In past years, lithium-ion batteries (LIBs) can be found in every aspect of life, and batteries, as energy storage systems (ESSs), need to offer electric vehicles (EVs) more competition to be accepted in markets for automobiles. Thick electrode design could reduce the use of non-active materials in batteries that its energy density would be improved and its cost would be cut. However, thick electrodes are limited by their weak mechanical stability and poor electrochemical performance, these limitations could be classified as the critical cracking thickness (CCT) and the limited penetration depth (LPD). The understanding of the CCT and the LPD have been proposed and the recent works on breaking the CCT and improving the LPD are listed in this article. By comprising these attempts, some thick electrodes could not offer higher mass loading or higher accessible areal capacity that would defeat the purpose.

The goal of our study was to examine the effect of regional characteristics of living environment on individual a priori and a posteriori dietary patterns of the Russian population. For the analysis, we used cross-sectional data from the Epidemiology of Cardiovascular Diseases in the Regions of the Russian Federation study of 2013-2014. The sample included 18,054 men and women 25-64 years of age from 12 regions. Based on the frequency of consumption of basic foods, four a posteriori empirical dietary patterns (EDPs), along with an a priori cardioprotective dietary pattern (CPDP) were identified. To describe the regional living environment, 5 regional indices were used. The adherence to the meat-based EDP was directly associated with deterioration of social living conditions and the more northerly location of the region of residence. The probability of CPDP increased with deterioration of social living conditions, an aggravation of demographic crisis, higher industrial development of the region, as well as with a decline in the economic development of the region, income and economic inequality of the population. We detected some gender-dependent differences in associations. The revealed patterns reflect the national dietary preferences of Russians, and regional indices characterize the effect of living environment.

Vacuum pump wear is the most prevalent failure mode of the IBA Synthera® automated radiochemistry system. Rebuilding or replacing the pump causes equipment downtime and increases the radiation exposure of the service personnel. We built a dedicated test device to assess new or rebuilt pumps prior to installation, thus reducing downtime and radiation exposure during repairs. The Testbed incorporates a microprocessor that actuates the pump, valves, pressure sensor, and communicates with the user through lights, buttons, and an alphanumeric screen. The Testbed increases productivity and safety in the radiochemistry laboratory.

Today, concepts such as Emotional Education and Emotional Intelligence are recognized by many authors and researchers, emphasizing the importance of their development in all people, but especially in schools, specifically in teachers or educators and children. However, we still find many shortcomings within the schools that are needed to continue working. The aim of this study is to investigate the presence of emotional intelligence and empathy in teachers, through a sample of 42 participants. The research was conducted based on two validated and highly reliable assessment tools, called TMMS-24 and TECA. The results of the TMMS-24 indicate that a large percentage of participants have an adequate score of emotional intelligence. The results of the TECA indicate that most of the participants are in the middle, high or extremely high scores in relation to the level of empathy.

To have a better protection, strong toughness and good flexibility, all lives and plants must have skins, similarly, all books should have covers. In this paper, considering interlayer friction as a perturbation, we formulate a hardcover-book-like laminated plates with internal friction. For a quasi-static problem, detail analysis on the bending response and energy dissipation are carried out for a three-point-supported plates. Our numerical investigations show that the hardcover is more essential than the core layers in terms of bending response, and the energy dissipation within per loading-unloading cycle can be made to vary by a considerable amount. The central goal of studying is not only to predict the bending deformation of the book-like-plates, but also as a representative case to help finding some clue on the universal behaviours of multilayered architectures with internal friction.

The stick-free flight stability is an old-fashioned and non-progressive issue; nevertheless, it is still existent, and of significant importance to the design of aircraft whose control system is reversible. The problem’s existence necessitates a deep assessment of stick-free flight stability throughout the aircraft design. Up to now, this problem has been addressed using either analytical approaches, which are only related to the static stability evaluation, or performing flight tests. In this study, the problem is handled in its entirety, from static and dynamic flight stability assessment to design criteria with a comprehensive perspective. Moreover, it is also exhibited that contrary to what has been generally proposed in the literature, limiting the problem of stick-free flight stability through static stability assessment is far from the main challenge. As a brief scope, the derivation of the control surface dynamics, a stick-free trim algorithm, and assessment rationale of the stick-free static and dynamic flight stability using a simulation approach are proposed. As a consequence, the aim is to set a broad understanding for designers related to this phenomenon and add adjunct design criteria in the design optimization process by approaching it in terms of modeling, simulation, and flight test perspective.

Gravity played an important role in the development of life on earth. The effect of gravity on a living organisms can be investigated by controlling the magnitude of gravity. Most reduced gravity experiments are conducted on the lower earth orbit (LEO) in the International Space Station (ISS). However, running experiments in ISS face challenges such as high cost, extreme condition, lack of direct accessibility, and long waiting period. Therefore, researchers have developed various ground-based devices and methods to perform reduced gravity experiments. However, the search for life outside the earth requires more intensive research. Advancements in conventional methods and the development of new tools are required for this purpose. The advantages of Lab-on-a-Chip (LOC) devices make them an attractive option for simulating microgravity. This paper briefly reviews the advancement of LOC technologies for simulating microgravity in a earth-based laboratory.

Several studies have highlighted the importance and evolution of process safety leadership and culture in various industries. However, none has focused on the palm oil milling industry yet. This paper critically reviews the latest developments in the palm oil milling process and unit operations leading to process safety concerns. It also discusses the Principle of 3C that is applied to explain repeat accidents and the four-level safety culture in the palm oil milling industry. For this purpose, the author presents case studies of two key palm oil companies in Malaysia. Overall, this paper offers guidelines to leaders in the palm oil milling industry about the required process safety leadership and culture to be understood in order to improve their safety outcomes.

In the olive orchard on the western part of Istria, Croatia, twig and branch dieback was observed on several olive trees from the local cultivar ‘Buza’. In total, seven samples from symptomatic trees were collected. Samples were analyzed, and four fungal isolates showed morphological similarities to the species Phaeoacremonium iranianum. One isolate, chosen as a representative, was taken for molecular identification and pathogenicity tests. Based on DNA sequence data of ITS1/ITS4, βt2a/βt2b, EF1-728F/EF1-986R, the isolate was identified as P. iranianum. Pathogenicity tests were conducted on detached olive branches and olive trees in the greenhouse, and identified fungal species was pathogenic on olive. To the best of our knowledge, this is the first report of twig and branch dieback on olive causing by phytopathogenic specie of fungus Phaeoacremonium iranianum.

The quality of yarn is essential in the control of the fabrics processes. There is some commercial equipment that measures the quality of yarn based on sensors, of different types, used for collecting data about some yarn characteristic parameters. The existing equipment, for the above-mentioned purpose, is characterized by its high size and cost, and for allowing the analysis of only few yarn quality parameters. Thus, this paper presents the development and results obtained with the design of a mechatronic prototype integrating a computer vision system that allows, among other parameters, the analysis and classification, in real time, of the hairs of the yarn using artificial intelligence techniques. The system also determines other characteristics inherent to the yarn quality analysis, such as: linear mass, diameter, volume, twist orientation, twist step, average mass deviation, coefficient of variation, hairiness coefficient, average hairiness deviation and standard hairiness deviation, as well as performs spectral analysis. A comparison of the obtained results with the designed system and a commercial equipment was performed validating the undertaken methodology.

In recent years, Nigeria is witnessing increasing frequency of flood occurrence with devastating impact translating into significant loss of lives (in Nigeria, over 300 people died in September 2022) and properties. Addressing flood disaster requires holistic approach from policy and governance perspectives, integration of policies and programs and synergies between institutions. Using synergies and eliminating trade-offs, flood governance and policy coherence integrate all relevant policy fields and institutions to achieve common policy outcomes. The objective of this study is to examine and understand how flood governance and policy coherence are approached, as well as institutional design and implementation for coherence in Nigeria. The findings revealed that there is no single flood policy in Nigeria. Due to this, there is no focus and no defined objectives for flood governance, prevention, control, and management, and no imperative for the government to seek both short-term and long-term flood solutions. There is no synergy and coordination among institutions for flood governance in the country. Since the country established the federal ministry of environment in 1999, the environment, floods, and climate-related hazards were given less priority. State and local governments handle most flood disasters and emergencies. Federal assistance is provided, however, when flood disasters exceed the capabilities of local and state governments. This study recommends that across the country, flood policy needs to be designed, formulated, and implemented while assigning governance responsibility and decentralizing policy to state and local governments.

Tin (IV) oxide nanoparticles (SnO2 NPs) have received a lot of interest because of their interesting features. SnO2 NPs have proven productive in a range of fields, including water purification, supercapacitors, batteries, antibacterial and antioxidant agents, and others. SnO2-based nanoparticles found a wide range of applications after incorporating materials with varying chemical compositions. SnO2 NPs and their nanocomposites have been used effectively as antibacterial agents against various pathogenic bacteria, photocatalysts for dye degradation, and electrode materials for supercapacitors (SCs). This article covers the characteristics of SnO2 NPs, SnO2 nanocomposite materials, applications of SnO2 NPs and their composite materials, including antibacterial, energy storage, and photocatalysis, as well as some significant recent studies.

Tubulin superfamily (TSF) proteins are widespread and known for multifaceted roles as cytoskeletal proteins underpinning many basic cellular functions including morphogenesis, division, and motility. In eukaryotes, tubulin assembles into microtubules, a major component of the dynamic cytoskeletal network of fibres, whereas the bacterial homolog FtsZ assembles the division ring at midcell. Functions of the lesser-known archaeal TSF proteins are beginning to be identified, and show surprising diversity, including homologs of tubulin and FtsZ, and a third archaea-specific family, CetZ, implicated in the regulation of cell shape and possibly other unknown functions. In this study, we defined sequence and structural characteristics of the CetZ family and CetZ1 and CetZ2 subfamilies, identified CetZ groups and diversity amongst archaea, and identified potential functional relationships through analysis of the genomic neighbourhoods of cetZ genes. At least three subfamilies of orthologous CetZ proteins were identified in the archaeal class Halobacteria, including CetZ1 and CetZ2 and a novel uncharacterized subfamily. CetZ1 and CetZ2 were correlated to one another and to cell shape and motility phenotypes across diverse Halobacteria. Amongst other known CetZ clusters in orders Archaeoglobales, Methanomicrobiales, Methanosarcinales, and Thermococcales, an additional uncharacterized group from Archaeoglobales and Methanomicrobiales affiliated strongly with Halobacteria CetZs, suggesting they originated via horizontal transfer. Subgroups of Halobacteria CetZ2 and Thermococcales CetZ genes were found adjacent to different type IV pili regulons, suggesting a potential utilization of CetZs by type IV systems. More broadly conserved cetZ gene neighbourhoods included nucleotide and cofactor biosynthesis (e.g., F420) and predicted cell surface sugar epimerase genes. The findings imply that CetZ subfamilies are involved in multiple functions linked to the cell surface, biosynthesis and motility.

Evolution of SARS-CoV-2 has followed similar trends as other RNA viruses, e.g., HIV-1 and influenza A. Rapid initial diversification was followed by strong competition and rapid succession of dominant variants. Host-initiated RNA editing has been the primary mechanism for introducing mutations. A significant number of mutations were detrimental and were quickly purged. Fixed mutations are mostly diversifying mutations selected for host adaptation and immune evasion, with the latter accounting for more of the changes. However, immune evasion often comes at the cost of functionality, so that optimal functionality is still far from being accomplished. Instead, selection for antibody-escaping variants and accumulation of near-neutral mutations has led to suboptimal codon usage and reduced replicative capacity as demonstrated in non-respiratory cell lines. Beneficial adaptation of the virus includes reduced infectivity in lung tissues and increased tropism for the upper airway, resulting in shorter incubation periods, milder diseases, and more efficient transmission between people.

