved in oncogenesis. Human BRD4 is involved in multiple processes of the DNA virus life cycle, including viral replication, genome maintenance, and gene transcription through interaction with viral proteins. Aberrant BRD4 expression contributes to carcinogenesis by mediating hyperacetylation of the chromatin containing the cell proliferation-promoting genes. BET bromodomain blockade using small-molecule inhibitors gives rise to selective repression of the transcriptional network driven by c-Myc. These inhibitors are expected to be potential therapeutic drugs for a wide range of cancers. This review presents an overview of the basic roles of BET proteins and highlights the pathological functions of BET and the recent developments in cancer therapy targeting BET proteins in animal models.

Finite element discretizations of problems in computational physics often rely on hand-generated initial mesh and adaptive mesh refinement (AMR) to preferentially resolve regions containing important features during simulation. We propose Adaptnet, a Graph Neural Networks (GNNs) framework for learning mesh generation and adaptation. The model is composed of two GNNs: the first one, Meshnet, learns mesh parameters commonly used in open-source mesh generators, to generate an initial mesh from a Computer Aid Design (CAD) file; while the second one, Graphnet, learns mesh-based simulations to predict the components of an Hessian-based metric to perform anisotropic mesh adaptation. Our approach is tested on structural (Deforming plate - Linear elasticity) and fluid mechanics (Flow around cylinders - steady-state Stokes) problems. Our results show it can accurately predict the dynamics of the system and adapt the mesh accordingly. The adaptivity of the model supports learning resolution-independent dynamics and can scale to more complex state spaces at test time.

The viewpoint and reaction of a country towards climate change are shaped by its political, cultural, and scientific backgrounds, in addition to the distinct characteristics of its evolving climate and the anticipated and actual consequences of the phenomenon in the times ahead. A region's climate has a significant impact on how water is managed and used, mostly in the primary sector (agroforestry systems, livestock, and agriculture), and both the distribution of ecosystem types and the amount and spreading of species on Earth. As a result, the environment and agricultural practices are affected by climate, so evaluating both distribution and evolution is extremely pertinent. Towards this aim, the climate distribution and evolution in the S. Francisco River basin (SFRB) is assessed in three periods (1970−2000, 1981−2022) in the past and 20412060 in the future from an ensemble of GCMs under two SSPs (Shared Socioeconomic Pathways), SSP2-4.5 and SSP5-8.5. The Köppen-Geiger (KG) climate classification system is analyzed, and climate change impacts are inferred for this watershed located in central-eastern Brazil, covering an area equivalent to 8% of the country. Results predict the disappearance of the hot-summer (Csa) and warm-summer (Csb) Mediterranean climates, and a reduction/increase in the tropical savanna with dry winter (Aw)/dry summer (As). A striking increase in the semi-arid hot (BSh-steppe) climate is predicted with a higher percentage (10%) under SSP5-8.5. The Source and the mouth of SFRB are projected to endure the major impacts of climate change that is followed by a predicted increase/decrease in temperature/precipitation. Future freshwater resource availability, and quality for human use will all be impacted. Consequences on ecosystems, agricultural and for the socioeconomic sectors within the SFRB might deepen the current contrasts between regions, urban and rural areas, and even between population groups, thus translating to a larger extent, the inequality that still characterizes Brazilian society. Maps depicting land use and cover changes in the São Francisco River basin from 1985 to 2022 highlight tendencies such as urbanization, agricultural expansion, deforestation, and changes in shrubland and water bodies. Urban areas fluctuated slightly, while cropland significantly increased from 33.57% to 45.45%, and forest areas decreased from 3.88% to 3.50%. Socioeconomic data reveals disparities among municipalities: 74.46% with medium Human Development Index (HDI), 0.59% with very high HDI, and 9.11% with low HDI. Most municipalities have a Gross Domestic Product (GDP) per capita below US$ 6,000. Population distribution maps show a predominance of small to medium-sized urban and rural communities, reflecting the basin's dispersed demographic and economic profile. To build routes towards a shared sustainable goal—namely, to adapt and reduce the expected climate change impacts in SFRB—it is imperative that integrated measures be conducted with the cooperation of stakeholders, local population, and decision-makers.

This research investigates entrepreneurial attitudes among 415 students in the academic sports domain. Utilizing a 15-item questionnaire, participants were categorized into four groups based on their sports background: individual and team sports with competitive history, amateur athletes, and non-practitioners. The questionnaire exhibited good internal consistency (Cronbach's alpha coefficient = 0.755). Statistical analysis using the Chi-Square Test of Homogeneity across criteria such as gender, competitive sports involvement, type of competitive sports, and engagement in amateur sports revealed significant differences in nine out of 60 comparisons. Furthermore, this study employs an explanatory approach to elucidate the distinctions among athletes, amateur, and non-athlete students in academic sports programs regarding assumption of responsibilities, proactive initiative, determination to establish their own businesses, perception of the comfort zone, and the perception of post-sports career adaptation. Additionally, a gender comparison is conducted concerning the perception of business risk, comfort and security in a stable workplace, and the applicability of sports-oriented approaches in business. These results provide valuable insights into how sports backgrounds shape entrepreneurial perspectives, thereby contributing to the development of customized entrepreneurial programs. Moreover, the study seeks to offer a nuanced comprehension of the diverse factors influencing the entrepreneurial attitudes and perceptions of students in sports academic programs.

Reticulate evolution through the interchanging of genetic components across organisms can impact significantly on the fitness and adaptation of species. Bread wheat (Triticum aestivum subsp. aestivum) is one of the most important crops in the world. Allopolyploid speciation, frequent hybridization, extensive introgression, and occasional horizontal gene transfer (HGT) have been shaping a typical paradigm of reticulate evolution in bread wheat and its wild relatives, which is likely to have a substantial influence on phenotypic traits and environmental adaptability of bread wheat. In this review, we outlined the evolutionary history of bread wheat and its wild relatives with a highlight on the interspecific hybridization events, demonstrating the reticulate relationship between species/subspecies in the genera Triticum and Aegilops. Furthermore, we discussed the genetic mechanisms and evolutionary significance underlying the introgression of bread wheat and its wild relatives. An in-depth understanding of the evolutionary process of Triticum species should be beneficial to future genetic study and breeding of bread wheat.

In Korea, the underground silo structure for low- and intermediate-level radioactive waste disposal facilities has been constructed and operated since 2014. Large-scale earthquakes occurred in 2016 and 2017, respectively, in Gyeongju and Pohang areas near the underground silo structures, and interest in the stability of the underground silo increased significantly. In this paper, one-dimensional free field analyses have been carried out before the three-dimensional silo dynamic analyses subjected to earthquake loadings. As an additional study, a new form of finite element equilibrium equation is derived in terms of relative motions, which is essentially the same equation as that expressed in terms of total motions where the base shear force is applied for earthquake load. The accuracy of conventional finite element solutions is evaluated by directly comparing them with closed-form solutions by frequency domain analysis such as SHAKE91.

Failure to adapt to climate change is currently considered one of the major threats affecting humanity. Hence, much effort is being put into discussing adaptation approaches. While many adaptation options have been identified, the academic literature does not present a simple process that local councils and community members can use to rank adaptation options. In this context, community members participating on planning processes are presented with many adaptation options, but with no objective approach for selection, which adds challenge to the planning process. With the objective of addressing this issue, this work proposes a simple equation that allows calculating the applicability level of adaptation options. Results can then be plotted into graphs that allow correlating adaptation options and applicability level, which can be easily understood by community members. To develop such equation, this work built on existing sophisticated models from where the indicators used on the equation were identified, as well as the relationship between them. A scale was proposed to help on identifying adaptation options that should be implemented on the short, medium and long term, and options that should only be implemented if the circumstance change.

A multiscale model that enables quantitative understanding and prediction of the size effect on scattering properties of micro- and nanostructures is crucial for the design of LED surface textures optimized for high light extraction efficiency (LEE). In this paper, a hybrid process for combining full-wave finite-difference time-domain simulation and a ray-tracing technique based on a bidirectional scattering distribution function model is proposed. We apply this method to study the influence of different pattern sizes of a patterned sapphire substrate on GaN-based LED light extraction from the microscale to the nanoscale. The results show that near-wavelength–scaled patterns with strong diffraction are not expected to enhance LEE. By contrast, microscaled patterns with optical diffusion behavior have the highest LEE at a specific aspect ratio, and subwavelength-scaled patterns that have antireflection properties show marked enhancement of LEE for a wide range of aspect ratios.

