Lung carcinoma is one of the most deadly malignant tumors in mankind. With the rising incidence of lung cancer, searching for the high effective cures become more and more imperative. There has been sufficient research evidence that living habits and situations such as smoking and air pollution are associated with an increased risk of lung cancer. Simultaneously, the influence of individual genetic susceptibility on lung carcinoma morbidity has been confirmed, and a growing body of evidence has been accumulated on the relationship between various risk factors and the risk of different pathological types of lung cancer. Additionally, the analyses from many large-scale cancer registries have shown a degree of familial aggregation of lung cancer. To explore lung cancer-related genetic factors, Genome-Wide Association Studies (GWAS) have been used to identify several lung cancer susceptibility sites and have been widely validated. However, the biological mechanism behind the impact of these site mutations on lung cancer remains unclear. Therefore, this study applied the Summary data-based Mendelian Randomization (SMR) model through the integration of two GWAS datasets and four expression Quantitative Trait Loci (eQTL) datasets to identify susceptibility genes. Using this strategy, we found ten of Single Nucleotide Polymorphisms (SNPs) sites that affect the occurrence and development of lung tumors by regulating the expression of seven genes. Further analysis of the signaling pathway about these genes not only provides important clues to explain the pathogenesis of lung cancer but also has critical significance for the diagnosis and treatment of lung cancer. Pulmonary carcinoma is the leading cause of cancer deaths in the world. According to the Global Cancer Statistics report, there were about 2 million newly detected lung cancer cases worldwide in 2018, accounting for 11.6% of all tumors . Deaths It is immediately unclear how pulmonary carcinoma develops. As shown in many kinds of research, the occurrence of lung cancer can be driven by multiple factors including genetic and environmental factors. In particular, SCLC is generally related to lifestyle and environment . BesidesIn recent decades, many genetic studies have been utilized to determine the genetic factors of lung cancer. Genome-wide association studies (GWAS) as a hot research tool at present have been applied in lung cancer, and have successfully identified many genetic susceptibility sites . GWAS peAccordingly, this research adopted the method of multi-omics analysis to consolidate various sources of data in an attempt to identify genes related to lung cancer incidence. With the development of high-throughput omics platforms, multi-omics analysis is one of the most important and widely used in biomedical research . As thesSMR was conducted to investigate the pathogenic genes of lung cancer. SMR analysis is an improved model based on Mendelian randomization study that uses genetic variation as a tool to predict the effects of simulated exposure factor on disease using gene-disease causal inference . CompareHowever, the existence of linkage disequilibrium (LD) or pleiotropy of instrumental variables can be contrary to the exclusion restriction. Once two genes are not completely independent, they will show some degree of linkage, which is called linkage imbalance. Pleiotropy is the phenomenon that one gene can affect more than one trait. Due to the coordinate positions in the genome between SNPs and the complicated biological effects between genetic variants and traits, LD and pleiotropy are often unavoidable . TherefoIn this research, we integrated lung cancer GWAS data and eQTL data to recognize functional genes and regulatory elements in pulmonary carcinoma. Statistical analysis of the relationship between a single SNP and gene expression is referred to as eQTL analysis . Namely,We downloaded two available GWAS summary data of lung cancer from the GWAS Catalog website. One of the GWAS datasets was obtained from the meta-analysis of the United Kingdom Biobank (UKB) and the Kaiser Permanente Genetic Epidemiology Research on Adult Health and Aging (GERA) . The UKBThe other GWAS summary data was collected from the analysis of the BioBank Japan Project (BBJ) data . BBJ is Four eQTL data containing three blood eQTL data and one lung eQTL data were applied in our research. Two of eQTL data were obtained by sequencing whole peripheral blood mRNA of 2,116 healthy adults from four Dutch cohorts . The difp values or the SE value. The core algorithms of eQTL and GWAS are based on linear regression. Therefore, the Beta/OR value of GWAS and eQTL represents the value of the regression coefficient. SE value refers to the standard error of regression coefficient. The p value means the value of whether the association is significant. We listed two calculation formulas of the Z value below  