Leptospirosis is an important zoonotic disease, causing about 60,000 deaths annually. One of the reasons for the severe course of leptospirosis is a cytokine storm, which develops as a result of an excessive immune response. The gut microbiota that resides in the gastrointestinal tract provides essential health benefits to its host, particularly by regulating immune homeostasis and a bidirectional relationship with many internal organs. A change in the gut microbiota can be caused not only by antibiotics, but also by infectious agents such as the coronavirus or the flu virus. It is known that L. interrogans can change the microbiota of mice. Thus, gut dysbiosis in leptospirosis can affect the clinical course of the disease, through the gut–organ axis. Modulation of intestinal microbiota by probiotics and/or fecal microbiota transplantation in leptospirosis may become an important area of scientific research.

Cancer metastasis into the brain constitutes one of the most severe but not uncommon development of cancer growth. Several factors control how cancer types interact with the brain to establish metastasis These factors include system of migration, system of infiltration of the blood-brain barrier and the interaction with host cells (e.g., neurons, astrocytes) and the immune system fight against metastasis in the brain. Although, brain radiotherapy is the main treatment procedure carried out to treat, new therapies are emerging that constitute a glimpse of hope for increasing the diminutive life expectancy currently forecasted to cancer patients who are facing the prospectus of brain metastasis. However applying these therapeutic strategies has been has not been effective. Thus there is a need for better understanding of the metastasis process in order to suggest novel therapeutic targets. In this review, we follow the journey of various cancer cells from their primary location through the various process that they undergo including EMT, penetration of the ECM, intravasation, extravasation and infiltration of the blood brain barrier ending up with colonization and angiogenesis. In each phase, we focus on molecules that could potentially be drug target candidates.

As a model for economic and ecological systems, replicator dynamics represents a basic form of agent competition for finite resources. Here, we investigate the effects of stochastic resetting in this kind of processes. Random reset events abruptly lead individual resources to a small value from which dynamics must start anew. Numerical results show that resource distribution over the population of competing agents develops highly nonuniform profiles, exhibiting clustering and fluctuations with anomalous dependence on the population size. This non-standard statistical behavior jeopardizes an analytical treatment based on mean-field assumptions. We propose alternative simplified analytical approaches which provide a stylized description of entropy evolution for the clustered distribution of resources and explain the unusually slow decrease of fluctuations.

As the global energy crisis continues, efficient hydrogen production is one of the hottest topics these days. In this sense, establishing catalytic trends for hydrogen production is essential for choosing proper H2 generation technology and catalytic material. Volcano plots for hydrogen evolution in acidic media are well-known, while volcano plot in alkaline media was constructed ten years ago using theoretically calculated hydrogen binding energies. Here we show for the first time that the volcano-type relationships are largely maintained in a wide range of pH values, from acidic to neutral and alkaline solutions, using theoretically calculated hydrogen binding energies on clean metallic surfaces and experimentally measured hydrogen evolution overpotentials. If metallic surfaces are exposed to high anodic potentials, hydrogen evolution can be boosted or significantly impeded, depending on the metal and the electrolyte in which the reaction occurs. Such effects are discussed here and can be used to properly tailor catalytic materials for hydrogen production via different water electrolysis technologies.

In this paper, we give some upper and lower bounds for the multiplicative Randic index, reduced reciprocal Randic index, Narumi-Katayama index and symmetric division index a graph using solely the vertex degrees. Then we obtain upper and lower bounds for these indices for the complete graphs, path graphs and Fibonacci-sum graphs. Finally, we compared the bounds of these indices for a general graph and some special graphs.

Diffuse intrinsic pontine glioma (DIPG) is recognized as a pediatric brainstem cancer with a 0% survival rate. On a molecular basis, DIPG commonly results from mutations in histone H3, specifically a mutation in the H3K27M gene, that promotes tumorigenesis and results in presentation of this fatal brainstem tumor. DIPG is challenging to treat, as surgical intervention is inefficacious due to the location where the glioma resides. To date, traditional treatments such as radiation, chemotherapy, and immunotherapy have not increased survival rates and have only been successful at relieving symptoms. Future therapeutic approaches such as proton beam radiation, Chimeric Antigen Receptor T Cell (CAR-T) immunotherapy, and alternative epigenetic pharmaceuticals are under investigation for potential benefits. Various clinical trials have also explored these treatment procedures to discover potential increases in survival rates in both animal and human studies. In this review, we will evaluate the pathology and molecular characteristics of DIPG, the current and future approaches to DIPG treatment; and, lastly, we will discuss clinical trials that have been completed to develop successful treatment options.

By constructing a static map, the library robot can navigate and access documents autonomously, which greatly improves efficiency; however, for libraries where shelves can be moved, map changes do not allow for direct navigation, and also sensors such as radar and camera are relatively expensive, so we propose a low-cost navigation algorithm based on real-time ranging data and map matrices, and designed a mobile archive access robot. In order to obtain real-time distance data, nine near/long range sensors were mounted on the chassis and a composite digital filtering algorithm was designed based on different moving area characteristics; then the access task matrix and map matrix were designed based on the archive access task and the location characteristics of the archive shelf placement, the robot can rely on the range data to update the map matrix during the moving process, complete its own positioning, and use the task matrix The robot can use the task matrix to complete autonomous navigation and access to multiple files. Experiments show that the filtered positioning accuracy can reach 1cm, while the robot can move to the target shelf autonomously, which is more practical and less costly.

Abstract: Background: COVID-19 is a respiratory disease that eventually became pandemic with 300 million people infected around the world. Alongside the improvement in COVID-19 management and vaccine development, identifying biomarkers for COVID-19 has recently reported to help in early prediction and managing severe cases which might improve outcome. The Aim of our study was to find out if there is any correlation between clinical severity and elevated hematological and biochemical markers in Covid-19 patients and its effect on the outcome Methods: We have collected data retrospectively of socio-demographic, medical history, biomarkers, and diseases outcome from five hospitals and health institutions in the Kingdom of Saudi Arabia. Results: Pneumonia was the most common presentation of COVID 19 in our cohort. All biomarkers (D-Dimer, CRP, troponin, LDH and ferritin) except for the mean lowest white blood cells were found to have significant correlation (p<0.05) with worse COVID-19 disease outcomes. There was a sig-nificant association between the inflammatory biomarkers and the disease severity of COVID 19 patients (p<0.001). Patients with evidence of severe respiratory disease particularly who required mechanical ventilation (MV) had higher biomarkers, when compared to those with stable respiratory condition (p<0.001). Conclusion: Identifying biomarkers predicts outcome for COVID-19 patients and may significantly help in the management.

Humans are a vision-dominated species, and what we see depends on where we look. Therefore, eye movements (EM) are essential to our interactions with the environment, and experimental findings show EM is affected in neurodegenerative disorders (ND). It could be a reason for some cognitive and movement disorders in ND. Therefore, we aim to determine if changes in EM-evoked responses can tell us about ND, such as Alzheimer’s (AD) and Parkinson’s Disease (PD) progression in different stages. In the present review, we have analyzed the results of neurological, psychological, and EM (saccades, antisaccades, pursuit) tests to predict disease progression with Machine Learning (ML) methods. Described predictive algorithms are using various approaches, including Granular Computing, Naive Bayes, Decision Trees/Tables, Logistic Regression, C-/LinearSVC, KNC, and Random Forest. We demonstrated that EM is a robust biomarker for assessing symptom progression in PD and AD. There are also navigation problems in 3D space in both diseases. Consequently, we investigated EM experiments in the virtual space and how they may help find neurodegeneration-related brain changes. In conclusion: EM parameters with clinical symptoms are powerful precision instruments that, in addition to predictions of ND progression with the help of ML, could be used to indicate the different preclinical stages of both diseases.

Biochemical oxygen demand (BOD) is one of the most important factors to consider when evaluating water contamination. BOD5 is the amount of oxygen consumed in five days by microorganisms that oxidize biodegradable organic materials in an aerobic biochemical manner. The primary objective of this effort is to use microbial fuel cells (MFCs) to shorten the time required for BOD5 measurements. We created a regression artificial neural network (AI), and the predictions we obtained for BOD5 measurements were taken over 6 – 24 hours with an average error of just 7%. The outcomes demonstrated by our AI MFC/BES BOD5 sensor’s viability for use in real-world scenarios.

Real estate has the dual characteristics of consumption and investment. Under the inherent concept of "where there is land, there is wealth", real estate has become the investment target of the main asset. In addition to the supply and demand of the market will affect the price of real estate, many general economic factors will also have a certain impact on the price of real estate. This paper pays a special focus on the effects of MRT on housing prices, finding the closer a house is to an MRT station, the higher its price. In addition, convenience stores are very popularized, and this research report is also interested in whether the number of convenience stores within walking distance will affect the house price. In a word, these two factors do affect housing prices significantly.

Rose (Rosa spp.), especially R. hybrida, is one of the most popular ornamental plants in the world and the third largest cut flower crop in Taiwan. Rose mosaic disease (RMD), showing mosaic, line patterns and ringspots on leaves, is a common rose disease caused by complex infection of various viruses. Due to pests and diseases, the rose planting area in Taiwan has been decreasing since 2008; however, no rose virus disease has been reported in the past five decades. In the spring of 2020, rose samples showing RMD-like symptoms were observed at an organic farm in Chiayi, central Taiwan. The virome in the farm was analyzed by RNA-seq. Rose genomic sequences were filtered from the obtained reads. The remaining reads were de novo assembled to generate 294 contigs, 50 of which were annotated as viral sequences corresponding to 10 viruses. Through reverse transcription-polymerase chain reaction validation, a total of seven viruses were detected, including six known rose viruses, namely apple mosaic virus, prunus necrotic ringspot virus, rose partitivirus, apple stem grooving virus, rose spring dwarf-associated virus and rose cryptic virus 1, and a novel ilarvirus. After completing the whole genome sequencing and sequence analysis, the unknown ilarvirus was demonstrated as a new species, tentatively named rose ilarvirus 2. This is the first report of the rose viruses in Taiwan.

One of the possible mechanisms causing significant emissions of methane into the atmosphere within the Arctic shelf may be the decomposition of gas hydrates. Their accumulations within the Arctic shelf formed during Ice Age almost simultaneously with the formation of permafrost, which contributed to the emergence of a zone of stable existence of gas hydrates. The subsequent flooding of the Arctic shelf led to the degradation of the permafrost and the violation of the conditions for the existence of hydrates. To assess the state of the stability zone, methods of mathematical numerical modeling are used. Standard seismic methods are widely used to localize gas hydrates, but monitoring their physical state requires the development of fundamentally new approaches based on solving multiparameter inverse seismic problems. In particular, the degree of attenuation of seismic energy is one of the objective parameters for assessing the consolidation of gas hydrates: the closer they are to the beginning of decomposition, the higher the attenuation, and hence the lower the quality factor. Thus, the methods of seismic monitoring of the state of gas hydrates in order to predict the possibility of developing dangerous scenarios should be based on solving a multi-parameter inverse seismic problem. This publication is devoted to the presentation of this approach.

Others have proposed that living systems are characterized by the possession of a kind of information unique to them. This information, variously called semantic, symbolic, or coding information conveys meaning from senders to recipients, most commonly among genome and other cellular components, different cells, organisms, etc., enabling all biological functions. Unlike other kinds of information, it is only indirectly quantifiable and does not increase by copying. Here, the term system information is used to encompass the entire package of coding information enabling all organisms of a species to complete their life cycles through possession of its copy (Crkvenjakov and Heng 2021). System information grows in a nonreductive evolution only during speciation proper by the acquisition of novel functions by daughter species. Contemporary science agrees that species are units of evolution but has failed to recognize and parse the importance of phases of speciation and stasis for evolution. Confinement of system information change to speciation only explains for the first time why there are species and why their number increases nearly exponentially throughout evolution. More importantly, here we define that evolution as a process operating on a planetary scale, while chance based, is non-random mathematically since it obeys the Laws of Information which dictate the behavior of System information in organisms. Growth and persistence of system information on the non-degenerative portion of Tree of Life follow the law of conservation, which states that its growth is as parsimonious (minimal) as possible and the persistence of parental system information the maximal possible in each unit speciation step. This formulation is a restatement of Loewnstain's (1999) principle of information economy and our lock-in law (Crkvenjakov and Drmanac 2007). The principle holds that since, on chance assumption, the system information change cost is directly proportional to time, more costly pathways of achieving the same new functional goal will be, almost without exception, competed out by the less costly ones. As a corollary of the conservation law, the speciation law states that speciation follows the most economical way of growth of system information while conserving the existing one, which is horizontal information transfer by molecular sex (uptake of exogenous nucleic acid into the progeny genome) by exchange among two parents from maximally possible divergent lineages. The speciation law dictates that molecular sex mode be used since it is the most parsimonious one. The molecular sex cost in information bits is zero to the recipient, which is not the case with the generally accepted mode of vertical accumulation of information bits in a population gene pool within a lineage.

