Miracle-dz/UniFAR-FTUs huggingface dataset:
An Organismal Perspective on the Warburg Effect and Models for Proliferation Studies

Simple Summary Considerable interest in the physiology of proliferation has been generated by human cancers. Cancers often share features with other proliferative cells that are exemplified by the Warburg effect, which involves an altered metabolism with less oxygen uptake and greater lactate secretion. While some of the Warburg effect can be rationalized as production of biosynthetic precursors to fuel proliferation, lactate secretion does not fit this paradigm as it wastes these valuable precursors. In this context, examining proliferation in organisms that are capable of anaerobic pathways other than lactate production can be illuminating. Many animal species, and indeed eukaryotes in general, can carry out other bioenergetic reactions that do not use oxygen. These reactions frequently involve mitochondria and employ such seemingly baroque processes as running the Krebs cycle, the metabolic heart of the cell, backwards. Detailed study of these processes may require development of new animal model systems. For example, colonial marine hydroids frequently exhibit a stage in the life cycle that only undergoes cellular proliferation and never produces gametes. In contrast, traditional animal models such as worms, flies, and mice undergo only limited proliferation before gamete production. The crucial unresolved issues related to the Warburg effect could thus be understood. Abstract Interest in the physiology of proliferation has been generated by human proliferative diseases, i.e., cancers. A vast literature exists on the Warburg effect, which is characterized by aerobic glycolysis, diminished oxygen uptake, and lactate secretion. While these features could be rationalized via the production of biosynthetic precursors, lactate secretion does not fit this paradigm, as it wastes precursors. Forming lactate from pyruvate allows for reoxidizing cytosolic NADH, which is crucial for continued glycolysis and may allow for maintaining large pools of metabolic intermediates. Alternatively, lactate production may not be adaptive, but rather reflect metabolic constraints. A broader sampling of the physiology of proliferation, particularly in organisms that could reoxidize NADH using other pathways, may be necessary to understand the Warburg effect. The best-studied metazoans (e.g., worms, flies, and mice) may not be suitable, as they undergo limited proliferation before initiating meiosis. In contrast, some metazoans (e.g., colonial marine hydrozoans) exhibit a stage in the life cycle (the polyp stage) that only undergoes mitotic proliferation and never carries out meiosis (the medusa stage performs this). Such organisms are prime candidates for general studies of proliferation in multicellular organisms and could at least complement the short-generation models of modern biology.
Detection of DDoS Attacks using Artificial Gorilla Troops Optimizer based Deep Learning Model

The importance of security has skyrocketed alongside the adoption of Internet of Things (IoT) services. Software-defined networking (S DN) provides a means of securely managing IoT devices, which were exposed in a current distributed denial-of-service (DDoS) attack. Many IoT devices unwittingly contributed to the DDoS attack. DDoS attacks, one of the most common types of cyberattack, are particularly pernicious since they can cripple a network’s ability to function and render many of its services inaccessible to users. This research used optimised deep learning-based models to detect DDoS in SDN. At first, the dataset’s normal and DDoS attack traffic characteristics were gathered from the SDN. The models are recommended to be simpler, easier to read, and to have a shorter training period when using an NSL-KDD dataset for feature selection approaches. Real-time DDoS attack detection in SDN is proposed in this research using an Long Short-Term Memory (LS TM) models. High accuracy in classification is achieved by utilising an artificial gorilla troop optimizer to pick the features of NSL-KDD. Using less time and material, the proposed IDS was able to achieve a detection accuracy of 97.59%. These findings provide encouraging evidence that DDoS attack detection in SDN could benefit from the use of deep learning and feature selection techniques, which could significantly reduce processing loads and runtimes.
Adaptation and Validation of the Turkish Version of the Ford Insomnia Response to Stress Test in University Students