Agriculture contributes towards South African economy, but this sector is highly vulnerable to risks imposed by climate change. Smallholder maize farmers are specifically susceptible to climate change impacts. Maize crop plays a crucial role in the country's food security as it is considered a stable food for households and source of feed for livestock. The study aimed to analyze the socio-economic factors influencing smallholder maize farmers’ willingness to adopt climate-smart agriculture. It was conducted in three different areas due to their specific agro-ecological zones. Multipurpose research design was used to gather data, and multistage random sampling to choose the study areas. Subsequently, 209 purposively selected farmers were interviewed face-to-face using structured questionnaires and focus discussion groups. Descriptive statistics and Double-hurdle regression model were used to analyze the data. Descriptive results indicated that 81%, 67%, and 63% farmers were willing to adopt CSA in Ga-Makanye, Gabaza, and Giyani, respectively. Empirical results from the first and second hurdles of the regression analysis showed that farmers’ education, crop diversification, information about CSA positively influenced the decision to adopt CSA while household size and agricultural experience negatively influenced the decision to adopt CSA. It is recommended that the Department of Agriculture, Land Reform, and Rural Development should provide CSA workshops and educational programs to farmers to enhance their knowledge and decision-making processes regarding adaptation strategies.

Hepatitis B surface antigen (HBsAg) is not only the biomarker of hepatitis B virus (HBV) infection and expression activity in hepatocytes, but also contributes to viral specific T cell exhaustion and HBV persistent infection. Therefore, anti-HBV therapies targeting HBsAg to achieve HBsAg loss are key approaches for HBV functional cure. In this study, we found that YZH-106, a rupestonic acid derivative, inhibited HBsAg secretion and viral replication. Further investigation demonstrated that YZH-106 promoted the lysosomal degradation of viral L- and M-HBs proteins. Mechanistic study by Biacore and docking analysis revealed that YZH-106 bound directly to PreS2 domain of L- and M-HBsAg, thereby blocking their entry to the endoplasmic reticulum (ER) and promoting their degradation in cytoplasm. Our work thereby provide basis for design of novel compound therapy to target HBsAg against HBV infection.

Named Entity Recognition (NER) is a fundamental and pivotal stage in the development of various knowledge-based support systems, including knowledge retrieval and question-answering systems. In the domain of pig diseases, Chinese NER models encounter several challenges such as the scarcity of annotated data, domain-specific vocabulary, diverse entity categories, and ambiguous entity boundaries. To address these challenges, we propose PDCNER, a Pig Disease Chinese Named Entity Recognition method leveraging lexicon-enhanced BERT and contrastive learning. Firstly, we construct a domain-specific lexicon and pre-train word embeddings in the pig disease domain. Secondly, we integrate lexicon information of pig diseases into the lower layers of BERT using a Lexicon Adapter layer, which employs char-word pair sequences. Thirdly, to enhance feature representation, we propose a lexicon-enhanced contrastive loss layer on top of BERT. Finally, a Conditional Random Field (CRF) layer is employed as the model's decoder. Experimental results show that our proposed model demonstrates superior performance over several mainstream models, achieving a precision of 87.76%, a recall of 86.97%, and an F1-score of 87.36%. The proposed model outperforms BERT-BiLSTM-CRF and LEBERT by 14.05% and 6.8%, respectively, with only 10% of the samples available, showcasing its robustness in data scarcity scenarios. Furthermore, the model exhibits generalizability across publicly available datasets. Our work provides reliable technical support for the information extraction of pig diseases in Chinese and can be easily extended to other domains, thereby facilitating seamless adaptation for named entity identification across diverse contexts. The codes have been open-sourced at https://github.com/tufeifei923/pdcner.

In the realm of artificial intelligence, knowledge graphs have become a fascinating area of research. Relationships between entities are depicted through a structural framework in knowledge graphs. In this paper, we propose to build a domain-specific medicine dictionary (DSMD) based on the principles of knowledge graphs. Our dictionary is composed of structured triples, where each entity is defined as a concept, and these concepts are interconnected through relationships. This comprehensive dictionary boasts more than 348,000 triples, encompassing over 20,000 medicine brands and 1,500 generic medicines. It offers a groundbreaking approach to storing and accessing medical data. Our dictionary facilitates various functionalities, including medicine brand information extraction, brand-specific queries, as well as queries involving two words or question answering. We anticipate that our dictionary will serve a broad spectrum of users, catering to both human users, such as diverse range of healthcare professionals as well as AI applications.

Stroke survivors often face diverse unmet needs, highlighting the significance of identifying and addressing these needs to enhance rehabilitation outcomes and overall quality of life. This study aimed to validate the modified Needs Assessment Questionnaire (mNAQ) as a reliable and valid tool for assessing the needs of stroke patients in the Greek context. Additionally, the research sought to identify potential differences in the assessment of stroke patients' needs based on their stroke phase and National Institutes of Health Stroke Scale (NIHSS) scores. A sample of 71 adult stroke survivors, adhering to World Health Organization guidelines and providing autonomous consent, participated in the study. The mNAQ, comprising 141 items across 12 domains, was uti-lized to evaluate stroke patients' needs. The NIHSS and Barthel Index (BI) were employed for functional independence and mobility assessment. Data analysis incorporated confirmatory fac-tor analysis, exploratory factor analysis, and Cronbach's reliability analysis to establish con-struct validity and internal consistency. Concurrent and known-groups validity analyses were conducted, and Spearman’s rho correlation explored the relationship between mNAQ and BI scores. Non-parametric analyses were applied to identify differences based on stroke phase and NIHSS scores. The study revealed that the mNAQ initially lacked satisfactory psychometric properties in the Greek context. Subsequent modifications, guided by confirmatory and explora-tory factor analyses, resulted in a refined three-factor scale encompassing 31 items in the domains of communication, mobility, and social functioning needs. This adapted measure effectively dif-ferentiated between acute and chronic stroke patients and those with minor and moderate strokes. In conclusion, the validated 31-item Greek mNAQ emerges as a crucial tool for compre-hensively assessing the needs of stroke patients. Its application holds promise for optimizing post-stroke care, improving functional outcomes, and ultimately enhancing the overall well-being and quality of life for stroke survivors.

The literature on contract farming and climate change in Zimbabwe has blind spots in relation to the study of contract farming as a climate change response. While the literature on contract farming and climate change abounds, such literature is lacking when it comes to the exploration of how contract farming can facilitate climate change coping and adaptation strategies by smallholder farmers. This paper fills this gap. It draws on in-depth interviews with 10 contracted and 10 non-contract farmers who were engaged through face-to-face in-depth interviews in the Chipinge South Constituency. It found that contract farming does not only boost productivity, but it also enables farmers to positively respond to the ravages of climate change, and therefore, it should be supported and encouraged. Future research should explore more viable and sustainable way through which the state, instead of private sector actors, should be at the centre of contract farming.

DNA Topoisomerase IIa (Top2A) is a nuclear enzyme that is a cancer drug target, and there is interest in identifying novel sites on the enzyme to inhibit cancer cells more selectively and to reduce off-target toxicity. The C-terminal domain (CTD) is one potential target, but it is an intrinsically disordered domain, which prevents structural analysis. Therefore, we set out to analyze the sequence of Top2A from 105 species using bioinformatic analysis including the PSICalc algorithm, entropy analysis, and other approaches. Our results demonstrate that large (10th-order) interdependent clusters are found including non-proximal positions across the major domains of Top2A. Further, CTD-specific clusters of 3rd, 4th, and 5th-order were identified that included positions that had been previously analyzed via mutation and biochemical assays. Some of these clusters coincided with positions that when mutated either increased or decreased relaxation activity. Finally, sites of low entropy (i.e., high information content) were identified and mapped as key positions in the CTD. Included in the low entropy sites are phosphorylation sites and charged positions. Together, these results help to build a clearer picture of the critical positions in the CTD and provide potential sites/regions for further analysis.

Adaptation to climate change is being addressed in many domains. This means that there are multiple perspectives on adaptation; often with differing visions resulting in disconnected responses and outcomes. Combining singular perspectives into coherent, combined perspectives that include multiple needs and visions can help to deepen the understanding of various aspects of adaptation and provide more effective responses. Such combinations of perspectives can help to increase the range and variety of adaptation measures available for implementation or avoid maladaptation compared with adaptations derived from a singular perspective. The objective of this paper is to present and demonstrate a framework for structuring the local adaptation responses using the inputs from multiple perspectives. The adaptation response framing has been done by: (i) contextualizing climate change adaptation needs; (ii) analyzing drivers of change; (iii) characterizing measures of adaptation; and (iv) establishing links between the measures with a particular emphasis on taking account of multiple perspectives. This framework was demonstrated with reference to the management of flood risks in a case study Can Tho, Vietnam. The results from the case study show that multiple perspective framing of adaptation responses enhance the understanding of various aspects of adaptation measures, thereby leading to flexible implementation practices.

