Miracle-dz/UniFAR-FTUs huggingface dataset:
Long Noncoding RNAs and Circular RNAs Regulate AKT and Its Effectors to Control Cell Functions of Cancer Cells

AKT serine-threonine kinase (AKT) and its effectors are essential for maintaining cell proliferation, apoptosis, autophagy, endoplasmic reticulum (ER) stress, mitochondrial morphogenesis (fission/fusion), ferroptosis, necroptosis, DNA damage response (damage and repair), senescence, and migration of cancer cells. Several lncRNAs and circRNAs also regulate the expression of these functions by numerous pathways. However, the impact on cell functions by lncRNAs and circRNAs regulating AKT and its effectors is poorly understood. This review provides comprehensive information about the relationship of lncRNAs and circRNAs with AKT on the cell functions of cancer cells. the roles of several lncRNAs and circRNAs acting on AKT effectors, such as FOXO, mTORC1/2, S6K1/2, 4EBP1, SREBP, and HIF are explored. To further validate the relationship between AKT, AKT effectors, lncRNAs, and circRNAs, more predicted AKT- and AKT effector-targeting lncRNAs and circRNAs were retrieved from the LncTarD and circBase databases. Consistently, using an in-depth literature survey, these AKT- and AKT effector-targeting database lncRNAs and circRNAs were related to cell functions. Therefore, some lncRNAs and circRNAs can regulate several cell functions through modulating AKT and AKT effectors. This review provides insights into a comprehensive network of AKT and AKT effectors connecting to lncRNAs and circRNAs in the regulation of cancer cell functions.
Current Account Dynamics: On Income and Trade Balance

We investigate the dynamics of Japan’s income and trade balance between 1996:Q1 and 2019:Q4 via a structural VAR model. The two most important constituents of the current account (trade balance and income balance) and seven other macroeconomic variables are entered in our VAR model. We implement a shadow rate for the measure of monetary policy under the unconventional monetary policy regime, including a zero lower bound interest rate. By using a standard SVAR model from the literature, we find that world shocks dominate and rule the dynamics of Japan’s current account. Through additional short-run zero restrictions, we also find that exogenous exchange rate shocks affect the current account.
Using scaling to compute moments of inertia of symmetric objects

Moment of inertia is a very important property in the study of rotational mechanics. The concept of moment of inertia is analogous to mass in the linear motion, and its calculation is routinely done through integration. This paper provides an alternative way to compute moments of inertia of rigid bodies of regular shape using their symmetrical property. This approach will be very useful and preferred for teaching rotational mechanics at the undergraduate level, as it does not require the knowledge or the application of calculus. The seven examples provided in this paper will help readers to understand clearly how to use the method.
A pH-responsive biopolymer-based multiple emulsion prepared in a helicoidal contactor for chemotherapeutics delivery

Among biopolymers, pH-responsive ones have much potential for drug delivery systems by exploiting the pH gradient between physiological and pathological states. In this paper multiple emulsions with a pH-responsive biopolymer for controlled drug delivery in brain tumor treatment were discussed. The characteristic and rheological properties of multiple emulsions, biopolymer adsorption, zeta potential, and release processes were examined. The results showed higher drug release rates in the acidic tumor microenvironment compared to the simulated conditions of normal cells.
Erratum: “The Internal Energy for Molecular Hydrogen in Gravitationally Unstable Protoplanetary Disks” (ApJ, 656, L89 [2007])

