There have been many discussions on the globalisation of SMEs, but it is true that there is not enough academic achievement after such the study of Born global (BG) ventures. The internationalisation of SMEs (Small and Medium Enterprises) is not easy because they lack resources or capabilities compared to multinational corporations. This study investigated the role of government in assisting the internationalisation of SMEs. In particular, SMEs lacked the ability to acquire market-oriented information, so we’ve established the scheme of efficient information support system for the internationalisation of SMEs. In other words, we proposed an information analysis system through the establishment of a relational database constructed for market-oriented information support. KISTI (Korea Institute of Science and Technology Information), which is one of the government-funded research institutes in the Republic of Korea, provided information support to the SMEs dealing with hydrazine related products. This study suggests this case for the market-oriented information support of the government in the internationalisation of SMEs. The research on information support of the government is meaningful in that it suggests a way to support SMEs in practical level.

Mercury is a toxic pollutant emitted from both natural sources and through human activities. A global interest in atmospheric mercury has risen ever since the discovery of the Minamata disease in 1956. Properties of gaseous elemental mercury enable long range transport which can cause pollution even in pristine environments. Total gaseous mercury (TGM) was measured from winter 2016 to spring 2017 over the Baltic Sea. A Tekran 2357A mercury analyser was installed aboard the research and icebreaking vessel Oden for the purpose of continuous measurements of gaseous mercury in ambient air. Measurements were performed during a campaign along the Swedish east coast and in the Bothnian Bay near Lulea during the icebreaking season. Data was evaluated from Gothenburg using a plotting software and back trajectories for air masses were calculated. The TGM average of 1.365 ± 0.054 ng/m³ during winter and 1.288 ± 0.140 ng/m³ during spring was calculated as well as a total average of 1.362 ± 0.158 ng/m³. Back trajectories showed a possible correlation of anthropogenic sources elevating the mercury background level in some areas. There were also indications of depleted air, i.e., air with lower concentrations than average, being transported from the Arctic to northern Sweden resulting in a drop in TGM levels.

The Ni-Co-Cr-W-Mo system is critical for the design of nickel-based super-alloys. This system stabilizes different topologically close packed (TCP) phases in many of the commercially super-alloys with high W and Mo contents. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), and thermodynamic calculations are applied to investigate the thermodynamics of the precipitates in two different W content Ni-Co-Cr-W-Mo super-alloys. Computational thermodynamics verified experimental observation of the f u phase formation as a function of temperature and alloy chemistry, but the kinetics for the precipitation of M6C phase do not agree with the experimental findings. The major precipitates of alloy 1 at temperatures 700 and 750 °C during long time exposure are M23C6, γ′ phase, and MC, and alloy 2 are M23C6, γ′ phase, MC, M6C and u phase. The W addition is found to promote the precipitation of M6C and u phase during exposure. The M6C has higher W and lower Ni content than that of u phase, meanwhile, M6C is an unstable phase would transform into M12C after 5000 h exposure at 750 °C. A great quantity of needle-like u phases precipitated after exposure at 750 °C for 5000h, which have no effect on the impact property of alloy 2.

Technological advances such as blockchain, artificial intelligence, big data, social media, geographic information systems represent a building block of the digital transformation that supports the resilience of the food supply chain (FSC) and increases its efficiency. This paper reviews the literature surrounding digitalization in FSCs. A bibliometric and key-route main path analysis was carried out to objectively and analytically uncover the knowledge development in digitalization within the context of sustainable FSCs. The research began with the selection of 2140 articles published nearly over five decades. Then, the articles were examined according to several bibliometric metrics such as year of publication, countries, institutions, sources, authors, and keywords frequency. A keyword co-occurrence network was generated to cluster the relevant literature. Findings of the review and bibliometric analysis indicate that research at the intersection of technology and the FSC has gained substantial interest from scholars. On the basis of keyword co-occurrence network, the literature is focused on the role of information communication technology for agriculture and food security, food waste and circular economy, and the merge of the Internet of Things and blockchain in the FSC. The analysis of the key-route main path uncovers three critical periods marking the development of technology-enabled FSCs. The study offers scholars a better understanding of digitalization within the agri-food industry and the current knowledge gaps for future research. Practitioners may find the review useful to remain ahead of the latest discussions of technology-enabled FSCs. To the authors’ best knowledge, the current study is one of the few endeavors to explore technology-enabled FSCs using a comprehensive sample of journals articles published during the past five decades.

Recently, several reports have emerged describing the long-term consequences of COVID-19 that may affect multiple systems, suggesting its chronicity. As further research is needed, we conducted a longitudinal observational study to report the prevalence and associated risk factors of long-term health consequences of COVID-19 by symptom clusters in patients discharged from the Temporary COVID-19 Hospital (TCH) in Mexico City. Self-reported clinical symptom data were collected via telephone calls over 90 days post-discharge. Among 4670 patients discharged from the TCH, we identified 45 symptoms across eight symptom clusters (neurological; mood disorders; systemic; respiratory; musculoskeletal; ear, nose, and throat; dermatological; and gastrointestinal). We observed that the neurological, dermatological, and mood disorder symptom clusters persisted in >30% of patients at 90 days post-discharge. Although most symptoms decreased in frequency between day 30 and 90, alopecia and the dermatological symptom cluster significantly increased (p<0·00001). Women were more prone than men to develop long-term symptoms and invasive mechanical ventilation also increased the frequency of symptoms at 30-days post-discharge. Overall, we observed that symptoms often persisted regardless of disease severity. We hope these findings will help promote public health strategies that ensure equity in the access to solutions focused on the long-term consequences of COVID-19.

In higher eukaryotes, enhancers determine the activation of developmental gene transcription in specific cell types and stages of embryogenesis. Enhancers transform the signals produced by various transcription factors within a given cell, activating the transcription of the targeted genes. Often, developmental genes can be associated with dozens of enhancers, some of which are located at large distances from the promoters that they regulate. Currently, the mechanisms that underly the specific distance interactions between enhancers and promoters remain unknown. This review describes the properties and activities of enhancers and discusses the mechanisms of distance interactions and potential proteins involved in this process.

With the rise of long-read sequencers and long-range technologies, delivering high-quality plant genome assemblies is no longer reserved to large consortia. Indeed, sequencing techniques but also computer algorithms have reached a point where the reconstruction of assemblies at the chromosome-scale is now feasible at the laboratory scale. Current technologies, and especially long-range technologies, are numerous and selecting the most promising one for the genome of interest is crucial to obtain optimal results. In this study, we resequenced the genome of the yellow sarson, Brassica rapa cv. Z1, using the Oxford Nanopore PromethION sequencer and assembled the sequenced data using current assemblers. To reconstruct complete chromosomes, we used and compared three long-range techniques, optical mapping, Omni-C and Pore-C sequencing libraries commercialized by Bionano Genomics, Dovetail Genomics and Oxford Nanopore Technologies respectively, or a combination of the three, in order to evaluate the capability of each technology.

A comprehensive model for designing robust all-in-fiber microresonator-based optical sensing setups is illustrated. The investigated all-in-fiber setups allow light to selectively excite high-Q whispering gallery modes (WGMs) into optical microresonators, thanks to a pair of identical long period gratings (LPGs) written in the same optical fiber. Microspheres and microbubbles are used as microresonators and evanescently side-coupled to a thick fiber taper, with a waist diameter of about 18 µm, in-between the two LPGs. The model is validated by comparing the simulated results with the experimental data. A good agreement between the simulated and experimental results is obtained. As an application example, the sensing of the concentration of an aqueous glycerol solution is demonstrated. The model is general and by exploiting the refractive index and/or absorption characteristics at suitable wavelengths, the sensing of other substances or pollutants can be also predicted.

Background: This study investigated the prevalence of worsening problems using Post Stroke Checklist (PSC) at 3, 6, and 12 months post-stroke and their associations with health-related quality of life. Methods: In stroke patients admitted between June 2014 and December 2015, PSC and EuroQol-5Dthree level (EQ-5D-3L) were assessed at post-stroke 3 (n=181), 6 (n=175), and 12months (n=89). The prevalence of worsening problems and its association withEQ-5D-3L at post-stroke 3 and 6months were analyzed. Results: An average of 0.59 (range 0–12), 1.47 (range 0–12), and 1.00 (range 0–10) worsening problems per patient was identified at 3, 6, and 12months after stroke, respectively. The most frequently and continuously identified worsening problems were mood disturbances (reported by 8.8%, 16.0% and13.5% of patients at 3, 6, and 12 months post-stroke, respectively). Worsening mobility was significantly associated with worse EQ-5D index at post-stroke 3 months (β,-0.583; 95% CI, -1.045 to -0.120). The worsening of mobility and communication was significantly associated with worse EQ-5D index at post-stroke 6 months (mobility: β,-0.170; 95% CI, -0.305 to -0.034, communication: β,-0.164; 95% CI, -0.309 to -0.020). Conclusions: PSC may be useful for the detection of various subjective worsening problems during serial clinical follow-up after stroke. Appropriate rehabilitation and management strategy to solve the identified problems could improve the quality of life in stroke survivors.

Molecular dynamics simulation is currently the theoretical technique eligible to simulate a wide range of systems from soft condensed matter to biological systems. However, of the excellent results that the technique has arrogated, this approach remains computationally expensive, but with the emergence of the new supercomputing technologies bases on graphics processing units graphical processing units-based systems GPUs, the perspective has changed. The GPUs allow performing large and complex simulations at a significantly reduced time. In this work, we present recent innovations in the acceleration of molecular dynamics in GPUs to simulate non-Hamiltonian systems. In particular, we show the performance of measure-preserving geometric integrator in the canonical ensemble, that is, at constant temperature. We provide a validation and performance evaluation of the code by calculating the thermodynamic properties of a Lennard-Jones fluid. Our results are in excellent agreement with reported data reported from literature, which were calculated with CPUs. The scope and limitations for performing simulations of high-throughput MD under rigorous statistical thermodynamics in the canonical ensemble are discussed and analyzed.

There are a large number of wildlife and insect species that are in trouble on this planet, and most believe that monarch butterflies in eastern North America are too, because of the well-publicized declines of their winter colonies in central Mexico in the last 25 years. A small number of studies over the last decade have cast doubt on this claim by showing declines are not evident at other stages of the annual cycle. To determine how extensive this pattern is, I conducted an exhaustive review of peer-reviewed and grey literature on (eastern) monarch population censuses and studies, conducted across all seasons, and extracted data from these sources to evaluate how monarch abundance has or has not changed over time. I identified 20 collections of data that included butterfly club reports, compilations of citizen-science observations, migration roost censuses, long-term studies of isotopic signatures, and even museum records. These datasets range in duration from 15 years to over 100 years, and I endeavored to also update each with information from the most current years. I also re-examined the winter colony data after incorporating historical records of colony measurements dating back to 1976. This represents the most complete and up-to-date synthesis of information regarding this population. When I examined the long-term trajectory within each dataset a distinct pattern emerged. Modest declines are evident within the winter colonies (over the full 45 year dataset), and, within three censuses conducted during the spring recolonization. Meanwhile, 16 completely separate monitoring studies conducted during the summer and fall (and from varying locations) revealed either no trend at all or in fact an increase in abundance. While each of these long-term studies has inherent limitations, the fact that all 16 sources of data show the same pattern is undeniable. Moreover, this evidence is consistent with recently-conducted genetic work that shows a lack of decline. Collectively, these results indicate that despite diminishing winter colonies and spring migrations, monarchs in eastern North America are capable of rebounding fully each year, implying that milkweed is not limiting within their collective range. Moreover, there is no indication from these data that the summer population was ever truly diminished by changing agricultural practices in the Midwest that reduced milkweed in crop fields within that region. It is possible that the larger population is not as dependent on Midwestern agricultural milkweed as once thought, and/or that monarchs are adapting to increasingly human-altered landscapes. These results are timely and should bear on the upcoming USFWS decision on whether the monarch requires federal protection in the United States. Importantly, they argue that despite losses of many insects globally, the eastern North American monarch population is not in the same situation.

With the development of big data analysis, blockchain and other technologies, the supply chain of enterprises is transforming to lean and intelligent. As an important link in the enterprise supply chain, the intelligent transformation of procurement plays an important role in the improvement of the supply chain efficiency, therefore, the construction of a common method supporting the intelligent upgrade of the enterprise procurement business has become a key concern for enterprise managers. Based on the balanced scorecard theory and the supply chain maturity model, this study combines the actual situation of procurement management in Chinese energy enterprises and constructs a procurement benchmarking system that balances the development direction of the industry and the actual needs of enterprises. Meanwhile, based on the grounded theory, three major themes of the intelligent procurement system (digital business module, procurement synergy mechanism and procurement ecosystem) are extracted to provide a methodological reference for the construction of intelligent procurement systems of energy enterprises. The study concludes with a case study of China National Energy Group Materials Company to demonstrate the application of the intelligent procurement system built in this paper, with a view to providing methodological reference for the intelligent procurement management in energy enterprises.