Currently SARS-CoV-2 is spreading around the world as an Omicron strain. Recently, the Omicron variants (BQ.1 and BQ.1.1) were identified as novel Variants of Concern. UpToDate, there is little information about the Omicron Variants BQ.1 and BQ.1.1. The widely altered Omicron variants are spread globally, providing a high risk of infection surges with devastating consequences in some areas. The Omicron type of SARS-CoV-2 has a harm risk of reinfection, according to early reported findings. COVID-19 Variants particularly, BQ.1 and BQ.1.1 have gained global attention and caused a worldwide sensation since their discovery. Therefore, this communication discusses the present status of COVID-19 Variants BQ.1 and BQ.1.1, and their consequences.

Monkeypox virus infection is a recognized public health emergency. Little research has been done on treatment options for this disease. Until recently, there was not a single published work describing the usage of specific drugs in human patients with monkeypox virus infection. This paper gives the first bibliometric analysis of monkeypox treatment options based on data available on PubMed and Scopus. It also reviews the specific drugs used in the treatment of monkeypox. That includes data on Tecovirimat, Cidofovir, Brincidofovir, and Vaccinia Immune Globulin. Tecovirimat is a promising option in progressive disease in terms of efficacy and safety. However, Brincidofovir has been associated with discontinuation of treatment. Cidofovir is also not the preferred drug among physicians. Currently, Tecovirimat can be further used for the management of aggravating cases. More studies should be conducted on Tecovirimat to treat this condition, mainly through controlled trials.

Ochna kibbiensis (Family: Ochnaceae) has been employed in ethnomedicine for the treatment of malaria, and inflammation among others. The aim of this study was to isolate and characterize prophylactic antimalarial agents from the leaves of O. kibbiensis against Plasmodium berghei, in vivo and in silico. The median lethal dose (LD50) of the methanol extract and its fractions (n-hexane, dichloromethane, ethylacetate and n-butanol) was determined according to Lorke’s method while the antimalarial effect of the extract and its fractions was investigated according to the method described by Peters prophylactic test using Chloroquine-sensitive Plasmodium berghei (NK65). All the extract/fractions exhibited LD50 values ≥ 5000 mg/kg with the exception of the n-butanol fraction (1702.94 mg/kg) which indicate that the plant is non-toxic. Dichloromethane fraction exhibited significant (p&lt;0.05) and dose-dependent prophylactic effect with 47.62, 85.12 and 100.0 % prophylaxis (at 500, 250 &amp; 125 mg/kg) while the least effect was observed by the n-butanol fraction with percentage prophylaxis of 64.29 and 76.19, respectively; the standard drug, pyrimethamine had 95.24 % prophylaxis. Based on the result obtained, dichloromethane fraction of O. kibbiensis was subjected to chromatographic purification which led to the isolation of a mixture of two compounds identified as stigmasterol and β-sitosterol by analysis of the NMR spectral data and comparison with existing literature; the compounds exhibited good binding affinities (- 5.129 and - 4.889 kcal/mol) against pfLDH and favorable ADMET profile. In conclusion, the leaf of O. kibbiensis have demonstrated a significant prophylactic antimalarial activity and the two known steroids (stigmasterol and β-sitosterol) isolated from the dichloromethane fraction for the first time.

Hox genes represent an important gene family that is involved in the segmentation pattern and identity of the segments during the formation of the body plan in metazoans. For many years, several studies have sought to establish a correlation between the evolution of these genes and the evolution of large groups of metazoans. Here, we use publicly available sequences of Hox gene clusters to reconstruct the evolutionary history of anterior Hox genes. We show that information harbored by these genes, in part, reflects the evolution and diversification of most animal archetypes, but in many cases, there were conflicts between the evolutionary history of some genes and the history of large groups, these cases may have occurred due to specific and similar selective pressures in relatively distant groups, which may have led to evolutionary convergences. Our findings also reveal that the evolution of Hox genes (and clusters) as a multigene family is consistent to a birth-and-death model constrained by development, where there is a trade-off between a relatively fast gene turnover and their central developmental roles

Obesity in pregnancy induces metabolic syndrome, low-grade inflammation, altered endocrine factors, placental function, and the maternal gut microbiome. All these factors impact fetal growth and development, including brain development. The lipid metabolic transporters of the maternal-fetal-placental unit are dysregulated in obesity. Consequently, the transport of essential long-chain PUFAs for fetal brain development is disturbed. The mother’s gut microbiota is vital in maintaining postnatal energy homeostasis and maternal-fetal immune competence. Obesity during pregnancy changes the gut microbiota, affecting fetal brain development. Obesity and a high-fat diet in pregnancy can induce placental and intrauterine inflammation and thus influence the neurodevelopmental outcomes of the offspring. Several epidemiological studies observed an association between maternal obesity and adverse neurodevelopment. This review discusses the effects of maternal obesity and gut microbiota on fetal neurodevelopment outcomes. In addition, the possible mechanisms of the impacts of obesity and gut microbiota on fetal brain development are discussed.

Fabry disease (FD) is a X-linked inheritable storage disease caused by deficiency of al-pha-galactosidase causing lysosomal overload of sphingolipids. FD cardiomyopathy is character-ized by left ventricular (LV) hypertrophy and should be considered in differential diagnosis with all the other causes of LV hypertrophy. An early diagnosis of FD is very important because the enzyme replacement therapy (ERT) may change the fate of patients by blocking both cardiac and systemic involvement and improving prognosis. Diagnosis may be relatively easy in young patients with the typical signs ans symptoms of FD, but in male patients with late onset of disease and in females, diagnosis may be very challenging. Morphological and functional aspects are not specific for FD, which cannot be diagnosed or excluded by echocardiography. Cardiac magnetic resonance (CMR) with tissue characterization capability, is the preferred technique for the differential diagnosis of LV hypertrophy. The finding of decreased myocardial T1 value in LV hypertrophy is very specific for FD. Late gadolinium enhancement (LGE) is found in late stage of disease but it is useful to predict the cardiac response to ERT and to stratify the prognosis.

This short communication reports on the initial results of a much larger, ongoing project, the aim of which is to investigate the question: could the skin microbiome, just like the gut microbiome, play a role in sports recovery and performance – and if so, could this role be as significant a one as that played by the gut microbiome? 17 high performance college athletes addressed their skin microbiome by minimizing contact with synthetic chemicals and by using topical skin supplements, shown previously to significantly increase skin microbiome biodiversity, for two weeks after training. 76% said their skin softness improved, 35% said their muscle stiffness and recovery after sport improved, 12% said their sleep quality improved, and 100% said they would be likely to use skin supplements again. Future work will use hundreds of athletes.

A series of novel indole Schiff base derivatives (2a–2t) containing a 1,3,4-thiadiazole scaffold modified with a thioether group were synthesized, and their structures were confirmed using FT-IR, ¹H NMR, ¹³C NMR, andHR-MS. In addition, the antifungal activity of synthesized indole derivatives was investigated against Fusarium graminearum (F. graminearum), Fusarium oxysporum (F. oxysporum), Fusarium moniliforme (F. moniliforme), Curvularia lunata (C. lunata), and Phytophthora parasitica var. nicotiana (P. p. var. nicotianae) using the mycelium growth rate method. Among the synthesized indole derivatives, compound 2j showed the highest inhibition rates of 100%, 95.7%, 89%, and 76.5% at a concentration of 500 μg/mL against F. graminearum, F. oxysporum, F. moniliforme, and P. p. var. nicotianae, respectively. Similarly, compounds 2j and 2q exhibited higher inhibition rates of 81.9% and 83.7% at a concentration of 500 μg/mL against C. lunata. In addition, compound 2j has been recognized as a potential compound for further investigation in the field of fungicides.

Human oncoviruses are able to subvert telomerase function in cancer cells through multiple strategies. The activity of the catalytic subunit of telomerase (TERT) is commonly enhanced in virus-related cancers. Viral oncoproteins, such as high-risk human papillomavirus (HPV) E6, Epstein-Barr virus (EBV) LMP1, Kaposi sarcoma-associated herpesvirus (HHV-8) LANA, hepatitis B virus (HBV) HBVx, hepatitis C virus (HCV) core protein and human T-cell leukemia virus-1 (HTLV-1) tax protein, interact with regulatory elements in the infected cells and contribute to the transcriptional activation of TERT gene. Specifically, viral oncoproteins have been shown to bind TERT promoter, to induce post-transcriptional alterations of TERT mRNA and to cause epigenetic modifications, which have important effects on the regulation of telomeric and extra-telomeric functions of the telomerase. Other viruses, such as herpesviruses, operate by integrating their genomes within the telomeres or by inducing alternative lengthening of telomeres (ALT) in non-ALT cells. In this review, we recapitulate recent findings on virus-telomerase/telomeres interplay and the importance of TERT-related oncogenic pathways activated by cancer causing viruses.

Mechanistic models have been used for centuries to describe complex interconnected processes, including biological ones. As the scope of these models has widened, so have their computational demands. This complexity can limit their suitability when running many simulations or when real-time results are required. Surrogate machine learning models can be used to approximate the behaviour of complex mechanistic models, and once built, their computational demands are several orders of magnitude lower. This paper provides an overview of the relevant literature, both from an applicability and a theoretical perspective. For the latter, the paper focuses on the design and training of the underlying machine learning models. Application-wise, we show how machine learning surrogates have been used to approximate different mechanistic models. We present a perspective on how these approaches can be applied to models representing biological processes with potential industrial applications (e.g., metabolism and whole-cell modelling) and show why surrogate machine learning models may hold the key to making the simulation of complex biological models possible using a typical desktop computer.

Introduction: Cancer is a group of diseases caused by uncontrolled and abnormal cell growth caused by the accumulation of genetic and epigenetic changes. Based on data from Dr Kariadi Hospital in 2021, lung cancer, breast cancer, and cervical cancer were 1,259 cases, this incident increased significantly from 2020. Based on the data obtained, the sequence of lung cancer cases was 241, breast cancer 623 , and cervical cancer 395. Both cancer and its treatment can weaken the patient's immune system, this is what makes cancer sufferers have a risk of anxiety and depression. Objective: To find out the description of anxiety and depression in cancer patients at RSUP Dr. Kariadi Semarang. Methods: The design of this study was descriptive quantitative, with a population of 56 patients and calculated using a stratified random sampling formula so that a sample of 49 respondents was obtained. The instrument or measuring instrument in this study used the Hospital Anxiety and Depression Scale (HADS). Results: Most cancer patients experienced mild anxiety and did not experience depression or at normal levels, from the number of respondents as many as 49 respondents, 27 respondents experienced mild anxiety (55.1%) and did not experience depression or at normal levels as many as 22 respondents (44, 9%). Suggestion: It is hoped that it can provide input for nurses to provide counseling to patients as an effort to reduce the anxiety and depression felt by patients.

Although impending urbanization is a well-acknowledged problem, there is a rising concern about how the urban forms will change and what can be the impacts on the global energy demand. As hubs of economic, social and cultural activities, cities are major energy consumers and GHG emissions. Energy consumption is a technical or a spatial problem? From Newman and Kenworthy to today, several studies have tried to shed light on this nexus. In this work, the controversial paradigm of urban density is discussed as a key component of the fight against climate change impacts. Concerning energy consumption, an in-depth bibliometric analysis is developed to identify the interdependencies of the terms. As a key ‘promise’ of an efficient urban configuration, density has been the core of diverse studies but with still under exploration arguments. This work provides a way forward for planners seeking to design strategies related to dense urban tissues exploring controversial paradigms as a key solution for energy-efficient problems.

