Cinnamomum tamala leaf (CTL), also known as tejpat and Indian bay leaf, is used all over the world for seasoning, flavouring, and medicinal purposes. Numerous researchers are interested in exploring the nutritional and medicinal benefits of CTL due to their potential as nutraceuticals. These characteristics could be explained by the presence of several essential bioactive substances and lipid derivatives. There are no reports available on this species about its metabolites profile. In this work, rapid screen and identify chemical compounds in supercritical (SC)-CO2 extracts of CTL by use of UPLC-Q-TOF-MSE with multivariate statistical analysis approach was established in both negative and positive mode. As a results, a total of 166 compounds, including 66 monocarboxylic fatty acids, 52 dicarboxylic fatty acids, 27 fatty acid amides, and 21 others were tentatively identified based on accurate mass, and the mass spectrometric fragmentation pattern, out of which 142 compounds are common and found in all five CTL extracts. They displayed robust [M+H]+ and/or [M-H]- ions in both low- and high-energy collision-induced dissociations (CIDs). Based on chemical profiling and chemometric analysis, CTL4 (300bar/55°C) extract was found significantly more potent in other CTL’s extracts. A new mono- and di-carboxylic fatty acids, fatty acid amides and other essential bioactive compounds were separated within 20 min runtime and identified in CTL for the first time. The combination of UPLC-Q-TOF-MSE and chemometric analysis is a powerful method to rapidly screen the metabolites profile for the quality control of C. tamala leaf.

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.

In addition to their importance in statistical thermodynamics, probabilistic entropy measurements are crucial for understanding and analyzing complex systems, with diverse applications in time series and one-dimensional profiles. However, extending these methods to two- and three-dimensional data still requires further development. In this study, we present a new method to classify spatiotemporal processes based on entropy measurements. To test and validate the method, we selected four classes of similar processes related to the evolution of random patterns: dynamic colored noises ((i)~white and (ii)~red); (iii)~weak turbulence from reaction-diffusion; (iv)~hydrodynamic fully developed turbulence, and (v)~plasma turbulence from MHD. Considering seven possible ways to measure entropy from a matrix, we present the method as a parameter space composed of the two best separating measures of the five selected classes. The results highlight better combined performance of Shannon Permutation Entropy ($S^p_H$) and a new approach based on Tsallis Spectral Permutation Entropy ($S^s_q$). Notably, our observations reveal the segregation of reaction terms in this $S^p_H \times S^s_q$ space, a result that identifies specific sectors for each class of dynamic process, and can be used to train machine learning models for automatic classification of complex spatiotemporal patterns.

The treasury of a DAO is purported to uphold the value of the DAO-issued token, and this study represents a pioneering effort to delve into these realms. We explore the relationship between the treasury and market capitalization in two DAO projects, namely Olympus DAO and KlimaDAO. Employing Granger Causality tests, we scrutinize the relationship between market capitalization and the treasuries of these DAOs. Furthermore, we use an EGARCH model to investigate the in-fluence of carbon offset tokens on KlimaDAO’s market capitalization, incorporating this variable into the model's conditional mean equation. The findings reveal a significant relationship between market capitalization and the treasuries of the DAOs, indicating a potential impact of market capitalization on treasuries. Additionally, the well-specified EGARCH model highlights the sig-nificance of carbon offset tokens, suggesting their potential to influence the overall market capi-talization of a DAO project and support its value.

Rice blast fungus (Magnaporthe oryzae) causes massive yield losses annually worldwide. The fungus secreted bunch of effector proteins target different rice cellular compartments to facilitate its infection. However, most of intensively studied effectors are small secreted proteins with high presence/absence polymorphism in the fungus, and effectors conserved among different species were rarely investigated. In this study, we identified MoPce1, a CAP/PR domain containing protein common in different species, as an important virulence factor from a screening of 145 putative core effectors (PCE). MoPCE1 is required for pathogenicity but not the asexual development. Ectopic expression of MoPCE1sp in ZH11 background compromised the plant resistance. We also found that MoPce1 lacks the conserved cysteine residuals in CAP domain, and is BIC-localized in invasive hyphae and nuclei-localized when ectopically expressed in tobacco leaves and rice protoplasts. These results suggested that MoPce1 may bind novel ligand(s) rather than sterol. Indeed, we found that MoPce1 could interact with OsDi19-5, a transcription factor in rice. We inferred from these results that MoPce1 is required for pathogenicity by suppressing the immune response in rice, likely through the interaction with OsDi19-5.

Financial bubble prediction has been a significant area of interest in empirical finance, garnering substantial attention in the literature. This study aimed to detect and forecast financial bubbles in the Vietnamese stock market from 2001 to 2021. The PSY procedure, which involves a right-tailed unit root test to identify the existence of financial bubbles, was employed to achieve this goal. Machine learning algorithms were then utilized to predict real-time financial bubble events. The results revealed the presence of financial bubbles in the Vietnamese stock market during 2006-2007 and 2017-2018. Additionally, the empirical evidence supported the superior performance of the Random Forest and Artificial Neural Network algorithms over traditional statistical methods in predicting financial bubbles in the Vietnamese stock market.

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.

While previous research has shown the use of attachable air-caps on windows to efficiently reduce a building’s energy consumption, the air-caps considered had to be attached to the entire window’s surface, thus limiting the occupants’ view and creating the inconvenience of needing to detach and attach the air-caps. In this study, a window-mounted air-cap roller module using Velcro tape that may be easily attached, detached, and rolled up or down was developed and performance tested in a full-scale test bed. It was found that as the area of the air-caps attached on a window increased, the required indoor lighting energy increased. However, the window insulation improved, thus reducing the cooling and heating energy needed. Attaching the air-caps to the entire window surface effectively reduced the building’s energy consumption, but views through the window may be disturbed. Thus, the developed window-mounted air-caps enable an occupant to reduce the building energy consumption and maintain their view according to their need. The findings of this study may contribute to a reduction in building energy consumption without sacrificing a pleasant indoor environment. Further studies may be needed to verify their efficacy under varying indoor and outdoor conditions.

Although RNA modifications were discovered decades ago, the identification of enzymes that write, read, and erase RNA modifications enabled their functional study and spawned the field of epitranscriptomics. Coupling that knowledge to new methods has enabled the precise pinpointing of epitranscriptomic modifications across the transcriptome plus the elucidation of their functional consequences. PCIF1 (Phosphorylated CTD Interacting Factor 1) was shown to add N6, 2’-O-dimethyladenosine (m⁶Am) marks at the first nucleotide after the 5’ N7-methylguanosine (m⁷G) cap. In this review, we discuss the epitranscriptomic regulation of mRNA in general, and focus on m⁷G cap-adjacent m⁶Am in particular. m⁶Am positions can now be distinguished from N6-methyladenosine (m⁶A) using new techniques leveraging PCIF1-knockout cells. Although m⁶Am modification sites can be detected precisely, conflicting data have been published regarding how cap-adjacent m⁶Am marks affect their host mRNA. Discrepancies in the data mean that the effects of cap-adjacent m⁶Am on mRNA stability, decapping, and translation continue to be debated. Finally, while PCIF1 is predominantly nuclear, a subset of results suggest a possible cytoplasmic role as well. Taken together, these contradictory results which employed different methodologies and cell lines means that further experiments are required to determine the ultimate biological function(s) of m⁷G cap-adjacent m⁶Am.

