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This paper uses empirical evidence to address the subject of when and how the processes for the homeostatic influence of the biota on planetary-scale biogeochemical processes first arose. To set the scene for the study, the paper first proposes three core necessary attributes for Gaia: (1) individual organisms possessing control systems using feedback loops to regulate their external environment (the environmental regulation core attribute); (2) joint action of groups of such organisms leading to the achievement of larger scale environmental regulation (the joint action core attribute); and (3) such action occurring at a planetary scale. Doolittle (1981) and Dawkins (1982) stated, in essence, that it was impossible for them to imagine any evolutionary way by which Gaia could have arisen. The Doolittle and Dawkins positions were influential and interpreted by some as evidence that Gaia could not exist at all. New evidence against this interpretation is provided in this paper by reference to recent studies providing multiple lines of empirical evidence at planetary scale that Gaia exists. In the present paper these results are put to tests from the epistemic position of scientific realism, the dominant view held by scientists, philosophers, and the educated public about the relationship between scientific evidence and truth. This is the position that empirically successful theories are likely to be at least approximately true. The scientific realism tests applied to the recent studies providing multiple lines of empirical evidence at planetary scale that Gaia exists are empirical success, falsifiability, the no-miracles argument, abduction, and predictive success. This body of results passes the scientific realism tests applied, showing that the statement that Gaia exists is likely to be at least approximately true. If Gaia exists, it had to have evolved somehow. One way is that proposed by Doolittle (2014) and Boyle and Lenton (2022). This is that Gaia could evolve not by genes but by persistence, at clade level. This paper complements that work by seeking to determine whether Gaia could evolve on the precise ground stated by Dawkins and Doolittle in their objections – that is, by standard gene-based Darwinian natural selection. To do this, the paper identifies genes in modern organisms that relate to functionality that is analogous to that of each of two of the core attributes of modern Gaia. A number of these modern genes are also found from phylogenetics to be statistically significantly likely to be present in the Last Universal Common Ancestor or the Last Bacterial Common Ancestor 3.5 to 4 billion years ago. These results provide statistically significant evidence that functionality analogous to core attributes of Gaia was present in LBCA and/or LUCA. In other words, the potentiality for Gaia arose between 3.5 to 4 billion years ago. Doolittle (1981) and Dawkins (1982) as quoted at the outset wrote of the impossibility of imagining any evolutionary way in which Gaia could have arisen. In this paper we provided evidence (i) that Gaia likely exists, and (ii) that clade-based (Doolittle, 2014 and Boyle and Lenton, 2022) and now standard – that is, gene-based – Darwinian natural selection (this paper) provide a range of ways for Gaia to evolve. We submit that this evidence shows that, in contrast to when Doolittle (1981) and Dawkins (1982) wrote, it is now not impossible to imagine evolutionary ways in which Gaia could have arisen.

Measles virus (Morbillivirus abbreviated as MV, but more recently MeV) is the causal agent of Measles disease, thought to have existed at least 4000 years ago, affecting predominantly infants, but also immunocompromised individuals and others remaining a public health issue today globally. In this review, we are discussing the historical background about MeV infection to modern–day research, then delving into Measles disease and discussing what is known about immunisation against the disease. We elucidate what is known about the viral structure and the function of the viral proteins. The genomic stability of the MeV particle is suggestive that the third pathogen with the potential to be eradicated (after the Variola and Rinderpest viruses) requires further biological and immunological clarification. Here therefore covers a bow from structure and mechanism to clinical aspects of MeV infection touching topics like cellular receptor–associated factors to the immunology of MeV infection. We highlight the actual knowledge about innate immune response during MeV infection, including chemokine and cytokine expression finalised by the current understanding of adaptive immune responses to MeV.

Interferons (IFNs) were the original prototype cytokine system discovered in 20th century research. As the name interferon implies (derived from the Latin interfere-on), these proteins have immunostimulatory, primarily antiviral and antitumour properties and are synthesised and secreted between cells. Due to technological advances, processes and variable factors involved in IFN regulation can be comparatively explained by proteins expressed and genes expressed. In this review, we provide a brief introduction and background on the history of IFN research. We then provide an overview of type I IFNs, associated cells, and their receptors and outline the characteristics of type I IFN subtypes. We distinguished between the three types of IFN in the immune system of higher mammals and the associated cellular signalling mechanisms of IFNs together with IFN–inducible transmembrane proteins (IFITM) during viral infection. Additionally, we elucidated the role of IFN in viral diseases, as well as type II IFN and immunological disorders, in infections and deficiency followed by type I IFN subtypes. Errors in the IFN signal transduction and activator of transcription (STAT) protein signalling pathway during disease were analysed. This paper concludes with an examination of the role of type I/II/III interferon signalling since the discovery of the timing of interferon synthesis within immune cell pathways, examining autoantibodies, interferons and errors, and finally closing with the current understanding of interferon and immunotherapy regulation in cancer.

