Miracle-dz/UniFAR-FTUs huggingface dataset:
Reinventing Therapeutic Proteins: Mining a Treasure of New Therapies

Reinventing approved therapeutic proteins for a new dose, a new formulation, a new route of administration, an improved safety profile, a new indication, or a new conjugate with a drug or a radioactive source is a creative approach to benefit from the billions spent on developing new therapeutic proteins. These new opportunities were created only recently with the arrival of AI/ML tools and high throughput screening technologies. Furthermore, the complex nature of proteins offers mining opportunities that are not possible with chemical drugs; bringing in newer therapies without spending billions makes this path highly lucrative financially while serving the dire needs of humanity. This paper analyzes several practical reinventing approaches and suggests regulatory strategies to reduce development costs significantly. This should enable the entry of hundreds of new therapies at affordable costs.
An ultrasound assisted green protocol for the synthesis of quinoxaline based bisspirooxindoles: Crystal structure analysis, enone umpolung, DFT calculations, anti-cancer activity, and molecular docking studies

Abstract A series of novel quinoxaline based bisspirooxindolo-pyrrolizidines were synthesized through 1,3-dipolar cycloaddition under ultrasonication with shorter reaction time and good yields. The compounds were well characterized by various spectroscopic methods and finally single crystal X-ray diffraction method (4c, 4d). DFT energy calculations confirm the regioselectivity due to enone umpolung effect. The in vitro anti-cancer activity of the synthesized compounds (4a–s) shows that the compounds 4g and 4q exhibited good anti-cancer activity with IC50 values14.51 ± 1.1 and 11.36 ± 0.23 µM against DU-145 prostate cancer cell line; and 16.78 ± 0.95 and 14.28 ± 0.64 µM against Hela cervical cancer cell lines when compared to the standard anti-cancer drug doxorubicin (1.75 ± 0.06 and 1.35 ± 0.09 µM). In silico molecular docking studies indicated that the synthesized compounds may serve as a potential lead for the further development of novel anti-cancer agents. GRAPHICAL ABSTRACT
A Proper Orthogonal Decomposition Approach to Approximate Balanced Truncation of Innite Dimensional Linear Systems (SI-CMSSE 07)

dimensional systems. The algorithm is related to standard balanced truncation, but includes aspects of the proper orthogonal decomposition in its com- putational approach. The method can be also applied to nonlinear systems. Numerical results are presented for a convection diusion system. dimensional systems ow). Although we do not at- tempt to provide a complete convergence analysis of the algorithm in this work, we present numerical results for a model problem that indicate that the algorithm is convergent. Our derivation of the algorithm also points to the analysis that remains in order to prove convergence. We also note how the present algorithm can dier from the popular method of
The Use of Machine Learning for Inferencing the Effectiveness of a Rehabilitation Program for Orthopedic and Neurological Patients

Advance assessment of the potential functional improvement of patients undergoing a rehabilitation program is crucial in developing precision medicine tools and patient-oriented rehabilitation programs, as well as in better allocating resources in hospitals. In this work, we propose a novel approach to this problem using machine learning algorithms focused on assessing the modified Barthel index (mBI) as an indicator of functional ability. We build four tree-based ensemble machine learning models and train them on a private training cohort of orthopedic (OP) and neurological (NP) hospital discharges. Moreover, we evaluate the models using a validation set for each category of patients using root mean squared error (RMSE) as an absolute error indicator between the predicted mBI and the actual values. The best results obtained from the study are an RMSE of 6.58 for OP patients and 8.66 for NP patients, which shows the potential of artificial intelligence in predicting the functional improvement of patients undergoing rehabilitation.
Personalized Machine Learning Approach to Estimating Knee Kinematics Using Only Shank-Mounted IMU