method can control false positive events by correcting the p values, we used FDR to adjust the PSMR values, resulting in obtaining the Padj values. The Padj value of 0.05 was taken as the threshold of statistical significance, which means that the genes corresponding to Padj less than 0.05 could be regarded as lung cancer-related pathogenic genes found in this study.The Z values of GWAS and eQTL were used to calculate the TIn this SMR analysis, the first step was to find the same SNPs between one of the GWAS datasets and one of the eQTL datasets and generate a new SMR dataset containing the details of all the overlapping SNPs . We perfSMR formula. After the Chi-squared test, TSMR values were transferred to PSMR  were screened out  database. According to the analysis results, PSMA4 is an essential subunit of the proteasome protein. The proteasome is a large protein complex and is the key to regulating cell function cellular functions, such as cell differentiation, cell cycle . Many stMoreover, the CLPTM1L gene can encode cleft lip and palate transmembrane protein 1-Like protein. Recent research shows that the SNPs of CLPTM1L were associated with many human malignancies, especially in some highly aggressive and metastatic cancers . RAD52 fWe applied multi-omics analysis to integrate two of the GWAS datasets and four of eQTL datasets based on the SMR method, resulting in the functionally important genes related-lung cancer being found. SMR research can facilitate more accurate and effective identification of the potential causal relationship between exposure and outcome, for disease risk factors had a further understanding. Once risk factors have been successfully discovered, they can be prevented or targeted for cancer treatment. By analyzing the causal genes associated with lung cancer, we recognized seven genes associated with lung cancer, suggesting a possible mechanism for the development of lung cancer by altering the expression of risk genes.With the analysis of these SMR datasets, the PSMA4 gene was repeatedly identified. It has been reported that down-regulation of proteasome subunit encoding gene PSMA4 reduces proteasome activity . The pro+ T cells. There is evidence that HLA II on tumor cells affects tumor migration and invasion, cancer progression, immune response and prognosis of a variety of malignancies in vitro and in vivo. For example, some studies have shown that tumor HLA-DQB1 level is associated with the recurrence free survival (RFS) of early Lung Adenocarcinoma, and the mechanism may be related to anti-tumor immunity (Additionally, HLA-DQB1, HLA-DRB9, and HLA-DQB2 are all homologs of HLA class II molecules, which are essential for immune response. HLA plays a key role in the interaction between tumor cells and the human immune system. HLA II molecules are involved in anti-tumor immunity and exogenous antigen presentation by CD4immunity . CurrentCLPTM1L is a mitochondrial protein and is thought to be involved in lung cancer. Some studies have found that the expression of CLPTM1L is significantly increased in lung cancer tissues compared with normal tissues by immunohistochemistry, especially in lung adenocarcinoma . Also, tMoreover, according to the GWAS results, the SNPs of RAD52 gene were associated with increased risk of lung cancer, particularly the development of lung squamous cell carcinoma . As the The iron-binding protein encoded by IREB2 is involved in maintaining iron homeostasis in human cells. IREB2 is located in the susceptibility locus of lung cancer. A previous study showed that iron levels in the lungs increase with age, with higher concentrations in the lungs of smokers. Therefore, abnormal IREB2 expression or function may lead to iron metabolism disorder. Iron load leads to cell proliferation in cancer cell lines, and cancer cell lines have a poor ability to regulate IREB2 expression . In addiIn summary, our study integrated genomic and transcriptome information and found seven genes associated with lung cancer by the SMR method, revealing possible regulatory mechanisms that alter the expression of these genes and further pathogenicity. This provides a research direction for understanding the precise treatment and targeted drug design of lung cancer. Although the SMR method can avoid the interference of confounding effect and reverse causal association in the study, and can be not limited by the sample size, thus producing more reliable results, this method can only preliminarily judge that the gene expression may affect the development of lung cancer, and cannot show how gene expression regulates the onset and progression of this disease. It takes experiments and other analytical methods to confirm a causal link between genes and disease. In the following study, we will continue to use SMR analysis method to integrate GWAS and other types of QTL data to mine more genes affecting lung cancer.


