Toxins present in cigarette and e-cigarette smoke constitute a significant cause of illnesses and are known to have fatal health impacts. Specific mechanisms by which toxins present in smoke impair cell repair are still being researched and are of prime interest for developing more effective treatments. Current literature suggests toxins present in cigarette smoke and aerosolized e-vapor trigger abnormal intercellular responses, damage mitochondrial function, and consequently disrupt the homeostasis of the organelle’s biochemical processes by increasing reactive oxidative species. Increased oxidative stress sets off a cascade of molecular events, disrupting optimal mitochondrial morphology and homeostasis. Furthermore, smoking-induced oxidative stress may also amalgamate with other health factors to contribute to various pathophysiological processes. An increasing number of studies show that toxins may affect mitochondria even though exposure to secondhand or thirdhand smoke. This review assesses the impact of toxins present in tobacco smoke and e-vapor on mitochondrial health, networking, and critical structural processes including mitochondria fission, fusion, hyperfusion, fragmentation, and mitophagy. The efforts are focused on discussing current evidence linking toxins present in first, second, and thirdhand smoke to mitochondrial dysfunction

There are complex ethical dilemmas inherent in medicine teaching, particularly in clinical prac-tice involving actual patients. Questions must be raised on fulfilling medical students' training needs while still respecting patients' fundamental rights to autonomy and privacy. We aimed to assess patients' perspectives regarding medical students' involvement in their medical care. An observational, cross-sectional study was developed, and an interview-like questionnaire was ap-plied randomly to patients waiting for a consult/admitted to three distinct departments: General Surgery, Obstetrics/Gynaecology, and Infectious Diseases. Of the 77% interviewed patients who reported previous experiences with medical students, only 59% stated that they were asked for consent for their participation and 28% that students had adequately introduced themselves. Pa-tients from Gynaecology/Obstetrics were the ones who reported lower rates of these practices and were also the ones who were most bothered by students' presence, stating that they would refuse students' participation in the future. Male patients received more explanations than female pa-tients regarding the same matters. 35% of patients stated they would feel more comfortable without the medical students' presence. The study shows a need to pay closer attention to ful-filling patients' fundamental rights.

The urgency of the COVID19 pandemic caused a surge in related scientific literature. This surge made the manual exploration of scientific articles time-consuming and inefficient. Therefore, a range of exploratory search applications have been created to facilitate access to the available literature. In this survey, we give a short description of certain efforts in this direction and explore the different approaches that they used.

Cohesion in the refractory metals Cr, Mo, and W is phenomenologically described in this work via a n-body energy functional with a set of physically motivated parameters that were optimized to reproduce selected experimental properties characteristic of perfect and defective crystals. The functional contains four terms accounting for the hard-core repulsion, the Thomas-Fermi kinetic energy repulsion and for contributions to the binding energy of s and d valence electrons. Lattice dynamics, molecular statics, and molecular dynamics calculations show that this model describes satisfactorily thermodynamic properties of the studied metals whereas, unlike other empirical approaches from the literature, predictions of phonon dispersion relations and of surface and point defect energetics reveal in fair good agreement with experiments. These results suggest that the present model is well adapted to large-scale simulations and whenever total energy calculations of thermodynamic properties are unfeasible

In this part of the series of two papers, we extend the theorems discussed in part I for infinite series. We then use these theorems to develop distinct novel results involving the Hurwitz zeta function, Riemann zeta function, Polylogarithm and Fibonacci numbers. In continuation, we obtain some zeros of the newly developed zeta functions and explain their behaviour using plots in complex plane. Furthermore, we provide particular cases for the theorems and corollaries which show that our results generalise the currently available functions and series such as the Riemann zeta function and the geometric series. Finally, we provide four miscellaneous examples to showcase the vast scope of the developed theorems.

Ciliopathies are genetic syndromes that link skeletal dysplasias to dysfunction of primary cilia. Primary cilia are sensory organelles synthesized by intraflagellar transport (IFT) - A and B complexes, which traffic protein cargo along a microtubular core. We have reported that deletion of IFT-A gene, Thm2, together with a null allele of its paralog, Thm1, causes a small skeleton with small mandible or micrognathia in juvenile mice. Using micro-computed tomography, here we quantify the craniofacial defects of Thm2^-/-;Thm1^aln/- triple allele mutant mice. At postnatal day 14, triple allele mutant mice exhibit micrognathia, maxillary hypoplasia, and a decreased facial angle due to shortened maxilla, premaxilla, and nasal bones, reflecting altered development of facial anterior-posterior elements. In contrast, other ciliopathy-related craniofacial defects, such as cleft lip and/or palate, hypo-/hypertelorism, broad nasal bridge, craniosynostosis, and facial asymmetry, were not observed, suggesting development of the facial transverse dimension is intact. Calvarial-derived osteoblasts of triple allele mutant mice showed reduced bone formation in vitro that was ameliorated by Hedgehog agonist, SAG. Together, these data indicate that Thm2 and Thm1 genetically interact to regulate bone formation and sculpting of the postnatal face. The triple allele mutant mice present as a novel model to study craniofacial bone development.

This venture targets giving a correspondence framework that can improve the legal framework execution and cooperation and furthermore make it simple to lead execution audits. This examination depicts restricted admittance to the right legitimate data just as admittance to courts and court administrations as the essential factors that limit admittance to equity. The basic target fundamental for accomplishing the point incorporates planning an online easy to understand data framework that would improve correspondence and association between legal professionals like legal advisors and disputants. The undertaking uses a subjective methodology that zeroed in on the substance investigation of essential information. Key members were court laborers like attorneys and agents, just as court clients like disputants. Pre-plan discoveries uncovered that current legitimate data frameworks – basically court sites – neglect to recognize their crowd by treating all court clients similarly comparative with the correspondence of lawful data. Likewise, the issue of admittance to equity includes the significant expense of equity also, restricted legal data that elevates admittance to equity. Post-plan discoveries from the online overview directed after the turn of events and execution of the IT curio uncovered a powerful and productive legitimate data framework. Exploration discoveries supported the advancement of an IT antiquity that records for huge shortcomings in current legal data frameworks, for example, their failure to pass on fitting legitimate data intelligently and productively. In outline, the investigation prescribes the reception of saw control to work fair and square of certainty of court clients and the differentiation between court clients to convey the right data to the right crowd.

In several mammalian species including humans, complex stimulation patterns such as cognitive challenge and physical exercise lead to improvements in organ function, organism health and performance, as well as possibly longer lifespans. The hypothesis is presented here that activity-dependent transcriptional programs, induced by these environmental stimuli, temporarily and lightly de-differentiate somatic cells such as neurons and muscle cells into a state that resembles functionally younger cells to allow cellular remodeling and adaptation of the organism to environmental change. This cellular adaptation program targets several process classes that are heavily implicated in aging, such as mitochondrial metabolism, cell-cell communication, intracellular signaling and epigenetic information processing and leads to functional improvements in these areas. I reverse engineer these activity-dependent gene programs, identify critical molecular nexus points such as CREB, MEF2 and cFos and speculate as to how one might leverage them to prevent and attenuate human aging-related decline of body function, enhance human performance and restore more youthful levels of function and morphology. The findings presented here can serve as a basis for the study and development of effective longevity efforts as the underlying gene programs could be used as markers for treatment success and as targets for therapy development.

Flavonoids are a biochemically diverse group of specialized metabolites in plants that are derived from phenylalanine. While the biosynthesis of the flavonoid aglycone is highly conserved across species and well characterized, numerous species-specific decoration steps and their relevance remained largely unexplored. The flavonoid biosynthesis takes place at the cytosolic site of the endoplasmatic reticulum (ER), but accumulation of various flavonoids was observed in the central vacuole. A universal explanation for the subcellular transport of flavonoids has eluded researchers for decades. Current knowledge suggests that a glutathione S-transferase-like protein (ligandin) protects anthocyanins and potentially proanthocyanidin precursors during the transport to the central vacuole. ABCC transporters and to a lower extend MATE transporters sequester anthocyanins into the vacuole. Glycosides of specific proanthocyanidin precursors are sequestered through MATE transporters. A P-ATPase in the tonoplast and potentially other proteins generate the proton gradient that is required for the MATE-mediated antiport. Vesicle-mediated transport of flavonoids from the ER to the vacuole is considered as an alternative or additional route.

This work reviews three frameworks for responding to economic disruption: risk mitigation, systemic recovery, and economic resilience. Specifically, by reviewing extant literature in economics, communication, and other disciplines, we argue that current approaches to understanding resilience in economics largely fail to address ongoing and emergent disruptions to the economic and social world. In response to these issues, we work to synthesize economic frameworks and the communication theory of resilience to forward a new way of examining the overlapping questions of economic resilience related to metatheoretical commitments, analytic contexts, and implications for theory, method, and practice.

Kynurenic acid (KYNA) is an endogenous tryptophan (Trp) metabolite known to possess neuroprotective property. KYNA plays critical roles in nociception, neurodegeneration, and neuroinflammation. A lower level of KYNA is observed in patients with neurodegenerative diseases such as Alzheimer’s and Parkinson’s diseases or psychiatric disorders such as depression and autism spectrum disorders, whereas a higher level of KYNA is associated with the pathogenesis of schizophrenia. Little is known about the optimal concentration for neuroprotection and the threshold for neurotoxicity. In this study the effects of KYNA on memory functions were investigated by passive avoidance test in mice. Six different doses of KYNA were administered intracerebroventricularly to previously trained CFLP mice and they were observed following 24 hours. High doses of KYNA (i.e., 20-40 μg/2 μl) significantly decreased the avoidance latency, whereas a low dose of KYNA (0.5 μg/2 μl) significantly elevated it compared with controls, suggesting that the low dose of KYNA enhanced memory function. Furthermore, six different receptor blockers were applied to reveal the mechanisms underlying the memory enhancement induced by KYNA. The series of tests revealed the possible involvement of the serotonergic, dopaminergic, α and β adrenergic, and opiate systems in the nootropic effect. The study confirmed that a low dose of KYNA improved a memory component of cognitive domain, which was mediated by, at least in part, four systems of neurotransmission in an animal model of learning and memory.

Industry 4.0 or the Fourth Industrial Revolution is driven by innovative technologies that have profound effects on both production systems and business models. This revolution is characterized by the addition of disruptive technologies and methods. These aspects of Industry 4.0 have a significant impact on marketing and have led to an evolution to ensure that marketing activities align with technological advancements and address consumers' current needs. The purpose of this paper is to formulate and discuss future research avenues for marketing considering the changes brought by Industry 4.0. The approach taken in the paper is to review the relevant literature and focus on the key themes most important for future research on industry 4.0 and marketing. Therefore, a systematic literature review was conducted based on SCOPUS indexing online database of scientific articles, the most important peer-reviewed peer in the academic world. The paper finds that there are a number of research avenues for marketing researchers to conduct investigations on, but the most important areas are five marketing principles in Industry 4.0: cooperation, conversation, co-creation, cognitivity, and connectivity.

We consider partition functions, in the form of state sums, and associated probabilistic measures for aperiodic substrates described by model sets and their associated tiling spaces. We propose model set tiling spaces as microscopic models for small scales in the context of quantum gravity. Model sets possess special self-similarity properties that allow us to consider implications on large and observable scales from the underlying (non-ergodic) dynamics. In particular we consider the implication of the underlying aperiodic substrate for the well known problem of time in quantum gravity, and propose a correspondence between small and large scales, the so-called ergodic correspondence, that addresses the emergence of matter properties and spacetime structure. In the process we find a possible bound in the mass spectrum of fundamental particles.