Approximately 20% of the world's population resides in coastal areas at risk of rising sea levels due to climate change. This could lead to severe weather events and ecological impact on critical infrastructure. To combat the effects of water-based environmental forces on the land, hard engineering methods such as seawalls have been utilized. However, a more sustainable solution is emerging in nature-based engineering, which involves growing vegetation. This approach can help extend the lifespan of existing technology and provide climate change adaptation and resilience for coastal and riverine communities. The following paper will review seaweed farms as an advanced nature-based mitigation approach. The paper also outlines the findings of experiments at RWTH Aachen University's hydraulic laboratory. These tests involved using seaweed as a natural solution to dampen waves in a model system to test a hypothesis. One result involving a system with two lines of seaweed revealed 15 percent wave damping. Using seaweed as a nature-based solution, a gentle engineering approach to designing future vegetated protection systems can extend the design life of existing coastal infrastructure. This can also provide protection against climate-induced sea level rise, promote coastal risk mitigation, restore ecosystems, and foster blue bio-economic development.

This study presents a detailed computational investigation aimed at identifying and evaluating potential small organic molecular scaffolds as inhibitors targeting the Pks13 protein, a critical enzyme in tuberculosis (TB) drug discovery. Our approach integrates multiple computational techniques, including quantitative structure-activity relationship (QSAR) modelling, compound library generation, molecular docking, biological activity prediction, re-evaluation of screened compounds, molecular dynamics (MD) analysis, and binding free energy estimation using the MM-GBSA approach. We developed a robust QSAR model using multiple linear regression (MLR) techniques, specifically tailored to predict the minimum inhibitory concentration (MIC) values of potential anti-TB agents. This model demonstrated excellent predictive performance, validated through rigorous internal and external validation procedures. Through molecular docking simulations, we identified five promising inhibitors Ethyl (R,Z)-2-(2-((2-chlorobenzyl) oxy)benzylidene)-5-(4-chlorophenyl)-7-methyl-3-oxo-2,3-dihydro-5H-thiazolo [3,2-a] pyrimidine-6-carboxylate (PKD1), (7R,9aR)-7-(2-hydroxyphenyl)-4-(2-methoxyphenyl)-3-methyl-7,8,9,9a-tetrahydroisoxazolo[5,4-b]quinolin-5(6H)-one (PKD2), (7-(4-methoxy phenyl)-2-methyl-3-phenylpyrazolo[1,5-a]pyrimidin-5-yl)(4-(3-(trifluoromethyl)phenyl) piperazin-1-yl)methanone (PKD3), (8S,10S)-10-(((2R,4S,5S,6S)-4-amino-5-hydroxy-6-methyl tetrahydro-2H-pyran-2-yl)oxy)-6,8,11-trihydroxy-8-(2-hydroxyacetyl)-1-methoxy-7,8,9,10-tetra-hydrotetracene-5,12-dione (PKD4), 3-(3,4-dimethylphenyl)-5-((2-(4-fluorophenyl)-2-oxoethyl) thio)-6-phenyl-2-thioxo-2,3,5,7a-tetrahydrothiazolo[4,5-d]pyrimidin-7(6H)-one (PKD5)with high binding affinities, prioritizing them based on their binding energies. These selected compounds were further assessed for their biological activity predictions, guiding the selection of candidates for experimental validation. Subsequent re-evaluation using absolute binding free energy estimation and MD simulations provided insights into the stability and dynamics of the protein-ligand complexes over time. Our findings highlighted the potential of identified compounds as stable binders within the Pks13-TE domain binding pocket, underscoring their viability as drug candidates. Additionally, binding free energy analysis using MM-GBSA reaffirmed the strong affinity of the proposed compounds towards Pks13. Our integrated computational approach offers a systematic and detailed framework for the identification and characterization of potential Pks13 inhibitors with specific relevance to TB drug discovery.

Although there are a large number of databases available for regulatory elements, bottleneck has been created by the lack of bioinformatics tools for predicting types of mechanisms underlying actions of regulatory elements. To reduce the gap, we developed ARabidopsis Transcription regulatory Factor Domain-domain interaction Analysis Tool- Liquid-liquid phase separation (LLPS), Oligomerization, GO analysis (ART FounDATion-LOG), a useful toolkit for protein-nucleic acid interactions (PNI) and protein-protein interactions (PPI) analysis based on domain-domain interaction (DDI). LLPS, protein oligomerization, structural properties of protein domains, and protein modifications are major components in orchestrating spatio-temporal dynamics of PPI and PNI. Our goal is to integrate PPI/PNI information into development of prediction model for identifying important genetic variants in peach. The program unified inter-database relational keys by protein domains for facilitating inference from the model species. Key advantage of the program lies in the integrated information of related features: LOG, structural characterization of domain (e.g. domain linker, intrinsically disordered regions, DDI, domain-motif (peptide) interaction, beta-sheet and transmembrane helices), and post-translational modification. We provided simple tests to demonstrate how to use the program. The program may be applied to other eukaryotic organisms. The program codes and data are freely available for download at and https://sourceforge.net/projects/artfoundation-log/.

In this work, we report the electric energy storage properties of (1-x)Bi0.5(Na0.8K0.2)0.5TiO3-xBi0.2Sr0.7TiO3 (BNKT-BST; x = 0.15-0.50) relaxor ferroelectric ceramics enhanced via a domain engineering method. A rhombohedral–tetragonal phase, the formation of highly dynamic PNRs, and a dense microstructure are confirmed from XRD, Raman vibrational spectra, and microscopic investigations. The relative dielectric permittivity (2664 at 1 kHz) and loss factor (0.058) were gradually improved with BST (x=0.45). The incorporation of BST into BNKT can disturb the long-range ferroelectric order, lowering the dielectric maximum temperature Tm and inducing the formation of highly dynamic polar nano-regions. In addition, the Tm shifts toward high temperature with frequency and a diffuse phase transition, indicating relaxor ferroelectric characteristics of BNKT-BST ceramics, which is confirmed by the modified Curie Weiss law (γ=1.83). The rhombohedral–tetragonal phase, fine grain size, and lowered Tm with relaxor properties synergistically contribute to a high recoverable energy density Wrec of 0.81 J/cm3 and a high energy efficiency η of 86.95% at 90 kV/cm for x = 0.45.

Federated Distillation (FD) has recently attracted increasing attention for its efficiency in aggregating multiple diverse local models trained from statistically heterogeneous data of distributed clients. Existing FD methods generally treat these models equally by merely computing the average of their output soft predictions for some given input distillation sample, which does not take the diversity across all local models into account, thus leading to degraded performance of the aggregated model, especially when some local models learn little knowledge about the sample. In this paper, we propose a new perspective that treats the local data in each client as a specific domain and design a novel domain knowledge aware federated distillation method, dubbed DaFKD, that can discern the importance of each model to the distillation sample, and thus is able to optimize the ensemble of soft predictions from diverse models. Specifically, we employ a domain discriminator for each client, which is trained to identify the correlation factor between the sample and the corresponding domain. Then, to facilitate the training of the domain discriminator while saving communication costs, we propose sharing its partial parameters with the classification model. Extensive experiments on various datasets and settings show that the proposed method can improve the model accuracy by up to $6.02\%$ compared to state-of-the-art baselines.

Gradient-domain image processing is a technique where, instead of operating directly on the image pixel values, the gradient of the image is computed and processed. The resulting image is obtained by reintegrating the processed gradient. This is normally done by solving the Poisson equation, most oftenly by means of a finite difference implementation of the gradient descent method. However, this technique in some cases lead to severe haloing artefacts in the resulting image. To deal with this, local or anisotropic diffusion has been added as an ad-hoc modification of the Poisson equation. In this paper, we show that a version of anisotropic gradient-domain image processing can result from a more general variational formulation through the minimisation of a functional formulated in terms of the eigenvalues of the structure tensor of the differences between the processed gradient and the gradient of the original image. Example applications of linear and non-linear local contrast enhancement and colour image daltonisation illustrate the behaviour of the method.