Integrating triple bottom line (TBL) goals into supply chains (SCs) is a challenging task which necessitates the careful coordination of numerous stakeholders’ individual interests. Recent technological advancements can impact TBL sustainability by changing the design, structure and management of modern SCs. Blockchain technology enables immutable data records and facilitates a shared data view along the supply chain. The Physical Internet (PI) is an overarching framework that can be applied to create a layered and comprehensive view of the SC. In this conceptual paper I define and combine these technologies and derive several high-level research areas and research questions to investigate adoption, management as well as structural SC issues. I suggest a theory-based research agenda for the years to come that exploits the strengths of rigorous academic research, while remaining relevant for the industry. Furthermore, I suggest various well-established theories to tackle the respective research questions and provide specific directions for future research.

In this study, 6 residents of a long-term care facility were asked to try on Virtual Reality glasses and report their first experiences with Virtual Reality. The results show that Virtual Reality is of great interest to elderly residents of in-patient long-term care facilities. The wearing period was longer than expected and no symptoms of cyber sickness occurred. For the residents it was exciting to explore the virtual environments. Austrian destinations, nature scenes in the mountains and forests but also trips to the zoo, the museum, in churches or even densely populated areas like shopping streets or train stations would be places for the residents, they would like to explore virtually. Far-off destinations such as Rio de Janeiro or the Caribbean are more of an exception. Biographically relevant places such as the parental home or the location of their wedding were not named. Concerning the usability, an adjustment of the VR glasses is necessary for a longer-term use in any case.

Logistics Engineering has transcended over the decades into an approach for competitive benefits in organisational performance and logistics efficiency. Most industries are recognising that significant savings are available to companies that are able to ordinate and improve within their logistics operations. Companies today face great challenges because the successful supply of many products and services needs to the effective integration of logistics activities across a prolongation supply chain and an increasing geographical separation. Moreover, logistics integration approach involves both internal integration for an ordinated, unified process as well as relationships to react flexibly, changeability, and responsibility to customer's demands. In this study, by using of Fuzzy Vikor (F-Vikor), the best combination of RIR was selected and RIR Locating was done. The main aim of this paper is survey and design of the integrated operational logistics network (IOLN) and also, a proposed IOLN to integration of the Iran Khodro Co Supply Network.

Polymers in highly confined geometries can display complex morphologies including ordered phases. A basic component of a theoretical analysis of their phase behavior in confined geometries is the knowledge of the number of possible single-chain conformations compatible with the geometrical restrictions and the established crystalline morphology. While the statistical properties of unrestricted self-avoiding random walks (SAWs) both on and off-lattice are very well known, the same is not true for SAWs in confined geometries. The purpose of this contribution is a) to enumerate the number of SAWs on the simple cubic (SC) and face-centered cubic (FCC) lattices under confinement for moderate SAW lengths, and b) to obtain an approximate expression for their behavior as a function of chain length, type of lattice, and degree of confinement. This information is an essential requirement for the understanding and prediction of entropy-driven phase transitions of model polymer chains under confinement. In addition, a simple geometric argument is presented that explains, to first order, the dependence of the number of restricted SAWs on the type of SAW origin.

This paper deals with the analysis of trends in road accidents on major highways in Brazil. Using updated time series techniques, our results indicate that a low degree of long memory was detected in the series with shocks having transitory effects over time. We further find that the number of accidents taking place in Brazil has been reducing over time, though in the presence of negative shocks, the recovery is not going to be immediate due to the long memory nature of the data. Despite the absence of relevant investment relating to infrastructure expansion, it is worth mentioning the consolidation of a nationwide tolled road system in Brazil involving concessions to private administrators, alongside more severe traffic laws that can impose limitations on driving licences.

Resilience is a particularly important quality for supply chains in this turbulent environment. Resilience in supply chain is the ability to retain, resume, and recover operations after an intense destructive incident. One of the strategic solutions for managing supply chain disruptions is to establish collaboration and strategic alliances in order to achieve competitive advantage. Therefore, given the increasing publication of articles in areas of strategic alliances and supply chain resilience, it is a good opportunity to review these articles, identify gaps in the current literature, demonstrate links between the two areas, and provide suggestions for future research. For these purposes, a bibliometric analysis has been performed on literature available on the Web of Science database. The distribution of articles based on year and country, influential journals, research areas, authors, affiliations, keywords, citations, and reference co-citation analysis are discussed. Results indicate that studies about strategic alliances, meant to increase resilience, are growing in areas such as “Management”, “Operations research”, “Management science”, and “Business”. Furthermore, the sources could be categorized in five clusters; namely “From knowledge concept to value creation”, “Internal and external relationships”, “Logistics and supply chain performance”, “Intellectual capital and strategic management”, and “Critical success factors and alliances”. This article can be useful for both practitioners and academics who explore the topic of strategic alliances and resilience in supply chain and also offers managers the opportunity to overcome supply chain disruptions and negative consequences of risks by becoming familiar with the key concepts of resilience. The persistence of businesses and supply chains is guaranteed through communicating with partners and even competitors in the light of alliance according to the findings of this research. Managers can pay attention to the integration of the supply chain to improve resilience and increase collaboration between suppliers and customers. Given the research results, strategic alliances can be noted in expanding organizational entrepreneurship, and shaping strategic collaboration networks in light of strategic alliances.

This paper outlines a new methodology for developing strategy for supply chain integration of Autonomous Electrical Vehicles (AEV) to the Internet of Things (IoT). The methodology consists of external architecture and internal design that anticipates the business strategy in the development process. The methodology is designed to anticipate the impact of developments in new road transport technologies, such as Tesla Truck or Tesla Pickup. Since the methodology is designed to anticipate the impact of non-existing technologies, it represents green-field analysis. Green-field is defined as a new and non-existent operation. Green-field strategy architecture in this paper is presented as a process of accepting the world and acting upon that version of the world. The results of the analysis are presented as pathways and outcomes, emerging from the interrelated relationship between AEV and IoT. The emerging methodology is applied through two case studies to evaluate the impact to environment, performance and operationalisation. The methodology proposes architecture and design for integrating AEV and IoT in the supply chain strategy, and a set of new evaluation criteria that promote acceptance of Artificial Intelligence (AI) in the design process. The main contribution to knowledge is a new methodology for integrating AEV and the IoT to the supply chains. The paper applies interplay between inductive and deductive case study and grounded theory approach to build upon the concept of supply chain architecture and contribute to knowledge to the topic of formulating green-field integrated AEV- IoT supply chain strategy.

LiFePO₄ (LFP) or Lithium-ion battery with its advantages compared to common current motorcycle battery is an appropriate alternative in substituting wet and dry cell battery. Huge amount of demand of motorcycle along with the battery in Indonesia also make it an interesting product for business. In order to assess the commercial potential for such a new technology, market share needs to be estimated as well as the techno-economic feasibility. Hence, market share prediction using the residents of Surakarta Region and techno-economic analysis using NPV, IRR and PBP indicators have been conducted in this study. Calculation using markov chain method shows that LFP battery tends to dominate the market after certain period. Techno-economic analysis also figures out that the commercialization is feasible in three conditions - first mover, even with market leader and equilibrium point. Therefore, there is a great commercial potential for LFP battery especially in Indonesia.

Objectives: A wealth of literature has established risk factors for social isolation among older people, however much of this research has focused on community-dwelling populations. Relatively little is known about how risk of social isolation is experienced among those living in long-term care (LTC) homes. We conducted a scoping review to identify possible risk factors for social isolation among older adults living in LTC homes. Methods: A systematic search of five online databases retrieved 1535 unique articles. Eight studies met the inclusion criteria. Results: Thematic analyses revealed that possible risk factors exist at three levels: individual (e.g., communication barriers), systems (e.g., location of LTC facility), and structural factors (e.g., discrimination). Discussion: Our review identified several risk factors for social isolation that have been previously documented in literature, in addition to several risks that may be unique to those living in LTC homes. Results highlight several scholarly and practical implications.

Software reliability is an important characteristic for ensuring the qualities of software products. Predicting the potential number of bugs from the beginning of a development project allows practitioners to make the appropriate decisions regarding testing activities. In the initial development phases, applying traditional software reliability growth models (SRGMs) with limited past data does not always provide reliable prediction result for decision making. To overcome this, herein we propose a new software reliability modeling method called deep cross-project software reliability growth model (DC-SRGM). DC-SRGM is a cross-project prediction method that uses features of previous projects’ data through project similarity. Specifically, the proposed method applies cluster-based project selection for training data source and modeling by a deep learning method. Experiments involving 15 real datasets from a company and 11 open source software datasets show that DC-SRGM can more precisely describe the reliability of ongoing development projects than existing traditional SRGMs and the LSTM model.

DNA sequencing methods were first developed more than 20 years ago with the publication of two approaches to sequencing methodology that became known as Sanger sequencing (1), based on enzymatic synthesis from a single-stranded DNA template with chain termination using dideoxynucleotides (ddNTPs) and Maxim-Gilbert sequencing (2),which involved chemical degradation ofend-radio-labeled DNA fragments. Both methods relied on four-lane,high resolution polyacrylamide gel electrophoresis to separate the labeled fragment and allow the base sequence to be read in a staggered ladder-like fashion. Sanger sequencing was technically easier and faster, and thus became the main DNA sequencing method for the vast majority of applications.

Dietary factors play an important role in shaping the gut microbiome which, in turn, regulates the molecular events in colonic mucosa. The composition and resulting metabolism of the gut microbiome have been implicated in the development of colorectal cancer (CRC). Diets low in dietary fibers and phytomolecules as well as other lifestyle-related factors may predispose to CRC. Emerging evidence demonstrates that the predominance of microbes, such as Fusobacterium nucleatum, can predispose the colonic mucosa to malignant transformation. Dietary and lifestyle modifications have been demonstrated to restrict the growth of potentially harmful opportunistic organisms. In this study, we aim to present evidence regarding the relationship of dietary factors to the gut microbiome and development of CRC.

This review article aims at improving understanding of the theoretical discussion on technonationalism. Reviewing conventional works of the literature proposes protectionist, innovationist, and strategic industrial approaches toward high-tech industries. The findings show that technonationalism is a concept linking technology and national security, seeking geoeconomic interests in the integrated global market to exert state power for economic statecraft. The nature of industry highly relies on the global value chain, and technological innovation in the high-tech sectors occurs mainly in the private sector; thus, this article points out the issue of embracing business interests into the state’s strategic addenda as a critical criterion of technonationalism. Also, technonationalism is a narrowly defined concept with state actors involved in national security affairs and high-tech business actors.

The objective of this study is to determine the type of governance of the four main agricultural value chains in Tamaulipas, northeast of Mexico. For the preparation of this research used a qualitative design, using a questionnaire and semi-structured interviews to a sample of representatives of the four selected chains. The results showed that in all the studied networks, control and coordination capacity is limited by the influence that has a link on the rest of the chain. In all cases, was that the industrial link is who leads the chains and exert control over the rest of the links. The results showed that, when the industrial link is located close geographically to the rest of the links (chains of rice and sugar cane), the chains showed a hierarchical type, where the industrial exercised dominion over the rest of the links and captures most of the income. On the other hand, in chains where control is exercised by links that are outside the territory (chains of soybeans and sorghum), they function as captive chains, putting the rest of the chain to its influence and control.

Raw material logistics reflects an important aspect of global trade. Raw materials form an essential basis for society, for daily life, and range from apples to zinc. This paper addresses the analysis and optimization of supply chains of raw materials in terms of their economic viability and their sustainability. Type representatives are chosen according to annual transported quantities. Hard coal represents bulk goods, aluminum primary raw materials with medium quantities, and rare earths primary raw materials with small quantities. Their respective supply chains are analyzed and subsequently possible strategies and methods and their application are discussed. The paper shows for the first time that the selection and application of optimization priorities (e.g. profitability or sustainability) depends on the primary raw material and its integration into global production chains.

Beer fermentation is typically monitored by periodic sampling and off-line analysis. In-line sensors would remove the need for time-consuming manual operation and provide real-time evaluation of the fermenting media. This work uses a low-cost ultrasonic sensor combined with machine learning to predict the alcohol concentration during beer fermentation. The highest accuracy model (R2=0.952, MAE=0.265, MSE=0.136) used a transmission-based ultrasonic sensing technique along with the measured temperature. However, the second most accurate model (R2=0.948, MAE=0.283, MSE=0.146) used a reflection-based technique without the temperature. Both the reflection-based technique and the omission of the temperature data are novel to this research and demonstrate the potential for a non-invasive sensor to monitor beer fermentation.