In 2018, the total number of Lassa fever cases in Nigeria was significantly higher than that observed in previous years. Hence, studies had attempted to determine the underlying cause. However, reports using phylogenetic methods to analyze this finding ruled out the emergence of potentially more transmissible Lassa virus strains or an increase in human-to-human viral transmission as the cause underlying the increase in cases. Two years later, the situation seems even worse as the number of confirmed cases has reached an all-time high according to situational reports released by the Nigerian Center for Disease Control.Considering the increasing trend of Lassa fever cases and related mortality, the major objective of this study is to map mutations within the genomes of Lassa virus isolates from 2018 and 2019 using the reference sequence available at the National Center for Biotechnology Information as a benchmark and compare them to the genomes of viruses isolated during 1969-2017. This study would also attempt to identify a viral marker gene for easier identification and grouping. Finally, the time-scaled evolution of Lassa virus in Nigeria will be reconstructed.After collecting the sequence data of Lassa virus isolates, Bayesian phylogenetic trees, a sequence identity matrix, and a single nucleotide polymorphism matrix will be generated using BEAST (version 2.6.2), Base-By-Base, and DIVEIN , respectively.Mining and alignment of Lassa virus genome sequences have been completed, while mutational analysis and the reconstruction of time-scaled maximum clade credibility trees, congruence tests for inferred segments, and gene phylogeny analysis are ongoing.The findings of this study would further the current knowledge of the evolutionary history of the Lassa virus in Nigeria and would document the mutations in Nigerian isolates from 1969 to 2019.DERR1-10.2196/23015 Lassa fever, a viral hemorrhagic fever, is caused by Lassa virus. Viral hemorrhagic fevers are a group of viral illnesses that are characterized by damage to the vascular system; hence, they are described as being “hemorrhagic” . Viral hMammarenavirus. There are 35 currently recognized viruses within this genus, which are classified into Old World and New World viruses, and Lassa virus is an Old World virus ) in the Lassa virus genomes may provide mutation signatures of Lassa virus. Although the potential mutational differences between strains isolated in 2018-2019 and before 2018 (1969-2017) would not provide insights into the increased transmissibility of the virus in Nigeria in and after 2018, functional analysis of the identified mutations in future studies would provide valuable insights into the role of indels and SNPs (if any) in Lassa virus infections. Hence, Although Ehichioya et al  construcLassa virus sequences would be collected from GitHub  followinMammarenavirus genome would be obtained using the Base-By-Base bioinformatics program [The percent identity matrix of both segments of the  program . Owing tThe 2018 and 2019 isolates would be extracted from these gene sequence alignments. All gaps would be deleted to produce the raw order of bases in all isolates. Thereafter, all sequences would be translated in silico into amino acid sequences, and the reference sequence for each protein -27 wouldThis study commenced in January 2020 as an obligatory senior undergraduate research project of the first author (DK) and will be completed in February 2021 with a thesis presentation and defense. Data mining and alignment of Lassa virus genome sequences have been completed. Phylogenetic and genomic analysis of aligned Lassa virus genome sequences are currently ongoing. A total of 133 L and 145 S segments from 1969 to 2019 were used for the genome-wide mutational and phylogenetic analyses . SegmentUnlike previous reports, this proposed study will focus on determining SNPs and indel profiles of the 2018 and 2019 isolates of Lassa virus using the reference sequences as a benchmark for comparison. The identification of such SNPs and indels may further our understanding of the molecular factors contributing to the upsurge in Lassa fever cases in Nigeria in 2018 till date. SNP and indel identification may also help explain the limited instances of potential human-to-human transmission of Lassa fever as suggested in a previous report . The ideA major limitation of this proposed study is that the genome sequences of viruses isolated from rodent reservoirs during the 2018 outbreak are unavailable; hence, this study will not be able to examine whether the upsurge resulted from increased rodent-to-human transmission. A comparison of the genome sequences of Lassa virus isolated from rodent reservoirs with those of the 2018 and 2019 human isolates would reveal sequence signatures that may be associated with increased rodent-to-human transmission. Thus, viral adaptation for increased transmissibility may have occurred in the rodent reservoir rather than in humans. Extensive retrospective and prospective sampling of the Lassa virus in reservoir hosts is needed to examine our hypothesis of rodent-to-human transmission. Another limitation is that Lassa virus genome sequences from 2020 are not readily available.The strengths of this proposed study include the public availability of Lassa virus genomes isolated during the 2018 and 2019 outbreaks, application of congruence tests to inferred phylogenetic trees, and mapping of mutations that may be masked by phylogenetic signals. Collectively, the strengths of this proposed study will more comprehensively elucidate the evolutionary history of Lassa virus in Nigeria.