Artificial Intelligence is increasingly being used by social media platforms to tackle online hate speech. The sheer quantity of content, the speed at which is it developed and the enhanced pressure companies are facing by States to remove hate speech quickly from their platforms have led to a tricky situation. This commentary argues that automated mechanisms, which may have biased datasets and be unable to pick up on the nuances of language, should not be left unattended with hate speech as this can lead to issues of violating freedom of expression and the right to non-discrimination.

Ticks drive a wide diversity of pathogens to a great variety of hosts, including humans. We conducted a tick surveillance study in northwestern Spain between 2014 and 2019. Ticks were removed from people and were identified. Tick numbers, species, development stages, evolution over time, seasonal and geographical distribution, and epidemiological characteristics of people bitten by ticks were studied. We collected ticks from 8143 people. Nymphs of Ixodes ricinus were the most frequently collected. Rhipicephalus bursa, Rhipicephalus sanguineus sensu lato (s.l), Hyalomma marginatum, Hyalomma lusitanicum, Dermacentor marginatus, Dermacentor reticulatus and Haemaphysalis punctata were also found, with adults as the main stage. Hyalomma spp. and R. bursa have been progressively increasing over time. Although bites occurred throughout the year, the highest number of incidents were reported from April to July. The distribution patterns of the tick species were different between the north and the south of the region, which was related to cases detected in humans of the pathogens they carried. Adult men were more likely to be bitten by ticks than women. Ticks were most frequently removed from adults from the lower limbs, while for children they were mainly attached to the head. Epidemiological surveillance is essential given the increase in tick populations in recent years, mainly of species potentially carrying pathogens causing emerging diseases in Spain, such as the Crimean Congo Hemorraghic Fever (CCFH).

According to the World Health Organization (WHO), dementia is the seventh leading reason of death among all illnesses and one of the leading causes of disability among the world’s elderly people. Day by day the number of Alzheimer’s patients is raising. Considering the increasing rate and the dangers, Alzheimer’s disease should be diagnosed carefully. Machine learning is a potential technique for Alzheimer’s diagnosis but general users do not trust machine learning models due to the black-box nature. Even, some of those models do not provide the best performance because of using only neuroimaging data. To solve these issues, this paper proposes a novel explainable Alzheimer’s disease prediction model using a multimodal dataset. This approach performs a data-level fusion using clinical data, Freesurfer MRI segmentation data, and psychological data. For Alzheimer’s disease vs cognitively normal prediction, the random forest classifier provides 100% accuracy. Furthermore, Alzheimer’s disease and non-Alzheimer’s dementia should be classified properly because their symptoms are similar. To the best of our knowledge, we are the first to present a three-class classification on Alzheimer’s disease vs cognitively normal vs non-Alzheimer’s dementia and achieved 99.86% accuracy using an ensemble model. Besides, a novel Alzheimer’s patient management architecture is also proposed in this work..

Neurological / neurovascular disorders constitute the leading cause of disability and second leading cause of deaths in the world. Some of the major neurological / neurovascular disorders or diseases include stroke, Alzheimer’s disease, spinal cord injury, neonatal hypoxic-ischemic encephalopathy, etc. Their pathophysiology is considered as highly complex and has been blamed as the main obstacle in the development of any drugs for these diseases. In this review, we have described the endothelin system, their involvement in neurovascular disorders, importance of endothelin B receptors (ETBRs) as a novel potential drug target, and its agonism by IRL-1620 (INN - sovateltide), which we are developing as a drug candidate for treating the above-mentioned neurological disorders / diseases. We have highlighted the results of our pre-clinical and clinical studies related to these diseases. The phase I, safety and tolerability study of sovateltide has shown it as a highly safe and well tolerable compound at tested dosages, while pre-clinical and clinical phase II studies have demonstrated its efficacy in treating acute ischemic stroke, where it is currently being developed as a “First in Class” drug. Its testing in Alzheimer’s disease (AD), acute spinal cord injury and neonatal hypoxic-ischemic encephalopathy (HIE) is currently being carried out and are at different stages of drug development. Successful completion of these studies will prove the involvement of ETBRs signaling in neurological / neurovascular diseases and help in development of novel neurological drugs in future.

Using an original method for measuring weak radiothermal electromagnetic signals, experimental data were obtained on the analysis of dispersions of noise attractors of the radiobrightness temperature of aqueous solutions of NaCl, Al (NO₃)₃ at dilutions C1 (1:100) – C12 (12:100). The values of dispersions of noise attractors significantly differ from those for the control experiment with water. On the basis of the changed values of the noise attractors of the radiobrightness temperature, the values of the absorption coefficients of the solutions are calculated. The difference between the values of the solutions absorption coefficients and those for water is shown with a successive increase in their values with an increase in the degree of dilution. The difference between the values of the absorption coefficients of solutions of high dilutions and those for water is interpreted as a mechanism of cooperative interaction of ions with water molecules located outside the first hydration shell. A network of water molecules perturbed by ions contributes to the dielectric permittivity was obtained due to new properties at supramolecular interactions on the basis of dispersion interaction.

This paper presents some results from a computational fluid dynamics (CFD) model of a multi-megawatt crosswind kite spinning on a circular path in a straight downwind configuration. The unsteady Reynolds averaged Navier-Stokes equations closed by the k−ω SST turbulence model are solved in the three-dimensional space using ANSYS Fluent. The flow behaviour is examined at the rotation plane, and the overall (or global) induction factor is obtained by getting the weighted average of induction factors on multiple annuli over the swept area. The wake flow behaviour is also discussed in some details using velocity and pressure contour plots. In addition to the CFD model, an analytical model for calculating the average flow velocity and radii of the annular wake downstream of the kite is developed. The model is formulated based on the widely-used Jensen’s model (Technical Report Risø-M; No. 2411, 1983), which was developed for conventional wind turbines, and thus has a simple form. Expressions for the dimensionless wake flow velocity and wake radii are obtained by assuming self-similarity of flow velocity and linear wake expansion. Comparisons are made between numerical results from the analytical model and those from the CFD simulation. The level of agreement was found to be reasonably good. Such computational and analytical models are indispensable for kite farm layout design and optimization, where aerodynamic interactions between kites should be considered.

Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-term electrocardiogram (ECG) analysis algorithms could provide diagnosis suggestion even early warning for physicians, however, they are mutually-exclusive in terms of robustness, generalization and low complexity. In this study, a novel PVC recognition algorithm that combines deep learning-based heartbeat template clusterer and expert system-based heartbeat classifier is proposed. Long short-term memory-based auto-encoder (LSTM-AE) network was used to extract features from ECG heartbeats for K-means clustering. Thus, the templates were constructed and determined based on clustering results. Finally, the PVC heartbeats were recognized based on a combination of multiple rules, including template matching and rhythm characteristics. Three quantitative parameters, sensitivity (Se), positive predictive value (P+) and accuracy (ACC), were used to evaluate the performances of the proposed method on the MIT-BIH Arrhythmia database and the St. Petersburg Institute of Cardiological Technics database. Se on the two test databases was 87.51% and 87.92%, respectively; P+ was 92.47% and 93.18%, respectively; and ACC was 98.63% and 97.89%, respectively. The PVC scores on the 3rd China Physiological Signal Challenge 2020 training set and hidden test set were 36,256 and 46,706, respectively, which could rank first in the open-source codes. The results showed that the combination strategy of expert system and deep learning can provide new insights for robust and generalized PVC identification from long-term single-lead ECG recordings.

The article reports morphotectonic and petrological characteristics of magmatic systems of Permo-Triassic traps in the sedimentary cover of the Siberian Craton, Taimyr, as well as in the crystalline basement of the Mesozoic cover of the West Siberian Plate and the Kara sedimentary basin based on the relief analysis, seismotomography data, magnetic and gravitational anomalies. Four development sectors of magma-permeable zones were distinguished along the perimeter of the craton of the Anabar Shield. The western sector is characterized by an extensive stretching area, on which the lava region of volcanic ridges junction of the Tunguska syncline formed. A striking feature of subaerial volcanism is the meridional petrochemical trend of increasing the silica of basic magmas in intrusive rocks of the Siberian Craton while the volumes of embedded melts are reduced.

The spin magnetic moments and spin g factors (g_s = -2) of electron, muon and tau are explained based on the electric charges (EC) and lepton charges (LC) in terms of the three-dimensional quantized space model. The spin g factors of electron, muon and tau are g_s = -2 which is the sum of the EC g factor (g_EC = -1) and the LC g factor (g_LC = -1). The spin g factor (g_s = -2) of the electron is predicted by the Dirac’s equation. The orbit g factors of electron, muon and tau are g_L = g_EC = -1 from the EC g factor (g_EC = -1) without the contribution of the LC g factor (g_LC = -1). The spin g factors of the elementary fermions are calculated from the equation of g_s = g_EC + g_LC + g_CC where g_EC = EC/|EC|, g_LC = LC/|LC| and g_CC = CC/|CC|. For example, the spin g factors of the neutrinos and dark matters are g_s = -1. The spin g factors of the u and d quarks are g_s = 0 and g_s = -2, respectively. The g factor problem of neutrinos with the non-zero LC charges are solved by the LC Coulomb force of F_c(LC) ≈0. It is, for the first time, proposed that the binary motion (fluctuations) of the m_EC and m_LC masses for the electron, muon and tau leptons make the anomalous g factor. This binary motion could be originated from the virtual particle processes including the photons. Also, the weak force (beta) decay is closely related to the binary motion of the m_EC and m_LC for the electron, muon and tau leptons.

Charcot-Marie-Tooth (CMT) syndrome is the most common progressive human motor and sensory peripheral neuropathy. CMT type 1E is a demyelinating neuropathy affecting Schwann cells due to pmp22 mutations, modelized by Trembler-J mice. Curcumin, a natural polyphenol com-pound obtained from turmeric (Curcuma longa), exhibits dose- and time-varying antitumor, antioxidant and neuroprotective properties, however, the neurotherapeutic actions of curcumin still remain elusive. Here, we propose curcumin as a possible natural treatment capable of enhancing cellular detoxification mechanisms, resulting in an improvement of the neurodegenerative Trembler-J phenotype. Using a refined method for obtaining enriched Schwann cell cultures, we evaluated the neurotherapeutic action of low dose curcumin treatment on the PMP22 expression, and on the chaperones (HSF1 and Hsp27) and autophagy/mTOR (HDAC6 and ribosomes) pathways in Trembler-J and wild-type genotypes. In wild-type Schwann cells, the action of cur-cumin resulted in strong stimulation of the chaperone and macroautophagy pathway, whereas the modulation of ribophagy showed a mild effect. However, despite the promising neuroprotective effects for the treatment of neurological diseases, we report (demonstrate?) that the action of cur-cumin in Trembler-J Schwann cells could be impaired due to the irreversible impact of ethanol used as a common curcumin vehicle necessary for administration. These results contribute to expand our still limited understanding of PMP22 biology in neurobiology, and expose the intrinsic lability of the neurodegenerative Trembler-J genotype. Furthermore, they unravel interesting physiological mechanisms of cellular resilience relevant for to the pharmacological treatment of the neurodegenerative Tremble J phenotype with curcumin and ethanol. We conclude that the analysis of the effects of the vehicle itself is an essential and in-escapable step to comprehensibly assess the own effects and full potential of curcumin treatment for therapeutic purposes.