Background: Study to assess glucose levels of people with type 1 diabetes (T1D) overnight based on insulin type and timing. Methods: Real-world, retrospective study in T1D using multiple daily insulin injections. Contin-uous glucose monitoring and insulin injections data were collected for ten hours after dinner us-ing the Insulclock® connected cap. Meal events were identified using ROC detection methodology. The timing of rapid insulin, second injections, and the type of insulin analogs used were evaluat-ed. Results: Nocturnal profiles (n=775, 49 subjects) were analyzed. Higher glucose AUC over 180mg/dL was observed in delayed injections (number; %; mg/dL x h): -45-15min (n=136; 17.5%, 175.9 ±271.0); -15-0min (n= 231; 29.8%, 164.0 ±237.1); 0+45min (n= 408; 52.6%, 203.6 ±260.9), (p 0.049). The use of ultrarapid (FiAsp®) (URI) vs rapid insulin analogs (RI) was associated with less hypoglycemia events (7.1 vs 13.6%; p 0.005) and TBR70 (1.7 +6.9 vs 4.6 +13.9%; p 0.003). Us-ers of glargine U300 vs degludec had higher TIR (70.7 vs 58.5 %) (Adjusted R-squared: 0.22, p < 0.001). The use of a correction injection (n= 144, 18.6%) was associated with higher hypoglycemia events (18.1 vs 9.5%; p 0.003), TBR70 (5.5 +14.2 vs 3.0 +11.1%; p 0.003), glucose AUC over 180 mg/dL (226.1 +257.8 vs 178.0 +255.3 mg/dL x h; p 0.001), and lower TIR (56.0 +27.4 vs 62.7 +29.6 mg/dL x h; p 0.004). Conclusions: Dinner rapid insulin timing, insulin types and correction injections affect nocturnal glucose profile in T1D.

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.

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.

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.

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.

Due to the advancement and availability of internet in present age, device tend to become wireless as well. Mobile ad-hoc network is a wireless environment that consist of advanced agility of nodes and does not contain on any central administrative authority. As it does not contain any central administrative authority so being wireless environment, the network is vulnerable to security threats and suffers in performance as well. Security of MANET is the core issue in this era, meanwhile the data is sent over untrustworthy network. Several techniques were proposed to secure data over Mobile Adhoc Network though cryptography that is one of the most reliable techniques to protect data. In our proposed methodology a Secure Cryptographic technique for Mobile Adhoc Networks (SCTMA) we proposed A Collusion attack prevention (CAP) scheme with ElGamal key generator along with DNA encryption Algorithm is used to make it more secure. The proposed scheme protects the network against collusion attacks and improves the performance as well. Performance analysis will be measured on the basis of key generation time, response time, encryption time and decryption time. The proposed scheme also protects the network against collusion attacks.

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.

Virtual Histology- Intravascular Ultrasound (VH-IVUS) image is an available method for visualizing plaque component to detect thin cap fibroatheroma. Nevertheless, this imaging modality has considerable limitations to extract the plaque component features and identifying the TCFA plaque. The aim of this paper is to improve the identification of TCFA using fusion of IVUS and VH-IVUS images. In order to generate the automatic technique for reducing the human interaction, a new method namely Active Contour based Plaque Border Detection (ACPB) is proposed. In order to perform the pixel wise classification, hybrid of K-means algorithm with Particle Swarm Optimization and Plaque based Minimum Euclidean Distance (KMPSO-PMED) method is presented to classify the plaque region as well. Moreover, to obtain more significant information of imaging modalities, fusion of two different images consisting of VH-IVUS and IVUS is performed. Therefore, geometric features are extracted from the plaque region and combine with IVUS features. Furthermore, different group of plaque features are divided by means of the histopathological studies. SVM classifiers is applied to detect the TCFA and non-TCFA plaques. The proposed method is applied on 566 in-vivo IVUS and their matching VH-IVUS images obtained from 9 patients. The best result of SVM illustrates the accuracy rates of 99.41% for classification of TCFA plaque. The results prove that the highest accuracy is achieved by integrated features of IVUS and VH-IVUS images.

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.

One of the stated goals of the Common Agricultural Policy reforms has been to provide a fairer distribution of payments across and within Member States, but little progress has been accomplished, with about 20% of farmers receiving 80% of the total amount of Direct Payments. The structural factors that underlie this inequity, notably the preponderance of specific types of farming systems, are investigated in this research. A logit model was developed using Agricultural Census data at the Commune level, using the evolution of the percentage of farmers receiving Direct Payments in Portugal as the dependent variable. The findings reveal that the local importance of arable crops (cereals) and cattle farming systems, as well as the existence of larger farms and younger farmers, all contributed to farmers' increasing access to DP between 2009 and 2019. In traditional Mediterranean farming systems, however, access to DP has been restricted to a smaller proportion of farmers. However, there appears to have been some redistribution in the previous two CAP programming cycles, from bigger to smaller farmers, older to younger farmers, and from olives, cereals, and cattle to other types of production, notably vineyards.

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.

Basaltic activity is the most common class of volcanism on Earth, characterized by magmas of sufficiently low viscosities such that bubbles can move independently of the melt. Following exsolution, spherical bubbles can then expand and/or coalesce to generate larger bubbles of spherical-cap or Taylor bubble (slug) morphologies. Puffing and strombolian explosive activity are driven by bursting of these larger bubbles at the surface. Here, we present the first combined model classification of spherical-cap and Taylor bubble driven puffing and strombolian activity modes on volcanoes. We furthermore incorporate the possibility that neighboring bubbles might coalesce, leading to elevated strombolian explosivity. The model categorizes behavior in terms of the temporal separation between the arrival of successive bubbles at the surface and bubble gas volume or length, with the output presented on visually intuitive two-dimensional plots. The categorized behavior is grouped into the following regimes: puffing from a) cap bubbles; and b) non-overpressurised Taylor bubbles; and c) Taylor bubble driven strombolian explosions; each of these regimes is further subdivided into scenarios whereby inter-bubble interaction does/doesn't occur. The model performance is corroborated using field data from Stromboli (Aeolian Islands, Italy), Etna (Sicily, Italy) and Yasur (Vanuatu), representing one of the very first studies, focused on combining high temporal resolution degassing data with fluid dynamics, as a means of deepening our understanding of the processes which drive basaltic volcanism.

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.

This review covers the last 25 years literature on analogs of suberoylanilide hydroxamic acid (SAHA, known also as Vorinostat) acting as HDAC inhibitor. In particular, the topic has been focused on the synthesis and biological activity of compounds where the phenyl group (the surface recognition moiety, CAP) of SAHA has been replaced by an azaheterocycle through a direct bond with amide nitrogen atom, and the methylene chain in the linker region is of variable length. Most of the compounds reported displayed good to excellent inhibitory activity against HDACs and in many cases showed anti-proliferative activity against human cancer cell lines.