Spiritual intelligence had an impact on different biblical leaders, and in this text, we see some cases to serve as a sample (Joseph, Bezalel, and Daniel). In the Bible, this impact is demonstrated in innovations introduced by Joseph in Egypt, Bezalel the manager of the macro project of building in crisis time, Daniel the politician. It is the supreme intelligence and leaders are invited to make a shift from rationality to spirituality. The more leaders of organizations will use spiritual intelligence, the more leaders and followers will experience satisfaction, joy, accomplishment.

Secondary transportation of raw and comminuted forest products is a major component in forest harvesting operations in terms of economics, public perception, and safety. Consequently, there is a substantial amount of literature on this topic. The existing literature has dealt with many of the technical aspects of transportation with a majority of them focusing on improving supply chain issues; However, there are only few specific to secondary transportation issues in general. This annotated bibliography will help practitioners, researchers, and stakeholders gain a better understanding of the existing literature from 2000 to 2015. To this end, we began by classifying the selected literature into six themes: cost, roads and routes, trucking, efficiency & safety, other modes of transportation, and supply chain & optimization. Woody biomass for bioenergy production was the most researched forest product with respect to transportation. About one-third of the articles were presented in the context of supply chain modeling and optimization. More than half of the studies originated from Europe while the United States had the most publications for any given country. The most articles (16) were published in 2013. Biomass and Bioenergy published the highest number of articles (29) during the timeframe.

The classical Rotational Isomeric State (RIS) model, originally proposed by Flory, has been used to rationalize a wide range of physicochemical properties of neutral polymers. However, many weak polyelectrolytes of interest are able to regulate their charge depending on the conformational state of the bonds. Recently, it has been shown that the RIS model can be coupled with the Site Binding (SB) model, for which the ionizable sites can adopt two states: protonated or deprotonated. The resulting combined scheme, the SBRIS model, allows to analyse ionization and conformational equilibria on the same foot. In the present work this approach is extended to include pH-dependent electrostatic Long Range (LR) interactions, ubiquitous in weak polyelectrolytes at moderate and low ionic strengths. With this aim the original LR interactions are taken into account by defining effective Short Range (SR) and pH-dependent parameters, such as effective microscopic protonation constants and rotational bond energies. The new parameters are systematically calculated using variational methods. The machinery of statistical mechanics for SR interactions, including the powerful and fast transfer matrix methods, can then be applied. The resulting technique, to which we will refer as Local Effective Interaction Parameters (LEIP) method, is illustrated with a minimal model of a flexible linear polyelectrolyte containing only one type of rotating bonds. LEIP reproduces very well the pH dependence of the degree of protonation and bond probabilities obtained by semi-grand canonical Monte Carlo simulations, where LR interactions are taken explicitly into account. The reduction in the computational time in several orders of magnitude suggests that the LEIP technique could be useful in a range of areas involving linear weak polyelectrolytes, allowing direct fitting of the relevant physical parameters to the experimental quantities.

This paper developed a new theory for supply chain architecture, and engineering design that enables integration of the business and supply chain strategies. The architecture starts with individual supply chain participants and derives insights into the complex and abstract concept of green-field integration design. The paper presented a conceptual system for depicting the interactions between business and supply chain strategy engineering. The system examines the decisions made when engineering the business strategy, with regards to the supply chain design. The system derived with a new understanding of how strategies are integrated, and what are the implications for engineering successful strategies. The study revealed that supply chain design is not considered in great detail before architecting the business strategies. Thus, companies consequentially experience supply chain problems that are likely to be detrimental to the growth potentials. The paper also derived with the findings that proactive and pre-emptive involvement of supply chain participants in the strategy engineering process, would lead to a more robust strategic design.

The lean supply chain of construction engineering projects is to achieve the maximum satisfaction of the owners' needs in order to effectively achieve the goal of supply chain management. This paper explores an effective method of lean supply chain cost management for construction engineering projects with target cost management, so that each participating unit on the supply chain node can fully utilizes its core competencies to minimize internal consumption and waste, and achieve the optimal overall efficiency of the supply chain. According to the requirements of the goal planning theory of the construction project company, establish a lean supply chain cost planning system for the construction project, realize the basic model of the lean supply chain cost management of the construction project, and set the target cost from the lean project of the construction project. The technical decomposition is established by the process of cost decomposition and cost pressure transmission and sub-target cost planning.

Precipitation downscaling is widely employed for enhancing the resolution and accuracy of precipitation products from general circulation models (GCMs). In this study, we propose a novel statistical downscaling method to foster GCMs’ precipitation prediction resolution and accuracy for monsoon region. We develop a deep neural network composed of convolution and Long Short Term Memory (LSTM) recurrent module to estimate precipitation based on well-resolved atmospheric dynamical fields. The proposed model is compared against GCM precipitation product and classical downscaling methods in the Xiangjiang River Basin in South China. Results show considerable improvement compared to the ECMWF-Interim reanalysis precipitation. Also, the model outperforms benchmark downscaling approaches, including 1) quantile mapping, 2) support vector machine, and 3) convolutional neural network. To test the robustness of the model and its applicability in practical forecast, we apply the trained network for precipitation prediction forced by retrospective forecasts from ECMWF model. Compared to ECMWF precipitation forecast, our model makes better use of the resolved dynamical field for more accurate precipitation prediction at lead time from 1 day up to 2 weeks. This superiority decreases along forecast lead time, as GCM’s skill in predicting atmospheric dynamics being diminished by the chaotic effect. At last, we build a distributed hydrological model and force it with different sources of precipitation inputs. Hydrological simulation forced with the neural network precipitation estimation shows significant advantage over simulation forced with the original ERA-Interim precipitation (with NSE value increases from 0.06 to 0.64), and the performance is just slightly worse than the observed precipitation forced simulation (NSE=0.82). This further proves the value of the proposed downscaling method, and suggests its potential for hydrological forecasts.

Probiotics make up a large and growing segment of the commercial market of dietary supplements and are touted as offering a variety of human health benefits. Some of the purported positive impacts of probiotics include, but are not limited to, stabilization of the gut microbiota, prevention of gastrointestinal disorders and modulation of the host immune system. Current research suggests that the immunomodulatory effects of probiotics are strain specific and vary in mode of action. Here, we examined the immunomodulatory properties of Bacillus subtilis strain DE111 in a healthy human population. In a randomized, double blind, placebo-controlled four-week intervention, we examined peripheral blood mononuclear cells (PBMCs) at basal levels pre- and post-treatment as well as in response to stimulation with bacterial lipopolysaccharide (LPS). We observed an anti-inflammatory effect of B. subtilis, manifested as a decrease in immune cell populations within the basal state along with an increase in anti-inflammatory immune cells in response to LPS stimulation. Overall gastrointestinal health, microbiota, and circulating and fecal markers of inflammation and gut barrier function were largely unaffected by DE111 treatment. These data suggest that the novel probiotic B. subtilis DE111 may have clinical applications in modulating immune homeostasis via anti-inflammatory mechanisms.

Modeling brain dynamics to better understand and control complex behaviors underlying various cognitive brain functions are of interests to engineers, mathematicians, and physicists from the last several decades. With a motivation of developing computationally efficient models of brain dynamics to use in designing control-theoretic neurostimulation strategies, we have developed a novel data-driven approach in a long short-term memory (LSTM) neural network architecture to predict the temporal dynamics of complex systems over an extended long time-horizon in future. In contrast to recent LSTM-based dynamical modeling approaches that make use of multi-layer perceptrons or linear combination layers as output layers, our architecture uses a single fully connected output layer and reversed-order sequence-to-sequence mapping to improve short time-horizon prediction accuracy and to make multi-timestep predictions of dynamical behaviors. We demonstrate the efficacy of our approach in reconstructing the regular spiking to bursting dynamics exhibited by an experimentally-validated 9-dimensional Hodgkin-Huxley model of hippocampal CA1 pyramidal neurons. Through simulations, we show that our LSTM neural network can predict the multi-time scale temporal dynamics underlying various spiking patterns with reasonable accuracy. Moreover, our results show that the predictions improve with increasing predictive time-horizon in the multi-timestep deep LSTM neural network.

Bayesian nonlinear mixed effects models for data in the form of continuous, repeated measurements from a population, also known as Bayesian hierarchical nonlinear models, are a popular platform for analysis when interest focuses on individual specific characteristics and relevant uncertainty quantification. Due to the limitation of computational power, this framework was relatively dormant until the late 1980s, but in recent years, the statistical research community saw vigorous development of new methodological and computational techniques for these models, the emergence of software, and wide application of the models in numerous industrial and academic fields. This article presents an overview of the formulation, interpretation, and implementation of Bayesian nonlinear mixed effects models and surveys recent advances and applications.

Human milk fat is a concentrated source of energy and provides essential and long chain polyunsaturated fatty acids. According to previous experiments, human milk fat is partially lost during continuous enteral nutrition. However, these experiments were done over relatively short infusion times, and a complete profile of the lost fatty acids was never measured. Whether this lost happens considering longer infusion times or if some fatty acids are lost more than others remain unknown. Pooled breast milk was infused through a feeding tube by a peristaltic pump over a period of 30 minutes and 4, 12 and 24 hours at 2 ml/hour. Adsorbed fat was extracted from the tubes, and the fatty acid composition was analyzed by Gas chromatography-mass spectrometry. Total fat loss (average fatty acid loss) after 24 hours was 0.6 ± 0.1%. Short-medium chain (0.7%, p=0.15), long chain (0.6%, p=0.56) saturated (0.7%, p=0.4), monounsaturated (0.5%, p=0.15), polyunsaturated fatty (0.7%, p=0.15), linoleic (0.7%, p=0.25), and docosahexaenoic acids (0.6%, p=0.56) were not selectively adsorbed to the tube. However, very long chain fatty (0.9%, p=0.04), alpha-linolenic (1.6%, p=0.02) and arachidonic acids (1%, p=0.02) were selectively adsorbed and therefore lost in a greater proportion than other fatty acids. In all cases, the magnitude of the loss was clinically low.

Abstract: Early life gut microbiota have been increasingly recognized as major contributors to short and/or long-term human health and diseases. Numerous studies have demonstrated that human gut microbial colonization begins at birth but continues to develop a succession of taxonomic abundances for two to three years until the gut microbiota reaches adult-like diversity and proportions. Several factors, including gestational age (GA), delivery mode, birth weight, feeding types, antibiotic exposure, maternal microbiome and diet influence the diversity, abundance and function of the early life gut microbiota. Gut microbial life is essential for assisting with the digestion of food substances to release nutrients, exerting control over pathogens, stimulating or modulating the immune system and influencing many systems such as the liver, brain, and endocrine system. Microbial metabolites play multiple roles in these interactions. Furthermore, studies provide evidence supporting that imbalances of the gut microbiota in early life, referred to as dysbiosis, are associated with specific childhood or adult disease outcomes, such as asthma, atopic dermatitis, diabetes, allergic diseases, obesity, cardiovascular diseases (CVD) and neurological disorders. These findings support that the human gut microbiota may play a fundamental role in the risk of acquiring diseases that may be programmed during the early life stage. In fact, it is critical to explore the role of the human gut microbiota in early life. In this review, we summarize the general understanding of the colonization and development of the gut microbiota in early life, highlighting the recent findings regarding the relationship between the gut microbiota composition and their metabolites, and immune functions, which could significantly influence long-term health and disease. We then review known pathophysiological interactions of the early gut microbiome with a number of well characterized diseases and pose potential etiological mechanisms.

Refractive surgery is an increasingly popular procedure to decrease spectacle or contact lens dependency. The two most commonly used surgical techniques to correct myopia is Photorefractive keratectomy (PRK) and Femtosecond- Lasik (FS-LASIK)There are few publications that gathers such a long term follow up of both surgical techniques (2) Methods It has been performed a retrospective non-randomized study 509 PRK eyes and 310 FS-LASIK surgeries were followed for 10 years for the treatment of myopia and compound myopic astigmatism. Patients were followed up three months, one year, 2 years, 5 and 10 years. The safety index of both procedures was defined as a quotient between the postoperative BCVA (Best Corrected Visual Acuity) and the preoperative BCVA. The predictability is calculated as difference between the expected spherical equivalent and the achieved spherical equivalent. The efficacy index was calculated as a quotient between postoperative UCVA divided by the preoperative BCVA (3) Results. The results were: a safety index higher than 100% (109%) and an efficacy index of 82.4% after 10 years of PRK surgery in both groups. FS-LASIK was the safest surgery after 10 years and the most efficacy technique although in this case there were no statistically significant differences (4) Conclusions. All these data demonstrated better indexes for FS-LASIK

The increased consumption of high fat-containing foods has been linked to the prevalence of obesity and abnormal metabolic syndromes. Rhizopus oligosporus, a fungus in the family Mucoraceae, is widely used as a starter for homemade tempeh. Although R. oligosporus can prevent the growth of other microorganisms, it grows well with lactic acid bacteria (LAB). Lactobacillus plantarum can produce β-glucosidase, which catalyzes the hydrolysis of glucoside isoflavones into aglycones (with greater bioavailability). Therefore, the development of a soybean-based functional food by the co-inoculation of R. oligosporus and L. plantarum is a promising approach to increase the bioactivity of tempeh. In this study, the ameliorative effect of L. plantarum in soy tempeh on abnormal carbohydrate metabolism in high-fat diet (HFD)-induced hyperglycemic rats was evaluated. The co-incubation of L. plantarum with R. oligosporus during soy tempeh fermentation reduced the homeostatic model assessment of insulin resistance, HbA1c, serum glucose, total cholesterol, triglyceride, free fatty acid, insulin, and low-density lipoprotein contents and significantly increased the high-density lipoprotein content in HFD rats. It also increased the LAB counts as well as the bile acid, cholesterol, triglyceride, and short-chain fatty acid contents in the feces of HFD rats. Our results suggested that the modulation of serum glucose and lipid levels by LAB occurs via alterations in the internal microbiota, leading to the inhibition of cholesterol synthesis and promotion of lipolysis. Tempeh, produced with both L. plantarum and R. oligosporus, may be a beneficial dietary supplement for individuals with abnormal carbohydrate metabolism.