Knee kinematics is a valuable measure of knee joint function. However, collecting that data outside the clinic is difficult, especially with a limited number of wearable sensors and when you only use an ankle-mounted inertial measurement unit (IMU) to estimate knee kinematics. Due to the cyclic nature of gait, it is possible to use machine learning to extract joint angles from only ankle-mounted sensors. This study aimed to use time-series feature extraction and a random forest regressor to generate a person-specific surrogate model for estimating knee joint flexion angles from a single-mounted IMU above the ankle. Optical motion capture (OMC) and inertial data from ten healthy participants walking on a treadmill were collected to create ten personalized surrogate models for estimating right knee flexion angles during gait. An additional ten models were created for a leave-one-out analysis to test the generalisability of the models. Temporal cross validation of the personalized models and a leave-one-out analysis was performed on the selected feature set. The personalized models achieved an average root-mean-square error (RMSE) of <inline-formula> <tex-math notation="LaTeX">$2.45^{\circ } \pm {0.65}^{\circ }$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">${R}^{{2}}$ </tex-math></inline-formula> of 0.98) compared to a gold-standard OMC. The generalized models achieved an average RMSE of <inline-formula> <tex-math notation="LaTeX">$6.77^{\circ } \pm {3.38}^{\circ }$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">${R}^{{2}}$ </tex-math></inline-formula> of 0.83) in the leave-one-out analysis. Time-series feature-based personalized surrogate models could be used to accurately estimate knee kinematics by using a single ankle-mounted sensor. However, more data are required to train a generalized model using the presented method.
Dissolving Microneedles Loaded with Nanoparticle Formulation of Respiratory Syncytial Virus Fusion Protein Virus-like Particles (F-VLPs) Elicits Cellular and Humoral Immune Responses

Respiratory syncytial virus (RSV) is one of the leading causes of bronchiolitis and pneumonia in children ages five years and below. Recent outbreaks of the virus have proven that RSV remains a severe burden on healthcare services. Thus, a vaccine for RSV is a need of the hour. Research on novel vaccine delivery systems for infectious diseases such as RSV can pave the road to more vaccine candidates. Among many novel vaccine delivery systems, a combined system with polymeric nanoparticles loaded in dissolving microneedles holds a lot of potential. In this study, the virus-like particles of the RSV fusion protein (F-VLP) were encapsulated in poly (D, L-lactide-co-glycolide) (PLGA) nanoparticles (NPs). These NPs were then loaded into dissolving microneedles (MNs) composed of hyaluronic acid and trehalose. To test the in vivo immunogenicity of the nanoparticle-loaded microneedles, Swiss Webster mice were immunized with the F-VLP NPs, both with and without adjuvant monophosphoryl lipid A (MPL) NPs loaded in the MN. The mice immunized with the F-VLP NP + MPL NP MN showed high immunoglobulin (IgG and IgG2a) levels both in the serum and lung homogenates. A subsequent analysis of lung homogenates post-RSV challenge revealed high IgA, indicating the generation of a mucosal immune response upon intradermal immunization. A flowcytometry analysis showed high CD8+ and CD4+ expression in the lymph nodes and spleens of the F-VLP NP + MPL NP MN-immunized mice. Thus, our vaccine elicited a robust humoral and cellular immune response in vivo. Therefore, PLGA nanoparticles loaded in dissolving microneedles could be a suitable novel delivery system for RSV vaccines.
Reference values in adults

Exercise testing provides an array of measurements characterising an individual’s physiological response. The amount of information obtained depends on the type of test: some tests, such as field tests, yield one or, at most, a few variables, while the classical laboratory cardiopulmonary exercise test (CPET) yields a large amount of data. When exercise testing is used to find out whether a response is abnormal or not, it becomes necessary to compare the response with what it would be expected to be in ‘‘normal’’ circumstances, i.e. a ‘‘reference’’ or ‘‘predicted’’ value is needed. Reference values exist for many of the physiological variables monitored or derived during a CPET. They are estimations of the population mean commonly obtained from large samples of presumed healthy subjects. It should be recognised that inherent biological variability imposes a distribution of individual values around the mean, usually in a symmetrical fashion (or closely so) that is well described by a Gaussian or normal distribution. Thus, lower probabilities are associated with values which lie at greater distances from the distribution mean. This form of distribution is well characterised by the mean or central value and a measure of the spread of the distribution, i.e. the SD. The distributions of most of the commonly used CPET indices follow a Gaussian distribution, and hence y95% of the values falling on both sides of the mean will lie within 2 SD of the mean (actually 1.96 SD). To decide whether the measured response is abnormal or not, statistical inferential procedures are relied upon, which consist of the following steps: 1) stating the statistical (null) hypothesis; 2) specifying the significance level; 3) specifying a decision rule; and 4) drawing a conclusion. A decision can be made only as to whether the observed measurement is different from the reference value obtained in a healthy population, as statistical descriptions of diseased populations are rarely available. Therefore, the only hypotheses that can be tested statistically would be either whether the measurement in question yields a result that is not different from the healthy population mean, regardless of direction (i.e. a two-tailed hypothesis), or whether the measured value is lower or higher than the population mean (i.e. a one-tailed hypothesis). One-tailed hypotheses are logically sound if the concern is only about deviations in one direction, as is frequently the case [1]; i.e. usually the only point of interest is whether a subject is below or above the population reference value for the parameter of interest. However, two-tailed hypotheses are generally adopted because they are considered safer. The 95% confidence interval (CI) is broader (1.96 versus 1.65) which, therefore, reduces the risk of false-positive decisions. There are two types of error that can be made when performing a statistical inference (table 1): a type I error, which is when the alternative hypothesis is incorrectly chosen; and a type II error, when the statistical (null) hypothesis fails to be rejected when it is
Diffusion-flame flickering as a hydrodynamic global mode