Research Highlights: Data of advanced-age provenance tests were reanalyzed applying a new approach, to directly estimate the growth of populations at their original sites under individually generated future climates. The results reveal surprisingly high resilience potential of fir species. Background and Objectives: The growth and survival of silver fir under future climatic scenarios is insufficiently investigated at the xeric limits. The selective signature of past climate determining the current and projected growth was investigated to analyze the prospects of adaptive silviculture and assisted transfer of silver fir populations, and of the introduction of non-autochthonous species. Materials and Methods: Hargreaves’ climatic moisture deficit was selected to model height responses of adult populations. Climatic transfer distance was used to assess the relative drought stress of populations at the test site, relating these to the past conditions to which the populations had adapted. ClimateEU and ClimateWNA pathway RCP8.5 data served to determine individually past, current, and future moisture deficit conditions. Beside silver fir, other fir species from South Europe and the American Northwest were also tested. Results: Drought tolerance profiles explained the responses of transferred provenances and predicted their future performance and survival. Silver fir displayed significant within-species differentiation regarding drought stress response. Applying the assumed drought tolerance limit of 100mm relative moisture deficit, most of the tested silver fir populations seem to survive their projected climate at their origin until the end of the century. Survival is likely also for transferred Balkan fir species and for grand fir populations, but not for the Mediterranean species. Conclusions: The projections are less dramatic than provided by usual field assessments. Some results contradict generally accepted concepts. The method fills the existing gap between experimentally determined adaptive response and the predictions needed for management decisions. It also underscores the unique potential of provenance tests.

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.

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.

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.

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.

Sustainability in material purchasing is a growing area of research. Goods purchasing decisions strongly affect transportation path flows, vehicle consolidation, inventory levels and related obsolescence costs. Within a global sourcing context, companies experience the need of new decision making approaches capable to consider a large variety of factors, also linked with society and environment. Environmental impact assessment has become a key requirement for materials purchasing and transportation decisions since global warming is a rising concern both in academic and industrial researches. In fact, it is well known that the freight transport industry is responsible for large amounts of carbon emissions contributing to global warming. In this paper, we initially analyse and compare the environmental economic policies established by the International Governments in relation to the carbon trading systems adopted. Then, we develop a multi-objective lot sizing approach useful in practice to define the sustainable quantity to purchase when a Cap and Trade mitigation policy is present. We further analyse the model behaviour according to different carbon price values by demonstrating that carbon prices are still far too low to motivate managers towards sustainable purchasing choices.

The paper discusses the electroreduction of Bi(III) ions in the aspect of expanding the ”cap – pair” effect. The ”cap – pair” rule is associated with the acceleration of the electrode’s processes by organic substances. The interpretation of the ”cap – pair” effect mechanism was expanded to include the effect of supporting electrolyte concentration on the acceleration process and the type of electrochemical active as well as used protonated organic substances. It has also been shown that the phenomena occurring at the electrode/solution interface can influence a change in the dynamics of the electrode’s process according to the ”cap – pair” rule.

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.

In this paper, low circumferential reciprocating load foot-scale tests were performed on two truncated PHC B 600 130 tubular piles with bearing nodes to characterize the damage process and morphology of the specimens and to investigate the load-carrying performance of the members. The test results reveal that under the action of tensile-bending-shear loading, the bearing concrete in the node area buckles and is damaged, the anchored reinforcement in the node area yields, the constraint is weakened, an articulation point is formed, and the node rotational capacity increases. When the embedment depth increases from 200 mm to 300 mm, the ultimate bearing capacities of the positive and negative nodes increase by 57.60% and 54.60%, respectively. A numerical simulation is used to verify the test results. Considering the three types of piles with truncated nodes, the numerical simulation is used to analyze the node-bearing capacity at different embedment depths. Finally, two preferred node types are proposed as follows: pipe pile core-filled longitudinal reinforcement anchored into the bearing node and pipe pile body longitudinal reinforcement anchored into the bearing node + pipe pile core-filled longitudinal reinforcement anchored into the bearing node, with preferred embedment depths of 350 mm and 200 mm, respectively.

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.

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.

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.

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.

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/.

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.

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.

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.

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.

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.

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.

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

Geothermal energy aims at harnessing the heat of the Earth to produce energy, from the near-surface to great depths. Emphasis is put on geothermal reservoirs in crystalline rocks, hence vol-canic islands and crystalline basements of sedimentary basins based on three examples world-wide. Hot brines circulate in those rocks and are responsible for their hydrothermal alteration because of fluid-rock interactions. This includes dissolution of preexisting minerals and crystal-lization of newly formed ones which provide many information of crucial importance for a better exploration of those reservoirs. For example, the crystallization of clay minerals results from the dissolution of preexisting minerals (feldspar and ferro-magnesian minerals in plutonic, volcanic or metamorphic rocks) or from the direct precipitation from geothermal brines, in specific condi-tions. Clay minerals are found in both the geothermal reservoirs and in the clay-cap found above them. Other newly formed minerals such as quartz, carbonates or sulfates have also recorded the conditions of temperature and salinity encountered by fossil reservoirs which can be evaluated thanks to the study of fluid inclusions trapped during their crystallization. This paper presents results from several geothermal deep sites and surface analogues to pinpoint the role played by newly-formed minerals in the characterization of geothermal reservoirs and their cap-rock.

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.

The central nervous system (CNS) is the final frontier in drug delivery because of the blood-brain barrier (BBB), which poses significant barriers to the access of most drugs to their targets. Kynurenic acid (KYNA), a tryptophan (Trp) metabolite, plays an important role in behavioral functions and abnormal KYNA levels have been observed in neuropsychiatric conditions. The current challenge lies in delivering KYNA to the CNS owing to its polar side chain. Recently, C-3 side chain modified KYNA analogs have been shown to cross the BBB; however, it is unclear whether they retain the biological functions of the parent molecule. This study examined the impact of KYNA analogs, specifically SZR-72, SZR-104, and the newly developed SZRG-21, on behavior. The analogs were administered intracerebroventricularly (i.c.v.), and their effects on the motor domain were compared with those of KYNA. Specifically, open field (OF) and rotarod (RR) tests were employed to assess motor activity and skills. SZR-104 increased horizontal exploratory activity in the OF test at a dose of 0.04 μmol/4 μL, while SZR-72 decreased vertical activity at doses of 0.04 and 0.1 μmol/4 μL. In the RR test, however, neither KYNA nor its analogs showed any significant differences in motor skills at either dose. Side chain modification affects affective motor performance and exploratory behavior, as the results show for the first time. In this study, we showed that KYNA analogs alter emotional components such as motor-associated curiosity and emotions. Consequently, drug design necessitates the development of precise strategies to traverse the BBB while paying close attention to modifications in their effects on behavior.

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.