In the above-mentioned Letter, we stated that Boss uses a discontinuous internal energy for H 2, where H2 contributes to 3kT/4mp the specific internal energy e for K and for K, based on his own citations to A. P. Boss (ApJ, 277, 768 T ≤ 100 5kT/4m T 1 100 p [1984]). It was brought to our attention by A. P. Boss (2007, private communication) that he now uses a quadratic interpolation for e between 100 and 200 K (see Fig. 1, left panel). This change is not mentioned in the literature but is alluded to in A. P. Boss (ApJ, 346, 336 [1989]). According to Boss, the interpolation has been used in all his simulations since Boss (1989). In Figure 1 (right panel), we also illustrate the consequences for of Boss’s latest e approximation by calculating the specific G1 heat at constant volume and by relating that to (see J. P. Cox & R. T. Giuli, Principles of Stellar Structure [New York: c p de/dT G1 v Gordon & Breach, 1968]). This curve is compared with curves for pure para-hydrogen ( short-dashed curve), a 3 : 1 ortho/parahydrogen mixture ( long-dashed curve), where the species are treated as independent, and an equilibrium mixture ( solid curve). Because fragmentation becomes more likely as is lowered (W. K. M. Rice, G. Lodato, & P. J. Armitage, MNRAS, 364, L56 [2005]), Boss’s G1 approximation between 100 and 200 K is likely to make his disks susceptible to fragmentation for that temperature range. We would like to thank A. P. Boss for his discussions and clarification of his code.
Is Composition of Brain Clot Retrieved by Mechanical Thrombectomy Associated with Stroke Aetiology and Clinical Outcomes in Acute Ischemic Stroke?—A Systematic Review and Meta-Analysis

Background: Brain clots retrieved following endovascular thrombectomy in acute ischemic stroke patients may offer unique opportunities to characterise stroke aetiology and aid stroke decision-making in select groups of patients. However, the evidence around the putative association of clot morphology with stroke aetiology is limited and remains inconclusive. This study aims to perform a systematic review and meta-analysis to delineate the association of brain clot composition with stroke aetiology and post-reperfusion outcomes in patients receiving endovascular thrombectomy. Methods: The authors conducted a systematic literature review and meta-analysis by extracting data from several research databases (MEDLINE/PubMed, Cochrane, and Google Scholar) published since 2010. We used appropriate key search terms to identify clinical studies concerning stroke thrombus composition, aetiology, and clinical outcomes, in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results: The authors identified 30 articles reporting on the relationship between stroke thrombus composition or morphology and aetiology, imaging, or clinical outcomes, of which 21 were included in the meta-analysis. The study found that strokes of cardioembolic origin (SMD = 0.388; 95% CI, 0.032–0.745) and cryptogenic origin (SMD = 0.468; 95% CI, 0.172–0.765) had significantly higher fibrin content than strokes of non-cardioembolic origin. Large artery atherosclerosis strokes had significantly lower fibrin content than cardioembolic (SMD = 0.552; 95% CI, 0.099–1.004) or cryptogenic (SMD = 0.455; 95% CI, 0.137–0.774) strokes. Greater red blood cell content was also significantly associated with a thrombolysis in cerebral infarction score of 2b–3 (SMD = 0.450; 95% CI, 0.177–0.722), and a positive hyperdense middle cerebral artery sign (SMD = 0.827; 95% CI, 0.472–1.183). No significant associations were found between red blood cell, platelet, or white blood cell content and aetiology, or between clot composition and bridging thrombolysis. Conclusions: This meta-analysis found that fibrin composition is significantly higher in strokes of cardioembolic and cryptogenic origin, and that red blood cell content is positively associated with the hyperdense middle cerebral artery sign and better reperfusion outcomes. Important advances to stroke clinical workup can be derived from these findings, in which many aspects of stroke workflow remain to be optimised. As data are still limited in terms of the association of various thrombus components with stroke aetiology as well as a standardised method of analysis, further studies are required to validate these findings to guide their use in clinical decision-making.
Optimal Facial Feature Based Emotional Recognition Using Deep Learning Algorithm