The present study employs a linear global stability analysis to investigate buoyancy-induced flickering of axisymmetric laminar jet diffusion flames as a hydrodynamic global mode. The instability-driving interactions of the buoyancy force with the density differences induced by the chemical heat release are described in the infinitely fast reaction limit for unity Lewis numbers of the reactants. The analysis determines the critical conditions at the onset of the linear global instability as well as the Strouhal number of the associated oscillations in terms of the governing parameters of the problem. Marginal instability boundaries are delineated in the Froude number/Reynolds number plane for different fuel jet dilutions. The results of the global stability analysis are compared with direct numerical simulations of time-dependent axisymmetric jet flames and also with results of a local spatio-temporal stability analysis.
Two-Stage Deep Single-Image Super-Resolution With Multiple Blur Kernels for Internet of Things

Single-image super-resolution (SISR) aims to reconstruct a high-resolution image from a single low-resolution (LR) image. Although convolutional neural network (CNN)-based SISR methods greatly enhance image restoration, they face critical challenges. First, SISR models using CNNs process image patches uniformly regardless of importance, causing spatial inefficiency in computation and representation. However, due to resource constraints, edge devices in the Internet of Things (IoT) cannot bear heavy computations or large memory storage. Second, most of the existing SISR methods are designed only for the widely adopted bicubic degradation and cannot handle LR images with arbitrary blur kernels, resulting in poor recovery performance. To address these issues, in this article, we propose a two-stage semantic and spatial deep super-resolution (SSDSR) model suitable for the IoT environment. The proposed SSDSR model is capable of handling a variety of blur kernels (e.g., isotropic Gaussian, motion, and disk blur) by effectively using their prior information. Moreover, the semantic feature extraction (SFE) module enables the proposed model to focus on key areas of LR images rather than treating all pixels equally, which significantly reduces the computational load. The semantic information from the SFE module and the spatial information from the spatial attention module are fused adaptively, allowing the proposed model to extract key information in LR images, thereby increasing the representation capacity of the CNN and improving image recovery. Compared with state-of-the-art SISR methods on benchmark datasets, the proposed SSDSR model demonstrates superior performance. When run in real time on an IoT edge device, our model exhibits high computational efficiency and excellent image quality.
Functional Movement Patterns, Spinal Posture and Prevalence of Musculoskeletal Symptoms among Elite Ice Hockey Players: A Cross Sectional Study

The aim of this study was to evaluate functional movement patterns and spinal posture of elite ice hockey players and to examine the association between spinal posture, prevalence of musculoskeletal symptoms and Functional Movement Screen (FMSTM) scores. The study included 86 elite male ice hockey players aged 18 to 38 years. Sagittal spinal curvatures were measured with a Saunders digital inclinometer, and functional movement patterns were assessed by the FMSTM. Spinal posture of the studied ice hockey players was characterized by normal kyphosis (46%) or hyperkyphosis (41%) and decreased lumbar lordosis (54%). The mean total FMSTM score was 14.8. Most of the hockey players (57%) achieved a total FMSTM score in the range of 14–17 points, whereas 28% had a total FMSTM score of <14. Seventy-two percent of the studied athletes had at least one asymmetry. Significant differences between performing the movements on the right and the left sides of the body were observed in in-line lunges (p = 0.019) and shoulder mobility sub-tests (p < 0.001). The FMSTM sub-tests performed with the lowest success rates were rotatory stability and the hurdle step. A lower score in the rotatory stability test is related to shoulder pain. It is highly important to develop appropriate exercise programs to reduce or prevent muscle imbalances in ice hockey players.
Eigenvalue knots and their isotopic equivalence in three-state non-Hermitian systems

