Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Nematode-Killing Protein-1 (Nkp-1) from a Marine Epiphytic Bacterium Pseudoalteromonas tunicata

Drug resistance among parasitic nematodes has resulted in an urgent need for the development of new therapies. However, the high re-discovery rate of anti-nematode compounds from terrestrial environments necessitates a new repository for future drug research. Marine epiphytes are hypothesised to produce nematicidal compounds as a defence against bacterivorous predators, thus representing a promising yet underexplored source for anti-nematode drug discovery. The marine epiphytic bacterium Pseudoalteromonas tunicata is known to produce several bioactive compounds. Screening heterologously expressed genomic libraries of P. tunicata against the nematode Caenorhabditis elegans, identified as an E. coli clone (HG8), shows fast-killing activity. Here we show that clone HG8 produces a novel nematode-killing protein-1 (Nkp-1) harbouring a predicted carbohydrate-binding domain with weak homology to known bacterial pore-forming toxins. We found bacteria expressing Nkp-1 were able to colonise the C. elegans intestine, with exposure to both live bacteria and protein extracts resulting in physical damage and necrosis, leading to nematode death within 24 h of exposure. Furthermore, this study revealed C. elegans dar (deformed anal region) and internal hatching may act as a nematode defence strategy against Nkp-1 toxicity. The characterisation of this novel protein and putative mode of action not only contributes to the development of novel anti-nematode applications in the future but reaffirms the potential of marine epiphytic bacteria as a new source of novel biomolecules.
CNN based Gearbox Fault Diagnosis and Interpretation of Learning Features

Machine learning based fault diagnosis schemes have been intensively proposed to deal with faults diagnosis of rotating machineries such as gearboxes, bearings, and electric motors. However, most of the machine learning algorithms used in fault diagnosis are pattern recognition tools, which can classify given data into two or more classes. The underlined physical phenomena in fault diagnosis are not directly interpretable in machine learning schemes, thus it is usually called black/gray box models. In this study, convolutional neural networks (CNN) machine learning algorithm is proposed to classify gearbox faults, and the learning features of the CNN filters are visualized to understand the physical fault diagnosis phenomena. Within the framework, a detailed explanation is given based on domain knowledge of gearbox fault diagnosis, and the physical phenomena are explained by fault characteristic frequencies, allowing for observing the relationship between the characteristic frequencies and the CNN feature learning filters. The proposed algorithm is validated using an in-house experimental setup.
The Impact of Free Cash Flows to the Financial Flexibility of the Banks listed in the Colombo Stock Exchange

There are considerable arguments in favour of and against the positive relationship between free cash flows (FCF) and financial flexibility. The aim of the study is to determine the impact of free cash flows on the financial flexibility of the banks listed in the Colombo Stock Exchange (CSE). The free cash flow will measure according to the model in Journal of Finance: Agency costs and ownership structure in 2000 and financial flexibility will determine using the financial leverage based on the model captured according to the Accounting Horizons Journal: Financial flexibility and investment decisions in 2007 . The population of the study is the banks listed in the CSE. The sample consists of 60 observations covering 12 banks for a period of over 05 years from 2015 to 2019. The panel regression model has been used to test hypotheses. The results indicate that there is a positive significant relationship between free cash flows and the financial flexibility of the banks listed in CSE.
Machine‐Learning Characterization of Tectonic, Hydrological and Anthropogenic Sources of Active Ground Deformation in California