Humans have traditionally found it simple to identify emotions from facial expressions, but it is far more difficult for a computer system to do the same. The social signal processing subfield of emotion recognition from facial expression is used in a wide range of contexts, particularly for human-computer interaction. Automatic emotion recognition has been the subject of numerous studies, most of which use a machine learning methodology. The recognition of simple emotions like anger, happiness, contempt, fear, sadness, and surprise, however, continues to be a difficult topic in computer vision. Deep learning has recently drawn increased attention as a solution to a variety of practical issues, including emotion recognition. In this study, we improved the convolutional neural network technique to identify 7 fundamental emotions and evaluated several preprocessing techniques to demonstrate how they affected the CNN performance. This research focuses on improving facial features and expressions based on emotional recognition. By identifying or recognising facial expressions that elicit human responses, it is possible for computers to make more accurate predictions about a person's mental state and to provide more tailored responses. As a result, we examine how a deep learning technique that employs a convolutional neural network might improve the detection of emotions based on facial features (CNN). Multiple facial expressions are included in our dataset, which consists of about 32,298 photos for testing and training. The preprocessing system aids in removing noise from the input image, and the pretraining phase aids in revealing face detection after noise removal, including feature extraction. As a result, the existing paper generates the classification of multiple facial reactions like the seven emotions of the facial acting coding system (FACS) without using the optimization technique, but our proposed paper reveals the same seven emotions of the facial acting coding system.
Fully Distributed Robust Formation Flying Control of Drones Swarm Based on Minimal Virtual Leader Information

This paper studies the robust formation flying problem for a swarm of drones, which are modeled as uncertain second order systems. By making use of minimal virtual leader information, a fully distributed robust control scheme is proposed, which includes three parts. First, the output based adaptive distributed observer is adopted to recover the global flying path vector as well as the coefficients of the minimal polynomial of the system matrix of the virtual leader system for each drone based on neighboring information from the communication network. Second, based on the estimated minimal polynomial of the system matrix of the virtual leader system, an asymptotic internal model is conceived to deal with uncertain system parameters. Third, by combining the asymptotic internal model and a certainty equivalent dynamic state feedback control law, a local trajectory tracking controller is synthesized to solve the robust formation flying problem. Numerical simulations are provided to validate the proposed control scheme.
Cold spots are universal in protein–protein interactions

Proteins interact with each other through binding interfaces that differ greatly in size and physico‐chemical properties. Within the binding interface, a few residues called hot spots contribute the majority of the binding free energy and are hence irreplaceable. In contrast, cold spots are occupied by suboptimal amino acids, providing possibility for affinity enhancement through mutations. In this study, we identify cold spots due to cavities and unfavorable charge interactions in multiple protein–protein interactions (PPIs). For our cold spot analysis, we first use a small affinity database of PPIs with known structures and affinities and then expand our search to nearly 4000 homo‐ and heterodimers in the Protein Data Bank (PDB). We observe that cold spots due to cavities are present in nearly all PPIs unrelated to their binding affinity, while unfavorable charge interactions are relatively rare. We also find that most cold spots are located in the periphery of the binding interface, with high‐affinity complexes showing fewer centrally located colds spots than low‐affinity complexes. A larger number of cold spots is also found in non‐cognate interactions compared to their cognate counterparts. Furthermore, our analysis reveals that cold spots are more frequent in homo‐dimeric complexes compared to hetero‐complexes, likely due to symmetry constraints imposed on sequences of homodimers. Finally, we find that glycines, glutamates, and arginines are the most frequent amino acids appearing at cold spot positions. Our analysis emphasizes the importance of cold spot positions to protein evolution and facilitates protein engineering studies directed at enhancing binding affinity and specificity in a wide range of applications.
Learning Despite Complex Attribute Interaction: An Approach Based on Relational Operators