Objective We adapted the Ford Insomnia Response to Stress Test to Turkish (FIRST-T) and validated it. Methods We randomly divided 774 Turkish university students into two equal groups for exploratory (EFA) and confirmatory factor analysis (CFA). McDonald’s omega and Cronbach’s alpha values were utilized for reliability analyses. Item response theory (IRT) approach also used for psychometric properties on the full sample. For discriminant validity, study sample were classified into high and low sleep reactivity groups, and their sociodemographic and sleep data were compared. Results EFA results suggested a one-factor structure of the FIRST-T, which was confirmed by CFA results. The FIRST-T had solid internal reliability. Item analysis results showed that all the items could distinguish between low and high scorers. This scale showed the same construct (clinical insomnia vs good sleepers) across the sexes in multi-group CFA and differential item functioning results. In the high FIRST-T score group, sleep quality, severity of insomnia, and anxiety scores were higher. In this group, more participants had clinical insomnia according to the Insomnia Severity Index (ISI) and were poor sleepers according to the Pittsburg Sleep Quality Index (PSQI) (p < 0.01). Conclusion The FIRST-T has robust psychometric properties that assesses sleep reactivity among university students.
Modelling the distribution of the cluster maxima of exceedances of subasymptotic thresholds

A standard approach to model the extreme values of a stationary process is the peaks over threshold method, which consists of imposing a high threshold, identifying clusters of exceedances of this threshold and fitting the maximum value from each cluster using the generalized Pareto distribution. This approach is strongly justified by underlying asymptotic theory. We propose an alternative model for the distribution of the cluster maxima that accounts for the subasymptotic theory of extremes of a stationary process. This new distribution is a product of two terms, one for the marginal distribution of exceedances and the other for the dependence structure of the exceedance values within a cluster. We illustrate the improvement in fit, measured by the root mean square error of the estimated quantiles, offered by the new distribution over the peaks over thresholds analysis using simulated and hydrological data, and we suggest a diagnostic tool to help identify when the proposed model is likely to lead to an improved fit. Copyright 2012, Oxford University Press.
Hi-C Data Resolution Improvement Method based on Ensemble Learning

The multi-level spatial structure of chromosomes allows remote regulatory elements in the linear coordinate space to closely regulate the expression level of the target genes in the three-dimensional structural space, so, the efficient analysis will be essential. Especially, this paper focuses on the Hi-C data resolution improvement method based on ensemble learning. Hi-C data standardization is used to remove the systematic bias between samples introduced by the various unavoidable nonrandom factors, hence, the accuracy is essential. Therefore, this study utilizes the stacking integration model to achieve the ensemble task, the designed model can avoid the problems of low prediction accuracy and the poor model robustness. Similarly, the multi-objective regression evolved based on the idea of multi-label classification. After testing the designed model on the public data sets, the accuracy can reach more than 99%. Compared with the traditional tools, our designed algorithm reaches better results.
Received Signal Strength and Optimized Support Vector Machine based Sybil Attack Detection Scheme in Smart Grid

As smart grid enables two-way flow of data and electricity with Advanced Metering Infrastructure, it is prone to security vulnerabilities. Sybil attack, one such vulnerability exhibits multiple identities of same node. As a consequence, the compromised or malicious nodes present in smart grid inject false information that would cause a serious impact in a critical infrastructure i.e. smart grid. Hence, the proposal aims to detect this attack based on node's Received Signal Strength, address, energy consumption and distance using machine learning algorithm. Support vector machine outperforms other machine learning algorithms like logistic regression, K-Nearest Neighborhood, Naive Baye's, and K-Nearest Neighborhood in terms of accuracy, training time, misclassification cost, prediction speed, sensitivity or recall, specificity, F1 score, precision, and Area Under the Curve (AUC) and Receiver Operating Characteristic Curve (ROC). Further, the performance of the model is optimized using hyper parameter tuning. The proposal is implemented in MATLAB. The results exhibit 96.5% accuracy that clearly demonstrates the efficacy of the model.
Motion chaos in the pseudoinverse control of redundant robots