The access to cardiac rehabilitation (CR) is contingent upon physician referrals, yet these are often inadequate, particularly in low-resource settings. This multi-method study aimed to translate, culturally adapt, and validate the Portuguese version of the Provider Attitudes toward CR and Referral (PACRR-P) scale, as well as to identify key factors influencing CR referral in a Latin American context for the first time. The PACRR was translated into Brazilian-Portuguese through a rigorous process involving independent translation, back-translation, and expert panel review to ensure face, content, and cross-cultural validity. A total of 44 Brazilian physicians completed the questionnaire, allowing for an assessment of internal consistency, criterion validity, and convergent validity. The findings confirmed the face, content, and cultural validity of the 20 translated items, with a mean item clarity rating of 4.8/5. The final version included 17 of the original 19 PACRR-P items, with a Cronbach’s alpha of 0.73. Referral rates were significantly associated with specific PACRR-P items, supporting the scale's criterion validity, while correlations with ReCaRe scores further supported convergent validity. The most prominent barriers to referral were a lack of familiarity with CR site locations, absence of a standard referral form, and lack of automatic referral processes. The PACRR's validity and reliability among Portuguese-speaking providers are preliminarily supported.

The agricultural sector provides employment and income to the majority of Myanmar's population. The sector, however, is extremely susceptible to severe weather, rising temperatures, and changes in precipitation. This paper aims to analyze how vulnerable Myanmar's agricultural sector is to climate change and highlights the key sectors that are most at risk. This report also examines historical climate information, and the anticipated effects of climate change on Myanmar's agricultural sectors. In-depth literature reviews and summaries of Myanmar's climate change adaptation efforts are included in the report, along with recom-mendations for targeted, locally appropriate actions to strengthen the country's agricultural sector's resilience. Additionally, the study reviews Myanmar's in-stitutional framework for climate action and government priorities for adaptation measures, emphasizes the urgent need for climate action in Myanmar's agricul-ture sectors and calls for more research and development efforts on the effects of climate change on rural livelihoods and agriculture.

Cancer, a disease of multicellular organisms, probably developed almost immediately following the transition from unicellular to metazoan life, about one billion years ago. Great efforts have been made to understand the carcinogenesis for many years. In this paper, We tried to explain the cancer based on “chaos”, “adaptation” and “information” with the context of new literature findings.

This study examines the evolution of the two-factor authentication method and its adaptability to the online education system. Two-factor authentication is a security measure used especially in areas where information such as banking is valuable. Parallel to technological developments, it has developed as much as daily. It aims to take security one step forward because it is composed of two phases. Today, banking, IOT devices, public transport tickets and many other areas are used. Two-factor authentication methods against security attacks in the field of information are also being updated. In recent years, new technologies such as biometric (iris pattern, retinal pattern, etc.) or behavioral biometry (location tracking, walking information, touch speed etc.) were studied. Instead of physically studying somewhere like going to a course in modern society, online trainings become more advantageous. Most of these online trainings are given certificates such as participation certificate, success certificate, etc. The main problem here is whether the person who is being certified is true. In the study conducted in line with these details, there is a proposal for the application of the Mimic Control Method with Sound Intensity (MCMSI) method for on-line training by examining the two-factor authentication techniques up to the day.

Not only are humans responsible for the anthropogenic causes of currently observed climate change, but we are also responsible for our responses to climate change. How we choose to respond provides important insights into our ability to collectively act in the face of threats with the unique characteristics of climate change. This communication attempts to provide an overview of some the difficulties in forging new policy directions along our coastlines in an era of climate change. It is meant as a referential framing for the research presented in this special issue. As this communication is being written, the world is gripped by a global pandemic caused by a variant of the coronavirus. There are important corollaries between the underlying characteristics of the coronavirus and the causes and effects of climate change. Seeing how the global citizenry is responding to the current epidemic provides some insight into the difficulties in fostering collective action towards climate change. As with the pandemic, the issue is not really one of understanding the problem, but rather the varying human responses to the problem. We can expect the same difficulties as we continue to confront the ever-growing problem of climate change.

Spacetime torsion is known to be highly suppressed at the end of inflation, called preheating. Of course this fact does not avoid that metric could affect the cosmic dynamos at preheating phase as was showed by Bassett et al, in the general relativity (GR) case. In this paper, is to show that a torsionful magnetogenesis in QED effective lagrangean, drives either a torsion damping or an amplification mechanism or dynamo effect, depending upon torsion chirality. The present extension of Basset et al theory yields a new magnetic field expression $B\sim{a^{(1\pm{\beta})}}$, obtained from dynamo equation in QED. Here a cosmic factor ${\beta}=g_{T}{\eta}k$ is present. In a second example we find Higgs inlationary fields in Einstein-Cartan gravity thick domain walls (DWs). Recently a moodified Einstein-Cartan gravity was given by M Shaposhnikov et al [PRL (2020)] to obtain higgs like inflatons as a portal to dark energy.

Community engagement in climate action and adaptation planning is an essential prerequisite for overcoming existing and projected environmental injustices associated with the negative impacts of climate change. Diversity and inclusion of stakeholders are crucial for addressing equity both in the development and implementation of local climate plans. Our study attempts to evaluate and compare consideration of equity in climate action and climate adaptation plans of Michigan cities and its association with a diversity of stakeholders involved in the planning process. Data analysis is based on the content of eight municipal climate action and/or climate adaptation plans, related documents, and interviews with city planners along with community activists. Data derived from climate action and adaptation plans were also compared to the strategies outlined in the Tribal Climate Adaptation Menu, which integrates climate science and indigenous knowledge. The study concludes that municipalities that engage more diverse groups of stakeholders appear to be more attentive to social equity and more likely to offer specific climate action and adaptation measures focusing on vulnerable groups. Integration of indigenous knowledge could provide valuable insights for municipalities through collaboration with tribal communities and climate adaptation experts.

Coastal ecosystems provide a wide range of goods and services for the lives of human beings and aquatic species. Nevertheless, climate-induced extreme events cause unprecedented impacts on these areas, instigating a reduction of aquatic goods and services, destruction of infrastructure, and loss of human lives. Hence, this review investigated the impacts of climate-induced coastal hazards and adaptation responses. A systematic review method was used to address the objectives of this literature review. The main data sources were Research Gate, Google Scholar, Scopus, Web of Science, and PubMed databases. A total of 40 research articles (2012-2023) were selected using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA 2020) methodology. The findings of this review revealed that Bangladesh was the leading country based on the number of studies published on the impacts of climate-induced coastal hazards and coastal adaptations (20%), followed by the USA, which accounts for about 15%. Sea-level rise, coastal flooding, and climate change and variability were the most studied climate-induced coastal hazards, each accounting for 55%, 52.5% and 52.5% respectively. Coastal hazards have had significant impacts on the fishery, water, coastal ecosystems and biodiversity, agriculture, tourism, and infrastructure sectors. Besides, they caused migration and death of humans and animals. About 35% of the articles explored the assessment of vulnerability to climate-induced hazards. Moreover, 32.5% of the articles investigated anticipated climate-induced hazards. Hard, soft, ecosystem-based, and hybrid adaptation measures were used to adapt to the impacts of diverse climate-induced coastal hazards. In conclusion, developing countries are more vulnerable to the adverse impacts of climate-induced coastal hazards than developed countries. The coastal area’s vulnerability to climate change risks will continue unless all stakeholders act proactively. Thus, it is suggested that the adaptation policies of vulnerable coastal areas should give due attention to nature-based solutions to reduce the adverse impacts of coastal hazards sustainably.

Polycystic ovary syndrome (PCOS) is increasingly recognized as a complex metabolic disorder that manifests in genetically susceptible women following a range of negative exposures to nutritional and environmental factors related to contemporary lifestyle. The hypothesis that PCOS phenotypes are derived from a mismatch between ancient genetic survival mechanisms and modern lifestyle practices is supported by a diversity of research findings. The proposed evolutionary model of the pathogenesis of PCOS incorporates evidence related to evolutionary theory, genetic studies, in-utero developmental epigenetic programming, transgenerational inheritance, metabolic features including insulin resistance, obesity and the apparent paradox of lean phenotypes, reproductive effects and subfertility, the impact of the microbiome and dysbiosis, endocrine disrupting chemical exposure, and the influence of lifestyle factors such as poor quality diet and physical inactivity. Based on these premises, the diverse lines of research are synthesized into a composite evolutionary model of the pathogenesis of PCOS. It is hoped that this model will assist clinicians and patients to understand the importance of lifestyle interventions in the prevention and management of PCOS and provide a conceptual framework for future research. It is appreciated that this theory represents a synthesis of the current evidence and that it is expected to evolve and change over time.