In this paper we consider the congruence problem that arises in the post-analysis of Bayesian network models reconstructed from different datasets. Apart from the structure, a typical network numerically encodes relationship intensities, assigning numerical score to network edges via the scoring criterion used in the reconstruction process. This scoring is rarely a directly interpretable quantity with proper units of measure and an absolute scale, and often comes short in desirable characteristics of a true metric. This leads to poor portability of edge magnitude considerations between similar networks, originating from different sources. In this work, we address this problem by estimating the effect that data-specific resolution limit has on conditional independence, as reflected by information-theoretic entropy, and by the appropriate modification of MDL score, which removes the inconsistency between the score components in both the meaning and units. We also numerically validate our findings and expose additional performance advantages obtained via this modification.

We present a summary of the main results within the Scale Invariant Vacuum (SIV) paradigm as related to the Weyl Integrable Geometry. After a brief review of the mathematical framework, we will highlight the main results related to inflation within the SIV [9], the growth of the density fluctuations [8], and the application of the SIV to scale-invariant dynamics of Galaxies, MOND, Dark Matter, and the Dwarf Spheroidals [7]. The connection of the weak-field SIV results to the un-proper time parametrization within the reparametrization paradigm is also discussed [14].

This paper is focused on understanding multiscale modelling to obtain a bridge among different time and length scales of simulation techniques. These techniques are vital as it holds the potential to understand and predict the capabilities of polymer nanocomposites (PNCs). However, an appropriate approach in controlling the interfacial interaction between nanoparticle and polymer in nanocomposites structure is still needed to develop. In this review, an initial brief introduction to various trending simulation techniques has been discussed at all three levels of scale (nm, μm, mm). Later, descriptive study on fundamental issues such as thermodynamics, kinetics, mechanical properties, and morphology has been studied deeply. The multiscale modeling bridges the gaps of simulation between the different scales of models from molecular to mesoscale levels working over the broad range of length and timescale. Through the sequential, adaptive, and concurrent approaches, we can develop a system that may comprehend multiscale mode modeling adaptive resolution approach has recently added approach the molecule of the subject can shift their position freely in the domain and through this approach and studied the Brownian motion. Co-The co-current coach is also termed as handshaking path and it is linked aiming at different scale models. Covering the rigid techniques smoothly and linking them at different scales helps in normalizing the statistical behaviour.

Recently, the use of drones/unmanned aerial vehicles (UAVs) has notably increased due to their broad commercial spread and low cost. The wide diffusion of drones increases the hazards of their misuse in illegitimate actions such as drug smuggling and terrorism. Thus, the surveillance and automated detection of drones are crucial for safeguarding restricted regions or special zones from illegal drone interventions. One of the most challenging issues in drone detection in surveillance videos is the apparent similarity of drones and birds against complex backgrounds. In this work, an automated image-based drone-detection system utilizing an advanced deep-learning-based object-detection method known as you only look once (YOLOv5) is introduced for protecting restricted regions or special zones from unlawful drone interventions. Due to the lack of sufficient data, transfer learning was utilized to pretrain the object-detection method to increase the performance. The experiments showed outstanding results, and an average precision of 94.7% was accomplished.

Externalizing behavior in its more extreme form is often considered a problem to the individual, their families, teachers and society as a whole. Several brain structures have been linked to externalizing behavior and such associations may arise if the (co)development of externalizing behavior and brain structures share the same genetic and/or environmental factor(s). We assessed externalizing behavior with the Child Behavior Checklist and Youth Self Report, and brain volumes and white matter integrity (FA and MD) with magnetic resonance imaging in the BrainSCALE cohort, consisting of twins and their older siblings from 112 families measured longitudinally at ages 10, 13, and 18 years of the twins. Genes influenced externalizing behavior and changes therein (h2 up to 88%). More pronounced externalizing behavior was associated with higher FA (observed correlation rph up to +0.20) and lower MD (rph up to –0.20); with sizeable genetic correlations (FA ra up to +0.42; MD ra up to –0.33). Cortical gray matter (CGM; rph up to –0.20) and cerebral white matter (CWM; rph up to +0.20) volume were phenotypically but not genotypically associated with externalizing behavior. These results suggest a potential mediating role for global brain structures in the display of externalizing behavior during adolescence that are both partially explained by the influence from the same genetic factor.

Recent progress in microfabrication technique allowed the rapid development of neural implants. They are getting categorized as effective tools for clinical practice, especially to treat traumatic and neurodegenerative disorders. Microelectrode arrays already have been used in numerous neural interface devices. Basically, almost all neural implants have been developed based on BCI (Brain Computer Interface) system. When BCI system falls under invasive technique, it is referred as BMI or Brain Machine Interface. BMIs hold promises for neurorehabilitation of motor and sensory function, cognitive state evaluation and treatment of neurological chaos. A directed overview of the field of neural implants is discussed in this article. The aim of this review is to give a brief introduction of neural prosthetics as well as their exciting applications in treating neurological disorders and a deep discussion on their functionality are mentioned. BCI system and their different types, their functionality, their pros and cons, how other neural implants developed, and their present status have been covered. Different possibilities and possible future of deep brain stimulation (DBS), Neuralink, motor and sensory neural prosthetics are further discussed.

What if oxygenic photosynthesis is a primordial process with roots at the origin of life? What would the impact of this change in perspective be on our understanding of the early Earth and of the emergence and diversification of life? In here, I will examine some of the historical context of the study of the evolution of photosynthesis, which led to the consolidation of the current notion that the origin of anoxygenic photosynthesis occurred before the origin of oxygenic photosynthesis. I will show with a few examples how the mainstream view on the evolution of photosynthesis traces back to Oparin’s ‘primordial soup’ scenario for the origin of life, fuelled by the century-old perception that oxygenic photosynthesis is a plant rather than a bacterial trait. However, it has become more evident than ever before that the mainstream view is not supported by the evolution of the photosystems. In other words, the origin of biological water oxidation appears to be the seed from where photosynthesis sprout. Somewhat troubling and contrary to all predictions that derive from the mainstream view, photosystem II—the water-splitting and oxygen-evolving enzyme—shows features that are better explained if photochemical reaction centres originated during the establishment of oxygenic photosynthesis. An urgent revision of the evolution of photosynthesis procured to be free from biases of interpretations and presuppositions is strongly encouraged from all angles of the Life and Earth Sciences.

Extracellular vesicles (EV) are a very heterogeneous group of cell-derived vesicles released by almost all kind of living cells. EV are involved in intercellular communication, both locally and systemically, since they induce signals and transfer their contents (proteins, lipids, RNAs) to other cells, which subsequently trigger a wide variety of biological responses in the target cells. How-ever, cell surface receptor-induced EV release is limited to cells from the immune system, includ-ing T lymphocytes. T cell receptor activation of T lymphocytes induces secretion of EV containing T cell receptor for antigen and several bioactive molecules, including proapoptotic proteins. These EV are thus specific for antigen-bearing cells, which make them ideal candidates for a cell-free, EV-dependent cancer therapy. In this review we discuss the generation of EV by T lymphocytes and some potential therapeutic approaches of these EV.

We study the brain segmentation by dividing the brain into multiple tissues. Given possible brain segmentation by deep, machine learning can be efficiently exploited to expedite the segmentation process in the clinical practice. To accomplish segmentation process, a MRI and tissues transfer using generative adversarial networks is proposed. Given the better result, we propose the transfer model using GAN. For the case of the brain tissues, white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) are segmented. Empirical results show that this proposed model significantly improved segmentation results compared to the stat-of-the-art results. Furthermore, a dice coefficient (DC) metric is used to evaluate the model performance.

Tinnitus is an auditory phantom perception in the ears or head in the absence of a corresponding external stimulus. There is currently no effective treatment available that reliably reduces tinnitus. Educative counseling is a treatment approach that aims to educate patients and inform them about possible coping strategies. For this feasability study, we implemented educational material and self-help advice in a smartphone app. Participants used the educational smartphone unsupervised during their daily routine over a period of 4 months. Comparing the tinnitus outcome measures before and after smartphone-guided treatment, we measured changes in the tinnitus-related distress, but not in tinnitus loudness. Improvements on the Tinnitus Severity numeric rating scale reached an effect size of .408, while the improvements on the THI were much smaller with an effect size of .168. Analysis on the user behavior showed that frequent and intensive use of the app is a crucial factor for treatment success: participants that used the app more often and interacted with the app intensively, reported a stronger improvement of the tinnitus. Between study allocation and final assessment, 26 of 52 participants dropped out of the study. Reasons for the dropouts and lessons for future studies are discussed in this paper.

The formulation used by most of the studies on an elastic torus are either Reissner mixed formulation or Novozhilov's complex-form one, however, for vibration and some displacement boundary related problem of a torus, those formulations face a great challenge. It is highly demanded to have a displacement-type formulation for the torus. In this paper, I will carry on my previous work [ B.H. Sun, Closed-form solution of axisymmetric slender elastic toroidal shells. J. of Engineering Mechanics, 136 (2010) 1281-1288.], and with the help of my own maple code, I am able to simulate some typical problems and free vibration of the torus. The numerical results are verified by both finite element analysis and H. Reissner's formulation. My investigations show that both deformation and stress response of an elastic torus are sensitive to the radius ratio, and suggest that the analysis of a torus should be done by using the bending theory of a shell, and also reveal that the inner torus is stronger than outer torus due to the property of their Gaussian curvature. Regarding the free vibration of a torus, our analysis indicates that both initial in u and w direction must be included otherwise will cause big errors in eigenfrequency. One of the most intestine discovery is that the crowns of a torus are the turning point of the Gaussian curvature at the crown where the mechanics' response of inner and outer torus is almost separated.

Next generation sequencing and metabolomics have become very cost and work efficient and are integrated into an ever growing number of life science research projects. Typically, well established software pipelines provide quantitative data informing about gene expression or concentrations of metabolites from the raw data. This data needs to be visualized and further analyzed in order to support scientific hypothesis building and identification of underlying biological patterns. Some tools exist, but require installation or manual programming. We developed “Gene Expression Plotter” (GXP), an RNA-Seq and Metabolomics data visualization and analysis tool entirely running in the user’s web browsers, thus not needing any custom installation, manual programming or upload of confidential data to third party servers. GXP enables the user to generate interactive plots, visually summarize genetic or metabolic responses in scientific sketches (Mapman), carry out cluster and principal component analysis, and conduct overrepresentation analyses. GXP can be used to publish research data along with interactive plots and results of analyses carried out with it. GXP is freely available on GitHub: https://github.com/usadellab/GeneExpressionPlots

Surrogate model based optimization method is widely-used to accelerate the design and optimization process~\cite{marler2004survey}. The input of regression model used in the surrogate model are numbers, which requires users to parametrize the geometries. In this paper, a new parameterization-free surrogate model is introduced and its corresponding uncertainty quantification and sensitivity analysis method are discussed. The input of new surrogate model methods is surface mesh of simulation domain. \Gls{gnns} is used to extract geometric information, and \Gls{cnns} is used to predict contours. This framework bypasses parameterization,as a consequence, uncertainties introduced by manual parameterization is reduced. However, such changes compared with conventional surrogate model methods impose great challenge on uncertainties quantification and sensitivity analysis. Uncertainties quantification in this paper means the error bar of prediction results, which is calculated by Gaussian Process Regression method in current surrogate method. In this paper, a new quantification method achieved by Kullback-Leibler divergence (KLD) is introduced. And the sensitivity analysis is conducted by Automatic Differentiation, which gives a Jacobian matrix of inputs. The method and analysis mentioned above are demonstrated by a low-pressure steam turbine rotator and its exhaust system.