When a manipulator is redundant, it is anticipated that the inverse kinematics admits an infinite number of solutions. Several kinematic control algorithms for redundant manipulators adopt generalized inverse matrices. In this line of thought, the generalized inverse control scheme is tested through experiments that reveal the difficulties that often arise. Motivated by these problems the paper studies kinematics and dynamics of the trajectory planning scheme. The experiment confirms the nonlinear and chaotic performance of the algorithm and gives a deeper insight towards the future development of superior trajectory control systems.
The use of azithromycin and pyrimethamine for treatment of cerebral toxoplasmosis in human immunodeficiency virus-infected patients: a systematic review

Purpose Toxoplasma gondii is a parasite that is widely distributed around the globe and can cause brain inflammation, particularly in immunosuppressed patients such as those diagnosed with human immunodeficiency virus (HIV). This paper reviews the efficacy of azithromycin and pyrimethamine combination therapy for cerebral toxoplasmosis in patients with HIV. Methods The scope of the studies included in this review was limited from 1992 to 2022, with studies primarily being randomized, controlled clinical trials available on online scientific journal databases. The authors screened eligible records for review, removing those that did not fit the inclusion and exclusion criteria. The risk of bias of the extracted data was analyzed through the Cochrane risk-of-bias tool for randomized trials. Results A broad search of major online databases such as PubMed, Medline, Google Scholar, and Cochrane using keywords, limit fields, and Boolean operators yielded 3,130 articles. After thoroughly screening the search results, two studies were included in this review. Results from the studies included in the review demonstrate that the combination therapy of azithromycin and pyrimethamine is favorable for cerebral toxoplasmosis. However, the net response is less effective than the standard treatment regimen (pyrimethamine and sulfadiazine). Conclusion The combination therapy of azithromycin and pyrimethamine is less effective than the standard treatment regimen for maintenance therapy for cerebral toxoplasmosis; thus, administering these medications for this indication must be met with caution.
The linear polarization of 3C 345 at milliarcsecond resolution

A polarization-sensitive 6 cm map of the superluminal quasar 3C 345 with angular resolution 2.5 x 4.7 mas was made using four stations of the US VLBI network. The optically thick core is essentially unpolarized, while both of the knots in the jet are highly polarized. The inner knot C3 has m = 11 percent at electric vector position angle chi = 22 deg, and the outer knot C2 has m = 6 percent at chi = 83 deg. Thus the magnetic field appears to be parallel to the jet near its base and to change direction down the jet. 19 references.
Japanese Encephalitis Virus: An Update on the Potential Antivirals and Vaccines

Japanese encephalitis virus (JEV) is the causal agent behind Japanese encephalitis (JE), a potentially severe brain infection that spreads through mosquito bites. JE is predominant over the Asia-Pacific Region and has the potential to spread globally with a higher rate of morbidity and mortality. Efforts have been made to identify and select various target molecules essential in JEV’s progression, but until now, no licensed anti-JEV drug has been available. From a prophylactic point of view, a few licensed JE vaccines are available, but various factors, viz., the high cost and different side effects imposed by them, has narrowed their global use. With an average occurrence of >67,000 cases of JE annually, there is an urgent need to find a suitable antiviral drug to treat patients at the acute phase, as presently only supportive care is available to mitigate infection. This systematic review highlights the current status of efforts put in to develop antivirals against JE and the available vaccines, along with their effectiveness. It also summarizes epidemiology, structure, pathogenesis, and potential drug targets that can be explored to develop a new range of anti-JEV drugs to combat JEV infection globally.
Critical Analysis and Cross-Comparison Between English and Chinese Websites Providing Online Medical Information for Patients With Adenoid Hypertrophy: Cross-sectional Study