Abiotic and biotic stresses often impede optimal plant germination by disrupting natural growth and development mechanisms. Gram-negative Azotobacter, among crop growth-promoting rhizobacteria, emerges as a potent agent for enhancing plant health. Azotobacter employs various mechanisms, such as nitrogen fixation, phosphorus solubilization, pesticide and fungicide degradation, siderophore production, and synthesis of growth-promoting hormones, collectively contributing to improved plant vigor. Furthermore, Azotobacter-based biofertilizers offer additional benefits for soil fertility enhancement. As a favorable and cost-effective alternative to chemical fertilizers, the utilization of biofertilizers has gained traction. Nonetheless, commercial-scale microbial biofertilizer formulation remains a challenge. This study aims to consolidate the advantageous attributes and effective research endeavors regarding Azotobacter biofertilizers for fostering sustainable agroecosystems.

I believe consciousness is a property of advanced nervous systems, and as such a product of evolution. Thus, to understand consciousness we need to describe the trajectory leading to its evolution and the selective advantages conferred. A deeper understanding of the neurology would be a significant contribution, but other advanced functions, such as hearing and vision, are explained with a comparable lack of detailed knowledge as to the brain processes responsible. In this paper, I try to add details and credence to a previously suggested, evolution-based model of consciousness. According to this model, the feature started to evolve in early amniotes (reptiles, birds, and mammals) some 320 million years ago. The reason was the introduction of feelings as a strategy for making behavioral decisions.

Agriculture is a critical sector contributing significantly to the global economy and food security. However, it faces several challenges such as climate change, water scarcity, soil nutrient depletion, and others. Efficient water management and nutrient monitoring are essential for sustainable agricultural practices; therefore, the main purpose of this research is to develop and evaluate a cost effective and efficient soil profile system, suitable for irrigation systems control, acquisition and wireless transmission of soil property data in real-time. The performance of the prototype equipment was evaluated through field testing and validation. The developed system showcased a coefficient of determination for the selected soil parameters ranging from a very strong (0.98) R2 to a strong R2 (0.88) when compared with the standard measurement. It was observed that the developed system addressed the limitations of conventional soil monitoring methods, which often lack real-time and in-situ measurement capabilities. By automating the monitoring process, the system enabled precise irrigation scheduling, optimized water usage, and reduced wastes. Additionally, the real-time nutrient data facilitate site-specific fertilizer application, promoting sustainable and efficient agricultural practices. The developed system also enables users to make informed decisions regarding irrigation scheduling, fertilizer application, and crop management, ultimately leading to increased crop yields, reduced input costs, and minimized environmental impacts. The system's affordability and wireless communication capabilities will make it accessible to a broader range of agricultural stakeholders, fostering sustainable and efficient farming practices.

Forest transition describes a land use change process where an initial period of forest cover loss is followed by a phase of recovery. Previous studies on forest transition, often limited by geographic or temporal constraints, have yielded mixed results about the plausibility of this hypothesis. This research analyzes the relationship between the Human Development Index (HDI) and deforestation rates using quantile regression method. The study encompasses 165 countries across four continents over the period 1990-2021. Our findings demonstrate that forest transition provides a plausible framework for understanding land use dynamics, with significant regional variations that elucidate current trends in forest cover changes. These results underscore the importance of considering regional contexts in forest conservation and development policies.

In this paper, I present the results of the application of an entropy function (H(C)), which I suggested in 1972, for matrices of pairwise co-occurrence of species in plant communities, to two sets of very simple “toy” matrices representing different situations of co-occurrence that species or “traits” may have in ecological communities. The first set consists of 7 binary matrices X(M, N) from which 7 pairwise co-occurrence matrices (C(M, M)) are generated by self-matrix multiplication. The second set consists of 6 co-occurrence matrices generated by 6 “toy” graphs that have the minimum number of connections (M-1) and that represent different situations as far as the centrality of nodes is concerned. The entropy (H(C)) is given by the difference between the entropy H(D) of the diagonal D of the co-occurrence matrix C(M, M) and the entropy H(T) of its upper or lower triangular part T, with M(M-1)/2 cells. H(C) is relativized in two ways to give origin to (K(C)) and (R(C)), which I call respectively “index of weighted connectance” and “redundancy”; these have been compared for both the sets of co-occurrence matrices with: a) the ratio (H(D)+H(T))/ ln ((M(M+1)/2), that is, the relative entropy or the evenness of the upper or lower triangular part of C(M, M) including the diagonal D (M, M) (i.e., D+T); b) the formula of Ricotta and Szeidel (2006), which in this case incorporates in the entropy H(D) the entropy of the matrix of co-occurrence itself with values relativized between 0 and 1; c) the evenness of the eigenvalues of C(M, M); d) the chi-square of C(M, M); e) the average nested free; and f) the nested-based similarity between the N vectors of the X(M, N) matrices; and g) the number of centres of the graphs corresponding to the co-occurrence matrices. I used the Pearson correlation coefficient to estimate the correlation between all the parameters used to calculate H(C) and the parameters for the comparisons from a) to g) of the two “toy” data sets. Finally, I used the two correlation matrices for ranking the parameters by the method suggested by Orlóci (1978), based on the sum of squares, in order to evaluate the capacity of each parameter in explaining the variation of the others. The results show that the correlation between the parameters is very high and that H(C) can be considered a good tool to describe the pattern of the graphs of the corresponding co-occurrence matrices.