Much evidence has accumulated over the past decade in favor of a significant association between dysbiosis, neuroinflammation and neurodegeneration. Presently, the pathogenetic mechanisms triggered by molecules produced by the altered microbiota, also responsible for the onset and evolution of Alzheimer Disease will be described. Our attention will be focused on the role of astrocytes and microglia. Numerous studies have progressively demonstrated how these glial cells are important to ensure an adequate environment for neuronal activity in healthy conditions. Furthermore, it is becoming evident how both cell types can mediate the onset of neuroinflammation and lead to neurodegeneration when subjected to pathological stimuli. Based on this information, the role of major microbiota products in shifting the activation profiles of astrocytes and microglia from a healthy to a diseased state will be discussed focussing on Alzheimer Disease pathogenesis.

Metastatic pancreatic ductal adenocarcinoma pancreatic (PDAC) is characterized by poor prognosis and short survival. Today, the use of new polytherapeutic regimens increases clinical outcome of these patients opening new clinical scenario. A crucial issue related to the actual improvement achieved with these new regimens is represented by the occasional possibility to observe a radiological complete response of metastatic lesions in patients with synchronous primary tumor. What could be the best therapeutic management of these patients? Could surgery represent an indication? Herein we reported a case of a patient with a PDAC of the head with multiple liver metastasis, who underwent first line chemotherapy with mFOLFIRINOX. After 10 cycles, he achieved a complete radiological response of liver metastases and a partial response of pancreatic lesion. A, duodenocephalopancreasectomy was performed. Due to liver a lung metastases after 8 months from surgery, a second line therapy was started with a disease free survival and overall survival of 8 months and 45 months, respectively. Improvement in the molecular characterization of PDAC could help in the selection of patients suitable for multimodal treatments.

Athletes living with and beyond cancer can continue to train and, in some cases, compete during treatment. Following cancer treatment, athletes can return to competitive sport but need to learn to adapt their physical strength and training to lingering effects of cancer. It is critical for oncology healthcare providers to use the principles of assess, refer and advise to exercise oncology programs that are appropriate for the individual. Managing side effects of treatment is key to being able to train during and immediately following cancer treatment. Keen attention to fatigue is important at any point in the cancer spectrum to avoid overtraining and optimize the effects of training.

Irish beef farms have experienced poor viability longitudinally, with officially acknowledged ‘crisis’ levels in 2013. In response, beef Producer Organisation (PO) legislation was introduced. Through the lens of international evidence in the literature about how POs function, this paper presents an analysis of Irish stakeholders’ views in the context of a public consultation process. While stakeholders indicate the need for individual POs to collaborate, little emphasis is placed on other factors necessary for success: collaboration with other chain actors; and market differentiation of products. Stakeholders identified primary threats as poor engagement on the part of both farmers and processors/purchasers.

Advances in next-generation sequencing have facilitated the discovery of a multitude of long non-coding RNAs (lncRNAs) with pleiotropic functions in cellular processes, disease and viral pathogenesis. It came as no surprise when viruses were also revealed to transcribe their own lncRNAs. Among them, gammaherpesviruses, one of the three subfamilies of the Herpesviridae, code their largest number. These structurally and functionally intricate non-coding (nc) transcripts modulate cellular and viral gene expression to maintain viral latency or prompt lytic reactivation. The lncRNAs allow the virus to escape cytosolic surveillance, sequester and re-localize essential cellular factors and modulate the cell cycle and proliferation. Some viral lncRNAs act as “messenger molecules”, transferring information about viral infection to neighboring cells. This broad range of lncRNA functions is achieved through lncRNA structure-mediated interactions with effector molecules of viral and host origin, including other RNAs, proteins and DNAs. In this review, we discuss examples of gammaherpesvirus-encoded lncRNAs, emphasize their unique structural attributes, and link them to viral life cycle, pathogenesis and disease progression. We will address their potential as novel targets for drug discovery and propose future directions to explore lncRNA structure and function relationship.

The novel coronavirus disease 19 (COVID-19) has resulted in an estimated 20 million excess deaths and the recent resurgence of COVID-19 in China is predicted to result in up to 1 million deaths over the next few months. With vaccines unable to halt transmission it is important to continue our quest for safe, effective, affordable drugs that will be available to all countries. Drug repurposing is one of the strategies being explored in this context. Recently, out of 7,817 approved drugs, 214 candidates were systematically down-selected using a combination of 11 filters including approval status, assay data against SARS-CoV-2, pharmacokinetic, pharmacodynamic and toxicity profiles. These drugs were subjected in this study to virtual screening against various targets of SARS-CoV-2 followed by molecular dynamic studies of the best scoring ligands against each target. The chosen molecular targets were Spike receptor binding domain, Nucleocapsid protein RNA binding domain, and key non-structural proteins 3, 5, 12, 13 and 14. Four drugs approved for other indications — alendronate, cromolyn, natamycin and treprostinil — look sufficiently promising from our in silicostudies to warrant further in vitro and in vivo investigations as appropriate to ascertain their extent of anti-viral activities.

The main features of the instrumental global mean surface temperature (GMST) are reasonably well described by a simple linear response model driven by anthropogenic, volcanic and solar forcing. This model acts as a linear long-memory filter of the forcing signal. The physical interpretation of this filtering is the delayed response due to the thermal inertia of the ocean. This description is considerably more accurate if El Ni\~{n}o Southern Oscillation (ENSO) and the Atlantic Multi-decadal Oscillation (AMO) are regarded as additional forcing of the global temperature and hence subject to the same filtering as the other forcing components. By considering these as predictors in a linear regression scheme, more than 92\% of the variance in the instrumental GMST over the period 1870-2017 is explained by this model, and in particular all features of the 1998 -- 2015 hiatus, including its death. While the more prominent pauses during 1870 -- 1915 and 1940 -- 1970 can be attributed to clustering in time of strong volcanic eruptions, the recent hiatus is an unremarkable phenomenon that is attributed to ENSO and a small contribution from solar activity.

Earth's global surface temperature shows variability on an extended range of temporal scales and satisfies an emergent scaling symmetry. Recent studies indicate that scale invariance is not only a feature of the observed temperature fluctuations, but an inherent property of the temperature response to radiative forcing, and a principle that links the fast and slow climate responses. It provides a bridge between the decadal- and centennial-scale fluctuations in the instrumental temperature record, and the millennial-scale equilibration following perturbations in the radiative balance. In particular, the emergent scale invariance makes it possible to infer equilibrium climate sensitivity (ECS) from the observed relation between radiative forcing and global temperature in the instrumental era. This is verified in ensembles of Earth system models (ESMs), where the inferred values of ECS correlate strongly to estimates from idealized model runs. For the range of forcing data explored in this paper, the method gives best estimates of ECS between 2.3 and 3.4 K.

This pilot study of Streptococcus mutans ATCC 35668 grown in media with and without polyols (erythritol) measured the resultant metabolites, including the short chain fatty acids by using head space analysis. Brain Heart Infusion Broth (BHI2 or BHI10) supplemented with 2% or 10% sucrose containing no polyols or either erythritol or xylitol and Streptococcus mutans (ATCC 35668) was grown aerobically. After 48 hours of growth the supernatant were harvested and centrifuged to pellet bacteria. Supernatants were removed from bacterial pellets then submitted for Short Chain Fatty Acid (SCFA) analysis with an Agilent Technologies (Santa Clara, CA 95051) system configured from three components, a 5973 mass selective detector, a 6890N gas chromatographer, and a 7697A headspace sampler. Streptococcus mutans growing in Brain Heart Infusion Broth (BHI2 or BHI10) supplemented with 2% or 10% sucrose but containing no polyols produced the following short chain fatty acids: methyl isovalerate, acetic acid, propionic acid, butanoic acid, pentanoic acid, ethyl butaric acid, 4-methylvaleric acid, hexanoic acid. When the Brain Heart Infusion Broth (BHI2 or BHI10) supplemented with 2% or 10% sucrose containing erythritol was used as media for this Streptococcus mutans strain, the following were produced: ethanol, acetoin, and acetic acid. Our results would suggest that constituents of the media may affect the bacterial metabolite production.

The genus Pseudomonas hosts an extensive genetic diversity and is one of the largest genera among Gram-negative bacteria. Type strains of Pseudomonas are well-known to represent only a small fraction of this diversity and the number of available Pseudomonas genome sequences is increasing rapidly. Consequently, new Pseudomonas species are regularly reported and the number of species within the genus is in constant evolution. In this study, whole genome se-quencing enabled us to define 43 new Pseudomonas species and to provide an update of the Pseu-domonas evolutionary and taxonomic relationships. Phylogenies based on the rpoD gene and whole genome sequences, including 316 and 313 type strains of Pseudomonas, respectively, re-vealed sixteen groups of Pseudomonas and justified the partitioning of the P. putida group into fifteen subgroups. Pairwise average nucleotide identities were calculated between type strains and a selection of 60 genomes of non-type strains of Pseudomonas. Forty-one strains were incor-rectly assigned at the species level and among those, 19 strains were shown to represent an addi-tional 13 new Pseudomonas species that remain to be formally classified. This work pinpoints the importance of correct taxonomic assignment and phylogenetic classification in order to perform integrative studies linking genetic diversity, lifestyle and metabolic potential of Pseudomonas spp.

In recent years supply chains have evolved into huge ecosystems, demanding trust, provenance, and data privacy. Since blockchain technology (BCT) allows for the development of a distributed environment, it is ideal for supply chain management (SCM) applications. However, concerns regarding data privacy have impeded the development of blockchains. Despite the fact that some blockchains can restrict participants to read and write data, the blockchain’s transparency makes protecting sensitive data challenging. To solve the data privacy challenge, this paper proposes a framework, AccessChain, that is an SCM access control framework based on an attribute-based access control (ABAC) model that restricts access to competing parties while allowing for network scalability. This proposed AccessChain model has two types of ledgers in the system: local and global. Local ledgers are used to store business contracts between stakeholders and the ABAC model management, whereas the global ledger is used to record transaction data. AccessChain can enable decentralized, fine-grained and dynamic access control management in SCM when combined with the ABAC model and BCT. This paper’s experimental results illustrate that high throughput can be achieved in a large-scale request environment while maintaining data privacy and sustaining a scalable network.

There is mounting evidence that patients with severe COVID-19 disease may have symptoms that continue beyond the acute phase, extending into the early chronic phase. Often referred to as 'Long COVID'. Simultaneously, case investigations have shown that COVID-19 individuals might have a variety of neurological problems. The accurate and accessible assessment of cognitive function in patients post COVID-19 infection is thus of increasingly high importance for both public and individual health. Little is known about the influence of COVID-19 on the general cognitive levels but more importantly, at sub functions level. Therefore, we first aim to summarize current level of evidence supporting a negative impact of COVID-19 infection on cognitive functions. 27 studies have been included in the systematic review representing a total of 94,103 participants (90,317 COVID-19 patients and 3,786 healthy controls). We then performed a meta-analysis summarizing the results of 5 studies (959 participants, 513 patients) to quantify the impact of COVID-19 on cognitive functions. The overall effect, expressed in Standardized Mean Differences, is -0.56 [95%CI -0.79 ; -0.34]. To prevent disability, we finally discuss the different approaches available in rehabilitation to help these patients and to avoid long-term complication.