When shortage of knowledge prevents human experts from chosing good attributes to represent empirical observations, learning is difficult. Expressing concepts by using only primitive (low-level) attributes is intricate because the contribution of each attribute to the definition is almost unnoticeable. This aggravates attribute interaction , a situation where an attribute''s effect on classification depends on the value of other attributes. When attribute interaction is complex, involving many combinations of several attributes, current learners cannot handle it. Such complex interaction appears in real-world domains (e.g., protein folding). These domains are not random; they have structure although it may be concealed by the complexity of the interactions. Concepts in these domains often have embedded, implicit structure, which may be revealed through explicit relations . Because of the existence of structure, there is still hope for learning despite complex attribute interaction if appropriate techniques are devised. The design of these techniques, however, must be guided by a required functionality: finding interactions, more precisely, finding relations. In particular, this thesis focuses on interactions due to complex relations among primitive attributes. Then, concepts can be simple in terms of relations, but complex in terms of primitive attributes. Such focus is motivated by relations that appear in protein folding and other domains. To facilitate learning when relations create complex interactions, an approach based on the algebraic notions of relation and relational operators is proposed. The approach is implemented in MRP, a learning system that relies on multidimensional relational projection to find relations and hence, find interactions that can be captured as relations among attributes. Synthetic and real-world problems are used to empirically evaluate MRP with respect to five classical and advanced machine learning systems. MRP''s distinctive behavior is analyzed in terms of concept characteristics (such as DNF size, entropy, variation, and Fourier spectrum), and related to the system''s performance in real-world domains. Finally, a small family of simplified versions of MRP is evaluated empirically to analyze what components of MRP are responsible for its improved performance, and this analysis is used to focus the proposal for future research directions and system extensions.
Cognitive Processes during Recovery: Moving toward Personalized Spine Surgery Outcomes

This paper focuses on a novel application of personalized medicine: the ways one thinks about health (i.e., appraisal processes) as relevant predictors of spine-surgery response. This prospective longitudinal cohort study (n = 235) investigated how appraisal processes relate to outcomes of spinal decompression and/or fusion surgery, from pre-surgery through one-year post-surgery. Patient-reported outcomes assessed spine-specific disability (Oswestry Disability Index (ODI)), mental health functioning (Rand-36 Mental Component Score (MCS)), and cognitive appraisal processes (how people recall past experiences and to whom they compare themselves). Analysis of Variance examined the appraisal-outcomes association in separate models at pre-surgery, 3 months, and 12 months. We found that appraisal processes explained less variance at pre-surgery than later and were differentially relevant to health outcomes at different times in the spine-surgery recovery trajectory. For the ODI, recall of the seriousness of their condition was most prominent early in recovery, and comparing themselves to positive standards was most prominent later. For the MCS, not focusing on the negative aspects of their condition and/or on how others see them was associated with steady improvement and higher scores at 12 months. Appraisal processes are relevant to both spine-specific disability and mental-health functioning. Such processes are modifiable objects of attention for personalizing spine-surgery outcomes.
Impact of cultural tightness on vaccination rate

Abstract We find that cultural tightness, that is, the level of social punishment for violating norms, is associated with lower vaccination rates against COVID‐19 across both states and counties in the United States. This is consistent with individuals in tighter cultures being more likely to base risk management decisions on social norms rather than on advice from experts and leaders. It is also consistent with our documentation of a social norm against COVID‐19 vaccination. This implies that when a society depends on individual action to help manage society‐wide risks, social norms can influence the degree to which individuals in tighter societies will engage in actions that minimize the overall risk to the society.
Highly conducting corn starch doped ionic liquid solid polymer electrolyte for energy storage devices-RAFM 2022

In this study, a new biopolymer electrolyte (BPE) is successfully developed using corn starch complexed with ammonium iodide (NH4I) as host, and low viscosity ionic liquid 1-Ethyl, 3-Methyl Imidazolium Thiocyanate (EMImSCN), as a plasticizer. This ionic liquid doped BPE shows high ionic conductivity of the order 10−4 S/cm and electrochemical stability window more than 3 V, which is suitable for electrochemical application. The ionic transference number (tion) of 0.98 confirms the system to be predominantly ionic in nature. Fourier transform infrared spectroscopy (FTIR) shows complex nature, while polarized optical microscopy (POM) gives us an idea about the semi-crystalline nature of BPE. Maximum conducting ionic liquid doped BPE sandwiched electric double layer capacitor (EDLC) and dye sensitize solar cell (DSSC) shows efficient performance.
Multi-Task Output Space Regularization