The Water-Cooled Lead Lithium (WCLL) Breeding Blanket (BB) is one of the two BB concepts candidate to be selected as driver blanket for the EU-DEMO fusion reactor. In this regards, the development of a sound architecture of the WCLL Central Outboard Blanket (COB) Segment, ensuring the fulfilment of the thermal and structural design requirements, is one of the main goals of the EUROfusion consortium. To this purpose, an exploratory research campaign has been launched to preliminarily investigate the thermo-mechanical performances of the Bottom Cap (BC) region of the WCLL COB segment, because of its peculiarities making its design different from the other regions. The assessment has been carried out considering the nominal BB operating conditions, the Normal Operation (NO) scenario, as well as a steady-state scenario derived from the in-box LOCA accident, the Over-Pressurization (OP) scenario. Starting from the reference geometric layout of the WCLL COB BC region, a first set of analysis has been launched in order to evaluate its structural performances under a previously calculated thermal field and to select potential geometric improvements. Then, the analysis of a more complete BC region has been conducted from both the thermal and structural standpoints, evaluating its structural behaviour in compliance with the RCC-MRx code. Finally, after some iterations and geometric updates, a promising geometric layout of the BC region has been obtained even though some criticalities still persist in the internal Stiffening Plates and First Wall. However, the obtained results clearly showed that the proposed layout is worthy to be further assessed to achieve a robust enough configuration. The work has been performed following a theoretical-numerical approach based on the Finite Element Method (FEM) and adopting the quoted Ansys commercial FEM code.

In this paper, we propose and demonstrate a network analysis optical frequency domain reflectometer (NA-OFDR) for distributed temperature measurements at high spatial (down to 3 cm) and temperature resolution. The system makes use of a frequency-stepped, continuous-wave (cw) laser, whose output light is modulated using a vector network analyzer. The latter is also used to demodulate the amplitude of the beat signal obtained by coherently mixing the Rayleigh backscattered light with a local oscillator. The system is capable of high measurand resolution (50 mK at 3-cm spatial resolution), thanks to the high sensitivity of coherent Rayleigh scattering to temperature. Furthermore, compared to the conventional optical-frequency domain reflectometry (OFDR), the proposed system does not rely on the use of a tunable laser, therefore it is less prone to limitations related to the laser coherence or sweep nonlinearity. Two configurations are analyzed, both numerically and experimentally, based on either a double sideband or single sideband modulated probe light. The results confirm the validity of the proposed approach.

As synthetic/artificial life forms become more abundant and sophisticated, an increasing number of bizarre creatures - xenobots, robots, soft A-life entities, genetically engineered organisms, etc. - are invading our society. Therefore, we need to bring order to all this, to establish what is living and what is not. Here, I intend to classify all these non-natural entities and clarify their status with reference to their consideration or not as living beings, leaving the door open to an uncertain future in which perhaps we can see how "the artificial" and "the natural" merge to originate something new. To order all this "new biodiversity" and to also give entry to viruses (which are excluded of the three-domains tree of life), I propose the creation of a new domain, Lithbea (from the name: life-in-the-border entities), in which all these new human-made entities as well as the viruses will be included. Within this domain there would be two kingdoms, Virus and Humade (contraction of human-made), based on their origin, natural or human-made. A brief description of each component of Lithbea is included and the implications for society and biology of this “new biodiversity” is briefly discussed.

The commonly-used large-scale knowledge bases have been facing challenges in open domain question answering tasks which are caused by the loose knowledge association and weak structural logic of triplet-based knowledge. To find a way out of this dilemma, this work proposes a novel metaknowledge enhanced approach for open domain question answering. We design an automatic approach to extract metaknowledge and build metaknowledge network from Wiki documents. For the purpose of representing the directional weighted graph with hierarchical and semantic features, we present an original graph encoder GE4MK to model the metaknowledge network. Then a metaknowledge enhanced graph reasoning model MEGr-Net is proposed for question answering, which aggregates both relational and neighboring interactions comparing with R-GCN and GAT. Experiments have proved the improvement of metaknowledge over main-stream triplet-based knowledge. We have found that the graph reasoning models and pre-trained language models also have influences on the metaknowledge enhanced question answering approaches.

This paper develops a detail image signal enhancement that makes images perceived as clearer and more resolved and so is more effective for higher resolution displays. We observe that the local variant signal enhancement makes images more vivid, and the more revealed granular signals harmonically embedded on the local variant signals make images more resolved. Based on this observation, we develop a method that not only emphasizes the local variant signals by scaling up the frequency energy in accordance with human visual perception, but also strengths up the granular signals by embedding the alpha-rooting enhanced frequency components. The proposed energy scaling method emphasizes the detail signals in texture images and rarely boosts noisy signals in plain images. In addition, to avoid the local ringing artifact, the proposed method adjusts the enhancement direction to be parallel to the underlying image signal direction. It was verified through the subjective and objective quality evaluations that the developed method makes images perceived as clearer and highly resolved.

In this paper, a technique of image watermarking using multiple images as watermarks is presented. The technique is based on transform domain functions including discrete wavelet transform (DWT), discrete cosine transform (DCT) and singular value decomposition (SVD) with an image as the host signal i.e. the watermarks will be used as proofs of the authenticity of the host image. The technique is executed by performing multilevel DWT followed by applying DCT and SVD to both the host and watermark. Multiple watermarks are used for the insurance of better security level. The scheme is immune to common image processing operations & some attacks and exhibits PSNR of 108.3781dB, normalized cross correlation (NCC) over 0.99 and normalized correlation (NC) over 0.99.

Microwave transversal filters, which are implemented based on the transversal filter structure in digital signal processing, offer a high reconfigurability for achieving a variety of signal processing functions without changing hardware. When implemented using microwave photonic (MWP) technologies, also known as MWP transversal filters, they provide competitive advantages over their electrical counterparts, such as low loss, large operation bandwidth, and strong immunity to electromagnetic interference. Recent advances in high-performance optical microcombs provide compact and powerful multi-wavelength sources for MWP transversal filters that require a larger number of wavelength channels to achieve high performance, allowing for the demonstration of a diverse range of filter functions with improved performance and new features. Here, we present a comprehensive performance analysis for microcomb-based MWP spectral filters based on the transversal filter approach. First, we investigate the theoretical limitations in the filter spectral response induced by finite tap numbers. Next, we analyze the distortions in the filter spectral response resulting from experimental error sources. Finally, we assess the influence of input signal’s bandwidth on the filtering errors. These results provide a valuable guide for the design and optimization of microcomb-based MWP transversal filters for a variety of applications.

The popularity of cloud computing is growing owing to its large data storage capacity and high computation power. It provides online, on-demand, scalable application solution, removes hardware and software barriers for non-specialist, rapidly integrates and deploys desired and necessary facilities, supports quick upgrading and addition of features. Users get benefitted with the selection of the appropriate cloud computing platform for their projects. Here, our paper provides a comprehensive overview of the services provided to the users by the most common cloud computing service providers. This paper could be used as a reference while selecting the best service provider based on the requirements of the projects.