(1) Background: COVID-19 was responsible for the latest pandemic, shaking and reshaping healthcare systems worldwide. Its late clinical manifestations make it linger in medical memory as a debilitating illness over extended periods. (2) Methods: Recent literature was systematically analyzed to categorize and examine the symptomatology and pathophysiology of Long COVID across various bodily systems, including pulmonary, cardiovascular, gastrointestinal, neuropsychiatric, dermatological, renal, hematological, and endocrinological aspects. (3) Results: The review outlines the diverse clinical manifestations of Long COVID across multiple systems, emphasizing its complexity and challenges in diagnosis and treatment. Factors such as pre-existing conditions, initial COVID-19 severity, vaccination status, gender, and age were identified as influential in the manifestation and persistence of Long COVID symptoms. This condition is highlighted as a debilitating disease capable of enduring over an extended period and presenting new symptoms over time. (4) Conclusions: Long COVID emerges as a condition with intricate multi-systemic involvement, complicating its diagnosis and treatment. The findings underscore the necessity for a nuanced understanding of its diverse manifestations to effectively manage and address the evolving nature of this condition over time.

The present article proposes a complete framework for Supply Chain Strategy (SCS) analysis that is adapted to the specific characteristics of the agrifood chain, thereby facilitating management of the former. As a specific case of analysis, the horticultural supply chain, originating in Spain and ending with the European consumer, is analyzed, taking as a reference the marketing companies at origin (mainly social economy companies, i.e.; cooperatives). In addition, a survey of marketing companies is conducted to possibly determine which explicit cooperative growth strategies may include horizontal and vertical collaboration relationships with other members of the chain. The aim is to analyze with whom the cooperative collaborates within the supply chain and the key points of such collaboration. A model analyzing the influence of collaboration on company performance is also considered. The results reveal that, in recent years, aspects related to quality and health have been surpassed and replaced by the concept of sustainability within a framework of collaboration with customers. In any case, it becomes evident that there is a need to expand collaboration within the chain by incorporating the supplier of the supplier, with the aim of making the chain more profitable.

Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) infection triggers various events from molecular to tissue level, which in turn is given by the intrinsic characteristics of each patient. Given the molecular diversity characteristic of each cellular phenotype, the possible cytopathic, tissue and clinical effects are difficult to predict, which determines the heterogeneity of COVID-19 symptoms. The purpose of this article is to provide a comprehensive review of the cytopathic effects of SARS-CoV-2 on various cell types, focusing on the development of COVID-19, which in turn may lead, in some patients, to a persistence of symptoms after recovery from the disease, a condition known as long COVID. We describe the molecular mechanisms underlying virus-host interactions, including alterations in protein expression, intracellular signaling pathways, and immune responses. In particular, the article highlights the potential impact of these cytopathies on cellular function and clinical outcomes, such as immune dysregulation, neuropsychiatric disorders, and organ damage. The article concludes by discussing future directions for research and implications for the management and treatment of COVID-19 and Long-COVID.

Healthcare workers (HCWs) in long-term care (LTC) faced and continue to experience significant emotional and psychological distress throughout the pandemic. Despite this, little is known about the unique experiences of LTC workers. This scoping review synthesizes existing research on the experiences of HCWs in LTC during the COVID-19 pandemic. Following Arksey and O’Malley’s framework, data were extracted from six databases from inception of the pandemic to June 2022. Among 3,808 articles screened, 40 articles were included in the final analysis. Analyses revealed three interrelated themes: carrying the load (moral distress); building pressure and burning out (emotional exhaustion); and working through it (a sense of duty to care). Given the impacts of the pandemic on both HCW wellbeing and patient care, every effort must be made to address the LTC workforce crisis and evaluate best practices for supporting HCWs experiencing mental health concerns during and post-COVID-19.

Coincident changes in abundance and behavior pose a challenge for interpreting abundance data from monitoring programs. In the San Francisco Estuary, long-term monitoring documented the declines of many species including the anadromous Longfin Smelt (Spirinchus thaleichthys). We identified seasonal patterns in reginal presence of Longfin Smelt through its life cycle using monitoring data and generalized additive modelling. We then investigated the year-to-year variability in the seasonal patterns of presence using functional data analysis (FDA). FDA separated the variability due to population size from variability due to differences in timing of presence. We found that Longfin Smelt have consistent seasonal distribution patterns and that two trawl types were needed to accurately describe those patterns. After accounting for variability due to year-class strength, shifts in the timing of presence were evident in three regions. The most variable period for the upstream regions Suisun Bay and West Delta was for age-0 fish in summer and for the downstream region Central Bay was for age-0 fish in late fall. This manifested as a delay in the typical fall re-occupation of upstream regions that comprise the study area for another monitoring study (Fall Midwater Trawl). Thus, a portion of the recent reductions in Fall Midwater Trawl abundance of Longfin Smelt resulted from changes in behavior rather than a decline in abundance. The presence of multiple monitoring surveys allowed analysis of distribution from one data set to aid interpretation of patterns in abundance from another monitoring survey. This study highlights how identifying portions of the life cycle with the most and least variability in distribution can help inform the types of management strategies that will be most effective. It also illustrates an analytical method that can be used to address the problem of confounded effects of abundance and behavior on patterns in monitoring data.

Although the signaling function of Na/K ATPase is been studied for decades, the chasm between the pumping function and the signaling function of Na/K – ATPase is still an open issue. This article explores the relationship between ion pumping and signaling with attention to the amplification of oxidants through this signaling function. Starting with some experimental observations published by our laboratories and others, we develop some predictions regarding cellular oxidant stress.

Due to its devastating consequences, late life depression is an important public health problem. The aim of the study was the analysis of variables which may potentially influence risk of depression (GDS-SF). Furthermore, the aim was to study possible mediating effect of given variables on the relationship between the total protein concentration and risk of depression in older-adults with chronic diseases, and physical function impairment. The research sample included a total of 132 older adults with chronic conditions and physical function impairments, remaining under a long-term care in residential environment. Negative linear correlation was observed between patients’ physical functionality, total protein concentration, concentration of HDL cholesterol, arm circumference, and the risk of depression. Considerably stronger relationship was observed between total protein concentration, and GDS-SF, in elderly suffering from sensory dysfunction (b = −6.42, 95% CI = −11.27; −1.58). The effect of the mediation between depression risk is correlated to total protein concentration in blood serum, and the mediators are probably low function impairment and low levels of 25 (OH)D vitamin. Cohort control research is suggested to confirm the hypothesis.

Wearable technology will become available and allow prolonged electroencephalography (EEG) monitoring in the home environment of patients with epilepsy. Neurologists analyse the EEG visually and annotate all seizures, which patients often under report. Visual analysis of a 24 hour EEG recording typically takes one to two hours. Reliable automated seizure detection algorithms will be crucial to reduce this analysis. We study a dataset of behind-the-ear EEG measurements. Our first aim was to develop a methodology to reduce the EEG dataset by classifying part of the data automatically, while retaining 100% detection sensitivity (DS). Prediction confidences are determined by temperature scaling of the classification model outputs and trust scores. A DS of approximately 90% (99%) can be achieved when automatically classifying around 90% (60%) of the data. Perfect DS can be achieved when automatically classifying 50% of the data. Our second contribution demonstrates that a common modelling strategy, where predictions from several short EEG segments are used to obtain a final prediction, can be improved by filtering out untrustworthy segments with low trust scores. The false detection rate shows a relative decrease between 21% and 43%, and the DS shows a small increase or decrease.

This article presents the implementation of a model to estimate the future price of commodities in the Brazilian market from time series of short-term technical evaluation. For this, data from two databases were used, one referring to the foreign market (opening values, maximum, minimum, closing, closing adjustment and volume) and the other, from the Brazilian market (the price of the day), considering commodities, sugar, cotton, corn, soybean and wheat. Subsequently, the technical indicators were calculated from the TA-Lib technical analysis library. Pearson’s correlation coefficient was applied, records with low correlation were removed, and then the database was consolidated. From the pre-processed data, Long Short-Term Memory (LSTM) recurrent neural networks were used to perform data prediction at the one and three day interval. These models were evaluated using the mean square error (MSE), obtaining results between 0.00010 and 0.00037 on test data one day ahead, and from 0.00017 to 0.00042 three days ahead. However, based on the results obtained, it was observed that the developed model obtained a promising forecasting performance for all the commodities evaluated. As a main contribution, there is the consolidation of databases that can be used in future scientific research. Furthermore, based on its interpretation, it can assist in decision making regarding the buying and selling of commodities to increase financial gains.

“Clickase” single-chain nanoparticles (Ck-SCNPs) are folded, enzyme-mimetic unimolecular polymeric nano-objects containing copper (Cu) ions able to catalyze the azide-alkyne Huisgen cycloaddition reaction in water and/or selected organic solvents, often in the presence of a reductant. Herein, we investigate the effect of morphology on catalytic activity of Ck-SCNPs synthesized by means of two different routes. An amphiphilic random copolymer composed of oligo(ethylene glycol) methyl ether methyl methacrylate (OEGMA) and 2-acetoacetoxy ethyl methacrylate (AEMA) units was used as precursor of these Ck-SCNPs. Folding was promoted through metal complexation between Cu(II) ions and beta-ketoester-containing AEMA moieties. The first route resulted in Ck-SCNPs1 containing Cu ions homogeneously distributed within each nanoparticle, whereas the second one promoted intra-chain clustering of Cu ions inside Ck-SCNPs2. A model fluorogenic “click” reaction between 9-(azidomethyl)anthracene and phenylacetylene, which was catalyzed either by Ck-SCNPs1 or Ck-SCNPs2, was used to unravel the effect of morphology on catalytic activity. This work paves the way to improve the catalytic activity of metallo-folded SCNPs through control of the intra-chain distribution of catalytic sites.

A long-term investigation of D. suzukii dynamics in wild blueberry fields from 2012 - 2018 demonstrates relative abundance is still increasing seven years after initial invasion. Relative abundance is determined by physiological date of first detection and air temperatures the previous winter. Date of first detection of flies does not determine date of fruit infestation. The level of fruit infestation is determined by year, fly pressure, and insecticide application frequency. Frequency of insecticide application is determined by production system. Non-crop wild fruit and predation influences fly pressure; increased wild fruit abundance results in increased fly pressure. Increased predation rate reduces fly pressure, but only at high abundance of flies, or when high levels of wild fruit are present along field edges. Male sex ratio might be declining over the seven years. Action thresholds were developed from samples of 92 fields from 2012 - 2017 that related cumulative adult male trap capture to the following week likelihood of fruit infestation. A two-parameter gamma density function describing this probability was used to develop a risk-based gradient action threshold system. The action thresholds were validated from 2016-2018 in 35 fields and were shown to work well in two of three years (2016 and 2017).

Runtime verification (RV) is a lightweight approach to detecting temporal errors of system at runtime. It confines the verification on observed trajectory which avoids state explosion problem.To predict the future violation, some work proposed the predictive RV which uses the information from models or static analysis. But for software whose models and codes cannot be obtained, or systems running under uncertain environment, these predictive methods cannot take effect. Meanwhile, RV in general takes multi-valued logic as the specification languages, for example the " true, false and inconclusive" in three-valued semantics. They cannot give accurate quantitative description of correctness when "inconclusive" is encountered. We in this paper present a RV method which learns probabilistic model of system and environment from history traces and then generates probabilistic runtime monitor to quantitatively predict the satisfaction of temporal property at each runtime state. In this approach, Hidden Markov Model (HMM) is firstly learned and then transformed to Discrete Time Markov Chain (DTMC). To construct incremental monitor, the monitored LTL property is translated into Deterministic Rabin Automaton (DRA). The final probabilistic monitor is obtained by generating the product of DTMC and DRA, and computing the probabilities for each state. With such method, one can give early warning once the probability of correctness is lower than pre-defined threshold, and have the chance to do adjustment in advance. The method has been implemented and experimented on real UAS (Unmanned Aerial Vehicle) simulation platform.

DNA double-strand breaks (DSBs) are deleterious lesions that are generated in response to ionizing radiation or replication fork collapse that can lead to genomic instability and cancer. Eukaryotes have evolved two major pathways, namely homologous recombination (HR) and non-homologous end joining (NHEJ) to repair DSBs. Whereas the roles of protein-DNA interactions in HR and NHEJ have been fairly well defined, the functions of small and long non-coding RNAs and RNA-DNA hybrids in the DNA damage response is just beginning to be elucidated. This review summarizes recent discoveries on the identification of non-coding RNAs and RNA-mediated regulation of DSB repair

ME/CFS (Myalgic Encephalomyelitis / Chronic Fatigue Syndrome) is a chronic, complex, heterogeneous disease that affects millions and lacks both diagnostics and treatments. Big data, or the collection of vast quantities of data that can be mined for information, has transformed the understanding of many complex illnesses like cancer (1,2) and multiple sclerosis (3,4), by dissecting heterogeneity, identifying subtypes, and enabling the development of personalized treatments. It is possible that big data can reveal the same for ME/CFS. Solve M.E. developed and launched the You + ME Registry to collect longitudinal health data from people with ME/CFS, people with Long COVID (LC) and control volunteers using rigorous protocols designed to harmonize with other groups collecting data from similar groups of people. The Registry is an invaluable resource because it integrates with a symptom tracking app, as well as a biorepository, to provide a robust and rich dataset that is available to qualified researchers. Accordingly, it facilitates collaboration that may ultimately uncover causes and help accelerate the development of therapies.