Advances in science and technology depend on the work of research teams and the publication of results through peer-reviewed articles representing a growing socio-economic resource. Current methods to mine the scientific literature regarding a field of interest focus on content, but the workforce credited by authorship remains largely unexplored. Notably, appropriate measures of scientific production are debated. Here, a new bibliometric approach named TeamTree analysis is introduced that visualizes the development and composition of the workforce driving a field. A new citation-independent measure that scales with the H index estimates impact based on publication record, genealogical ties and collaborative connections. This author-centered approach complements existing tools to mine the scientific literature and to evaluate research across disciplines. Progress in science and technology depends on research teams working on specific topics of interest and on the publication of their results in peer-reviewed articles . The rapThe principal steps and key features of TTA are introduced in TTA explores three aspects of scientific production: the publication record of authors, their genealogical relations and their collaborations. Several parameters are calculated to assess performance in each category . To summThe workforce contributing to a field of interest is visualized by TTGs. TTGs are scatterplots where each author is represented by a symbol displayed by an author-specific color with the AI value and the earliest year of publication plotted on the x and y axis, respectively . TTGs prhttps://github.com/fw-pfrieger/TeamTree. It can be used to analyse publications in a user-defined field of interest. Bibliographic records were obtained from MEDLINE using PubMed (https://pubmed.ncbi.nlm.nih.gov/) and from Web of Science (WoS) . To compare citation-independent TTP values with citation-based metrics, the Hirsch indices and the total number of citations were calculated from bibliographic records (WoS).TTA is implemented with custom-written routines based on the open source software R  and seleTo expose the utility of TTA, the new approach was applied to scientific articles from selected fields of research in science and technology .Aplysia, a genus of sea slugs, started at the end of the 19th century. Since then, the field expanded slowly but steadily reaching less than 2000 authors total [A new type of visual named TTG reveals the ensemble of authors contributing to a topic of interest . To exemrs total  Fig 2A)Aplysia, rs total  Fig 2B)Aplysia, rs total . It is ars total , 44.TTA evaluates the publication record of authors, the generation of offspring and the establishment of collaborations. To illustrate this point, TTA was applied to publications related to "circadian clock" (Clock) , a well-TTA was used to explore how the workforce of the Clock field developed over time. Plotting the number of authors entering and exiting the field based on the first and last year of their publications, respectively, indicated strong growth of the workforce. The accuracy of exit counts decreases for the last years . The pubA key goal of bibliometric analyses is to gauge the impact of individual authors on a field of research. The new metric TTP calculated as product of PC × OC × CC takes into account an author’s publication record (PC), offspring generation (OC) and collaborations (CC) . The conTo further validate TTP as citation-independent measure of productivity, TTA was applied to publications from the fields of biomedical research shown in TTA fills a gap between global investigations of the scientific endeavour and the recurrent need to identify and evaluate the teams working on a user-defined topic of interest in science and technology.A prime feature is the new measure to estimate scientific production named TTP. Several properties distinguish this metric from existing author-level indicators. TTP takes into account three important aspects of research activity: the publication of peer-reviewed scientific articles, the training and mentoring of junior scientists, who continue their career within the field, and the establishment of collaborative connections that signify recognition due to specific expertise and capacities. The respective parameters are derived solely from the authors of scientific articles and the year of publication. Thus, TTP estimates scientific production independently from citation counts or journal impact and augments the group of indicators that do not rely on these factors –49. Notahttps://www.genealogy.math.ndsu.nodak.edu/, https://academictree.org/). However, TTA underestimates offspring counts in the case of co-first or co-last authorship, of alphabetical author lists or of field-specific author ranking [https://orcid.org/) and computational algorithms [TTA exposes factors that impact the workforce development of a field. For example, the analysis of publication periods revealed that few authors contributed for more than one year to the Clock field. This finding supports previous reports that in many research areas only a small fraction of the workforce publishes during long periods of time . The del ranking , 77. Oth ranking . Moreovegorithms , 79–83. gorithms , 85.Peer-reviewed articles were used to introduce the features of TTA as this form of publication represents the core of scientific production , but theS1 FileCsv file summarizing TTA data for the Clock field using PubMed articles related to "circadian clock".(CSV)Click here for additional data file.