Orthologs of human glucocorticoid receptor (GR) and human mineralocorticoid receptor (MR) first appear in cartilaginous fishes. Subsequently, the MR and GR diverged to respond to different steroids: the MR to aldosterone and the GR to cortisol and corticosterone. We report that cortisol, corticosterone and aldosterone activate full-length elephant shark GR, and progesterone, which activates elephant shark MR, does not activate elephant shark GR. However, progesterone inhibits steroid binding to elephant shark GR, but not to human GR. Together, this indicates partial functional divergence of elephant shark GR from the MR. Deletion of the N-terminal domain (NTD) from elephant shark GR (truncated GR) reduced the response to corticosteroids, while truncated and full-length elephant shark MR had similar responses to corticosteroids. Swapping of NTDs of elephant shark GR and MR yielded an elephant shark MR chimera with full-length GR-like increased activation by corticosteroids and progesterone compared to full-length elephant shark MR. Elephant shark MR NTD fused to GR DBD+LBD had similar activation as full-length MR, indicating that the MR NTD lacked GR-like NTD activity. We propose that NTD activation of human GR evolved early in GR divergence from the MR.

Oral vaccines are highly envisaged for veterinary applications due to their convenience and ability to induce protective mucosal immunity as the first line of defense. The present investigation harnessed live-attenuated Salmonella Typhimurium to orally deliver novel expression vector systems containing the Cap and Rep genes from porcine circovirus type 2 (PCV2), a significant swine pathogen. The antigen expression by the vaccine candidates JOL2885 and JOL2886, comprising eukaryotic pJHL204 and pro-eukaryotic expression pJHL270 plasmids respectively, was confirmed by western blot and IFA. We evaluated their immunogenicity and protective efficacy through oral vaccination in a mouse model. This approach elicited both mucosal and systemic immunity against PCV2d. Oral administration of the candidates induced PCV2-specific sIgA, serum IgG antibodies, and neutralizing antibodies, resulting in reduced viral loads in the livers and lungs of PCV2d-challenged mice. T-lymphocyte proliferation and flow-cytometry assays confirmed enhanced cellular immune responses after oral inoculation. The synchronized elicitation of both Th1 and Th2 responses was also confirmed by enhanced expression of TNF-α, IFN-γ, IL-4, MHC-I, and MHC-II. Our findings highlight the effectiveness and safety of the constructs with an engineered-attenuated S. Typhimurium, suggesting its potential application as an oral PCV2 vaccine candidate.

Robotic systems are increasingly being used in healthcare to improve patient care and support healthcare professionals. These robots are especially effective at performing repetitive tasks. In complex cancer cases, robots can enhance surgical precision and efficiency, resulting in reduced pain for patients and a lower risk of complications. Robot-assisted surgery offers high-quality 3D imaging, improved clarity, and increased efficiency. This review paper examines the application of Artificial Intelligence (AI) and robotic systems in the healthcare domain, specifically focusing on the interaction among operators, patients, and robots. The goal is to promote collaboration and improve healthcare provision through human-robot interaction. We considered already published articles by relevance, to identify the primary safety issues that need addressing for the integration of AI and robots in hospitals. Concluding with insights into understanding the focal points of social errors in Human-Robot Interaction (HRI) is crucial for mitigating these errors. While current technologies have reached a certain level of maturity, there are still numerous challenges in utilizing robot-assisted clinical practice, including technological, safety, clinical, financial, insurance, psychological, social, ethical, and legal issues. In addition, there is an urgent issue that needs to be addressed: the alignment of values in artificial intelligence, ensuring that AI systems reflect ethical principles and human values is crucial to avoid negative consequences such as bias, discrimination, and prejudicial decisions. Resolving this complex issue requires collaboration among clinical staff, researchers, programmers, and political decision-makers from various fields.

We delve into the axion-dilaton model within the supergravity framework, with a specific focus on the intricacies of domain wall construction and stability. Employing holographic vitrification, we unravel the dynamics of domain wall formation in gauge theories featuring periodic vacuum structures. Our model, incorporating a QCD-like axion term and a stabilizing dilaton, undergoes scrutiny for conductivity variations under weak disorder. The investigation reveals the model's resilience, manifesting near-perfect conductivity under mild disorder conditions. However, the rigorous mathematical motivation for the holographic setup demands further elucidation. The scattered nature of our results prompts the necessity for a more systematic interpretation of QCD phenomena and conductivity transitions. This study contributes to the mathematical understanding of the axion-dilaton model's behavior, highlighting the imperative for a refined holographic framework and a more coherent interpretation of observed phenomena.

Fine-tuning is an important technique in transfer learning that has achieved significant success in tasks that lack training data. However, as it is difficult to extract effective features for single-source domain fine-tuning when the data distribution difference between the source and the target domain is large, we propose a transfer learning framework based on multi-source domain called adaptive multi-source domain collaborative fine-tuning to address this issue. This approach utilizes multiple source domain models for collaborative fine-tuning, thereby improving the model's feature extraction capability in the target task. Specifically, this approach employs an adaptive multi-source domain layer selection strategy to customize appropriate layer fine-tuning schemes for the target task among multiple source domain models. The objective is to extract more efficient features. Furthermore, a novel multi-source domain collaborative loss function is designed to facilitate the precise extraction of target data features by each source domain model. Simultaneously, it works towards minimizing the output difference among various source domain models, thereby enhancing the adaptability of the source domain model to the target data. In order to validate the effectiveness of the proposed the adaptive multi-source domain collaborative fine-tuning framework, it is analyzed and compared with the most widely used fine-tuning methods by applying them to seven publicly available visual categorization datasets commonly employed in transfer learning. Experimental results demonstrate that, in comparison with the existing fine-tuning approaches, our method not only enhances the accuracy of feature extraction in the model but also provides precise layer fine-tuning schemes for the target task, resulting in state-of-the-art performance.

Dialogue State Tracking is an essential component in multi-domain dialogue systems, aiming to accurately determine the current dialogue state based on the dialogue history. Existing research has addressed the issue of multiple mappings in dialogues by employing slot self-attention as a data-driven approach. However, learning the relationships between slots from a single sample has limitations and introduces noise. In this paper, we propose an External Slot Relation Memory-based Dialogue State Tracking model (ER-DST). By utilizing an external memory storage, we learn the relationships between slots as a dictionary of multi-domain slot relations. Additionally, we employ a small filter to discard slot information irrelevant to the current dialogue state. Experimental results on the MultiWOZ2.0 and MultiWOZ2.1 benchmarks demonstrate significant improvements while reducing the time complexity to O(n).

The sentiment of a word varies based on its context of usage: the words used around it and the part-of-speech it is used as. This paper proposes a technique to suggest the sentiment of a word by combining its part-of-speech and the semantic similarities of its co-occurrences with both context-specific and pre-trained embeddings to achieve powerful and fast results. A study was conducted across domains and sub-domains to measure variance of sentiment by switching domains and switching context within the same domain. Re-scoring a commonly used polarity lexicon showed that 10% of words changed scores while switching domains and 8% changed scores within domains while switching context. Part of Speech analysis on 65,353 commonly used sentiment lexicons showed that 81% of sentiment bearing (non-neutral) lexicons were of the tags NN (Common Noun), JJ (Adjective) or NNS (Proper Noun).