Type 2 Diabetes Mellitus is being increasingly associated with dysfunction of cognition. Dementia, including vascular dementia and Alzheimer’s disease, is being recognized as comorbidities of this metabolic disorder. The progressive hallmarks of this cognitive dysfunction include mild impairment of cognition and cognitive decline. Dementia and mild impairment of cognition appear in older patients primarily. Studies on risk factors, neuropathology, and brain imaging have provided important suggestions for mechanisms that lie behind the development of dementia. It is a significant challenge to understand the disease processes related to diabetes which affect the brain and lead to dementia development. The connection between Diabetes Mellitus and dysfunction of cognition has been observed in many human and animal studies that have noted mechanisms related to Diabetes Mellitus are possibly responsible for aggravating cognitive dysfunction. This article attempts to narrate the possible association between type 2 diabetes and Dementia, reviewing studies that have noted this association in vascular dementia and Alzheimer’s disease and helping to explain the potential mechanisms behind the disease process. The Google search for ‘Diabetes Mellitus and Dementia’ was carried out. Also, the search was done using ‘Diabetes Mellitus,’ ‘Vascular Dementia,’ ‘Alzheimer’s Disease.’ The literature search was done from Google Scholar, Pubmed, Embase, ScienceDirect, and MEDLINE. Keeping in mind the increasing rate of Diabetes Mellitus, it is important to establish the type 2 diabetes effect on the brain and diseases of neurodegeneration. This narrative review aims to build awareness regarding different types of dementia and their relationship with diabetes.

Heat and power cogeneration plants based on fuel cells are interesting systems for energy- conversion at low environmental impact. Different fuel cells have been proposed, but experimental testing rigs are expensive and the development of commercial systems is time consuming if based on fully experimental activities. Furthermore, tight control of operation of fuel cells is compulsory to avoid damage, which must be based on accurate models, able to predict the cell behaviour and prevent stresses and shut-down. Some selected examples of steady state, dynamic and fluid dynamic modelling of different types of fuel cells are proposed, mainly PEMFC and SOFC type. The general ideas behind the thermodynamic, kinetic and transport description are recalled, with some examples of models derived for single cells, stacks and integrated power co-generation units.

We derive the thermal noise spectrum of the Fourier transform of the electric field operator of a given wave vector starting from the quantum-statistical definitions and relate it to the complex frequency and wave vector dependent complex conductivity in a homogeneous, isotropic system of electromagnetic interacting electrons. We analyze separately the longitudinal and transverse case with their peculiarities. The Nyquist formula for vanishing frequency and wave vector, as well as its modification for non-vanishing frequencies and wave vectors follow immediately. Furthermore we discuss also the noise of the photon occupation numbers. It is important to stress that no additional assumptions at all were used in this straightforward proof.

With the increased availability of sequence data and even of fully sequenced and assembled genomes, phylogeny estimation of very large trees (even of hundreds of thousands of sequences) is now a goal for some biologists. Yet, the construction of these phylogenies is a complex pipeline presenting analytical and computational challenges, especially when the number of sequences is very large. In the last few years, new methods have been developed that aim to enable highly accurate phylogeny estimations on these large datasets, including divide-and-conquer techniques for multiple sequence alignment and/or tree estimation, methods that can estimate species trees from multi-locus datasets while addressing heterogeneity due to biological processes (e.g., incomplete lineage sorting and gene duplication and loss), and methods to add sequences into large gene trees or species trees. Here we present some of these recent advances and discuss opportunities for future improvements.

Damage detection based on modal parameter changes becomes popular in the last decades. Nowadays are available robust and reliable mathematical relations to predict the natural frequency changes if damage parameters are known. Using these relations, it is possible to create databases containing a large variety of damage scenarios. Damage can be thus assessed by applying an inverse method. The problem is the complexity of the database, especially for structures with more cracks. In this paper, we propose two machine learning methods, namely the random forest (RF) and the artificial neural network (ANN) as search tools. The databases we developed contain damage scenarios for a prismatic cantilever beam with one crack and ideal and non-ideal boundary conditions. The crack assessment is made in two steps. First, a coarse damage location is found from the networks trained for scenarios comprising the whole beam. Afterward, the assessment is made involving a particular network trained for the segment of the beam on which the crack is previously found. Using the two machine learning methods, we succeed to estimate the crack location and severity with high accuracy for both simulation and laboratory experiments. Regarding the location of the crack, which is the main goal of the practitioners, the errors are less than 0.6%. Based on these achievements, we concluded that the damage assessment we propose, in conjunction with the machine learning methods, is robust and reliable.

We present a simple strategy to generate a family of carbon dot/iron oxide nanoparticles (C/Fe-NPs) that relies on the thermal decomposition of iron (III) acetylacetonate in the presence of a highly fluorescent carbon-rich precursor, while polyethylene glycol serves as the passivation agent. By varying the molar ratio of the reactants, a series of C/Fe-NPs have been synthesized with tuneable elemental composition in terms of C, H, O, N, Fe. The quantum yield is enhanced from 6% to 9% as the carbon content increases from 27% to 36%, while the room temperature saturation magnetization is improved from 4.1 emu/g to 17.7 emu/g as the iron content is enriched from 17 to 31%. In addition, the C/Fe-NPs show excellent antimicrobial properties, minimal cytotoxicity and demonstrate promising bioimaging capabilities, thus showing great potential for the development of advanced diagnostic tools.

Among the first discovered and most prominent cellular oncogenes is MYC, which encodes a bHLH-ZIP transcription factor (Myc) that both activates and suppresses numerous genes involved in proliferation, energy production, metabolism and translation. Myc belongs to a small group of bHLH-ZIP transcriptional regulators (the Myc Network) that includes its obligate heterodimerization partner Max and six “Mxd proteins” (Mxd1-4, Mnt and Mga) each of which heterodimerizes with Max and largely oppose Myc’s functions. More recently, a second group of bHLH-ZIP proteins (the Mlx Network) has emerged. It is comprised of the Myc-like factors ChREBP and MondoA, which, in association with the Max-like member Mlx, regulate smaller and more functionally restricted sets of target genes, some of which are shared with Myc. Opposing ChREBP and MondoA are heterodimers comprised of Mlx and Mxd1, Mxd4 and Mnt, which also structurally and operationally link the two Networks. We discuss here the functions of these “Extended Myc Network” members with particular emphasis on the roles played by Max, Mlx and Mxd proteins in suppressing normal and neoplastic growth. These roles are complex due to the temporally- and tissue-restricted expression of Extended Myc Network proteins in normal cells, their regulation of both common and unique target genes and, in some cases, their functional redundancy.

Current bridge inspection practices rely on paper-based data acquisition, digitization, and multiple conversions in between incompatible formats to facilitate data exchange. This practice is time-consuming, error-prone, cumbersome, and leads to information loss. One aim for future inspection procedures is to have a fully digitized workflow that achieves loss-free data exchange, which lowers costs and offers higher efficiency. On the one hand, existing studies proposed methods to automatize data acquisition and visualization for inspections. These studies lack an open standard to make the gathered data available for other processes. On the other hand, several studies discuss data structures for exchanging damage information through out different stakeholders. However, those studies do not cover the process of automatic data acquisition and transfer. This study focused on a framework that incorporates automatic damage data acquisition, transfer, and a damage information model for data exchange. This enables inspectors to use damage data for subsequent analyses and simulations. The proposed framework shows the potentials for a comprehensive damage information model and related (semi-)automatic data acquisition and processing.

In this work, a novel heterojunction based on ZnSnO3/ZnO nanofibers was prepared using electrospinning method. The crystal, structural and surface compositional properties of sample based on ZnSnO3 and ZnSnO3/ZnO composite nanofibers were investigated by X-ray diffractometer (XRD), Scanning electron microscope (SEM), X-ray photoelectron spectrometer (XPS) and Brunauer-Emmett-Teller (BET). Compared to pure ZnSnO3 nanofibers, the ZnSnO3/ZnO heterostructure nanofibers display high sensitivity and selectivity response with fast response towards ethanol gas at low operational temperature. The sensitivity response of sensor based on ZnSnO3/ZnO composite nanofibers were 19.6 towards 50 ppm ethanol gas at 225°C, which was about 1.5 times superior than that of pure ZnSnO3 nanofibers, which can be owed mainly to the presence of oxygen vacancies and the synergistic effect between ZnSnO3 and ZnO.

Avocado (Persea americana Mill.) plant fruits are well-known for their high nutritional value, unique test, and healthy oil. It has a history of about 10,000 years. Avocado fruit offers many health benefits, and its production is rapidly increasing. The Food and Agriculture Organization (FAO)’s recent data suggest that the Avocados produced in the world in 2019 was twice that of 2010 (3778010 tons). Avocado’s global Gross Production Value was about 5.812 billion USD in 2018, and it is likely to increase rapidly because of the increasing demand for Avocado fruits. Avocado oil is also used in the cosmetic industry because of its therapeutic properties, and it boosts the economic value of the Avocado industry. Avocado fruits have a rough green-gold skin; however, fruits are called ‘the green gold’ because of their massive global demand in the worldwide market and a lucrative business. The cultivation of Avocado has tremendous potential in increasing the rural economy, rural agriculture-based employment and reducing the poverty rate of growers. On the other hand, the Avocado industry is highly criticised because of deforestation, massive water utilisation, polluting water bodies with insecticides and fertilisers, posing a threat to other plant species, and environmental pollution. However, it doesn’t preclude the importance of Avocado. Cameroon’s average temperature is about 23 °C, which is considered optimal for Avocado propagation and commercial cultivation. Cameroon Association of Active Youths (CAMAAY) want to explore the possibilities of engaging Cameroon youths in Avocado cultivation. This review is aimed to provide an overview of Avocado. The review also highlights Avocado cultivation related issues from one health and sustainability perspective in line with the global goals.

B lymphocyte development has two DNA recombination processes: V(D)J recombination of the immunoglobulin (Igh) gene variable region and class switching of the Igh constant regions from IgM to IgG, IgA, or IgE. V(D)J recombination is required for successful maturation of B cells from pro-B to pre-B to immature-B and then to mature B cells in the bone marrow. CSR occurs outside of the bone marrow when mature B cells migrate to peripheral lymphoid organs, such as spleen and lymph nodes. Both V(D)J recombination and CSR depend on an “open chromatin” state that makes DNA accessible to specific enzymes, recombination activating gene (RAG), and activation-induced cytidine deaminase (AID). Acetyltransferases GCN5 and PCAF possess redundant functions acetylating histone H3 lysine 9 (H3K9). Here, we generated a mouse model that lacks both GCN5 and PCAF in B cells. We found that double-deficient mice possess low levels of mature B cells in the bone marrow and peripheral organs, an accumulation of pro-B cells in bone marrow, and reduced CSR levels. We conclude that both GCN5 and PCAF are required for B cell development in vivo.