Background In the information era, patients can easily be misled by inaccurate internet content, thus making not well-informed decisions about medical issues. Adenoid hypertrophy, one of the most common causes of chronic upper airway obstruction in children and adolescents, may lead to serious complications, including sleep apnea and craniofacial change. There have been no critical studies about the quality of websites on adenoid hypertrophy, posing a challenge for users without a medical background to determine which website offers more reliable information. Moreover, the blockage of access to internet search tools such as Google, Yahoo, and others has created an isolated internet environment for the enormous user population in mainland China. Differences in internet legislation, the commercial environment, and culture are also likely to result in varied quality of online health information inside and outside mainland China. To date, no study has compared the quality difference between mainland Chinese and English websites. Objective The aims of this study were to (1) analyze the quality of websites about adenoid hypertrophy accessible by patients, (2) investigate the quality differences between Chinese and English websites, (3) determine which type of website (eg, government-sponsored, health care provider) is more reliable in terms of medical information, and (4) determine whether the blockage of foreign websites is hindering users’ accessibility to better-quality websites in mainland China. Methods The first 100 websites (excluding advertisements) displayed on the top three search engines worldwide and in mainland China for the key search term “enlarged adenoids” were collected as the data source. The websites were evaluated based on accessibility, accountability, interactivity, structure, and content quality (accuracy, content coverage, and objectivity). Cohen κ was calculated, and one-way ANOVA and the Kruskal-Wallis test were performed to compare the results between groups and subgroups. Results The mean score for the content quality of English websites was significantly higher than that of Chinese websites (6.16 vs 4.94, P=.03 for Google, Bing, and Yahoo; 6.16 vs 4.16, P<.001 for Baidu, Sougou, and Bing China). Chinese users who are not influenced by the Internet Censorship System are more likely to access higher-quality online medical information (4.94 vs 4.16, P=.02). In within-group Student-Newman-Keuls q posthoc analysis, professional organization and government-sponsored websites were generally of better quality than other websites for both Chinese and English websites (P<.05). Conclusions Generally, the English websites on adenoid hypertrophy are of better quality than Chinese websites; thus, Chinese users residing outside of the Chinese mainland are less influenced by inaccurate online medical information.
Self-regulating writers’ uses and non-uses of peer feedback

Abstract This study contributes to knowledge about peer review by identifying how different components motivate self-regulating learners’ adaptations in later writing. The study followed the changes to writing approach that first-year undergraduates deployed before, during and after peer review workshops. The workshops consisted of: holistically reviewing and discussing examples, developing criteria, writing a practice essay, reviewing three peer essays and receiving peer comments. Case study analysis drew on self-regulated learning theory and triangulated self-report with textual and/or think-aloud evidence. Eight of the ten case writers made adaptations to their writing which could be traced back to workshop learning. Analysing examples sparked students’ initial adaptations to their writing approach in the workshop essay. They then refined, adjusted and added new adaptations in the post-workshops essay. Students made varied use of peer comments, which were not often new information as students had already reflected on writing. Only two writers demonstrated direct uptake of peer feedback information, implementing suggestions in their post-workshop essay. Yet comments also verified the success of strategies, boosted self-efficacy and confirmed priorities for next steps. Behind some apparent non-uses there were interim steps for development. The study contributes to understanding of what it means to use peer feedback.
Magnetic acceleration of relativistic AGN jets

This is a brief review of the recent developments in the theory of magnetic acceleration of relativistic jets. We attempt to explain the key results of this complex theory using basic physical arguments and simple calculations. The main focus is on the standard model, which describes steady-state axisymmetric ideal MHD flows. We argue that this model is over-restrictive and discuss various alternatives.
Multi-spectral photoplethysmography biosensor

A photoplethysmography (PPG) signal can provide very useful information about a subject's hemodynamic status in a hospital or home environment. A newly developed portable multi-spectral photoplethysmography device has been used for studies of 11 healthy subjects. The developed optical fiber biosensor comprises one multi-wavelength laser diode (405nm, 660nm and 780nm) and a single photodiode with multi-channel signal output processing and built in Li-ion accumulator; special software was created for visualization and measuring of the MS-PPG signals. ARM7TDMI-S LPC2148, NXP (founded by Philips) 32 bit processor with clock frequency of 60 MHz performs measurement and analysis of the signal.
Bone-Marrow-Derived Mesenchymal Stem Cells Attenuate Behavioral and Cognitive Dysfunction after Subarachnoid Hemorrhage via HMGB1–RAGE Axis Mediation