We present a novel approach for adapting text written in standard Finnish to different dialects. We experiment with character level NMT models both by using a multi-dialectal and transfer learning approaches. The models are tested with over 20 different dialects. The results seem to favor transfer learning, although not strongly over the multi-dialectal approach. We study the influence dialectal adaptation has on perceived creativity of computer generated poetry. Our results suggest that the more the dialect deviates from the standard Finnish, the lower scores people tend to give on an existing evaluation metric. However, on a word association test, people associate creativity and originality more with dialect and fluency more with standard Finnish.

African trypanosomes are an early branch in eukaryotic evolution and developed a unique endomembrane system as an adaptation to their parasitic life style. The key virulence mechanism of many pathogens is successful immune evasion to enable survival within the host, which is a feature requiring both genetic events and membrane transport in African trypanosomes. Intracellular trafficking not only plays a role in immune evasion, but also in homeostasis of intracellular and extracellular compartments and interactions with the environment. Significantly, historical and recent work has unravelled some of the connections between these processes and highlighted how immune evasion mechanisms associated with adaptations in membrane trafficking may have, paradoxically, provided specific sensitivity to drugs. Here we explore these advances in understanding the membrane composition of the trypanosome plasma membrane and organelles and provide a perspective for how transport could be exploited for therapeutic purpose.

Query reformulation is a fundamental task in information retrieval systems aimed at improving the accuracy and relevance of search results. This abstract presents a query reformulation approach that utilizes a domain-specific ontology to enhance the effectiveness of query reformulation. The use of ontology enables a more precise understanding of the domain and its associated concepts, leading to more accurate query interpretation and reformulation. The proposed approach consists of two main steps: ontology-based query interpretation and ontology-guided query expansion. The proposed approach use Power thesaurus tool that perform sentiment analysis for suggestion of the synonyms of the query words to improve the accuracy of the retrieved results. The proposed methodology gives high accuracy as compared to the previous approaches that used Wordnet for the suggestions of synonyms. The proposed approach demonstrates the potential of utilizing ontologies in query reformulation, offering a more accurate and context-aware search experience in domain-specific information retrieval systems.

In this work we present a novel steganography algorithm, based on Fractal Transform domain, combined with the principles of Cryptography. The proposed method uses a private key, which will indicate which segment of the image will be taken and encoded with the Fractal Transform, to then insert the secret bits in the transformed coefficients, thus generating the stego image.

Using Directed Transfer Function (DTF) and Partial Directed Coherence (PDC) in their information version, this paper extends their theoretical framework to incorporate instantaneous Granger Causality (iGC)’s frequency domain description into a single unified perspective. We show that standard vector autoregressive models allow portraying iGC’s repercussions associated with Granger Connectivity where interactions mediated without delay between time series can be easily detected.

We employ another approach to quantize electromagnetic fields in the coordinate space, instead of the mode (or Fourier) space, such that local features of photons can be efficiently, physically, and more intuitively described. To do this, coordinate-ladder operators are defined from mode-ladder operators via the unitary transformation of systems involved in arbitrary inhomogeneous dielectric media. Then, one can expand electromagnetic field operators through the coordinate-ladder operators weighted by non-orthogonal and spatially-localized bases, which are propagators of initial quantum electromagnetic (complex-valued) field operators. Here, we call them QEM-CV-propagators. However, there are no general closed form solutions available for them. This inspires us to develop a quantum finite-difference time-domain (Q-FDTD) scheme to numerically time evolve QEM-CV-propagators. In order to check the validity of the proposed Q-FDTD scheme, we perform computer simulations to observe the Hong-Ou-Mandel effect resulting from the destructive interference of two photons in a 50/50 quantum beam splitter.

Permafrost hydrology is an emerging discipline, attracting increasing attention as the Arctic region is undergoing rapid change. However, the research domain of this discipline had never been explicitly formulated. Both 'permafrost' and 'hydrology' yield differing meanings across languages and scientific domains, hence 'permafrost hydrology' serves as an example of linguistic relativity. The differing views of permafrost as either an ecosystem class or a geographical region, and hydrology as a discipline concerned with either landscapes or generic water bodies, maintain a language-specific touch in the definition of permafrost hydrology. From this point of view, the English and Russian usage of this term is explained. A universal process-based definition is further proposed, developed on a specific process assemblage, including (i) water table dynamics caused by migration of an upper aquitard through freeze–thaw processes; (ii) water migration in soil matrix, driven by phase transitions in the active layer; (iii) transient water storage in solid state in the subsurface compartment. This definition is shown to fill the niche in existing vocabulary, and other definitions from northern hydrology field are revisited.

Immunoglobulin G (IgG) adopts a modular multidomain structure that mediates antigen recognition and effector functions, such as complement-dependent cytotoxicity. IgG molecules are self-assembled into a hexameric ring on antigen-containing membranes, recruiting the complement component, C1q. To provide deeper insights into the initial step of the complement pathway, we report a high-speed atomic force microscopy study for quantitative visualization of the interaction between IgG and the C1 complex composed of C1q, C1r, and C1s. Results showed that C1q in the C1 complex is restricted regarding internal motion and has a stronger binding affinity for on-membrane IgG assemblages than C1q alone, presumably because of smaller conformational entropy loss upon binding. Furthermore, we visualized a 1:1 stoichiometric interaction between C1/C1q and an IgG variant that lacks the entire CH1 domain in the absence of antigen. In addition to the canonical C1q-binding site on Fc, their interactions are mediated through a secondary site on the CL domain that is cryptic in the presence of the CH1 domain. Our findings offer clues for novel-modality therapeutic antibodies.

Endophytic fungi (EF) have emerged as promising modulators of plant growth and stress tolerance in agricultural ecosystems. This review synthesizes current knowledge on the role of EF in en-hancing crop adaptation to abiotic stress. Abiotic stresses, such as drought, salinity, and extreme temperatures, pose significant challenges to crop productivity worldwide. EF have shown re-markable potential in alleviating the adverse effects of these stresses. Through various mechanisms including synthesis of osmolytes, production of stress-related enzymes, and induction of plant de-fense mechanisms, EF enhance plant resilience to abiotic stressors. Moreover, EF promote nutrient uptake and modulate hormonal balance in plants, further enhancing stress tolerance. Recent ad-vancements in molecular techniques have facilitated the identification and characterization of stress-tolerant endophytic fungal strains, paving the way for their utilization in agricultural prac-tices. Furthermore, the symbiotic relationship between EF and plants offers ecological benefits, such as improved soil health and reduced dependence on chemical inputs. However, challenges remain in understanding the complex interactions between EF and host plants, as well as in scaling up their application in diverse agricultural systems. Future research should focus on elucidating the mechanisms underlying endophytic fungal-mediated stress tolerance and developing sustainable strategies for harnessing their potential in crop production.

Both globally and in Hungary, agriculture is one of the industries that is most vulnerable to weather and climate extremes. Intense temperature rises, spatial and temporal variations in precipitation, and significant changes in extreme climatological and weather parameters have contributed to changes in the conditions of cropland, crop losses, and impacts on crop quality in recent years. This paper depicts the transformation of the domestic agricultural sector due to the extreme drought shock of 2022, as well exploring the adaptation strategies applied. The research is based on official agro-climate database and crop data, and the temperature, precipitation, and radiation during the growing season are all examined. The agro-meteorological properties in Hungary had to be investigated for the entire year and all four of its seasons, with indicator analysis projected onto the ever-increasing and dormant seasons. Long-term climate analysis is necessary to understand the historic drought of 2022 and the success of future adaptation and mitigation techniques. The results can help smallholders effectively reduce the adverse impacts of drought conditions, thereby increasing their adaptation to similar shocks.

In the contemporary business world, digital transformations have undergone vast and important developments over the last several decades, and they have aided in the development of the virtual team concept, in which geographically dispersed team members work to achieve a common goal. Virtual teams, according to the literature, suffer from process losses more frequently than their face-to-face counterparts. Although just a few studies have looked at the effects of individual differences in virtual teams, this study fills in the gaps by examining the impact of individual differences: Age Disparity (AD), Gender Disparity (GD), Language Competency (LC), and IT Competency (ITC) in management perspective on virtual team adaptation in the Sri Lankan private sector. A survey was used to collect data from a sample of 175 private sector companies in Sri Lanka during the COVID- 19 pandemic, and the data was analyzed using partial least squares path modeling (PLS) to test the study's hypotheses. The results indicated that the hypotheses were statistically significant only in the language competency and IT competency and their effects were in the expected direction. Future research could benefit from perceptions of employees in public sector organizations.