Alzheimer´s Disease (AD) has currently no effective treatment; however, preventive measures can significantly delay the progress/onset of the disease. Thus, accurate and early prediction of risk is an important strategy to alleviate the AD burden. Neuroinflammation is a major factor prompting the onset of the disease. Inflammation exerts its toxic effect via multiple mechanisms. Amongst others, it is affecting gene expression via modulation of non-coding RNAs (ncRNAs), such as miRNAs. Recent evidence supports that inflammation can also affect long non-coding RNAs (lncRNAs) expression. While the association between miRNAs and inflammation in AD has been extensively studied, the role of lncRNAs in neurodegenerative diseases has been less explored. In this review, we focus on lncRNAs and inflammation in the context of AD. Furthermore, since plasma-isolated extracellular vesicles (EVs) are increasingly recognized as an effective monitoring strategy of brain pathologies, we have focused on the studies reporting dysregulated lncRNAs in EVs isolated from AD patients and controls. The revised literature shows a positive association between pro-inflammatory lncRNAs and AD. However, the reports evaluating lncRNAs alterations in EVs isolated from plasma of patients and controls, although still limited confirm the value of specific lncRNAs associated with AD as reliable biomarkers. This is an emerging field that will open new avenues to improve risk prediction, patients’ stratification and may lead to the discovery of potential novel therapeutic targets for AD

Abstract: The gut microbiome refers to microorganisms and their genetic material influencing local and systemic inflammation. Inflammation is known to contribute to cancer development, progression, and treatment. Evidence suggests that modulating the gut microbiome may affect responses to various cancer therapies. The gut microbiota has been suggested to have an impact on immunotherapy efficacy, especially the currently widely used immune checkpoint inhibitors in various malignancies. Microbial Interventions like fecal microbiota transplantation, various probiotics, or even antibiotics can increase or de-crease the tumor's sensitivity to immunotherapy. However, not all tumors react in the same manner, highlighting the tumor microenvironment heterogeneity across tumor types and the influence this has on the crosstalk between the microbiome and therapy outcomes. In this study, we intend to review the association between the gut microbiota and immunotherapy response in cancer patients and the factors regulating this interaction.

The possibilities and reach of social networks are increasing, the designs are becoming more diverse, and the ideas more visionary. Most recently, the former company “Facebook” announced the creation of a metaverse. With these technical possibilities, however, the danger of fraudsters is also growing. Using social bots, consumers are increasingly influenced on such platforms and business transactions are brought about through communication, i.e. conversational commerce. Minors or the elderly are particularly susceptible. This technical development is accompanied by a legal one: it is permitted by the Digital Services Directive and the Sale of Goods Directive to demand the provision of data as consideration for the sale of digital products. This raises legal problems at the level of the law of obligations and data protection law, whose regulations are intended to protect the aforementioned groups of individuals. This protection becomes even more important the more manipulative consumers are influenced by communicative bots. We show that there is a lack of knowledge about what objective data value can have in business transactions. Sufficient transparency of an objective data value can maintain legal protection, especially of vulnerable groups, and ensure the purpose of the laws.

A time integration scheme based on semi-Lagrangian advection and Laplace transform adjustment has been implemented in a baroclinic primitive equation model. The semi-Lagrangian scheme makes it possible to use large time steps. However, errors arising from the semi-implicit scheme increase with the time step size. In contrast, the errors using the Laplace transform adjustment remain relatively small for typical time steps used with semi-Lagrangian advection. Numerical experiments confirm the superior performance of the Laplace transform scheme relative to the semi-implicit reference model. The algorithmic complexity of the scheme is comparable to the reference model, making it computationally competitive, and indicating its potential for integrating weather and climate prediction models.

In this study, we surveyed over 600 participants to determine: a) major causes to mental stress during the pandemic and its future impacts, and b) diversity in public perception and acceptance (specifically for children) of Covid-19 vaccination. Statistical results and intelligent clustering outcomes indicate significant relationships between sociodemographic diversity, mental stress causes, vaccination perception, and Covid-19 infections. For instance, statistical results indicate significant dependence between mental stress due to Covid-19 and gender (p = 1.7e-05). Over 25% of males indicated work related stress comparing 35% in females however, females indicated more stressed (17%) due to relationships comparing to males (12%). Around 30% of Asian/Arabic participants don’t feel vaccination being safe as compared to 8% of white-British and 22% of white-European indicating significant dependence (p = 1.8e-08) with ethnicity. More specifically, vaccination acceptance for children is significantly dependent to ethnicity (p = 3.7e-05) where only 47% participants show willingness towards children’s vaccination. Primary dataset in this study along with experimental outcomes identifying sociodemographic information diversity with respect to public perception and acceptance of vaccination to children and potential stress factors might be useful for public and policy makers to be better prepared for future epidemics as well as working globally to combat mental health issues and running more effective vaccination campaigns.

The paper investigates new robust code constructions based on bent functions and spline–wavelet transformation. Implementation of bent functions in codes construction increases the probability of error detection in the data channel and cryptographic devices. Meanwhile, use of spline–wavelets theory for constructing the codes gives the possibility to increase system security from the actions of an attacker. Presented constructions combines spline–wavelets functions and bent functions. Developed robust codes, compared to existing ones, have a higher parameter of maximum error masking probability. Illustrated codes are ensuring the security of transmitted information. Some of the granted constructions were implemented on FPGA.

In this article we analyze the determinants and the export trend of European countries of medium and high technology products. The data were analyzed using various econometric models, namely WLS, Pooled OLS, Dynamic Panel, Panel Data with Fixed Effects, Panel Data with Random Effects. The results show that exports of medium and high-tech products are positively associated, among other variables, with the value of “Average Annual GDP Growth”, “Total Entrepreneurial Activity” and “Sales Impacts”, and negatively associated with, among other variables, “Human Resources”, “Government and Procurement of Advanced Technology Products” and “Buyer Sophistication”. A cluster analysis was realized with the k-Means algorithm optimized with the Silhouette coefficient. The result showed the presence of only two clusters. Since this result was considered poorly representative of the industrial complexity of the European Union countries, a further analysis was carried out with the Elbow method. The result showed the presence of 6 clusters with the dominance of Germany and the economies connected to the German economy. In addition, a network analysis was carried out using the distance to Manhattan. Four complex network structures and two simplified network structures were detected. A comparison was then made between 10 machine learning algorithms for predicting the value of exports of medium and high-tech products. The result shows that the best performing algorithm is the SGD. An analysis with Augmented Data-AD was implemented with a comparison between 10 machine learning algorithms for prediction and the result shows that the Linear Regression algorithm is the best predictor. The prediction with the Augmented Data-AD allows to reduce the MAE by about 0.0022131 compared to the prediction with the Original Data-OD.

Regardless of the advances in our ability to detect early and treat breast cancer, it is still one of the common types of malignancy worldwide, with the majority of patients decease upon metastatic disease. Nevertheless, due to these advances, we have extensively characterized the drivers and molecular profiling of breast cancer and further dividing it into subtypes. These subgroups are based on immunohistological markers (Estrogen Receptor-ER, Progesterone Receptor-PR and Human Epidermal Growth Factor Receptor 2-HER-2) and transcriptomic signatures, with distinct therapeutic approaches and regiments. These therapeutic approaches include targeted therapy (HER-2+), endocrine therapy (HR+) or chemotherapy (TNBC) with optional combination radiotherapy, depending on clinical stage. Technological and scientific advances in the identification of molecular pathways that contribute to therapy-resistance and establishment of metastatic disease, have provided the rationale for revolutionary targeted approaches against Cyclin-Dependent Kinases 4/6 (CDK4/6), PI3 Kinase (PI3K), Poly ADP Ribose Polymerase (PARP) and Programmed Death-Ligand 1 (PD-L1), among others. In this review, we focus on the comprehensive overview of epidemiology and current standard of care treatment of metastatic breast cancer, along with ongoing clinical trials. Towards this goal, we utilized available literature from PubMed and ongoing clinical trial information from clinicaltrials.gov to reflect the up to date and future treatment options for metastatic breast cancer.

Soil texture is key information in agriculture for improving soil knowledge and crop performance, so the accurate mapping of this crucial feature is imperative for rationally planning cultivations and for targeting interventions. We studied the relationship between radioelements and soil texture in the Mezzano Lowland (Italy), a 189 km² agricultural plain investigated through a dedicated airborne gamma-ray spectroscopy survey. The K and Th abundances were used to retrieve the clay and sand content by means of a multi-approach method. Linear (simple and multiple) and non-linear (machine learning algorithms with deep neural networks) predictive models were trained and tested adopting a 1:50,000 scale soil texture map. The comparison of these approaches highlighted that the non-linear model introduces significant improvements in the prediction of soil texture fractions. The predicted maps of the clay and of the sand content were compared with the regional soil maps. Although the macro-structures were equally present, the airborne gamma-ray data permits us shedding light on finer features. Map areas with higher clay content were coincident with paleo-channels crossing the Mezzano Lowland in Etruscan and Roman periods, confirmed by the hydrographic setting of historical maps and by the geo-morphological features of the study area.

Background: A novel virulent coronavirus is what causes Novel Corona-virus Disease 2019 (nCOVID 19). It results in severe respiratory distress syndrome and potentially fatal infectious pneumonia. On March 12, 2020, the World Health Organization first labeled it a pandemic, which was then followed on the same day by a community health emergency of global concern. Vaccines against this deadly virus are now being created. Many drugs with different uses have been repurposed and tested for the prevention and treatment of the infection. Objective: The purpose of this review is to provide an in-depth analysis of data on possible pharmacological targets and available coronavirus treatments. Methods: Following the review protocol, a literature search was conducted. Results: Chloroquine phosphate and hydroxychloroquine, Remdesivir, and Lopinavir-Ritonavir in combination with or without interferon and convalescent plasma therapy are the main treatment candidates, according to the World Health Organization. This review article has elaborated on the current evidence of prospective pharmacological targets and related ongoing research, including inflammatory chemicals, bioactive peptides, beta cells, platelets, and the Angiotensin I Converting Enzyme 2 Receptor. This information was gathered from published journals. In addition, stories of medications and biological products like interferons and vaccinations that are utilized or could be utilized have been provided. Conclusion: There are a variety of pharmacological targets and therapeutic strategies that need more study.

We study C*-algebraic versions of following conjectures/theorems: (1) Bieberbach conjecture (de Branges theorem) (2) Robertson conjecture (3) Lebedev-Milin conjecture (4) Zalcman conjecture (5) Krzyz conjecture (6) Corona conjecture (Carleson theorem). We prove that the C*-algebraic Bieberbach Conjecture for invertible coefficients is true for second degree C*-algebraic polynomials.

A correctly obtained Land-use/land-cover (LULC) prediction map is essential to under-standing and assessing future patterns. In the study, the LULC map of Urmia/Iran in 2030 was produced using two different prediction methods CA-Markov and Artificial Neural Network (ANN). In general, the study followed a methodology consisting of three steps. In the first steps, Landsat satellite images acquired in 2000, 2010 and 2020 were classified with maximum likelihood algorithm and LULC maps were prepared for each year. In the second stage, to investigate the LULC prediction methods' validation (CA-Markov and ANN) the LULC prediction map of 2020 was produced using the LULC map of 2000 and 2010; In this step, the predicted LULC map of 2020 and the actual LULC map of 2020 were evaluated by correctness, completeness and quality indexes. Finally, The LULC map for 2030 was prepared using all two algorithms and the change map was extracted. The results show that the area of soil and vegetation decreased, and built-up regions increased during the research period. The methods validation results show that the two algorithms are much closer to each other. Nevertheless, in general, ANN has the highest completeness (96.21%) and quality (93.8%) and CA-Markov the most correctness (96.47). This study shows that the CA-Markov algorithm is most successful in predicting the future that had larger areas and a higher percentage in the region (urban and vegetation cover) and the ANN algorithm in predicting phenomena that had smaller levels with fewer percentages (soil and rock).