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.

IntroductionThe aim of this study is to describe the epidemiology of all COVID-19 patients in Vietnam and to describe the measures of disease control and prevention implemented. MethodsData were recovered from Wikipedia regarding the 2020 coronavirus pandemic in Vietnam. The period covered was from 23 January to 20 April 2020. Descriptive analysis was stratified by gender, age, country of origin, travel history, clinical symptoms and outcome. A survey of disease control and prevention measures was conducted at the Centre for Disease Control in the Thai Binh province, which is responsible for screening and isolating individuals at high risk of COVID-19. ResultsAs of 20 April 2020, Vietnam had recorded 268 confirmed COVID-19 patients. 55.2% were female. 67.9% were aged 20-49 years and 82.5% were Vietnamese. 60.4% of cases were imported from outside Vietnam. Other cases were acquired in Vietnam by individuals in close contact with imported cases. Only one patient who had not travelled had had no known contact with a confirmed case. 63.1% of patients were asymptomatic. 75.7% of patients were discharged. No deaths were recorded. The Thai Binh CDC surveyed a total of 2,203 persons at risk of COVID-19. 336 persons (15.2%) were isolated at hospitals and 1,411 (64.0%) in dedicated isolation facilities. 16.4% reported at least one respiratory symptom. No positive cases confirmed by RT-PCR have been reported in the Thai Binh province to date. ConclusionThe effect of the systematic screening and isolation strategy made it possible to limit local transmission in Vietnam. Vietnam needs to reinforce diagnostic capacities, prevention measures and provide the necessary epidemiological data on which to base interventions. The wider use of rapid serological tests is also advisable in order to be able to conduct extensive screening in the community.

We survey SoTA open-domain conversational AI models with the purpose of presenting the prevailing challenges that still exist to spur future research. In addition, we provide statistics on the gender of conversational AI in order to guide the ethics discussion surrounding the issue. Open-domain conversational AI are known to have several challenges, including bland responses and performance degradation when prompted with figurative language, among others. First, we provide some background by discussing some topics of interest in conversational AI. We then discuss the method applied to the two investigations carried out that make up this study. The first investigation involves a search for recent SoTA open-domain conversational AI models while the second involves the search for 100 conversational AI to assess their gender. Results of the survey show that progress has been made with recent SoTA conversational AI, but there are still persistent challenges that need to be solved, and the female gender is more common than the male for conversational AI. One main take-away is that hybrid models of conversational AI offer more advantages than any single architecture. The key contributions of this survey are 1) the identification of prevailing challenges in SoTA open-domain conversational AI, 2) the unusual discussion about open-domain conversational AI for low-resource languages, and 3) the discussion about the ethics surrounding the gender of conversational AI.

Artificial Intelligence (AI) is required since multiple resources are in need to complete depending on a daily basis. As a result, automating routine tasks is an excellent idea. This reduces the foundation's work schedules while also improving efficiency. Furthermore, the business can obtain talented personnel for the business strategy through Artificial Intelligence. Explainability in XAI derives from a combination of strategies that improve machine learning models' environmental flexibility and interpretability. When Artificial Intelligence is trained with a large number of variables to which we apply alterations, the entire processing is turned into a black box model which is in turn difficult to understand. The data for this research's quantitative analysis is gathered from the IEEE, Web of Science, and Scopus databases. This study looked at a variety of fields engaged in the (Explainable Artificial Intelligence) XAI trend, as well as the most commonly employed techniques in domain of XAI, the location from which these studies were conducted, the year-by-year publishing trend, and the most frequently occurring keywords in the abstract. Ultimately, the quantitative review reveals that employing Explainable Artificial Intelligence or XAI methodologies, there is plenty of opportunity for more research in this field.

Emerin is a nuclear envelope protein that contributes to genome organization and cell mechanics. Through its N-terminal LEM-domain, emerin interacts with the DNA-binding protein Barrier-to-Autointegration (BAF). Emerin also binds to members of the Linker of the Nucleoskeleton and Cytoskeleton (LINC) complex. Mutations in the gene encoding emerin are responsible for the majority of cases of X-linked Emery-Dreifuss Muscular Dystrophy (X-EDMD). Most of these mutations lead to an absence of emerin. A few missense and short deletion mutations in the disordered region of emerin are also associated with X-EDMD. More recently, missense and short deletion mutations P22L, ∆K37 and T43I were discovered in emerin LEM-domain, associated with isolated atrial cardiac defects (ACD). Here we reveal which defects, at both molecular and cellular levels, are elicited by these LEM-domain mutations. Whereas DK37 mutation impairs correct folding of the LEM-domain, P22L and T43I have no impact on the 3D structure of emerin. Surprisingly, all three mutants bind to BAF, albeit with a weaker affinity in the case of DK37. In human myofibroblasts derived from a patient’s fibroblasts, emerin ∆K37 is correctly localized at the inner nuclear membrane, but is present at a significantly lower level, indicating that this mutant is abnormally degraded. Moreover, SUN2 is reduced, and cells are defective in producing actin stress fibers when grown on a stiff substrate and after cyclic stretches. Altogether, our data suggest that the main effect of mutation DK37 is to perturb emerin function within the LINC complex in response to a mechanical stress.

Sialylated milk oligosaccharides (SMOs) is the most important oligosaccharides in human milk oligosaccharides (HMOs), and have a multifunctional health benefit, and the most abundant SMOs are sialyllactose (SLs). The previous studies indicate that the growth of tumor cells, and angiogenesis on tumor tissues with Lewis lung carcinoma, melanoma, and colon carcinoma cells could be prevented by SL. Thus, it has been proposed that SL might be an ideal candidate for the development of anti-angiogenic drugs without any side effects. It has been proposed that the Sialic acid (Sia) can be produced through SL’s hydrolysis, and the dietary Sia is further transported to the brain and other tissues though blood for formation of CMP-Sia, the activated precursor of Sia, and then is catalyzed by polysialyltransferases (polySTs) for synthesis of polysialic acid (polySia) or polysialylation of neural cell adhesion molecule (NCAM). Thus, it’s necessary to study and develop new inhibitors, which not only modulate a proper balance between the gut health and neuros system development, but also inhibit the overexpression of polySia, and thus block the tumor cell migration. In this study, we proposed that 3’-SL and 6’-SL may be more ideal inhibitors comparing with two previous inhibitors, the low molecular weight heparin (LMWH) and cytidine monophosphate (CMP) in the previous studies.

This study presents an extensive collection of data on the aerodynamic behavior at a low Reynolds number and geometric coefficients for 2900 airfoils obtained through the class shape transformation (CST) method. By employing a verified OpenFOAM-based CFD simulation framework, lift and drag coefficients were determined at a Reynolds number of 10^5. Considering the limited availability of data on low Reynolds number airfoils, this dataset is invaluable for a wide range of applications, including unmanned aerial vehicles (UAVs) and wind turbines. Additionally, the study offers a method for automating CFD simulations that could be applied to obtain aerodynamic coefficients at higher Reynolds numbers. The breadth of this dataset also supports the enhancement and creation of machine learning (ML) models, further advancing research into the aerodynamics of airfoils and lifting surfaces.