We evaluated the therapeutic effects of bone-marrow-derived mesenchymal stem cells (BMSCs) on behavioral and cognitive function in a mouse model of mild subarachnoid hemorrhage (SAH) and explored the underlying mechanisms in conjunction with the HMGB1–RAGE axis. The SAH models were generated in a total of 126 male C57BL/6J mice via endovascular perforation and evaluated 24 h and 72 h after the intravenous administration of BMSCs (3 × 105 cells). The BMSCs were administered once, at 3 h, or twice, at 3 h and 48 h after the model induction. The therapeutic effects of the BMSCs were compared to those of the saline administration. Compared to saline-treated SAH-model mice, at 3 h, the mice with mild SAH treated with the BMSCs showed significant improvements in their neurological scores and cerebral edema. The administration of the BMSCs decreased the mRNA expression of HMGB1, RAGE, TLR4, and MyD88, as well as the protein expression of HMGB1 and phosphorylated NF-kB p65. Furthermore, the numbers of slips per walking time, impairments in short-term memory, and the recognition of novel objects were improved. There was some improvement in inflammatory-marker levels and cognitive function according to the BMSCs’ administration times, but no large differences were seen. The administration of BMSCs improved behavioral and cognitive dysfunction by ameliorating HMGB1–RAGE axis-mediated neuroinflammation after SAH.
An efficient microfluidic pressure sensing structure optimization using microcantilever integration

Microfluidic pressure sensors are extensively present in a wide range of applications such as wearable devices, drug detection, and many healthcare applications. Integrated microfluidic pressure sensors are highly desirable in many fields where it offers high sensitivity, non-toxicity, and high biocompatibility. In the present work, an integrated microfluidic pressure sensing mechanism is analyzed in a microfluidic device. The device is composed of poly dimethyl siloxane (PDMS) based material with a microcantilever of the same material integrated on one side of the microchannel. The pressure of fluid in the microchannel is measured by deflection generated on the PDMS microcantilever while the fluid is made to be drive-in. The pressure-based deflection measurement process is analyzed for different types of fluids and the geometry of microcantilevers. The designs for the microcantilevers are considered rectangular-shaped, T-shaped, and Pi-shaped cantilever. The modelling and analysis are done in the commercially available software tool COMSOL Multiphysics®. The results have shown that maximum deflection is achieved with a Pi-shaped microcantilever in fluid plasma (37.05 μm) and in water (30.98 μm) at 8000 μm/s fluid inlet velocity. This maximum deflection was found to be in cooperation with the pressure value at the channel inlet 125.1 Pa for Pi-microcantilever. The optimization is achieved for improved fluid pressure sensing with an integrated microcantilever, which reduces the device setup for fluid pressure analysis. The purpose of research and study is to control fluid pressure inside microfluidic channels, which can pave the way for efficient small setup cytometry and cell separation microfluidic devices.
Sparse Attribute Network Representation Learning Method

In recent years, the network representation learning (NRL) technology have been widely used in learning low-dimensional vectors for graph structures. Although much previous work has been done in the joint analysis of node attributes and network structure, most of them do not consider the weighted correlation between nodes and attributes and have difficulty in identifying the structural role proximity of nodes. This is especially true when the nodes have very sparse attributes. In response to this, this paper proposes a sparse attribute NRL method. Firstly, to mitigate the sparsity, a deep self-encoder is used to capture the higher-order proximity and structural role proximity of the network, A pairwise nodal algorithm is then introduced to obtain the interaction between sparse attributes and nodes, and to mitigate sparsity again by aggregating information about the nodes' neighbourhood attributes. Finally, the similarity between the central node and the context node is calculated and assigned using the attention mechanism, based on the principle that the higher the similarity is with similar attributes. The attention mechanism is used in conjunction with the CBOW model to learn the correlation of the network topologies and node properties. Our approach achieves better performance on each of the selected mainstream baseline methods.