Global demand for sleep-tracking wearables, or consumer sleep technologies (CSTs), is steadily increasing. CST marketing campaigns often feature a scientific component, but the scientific relevancy and monetary value of CST features within the sleep research community remains unquantified. Sleep medicine experts were recruited through social media and nonprobability sampling techniques to complete a survey identifying sleep metrics and device features that are most desirable to the scientific community. A hypothetical purchase task (HPT) estimated economic valuation for devices with different features by price. Forty-six (N=46) respondents with an average of 10±6 years’ experience conducting research in real-world settings completed the online survey. Total sleep time was ranked as the most important measure of sleep followed by objective sleep quality while sleep architecture/depth and diagnostic information were ranked as least important. Experts preferred wrist-worn devices that could reliably determine sleep episodes as short as 20 minutes. Economic value was greater for hypothetical devices with longer battery life. These data set a precedent to determine how scientific relevance of a product impacts the potential market value of a CST device. This is the first known attempt to establish consensus opinion or economic valuation for scientifically-desirable CST features and metrics using expert elicitation.

In recent years, news media has been greatly disrupted by the potential of technologically driven approaches in the creation, production, and distribution of news products and services. Artificial intelligence (AI) has emerged from the realm of science fiction and has become a very real tool that can aid society in addressing many issues, including the challenges faced by the news industry. The ubiquity of computing has become apparent and has demonstrated the different approaches that can be achieved using AI. We analyzed the news industry’s AI adoption based on the seven subfields of AI: (i) machine learning; (ii) computer vision (CV); (iii) speech recognition; (iv) natural language processing (NLP); (v) planning, scheduling, and optimization; (vi) expert systems; and (vii) robotics. Our findings suggest that three subfields are being developed more in the news media: machine learning, computer vision, as well as planning, scheduling, and optimization. Other areas have not been fully deployed in the journalistic field. Most AI news projects rely on funds from tech companies such as Google. This limits AI’s potential to a small number of players in the news industry. We make conclusions by providing examples of how these subfields are being developed in journalism and present an agenda for future research.

Vocational interest inventories are widely used by career counselors to help individuals to make career choices. The most common approach to assess vocational interests is based on verbal or textual stimuli. However, some of them are based on pictorials to overcome reading limits and provide additional information about the working environment and the activities related to a particular job. This article aims to present two studies on the development and first validation of the Multilingual Iconographic Professional Interest Inventory (MIPII) on two samples, one composed of 792 high-school students, and one composed of 366 middle school students. The inventory aimed to assesses the vocational interests of people over 19 areas by illustrations representing 95 jobs, five for each one, combined with their title in six different languages (Arabic, German, English, Spanish, French and Italian in this order). Both illustrations and titles are provided separately in the male and female version on the same page.

Deep eutectic solvents (DESs) are one of the most rapidly evolving types of solvents, appearing in a broad range of applications such as nanotechnology, electrochemistry, biomass transformation, pharmaceuticals, membrane technology, biocomposite development, modern 3D-printing, and many others. The range of their applicability continues to expand, which demands the development of new DESs with improved properties. To do so requires an understanding of the fundamental relationship between the structure and properties of DESs. Computer simulation and machine learning techniques provide a fruitful approach as they can provide predictions, reveal physical mechanisms and readily be linked to experiments. This review is devoted to the computational research of DESs and describes technical features of DES simulations and the corresponding perspectives on various DES applications. The aim is to demonstrate the current frontiers of computational research of DESs and discuss future perspectives.

We all have in mind Einstein's famous thought experiment in the elevator where we observe the free fall of a body, and then the trajectory of a light ray. Simply here, in addition to the qualitative aspect, we carry out the exact calculation, and for the first time the worldlines equations are given. We consider a uniformly accelerated reference frame in rectilinear translation, and we show that the trajectories of the particles are semi-ellipses with the center on the event horizon. The frame of reference is non-inertial, the space-time is flat, and the computations are performed within the framework of special relativity. Some experimental consequences are discussed, especially the experiment with the accelerated Michelson-Morley interferometer is solved, and we described an experiment where a new relativistic paradox appears --- a particle of matter seems to go faster than light. The differences, compared to the classical case, are important at large scale and close to the horizon, but they are small in the lift where the interest is above all theoretical. The concepts of metric, coordinated velocity and horizon are discussed, and the analogy with the black hole is made.

Scientists and astronomers have attached Scientists and astronomers have attached great importance to the task of discovering new exoplanets, even more so if they are in the habitable zone. To date, more than 4300 exoplanets have been confirmed by NASA, using various discovery techniques, including planetary transits, in addition to the use of various databases provided by space and ground-based telescopes. This article proposes the development of a deep learning system for detecting planetary transits in Kepler Telescope lightcurves. The approach is based on related work from the literature and enhanced to validation with real lightcurves. A CNN classification model is trained from a mixture of real and synthetic data, and validated only with real data and different from those used in the training stage. The best ratio of synthetic data is determined by the perform of an optimisation technique and a sensitivity analysis. The precision, accuracy and true positive rate of the best model obtained are determined and compared with other similar works. The results demonstrate that the use of synthetic data on the training stage can improve the transit detection performance on real light curves.

Tumor necrosis factor receptor 2 or TNFR2 is considered as an appealing target protein due its limited frequency to Tregs which are highly immunosuppressive and its presence on human malignancies. Numerous studies have revealed that TNFR2 is primarily found on MDSCs (myeloid derived suppressor cells) and CD⁺Foxp3⁺ regulatory T cell (Tregs). It has a great importance in the proliferation and functional activity of Tregs and MDSCs. To treat malignancies and diseases like autoimmune disorder, the suppressor activity of TNFR2 must be eliminated by downregulation or upregulation. Therefore, at the molecular level, advances in comprehension of TNFR2's complex structure and its binding to TNF have opened the door to structure-guided drug development. Two key obstacles of cancer treatment are the dearth of Treg-specific inhibitors and the lack of widely applicable ways to directly target tumors via frequently expressed surface oncogenes. Many researchers have discovered potential antagonist and agonist of TNFR2 which were successful in the inhibition of Tregs proliferation, reduction of soluble TNFR2 secretion from normal cells and in the expansion of T effector cells. The representation of the data in the following review article elucidates the clinically administrated TNFR2 antagonist and agonist in the treatment of cancers.

Background: The concept of Quality of Work Life (QWL) has been built multidimensionally through social reproduction; it is impacted by the perceptions of each individual and by the relationship between workers and the work environment. Objective: to analyze the Work Process and QWL of assisting nurses in public health. Methods: Research in a critical paradigm, descriptive, exploratory with a qualitative approach. The population corresponds to Nurses who work in care work. Semi-structured guiding questions were applied and were analyzed with content analysis. Results: seven participants declared female; all Chilean; seven are young adults; six singles; only one has children, and one has a person dependent on her care; six are heads of household, and five receive help with housework. All have a nursing degree, five have a diploma, but none have a postgraduate. Work Process has three subcategories: work object, instrument, organization, and work conditions; the QWL category has six subcategories: definition and perception of QWL, QWL potentiating factors, QWL exhausting factors, QWL improvement strategies, the emotional burden associated with QWL, and Health problems. Conclusions: In this way, the lifestyle built by the assistance in the health area has repercussions on the quality of life and health in general.

Cooperative learning (CL) is a teaching and learning pedagogy that has been used widely in school but there is limited information on instructors’ perceptions of CL nor observations of how CL is implemented in higher education settings. In this study, we investigated the ad-vantages and challenges of embedding CL into instructors’ teaching in Iran and Australia. Data were collected through interview and observation. Results showed that despite challenges such as the lack of familiarity of CL and how it can be implemented in university curricula, issues associated with assessment, and time constraints, CL created an interactive, pleasant and safe environment for deep learning in both countries. The findings showed that there were challenges in Iran such as a tendency to use traditional approaches to teaching, insufficient understanding of how to establish teamwork, and a lack of up-to-date teaching resources. In Australia, changing courses, working with external students, catering for individual differences, and building positive relationships were some of the challenges of implementing CL. We believe that these challenges can be overcome if university instructors are prepared to address them.

We explore the deflection angle in the framework of improved Schwarzschild Black hole utilizing the most advance geometrical path adopted by Gibbon-Werner. To investigate deflection angle of the photon ray by weak gravitational lensing for this black hole, we derive the optical curvature and perform the application of Gauss-Bonnet theorem on the optical metric. Moreover, we study the impacts of the plasma medium in context of the weak gravitational lensing in relate to this black hole. Further, we also study the graphical analysis of the deflection angle in both the plasma and non-plasma mediums. Also, we find the rigorous bound base upon the greybody factor for improved Schwarzschild black hole. A while later, we contrast our conclusions about deflection angle with the deflection angles of Schwarzschild black hole within plasma and non-plasma mediums.

MRI shows in-vivo the three archetypal patterns of CNS volume loss underlying progressive ataxias, namely spinal atrophy (SA), cortical cerebellar atrophy (CCA) and olivopontocerebellar atrophy (OPCA). The MRI-based CNS atrophy pattern was reviewed in 128 progressive ataxias. A CNS atrophy pattern was identified in 91 conditions: SA in Freidreich’s ataxia, CCA in 5 acquired and 72 (24 dominant, 47 recessive,1 X-linked) inherited ataxias, OPCA in Multi-System Atrophy and 12 (9 dominant, 2 recessive,1 X-linked) inherited ataxias. The MRI-based CNS atrophy pattern may be useful for genetic assessment, identification of shared cellular targets, and repurposing therapies or enlargement of drugs indications in progressive ataxias.

The International System of Units (SI) was adopted in 1960 as a universal measuring system to be used for all areas of science. Sports Science papers have shown lots of inaccurate and inappropriate terms for quantification of athletes’ performance and the psychobiological responses to exercise (e.g., internal load). In biomechanics, external and internal loads are forces acting externally and internally, inducing stress and strain in the biological tissues. Therefore, the current review present simple proposals to correct the inappropriate terms: 1) do not use the term external load when referring to the assessment of exercise time, distance, displacement, speed, velocity, acceleration, torque, work, power, impulse, etc.; 2) do not use the term internal load when referring to the assessment of psychobiological stress markers (i.e., session rating of perceived exertion, heart rate, blood lactate, oxygen consumption, etc.); 3) do not use the term impulse when expressing other calculus than integrating force with respect to time, and neither strain, when expressing other phenomena than the body deformation. Instead, the term exercise intensity is universal and can be used to describe all forms of exercise. Finally, duration should precisely be described according to physical quantities (e.g., time, distance, displacement, speed, velocity, acceleration, force, torque, work, power, impulse, etc.) and the units accomplish by use of the SI. These simple quantifications can be performed for the exercises, sessions, microcycles, mesocycles and macrocycles of the athletes. Such standardization will provide a consistent and clear communication among sports scientists and all areas of science.

Classical methods for inverse problems are mainly based on regularization theory. In particular those which are based on optimization of a criterion with two parts: a data-model matching and a regularization term. Different choices for these two terms and great number of optimization algorithms have been proposed. When these two terms are distance or divergence measures, they can have a Bayesian Maximum A Posteriori (MAP) interpretation where these two terms correspond, respectively, to the likelihood and prior probability models.

This paper extends the application of the ROTASE model for the formation of spiral arms of disc galaxies, questions and confusions from readers about this model are addressed. The optical trail effect behind the spiral arm rotation is the natural consequence of the model. The morphologies of ring-galaxies are classified into four categories: type I: single ring; type II: 8-shaped double ring; type III: 8-shaped double ring wrapped by a larger outer ring; type IV: single ring without spiral and bar. All four types of ring galaxies can be described by the ROTASE model. The ROTASE model predicts that the false impression of spiral arm rotating ahead of the galactic bar in the galaxy MCG+00-04-051 will change with time, it will look like a normal galaxy with about 30° to 40° bar rotation in the future and the galactic bar ends will look like rotating ahead of the spiral arms with further 10 ° to 15 °bar rotation. The formation of one arm galaxies is due to X-matter at one side of supermassive black hole is much stronger than other side. More evidence is found to support the explanation of the formation and the evolution of the Hoag’s object. The possible evolution of spiral pattern of galaxies is illustrated by UGC 6093. The winding of the Milky Way could be tighter in the future based on the ROTASE model.