Tectonic, hydrological and industrial processes coexist in the dynamic natural environments. However, our knowledge of ground deformation associated with tectonic, hydrological and anthropogenic processes and their interactions remains limited. California represents a natural laboratory that hosts the San Andreas fault system, Central Valley and other aquifer systems, and extensive human extraction of natural resources. The attendant multi‐scale ground deformation that has been mapped using Copernicus Sentinel‐1 Synthetic Aperture Radar (SAR)‐satellite constellation from four ascending and five descending tracks during 2015–2019. We consider the secular horizontal surface velocities and strain rates, constrained from GNSS measurements and tectonic models, as proxies for tectonic processes, and seasonal displacement amplitudes from interferometric SAR (InSAR) time series as proxies for hydrological processes. We synergize 23 types of multidisciplinary datasets, including ground deformation, sedimentary basins, precipitation, soil moisture, topography, and hydrocarbon production fields, using a machine learning algorithm–random forest, and we succeed in predicting 86%–95% of the representative data sets. High strain rates along the SAF system mainly occur in areas with a low‐to‐moderate vegetation fraction (∼0.3), suggesting a correlation of rough/high‐relief coastal range morphology and topography with the active faulting, seasonal and orographic rainfall, and vegetation growth. Linear discontinuities in the long‐term, seasonal amplitude and phase of the surface displacement fields coincide with some fault strands, the boundary zone between the sediment‐fill Central Valley and bedrock‐dominated Sierra Nevada, and the margins of the inelastically deforming aquifer in the San Joaquin Valley, suggesting groundwater flow interruptions, contrasting elastic properties, and heterogeneous hydrological units.
Sirtuin 3 (SIRT3) Pathways in Age-Related Cardiovascular and Neurodegenerative Diseases

Age-associated cardiovascular and neurodegenerative diseases lead to high morbidity and mortality around the world. Sirtuins are vital enzymes for metabolic adaptation and provide protective effects against a wide spectrum of pathologies. Among sirtuins, mitochondrial sirtuin 3 (SIRT3) is an essential player in preserving the habitual metabolic profile. SIRT3 activity declines as a result of aging-induced changes in cellular metabolism, leading to increased susceptibility to endothelial dysfunction, hypertension, heart failure and neurodegenerative diseases. Stimulating SIRT3 activity via lifestyle, pharmacological or genetic interventions could protect against a plethora of pathologies and could improve health and lifespan. Thus, understanding how SIRT3 operates and how its protective effects could be amplified, will aid in treating age-associated diseases and ultimately, in enhancing the quality of life in elders.
Towards the utilization of Cloud-based Integration Platforms

Choosing between different integration implementations is an important but surprisingly understudied part of software projects. However, the world is digitalizing rapidly, and the number of different systems that require integration is increasing. In this situation the importance of scalable cloud-based integration platforms, iPaaS and iSaaS platforms, has grown. These digital platforms and their APIs have made the integration of different systems significantly faster and easier than before, when integrations were handled on the premises and custom-built from scratch. This study goes through, via thematical analysis, the interview data collected from the representatives ($\mathrm{n}=20$) of the Finnish software companies that have experience with integration projects. The most common integration types identified in this study are on-premises implementations, hybrid, and cloud-based integration platforms. The study found how software companies choose between these integration types. By using the cultural lag theory framework, the study can explain that the adoption of new cloud-based integration platforms is in a maladjusted situation. Furthermore, the study shows that choice between on-premises and cloud-based integration solutions is not always clear as there is frequently a lack of a broader strategy behind integration decisions.
Future Friend Recommendation System based on User Similarities in Large-Scale on Social Network

Friendship is one of the most important issues in online social networks (OSN). Researchers analyze the OSN to determine how people are connected to a network and how new connections are developed. Most of the existing methods cannot efficiently evaluate a friendship graphs internal connectivity and decline to render a proper recommendation. This paper presented three proposed algorithms that can apply in OSN to predict future friends recommendations for the users. Using network and profile similarity proposed approach can measure the similarity among the users. To predict the user similarity, we calculated an average weight that indicates the probability of two users being similar by considering every precise subset of some profile attributes such as age, profession, location, and interest rather than taking the only average of the superset profile attributes. The suggested algorithms perform a significant enhancement in prediction accuracy 97% and precision 96.566%. Furthermore, the proposed recommendation frameworks can handle any profile attribute’s missing value by assuming the value based on friends’ profile attributes. Keywords—Social networks; recommendation framework; profile similarity; network similarity
Different instruction methods for learning programming concepts in Scratch Testing the retention of programming misconceptions among primary school children