Poultry eggs are a critical source of protein, vitamins, and minerals for people worldwide; facing the current global egg shortage is a significant concern. The shortage results from various factors, including avian flu outbreaks, changes in consumer demand, and supply chain disruptions caused by the COVID-19 pandemic. The economic crisis caused by the pandemic has also impacted the availability and affordability of eggs, particularly in low-income countries. The global egg shortage has implications for public health, particularly for vulnerable populations who rely on eggs for essential nutrients. One Health, an approach that recognizes the interconnectedness of human, animal, and environmental health, provides a useful framework for understanding and addressing the egg shortage. One Health approaches to the egg shortage involve collaboration between agriculture and environmental sectors to address the root causes of the lack and ensure the sustainable production and distribution of eggs. Addressing the global egg shortage requires a multifaceted approach considering the complex social, economic, and environmental factors. One Health perspectives offer a way to understand and address the interconnected factors contributing to the shortage to ensure access to affordable, nutritious eggs for all in a healthy way

The purpose of this study is to estimate the mean transitioning probabilities from a Healthy state to malaria positive uncomplicated state or to malaria positive severe state. It also classifies the various transitioning probabilities of moving through the various states based on some baseline characteristics. Malaria test results for 2019 over a 12-month period were collected from the University of Ghana school clinic. An H-U model for the study was developed and the transition rates from the cross-sectional data are indicated. With two states Healthy (H) and Uncomplicated (U) forming a state space, there were four possible transitions. The results show that the probability of transitioning from a Healthy state to a malaria positive state is 0.03% while the probability that an individual will remain at Healthy state (H) after the test is 99.73%. It was found that if an individual is already positive and has taken medication the probability that its second test came out negative is 6.45% while the chances that it will remain positive but uncomplicated is 93.55%. The study also showed that in the long run, about 95.98% of persons who visited the student clinic with malaria symptoms recorded negative tests for malaria parasite while about 4% recorded positive for malaria. In terms of disaggregation by gender, it was realized that the number of reported negative test results were higher for females (97.08%) than for males (96.13%). However, the infection rate is higher for males (3.87%) than females (2.92%). It is recommended that in as much as the University of Ghana has two health centers (a clinic and hospital), there should be a centralized system to track students’ health so research done would not be biased.

One of creators of quantum mechanics P. Jordan in his work on quantum biology claimed that life's missing laws were the rules of chance and probability of the quantum world. The article presents author’s results of studying frequencies (or probabilities) of nucleotides on so-called epi-chains of long DNA sequences of various eukaryotic and prokaryotic genomes. DNA epi-chains are algorithmically constructed subsequencies of DNA nucleotide sequences. According to the algorithm of construction of any epi-chain of the order n, the epi-chain is such nucleotide subsequence, in which the numerations of adjacent nucleotides differ by natural number n (n = 1, 2, 3, 4,…). Correspondingly each epi-chain of order n ≥ 2 contains n times less nucleotides than the original DNA sequence. The presented results unexpectedly discover that in long single-stranded and double-stranded DNA of any tested genome its DNA epi-chains of different orders n (values n are not too large) have practically identical frequencies (or probabilities) of each kind of nucleotides. These data allow considering DNA as a regular rich set of epi-chains, which can play a certain role in genetic and epigenetic phenomena as the author belives. Appropriate rules of nucleotide frequencies on epi-chains of long DNA sequences are formulated for further their tests on a wider set of genomes. These results testify on existence of long-range coherence in long DNA and remind the Fröhlich's theory of long-range coherence in biological systems. The phenomenological data are discussed from different standpoints: the DNA double helices and helical antennas with circular polarizations of electromagnetic waves; relations with the Fröhlich's theory; numerical analysis of DNA epi-chains under binary representations of nucleotides. Results are useful for developing quantum and algebraic biology.

Distributed deformation based on Fiber Bragg Grating sensors or other kinds of strain sensors can be used to evaluate safety in operating periods of bridges. However, most of the published researches about distributed deformation monitoring are focused on solid rectangular beam rather than box girder—a kind of typical hollow beam widely employed in actual bridges. Considering that the entire deformation of a single-cell box girder contains not only bending deflection but also two additional deformations respectively caused by shear lag and shearing action, this paper again revises the improved conjugated beam method (ICBM) based on the LFBG sensors to satisfy the requirements for monitoring two mentioned additional deformations. The best choice for the LFBG sensor placement in box gilder is also proposed in this paper due to strain fluctuation on flange caused by shear lag effect. Results from numerical simulations show that most of the theoretical monitoring errors of the revised ICBM are 0.3%~1.5%, and the maximum error is 2.4%. A loading experiment for a single-cell box gilder monitored by LFBG sensors show that most of the practical monitoring errors are 6%~8%, and the maximum error is 11%.

The author's method of oligomer sums for analysis of oligomer compositions of eukaryotic and prokaryotic genomes is described. The use of this method revealed the existence of general rules for cooperative oligomeric organization of a wide list of genomes. These rules are called hyperbolic because they are associated with hyperbolic sequences including the harmonic progression 1, 1/2, 1/3, .., 1/n. These rules are demonstrated by examples of quantitative analysis of many genomes from the human genome to the genomes of archaea and bacteria. The hyperbolic (harmonic) rules, speaking about the existence of algebraic invariants in full genomic sequences, are considered as candidates for the role of universal rules for the cooperative organization of genomes. The described phenomenological results were obtained as consequences of the previously published author's quantum-information model of long DNA sequences. The oligomer sums method was also applied to the analysis of long genes and viruses including the COVID-19 virus; this revealed, in characteristics of many of them, the phenomenon of such rhythmically repeating deviations from model hyperbolic sequences, which are associated with DNA triplets. In addition, an application of the oligomer sums method are shown to the analysis of the following long sequences: 1) amino acid sequences in long proteins like the protein Titin; 2) phonetic sequences of long Russan literary texts (for checking of thoughts of many authors that phonetic organization of human languages is deeply connected with the genetic language). The topics of the algebraic harmony in living bodies and of the quantum-information approach in biology are discussed.

The rapid penetration of social media has been redefining every facet of the old marketing and customer engagement tactics, not only for the low-end and mass products but also for luxury brands. In this context, brands are dealing with the challenge of keeping the balance between using mass marketing strategies concurrent with accentuating the exclusivity of their offerings. Social media can be considered a boon if brands employ them to reach the right audience and use the right platform by incorporating the right content. In this work, we propose a sector-specific, integrated, and holistic investigation of the social media strategies of luxury brands, together with the impact they generate in terms of the engagement level of the users as an indicator of their success. We provide empirical validation of the method in the sector of luxury fashion brands in the Italian market, providing qualitative and quantitative analysis of the content shared on social media, considering the type, timing, and modality of the sharing. We evaluate consumer-brand engagement in different contexts, including important live events in the field.

The most extensive urban growths in the next 30 years are expected to occur in developing countries. Lagos, Nigeria - Africa’s second most populous megacity- is a prime example. To achieve more sustainable and resilient cities, there is a need for modeling the urban growth patterns of major cities and analyzing their implications. In this study, the urban growth of Lagos state was modeled using the Multi-Layer Perceptron (MLP) neural network for the transition modeling and the Markov Chain analysis for the change prediction, achieving a model accuracy of 81.8%. An innovative visual validation of the model results using the ArcGIS was combined with kappa correlation statistics. The results show that by 2031, built-up areas will be the most spatially extensive LULC class in the study area with percentage coverage of 34.1% as opposed to 9% in 1986. The coverage of bare areas is also expected to increase by 53% between 2016 and 2031. Conversely, 24.9% and 68.3% loss of forestlands and wetlands respectively, are expected between 2016 and 2031. In view of the 11^th goal of SDGs which focuses on achieving sustainable cities and communities, the objectives of African Union’s Agenda 2063, and based on the urban growth trends observed, the study recommends a prioritization of vertical expansion as opposed to the current horizontal urban growth trends in the study area.

We quantitatively investigated the statistical behaviors of semiflexible polymer chains, which are simultaneously subjected to force stretching and rectangular tube confinement. Based on the wormlike chain model and Odijk deflection theory, we derived a new deflection length, by which new compact formulas are obtained for the confinement free energy and force-confinement-extension relation. These newly derived formulas have been justified by numerical solutions of an eigenvalue problem associated with the Fokker-Planck governing equation and extensive Brownian dynamics simulations based on the so-called Generalized Bead-Rod (GBR) model. We found that, comparing to the classical deflection theory, these new formulas are valid for a much extended range of the confinement-size /persistence-length ratio, and have no adjustable fitting parameters for sufficient long semiflexible chains in the whole deflection regime.

Memory is probably one of the most complex cognitive functions of the human, and in many years, thousands of studies have helped us to better recognize this brain function. One of the reference textbooks in neuroscience, which has also elaborated on the memory function, is written by Prof. Kandel and his colleagues. In this book, I encountered a number of ambiguities when it was explaining the memory system. Here, I am sharing those points, either to find an answer for them, or to let them be a suggestion for our future works. Prof. Kandel has spent most of his meritorious lifetime on studying the memory system; however, the brain is extremely complex, and as a result, we still have many years to comprehensively understand the neural mechanisms of brain functions.

Fatty acids and glucose are the main bioenergetic substrates in mammals that are alternatively used during the transition between fasting and feeding. Impairment of mitochondrial fatty acid oxidation causes mitochondrial myopathy leading to decreased physical performance. Here, we report that haploinsufficiency of ADCK2, a member of the aarF domain-containing mitochondrial protein kinase family, in human is associated with liver dysfunction and severe mitochondrial myopathy with lipid droplets in skeletal muscle. In order to better understand the etiology of this rare disorder, we generated a heterozygous Adck2 knockout mouse model to perform in vivo and cellular studies using integrated analysis of physiological and omics data (transcriptomics-metabolomics). The data show that Aldh2+/- mice exhibits impaired fatty acid oxidation, liver dysfunction, and mitochondrial myopathy in skeletal muscle resulting in lower physical performance. Significant decrease in CoQ biosynthesis was observed and supplementation with CoQ partially rescued the phenotype both in the human subject and mouse model. These results indicate that ADCK2 is involved in organismal fatty acid metabolism and in CoQ biosynthesis in skeletal muscle. We propose that patients with isolated myopathies and myopathies involving lipid accumulation be tested for possible ADCK2 defect as they are likely to be responsive to CoQ supplementation.

The whole genome sequencing (WGS) has become a crucial tool to understand genome structure and genetic variation. The MinION sequencing of Oxford Nanopore Technologies (ONT) is an excellent approach for performing WGS and has advantages in comparison with other Next-Generation Sequencing (NGS): It is relatively inexpensive, portable, has simple library preparation, can be monitored in real-time, and has no theoretical limits on read length. Sorghum bicolor (L.) Moench is diploid (2n = 2x = 20) with a genome size of about 730 Mb, and its genome sequence information is released in the Phytozome database. Therefore, sorghum can be be used as a good reference. However, plant species have complex and large genomes compared to animals or microorganisms. As a result, complete genome sequencing is difficult for plant species. MinION sequencing that produces long-reads can be an excellent tool to overcome the weak assembly of short-reads generated from NGS by minimizing the generation of gaps or covering the repetitive sequence that appears on the plant genome. Here, we conducted the genome sequencing for S. bicolor cv. BTx623 using the MinION platform and obtained 895,678 reads and 17.9 gigabytes(Gb) (ca. 25X coverage of reference) from long-read sequence data. Through a de novo assembly using two different tools and mapped assembled contigs against the sorghum reference genome, a total of 6,124 contigs (covering 45.9%) were generated from Canu, and a total of 2,661 contigs (covering 50%) were generated from Minimap and Miniasm with a Racon pipeline. Our results provide a pipeline of long-read sequencing analysis for plant species using the MinION platform and a clue to determine the total sequencing scale for optimal coverage based on various genome sizes.

Long COVID (LC) encompasses a constellation of long-term symptoms experienced by at least 10% of people after the initial SARS-CoV-2 infection, and so far has affected about 65 million people. The etiology of LC remains unclear; however, many pathophysiological pathways may be involved, including viral persistence; chronic, low grade inflammatory response; immune dysregulation and defective immune response; reactivation of latent viruses; autoimmunity; persistent endothelial dysfunction and coagulopathy; gut dysbiosis; hormonal dysregulation, mitochondrial dysfunction; and autonomic nervous system dysfunction. There are no specific tests for the diagnosis of LC, and clinical features including laboratory findings and biomarkers may not specifically relate to LC. Therefore, it is of paramount importance to develop and validate biomarkers that can be employed for the prediction, diagnosis and prognosis of LC and its therapeutic response. Promising candidate biomarkers that are found in some patients are markers of systemic inflammation including acute phase proteins, cytokines and chemokines; biomarkers reflecting SARS-CoV-2 persistence, reactivation of herpesviruses and immune dysregulation; biomarkers of endotheliopathy, coagulation and fibrinolysis; microbiota alterations; diverse proteins and metabolites; hormonal and metabolic biomarkers; as well as cerebrospinal fluid biomarkers. At present, there are only two reviews summarizing relevant biomarkers; however, they do not cover the entire umbrella of current biomarkers or their link to etiopathogenetic mechanisms, and the diagnostic work-up in a comprehensive manner. Herein, we aim to appraise and synopsize the available evidence on the typical laboratory manifestations and candidate biomarkers of LC, their classification based on main LC symptomatology in the frame of the epidemiological and pathogenetic aspects of the syndrome, and furthermore assess limitations and challenges as well as potential implications in candidate therapeutic interventions.