Defect detection and identification from fruits and vegetables are particularly challenging for Indian agriculture. Defect Detection is a process to identify the defects or damages in vegetables and fruits, based on the shapes, colors and textures. The local market finds it difficult to cope with the defects and other infections in fruits and vegetables as quality evaluations and classification of vegetables and fruits have become tedious process. Recently, several approaches based on Image processing, Machine Learning and Artificial Intelligence methods have been proposed for the purpose of defect detection. On the basis of classifying the types of defects, related pathogens, and physical and morphological characteristics descriptors, we review the different approaches based on a corpus of 57 articles between 2016 and 2021. In the process of describing the defect analysis, steps from the target articles, algorithms, and methods including qualitative and quantitative evaluation are mainly summarized. The aim of this current review work is to present-day novel images and collects recent defective area calculation methods to detect surface defects of fruits and vegetables using RGB images and to classify whether the fruit is defected or fresh. A rigorous evaluation of many new algorithms provided for quality assurance by researcher’s probes of vegetables and fruits have been conducted in this work. This review work conveys that using the recent identification features will help to decrease the disadvantages in fruit storeroom owing to storage of the affected vegetables and fruits, ie. Preventing the spread of defects and other infections from the infected fruits and vegetables to the fresh ones.

Reservoir computing is a machine learning method that uses the response of a dynamical system to a certain input in order to solve a task. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible.

The data-oriented paradigm has proven to be fundamental for the technological transformation process that characterizes Industry 4.0 (I4.0) so that Big Data & Analytics is considered a technological pillar of this process. The literature reports a series of system architecture proposals that seek to implement the so-called Smart Factory, which is primarily data-driven. Many of these proposals treat data storage solutions as mere entities that support the architecture's functionalities. However, choosing which logical data model to use can significantly affect the performance of the architecture. This work identifies the advantages and disadvantages of relational (SQL) and non-relational (NoSQL) data models for I4.0, taking into account the nature of the data in this process. The characterization of data in the context of I4.0 is based on the five dimensions of Big Data and a standardized format for representing information of assets in the virtual world, the Asset Administration Shell. This work allows identifying appropriate transactional properties and logical data models according to the volume, variety, velocity, veracity, and value of the data. In this way, it is possible to describe the suitability of SQL and NoSQL databases for different scenarios within I4.0.

Optical properties of the cornea are responsible for correct vision, ultrastructure allows optical transparency and biomechanical properties governs the shape, elasticity or stiffness of the cor-nea affecting ocular integrity and intraocular pressure. Therefore, optical aberrations, corneal transparency, structure and biomechanics play a fundamental role in the optical quality of hu-man vision, ocular health and refractive surgery outcomes. However, the convergence of those properties is not yet reported at macroscopic scale within the hierarchical structure of the cornea. This work explores the relationships between biomechanics, structure and optical properties (corneal aberrations and optical density) at macrostructural level of the cornea through dual Placido-Scheimpflug imaging and air-puff tonometry systems in a healthy young adult popula-tion. Results showed convergence between optical transparency, corneal macrostructure and biomechanics.

The large-scale processing of Cassava (Manihot esculenta Crantz.) generates significant quantities of solid wastes annually. Cassava peels (CP) account for 5 wt.% - 30 wt.% of wastes from the processing of cassava tubers. The poor disposal and management of CP pose risks to human health, safety and the environment. Therefore, there is an urgent need to identify and examine low cost, socially acceptable and environmentally friendly strategies to mitigate the immediate and long terms disposal and management challenges. Lack of such measures results in the accumulation of CP wastes, which are currently buried, combusted, or dumped in open fields. Therefore, this paper reviewed the potential routes for the biochemical, thermochemical, and plasma valorisation of CP. The literature reviewed revealed that biochemical technologies such as anaerobic digestion (AD) and fermentation are the most widely utilised approaches currently adopted for CP valorisation. AD produces biogas (methane 50-72 vol. % and carbon dioxide 25-45 vol. %), whereas fermentation yields bioethanol. However, the numerous challenges such as substrate-induced inhibition, associated with the biochemical processes hamper microbial degradation, methane formation, and process efficiency. Furthermore, the processes generate secondary wastes or digestate/sludge, which requires additional processing before disposal. Therefore, innovative thermal, thermochemical, and plasma technologies were proposed to valorise CP into syngas, biofuels, bioenergy, biochemicals, and fertilizers, among others. However, the waste products of fermentation cannot be effectively utilised as bio-fertilizers, whereas bioethanol causes corrosion in engines. Overall, the biochemical, thermal, thermochemical and plasma technologies can effectively valorise CP for effective net energy generation.

Cannabidiol (CBD) is a non-intoxicating compound extracted from Cannabis sativa, showing antidepressant-like effects in different rodent models. However, inconsistent results have been described depending on the species and the strain used to assess depressive-like behaviour. Moreover, only a few studies have investigated the effect of CBD in female rodents. Therefore, we aimed to i) investigate the effects of CBD in two different strains of mice (Swiss and C57BL/6) and in a rat model of depression based on selective breeding (Flinders Sensitive and Resistant Lines, FSL and FRL) subjected to tests predictive of antidepressant-like effects; and ii) investigate the influence of sex in the effects of CBD in both mice and rats. CBD induced an antidepressant-like effect in male Swiss but not in female Swiss or C57BL/6 mice in the tail suspension test (TST). In male FSL rats, CBD produced an antidepressant-like effect one-hour post-injection. However, in female FSL, CBD induced a bimodal effect, increasing the immobility time at one hour and decreasing it at two hours. Ketamine produced an antidepressant-like effect in male and female FSL rats at different doses. In conclusion, strain, sex, and administration time affect CBD's behavioural response to rodents exposed to tests predictive of antidepressant effects.

The global trend towards increased demand for organic food, greener environments, and the integration of biological control agents into pest management strategies has greatly enhanced the need for biological pesticides (biopesticides). Biopesticides are generally environmentally friendly and are made from micro-organisms or other natural substances. Despite their great potential, relatively few have been registered and commercialised in Nigeria compared to other African countries such as South Africa and Kenya. Biological active agents are so diverse such that ap-plying the same safety standards or environmental conditions to all of them is almost impossible. A review of risk assessment processes and comparative assessments of Nigeria's biopesticide regulations with other developing African countries and developed regions was conducted. Prolonged field testing, lack of bridged risk assessments and technical checklists have been identified as key factors hampering the timely development and commercialisation of biopesti-cides in Nigeria. Recommendations on necessary changes to the existing Nigeria biopesticide regulations have been made. Risk assessment matrices for microbial and biochemical biopesti-cides and a scientific/technical checklist have also been developed. Harmonisation and data ex-change among other countries in the region will also enhance the advancement of scientific and technical knowledge for sustainable regulation and cross-border trade.

In this article, we calculate the deflection angle of tidal charged black hole (TCBH) in weak field limits. First we obtain the Gaussian optical curvature and then apply the Gauss-Bonnet theorem on it. With the help of Gibbons-Werner method, we are able to calculate the light's deflection angle by TCBH in weak field limits. After calculating the deflection angle of light, we check the graphical behavior of TCBH. Moreover, we further find the light's deflection angle in the presence of plasma medium and also check the graphical behavior in the presence of plasma medium. Moreover, we investigate the shadow of TCBH.For calculating the shadow, we first find the null geodesics around the TCBH and then find its shadow radius. We also obtain TCBH's shadow in the plasma medium. Hence, we discuss the shadow of the TCBH using the $M87^{*}$ parameters announced by the Event Horizon Telescope.

In the last four years, the evolution and adoption of blockchain and, more generally, distributed ledger systems have shown the affirmation of many concepts and models with significant differences in system governance and suitable applications. This work aims to update the critical analysis of blockchain technologies carried out by our previous contribution to this journal, extending the focus to distributed ledger components and systems. Starting from the topical concept of decentralization, we introduce concepts and building blocks currently adopted in the available systems centering on their functional aspects and impact on possible applications. We present some conceptual framing tools helpful in the application context, and we will propose the concept of process authenticity, which we will discuss through two use cases: blockchain document dematerialization and e-voting.

We investigate the power of time series analysis based on a variety of information-theoretic approaches from statistics (AIC, BIC) and machine learning (Minimum Message Length) - and we then compare their efficacy with traditional time series model and with hybrids involving deep learning. More specifically, we develop AIC, BIC and Minimum Message Length (MML) ARMA (autoregressive moving average) time series models - with this Bayesian information-theoretic MML ARMA modelling already being new work. We then study deep learning based algorithms in time series forecasting, using Long Short Term Memory (LSTM), and we then combine this with the ARMA modelling to produce a hybrid ARMA-LSTM prediction. Part of the purpose of the use of LSTM is to seek capture any hidden information in the residuals left from the traditional ARMA model. We show that MML not only outperforms earlier statistical approaches to ARMA modelling, but we further show that the hybrid MML ARMA-LSTM models outperform both ARMA models and LSTM models.

A growing body of evidences is showing that dysbiotic gut microbiota may correlate with a wide range of disorders; hence, the clinical use of microbiota maps and fecal microbiota transplantation (FMT) can be exploited in the clinic of some infectious diseases. Through direct or indirect ecological and functional competition, FMT may stimulate decolonization of pathogens or opportunistic pathogens, modulating immune response and colonic inflammation, and restoring intestinal homeostasis, with reduction of host damage. Herein, we discuss how diagnostic parasitology may contribute to design clinical metagenomics pipelines and FMT programs, especially in pediatric subjects. The consequences of a more specialized diagnostics in the context of gut microbiota communities may improve the clinical parasitology and extend its applications to the prevention and treatment of several communicable and even noncommunicable disorders.

An entropic origin of gravity is re-visited. Isothermal self-gravitating cloud seen as an ideal gas is analyzed. Gravitational attraction within the isothermal cloud in equilibrium is balanced by the pressure, which is of a pure entropic nature. The notion of the Jeans entropy of the cloud corresponding to the entropy of the self-gravitating cloud in mechanical and thermal equilibrium is introduced. Balance of the gravitational compression and the entropic repulsion yields the scaling relation hinting to the entropic origin of the gravitational force. The analysis of the Jeans instability enables elimination of the “holographic screen” or “holographic principle” necessary for grounding of the entropic origin of gravity.

A non-negligible fraction of quasars are red at optical wavelengths, indicating (in the vast majority of cases) that the accretion disc is obscured by a column of dust which extinguishes the shorter-wavelength blue emission. In this paper we summarise recent work by our group, where we find fundamental differences in the radio properties of SDSS optically selected red quasars. We also present new analyses, using a consistent colour-selected quasar parent sample matched to four radio surveys (FIRST, VLA Stripe 82, VLA COSMOS 3 GHz and LoTSS DR1) across a frequency range 150 MHz-3 GHz and four orders of magnitude in radio flux. We show this enhancement is driven by systems with small-scale radio emission (∼kpc) and peaks around the radio-quiet threshold (defined as the ratio of 1.4 GHz luminosity to 6μm luminosity) across the four radio samples. Exploring the potential mechanisms behind this enhancement, we rule out star-formation and propose either small-scale jets or dusty winds interacting with the interstellar medium; this will be tested in detail using new multi-band uGMRT data. Overall our results cannot be explained with a simple viewing angle hypothesis, and so may point towards red quasars representing a key phase in the evolution of galaxies.