We investigate multi-task learning from an output space regularization perspective. Most multi-task approaches tie together related tasks by constraining them to share input spaces and function classes. In contrast to this, we propose a multi-task paradigm which we call output space regularization, in which the only constraint is that the output spaces of the multiple tasks are related. We focus on a specic instance of output space regularization, multi-task averaging, that is both widely applicable and amenable to analysis. The multi-task averaging estimator improves on the single-task sample average under certain conditions, which we detail. Our analysis shows that for a simple case the optimal similarity depends on the ratio of the task variance to the task dierences, but that for more complicated cases the optimal similarity behaves nonlinearly. Further, we show that the estimates produced are a convex combination of the tasks’ sample averages. We discuss the Bayesian viewpoint. Three applications of multi-task output space regularization are presented: multi-task kernel density estimation, multi-task-regularized empirical moment constraints in similarity discriminant analysis, and multi-task local linear regression. Experiments on real data sets show statistically signicant gains.
Versatile Approach of Silicon Nanofabrication without Resists: Helium Ion-Bombardment Enhanced Etching

Herein, we report a helium ion-bombardment enhanced etching method for silicon nanofabrication without the use of resists; furthermore, we demonstrate its unique advantages for straightforward fabrication on irregular surfaces and prototyping nano-electro-mechanical system devices, such as self-enclosed Si nanofluidic channels and mechanical nano-resonators. This method employs focused helium ions to selectively irradiate single-crystal Si to disrupt the crystal lattice and transform it into an amorphous phase that can be etched at a rate 200 times higher than that of the non-irradiated Si. Due to the unique raindrop shape of the interaction volumes between helium ions and Si, buried Si nanofluidic channels can be constructed using only one dosing step, followed by one step of conventional chemical etching. Moreover, suspended Si nanobeams can be fabricated without an additional undercut step for release owing to the unique raindrop shape. In addition, we demonstrate nanofabrication directly on 3D micro/nano surfaces, such as an atomic force microscopic probe, which is challenging for conventional nanofabrication due to the requirement of photoresist spin coating. Finally, this approach can also be extended to assist in the etching of other materials that are difficult to etch, such as silicon carbide (SiC).
The Roles of Phosphorus and Nitrogen Nutrient Transporters in the Arbuscular Mycorrhizal Symbiosis

More than 80% of land plant species can form symbioses with arbuscular mycorrhizal (AM) fungi, and nutrient transfer to plants is largely mediated through this partnership. Over the last few years, great progress has been made in deciphering the molecular mechanisms underlying the AM-mediated modulation of nutrient uptake progress, and a growing number of fungal and plant genes responsible for the uptake of nutrients from soil or transfer across the fungal–root interface have been identified. In this review, we outline the current concepts of nutrient exchanges within this symbiosis (mechanisms and regulation) and focus on P and N transfer from the fungal partner to the host plant, with a highlight on a possible interplay between P and N nutrient exchanges. Transporters belonging to the plant or AM fungi can synergistically process the transmembrane transport of soil nutrients to the symbiotic interface for further plant acquisition. Although much progress has been made to elucidate the complex mechanism for the integrated roles of nutrient transfers in AM symbiosis, questions still remain to be answered; for example, P and N transporters are less studied in different species of AM fungi; the involvement of AM fungi in plant N uptake is not as clearly defined as that of P; coordinated utilization of N and P is unknown; transporters of cultivated plants inoculated with AM fungi and transcriptomic and metabolomic networks at both the soil–fungi interface and fungi–plant interface have been insufficiently studied. These findings open new perspectives for fundamental research and application of AM fungi in agriculture.
Dynamics of the Lorenz system under quasiperiodic driving

We have investigated the dynamics of the Lorenz system under quasiperiodic driving. When the forcing amplitude is increased to certain critical value，the dynamics changes fundamentally. Therefore a new mechanism for the creation of the strange nonchaotic attractor is advanced in this paper. When the forcing amplitude is increased, the strange attractor is replaced by a strange nonchaotic attractor, eventually the phase space is suppressed into an invariant quasiperiodic torus. Numerical results show that the critical amplitude of the force is roughly proportional to the Rayleigh parameter, but slightly influenced by its frequency.