Nuclear Magnetic Resonance (NMR) and its various forms are extensively utilized in both research and clinical settings to analyse molecules. NMR data pre-processing, while essential to NMR data analysis, can be uniquely complex. Despite the availability of software tools, understanding these processes can be challenging, complicating the selection of appropriate pre-processing steps. In this review, we elucidate pre-processing steps in the time domain from a mathematical and statistical perspective, explaining direct current offset removal, eddy current correction, shift and linear prediction, weighting, zero filling, and domain transformation using plain language and fundamental mathematical formulas. Our objective is to clarify these processes in simple terms and provide general guidance.

Magnetic Resonance Imaging (MRI) is widely used in clinics and research due to its accurate disease identification and non-invasive nature. MRI is based on Nuclear Magnetic Resonance (NMR), which is also extensively employed in various fields. NMR and its modern versions involve intricate pre-processing steps before data analysis. These steps are initially processed in the time domain and subsequently in the frequency domain. While our previous review focused on time domain pre-processing (https://www.preprints.org/manuscript/202310.2032/v1), this review delves into the mathematical and statistical aspects of frequency domain pre-processing. We discuss essential pre-processing steps like phase error correction, baseline correction, solvent filtering, calibration and alignment, reference deconvolution, binning/bucketing and peak picking, peak fitting/deconvolution and compound identification, integration and quantification, normalization and transformation. Furthermore, we offer practical recommendations for each step.

Analyzing the records of Advanced LIGO and Virgo gravitational observatories, we found a specific time-domain asymmetry inherent only to the signals of their gravitational detectors. Experiments with different periodic signals, Gaussian and non-Gaussian noises, made it possible to conclude that the noise of gravitational detectors is an unusual mixture of signals. We also developed a specialized Pearson correlation analyzer to recognize the gravitational-wave events. It turned out that the LIGO detectors’ output signals include a significant (– 6 dB) component, which has the properties of records of reliably recognized gravitational waves. It allows us to argue that the gravitational background noise is largely due to the processes of merging astronomical objects. Since the specific signal is registered by the detectors continuously, we can consider the sub-kilohertz band gravitational oscillations field as detected. Our analysis method also allows us to estimate the contribution of the gravitational background component to the total signal energy. With its help, it will be possible not only to provide the radio-frequency estimation of the magnitude of gravitational disturbances but also, possibly, to construct a map of the gravitational noise of the sky.

DNA damage causes the mutations that are the principal source of genetic variation. DNA damage detection and repair mechanisms therefore play a determining role in generating the genetic diversity on which natural selection acts. Speciation, it is commonly assumed, occurs at a rate set by the level of standing allelic diversity in a population. The process of speciation is driven by a combination of two evolutionary forces: genetic drift and ecological selection. Genetic drift takes place under conditions of relaxed selection, and results in a balance between rates of mutation and rates of genetic substitution. These two processes, drift and selection, are necessarily mediated by a variety of mechanisms guaranteeing genome stability in any given species. One of the outstanding questions in evolutionary biology concerns the origin of the widely varying phylogenetic distribution of biodiversity across the tree of life, and how the forces of drift and selection contribute to shaping that distribution. The following examines some of the molecular mechanisms underlying genome stability and the adaptive radiations that are associated with biodiversity and the widely varying species richness and evenness in the different eukaryotic lineages.

Drought is a long period of unusually low rainfall and can have serious effects on people's lives. It is caused by various factors such as climate change, deforestation, and the overuse of water re-sources. In the Somali region of Ethiopia, Kebri Dehar is the most at risk and most likely to have a drought. The lack of rainfall and high temperatures in Kebri Dehar have led to drought. Man-aging drought is the most challenging and complex problem. The purpose of this study is to as-sess household perception of drought hazards occurrence and adaptation. Additionally, the study aims to design a drought resilience mechanism matrix to mitigate its impact on the local commu-nity. This study used the Heckman two-stage model, which is useful in measuring drought per-ception as it accounts for potential selection bias and allows for the identification of factors that influence individuals' perceptions of drought. Gender, severity of drought, water shortage, tem-perature, drought frequency, and long dry season all have a positive effect on adaptation of resil-ience mechanisms to drought hazards, while food availability and rainfall have a negative effect. It is important for governments and individuals to act to mitigate the effects of the drought. Ef-forts to address the issue require a multi-participation approach that involves household, com-munity, and institutional level participation is needed for adopting drought resilience mecha-nisms.

The climate of Bangladesh has changed drastically which may put considerable adverse impacts on mangrove fishers but very few studies focused on this professional group. An attempt was made to perceive the impact and adaptation measures of the Sundarbans mangrove resource users, employing interviews and focus group discussions. A total of 150 respondents were randomly selected from the Sundarbans west under Shyamnagar Upazila of Satkhira District. It was revealed that the abundance of fishes, fuel woods, honey, golpata (Nypa fruticans), and shrimp post-larvae (PL) was reduced considerably. The resource users have adapted themselves by changing their occupation and becoming jobless and depending on the other family members. PL collection, honey collection, shrimp culture, and wood collection were found professional adapting strategies to adopt cyclone, flood, salinity intrusion, river erosion, and drought. Several recommendations are elicited, the implementation of which is important to ensure livelihood sustainability of the mangrove communities.

Climate change-induced events amplify existing social, political, economic, infrastructural and environmental concerns in many Global South cities, and perhaps no city is more vulnerable than Bangladesh’s capital of Dhaka. Climate-induced rural-urban migration is a profound concern, and Dhaka’s political leaders have embraced technology-based innovation as a solution pathway. This article explores the societal impact of Dhaka’s innovation environment strategies for climate change adaptation and mitigation. Employing a case study qualitative methodology, our three findings expand knowledge about innovation-urban climate mitigation as understood by Dhaka-based entrepreneurs: First, the most effective innovations were not the most technologically advanced, but those with the highest degree of participant ownership. Second, gaps between recipient, corporate and governmental understandings of effective mitigation and adaptation harmed projects, and were driven by different definitions of risk and competing understandings of vulnerability. Third, even the most technical climate adaptation measures were inherently political in their application. We discuss how to better position urban climate innovation infrastructures in Bangladesh and beyond, including developing a better recognition of innovation lifecycles for urban climate adaptation and widening our definitions of ‘innovation’ to better incorporate more effective and inclusive climate adaptation solutions.

This article considers the concepts of sustainability and sustainable development in relation to disaster risk reduction and climate change adaptation. We conceptualize sustainability from a social systemic perspective, that is, from a perspective that encompasses the multiple functionalities of a social system and their interrelationships in particular environmental contexts. The systems perspective is applied in our consideration and analysis of disaster risk reduction (DRR), climate change adaptation (CCA), and sustainable development (SD). Section 1 introduces briefly sustainability and sustainable development, followed by a brief presentation of the theory of complex social systems (Section 2). The theory conceptualizes interdependent subsystems, their multiple functionalities, and the agential and systemic responses to internal and external stressors on a social system. Section 3 considers disaster risk reduction (DRR) and climate change adaptation (CCA), emerging in response to one or more systemic stressors. It illustrates these with disaster risk reduction in the cases of food and chemical security regulation in the EU. CCA is illustrated by initiatives and developments on the island of Gotland, Sweden and in the Gothenburg Metropolitan area, which go beyond a limited CCA perspective, taking into account long-term sustainability issues. Section 4 discusses the limitations of DRR and CCA, not only their technical limitations but economic, socio-cultural, and political limitations, as informed from a sustainability perspective. It is argued that DRRs are only partial subsystems and must be considered and assessed in the context of a more encompassing systemic perspective. Part of the discussion is focused on the distinction between sustainable and non-sustainable DRRs and CCAs. Section 5 presents a few concluding remarks about the importance of a systemic perspective in analyzing DRR and CCA as well as other similar subsystems in terms of sustainable development.