Over the last few years, Earth Observation (EO) data has shifted towards increased use to produce official statistics, particularly in the agriculture sector. National statistics offices worldwide, including in Asia and the Pacific, are expanding their use of EO data to produce agricultural statistics such as crop classification, yield estimation, irrigation mapping, and crop loss estimation. The advances in image classification, such as pixel-based and phenology-based classifications, and machine learning create new opportunities for researchers to analyze EO data applied to agriculture statistics. However, it requires the ground truth (GT) data because classification result mainly depends on the quality of GT. Therefore, in this study, we introduced a random sampling approach to design and collect GT data using EO imagery and ancillary data. As a result of data collection, GT data improve the algorithms and validates classification results. Nevertheless, despite the importance of GT data, they are rarely disseminated as a data product in themselves. Thus, this results in an untapped opportunity to share GT data as a global public good, and improved use of survey and census data as a source of GT data.

Mental health and social resilience play a significant role in refugees’ adaptation during the resettlement process in the host country. Maintaining good mental wellbeing helps the refugees to respond to stressful experiences with healthy life choices. This study aimed to explore the mental wellbeing and social resilience of Eritrean refugees living in Germany and to identify social conditions and enablers to foster adaptation. This study employs a qualitative approach with a semi-structured, in-depth interview data collection method. Informants were identified among mostly young adult refugees living in Heidelberg, Germany, with a migration history of 3-6 years. In total, 15 informants were recruited through snowball sampling. Data were sorted and analyzed using the five pillars of the Adaptation and Development after Persecution and Trauma (ADAPT) model. The findings suggest that Eritrean refugees experience psychological distress after resettlement in Germany, however with time, their mental health has improved. The study revealed conditions that were experienced as hindrances, as well as ones that were considered to be resources of positive mental wellbeing and social resilience for resettled refugees. Challenges described were the language barrier, discrimination, unemployment, insecure residence status, loss of family and friends, conflict within the diaspora community, and isolation. The main sources of mental wellbeing and social resilience include the feeling of being welcomed by local communities, access to social services, adopting new relationships, and educational opportunities. These experiences encouraged refugees to have a favorable view of their lives and futures as well as also found to facilitate better integration and adaptation. Understanding refugee mental wellbeing and social resilience require a multidimensional perspective. Eritrean refugees living in Germany have experienced and still are experiencing resettlement challenges, as for example loss of family and friends, negative perception of the German system, loss of past achievements, or unemployment. But they have developed adaptive and resilience mechanisms, too, such as seeing an opportunity for a better life, adopting new roles, and accepting Germany as a “second home”. In addressing those by the refugees as hindrances reported issues, these could be turned into sources of mental well-being and resilience.

Lesion studies are crucial in establishing brain-behavior relationships, and accurately segmenting the lesion represents the first step in achieving this. Manual lesion segmentation is the gold standard for chronic strokes. However, it is labor-intensive, subject to bias, and limits sample size. Therefore, our objective is to develop an automatic segmentation algorithm for chronic stroke lesions on T1-weighted MR images. Methods: To train our model, we utilized an open-source dataset: ATLAS v2.0 (Anatomical Tracings of Lesions After Stroke). We partitioned the dataset of 655 T1 images with manual segmentation labels into five subsets and performed a 5-fold cross-validation to avoid overfitting of the model. We used a deep neural network (DNN) architecture for model training. Results: To evaluate the model performance, we used three metrics that pertain to diverse aspects of volumetric segmentation including shape, location, and size. The Dice similarity coefficient (DSC) compares the spatial overlap between manual and machine segmentation. The average DSC was 0.65 (0.61- 0.67; 95% bootstrapped CI). Average symmetric surface distance (ASSD) measures contour distances between the two segmentations. ASSD between manual and automatic segmentation was 12mm. Finally, we compared the total lesion volumes and the Pearson correlation coefficient (ρ) between the manual and automatically segmented lesion volumes, which was 0.97(p-value &lt; 0.001). Conclusions: We present the first automated segmentation model trained on a large multicentric dataset. This model will enable automated on-demand processing of MRI scans and quantitative chronic stroke lesion assessment.

As renewed interest in human space-exploration intensifies, a coherent and modernized strategy for mission-design and planning has become increasingly crucial. Biotechnology has emerged as a promising approach to increase mission resilience, flexibility, and efficiency by virtue of its ability to efficiently utilize in situ resources and reclaim resources from waste streams. Since its infancy during the Apollo years, biotechnology, and specifically biomanufacturing, have witnessed significant expansions of scope and scale. Here we outline four primary mission classes, on Luna and Mars, that drive a staged and accretive biomanufacturing strategy. Each class requires a unique approach to integrate biomanufacturing into the existing mission architecture and so faces unique challenges in technology development.These challenges stem directly from the resources available in a given mission class – the degree to which feedstocks are derived from cargo and in situ resources – and the degree to which loop-closure is necessary. We see that as mission duration and distance from Earth increase, the benefits of specialized sustainable biomanufacturing processes increases. Here we present a strategic approach, guided by technoeconomics, to development, testing, and deployment of these technologies serves to nucleate the larger effort of supporting a sustained human presence in space. The processes needed for each scenario spans the technical breadth of synthetic biology to design engineering, from sophisticated genetic tailoring of chassis-organisms to building scalable, automated, easily operable bioreactors and processing systems. As space-related technology development often does, these advancements are likely to have profound implications for the creation of a stable, resilient bioeconomy on Earth.

Developing new ways to estimate probabilities can be valuable for science, statistics, and engineering. By considering the information content of different output patterns, recent work invoking algorithmic information theory has shown that a priori probability predictions based on pattern complexities can be made in a broad class of input-output maps. These algorithmic probability predictions do not depend on a detailed knowledge of how output patterns were produced, or historical statistical data. Although quantitatively fairly accurate, a main weakness of these predictions is that they are given as an upper bound on the probability of a pattern, but many low complexity, low probability patterns occur, for which the upper bound has little predictive value. Here we study this low complexity, low probability phenomenon by looking at example maps, namely a finite state transducer, natural time series data, RNA molecule structures, and polynomial curves. Some mechanisms causing low complexity, low probability behaviour are identified, and we argue this behaviour should be assumed as a default in the real world algorithmic probability studies. Additionally, we examine some applications of algorithmic probability and discuss some implications of low complexity, low probability patterns for several research areas including simplicity in physics and biology, a priori probability predictions, Solomonoff induction and Occam's razor, machine learning, and password guessing.

Albanian migration has always reflected a family character, be that before 1945 when Albania was not yet completely isolated, as well as after 1990 when borders were reopened. This feature characterized all types of movement, internal or international, permanent or seasonal migration, return migration or transnational movements, and remigration. The role of the family has been very important in making decisions regarding migration and answering questions from why to how to migrate, from when to where, whom to ask for help or how to invest remittances. Based on the case study of a rural area in Northern Albania, The Administrative Unit of Dajç, this article explores in detail the roles of family and kinship on decisions regarding return migration, the re-adjustment process, remigration or transnational life. By exploring the role of the family context in remigration and vice-versa the article reflects that the family biography – including the lifestyle, plans for the future or expectations - has changed due to previous migration experiences or challenges and difficulties when returning to the home country. It demonstrates how individual decisions to migrate or to ‘return home’ are negotiated and supported within families making transnational life a family project. The article adopts a new approach in the Albanian Migration Studies which may be implied on broader areas for further research in the future.

The assessment of seismic risk in urban areas with high seismicity is certainly one of the most important problems that territorial managers have to face. A reliable evaluation of this risk is the basis for the design of both specific seismic improvement interventions and emergency management plans. Unappropriate seismic risk assessments may provide misleading results and induce bad decisions with relevant economic and social impact.The seismic risk in urban areas is mainly linked to three factors, namely, “hazard”, “exposure” and “vulnerability”. Hazard measures the potential of an earthquake to produce harm; exposure evaluates the amount of population exposed to harm; vulnerability represents the proneness of considered buildings to suffer damages in case of an earthquake. Estimates of such factors may not always coincide with the perceived risk of the resident population. The propensity to implement structural seismic improvement interventions aimed at reducing the vulnerability of buildings depends significantly on the perceived risk.This paper investigates on the difference between objective and perceived risk and highlights some critical issues. The aim of this study is to calibrate opportune policies, which allow addressing the most appropriate seismic risk mitigation options with reference to current levels of perceived risk. We propose the introduction of a Seismic Policy Prevention index (SPPi). This methodology is applied to a case-study focused on a densely populated district of the city of Catania (Italy).

This paper reports a new global Mittag-Leffler synchronization criterion with regard to fractional-order hyper-chaotic financial systems by designing the suitable impulsive control and the state feedback controller. The significance of this impulsive synchronization lies in the fact that the backward economic system can synchronize asymptotically with the advanced economic system under the effective impulse macroeconomic management means. Matlab LMI-toolbox is utilized to deduce the feasible solution in numerical example, which shows the effectiveness of the proposed methods. It is worth mentioning that the LMI-based criterion usually requires the activation function of the system to be Lipschitz, but the activation function in this paper is fixed and truly nonlinear, which cannot be assumed to be Lipschitz continuous. This is another mathematical difficulty overcome in this paper.

THz band-pass filters were fabricated by laser ablation of micro-foils of stainless steel and Kapton. Their spectral performance was tested in transmission and re- flection at the THz beamline at the Australian Synchrotron (AuSy). A 25 μm Kapton film performed as a Fabry-Pérot etalon with a free spectral range of FSR = 119 cm−1, high finesse Fc ≈ 17, and was tuneable over ~10 μm (at ~5 THz band) with β = 30° tilt. The structure of the THz beam focal region as extracted by the first mirror (slit) shows a complex polarisation-wavelength- position dependence across the beam. This is important for polarisation sensitive measurements (in transmission and reflection) and requires normalisation at each orientation of linear polarisation.

Intratumoral heterogeneity can exist along multiple axes: Cancer Stem Cells (CSCs)/non-CSCs, drug-sensitive/drug-tolerant states and a spectrum of epithelial-hybrid-mesenchymal phenotypes. Further, these diverse cell-states can switch reversibly among one another, thereby posing a major challenge to therapeutic efficacy. Therefore, understanding the origins of phenotypic plasticity and heterogeneity remains an active area of investigation. While genomic components (mutations, chromosomal instability) driving heterogeneity have been well-studied, recent reports highlight the role of non-genetic mechanisms in enabling both phenotypic plasticity and heterogeneity. Here, we discuss various processes underlying phenotypic plasticity such as stochastic gene expression, chromatin reprogramming, asymmetric cell division and the presence of multiple “attractors”. These processes can facilitate a dynamically evolving cell population such that a subpopulation of (drug-tolerant) cells can survive lethal drug exposure and recapitulate population heterogeneity on drug withdrawal, leading to relapse. These drug-tolerant cells can be both pre-existing and also induced by the drug itself through cell-state reprogramming. The dynamics of cell-state transitions both in absence and presence of the drug can be quantified through mathematical models. Such a dynamical systems approach to elucidating patterns of intratumoral heterogeneity by integrating longitudinal experimental data with mathematical models can help design effective combinatorial and/or sequential therapies for better clinical outcomes.

The Industrial Internet of Things (IIoT) is bringing evolution with remote monitoring, intelligent analytics, and control of industrial processes. A reference architecture provides the general layout information for the flexible integration of IIoT systems; however, as the industrial world is currently in its initial stage of adopting the full-stack development solutions with IIoT, some challenges need to be addressed. To cope with the rising challenges and provide the blueprint guidelines to develop and implement IIoT in real-time, researchers around the globe have proposed IIoT architectures based on different architectural layers and emerging technologies. In this paper, we first review and compare some widely accepted IIoT reference architectures and present a state-of-the-art review of conceptual and experimental IIoT architectures in literature. We highlight scalability, interoperability, security, privacy, reliability, and low latency as the main IIoT architectural requirements and compare how the current architectures address these challenges. We also highlight the role of emerging technologies in current IIoT architectures to address these requirements and present the literature gap for future research work to address the challenges.