Programming education is an important research topic but is mostly focused on students. Nowadays, there is more interest in giving programming lessons to young children, but still less programming research is focused on this sample group. Learning programming is a difficult process and it is almost unavoidable that misconceptions arise. A misconception is an incorrect understanding of a concept. Because misconceptions negatively affect performance it is important to avoid misconceptions while learning how to program. To avoid misconceptions, someone must have a correct understanding of the concept, which may be influenced by the used instruction method. In this study the effect of three different instruction methods, on the retention of programming misconceptions among 10-to 12-year old children is tested. The used instruction methods are: worked examples, given subgoal labelling and self-explaining subgoal labelling. Nineteen children of primary school took part in this study. Each participant followed three online programming classes in Scratch where the basic programming concepts, variables, loops, if/else statements, and input were learned. Misconceptions were measured twice, during and after the intervention, by using a multiple-choice questionnaire with programming exercises in Scratch. No significant differences were found between the number of misconceptions and used instruction method over time. So, on average no difference in the effect of misconceptions over time for different instruction methods is found. Analyzing the descriptive statistics suggests the subgoal labelling instruction methods to be more effective for learning programming concepts. In those conditions there were fewer misconceptions, less wrong answers and more correct answers compared to the worked example condition. This pilot study showed insights and directions for further research. Further research must show whether a specific instruction method is more effective in learning programming concepts.
Neural Mechanisms of Visual Motion Anomalies in Autism: A Two-Decade Update and Novel Aetiology

The 21st century has seen dramatic changes in our understanding of the visual physio-perceptual anomalies of autism and also in the structure and development of the primate visual system. This review covers the past 20 years of research into motion perceptual/dorsal stream anomalies in autism, as well as new understanding of the development of primate vision. The convergence of this literature allows a novel developmental hypothesis to explain the physiological and perceptual differences of the broad autistic spectrum. Central to these observations is the development of motion areas MT+, the seat of the dorsal cortical stream, central area of pre-attentional processing as well as being an anchor of binocular vision for 3D action. Such development normally occurs via a transfer of thalamic drive from the inferior pulvinar → MT to the anatomically stronger but later-developing LGN → V1 → MT connection. We propose that autistic variation arises from a slowing in the normal developmental attenuation of the pulvinar → MT pathway. We suggest that this is caused by a hyperactive amygdala → thalamic reticular nucleus circuit increasing activity in the PIm → MT via response gain modulation of the pulvinar and hence altering synaptic competition in area MT. We explore the probable timing of transfer in dominance of human MT from pulvinar to LGN/V1 driving circuitry and discuss the implications of the main hypothesis.
In vitro and in vivo antioxidant activity of Raphanus sativus leaves extract

Antioxidant are compounds of a natural or endogenous nature that either inhibit or interfere with toxic oxidants created and disable and there by prevent the spread of the chain reaction formed by these oxidants. The methanolic extract of Raphanus sativus leaves has been investigated for its in vivo and in vitro antioxidant activity. The in vitro antioxidant potency was evaluated by scavenging methods and it was observed that In DPPH Scavenging method Methanolic extract from Raphanus sativus leaves at concentration of (100μg/mL) had radical scavenging activity (IC 50 ) of 117.30 μg/ml) in contrast to standardize ascorbic acid (100μg/mL) IC 50 (78.73 μg/ml). Whereas in Nitric oxide Scavenging method Methanolic extract from Raphanus sativus at concentration of (100μg/mL) had radical scavenging activity (IC 50 ) of 213.04 μg/ml) in contrast to standardize ascorbic acid (100μg/mL) IC 50 (80.0 μg/ml). Apart from these actions, a significant in vivo antioxidant potential was also showed Raphanus sativus leaves methanolic extract exhibited a potent anti-oxidation action with respect to parameter like TBARS, GSH, Catalase and produced significant decrease in tissue MDA, lipid peroxidation & increase in Catalase and GSH. So, in the order of methanolic extract of Raphanus sativus leaves shows antioxidant activity.
An improved real visual tracking system using particle filter