Amino acid sequences of native proteins are generally not palindromic. The protein molecule obtained as a result of reading the amino acid sequence backwards, i.e., a retro-protein, having the same amino acid composition and the same hydrophobicity profile as the native sequence may behave as a different molecule. Here we have used R4, a directed evolution product of Scfv-13, a protein that binds β-galactosidase, and report the properties of the retro-R4 (rR4). rR4 remains a binding protein and its new target is Glutamine-fructose-6-phosphate aminotransferase (GLMS), a native E. coli protein. Additionally, a tight junction immunoglobulin domain- containing membrane protein, junctional adhesion molecule A (JAMA) retro-protein retains the ability to drive cell-cell interactions. Thus, protein engineering suggests a new potential application to retro-proteins of the immunoglobulin superfamily.

Biases in Artificial Intelligence (AI) or Machine Learning (ML) systems due to skewed datasets problematise the application of prediction models in practice. Representation bias is a prevalent form of bias found in the majority of datasets. This bias arises when training data inadequately represents certain segments of the data space, resulting in poor generalisation of prediction models. Despite AI practitioners employing various methods to mitigate representation bias, their effectiveness is often limited due to a lack of thorough domain knowledge. To address this limitation, this paper introduces human-in-the-loop interaction approaches for representation debiasing of generated data involving domain experts. Our work advocates for a controlled data generation process involving domain experts to effectively mitigate the effects of representation bias. We argue that domain experts can leverage their expertise to assess how representation bias affects prediction models. Moreover, our interaction approaches can facilitate domain experts in steering data augmentation algorithms to produce debiased augmented data and validate or refine the generated samples to reduce representation bias. We also discuss how these approaches can be leveraged for designing and developing user-centred AI systems to mitigate the impact of representation bias through effective collaboration between domain experts and AI.

This paper aims to provide an overview of the complete process in the development of a Domain-Specific Language (DSL). It explains the construction steps such as preliminary research, language implementation, and evaluation. Moreover, it provides details for different key components which are commonly found in the DSLs such as the abstraction layer, DSL metamodel, and the applications. It also explains the general limitations related to the Domain-Specific Languages for Workflows.

Daltonisation refers to the recolouring of images such that details normally lost by colour vision deficient observers become visible. This comes at the cost of introducing artificial colours. In a previous work, we presented a gradient-domain colour image daltonisation method that outperformed previously known methods both in behavioural and psychometric experiments. In the present paper, we improve the method by (i) finding a good first estimate of the daltonised image, thus reducing the computational time significantly, and (ii) introducing local linear anisotropic diffusion, thus effectively removing the halo artefacts. The method uses a colour vision deficiency simulation algorithm as an ingredient, and can thus be applied for any colour vision deficiency, and can even be individualised if the exact individual colour vision is known.

Cell plasma membranes display a dramatically rich structural complexity characterized by functional sub-wavelength domains with specific lipid and protein composition. Under favorable experimental conditions, patterned morphologies can also be observed in vitro on model systems such as supported membranes or lipid vesicles. Lipid mixtures separating in liquid-ordered and liquid-disordered phases below a demixing temperature play a pivotal role in this context. Protein-protein and protein-lipid interactions also contribute to membrane shaping by promoting small domains or clusters. Such phase separations displaying characteristic length-scales falling in-between the nanoscopic, molecular scale on the one hand and the macroscopic scale on the other hand, are named mesophases in soft condensed matter physics. In this Review, we propose a classification of the diverse mechanisms leading to mesophase separation in biomembranes. We distinguish between mechanisms relying upon equilibrium thermodynamics and those involving out-of-equilibrium mechanisms, notably active membrane recycling. In equilibrium, we show that the mechanisms generically dwell on an up-down symmetry breaking between the upper and lower bilayer leaflets. Symmetry breaking is an ubiquitous mechanism in condensed matter physics at the heart of several important phenomena. In the present case, it can be either spontaneous (domain buckling) or explicit, i.e. due to an external cause (global or local vesicle bending properties). Whenever possible, theoretical predictions and simulation results are confronted to experiments on model systems or living cells, which enables us to identify the most realistic mechanisms from a biological perspective.

Keywords: domain adversarial; channel reconstruction; transfer learning; fault diagnosis

Vascular cell adhesion molecule-1 (VCAM-1) is closely associated with tumor progression and metastasis. However, the relevance and role of VCAM-1 in lung cancer have not been clearly elucidated. In this study, we found that VCAM-1 was highly overexpressed in lung cancer tissue compared with that of normal lung, and high VCAM-1 expression correlated with poor survival of lung cancer patients. VCAM-1 knockdown reduced invasion in A549 human lung cancer cells, and competitive blocking experiments targeting the Ig-like domain 6 of VCAM-1 (VCAM-1-D6) demonstrated that the VCAM-1-D6 domain was critical for VCAM-1-mediated A549 cell invasion. Next, we developed a human monoclonal antibody specific to human and mouse VCAM-1-D6 (VCAM-1-D6 huMab), which was isolated from a human synthetic antibody library using phage display technology. Finally, we showed that VCAM-1-D6 huMab had a nanomolar affinity for VCAM-1-D6 and that it potently suppressed invasion in A549 and NCI-H1299 lung cancer cell lines. Taken together, these results suggest that VCAM-1-D6 is a novel therapeutic target in VCAM-1-mediated lung cancer invasion and that our newly developed VCAM-1-D6 huMab will be a useful tool for inhibiting VCAM-1-expressing lung cancer cell invasion.

We investigate perovskite oxides from different aspects, namely their pseudoharmonic dynamical properties, their dynamical properties when strong anharmonicity exists and the intriguing functionalities arising from domain walls. Taking these viewpoints together yields a rather complex picture of this material class which was not anticipated in previous approaches. It opens pathways to novel applications and reveals the rich ground states beyond the fictitious belief in ‘simplicity of perovskites and such structures’.

SRC homology 3 (SH3) domains are critical interaction modules that orchestrate the assembly of protein complexes involved in diverse biological processes. They facilitate transient protein-protein interactions by selectively interacting with proline-rich motifs (PRMs). A database search revealed 298 SH3 domains in 221 human proteins. Multiple sequence alignment of human SH3 domains is useful for phylogenetic analysis and determination of their selectivity towards PRM-containing peptides (PRPs). However, a more precise functional classification of SH3 domains is achieved by constructing a phylogenetic tree only from PRM-binding residues and using existing SH3-PRP structures and biochemical data to determine the specificity within each of the 10 families for particular PRPs. In addition, the C-terminal proline-rich domain of the RAS activator SOS1 covers 13 of the 14 recognized proline-rich consensus sequence motifs, encompassing differential PRP pattern selectivity among all SH3 families. To evaluate binding capabilities and affinities, we conducted fluorescence dot blot and polarization experiments using 25 representative SH3 domains and various PRPs derived from SOS1. Our analysis has identified 45 interacting pairs with binding affinities ranging from 0.2 to 125 micromolar out of 300 tested and potential new interactors of SOS1. Furthermore, it establishes a framework to bridge the gap between SH3 and PRP interactions and provides predictive insights into potential interactions of SH3 domains with PRMs based on sequence specifications. This novel framework has the potential to enhance the understanding of protein networks mediated by SH3-PRM interactions and be utilized as a general approach for other domain-peptide interactions.

Current State-of-the-Art (SotA) chatbots are able to produce high quality sentences, handling different conversation topics, and larger interaction times. Unfortunately, the generated responses highly depend on the data on which they have been trained, the specific dialogue history and current turn used for guiding the response, the internal decoding mechanisms, ranking strategies, among others. Therefore, it may happen that for semantically similar questions asked by users, the chatbot may provide a different answer, which can be considered as a form of hallucination or produce confusion in long-term interactions. In this research paper, we propose a novel methodology consisting of two main phases: a) hierarchical automatic detection of topics and subtopics in dialogue interactions using a Zero-Shot learning approach, and b) detecting inconsistent answers using K-Means and the Silhouette coefficient. To evaluate the efficacy of topic and subtopic detection, we used a subset of the DailyDialog dataset and real dialogue interactions gathered during the Alexa Socialbot Grand Challenge 5 (SGC5). The proposed approach enables detecting up to 18 different topics and 102 subtopics. For the purpose of detecting inconsistencies, we manually generate multiple paraphrased questions and employ several pre-trained SotA chatbot models to generate responses. Our experimental results demonstrate a weighted F-1 value of 0.34 for topic detection, a weighted F-1 value of 0.78 for subtopic detection in DailyDialog, then 81% and 62% accuracy for topic and subtopic classification in SGC5; finally, to predict the number of different responses, we obtained a mean squared error (MSE) of 3.4 when testing smaller generative models and 4.9 in recent large language models.