The spectrum of a non-Hermitian system generically forms a two-dimensional complex Riemannian manifold with distinct topology from the underlying parameter space. Spectral topology permits parametric loops to map the affiliated eigenvalue trajectories into knots. In this work, through analyzing exceptional points and their topology, we uncover the necessary considerations for constructing eigenvalue knots and establish their relation to spectral topology. Using an acoustic system with two periodic synthetic dimensions, we experimentally realize several knots with braid index 3. In addition, by highlighting the role of branch cuts on the eigenvalue manifolds, we show that eigenvalue knots produced by homotopic parametric loops are isotopic such that they can deform into one another by type-II or type-III Reidemeister moves. Our results not only provide a general recipe for constructing eigenvalue knots but also expand the current understanding of eigenvalue knots by showing that they contain information beyond that of the spectral topology.
The new Cypriot music curriculum: teachers’ interpretation and implementation of differentiation

Abstract Differentiation is a key concept in the Cypriot music curriculum, last revised in 2010. This paper aims to investigate teachers’ interpretation and implementation of differentiation in their music classrooms. Interviews with Cypriot classroom music teachers and focus groups with their students took place within the context of a larger research project that investigated how elements of the Cypriot music curriculum were interpreted and applied by teachers and how this pedagogy in turn influenced students’ motivation. Interviews were carried out with eight music teachers. Differentiation, as understood and implemented by these Cypriot music teachers, differed in fundamental ways to differentiation as conceptualised in academic literature concerned with this concept. These findings may serve as a point of reflection for future professional development in Cyprus relating to differentiation and its implementation.
Artificial Intelligence-Based Cyber Security in the Context of Industry 4.0—A Survey

The increase in cyber-attacks impacts the performance of organizations in the industrial sector, exploiting the vulnerabilities of networked machines. The increasing digitization and technologies present in the context of Industry 4.0 have led to a rise in investments in innovation and automation. However, there are risks associated with this digital transformation, particularly regarding cyber security. Targeted cyber-attacks are constantly changing and improving their attack strategies, with a focus on applying artificial intelligence in the execution process. Artificial Intelligence-based cyber-attacks can be used in conjunction with conventional technologies, generating exponential damage in organizations in Industry 4.0. The increasing reliance on networked information technology has increased the cyber-attack surface. In this sense, studies aiming at understanding the actions of cyber criminals, to develop knowledge for cyber security measures, are essential. This paper presents a systematic literature research to identify publications of artificial intelligence-based cyber-attacks and to analyze them for deriving cyber security measures. The goal of this study is to make use of literature analysis to explore the impact of this new threat, aiming to provide the research community with insights to develop defenses against potential future threats. The results can be used to guide the analysis of cyber-attacks supported by artificial intelligence.
PaCNN-LSTM: A Localization Scheme Based on Improved Contrastive Learning and Parallel Fusion Neural Network

The deep learning technique plays an important role in Wi-Fi localization systems as it could mine deep features of measurement data. The main challenges are to combat the signal fluctuation resulting in a decrease in sample discrimination and to leverage the broadest information of sample measurements during the training phase, since they are directly related to the location accuracy and robustness. Hence, to address the above issues, this article proposes an indoor Wi-Fi localization scheme which mainly contains two modules. First, an improved contrastive learning is introduced to handle the sample signal measurements to increase the discrimination. It is from the perspective of learning and encoding, and it avoids the drawbacks brought by traditional processing methods. Then, we build a parallel fusion network named as PaCNN-LSTM based on convolutional neural network (CNN) and long short-term memory network (LSTM). Compared with existing networks, PaCNN-LSTM connects neural networks in parallel rather than serial, which improves the generalization performance of model when extracting the spatial and temporal features of signal measurements. In addition, it also considers the large amount of middle layer information that is always ignored. By adding a flatten layer after the pooling layer, the available information of samples has been broadened. Extensive experimental results show that the localization performance of the proposed scheme is outperformed than others, where the location accuracy is improved by about 22%.
On Hom-Lie Pseudo-Superalgebras

The aim of this article is to introduce the notion of Hom-Lie H-pseudo-superalgebras for any Hopf algebra H. This class of algebras is a natural generalization of the Hom-Lie pseudo-algebras as well as a special case of the Hom-Lie superalgebras. We present some construction theorems of Hom-Lie H-pseudo-superalgebras, reformulate the equivalent definition of Hom-Lie H-pseudo-super-algebras, and consider the cohomology theory of Hom-Lie H-pseudo-superalgebras with coefficients in arbitrary Hom-modules as a generalization of Kac’s result.
Visualization of Liquids Flows in Microfluidics and Plasma Channels in Nanosecond Spark Microdischarges by Means of Digital Microscopy

An application of digital optical microscopes for visualization of single-pulse or pulsed-periodic processes in microfluidics and physics of spark microdischarges is studied. Multiple examples of coagulation processes of liquid microvolumes, nanosecond spark discharges near liquid drops and plant living tissues in a cell-size level are provided.