The importance of a properly regulated immune response has been explored through various research-oriented methods. It has proved that having an effective and well-regulated immune response helps prevent many minors and serious diseases. Any kind of dysregulation in the working of the immune system may lead to severe consequences. An autoimmune disorder is one such consequence in which the component of the immune system starts targeting self-cells of the body, treating them as self-antigen producing autoantibodies and immune complexes resulting from the failure of immune responses both at a central and peripheral level. Currently, many autoimmune disorders are prevailing across the world, and new diseases need attention.In this review, we have written about the functions of T cell, LncRNAs and also some interactions between the LncRNAs and miRNAs in the pathogenesis of Multiple sclerosis (MS), Systemic lupus erythematosus (SLE), Type 1 diabetes (T1D), Rheumatoid arthritis (RA). To find the functions of these autoimmune disorders we have done intensive literature search during this we also found that GAS5 and MALAT are common in MS, SLE, T1D, RA. And finally, we have also mentioned the therapeutic drugs which neutralizes the different cluster of differentiation (CDs) and also suppresses the T cell differentiation in MS, SLE and RA. For T1D therapeutic strategies and various types of insulin are mentioned.

Imitation Learning is a discipline of Machine Learning primarily concerned with replicating observed behavior of agents known to perform well on a given task, collected in demonstration data sets. In this paper, we set out to introduce a pipeline for collecting demonstrations and training models that can produce motion plans for industrial robots. Object throwing is defined as the motivating use case. Multiple input data modalities are surveyed, and motion capture is selected as the most practicable. Two model architectures operating autoregressively are examined -- feedforward and recurrent neural networks. Trained models execute throws on a real robot successfully, and a battery of quantitative evaluation metrics is proposed, including extrapolated throw accuracy estimates. Recurrent neural networks outperform feedforward ones in most respects, with the best models having an assessed mean throw error on the order of 0.1...0.2 m at distances of 1.5...2.0 m. The data collection, pre-processing, and model training aspects of our proposed approach show promise, but further work is required in developing Cartesian motion planning tools before it is suitable for application in production.

Sacrococcygeal pilonidal disease is a chronic inflammatory condition with an incidence of 26:100,000 in the United States. However, its etiology and optimal treatment remain controversial. We included 129 and 74 patients with simple and complex sacrococcygeal pilonidal disease, respectively. The primary outcome was pilonidal sinus recurrence after unroofing curettage. Secondary outcomes were pain scores, time to return to work/school, and time to complete recovery. At a median follow-up of 53 months, the recurrence rate was 4.9% in all patients, not significantly higher in subjects with the complex disease. Duration of surgery (15.4 vs. 12.2 min), time to return to school/work (9.8 vs. 7.7 days), and complete healing time (44 vs. 36 days) were longer in patients with the complex disease. Postoperative complication rates, pain scores, and quality of life scores between the two groups did not differ. Unroofing curettage may be a good first-choice treatment for both simple and complex sacrococcygeal pilonidal disease.

We describe a dataset that contains job description published on a popular online website in the information and technology sector. As the website focus mainly on United Kingdom based jobs, the data have a specific focus on this country. It contains 11.501 job vacancies and 13 related meta data information. The dataset is suitable for HR analysis using machine learning techniques such as natural language processing and neural networks.

Eccentric punching shear can occur in concrete slab-column connections when the connection is subjected to shear and unbalanced moments. Typically, this situation results in edge and corner columns and is thus a common practical case. However, most punching experiments in the literature are concentric punching shear. This paper presents a developed database of eighty-eight experiments of flat slabs under eccentric punching shear, including a summary of the testing procedure of each reference and a description of the slab specimens. Additionally, a linear finite element analysis of all the specimens is included to determine the relevant sectional shear forces and moments. Finally, the ultimate shear stresses from the database experiments are compared to the shear capacities determined with ACI 318-19, Eurocode 2 NEN-EN 1992-1-1:2005, and the Model Code 2010. The comparison shows that the Model Code 2010 is the most precise in the predictions with an average tested over predicted ratio of 0.96 and a coefficient of variation of 27.96%. It can be concluded that this study represents the inconsistencies of the currently used design methods and the lack of experimental information.

In this study, we evaluated and compared several major carbon sequestration methods based on the first principles, namely, energy use efficiency and elemental stoichiometry. This study suggested that, at present, wood burial is the only feasible carbon sequestration method because it can be implemented immediately on a large scale, is cost-effective and efficient, has a long sequestration time, has low technical requirements, and has a relatively low impact on agriculture.

Up to 25% of early human epidermal growth factor receptor 2-positive (HER2+) breast cancer (BC) patients will relapse despite the improvement achieved by the introduction of HER2-targeted therapy. Beyond trastuzumab, other agents approved for early HER2+ BC include the monoclonal antibody pertuzumab, the antibody-drug conjugate (ADC) trastuzumab-emtansine (T-DM1) and the reversible HER2 inhibitor lapatinib. New agents, such as trastuzumab-deruxtecan or tucatinib in combination with capecitabine and trastuzumab, have also shown an improvement in the metastatic setting. Other therapeutic strategies to overcome treatment resistance have been explored in HER2+ BC, mainly in HER2+ that also overexpress estrogen receptors (ER+). One strategy has been to target the cyclin-dependent kinases 4/6 (CDK4/6) as they are downstream of HER2 and many of the cellular pathways associated with resistance to HER2-targeted therapies play a key role in cell cycle and proliferation. Different trials have explored these strategies with encouraging results, but definitive results are needed. In addition, HER2+ BC is known to be more immunogenic than other BC subgroups, with high variability between tumors. Different immunotherapeutic agents have been investigated in this setting, with promising but controversial results obtained to date.

On a bleak therapeutic landscape unchanged since the 1980’s, IVF with egg donation still stand as the lone medical answer to diminished reserve and premature ovarian insufficiency. Intraovarian platelet-rich plasma (PRP) crossed the horizon in 2016 as hopeful answer to these intertwined problems. The once remote mirage of platelet cytokine effects on gene regulation or telomere stabilization is now in sharper focus, and current work is clarifying how PRP corrects oxidative stress, rectifies tissue hypoxia, downregulates apoptosis, and enhances cellular metabolism. Not yet ready for routine use, this investigational treatment does offer one point of general agreement: How intraovarian PRP results should be classified—Patients are either responders or non-responders. From this, it is intriguing that no published PRP protocol has reported a supranormal ovarian rebound or hyperstimulation effect. This could be explained by baseline age-related ovarian conditions prevalent among poor responders, but since dysregulated or malignant transformations are also absent in other tissue contexts following autologous PRP treatment, the contribution of some platelet product which intrinsically delimits regenerative action cannot be discounted. Here we summarize results with recent experimental and clinical platelet research, framing those most likely to help advance reproductive endocrinology practice.

Solid tumours are characterized by an altered microenvironment (TME) from the physi-cochemical point of view, displaying a highly hypoxic and acidic interstitial fluid. Hy-poxia results from uncontrolled proliferation, aberrant vascularization and altered can-cer cell metabolism. Tumour cellular apparatus adapt to hypoxia by altering its metab-olism and behaviour, increasing its migratory and metastatic abilities by acquisition of a mesenchymal phenotype and selection of aggressive tumour cell clones. Extracellular acidosis is considered a cancer hallmark, acting as a driver of cancer aggressiveness by promoting tumour metastasis and chemoresistance by selecting for more aggressive cell phenotypes, although the underlying mechanism is still not clear. In this context, Ca2+ channels represent good target candidates due to their ability to integrate signals from the TME. Ca2+ channels are pH and hypoxia sensors and alterations in Ca2+ homeostasis in cancer progression and vascularization have been extensively reported. The present review will focus on Ca2+ permeable ion channels, with a major focus on TRP, SOC and PIEZO channels, that are modulated by tumour hypoxia and acidosis as well as the role of the resulted altered Ca2+ signals on cancer progression hallmarks. A deeper compre-hension of the Ca2+ signaling and acidic pH/hypoxia interplay will break new ground for the discovery of alternative and attractive therapeutics targets.

Two extensions of the generalized double Lomax distribution are introduced - the spliced-scale generalized double Lomax distribution and the exponentiated generalized double Lomax distribution. Their properties are studied. Usefulness of the new distributions is shown by fitting them to stock returns datasets.

For decades since the central dogma, cancer biology research has been focusing on the involvement of genes encoding proteins. It has been not until more recent times that a new molecular class has been discovered, named non-coding RNA (ncRNA), which has been shown to play crucial roles in shaping the activity of cells. An extraordinary number of studies into shown that ncRNAs represent an extensive and prevalent group of RNAs, including both oncogenic or tumor suppressive molecules. Henceforth, various clinical trials involving ncRNAs as extra ordinary biomarkers or therapies have started to emerge. In this review, we will focus on the prognostic and diagnostic role of ncRNAs for breast cancer.

Rheumatoid arthritis (RA) is a chronic, systemic, abnormal inflammatory immune response. It is characterized by the involvement of the synovium and multiple organs and the destruction of joints and articular cartilage. Over the past 30 years, several promising novel compounds and antibodies have been developed for the treatment of RA. The introduction of new drugs and precision medicine for all forms of RA raises several issues related to access to novel treatments by patients, optimal regimen selection, cost-effectiveness, prognosis monitoring and outcome surveillance, particularly with regarding to the development of low drug response rates, drug resistance and adverse side effects. Tremendous attention has been given to the identification of optimized drug combinations for the treatment of RA, particularly in early high-risk vulnerable and early individuals. Addressing these issues requires novel therapeutic approaches with new mechanisms and the establishment of accurate guidelines for drug selection, drug recombination, and non-chemical therapeutic efforts. In this study, we reviewed the most exciting recently established or ongoing novel drugs and methods according to the clinical trial database maintained by the United States National Library of Medicine and discussed the trends in RA drug development and challenges in the treatment, providing a reference significant for the accurate treatment of RA and the research direction in the future.

In the development of the safety-critical systems, it is important to perform Failure Modes and Effects Analysis (FMEA) process to identify potential failures. However, traditional FMEA activities tend to be considered difficult and time-consuming tasks. To compensate for the difficulty of the FMEA task, various types of tools are used to increase the quality and the effectiveness of the FMEA reports. This paper explains an Automatic FMEA tool which integrates the Model-based Design (MBD), FMEA, and Simulated Fault Injection techniques in a single environment. The Automatic FMEA tool has the following advantages compared to the existing FMEA analysis tool. First, the Automatic FMEA tool automatically generates FMEA reports compared to the traditional spreadsheet-based FMEA tools. Second, the Automatic FMEA tool analyzes the causality between the failure modes and the failure effects by performing model-based fault injection simulation. In order to demonstrate the applicability of the Automatic FMEA, we used the electronic fuel injection system (EFI) Simulink model. The results of the Automatic FMEA were compared to that of the legacy FMEA.

Natural compounds have diverse structures and are present in different forms of life. Metabolites such as tannins, anthocyanins, and alkaloids, among others, serve as a defense mechanism in live organisms and are undoubtedly compounds of interest for the food, cosmetic and pharmaceutical industries. Plants, bacteria, and insects represent a source of biomolecules with diverse activities, poorly studied in many cases. To use these molecules for different applications, it is essential to know their structure, concentrations, and biological activity potential. In vitro techniques that evaluate the biological activity of the molecules of interest have been developed since the 1950s. Currently, different methodologies have emerged to overcome some of the limitations of these traditional techniques, mainly the reduction of time and costs. However, emerging technologies continue to appear due to the urgent need to expand the analysis capacity of a growing number of reported biomolecules and the lack of therapeutic options to treat various diseases. This review presents an updated summary of the conventional and current methods to evaluate natural compounds' biological activity, including a diagram that summarizes the minimum techniques essential for correctly assessing molecules with biological potential.