This paper presents a real hybrid visual tracking system based on a special-color model of target with improving the performance of this designed visual tracking system using various linear and nonlinear estimators like Kalman filter and particle filter. Moreover, the whole system has been designed and implemented in the laboratory by fusing the tracking algorithm that was created utilizing python software with a moving camera sensor. In addition, the designed visual tracking system has been simple, low cost and achieved all stages of visual tracking process like target initialization, appearance modeling, movement estimation, and target positioning with a great success. Finally, the graphical analysis results of the designed system illustrated had a great illustration on the validity of utilizing particle filer was very efficient and clearer with maneuvering targets than that were used with Kalman filter. Streszczenie. W niniejszym artykule przedstawiono hybrydowy system śledzenia wizualnego oparty na modelu celu w specjalnej kolorystyce z poprawą wydajności tego zaprojektowanego systemu śledzenia wizualnego przy użyciu różnych liniowych i nieliniowych estymatorów, takich jak filtr Kalmana i filtr cząsteczkowy. Co więcej, cały system został zaprojektowany i wdrożony w laboratorium poprzez połączenie algorytmu śledzenia, który został stworzony przy użyciu oprogramowania Pythona z ruchomym czujnikiem kamery. Ponadto zaprojektowany system śledzenia wizualnego był prosty, tani i osiągnął z dużym sukcesem wszystkie etapy procesu śledzenia wizualnego, takie jak inicjalizacja celu, modelowanie wyglądu, szacowanie ruchu i pozycjonowanie celu. Wreszcie, wyniki analizy graficznej zilustrowanego zaprojektowanego systemu doskonale ilustrują zasadność wykorzystania filtra cząstek, który był bardzo wydajny i wyraźniejszy w przypadku celów manewrujących niż ten, który był używany z filtrem Kalmana. (Ulepszony rzeczywisty system śledzenia wizualnego za pomocą filtra cząstek).
Tim-3ScFv-Transforming Lactobacillus Inhibits Transplanted Tumour of Renal Cell Carcinoma

Background T cell immunoglobulin-3(Tim-3) is an immune checkpoint molecule; Tim-3 antibody is suitable for treating malignant renal tumours. However, Tim-3 antibody drugs are expensive, which limits their application. To overcome the disadvantages of expensive immunotherapeutic drugs, Lactococcus lactis was used as the host bacteria to express Tim-3 single-chain antibody in the intestine, and its promotion of the mouse immune system and inhibitory effects on the transplanted tumour of kidney cancer in mice were tested. Methods Molecular cloning technology was used to construct plasmids pLAN-CTB-Tim3scFv and pLAN-Tim3scFv, which were transformed into Lactococcus lactis. The expression of the transformed bacteria was analysed using western blotting, and the immune activity of secreted proteins of the transformed bacteria was detected using ELISA in vitro. A subcutaneous transplanted tumour model of renal adenocarcinoma was constructed in RAG mice, and the promoting effect of transforming bacteria on the activation of mouse spleen lymphocytes, and its inhibitory effect on transplanted tumours in mice was analysed. Results (1) Transformed Lactococcus lactis NZ -CTB-Tim3scFv and NZ -Tim3scFv, which secrete CTB-Tim3scFv and Tim3scFv single-chain antibodies, were successfully constructed. (2) CTB-Tim3scFv secreted by NZ-CTB-Tim3scFv transformed bacteria showed immunological activity. (3) Compared with the NZ-Tim3scFv and NZ-Vector groups, the characteristics of the subgroups of splenic lymphocytes in the NZ-CTB-Tim3scFv group had a higher proportion of CD3+CD4+, CD3+CD8a+, and CD3+CD69+ cells. Ki67 and CD31 expression in the NZ-CTB-Tim3scFv group was significantly reduced. The tumour volume of the NZ-CTB-Tim3scFv group increased the least, and was statistically different from that of the other two groups. Conclusions The Tim-3 single-chain antibody gene was successfully constructed and transformed in Lactococcus lactis. After feeding mice NZ-CTB-Tim3scFv transforming Lactobacillus, the CTB-Tim3scFv secreted by the transforming Lactobacillus promoted the proliferation and activation of spleen lymphocytes and inhibited volume growth, cell proliferation, and angiogenesis of the tumour in mice. In summary, apply transgenic lactobacillus secreting CTB-Tim-3scFv, to perform its role in anti-tumor immunotherapy through oral approach, is low cost and convenient. It is expected to become a new way of immunotherapy for renal cell carcinoma.
HectorGrapher: Continuous-time Lidar SLAM with Multi-resolution Signed Distance Function Registration for Challenging Terrain