This paper studies long economic series to assess the long-lasting effects of pandemics. We analyze if periods of time that cover pandemics have a change in trend and persistence in growth, and in level and persistence in unemployment. We find that there is an upward trend in the persistence level of growth across the centuries. In particular, shocks originated by pandemics in recent times seem to have permanent effect in growth. Moreover, our results show that the unemployment rate increases and it becomes more persistent after a pandemic. In this regard, our findings support the design and implementation of counter-cyclical policies to soften the shock of the pandemic.

China is one of the world’s most important countries for waterfowl because of the large amount of potential habitat and its position along major migratory routes. Waterfowl poaching in China is a serious threat, and for over twenty years colleagues and I have tracked waterfowl poaching in China including hunting methods, trade routes and prices involved. According to the latest survey of a NGO, 11.8% of Chinese people have participated in wildlife consumption, and about 32.0% of people have seen wildlife consumption (Not necessarily involved in killing and eating the wildlife). The survey results come from 100 000 internet questionnaires. The current report provides an update focusing on waterfowl poaching in Xinjiang Province of the northwest China, where is highly pathogenic area on the avian influenza, SARS and the Wuhan coronavirus pneumonia (such as COVID-19). The cases in 2011, 2012, and 2014 involved about 1816 to 2760 birds of more than 20 species, with an estimated total of 200 000 wild birds being hunted by a group per year in Xinjiang. Strangely, the poacher was not punished by any law. We know a few waterfowl species are protected as a list of Key Protected Species in China, and hopefully this report will draw attention to the scope of waterfowl poaching in China. China has made great progress with protecting other wildlife, and hopefully more can be done to protect migrating waterfowl.

This study investigates optimal decisions in a green supply chain consisting of a manufacturer and a leading retailer considering the green marketing and fairness preferences of member firms. Four Stackelberg game decision models are constructed in which the manufacturer and the re-tailer engage in green marketing separately when the manufacturer has no and has fairness preferences. The impacts of fairness preferences and green marketing on the optimal decision in the green supply chain are comparatively analyzed. The study finds that member firms perform green marketing regardless of the presence or absence of fairness preferences and that such be-havior increases the wholesale price, retail price, and market demand of low-carbon products as well as the profits of member firms and the supply chain. A more interesting finding is that the profit growth of member firms and the supply chain due to the manufacturer’s green marketing is more pronounced than that due to the retailer’s green marketing. When the retailer and the manufacturer engage in conduct green marketing, the manufacturer's fairness preferences have different effects on the wholesale price, retail price, market demand, level of green marketing efforts, member enterprises and profits of supply chain. Therefore, firms should consider the impact of green marketing and fairness preferences to make pricing and performance decisions, so as to achieve efficient operation of the whole supply chain and avoid double marginal effects. Finally, the above conclusions are verified through numerical simulation, providing a reference for the decision-making of member firms in the green supply chain.

Heart Rate Monitors (HRMs) are an indispensable tool for controlling training parameters of healthy athletes. They became a source of information about stress heart rhythm disturbances, recognized as unexpected increases in heart rate (HR), which can be life-threatening for athletes. Most HRMs do not recognize the type of arrhythmia, confusing them with artifacts. The aim of the study was to assess the usefulness of ECG recording functions by sports HRMs among endurance athletes, coaches, and physicians in comparison with other basic and hypothetical functions. We conducted 3 surveys among endurance athletes (76 runners, 14 cyclists, and 10 triathletes), as well as 10 coaches and 10 sports doctors to obtain information on how important ECG recording is, and what functions of HRMs should be improved to meet their expectations in the future. The respondents were asked questions regarding use and hypothetical functions, as well as preference for HRM type (optical/strap). For athletes, the 4 most important functions were distance traveled, pace, instant heart rate, and information about reaching the oxygen threshold. ECG recording was the 8th and 9th most important for momentary and continuous, respectively. Coaches opined more importance to ECG recording. Doctors placed ECG recording as most important. All participants preferred optical HRMs to strap HRMs. Research on the improvement and implementation of HRM functions shows slightly different preferences of athletes compared to coaches and doctors. Suspected arrhythmia increases the value of the HRM’s ability to record ECGs during training by athletes and coaches. For doctors, this is the most desirable feature in any situation. Considering the expectations of all groups continuous ECG recording during training will significantly improve the safety of athletes.

Flails are one of the more contentious and misunderstood classes of medieval weaponry, despite their prevalence in popular media: some researchers question their existence entirely and the bulk of historians are sceptical of widespread temporal and geographical prevalence while others, and a significant volume of period evidence, would argue the contrary. While the expansive use of flails in Eastern Europe and Byzantium is familiar, many Central, Western, and Southern European sources are less well-known or largely forgotten, especially those stemming from the later-early and early-high medieval eras (up to 1250). In this work, I collate and discuss the bulk of the available literary references and artistic depictions of flails and their use alongside some of the archaeological finds from Western, Central, and Southern Europe, with an emphasis on the 12^th and 13^th Centuries. The significance of this volume of evidence is examined, and an assessment of flails as a part of medieval culture and warfare is considered. Collectively, this would suggest that knowledge of flails as instruments of war and associated cultural connotations, if not their actual prevalence and use in warfare, was far more widespread across Europe this time period than has been previously estimated.

A significant number of persons with coronavirus disease 2019 (COVID-19) experience persistent, recurrent, or new symptoms several months after the acute stage of severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) infection. This phenomenon, termed Post-Acute Sequelae of SARS-CoV-2 (PASC) or Long COVID, is associated with high viral titers during acute infection, a persistently hyperactivated immune system, tissue injury by NETosis-induced micro-thrombofibrosis (NETinjury), microbial translocation, complement deposition, fibrotic macrophages, the presence of auto-antibodies, and lymphopenic immune environments. Here, we review the current literature on the immunological imbalances that occur during PASC. Specifically, we focus on data supporting common immunopathogenesis and tissue injury mechanisms shared across this highly heterogenous disorder including NETosis, coagulopathy, and fibrosis. Mechanisms include changes in leukocyte subsets/functions, fibroblast activation, cytokine imbalances, lower cortisol, autoantibodies, co-pathogen reactivation, and residual immune activation driven by persistent viral antigens and/or microbial translocation. Taken together, we develop the premise that SARS-CoV-2 infection results in PASC as a consequence of acute and/or persistent single or multiple organ injury mediated by PASC determinants to include degree of host response (inflammation, NETinjury), residual viral antigen (persistent antigen) and exogenous factors (microbial translocation). Determinants of PASC may be amplified by co-morbidities, age, and sex. Keywords: long COVID, PASC, long haulers, NETosis, T cell, NK cell, DC, neutrophil,

In the case of vascular plants the process of water loss by leafs and water absorption by the root is well known. There is agreement on the passive nature of long-distance moisture movement in the dead cells of the xylem; however, controversy exists focusing on the long-distance water transport principle. Hales (1726) founded a view of bulk flow based on water suction after experiments with cut twigs. The previous doctrine of long-distance water transport within vessel elements and tracheid of the xylem of intact plants – the relevant cohesion theory in text books – was developed mainly by Boehm (1893), Renner (1911) and Dixon (1914) with plant artefacts. Water movement according to this theory is based on an assumed hydrodynamic bulk fluid flow in xylem in continuous water columns (free of water vapour space), under tension, according to the law of Poiseuille (see e.g. Dixon 1914). Physically hydrodynamics is part of fluid mechanics, as a result Poiseuille’s law is usually valid only for hydrodynamic bulk flow in ideal capillaries (Sutera & Skalak, 1993). Besides the basic requirement for transport, according to cohesion theory, the existence of ideal capillaries is not compatible with either: “Because vessel elements and tracheid do not stand as ideal capillaries. …” (Bresinsky et al. 2008, translated from German). Unlike ideal capillaries, the walls of vessel elements and tracheid interact with the transported water. These walls are able to function as a source or as a sink for the transported water because of interaction with the cell walls. With the interaction, vessel elements and tracheid, part of the xylem, can shrink and swell, unlike ideal capillaries. Because the xylem (in woody plants part of the wood) is inconsistent with the basic law of fluid flow, the equation of mass balance (Zimmermann et al. 2004) and cohesion theory are not strictly followed.Many plant physiologists view the cohesion theory as appropriate, however, this theory remains controver­sial, i.e. by Eisenhut (1988), Laschimke (1990) and Hahn (1993). Nultsch (1996) gives doubts referring to the present doctrine of plant water transport. Zimmermann et al. (2004) reject the cohesion theory and conclude: “... that the arguments of the proponents of the Cohesion Theory are completely misleading” (Zimmermann et al. 2004). Hence cohesion theory can be treated as inapplicable and the question arises: how does water transport in fact function? In the following, it is gone into in more detail. A sorption hypothesis of actual water transport, based on empirical fact, shall be addressed in this paper.

Abstract: Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing. However, such data are necessary to better understand long-term urbanization and land development processes and for the assessment of coupled nature-human systems, e.g., the dynamics of the wildland-urban interface. Herein, we propose a framework that jointly uses remote sensing derived human settlement data (i.e., the Global Human Settlement Layer, GHSL) and scanned, georeferenced historical maps to automatically generate historical urban extents for the early 20th century. By applying unsupervised color segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multi-temporal GHSL. We apply our method to study areas in countries across four continents, and evaluate our approach for two U.S. study sites against historical settlement extents derived from the Historical Settlement Data Compilation for the US, HISDAC-US, achieving Area-under-the-Curve values >0.9. Our results are largely in agreement with model-based urban areas from the HYDE database, and demonstrate that the integration of remote sensing derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization, and long-term land cover change in countries where historical maps are available.

This study aimed to characterize the long non-coding RNA (lncRNA) expression in the bovine mammary gland and to infer their functions in dietary response to 5% linseed oil (LSO) or 5% safflower oil (SFO). Twelve cows (six per treatment) in mid lactation were fed a control diet for 28 days followed by a treatment period (control diet supplemented with 5% LSO or 5% SFO) of 28 days. Mammary gland biopsies were collected from each animal on day-14 (D-14, control period), D+7 (early treatment period) and D+28 (late treatment period) and were subjected to RNA-Sequencing and subsequent bioinformatics analyses. Functional enrichment of lncRNA was performed via potential cis regulated target genes located within 50 Kb flanking regions of lncRNAs and having expression correlation of >0.7 with mRNAs. A total of 4955 lncRNAs (325 known and 4630 novel) were identified which potentially cis targeted 59 and 494 genes in LSO and SFO treatments, respectively. Enrichments of cis target genes of lncRNAs indicated potential roles of lncRNAs in immune function, nucleic acid metabolism and cell membrane organization processes as well as involvement in Notch, cAMP and TGF-β signaling pathways. Thirty-two and 21 lncRNAs were differentially expressed (DE) in LSO and SFO treatments, respectively. Six genes (KCNF1, STARD13, BCL6, NXPE2, HHIPL2 and MMD) were identified as potential cis target genes of six DE lncRNAs. In conclusion, this study indicated potential roles for lncRNAs in mammary gland immune functions and development and provided potential candidate genes and pathways via which lncRNAs can function in diet responses.

This paper applied case study research to design architectures for green-field supply chain integration. The integration design is based on a case study of a supply chain integration of 5 companies, operating in different, but supply chain complimenting industry sectors. The case study research is applied to design and validate the architectures in a real world scenario. The supply chain integration architectures enable the conversion of individual into integrated strategies. The architectures are categorised and the process develops into a conceptual system for identifying the correlations between individual participants’ strategic areas of interest and the integrated supply chain areas of interest. The novelty of this paper is a conceptual system for green-field supply chain integration architectures, which can be applied in real world by supply chain practitioners.