Chemotherapy-induced peripheral neuropathy (CIPN) develops as a challenging nerve-damaging adverse effect of anticancer drugs used in chemotherapy. The disorder may require a dose reduction of chemo-therapy and its most common sensory symptoms are severe pain, tingling, and numbness in the hands and feet. CIPN affects dramatically the patient's quality of life (QoL). Pain and sensory abnormalities may occur for months, or even years after the termination of chemotherapy. This disease has complicated pathophysiology featured by underlying mechanisms not completely known. Although many pharmaco-logical and non-pharmacological therapeutic approaches have been tested to overcome these symptoms, there is currently no standardized cure to prevent or treat CIPN. According to current guidelines, Duloxe-tine is the only recommended agent for painful neuropathic symptoms. Therefore, finding effective thera-pies for CIPN is mandatory. The purpose of this review is to dissect CIPN, the target and immunothera-py-based approaches to this disorder, as well as to offer new insights for novel therapeutic perspectives.

Zephyranthes citrina Baker is a bulbous herb, commonly known as yellow rain lily belongs to the family Amaryllidaceae. It is a native of tropical and subtropical America but nowadays it is cultivated as a popular ornamental herb in several parts of the world including India. This herb represents one of the richest sources of phytochemicals, especially alkaloids and possesses great potential for pharmaceutical applications. It shows remarkable antiprotozoal, antimicrobial, anti-Alzheimer, cytotoxic, antioxidant, anti-inflammatory, analgesic, and dye removal activities. This review is an effort to give a detailed study of the literature on the biological activities of Zephyranthes citrina. This review concludes that Zephyranthes citrina has a great potential to treat various diseases and could be used as a source for novel healthcare products in the near future, which requires further experimentation.

Since the discovery of the Coronavirus disease 2019 (COVID-19) outbreak, a vast majority of studies have been carried out that confirmed the worst outcome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in people with preexisting health conditions, including diabetes, obesity, hypertension, cancer, and cardiovascular diseases. Likewise, diabetes itself is one of the leading causes of global public health concerns that impose a heavy global burden on public health as well as socio-economic development. Both diabetes and SARS-CoV-2 infection have their independent ability to induce the pathogenesis and severity of multi-system organ dysfunction, while the co-existence of these two culprits can accelerate the pathophysiology and magnify the severity of the diseases. However, the exact pathophysiology of multi-system organ failure in diabetic patients after SARS-CoV-2 infection is still obscure. This review summarized the organ-specific possible molecular mechanisms of SARS-CoV-2 and diabetes-induced pathophysiology of several diseases of multiple organs, including the lungs, heart, kidney, brain, eyes, gastrointestinal system, and bones, and subsequent manifestation of multi-system organ failure.

As its capital, Jakarta plays a critical role in boosting Indonesia’s economic growth and setting the precedent for broader change outside of the city. One crucial avenue of inquiry to better understand, and prepare for, the future of a country so heavily impacted by disastrous weather events is understanding the effects of climate change through data. This study investigates meteorological data collected from 1996 to 2021 and compares the application of the SARIMA and the Holt-Winters methods to predict the future influence of climatic parameters on Jakarta’s weather. The performance of the SARIMA method is proven to provide better results than the Holt-Winter models and both methods showed the best performances when forecasting the humidity data. The results of the forecast are able to demonstrate the characteristic of the climate in Jakarta, with dry season ranging from May to October and wet season ranging from November to April.

The cuttlebone-like structure is a complex porous bionic structure with asymmetric sinusoidal S-shaped wall structure connecting laminar septa, and studies have shown that the cuttlebone-like structure has light weight, high strength and excellent energy absorption capability. It has become an important research to investigate the mechanical mechanism of cuttlebone-like structure and to design a bionic structure beyond nature structure based on it. In order to investigate the mechanical properties of the static compression and shearing processes of the cuttlebone-like structure, this paper establishes theoretical formulas and analyzes the influence of dimensionless parameters such as height-to-thickness ratio $\lambda$, period-to-thickness ratio $\xi$ and amplitude to period ratio $\eta$ on the compression and shearing processes with the help of ABAQUS finite element software.The parameter sensitivity analysis method was used to compare the important influence degree of each dimensionless parameter on the mechanical properties of the structure and to determine the relative optimal parameters of the structural mechanical properties under compression and shearing. Based on the relatively optimal parameter of the cuttlebone-like structure, the cross-sectional geometry of the structure is improved to build a new cuttlebone-like structure.The experimental results showed that the new cuttlebone-like structure with revised cross-section improved the mechanical properties of the cuttlebone-like structure.The study results are of theoretical guidance to enhance the mechanical properties and geometric design of cuttlebone-like structures.

Background The spread of COVID-19 continues, the mutation of SARS-COV-2 is still difficult to control, and the need for antiviral drugs to treat COVID-19 remains urgent. The use of arbidol in the treatment of COVID-19 is limited and controversial. Methods To clarify the efficacy of arbidol on COVID-19, we collected 25 cases and 178 related studies. We analyzed the treatment information of arbidol based on the obtained cases, expanded the scope of the study, and collected current studies on the treatment of COVID-19 in various databases for in-depth analysis. Results History analysis showed that arbidol was effective (76% cure rate) compared with other drugs. However, compared with other antiviral drugs or standard therapy, the arbidol group had no significant advantage in reducing the time to negative virus transformation, length of hospital stays, or improvement in CT (MD=0.22, 95%CI -0.29-0.73; MD = 0.61, 95% CI 1.46 to 2.67; RR=1.15, 95%CI 0.88-1.50); Analysis of adverse events showed no significant difference between the arbidol group and the other groups (RR=0.82, 95%CI 0.25-2.71). Conclusion Our study showed that arbidol had no significant effect on COVID-19, but showed a slight advantage in CT improvement and adverse events. Our study objectively evaluated the efficacy of arbidol in the treatment of COVID-19 and provided some guidance for arbidol in the treatment of COVID-19.

It is shown that the representation theory of some finitely presented groups thanks to their $SL_2(\mathbb{C})$ character variety is related to algebraic surfaces. We make use of the Enriques-Kodaira classification of algebraic surfaces and the related topological tools to make such surfaces explicit. We study the connection of $SL_2(\mathbb{C})$ character varieties to topological quantum computing (TQC) as an alternative to the concept of anyons. The Hopf link $H$, whose character variety is a Del Pezzo surface $f_H$ (the trace of the commutator), is the kernel of our view of TQC. Qutrit and two-qubit magic state computing, derived from the trefoil knot in our previous work, may be seen as TQC from the Hopf link. The character variety of some two-generator Bianchi groups as well as that of the fundamental group for the singular fibers $\tilde{E}_6$ and $\tilde{D}_4$ contain $f_H$. A surface birationally equivalent to a $K_3$ surface is another compound of their character varieties.

The one-to-one structural correspondence between any conjugated hydrocarbon CnHm and the borane BnHm+n is extended here, with the B3LYP/cc-pVDZ model, to pure conjugated carbon systems with the example of buckminsterfullerene C60 with the corresponding icosahedral isoelectronic system closo-borane B60H60, and the fluorine substituted systems B60F30H30 and B60F60 , all with icosahedral Ih symmetry. All systems correspond to energy minima in the potential energy hypersurface, except for B60(F30)in(H30)out . Selected electronic structure methods are used to characterize all systems: molecular electrostatic potentials (MEP), atomic charges, bond orders, and topological properties of the electron density within quantum theory of atoms-in-molecules (QTAIM) and electron-localization function (ELF) theory. In the particular case of B60H60 we use the recently developed Hückeloid model to characterize this system. The stability of the energy minimum icosahedral structure B60F60 could have an origin in F···F attractive interactions of the inner fluorine atoms of the cage.

Proteases or peptidases are hydrolases that catalyze the breakdown of polypeptide chains into smaller peptide subunits. Proteases exist in all life forms, including archaea, bacteria, protozoa, insects, animals, and plants, due to their vital functions in cellular processing and regulation. There are several classes of proteases in the MEROPS database based on their catalytic mecha-nisms. This review focuses on the post-proline cleaving enzymes (PPCEs), especially the prolyl endoprotease/oligopeptidase (PEP/POP). To date, most PPCEs studied are of microbial and ani-mal origins. Recently, there are reports of new plant PPCEs. The most common PEP/POP are members of the S9 family that comprise two conserved domains. The substrate-limiting β-propeller domain prevents unwanted digestion, while the α/β hydrolase catalyzes reaction at the carboxyl-terminal of proline residues. PPCEs have diverse applications, are widely used in the beer brewing industry, and have potential as therapeutic agents for Alzheimer’s disease and celiac disease by targeting proline-rich substrates. Protein engineering via mutagenesis has been performed to improve heat resistance, pepsin-resistant capability, specificity, and protein turno-ver of PPCEs for pharmacological applications. This is the first comprehensive review to cover the biotechnological applications of PPCEs and discuss the unique prolyl cleaving activity of dif-ferent enzymes based on the recent structure-function studies from diverse taxa.

Starting from a very simple economic scenario, we build on it a game and then we introduce a general strategy able to reduce a regression problem to an equivalent binary classification problem. This reduction scheme (that we call adaptive reduction or also dynamic reduction) can be also used to derive a new boosting algorithm for regression problems named bOOst^d. The bOOst^d algorithm is very simple to implement, and it can use any learning algorithm with no priori assumptions. We present a conjecture for bOOst^d performances, which ensures a little error on training set. More important we can also provide a very good theoretical upper bound for the generalization error. We give a set of preliminary experimental results that seems to confirm our conjecture for bOOst^d performances on training set and the theoretical assumptions for the generalization error. We also provide a possible justification of why boosting often does not overfit. Finally, we leave some open problems and argue that in the future an adaptive single boosting (with an unique code) algorithm for binary, multi class and regression problems can be derived.

During the last decades, zebrafish (Danio rerio) has become one of the most important model organisms to study different physiological and biological phenomena. Research field of carbonic anhydrases (CAs) and carbonic anhydrase related proteins (CARPs) is not an exception in this. The best-known known function of CAs is the regulation of acid-base balance. However, studies performed with zebrafish, among others, have revealed important roles for these proteins in many other physiological processes, some of which have been unpredicted in the light of the previous studies and suggestions. Examples are roles in zebrafish pigmentation as well as motor coordination. Disruption of the function of these proteins may generate even lethal outcomes. In this review, we summarize the current knowledge of CA-related studies performed in zebrafish.

Even though hard, low friction coatings such as diamond like carbon (DLC) would be beneficial for the per-formance and longevity of rubber seals, a crucial challenge – as graphically illustrated in Figure 1.a – remains. The elastic mismatch of rubber substrate and DLC coating prevents a fracture free coating application. In this work a nature inspired approach (Figure 1.b) is applied to render the stiff coating flexible and resilient to delamination at the same time by direct patterning. Rubber substrates were laser structured with tile patterns and subsequently DLC-coated. Tensile and tribology tests were performed on structured and unstructured samples. Unstructured DLC-coatings showed a crack pattern induced by the coating process, which was further fragmented by tensile stress. Coatings with tile patterns did not experience a further fragmentation under load. During continuous tribological loading, less heterogenous damage is produced for tile structured samples. The findings are ascribed to the relief of induced coating stress by the tile structure, meaning a more resilient coating.

A key goal of modern neuroscience involves understanding how connections in the brain form and function. Such a knowledge is essential to better inform how defects in the exquisitely complex steps of nervous system growth underlie neuropsychiatric and neurodevelopmental disorders. In the last 40 years, studies of the nervous system in the fruit fly Drosophila melanogaster enabled the discovery of a wealth of molecular and genetic mechanisms that drive the development of these synaptic connections – specialized cell-to-cell connections that are the essential substrate for information flow and processing in the nervous system. The major driver of knowledge focused on studies at the neuromuscular junction due to its ease of examination. Analogous studies in the central nervous system lagged behind due to a lack of genetic accessibility of specific central neuron classes, appropriate synaptic labels compatible with cell-type specific access, and high resolution, quantitative imaging strategies. However, understanding how synapses in central circuits form remains a prerequisite to understanding brain development. In the last decade, a host of new tools and techniques made possible the extension of genetic studies of synapse organization into central circuits and greatly enhanced our understanding of central synapse formation, organization, and maturation. In this review, we consider the current state-of-the-field, focusing on two major elements. We first discuss the tools, technologies, and strategies developed to visualize and quantify synapses in vivo in genetically identifiable neurons of the Drosophila CNS. Second, we explore in depth how these tools enabled a clearer understanding of synaptic development and organization in different circuits of the fly brain and discovered novel molecular mechanisms that underlie synapse formation. These studies establish multiple brain regions in the fly as powerful in vivo genetic models that offer novel insights into synaptogenic regulators and mechanisms of neural development.