The binding affinity of trastuzumab and pertuzumab to HER2 has been studied using both experimental and in-silico methods. The experiments were conducted using the antibodies in their complete IgG form, as used in clinical therapy, and the extracellular domain of the HER2 protein in solution. This approach provides a precise, reproducible, and reliable view of the interaction between them in physiological conditions. Dynamic light scattering and size exclusion chromatography coupled with tetra detection were utilized to characterize the protein complexes, measure their concentrations, and calculate the equilibrium free binding energy, ΔGbind. In addition, PRODIGY, a QSAR-like model with excellent predictive ability, was employed to obtain in-silico ΔGbind estimations. The results obtained indicate that pertuzumab exhibits a slightly higher binding affinity to HER2 than trastuzumab. The difference in binding affinity was explained based on the contribution of the different interfacial contact (IC) descriptors to the ΔGbind value estimated by the PRODIGY model. Furthermore, experiments revealed that the pertuzumab IgG antibody binds preferentially to two HER2 proteins, one per Fab fragment, while trastuzumab mainly forms a monovalent complex. This finding was interpreted based on a geometrical model that identified steric crowding in the trastuzumab-HER2 complex as compared to the pertuzumab-HER2 complex.

Abstract: The most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) is the presence of poly-PR/GR dipeptide repeats which are encoded by the C9orf72 gene. Recently, it was shown that poly-PR/GR alters chromatin accessibility which results in stabilization and enhancement of transcriptional activity of the tumor suppressor p53 in several neurodegenerative disease models. Reduction of p53 protein levels in cell and model organisms protects against neurotoxicity of poly-PR, and partially protects against neurotoxicity of poly-GR. Here, we aimed to study the detailed molecular mechanisms how p53 contributes to poly-PR/GR mediated neurodegeneration. Using a combination of biophysical techniques such as nuclear magnetic resonance (NMR) spectroscopy, fluorescence polarization, turbidity assays and differential interference contrast (DIC) microscopy, we found that p53 physically interacts with poly-PR/GR and triggers liquid-liquid phase separation of p53. We identified p53 transactivation domain 2 (TAD2) as the main binding site for PR25/GR25 and show that binding of poly-PR/GR to p53 is mediated by a network of electrostatic and/or hydrophobic interactions. Our findings might help to understand the mechanistic role of p53 in poly-PR/GR - associated neurodegeneration.

As a non-antibody scaffold, monobodies based on the fibronectin type III (FN3) domain overcome antibody size and complexity while maintaining analogous binding loops. However, antibodies and their derivatives remain the gold standard for design of new therapeutics. In response, clinical therapeutic proteins based on the FN3 domain are beginning to use native fibronectin function as a point of differentiation. The small and simple structure of monomeric monobodies confers increased tissue distribution and reduced half-life, whilst the absence of disulphide bonds improves stability in cytosolic environments. Where multi-specificity is challenging with an antibody format that is prone to mis-pairing of chains, FN3 domains in the fibronectin assembly already interact with a large number of molecules. As such, multiple monobodies engineered for interaction with therapeutic targets are being combined in a similar beads-on-a-string assembly which improves both efficacy and pharmacokinetics. Furthermore, full length fibronectin is able to fold into multiple conformations as part of its natural function and a greater understanding of how mechanical forces allow for the transition between states will lead to advanced applications that truly differentiate the FN3 domain as a therapeutic scaffold.

Despite recent scientific advancements, there is a gap in the use of technology to measure signals, behaviors, and processes of adaptation physiology of farm animals. Sensors present exciting opportunities for sustained, real-time, non-intrusive measurement of farm animal behavioral, mental, and physiological parameters with the integration of nanotechnology and instrumentation. This paper critically reviews the sensing technology and sensor data-based models used to explore biological systems such as animal behavior, energy metabolism, epidemiology, immunity, health, and animal reproduction. The use of sensor technology to assess physiological parameters can provide tremendous benefits and tools to overcome and minimize production losses while making positive contributions to animal welfare. Of course, sensor technology is not free from challenges; these devices are at times highly sensitive and prone to damage from dirt, dust, sunlight, colour, fur, feathers, and environmental forces. Rural farmers unfamiliar with the technologies must be convinced and taught to use sensor-based technologies in farming and livestock management. While there is no doubt that demand will grow for non-invasive sensor-based technologies that require minimum contact with animals and can provide remote access to data, their true success lies in the acceptance of these technologies by the livestock industry.

The public administrations (PAs) that have joined the Covenant of Mayors must now complete their adaptation plans with the climate chapter (SECAP). At the same time, the previous Plan for sustainable energy (SEAP) was mainly dedicated to mitigation actions (sustainable energy). It was often managed directly by municipalities' in-house energy companies or agencies. The WMO1234 Recommendation (Guidance on Integrated Urban Hydrometeorological, Climate and Environmental Services) is a fundamental Guidance to allow PAs to make policies and decisions regarding city regeneration and population health prevention. The purpose of this manuscript is to describe a process undertaken in the Emilia-Romagna region to build a coherent frame from the individuation of local environmental vulnerabilities and adopt specific policies. This frame was created by the contribution of various Institutions, including the Regional Authority, which furnished the main directives and tools for the mitigation and adaptation, the Municipality of Bologna, which prepared the first Adaptation Plan following the SECAP guidance, the Emilia-Romagna branch of Italian Association of Municipalities (ANCI), which took in charge the approach to harmonize different urban contexts and to communicate with stakeholders and population, and Research Institutes and Scientific Associations to explore, with specific studies and modeling application, the outcomes produced by a joint support to the new urban directives, such as the General Urban Planning Plan (PUG). To achieve this result, the WMO Guidance 1234 has been assumed as a reference methodology to ensure the best utilization of available data and widespread a common science-based methodological approach to other municipalities.

Background This paper draws learnings and successes based on field implementation experience spanning four years (2016–2019) of implementing CLTS by Prince of Peace Orphans and Widows Vision, a community-based organization located in Kaberamaido district in eastern Uganda. Methods The study aimed to document, disseminate and inform from an evidence-based point of view how adaptation and collaborative engagements triggers learning from mistakes to inform iterative changes from improvements and success.This is a descriptive paper that used project documents review based on field implementation experience. Existing project reports were synthesised, collated and curated for evidence. Data were drawn from project reports and records to inform narratives in writing. Implementation of the project was executed in homogenous rural communities occupied by people of the same dialect, cultural and social settings. Results We note that success in CLTS implementation can hardly be achieved by merely following prescriptions in handbooks and guidelines but rather by devising innovative community engagement and other participatory and community-driven techniques that foster adaptive management, promote ownership, and buy-in.Having learned from our failures, we used data to inform decisions and transformatively deviated from traditional CLTS implementation and introduced high impact and innovative approaches such as the use of CLTS helpdesks and Situation room, the Pamoja approach, learning labs and iterative feedback loops, innovatively tackling slippage and carefully introducing the follow-up mandona approach. These enhanced experiential learning and ultimately resulted in sustained sanitation behaviour. Conclusions The CLTS approach as outlined in the handbook needs to be flexibly adapted to address contextual needs. Reflective and learning sessions reinforced with routine feedback loops from implementers and beneficiaries yields tremendous results, propagates experiential learning, and ultimately results in a transformative deviation from undesirable to desired sanitation behaviours. These innovative approaches once carefully blended have proved to be sustainable, are adaptable and can work in an even larger scale and in a variety of contexts.

In a world where physical activity and social participation are fundamental pillars for a full, healthy, and quality life, confidence in walking emerges as a fundamental aspect to be assessed, especially in older adults. The purpose of this study was therefore to develop a Portuguese (Euro-pean) version, linguistically and psychometrically adapted, of the Ambulatory Self-Confidence Questionnaire (ASCQ); A translation method was used followed by an assessment of its validity and reliability. The Portuguese version was completed by 173 older adults. To assess reliability was used Cronbach’s Alpha and Intraclass Correlation Coefficients (ICC). For sociodemographic and clinical characterization, as well as for the questionnaire scoring was used descriptive statistical analysis. Pearson's correlation (r), Student´s t-test and ANOVA were used to analyse the criterion and con-struction validity; The Portuguese interactions of ASCQ was effectively translated and adjusted, revealing exceptional internal consistency and test-retest reliability, as reflected in Cronbach's alpha and ICC values of 0.95. No Floor Effect was observed, however, a Celling Effect was identified (3.5%). Criterion and construct validity were verified because all the correlations established were statistically significant; The adaptation of the ASCQ to Portuguese culture is adequate, making it valid for use within the Portuguese population of Portugal.