Immunotherapy has gained great momentum with chimeric antigen receptor T cell (CAR-T) therapy, in which patient’s T lymphocytes are genetically manipulated to recognize tumor-specific antigens increasing tumor elimination efficiency. In the last years, CAR-T cell immunotherapy for hematological malignancies achieved a great response rate on patients and is a very promising therapy for several other malignancies. Each new CAR design requires a preclinical proof-of-concept experiment using immunodeficient mouse models. The absence of a functional immune system in these mice makes them simple and suitable to be mathematically modeled. In this work, we developed a three population mathematical model to describe tumor response to CAR-T cell immunotherapy in immunodeficient mouse models, encompassing interactions between a non-solid tumor and CAR-T cells (effector and long-term memory). We account for several phenomena, such as tumor-induced immunosuppression, memory pool formation, and conversion of memory into effector CAR-T cells in the presence of new tumor cells. Individual donor and tumor specificities were considered as uncertainties in the model parameters. Our model is able to reproduce several CAR-T cell immunotherapy scenarios, with different CAR receptors and tumor targets reported in the literature. We found that therapy effectiveness mostly depends on some specific parameters such as the differentiation of effector to memory CAR-T cells, CAR-T cytotoxic capacity, tumor growth rate, and tumor-induced immunosuppression. In summary, our model can contribute to reduce and optimize the number of in vivo experiments with in silico tests to select specific scenarios that could be tested in experimental research. Such in silico laboratory was made available in a Shiny R-based platform called CARTmath. It is an open-source, easy to run simulator, available at github.com/tmglncc/CARTmath or directly on the webpage cartmath.lncc.br, containing this manuscript results as examples and documentation. The developed model, together with the CARTmath platform, provides potential use for assessing different CAR-T cell immunotherapy protocols and associated efficacy, becoming an accessory towards in silico trials.

Abstract: Many countries around the world suffer from the lack of a sea port directly linked to the rest of the world. Such countries are called "landlocked countries". This leads to Weak competitiveness of their products in the global market, as well as to the high cost of the imports. Africa has the largest share of these countries, with 16 of the 43 landlocked countries around the world. The aim of this paper is to propose a general framework for criteria that can be used to choose between ports in transit countries that can be used for import or export. These criteria are related to the assessment of the sea ports in terms of infrastructure and tariffs. It is also related to transport infrastructure from the transit country to the landlocked country and the level of safety. The study identified nine criteria that could be used to compare between ports in transit countries. Using Full Consistency Method (FUCOM) to evaluate those criteria showed that the number of navigation lines is the most important criteria followed by the port service level.

Background: Recent developments in our understanding of the interactions between long non-coding RNA (lncRNA) and cellular components have improved treatment approaches for various human diseases including cancer, vascular diseases, and neurological diseases. Although investigation of specific lncRNAs revealed their role in the metabolism of cellular RNA, our understanding of their contribution to post-transcriptional regulation is relatively limited. In this study, we explore the role of lncRNAs in modulating alternative splicing and their impact on downstream protein-RNA interaction networks. Results: Analysis of alternative splicing events across 39 lncRNA wildtype and knockout RNA-sequencing datasets from three human cell lines: HeLa (Cervical Cancer), K562 (Myeloid Leukemia), and U87 (Glioblastoma), resulted in high confidence (fdr < 0.01) identification of 4432 skipped exon events and 2474 retained intron events, implicating 759 genes to be impacted at post-transcriptional level due to the loss of lncRNAs. We observed that a majority of the alternatively spliced genes in a lncRNA knockout were specific to the cell type, in agreement with the finding that genes affected by alternative splicing also displayed enriched functions in a cell type specific manner. To understand the mechanism behind this cell-type specific alternative splicing patterns, we analyzed RNA binding protein (RBP)-RNA interaction profiles across the spliced regions. Conclusions: Despite limited RBP binding data across cell lines, alternatively spliced events detected in lncRNA perturbation experiments were associated with RBPs binding in proximal intron-exon junctions, in a cell type specific manner. The cellular functions affected by alternative splicing were also affected in a cell type specific manner. Based on the RBP binding profiles in HeLa and K562 cells, we hypothesize that several lncRNAs are likely to exhibit a sponge effect in disease contexts, resulting in the functional disruption of RBPs due to altered titration of the RBPs from their target loci. We propose that such lncRNA sponges can extensively rewire the post-transcriptional gene regulatory networks by altering the protein-RNA interaction landscape in a cell-type specific manner.

The out-of-equilibrium dynamics of many body systems has recently received a burst of interest, also due to experimental implementations. The dynamics of both observables, such as magnetization and susceptibilities, and quantum information related quantities, such as concurrence and entanglement entropy, have been investigated under different protocols bringing the system out of equilibrium. In this paper we focus on the entanglement entropy dynamics under a sinusoidal drive of the tranverse magnetic field in the 1D quantum Ising model. We find that the area and the volume law of the entanglement entropy coexist under periodic drive for an initial non-critical ground state. Furthermore, starting from a critical ground state, the entanglement entropy exhibits finite size scaling even under such a periodic drive. This critical-like behaviour of the out-of-equilibrium driven state can persist for arbitrarily long time, provided that the entanglement entropy is evaluated on increasingly subsytem sizes, whereas for smaller sizes a volume law holds. Finally, we give an interpretation of the simultaneous occurrence of critical and non-critical behaviour in terms of the propagation of Floquet quasi-particles.

Alzheimer’s disease and related dementias (ADRD) remain a public health challenge in developing counties. We developed a needs-based essential care service package (ECSP) for care planning of persons living with dementia (PLWD) using a cross-sectional survey among PLWD in institutions in six cities in China (n= 1,299). Face-to-face interviews were conducted with caregivers of PLWD by trained staff between 2018 and 2019. Care service needs and utilization by the level of cognitive impairment were summarized. The average age of PLWD was 80.7 years. 76% of participants had severe cognitive impairment. The needs-based ECSP with 30 service items would be sufficient in supporting care services of PLWD in China, of which seven items are core care. The selection plan for ECSP at different levels is designed as “General Care Services + Selective Care Services”, in which service items for low-, mid-and high-level care for PLWD are 7+3, 7+6, and 7+10, respectively. The findings provide the first large-scale data on service needs and utilization of PLWD in mainland China. The ECSP for PLWD advanced in the paper was a practicable and effective quantitative management means. It is deserved to application in a large scale.

Nowadays, frameworks and models are critical to enabling organizations to identify their current sustainability integration into business and to follow up on these initiatives over time. In this context, the maturity models offer a structured way of analyzing how a supply chain meets specific sustainability requirements and which areas demand attention to reach maturity levels. This study proposes a five-level maturity model to help supply chains identify their level of engagement with sustainability practices combining three perspectives: intra and inter-organizational sustainability practices, triple-bottom-line approach and critical areas for sustainability. All the steps followed in constructing the maturity model were based on a literature review, and case studies supported its improvement, application, and testing. The proposed model presents many advantages, such as being used as a self-assessment tool, a roadmap for sustainability behaviour improvement, and a benchmarking tool to evaluate and compare standards and best practices among organizations and supply chains.

This paper realizes early bearing fault warning through bearing fault time series prediction, and proposes a bearing fault time series prediction model based on optimized maximum correlation kurtosis deconvolution (MCKD) and long short-term memory (LSTM) recurrent neural network to ensure bearings operation reliability. The model is based on lifecycle vibration signal of the bearing, to begin, the cuckoo search (CS) is utilized to optimize the parameter filter length L and deconvolution period T of MCKD, taking into account the influence and periodicity of the bearing time series, the fault impact component of the optimized MCKD deconvolution time series is improved. Then select the LSTM learning rate α depending on deconvolution time series. Finally, the dataset obtained through various preprocessing approaches are used to train and predict the LSTM model. The average prediction accuracy of the optimized MCKD-LSTM model is 26 percent higher than that of the original time series, proving the efficiency of this method, and the prediction results track the real fault data well, according to the XI'AN JIAOTONG University XJTU-SY bearing dataset.

A long-term forecast study on the electricity demand of Taoussa of Mali is conducted in this paper, with various scenarios of socioeconomic and technological conditions. The analysis tool, which is applied in scenarios simulation, is the Model for Analysis of Energy Demand from the International Atomic Energy Agency. The analysis results are annual electricity demand and peak load forecast for the electrification from the period 2020 to 2035. During the planning period, the analysis results show that the electricity demand will increase to 49.40 MW (332.57 GWh) for the low scenario (LS), 66.46 MW (472.61 GWh) for the reference scenario (RS), and 89.47 MW (635 GWh) for the high scenario (HS). In addition, the total electricity demand increased at an average rate of 8.13% in the LS, 10.31% in the RS and 12.56% in the HS in all sectors. The electricity peak demand is expected to grow at 7.92%, 10.53% and 12.91% corresponding to the three scenarios; in this case, the system peak demand in 2035 will increase to 64.88 MW for the LS, 92.2 MW for the RS and 126.22 MW, the days of peak load are between 17th -23rd in May. The Industry sector will be the biggest electricity consumer of Taoussa area.

The Coronavirus disease 2019 (COVID-19) pandemic began in Jan. 2020 in Wuhan, China with a new coronavirus designated SARS-CoV-2. The principle cause of death from COVID-19 disease quickly emerged as Acute Respiratory Distress Syndrome (ARDS). A key ARDS pathogenic mechanism is the “Cytokine Storm”. This is a dramatic increase in the blood of inflammatory cytokines. In the last 2 years of the pandemic new pathology has emerged in COVID-19 survivors in which a variety of long-term symptoms emerge. This condition is called “Long COVID”. The spike protein on the surface of the virus (target for the new mRNA/DNA vaccines) is composed of joined S1-S2 subunits. Upon S1 bind-ing to the human ACE2 receptor on cells, the S1 subunit is cleaved and the S2 subunit me-diates entry of the virus. The S1 protein is then released into the blood, which might be one of the pivotal triggers for initiation and/or perpetuation of the cytokine storm. In this study, we tested the hypothesis that the spike S1 protein may activate inflammatory sig-naling and cytokine production independent of the virus. Our data support a potential role for spike S1 activation of inflammatory signaling and cytokine production in human lung and intestinal epithelial cells in culture. These data support a potential role for the SARS-CoV-2 spike S1 protein in COVID-19 pathogenesis.

In recent years, Blockchain has been favorably adopted in the Supply Chain industry as it provides guaranteed transparency and traceability. The flexibility of Blockchain allows different applications to enable to exchange information; a significant middleweight layer is responsible for information transfer in the Agricultural Sector. The products that are manufactured at a global level in the agriculture industry are improved in safety, validation of some criteria, and quality. In the agriculture industry, the increasing number of complications associated with food safety and impurity risks needs high-level effective traceability solutions that act as necessary quality management tools to make sure satisfactory safety of crops. The agricultural supply chain today has complex ecosystems, consisting of several stockholders to authenticate criteria which are important like crop development stages, monitoring and validation, and compliance with the quality standard. In this proposed research, a systematic literature review will be done that includes smart contracts, Blockchain, and business transactions exclusively for crop production traceability across the agricultural and food supply chain. By using Blockchain in the agriculture sector, productivity, consistency, safety, reliability, and advanced security are increased. All transactions are kept and recorded on the immutable record of Blockchain associates with a decentralized system. Thus, it provides more traceability and clarity in the agriculture system in asafe, trustworthy, and effective way. A systematic literature review is thus enforced to classify the papers which are selected by the following classification: crop traceability, contribution type, research type, and their approach. For this systematic review, the papers which extracted are classified according to defined criteria. The purpose of this study is to fill the gap by collecting and analyzing studies available within the literature aiming to firstly, gain complete insight on the integration of Blockchain in the agriculture sector. Secondly, provide a summary of the present state of research on this area and identify gaps in existing studies. To achieve this aim an SLR was conducted. The findings of this SLR are discussed and researchers were provided with suggestions on possible directions for future research.

In Cameroon, alcoholic beverages remain the main consumed drink. In the Northern regions, indigenous sorghum beers are popular and widely consumed in an actively fermenting state by the people. In this study, some physicochemical parameters of the alcoholic sorghum beverages and correlations between them were evaluated during fermentation for 10 days. The indigenous white and red beers were produced at the laboratory scale assisted by experimented producers and some parameters (pH, total acidity, alcohol, sugars, density, total solids, temperature, and conductivity) were measured on the wort and fermented beverages. The pH decreases from 3.2 to 2.4 and 3.11 to 2.41; total acidity increases from 1.07 to 5.1 g/l and 0.5 to 4.6 g/l; alcohol enhances from 0 to 9.5% and 0 to 6.8% (v/v); total solids drop from 13.6 to 5°P and 12.2 to 3.3°P were observed respectively in the white and red sorghum beers. The multivariate analysis showed a good correlation between consumption of sugar, the decrease of total solids and density with the decrease in pH. It was also shown that, a perfect link exist between the production of alcohol and organic acids. The hierarchical analysis showed that indigenous red beer samples fermented for 1 and 2 days and those fermented for 4 to 10 days are related and could be separate in two distinct groups, whereas white turbid beer samples are separated in three different groups, those fermented for 1 to 4 days, those 5 to 6 days and those 7 to 10 days. The results obtained could serve as a guide to better understand the fermentation process of the indigenous alcoholic sorghum-based beverages.