Optical motion capture systems are prone to the errors connected with markers recognition – occlusion, leaving the scene or mislabelling – all these errors are then corrected in the software, but still, the process is not perfect, resulting in artifact distortions. In the article, we examine four existing types of artifacts, then propose the method for detection and classification of the distortions. The algorithm is based on the derivative analysis, low-pass filtering, mathematical morphology and loose predictor. The tests involved multiple simulations using synthetically distorted sequences, comparison of performance to the human operators on real life data and applicability analysis for the distortion removal.

Epilepsy and Alzheimer's disease (AD) incidence increase with age. There are reciprocal relationships between epilepsy and AD. Epilepsy is a risk factor for AD and, in turn, AD is an independent risk factor for developing epilepsy in old age and abnormal AD biomarkers in PET and or CSF are frequently found in late onset epilepsies of unknown etiology. Accordingly, epilepsy and AD share pathophysiological processes including neuronal hyperexcitability and an early excitatory-inhibitory dysregulation leading to dysfunction in the inhibitory GABAergic and excitatory glutamatergic systems. Moreover, both β-amyloid and tau protein aggregates, the anatomopathological hallmarks of AD, have proepileptic effects. Finally, these aggregates have been found in the resection material of refractory temporal lobe epilepsies suggesting that epilepsy leads to amyloid and tau aggregates. Some epileptic syndromes, such as medial temporal lobe epilepsy share structural and functional neuroimaging findings with AD, leading to overlapping symptomatology such as episodic memory deficits and toxic synergistic effects. In this respect, the existence of epileptiform activity and electroclinical seizures in AD appears to accelerate progression of cognitive decline and the presence of cognitive decline is much more prevalent in epileptic patients than in elderly without epilepsy. Notwithstanding their clinical significance, the diagnosis of clinical seizures in AD is a challenge. Most are focal and manifest with altered level of consciousness without motor symptoms, and are often interpreted as cognitive fluctuations. Finally, despite the frequent association of epilepsy and AD dementia, there is a lack of clinical trials to guide the use of antiseizure medications (ASM). There is also a potential role for ASMs to be used as disease-modifying drugs in AD.

An increasing body of evidence shows that seaweeds, including kelp, can be used as a tool to neutralize or remove excess nutrients and metals from the water column. Here we report on a preliminary field assessment showing potential nutrient and carbon removal differences by sugar kelp and ribbon kelp grown in common gardens. Seawater and tissue samples were collected systematically from two farms in Alaska. Results show differences between % N and % C content between ribbon kelp (Alaria marginata) and sugar kelp (Saccharina latissima). Results also show that tissue nitrogen in ribbon kelp varies sharply due to nitrogen availability in the water column. In contrast, the percentage of tissue N in sugar kelp remains comparatively stable. Our outcomes provide insight into potential differences in nutrient removal and harvest timing for different kelp species.

Long-term, multi-decade research on planted tree survival in urban settings is sparse. One understudied urban environment is highway rights-of-way (ROW), lands adjacent to high-speed, unsignalized roadways. We conducted a re-inventory of tree planting cohort in northern Illinois, U.S. on a 48 km-long highway near Chicago which were 10-, 21-, and 30-years old to evaluate long-term patterns of survival and diversity. Using each randomly selected planting site along the highway as a unit of observation and analysis, we compared the number of trees documented in record drawing to the number of trees currently alive to determine percent survival. We evaluated 224 planting sites which originally contained 2,944 trees and collected data about the planting site location. For the oldest cohort, 26% of trees were still alive in 2018 (median survival by species = 16%, Q1 = 0%, Q3 = 48%), while 31% of the 21-year-old cohort (med. = 6%, Q1 = 0%, Q3 = 47%) and 86% of the 10-year-old cohort were still alive (med. = 85%, Q1 = 74%, Q3 = 96%). The survival of the 21- and 30-year-old cohort matches urban tree survival estimates by other researchers, while the 10-year-old survival is higher than expected. The only planting location characteristic that significantly affected survival was traffic islands (areas between the highway and entrance/exit ramps). Species with low drought tolerance were less likely to be alive for the 10-year-old cohort. Waterlogging tolerant species were more likely to be alive in the 10-year-old cohort. Since some species in the 21- and 30-year-old cohorts had very low survival, the tree species richness and diversity s in study areas declined between the initial record drawings and reinventory. This study demonstrates the challenges of maintaining long-term survival and diversity in the highway ROW and emphasizes the importance of species selection.

Two once-through steam generators and a combination of a steam generator and a gas preheater for supplying feed gases to solid-oxide electrolysis stacks were evaluated for their carryover characteristics of contaminants from the feed-water into the steam phase. The concentrations of various trace impurities in the steam were determined by sampling the steam condensates and screening them with inductively coupled plasma - mass spectrometry for 19 elements and liquid ion chromatography and continuous flow analysis for chloride and ammonium. Steam soluble species like boric acid undergo complete volatilization and transfer into the steam phase. During unstable evaporation in the steam generators an extensive physical carryover of alloying metal species was observed. At realistic operation conditions for steam electrolysis, the gas preheater caused a considerable release of silicon into the steam phase. Two stack experiments were performed with common preheater temperatures and showed largely increased cell voltage degradation at higher operation temperatures. The post-test chemical analysis of cell samples revealed significant concentrations of silicon in the samples that are regarded as primary cause for increased degradation. These findings could partially explain the wide spread of degradation rates reported for solid-oxide steam electrolysis experiments.

The time symmetry of T_c = CT is redefined based on the 4-D Euclidean space. The time inversion symmetry of T_c = CT changes the signs of the charge (q) and absolute time (ct) of the particle. The P symmetry changes the signs of the space momenta and handedness of the particle. The T_c symmetry changes the signs of the time momentum (P_t=E/c) and charges. The handedness are classified as the left-handedness and right-handedness on the 3-D space. The charges (|q| = cDt) and energies (E= cDtDV) of the particles are originated from the upward warping (q>0) and downward warping (q<0) of the particles along the time axis. The evolution of our matter universe with P_t > 0 since the big bang can be interpreted based on the CTP symmetry with the partner antimatter universe with p_t < 0. The photon space is called as the vacuum space. Then the E and M waves are the space fluctuations, and the gravitational G wave is the time fluctuations. And the E, M and G fields are newly explained. The zero E and M fields of the photon space indicates that the photon space and gamma ray are the 2EM waves. The rest mass energy of the particle is the 4-D space volume of the warped photon space. In the present work, the quantum wave function is considered as the 4-D vector with the space wave function and time axis wave function in the 4-D Euclidean space. The warped photon space corresponds to the squared wave function which is the probability density. It is concluded that, in the quantum mechanics, the imaginary number concept is introduced in the quantum wave functions instead of the time axis component of the 4-D wave function vector. The left-handed neutrino puzzle is explained from the handedness partner relation with the right-handed dark matters. Finally, the modified Lorentz transformations derived in the 4-D Euclidean space are compared with the Lorentz transformations of the special and general relativity theories in the 4-D Minkowski space.

Urban flood risk mapping is an important tool for the mitigation of flooding in view of human activities and climate change. Many developing countries, however, lack sufficiently detailed data to produce reliable risk maps with existing methods. Thus, improved methods are needed that can improve urban flood risk management in regions with scarce hydrological data. Given this, we estimated the flood risk map for Rasht City (Iran), applying a composition of decision-making and machine learning methods. Flood hazard maps were produced applying six state-of-the-art machine learning methods such as classification and regression trees (CART), random forest (RF), boosted regression trees (BRT), multivariate adaptive regression splines (MARS), multivariate discriminant analysis (MDA), and support vector machine (SVM). Flood conditioning parameters applied in modeling were elevation, slope angle, aspect, rainfall, distance to river (DTR), distance to streets (DTS), soil hydrological group (SHG), curve number (CN), distance to urban drainage (DTUD), urban drainage density (UDD), and land use. In total, 93 flood location points were collected from the regional water company of Gilan province combined with field surveys. We used the Analytic Hierarchy Process (AHP) decision-making tool for creating an urban flood vulnerability map, which is according to population density (PD), dwelling quality (DQ), household income (HI), distance to cultural heritage (DTCH), distance to medical centers and hospitals (DTMCH), and land use. Then, the urban flood risk map was derived according to flood vulnerability and flood hazard maps. Evaluation of models was performed using receiver-operator characteristic curve (ROC), accuracy, probability of detection (POD), false alarm ratio (FAR), and precision. The findings showed that the CART method is most accurate method (AUC = 0.947, accuracy = 0.892, POD = 0.867, FAR = 0.071, and precision = 0.929). The results also demonstrated that DTR, UDD, and DTUD played important roles in flood hazard modeling; whereas, the population density was the most significant parameter in vulnerability mapping. These findings indicated that machine learning methods can improve urban flood risk management significantly in regions with limited hydrological data.

IT auditing quality has been defined by results obtained in the process. Meanwhile, multiple studies have been done to enhance it therefore, it continues to be a topic interest and research so it is worth mentioning that its quality relies on trained competent auditors and experts able to develop a process correctly, adapting to clients and to manage auditing inherent risks According to results from the IT audit, low quality and security levels have been identified in terms of the human, technical and contextual factors, affecting audit quality. The objective of this investigation is to identify metrics and to determine their own corresponding factor applying an exploratory type of research. In order to achieve such aim, a targeted survey was designed and implemented at the Institute of Internal Auditors of Ecuador since they have the knowledge and expertise in the field. A factor analysis statistics technique was applied to data gathered to verify that it relates to the identified factors as dimensions are reduced, thus the most impacting metrics may assess the quality of IT audits. Analysis results yielded a mean score for each one of the assessed metrics, concluding that the technical factor is the most significative since it relates roles and task performance during the auditing process as well as control proce-dures. Finally, most auditing quality-related issues are mainly the outcome of an inferior management auditing process, therefore it is crucial that collegiate groups and professionals in the field validate the auditing process.

Analyzing the records of Advanced LIGO and Virgo gravitational observatories, we found a specific time-domain asymmetry inherent only to the signals of their gravitational detectors. Experiments with different periodic signals, Gaussian and non-Gaussian noises, made it possible to conclude that the noise of gravitational detectors is an unusual mixture of signals. We also developed a specialized Pearson correlation analyzer to recognize the gravitational-wave events. It turned out that the LIGO detectors’ output signals include a significant (– 6 dB) component, which has the properties of records of reliably recognized gravitational waves. It allows us to argue that the gravitational background noise is largely due to the processes of merging astronomical objects. Since the specific signal is registered by the detectors continuously, we can consider the sub-kilohertz band gravitational oscillations field as detected. Our analysis method also allows us to estimate the contribution of the gravitational background component to the total signal energy. With its help, it will be possible not only to provide the radio-frequency estimation of the magnitude of gravitational disturbances but also, possibly, to construct a map of the gravitational noise of the sky.

Tumor cells evolve in a complex and heterogeneous environment composed of different cell types and an extracellular matrix. Current 2D culture methods are very limited in their ability to mimic the cancer cell environment. In recent years, various 3D models of cancer cells have been developed, notably in the form of spheroids/organoids, using scaffold or cancer-on-chip devices. However, these models have the disadvantage of not being able to precisely control the organization of multiple cell types in complex architecture and are sometimes not very reproducible in their production, and this is especially true for spheroids. Three-dimensional bioprinting can produce complex, multi-cellular, and reproducible constructs in which the matrix composition and rigidity can be adapted locally or globally to the tumor model studied. For these reasons, 3D bioprinting seems to be the technique of choice to mimic the tumor microenvironment in vivo as closely as possible. In this review, we discuss different 3D-bioprinting technologies, including bioinks and crosslinkers that can be used for in vitro cancer models, and the techniques used to study cells grown in hydrogels; finally, we provide some applications of bioprinted cancer models.