For deployment in previously unknown, unstructured, and GPS-denied environments, autonomous mobile rescue robots need to localize themselves in such environments and create a map of it using a simultaneous localization and mapping (SLAM) approach. Continuous-time SLAM approaches represent the pose as a time-continuous estimate that provides high accuracy and allows correcting for distortions induced by motion during the scan capture. To enable robust and accurate real-time SLAM in challenging terrain, we propose HectorGrapher which enables accurate localization by continuous-time pose estimation and robust scan registration based on multiresolution signed distance functions. We evaluate the method in multiple publicly available real-world datasets, as well as a data set from the RoboCup 2021 Rescue League, where we applied the proposed method to win the Best-in-Class “Exploration and Mapping” Award.
Life Stories of Voluntarily Childless Older People: A Retrospective View on Their Reasons and Experiences

This article investigates the reasons for and experiences of voluntary childlessness throughout the life course. Thirteen voluntarily childless people aged 60 years and older (Belgium) were interviewed using the McAdams approach (2005). Four profiles were derived from the reasons given for voluntary childlessness: the “liberated careerist,” the “social critic,” the “acquiescent partner,” and “voluntarily childless because of life course circumstances. ” Results further indicate that older people experience feelings of acceptance, loss (missing familiarity with current trends, being helped, and children’s company), and relief concerning their voluntary childlessness. Moreover, they rarely seem to regret their choice. The discussion indicates the existence of voluntary childlessness among older people, a phenomenon sometimes questioned in the existing scientific literature. As part of a diverse target group, these older adults each have their personal reasons and experiences regarding childlessness.
SECURED RELAY SELECTION AND CONGESTION AWARE ROUTING FOR MANET ’ S

Due to decentralised architecture the nodes present in mobile ad-hoc networks nodes can able to move freely in the environment. Hence the nodes are characterised as self organised and self healing. Due to rapid movement of nodes link failures exists frequently between the nodes and if two nodes present in the range of communication, then the sensed data is passed directly to the sink node. To identify an efficient and concurrent bandwidth and trustable path between sending and receiving nodes; the communication link weight and node trust score factors are examined. The weight of the links is calculated by estimating longer link connectivity period and the bandwidth channel availability. Once the trust ratio for the nodes is evaluated through ratings then efficient bandwidth nodes are selected. Therefore the data is passed through the trusted node with high channel bandwidth towards the gateway.
RNNbow: Visualizing Backpropagation Gradients in Recurrent Neural Networks

We present RNNbow, an interactive tool for visualizing the gradient ﬂow during backpropagation training in recurrent neural networks. RNNbow is a web application that displays the relative gradient contributions from Recurrent Neural Network (RNN) cells in a neighborhood of an element of a sequence. By visualizing the gradient, as opposed to activations, it offers insight into how the network is learning. We use it to explore the learning of an RNN that is trained to generate code in the C programming language. We show how it uncovers insights into the vanishing gradient as well as the evolution of training as the RNN works its way through a corpus. We describe some future work in using RNNbow to illustrate the differences between RNN architectures and cell types.