According to global data, there is a male reproductive potential decrease. Pathogenesis of male infertility often is associated with autoimmunity towards sperm antigens essential for fertilization. Antisperm autoantibodies (ASAs) have immobilizing and cytotoxic properties, impairing spermatogenesis, causing sperm agglutination, altering spermatozoa motility and acrosomal reaction, thus preventing ovum fertilization. Infertility diagnosis requires mandatory check for the ASAs. The concept of blood-testis barrier currently is re-formulated with emphasis of informational paracrine and juxtacrine effects, rather than simple anatomical separation. Aetiology of male infertility includes both autoimmune and non-autoimmune diseases, but equally develops through autoimmune links of pathogenesis. Varicocele commonly leads to infertility due to testicular ischemic damage, venous stasis, local hyperthermia, and hypoandrogenism. However, varicocelectomy can alter blood-testis barrier facilitating ASAs production as well. There are contradictory data on the role of ASAs in pathogenesis of varicocele-related infertility. Infection and inflammation both promote ASAs production due to “danger concept” mechanisms and because of antigen mimicry. Systemic pro-autoimmune influences like hyperprolactinemia, hypoandrogenism and hypothyroidism also facilitate ASAs production. Diagnostic value of various ASAs was not yet clearly attributed, and their cut-levels not agreed neither in sera nor in ejaculate. The assessment of the autoimmunity role in pathogenesis of male infertility is ambiguous.

This study aims to identify benefits and barriers to distance education, particularly from the perspective of teachers in Saudi Arabia. As the applied data collection tool, a questionnaire was distributed to the general education teachers in three districts. The sample size of the study was 1076 teachers. The results revealed that despite several benefits gained from distance learning, there are also some barriers. Teachers found that the most important advantage in distance learning is the acquisition of technical skills during the online teaching processes, they learn more and use digital education platforms, they have sufficient time to prepare the scientific content, they were able to provide adequate technical solutions for their courses, and they have the opportunity to use multiple media to deliver their courses. With the introduction of distance learning, teachers have explored new ways to deliver course contents to students. It has fostered better ways to provide more interactive real-time and on-demand teaching and learning using modern technology, thus, helping teachers become familiar with the use of electronic resources. It seems that teachers invest in technical methods to enhance students’ performance. Also, teachers reported some obstacles that they face during remote teaching. Most of these problems are connection problems applied with devices and the internet, lack of students’ motivation to learn in distance, problems associated with urban learners.

This research contributes to the detailed discussion about the approach to secure, hygienic water and cleanliness in Uganda and its pastoral regions. The relationship between the sanitation and clean water access with destitution is also discussed. Although this document is not leading towards the policy recommendation but it is an overall idea of how Uganda progressed because of the provisions adopted by the government, local and international organizations, and NGOs, and how the country lacked before these steps taken. Most of the data mentioned is taken from the house surveys of a decade 2002-2013 alongside the qualitative data. Literature review is also considered and is divided in two sections: first included researches related to water accessibility and usage, while the second section included researches related to work done and progress for diarrheal diseases and sanitation. Afterwards, methodologies were discussed where, firstly, trends and then the limitations in access to the basic necessities of life that is clean water and hygiene are mentioned. At last, the implementations and how they affected the rural Ugandans was discussed.

Based on an ethnozoological study carried out from December 2018 to November 2019, this paper attempts to document the usage of squirrel species for various purposes by the Adi, Idu Mishmi, Miju Mishmi, Tangsa, Chakma and Monpa tribe living near the villages adjoining the protected areas located in the districts of East Siang, Lower Dibang Valley, Dibang Valley, Lohit, Changlang and West Kameng of Arunachal Pradesh, India. The paper also describes the use of certain squirrel species as a part of their traditional culture and lists their names in English, Vernacular and Latin names. The study has led to an understanding that eight squirrel species are used by the tribes for cultural, food and medicinal purposes. This work contributes to ethnozoological research by describing a knowledge system of squirrel species use and the association of squirrel species with the cultural believes of the tribes.

Brazil chicken production is around 13 million tons and about a third is exported to over 150 countries, placing Brazil as the world largest chicken meat producer, and therefore it is crucial to follow the legislation of all importer markets. This study conducted a survey by chance in 45 meat industries able to export. Therefore, 2580 chicken meat samples were collected and submitted to 11 analyte extraction and chromatographic verification of compliance in an accredited laboratory. Ten chemical residues (amoxicillin, bacitracin, colistin, dinitolmide + zoalene, spectinomycin, roxarsone, tiamulin, tylosin, trenbolone acetate and virginiamycin) were investigated in chicken meat and one (halofuginone hydrobromide) in chicken liver. The results showed that no compound exceeded the maximum residue limits established by seven legislations. All residue concentrations found were below the method quantification limit, thereby confirming the capability of Brazilian chicken meat industries in complying to foreign markets.

This study has investigated the effect of nutrition labeling on consumer guilt, in situations where consumers ingest products that have an unhealthy image. The first investigation has examined the effects of types of nutrition labeling (adding healthy ingredients/reducing unhealthy ingredients). The second investigation has examined the influence of two goal-activation types(utilitarian and hedonic) and nutrition labeling on consumer guilt. The findings reveal that the utilitarian-goal-activation group felt less guilty about consuming a product with a reduced unhealthy ingredient than they did about consuming a product with an added healthy ingredient. In addition, there were interactions between the two variables, since consumer goal activation had more effect on consumer guilt about products with reduced unhealthy ingredients than on products with added healthy ingredients. Based on these results, the academic and practical implications of the present study are discussed. and proposes several directions for future research.

Prominent among religions that claim direct or indirect lineage to Abraham are Judaism, Christianity and Islam. The three religions are known as monotheistic. Although, their interpretations of monotheism are closely similar, they have salient differences. Such differences have spurred unhealthy arguments, rivalry and hatred between some Christians and Muslims. Thus, this paper highlights some of the basic differences between Islam and Christianity traceable to scriptural differences and interpretations and the causes of disagreement between Christianity and Islam with a view to offering recommendations that would foster goodwill and engender better relationship between Christians and Muslims. The paper adopted a comparative and analytical system of research with library information and experiences as the major data gathering tools. The study finds that misinformation, ignorance, deliberate distortions, media irresponsibility among others is the major causes of disharmony among Christians and Muslims. The paper recommends mutual love and respect, constant dialogue, education and other factors that would improve the attitude of Christians and Muslims towards one another.

The relevance of bacteria to subjective experiences or qualia is under-appreciated. Here, I make four proposals. Firstly, living systems traverse sequences of active states that determine their behaviour; these states result from competitive coherence, which depends on a connectivity-based competition between a Next process and a Now process whereby elements in the active state at time n+1 are chosen between the elements in the active state at time n and those elements in the developing n+1 state. Secondly, bacteria should help us link the mental to the physical world given that bacteria were here first, are highly complex, influence animal behaviour and dominate the Earth. Thirdly, the operation of competitive coherence to generate active states in bacteria, brains and other living systems is inseparable from qualia. Fourthly, these qualia become particularly important to the generation of active states in the highest levels of living systems, namely, the ecosystem and planetary levels.

The production and consumption of nuts are increasing in the world due to strong economic returns and the nutritional value of their products. With the increasing role and importance given to nuts (i.e., walnuts, hazelnut, pistachio, pecan, almond) in a balanced and healthy diet and in the prevention of various diseases, breeding of the nuts species has also been stepped up. Most recent fruit breeding programs have focused on scion genetic improvement. However, the use of locally adapted grafted rootstocks also enhanced the productivity and quality of tree fruit crops. Grafting is an ancient horticultural practice use in nut crops to manipulate scion phenotype and productivity and overcome biotic and abiotic stresses. There are complex rootstock breeding objectives and physiological and molecular aspects of rootstock–scion interactions in nut crops. In this review, we provide an overview of these, considering the mechanisms involved in nutrient and water uptake, regulation of phytohormones, and rootstock influences on the scion molecular processes, including long-distance gene silencing and trans-grafting. Understanding the mechanisms resulting from rootstock × scion × environmental interactions will contribute to developing new rootstocks with resilience in the face of climate change, but also of the multitude of diseases and pests and of the possible increase of their aggressiveness. They will also have to offer the premises of economic production, respectively yield and the quality, according to multiple destinations of nuts in the current consumption and food industry, but also the increasing exigencies of the consumer market and the profile industry.

Appling the controlling relative permittivity and permeability in the equations for the gravitational-magnetic-electric field interaction, a very large variation of the gravitational acceleration of the Earth by electric/magnetic field could be arrived at. This conclusion may be supported by some of the experiments for the gravitational effect of superconductivity.

Based on the 2014-2015 China Education Panel Survey (CEPS) and using the propensity score matching method, we studied the causal relationship between physical exercise and prosocial behavior of junior middle school students in China. Ordinary least squares regression and propensity score matching estimation results showed that participation in physical exercise significantly increases students’ prosocial behavior by more than 0.2 standard points. The results of this study were tested and found to be robust.

MzSTools is a plugin for QGIS developed by the Institute of Environmental Geology and Geoengineering of the National Research Council (CNR-IGAG). The plugin has been designed as a set of practical and easy-to-use tools to carry out seismic microzonation (SM) studies, by producing standard compliant geographic database and maps, thus making them accurate, homogeneous and uniform for all municipalities in Italy. A geodatabase based on SQLite/SpatiaLite Relational Database Management System (RDBMS) has been designed to collect and store data related to elements such as: geognostic surveys; bedrocks and cover terrains; superficial and buried geomorphological elements; tectonic-structural elements; elements of geological instability such as landslide zones, liquefaction zones and zones affected by active and capable faults; homogeneous microzones in seismic perspective, microzones characterized by a seismic amplification factor. MzSTools assembles in a single software environment a set of useful tools in a configurable QGIS project template, comprising layers, symbol libraries, cartographic styles and print layouts for the SM maps. The plugin is open source and hosted on the GitHub platform, and available via the official QGIS plugins repository (https://plugins.qgis.org/plugins/MzSTools/).

It was observed that a cluster of ordered genes (Hox1, Hox2, Hox3,…) in the genome are activated in the ontogenetic units (1, 2, 3,…) of an embryo along the Anterior/Posterior axis following the same order of the Hox genes. This Spatial Collinearity (SC) is very strange since it correlates events of very different spatial dimensions. It was later observed in vertebrates, that, in the above ordering, first is Hox1expressed in ontogenetic unit 1, followed later by Hox2 in unit 2, and even later Hox3 in unit 3….This temporal collinearity (TC) is an enigma and even to-day is explored in depth. In 1999 T. Kondo and D. Duboule, after posterior upstream extended DNA excisions , concluded that the Hox cluster behaves ‘as if’ TC disappears. Here the consideration of TC really disappearing is taken face value and its repercussions are analyzed. Furthermore, an experiment is proposed to test TC disappearance. An outcome of this experiment could be the reappearance (partial or total) of TC.