Topological states of matter have attracted significant attention due to their intrinsic wave-guiding and localization capabilities robust against disorders and defects in electronic, photonic, and phononic systems. Despite the above topological features phononic crystals share with their electronic and photonic counterparts, finite-frequency topological states in phononic crystals may not always survive. In this work, we discuss the survivability of topological states in Su-Schrieffer-Heeger models with both local and non-local interactions with larger symmetry perturbation. Although such a discussion is still about ideal mass-spring models, the insights from this study set the expectations in continuum phononic crystals, which can further instruct the applications of phononic crystals for practical purposes.

Glioblastoma (GBM) is a representative malignant brain tumor characterized by a dismal prognosis, with survival rates of less than 2 years and high recurrence rates. Despite surgical resection and several alternative treatments, GBM remains a refractory disease due to its aggressive invasiveness and resistance to anticancer therapy. In this report, we explore the role of fibronectin type III domain containing 3B (FNDC3B) and its potential as a prognostic and therapeutic biomarker in GBM. GBM exhibited a significantly higher cancer-to-normal ratio compared to other organs, and patients with high FNDC3B expression had a poor prognosis (p < 0.01). In vitro studies revealed that silencing FNDC3B significantly reduced the expression of Survivin, an apoptosis inhibitor, and also reduced cell migration, invasion, extracellular matrix adhesion ability, and stem cell properties in GBM cells. Furthermore, we identified that FNDC3B regulates PTEN/PI3K/Akt signaling in GBM cells using MetaCore integrated pathway bioinformatics analysis and a proteome profiler phospho-kinase array with sequential western blot analysis. Collectively, our findings suggest FNDC3B as a potential biomarker for predicting GBM patient survival and for the development of treatment strategies for GBM.

Artificial Intelligence (AI) experienced significant advancements in recent years, and its impact is already recognized across various industries. Yet, the rise of AI has led to a growing concern about its impact on meeting the Sustainable Development Goals (SDGs). The aim of this paper was to evaluate contributions and the potential impact of AI to sustainable development in the society domain. Furthermore, the study analyzed GPT-3 responses, as one of the largest language models developed by OpenAI, descriptively. We conducted a set of queries on the SDGs to gather infor-mation on GPT-3`s perceptions of AI impact on sustainable development. Analysis of GPT-3’s contribution potential towards the SDGs showcased its broad range of capabilities for contributing to the SDGs in areas such as education, health, and communication. The study findings provide valuable insights into the contributions of AI to sustainable development in the society domain and highlight the importance of proper regulations to promote the responsible use of AI for sustainable development. We plan subsequent research for effects on ecologic and economic domains of SDGs. We highlighted the potential for improvement in neural language processing skills of GPT-3 by avoiding imitating weak human writing styles with more mistakes in longer texts.

Matrix Metaloproteinase-2 (MMP-2) is an extracellular Zn²⁺ protease specific to type I and IV collagens. Its expression is associated with several inflammatory, degenerative, and malignant diseases. Conformational properties, domain movements, and interactions between MMP-2 and its associated metal ions were characterized using a 1.0 µs molecular dynamics simulation. Dihedral principle component analysis revealed 10 families of conformations with the greatest degree of variability occurring in the link region connecting the catalytic and hemopexin domains. Dynamics cross correlation analysis indicated domain movements corresponding to opening and closing of the hemopexin domain in relation to the fibronectin and catalytic domains facilitated by the link region. Interaction energies were calculated using the MMPBSA-interaction entropy analysis method and revealed strong binding energies for the catalytic Zn²⁺ ion 1, Ca²⁺ ion 1, and Ca²⁺ ion 3 with significant conformational stability at the binding sites of Zn²⁺ ion 1 and Ca²⁺ ion 1. Ca²⁺ ion 2 diffuses freely away from its crystallographically defined binding site. Zn²⁺ ion 2 plays a minor role in conformational stability of the catalytic domain while Ca²⁺ ion 3 is strongly attracted to the highly electronegative sidechains of the Asp residues around the central β-sheet core of the hemopexin domain.

Abstract Introduction: Poor structural quality of health facilities for tuberculosis (TB) diagnosis and treatment contributes to unfavorable health outcomes among TB patients. Improving the quality of healthcare services helps to avert TB-related morbidity. Despite these facts, the level of structural quality of health facilitieSs to deliver TB services is not in line with what the national TB and leprosy program (NTLP) recommends. Hence, the present study was conducted to assess the structural quality of health facilities to deliver quality TB services to TB patients among public health facilities in the Sidama region.Objectives: To determine the quality of the structural domain of TB services at public health facilities in the Sidama region of southern Ethiopia.Methods: An institution-based cross-sectional study was conducted from June to July 2023 among randomly selected public health facilities in the Sidama region. We included 40 randomly selected public health facilities that delivered TB services in the study area. Data was collected by experienced data collectors using facility audits, clinical observation checklists, structured questionnaires, and in-depth interviews. An Open Kit Mobile (ODK) application was used to collect data and exported to SPSS version 26. All statistical analysis was done using SPSS version 26.Results: The overall mean quality score for the structural dimension was 50.25 among the 40 health facilities audited in the study area.Conclusion: The quality of TB services regarding structural dimensions was poor in the study area. Therefore, any strategies must give careful attention to increasing the comprehensive structural dimension of quality TB services.

In order to realize the performance degradation trend prediction accurately, a prediction method based on multi-domain features and temporal convolutional network (TCN) is proposed. Firstly, construct a high-dimensional feature set in the multi-domain of vibration signals, and use comprehensive evaluation indicators to preliminarily screen performance degradation indexes with good sensitivity and strong trend. Secondly, the kernel principal component analysis (KPCA) method is adopted to eliminate redundant information between multi-domain features, and construct a health index (HI) based on convolutional auto-encoder (CAE) network. Thirdly, a TCN-based performance degradation trend prediction model is constructed, and direct multi-step prediction is used to predict the performance degradation trend of the monitored object. On this basis, the validity of the proposed method is verified using the bearing public data, and it is successfully applied to performance degradation trend prediction of rolling contact fatigue specimen. The results show that the feature set can be reduced from 14 dimensions to 4 dimensions by using KPCA, while 98.33% of the information of the original feature set is retained. Furthermore, the method of constructing HI based on CAE network is effective. The change process of the HI constructed truly reflects the performance degradation process of the rolling contact fatigue specimen. Compared with the two commonly used HI construction methods, auto-encoding (AE) network and gaussian mixture model (GMM), this method has obvious advantages. At the same time, the prediction model based on TCN can accurately predict the performance degradation of the rolling contact fatigue specimen with the root mean square error 0.0146 and the mean absolute error 0.0105, which has better performance and higher prediction accuracy than the prediction model based on the long short-term memory (LSTM) network and the gated recurrent unit (GRU). This method has general significance and may be extended to the performance degradation prediction of other mechanical equipment/parts.

The ongoing COVID-19 pandemic has profoundly affected millions of lives globally, with some individuals experiencing persistent symptoms even after recovering. Understanding and managing the long-term sequelae of COVID-19 is crucial for research, prevention, and control. As a result, to monitor the health of individuals affected by these conditions, they must maintain up-to-date health records using digital health informatics apps for surveillance. In this review, we provide an overview of the existing literature on identifying long COVID manifestations through hierarchical classification and the characterization of long COVID by different hierarchical groups based on the Human Phenotype Ontology (HPO). We outline the aspects of the National COVID Cohort Collaborative (N3C) and Researching COVID to Enhance Recovery (RECOVER) in artificial intelligence (AI) to identify long COVID. Knowledge exploration, using the concept map for the clinical pathways of long COVID presented in this paper, provides an overview of the data needed to explore tackling the long-term effect of COVID-19 by integrating innovative cohesive frameworks and designing health informatics-based applications. To the best of our knowledge, this is the first paper to explore the potential incorporation of long COVID as a variable risk factor within a digital health informatics application.

Group-B enteroviruses (EV-B) are ubiquitous naked single-stranded positive RNA viral pathogens that are responsible for common acute or persistent human infections. Their genome is composed in the 5'end by a non-coding region, which is crucial for the initiation of the viral replication and translation processes. RNA domain-I secondary structures can interact with viral or cellular proteins to form viral ribonucleoprotein (RNP) complexes regulating viral genomic replication, whereas RNA domains-II to -VII (IRES) are known to interact with cellular ribosomal subunits to initiate the viral translation process. Natural 5’ terminally deleted viral forms lacking some genomic RNA domain-I secondary structures have been described in EV-B induced murine or human infections. Recent in vitro studies have evidenced that the loss of some viral RNP complexes in the RNA domain-I can modulate the viral replication and infectivity levels in EV-B infections. Moreover, the disruption of secondary structures of RNA domain-I could impair viral RNA sensing by RIG-I or MDA5 receptors, a way to overcome antiviral innate immune response. Overall, natural 5′ terminally deleted viral genomes resulting in the loss of various structures in the RNA domain-I could be major key players of host-cell interactions driving the development of acute or persistent EV-B infections.

Group IV phospholipase A₂α (cPLA₂α) regulates the production of prostaglandins and leukotrienes via the formation of arachidonic acid from membrane phospholipids. The targeting and membrane binding of cPLA₂α to the Golgi involves the N-terminal C2 domain whereas the catalytic domain produces arachidonic acid. Although most studies of cPLA₂α concern its catalytic activity, it is also linked to homeostatic processes involving the generation of vesicles that traffic material from the Golgi to the plasma membrane. Here we investigate how membrane curvature influences the homeostatic role of cPLA₂α in vesicular trafficking. The cPLA₂α C2 domain is known to induce changes in positive membrane curvature, a process which is dependent on cPLA₂α membrane penetration. We show that cPLA₂α undergoes C2 domain-dependent oligomerization on membranes in vitro and in A549 cells. We found that the association of the cPLA₂α C2 domain with membranes is limited to membranes with positive curvature, and enhanced C2 domain oligomerization was observed on vesicles ~50 nm in diameter. We demonstrated that the cPLA₂α C2 domain generates cholesterol enriched Golgi-derived vesicles independently of cPLA₂α catalytic activity. Our results therefore provide novel insight into the molecular forces that mediate C2 domain-dependent membrane localization in vitro and in cells.

Nanomagnetism and spintronics are currently active areas of research, with one of the main goals being the creation of low-energy-consuming magnetic memories based on nanomagnet switching. These types of devices could also be implemented in neuromorphic computing by crafting artificial neurons (ANs) that emulate the characteristics of biological neurons, through the implementation of neuron models such as the widely used leaky integrate-and-fire (LIF) with a refractory period. In this study, we have carried out numerical simulations of a 120 nm diameter, 250 nm length ferromagnetic nanowire (NW) with the aim of exploring the design of an artificial neuron based on the creation and destruction of a Bloch-point domain wall. To replicate signal integration, we applied pulsed trains of spin-currents to the opposite faces of the ferromagnetic NW. These pulsed currents (previously studied only in the continuous form) are responsible for inducing transitions between the stable single vortex (SV) state to the metastable Bloch point domain wall (BP-DW) state. To ensure the system exhibits the leak and refractory properties, the NW was placed in an homogeneous magnetic field of the order of mT in the axial direction. The suggested configuration fulfills the requirements and characteristics of a biological neuron, potentially leading to the future creation of Artificial Neural Networks (ANNs) based on reversible changes in the topology of magnetic NWs.

Entamoeba histolytica, the causative agent of amebiasis, is the third leading cause of death among parasitic diseases globally. Its life cycle includes encystation, which has been mostly studied in Entamoeba invadens, responsible of reptilian amebiasis. However, the molecular mechanisms underlying this process are not fully understood. Therefore, we focused on the identification and characterization of Myb proteins, that regulate the expression of encystation-related genes in various protozoan parasites. Through bioinformatic analysis, we identified 47 genes in E. invadens encoding MYB-domain-containing proteins. These were classified into single-repeat 1R (19), 2R-MYB proteins (27), and one 4R-MYB protein. The in silico analysis suggests that these proteins are multifunctional, participating in transcriptional regulation, chromatin remodeling, telomere maintenance, and splicing. Transcriptomic data analysis revealed expression signatures of eimyb genes, suggesting a potential orchestration in the regulation of early and late encystation-excystation genes. Furthermore, we identified 3135 probable target genes associated with reproduction, meiotic cell cycle, ubiquitin-dependent protein catabolism, and endosomal transport. In conclusion, our findings suggest that E. invadens Myb proteins regulate stage-specific proteins and a wide array of cellular processes. This study provides a foundation for further exploration of the molecular mechanisms governing encystation and unveils potential targets for therapeutic intervention in amebiasis.

The VEMoS AXL system is a new optical biometer based on spectral domain optical coherence tomography (SD-OCT) that has been tested in terms of intrasession repeatability and compared with a swept-source optical coherence tomography biometer (SS-OCT), which is considered the gold standard, for an agreement analysis. A biometric analysis was performed three consecutive times in 120 healthy eyes of 120 patients aged between 18 and 40 years with the SD-OCT system and afterwards a single measurement was obtained with the SS-OCT system. Within-subject standard deviations were 0.004 mm, 4.394 µm and 0.017 mm for axial length (AL), central corneal thickness (CCT), and anterior chamber depth (ACD) measures obtained with the SD-OCT biometer, re-spectively. The agreement between devices was good for AL (Limits of agreement, LoA: -0.04 to 0.03 mm) and CCT (LoA: -4.36 to 14.38 µm), whereas differences between devices were clinically relevant for ACD (LoA: 0.03 to 0.21 mm). In conclusion, the VEMoS AXL system provides consistent measures of anatomical parameters, being most of them interchangeable with those provided by the SS-OCT-based gold standard.