In this paper, we present the idea of Self Supervised learning on the Shape Completion and Classification of point clouds. Most 3D shape completion pipelines utilize autoencoders to extract features from point clouds used in downstream tasks such as Classification, Segmentation, Detection, and other related applications. Our idea is to add Contrastive Learning into Auto-Encoders to learn both global and local feature representations of point clouds. We use a combination of Triplet Loss and Chamfer distance to learn global and local feature representations. To evaluate the performance of embeddings for Classification, we utilize the PointNet classifier. We also extend the number of classes to evaluate our model from 4 to 10 to show the generalization ability of learned features. Based on our results, embedding generated from the Contrastive autoencoder enhances Shape Completion and Classification performance from 84.2% to 84.9% of point clouds achieving the state-of-the-art results with 10 classes.

As the Universe is dominated by of baryons, there must have been a process in early Universe creating such a asymmetry via baryon number violation. However, until recently all the confirmed experimental data indicate that lepton and baryon number are conserved in agreement with Standard Model of quarks and leptons. This paper proposes an alternative path of baryogenesis. Once the over abundant magnetic monopoles predicted by grand unification emerged at universe temperature of $10^{16}$ GeV, then their annihilation should first give rise to photons driving the expansion of the early universe. As the universe temperature dropped to the desert scale of around 10 EeV, departure from thermal equilibrium occurred. Thus the annihilation produced photons give rise to electron-positron pairs, which collide with remnant magnetic monopoles and antimonopoles. The resultant unstable intermediate X and antiX particle become asymmetric, and thereby leading to baryon asymmetry. The excess of antimonopoles corresponding to ten times of mass comparing with that of baryons provides a natural candidate for dark matter. The physics of such a desert regime baryogenesis can be simply characterized by three parameters. The collision predicts a ratio of particle and anti-particle equivalent to that of ultra-high-energy neutrino to their anti-particle, which can be tested by future observations, e.g., the constructing IceCube-Gen2.

The Transient Receptor Potential Ankyrin 1 (TRPA1) cation channels function as broadly-tuned sensors of noxious chemicals in many species. Recent studies identified four functional TRPA1 isoforms in Drosophila melanogaster (dTRPA1(A) to (D)), but their responses to non-electrophilic chemicals are yet to be fully characterized. Methods: We determined the behavioral responses of adult flies to the mammalian TRPA1 non-electrophilic activators citronellal and menthol, and characterized the effects of these compounds on all four dTRPA1 channel isoforms using intracellular Ca2+ imaging and whole-cell patch-clamp recordings. Results: Wild type flies avoided citronellal and menthol in an olfactory test and this behavior was reduced in dTrpA1 mutant flies. Both compounds activate all dTRPA1 isoforms in the heterologous expression system HEK293T, with the following sensitivity series: dTRPA1(C) = dTRPA1(D) > dTRPA1(A) ≫ dTRPA1(B) for citronellal and dTRPA1(A) > dTRPA1(D) > dTRPA1(C) > dTRPA1(B) for menthol. Conclusions: dTrpA1 was required for the normal avoidance of Drosophila melanogaster towards citronellal and menthol. All dTRPA1 isoforms are activated by both compounds, but the dTRPA1(B) is consistently the least sensitive. We discuss how these findings may guide further studies on the physiological roles and the structural bases of chemical sensitivity of TRPA1 channels.

Glyphosate (N-(phosphonomethyl)glycine) is a herbicide used to kill broadleaf weeds and grass, developed in the early 1970s. The widely occurring degradation product aminomethylphosphonic acid (AMPA) is a result of glyphosate and amino-polyphosphonate degradation. The massive use of the parent compounds leads to the ubiquity of AMPA in the environment, and particularly in water. Considering this, it can be assumed that glyphosate and its major metabolites could pose a potential risk to aquatic organisms. This review summarises current knowledge about residual glyphosate and their major metabolite AMPA in the aquatic environment, including status and toxic effects in aquatic organisms, mainly fish, are reviewed. Based on the above, we identify major gaps in the current knowledge and some directions for future research knowledge about the effects of worldwide use of herbicide glyphosate and its major metabolite AMPA. The toxic effect of glyphosate and their major metabolite AMPA has mainly influenced growth, early development, oxidative stress biomarkers, antioxidant enzymes, haematological, biochemical plasma indices, caused histopathological changes in the aquatic organism.

The importance of diet and the gut-brain axis for brain health and cognitive function is increasingly acknowledged. Dietary interventions are tested for their potential to prevent and/or treat brain disorders. Intermittent fasting (IF), the abstinence or strong limitation of calories for 12 to 48 hours, alternated with periods of regular food intake, has shown promising results on neurobiological health in animal models. In this review article, we discuss the potential benefits of IF on cognitive function and the possible effects on the prevention and progress of brain-related disorders in animals and humans. We do so by summarizing the effects of IF which - through metabolic, cellular and circadian mechanisms - lead to anatomical and functional changes in the brain. Our review shows that there is no clear evidence of a positive short-term effect of IF on cognition in healthy subjects. Clinical studies show benefits of IF for epilepsy, Alzheimer’s disease and multiple sclerosis on disease symptoms and progress. Findings from animal studies show mechanisms by which Parkinson’s disease, ischaemic stroke, autism spectrum disorder and mood- and anxiety disorders could benefit from IF. Future research should disentangle whether positive effects of IF hold true regardless of age or the presence of obesity. Also, variations in fasting patterns, total caloric intake and intake of specific nutrients may be relevant components of IF success. Longitudinal studies and Randomized Clinical Trials (RCTs) will provide a window into the long-term effects of IF on the development and progress of brain-related diseases.

An astonishing increase in temperature is posing several harmful impacts on crop plants. Heat stress is an abiotic environmental phenomenon that causes limits, inhibits plant growth, metabolism, and productivity worldwide, resulting in losses in production yields. Heat stress is caused by human activities and global warming,s such as greenhouse gases, carbon dioxide, methane, nitrous oxide, and water vapour. There are many pieces of evidence to support that heat stress reduces the crop plants yield worldwide, and the effects of heat stress are challenging to meet nutritional security and global food security for human beings. Heat stress has negative impacts on each developmental stage, including from germination to harvesting. Prevalent approaches for heat adaption is inadequate management that is unable either to increase the crop productivity or sustain ld. Several responses to dissect the relevant knowledge about heat stress mechanism involving morphological phenomena, physiological phenomena, reproductive replies, and molecular responses such as heat shock proteins act as mRNA synthesis, mRNA control (effects of genes during heat stress), the translation process, heat response element. There are such phenomena involving disseminating the knowledge concerning heat stress. In this review, we summarise the effect of heat stress on plant mechanisms, including morphological, biochemical and molecular responses.

Smartphone use has changed patterns of online and offline interaction. Phubbing (i.e., looking at one’s phone instead of paying attention to others) is an increasingly recognized phenomenon in offline interaction. We examined whether people who phub are more likely to have lower social intelligence, whether phubbing is considered more annoying than being ignored due to reading a magazine, and if people describe smartphones and magazines differently as sources of social distraction. We collected two survey samples (N = 112, N = 108) for a cartoon-based role-playing experiment (the Bystander Inaccessibility Experiment) in which a smartphone user and a person reading a magazine ignored the respondents’ conversational initiatives. Annoyance in each scenario was measured, and written accounts were collected on why the respondents rated the scenarios the way they did. Other measures used included the Generic Scale of Phubbing, Generic Scale of Being Phubbed, and Tromsø Social Intelligence Scale. The results showed that participants in both samples were more annoyed by phubbing than by being ignored due to reading a magazine. Linear regression analyses showed that phubbing was associated with lower social intelligence, even after adjusting for confounding factors. The annoyingness of phubbing was explained with negative attitudes toward smartphones, which were assumed to be used for useless endeavors, while magazines were more appreciated and seen as more cultivating. The role of bystanders’ epistemic access to the smartphone user’s activities is discussed.

This Paper intends to introduce the notion of prime ideals of Intuitionistic fuzzy normed Rings and to establish basic properties related to it. It investigates these notions and shown anew Result using intuinistic fuzzy points and non membership function incorparating with t-norm and s-norm to show aome results of fuzzy prime ideal.

The prevalence and incidence of cardiac pro-arrhythmic disorders are often influenced by sex due to specific effects on the QT interval. Androgens shorten QT, which may be protective against acquired long QT syndromes and their related arrhythmias in men such as torsade de pointes (TdP). On the other hand, androgens can potentiate Brugada and early repolarization syndromes, which are most prevalent in men. In this case series, we highlight four male patients with aborted SCD in the setting of abnormal testosterone status; two patients with TdP in a setting of testosterone deprivation (of which one drug-induced) and 2 patients with ventricular fibrillation associated with exogenous androgenic booster (Tribulus terrestris) intake. From this case series, we review the current available literature of the effects of androgen as a double-edged sword on the QTc interval and emphasize the importance of QTc monitoring in this subset of patients.

FinTech has been in the focus of the discussion for quite some time. However, the market share of FinTech companies is still relatively small in compared to more traditional financial services. The purpose of this paper is to analyse the status quo, current developments and challenges ahead for the Latvian FinTech sector. We combine three analyses: the political and legal, the economic, the social and the technological environment (PEST) analysis, an analysis of the size and performance of FinTech companies during the last 10 years, and a survey among FinTech companies. We find that the current status of regulation is one of the main obstacles to FinTech development, because it does not sufficiently consider FinTech-specific aspects. Problems in attracting skilled workforce, and an environment that is not very supportive of new developments in finance are further challenges and might explain at least part of the growth and performance difficulties. A revision, modernization and harmonization of regulation is essential to create a level playing field for all market participants: FinTech companies, traditional financial service providers and those originally traditional players that are integrating FinTech solutions in their business model. Further efforts are also required to foster the Latvia’s attractiveness for skilled workforce. We hope that this study helps increase the visibility of Latvian FinTech and contributes to the development of the new Latvian FinTech strategy.

The power function distribution is a flexible waiting time model that may provide better fit for some failure data. This paper presents the comparison of the maximum likelihood estimates and the Bayes estimates of two-parameter power function distribution. The Bayes estimates are obtained, using conjugate priors, under five loss functions consist of square error, precautionary, weighted, LINEX and DeGroot loss function. The Gibbs sampling algorithm is proposed to generate samples from posterior distributions and in result the Bayes estimates are computed. The comparison of the maximum likelihood estimates and the Bayes estimates are done through the root mean squared errors. One real-life data set is analyzed to illustrate the evaluation of proposed methods of estimation. Finally, results from the simulation are discussed to assess the performance behavior of the maximum likelihood estimates and the Bayes estimates.

Chemical modification of cellulose offers routes for structurally and functionally diverse biopolymer derivatives for numerous industrial applications. Among cellulose derivatives, cellulose ethers have found extensive use, such as emulsifiers, in food industries and biotechnology. Methylcellulose, one of the simplest cellulose derivatives, has been utilized for biomedical, construction materials and cell culture applications. Its improved water solubility, thermoresponsive gelation, and the ability to act as a matrix for various dopants also offer routes for cellulose-based functional materials. There has been a renewed interest in understanding the structural, mechanical, and optical properties of methylcellulose and its composites. This review focuses on the recent development in optically and mechanically tunable hydrogels derived from methylcellulose and methylcellulose-cellulose nanocrystal composites. We further discuss the application of the gels for preparing highly ductile and strong fibers. Finally, the emerging application of methylcellulose-based fibers as optical fibers and their application potentials are discussed.

Serendipity favors the prepared mind, but how does the brain make that lucky find? Analyzing the cerebral mechanisms behind an exceptional (albeit trivial) discovery, the author suggests that a combination of ‘concept cells’ and the Global Neuronal Workspace Hypothesis could explain how we make great discoveries.