Enchondromas originating in the epiphyses of long bones are rare and epiphyseal osteoid osteomas are also uncommon. Diagnosis can become elusive when enchondromas or osteoid osteomas occur in atypical locations and present with nonspecific clinical and imaging characteristics.We report a case of epiphyseal enchondroma of the left proximal femur in a 15-year-old girl with a 2-month history of left lower extremity pain. Preoperative CT displayed thickened cortex in the anterior surface of the left proximal femur with specks of calcification and inhomogeneity of the adjacent bone marrow cavity. She was diagnosed with osteoid osteoma. Postoperative pathological examination of surgically excised specimens revealed a diagnosis of enchondromas.Our case highlights that enchondroma should be considered in lesions of the epiphysis. Major clinical manifestations included lumbago and radiating pain along the femoral nerve distribution with decreased sensation and paresthesia of the left lower extremity. She also had limitation of motion of the lower limb, which was relieved after rest. The patient visited a local hospital in July 2018 and received no definite diagnosis. She was given vitamins and calcium supplementation, but her pain was not alleviated. In August 2018, her pain worsened and swelling also appeared in the left thigh. She visited the Department of Neurology of our hospital on September 6, 2018. Electromyography revealed injury of the left femoral nerve; she received mecobalamin and vitamin B12, but pain failed to improve. The patient had no history of trauma. Her medical history was unremarkable.Physical examination at admission revealed firm, tender mild swelling of the left proximal thigh, with normal overlying skin. Adduction and flexion of the left hip were limited and elicited pain. There were no other remarkable abnormalities. Plain radiographs showed increased density of the greater trochanter of the left femur Fig. . CT dispThe patient underwent surgical exploration. Soft tissue swelling was observed adjacent to lesion in the proximal femur and massive granulation tissues were present in the medullary canal. The periosteum, and the granulation tissue and cartilage tissue were excised by curettage and pathological study revealed thickening of the periosteum and bone Fig. . Iliac cEnchondromas originating in the epiphyses of the femur are very rare. In the current report, we describe a case of epiphyseal enchondroma of the proximal femur in a 15-year-old girl that was erroneously diagnosed as osteoid osteoma. Both conditions rarely occur in the epiphysis of the femur and have clinical and radiological manifestations that make diagnosis elusive. Lack of thorough understanding of the two disease entities, especially in an atypical location, indefinite radiological findings and misleading clinical and radiological features of the current case led to our erroneous diagnosis of the disease.Osteoid osteoma is a common benign tumor among young boys and adolescents , 7, typiThe osteolytic lesions of enchondromas manifest as local bone destruction on radiographs, with a well demarcated border and specks of calcification . EnchondOsteosarcoma, clear cell chondrosarcoma, chondroblastoma, giant cell tumor are often emphasized as differential diagnoses of lesions in the epiphysis  as epiph


A 29-year-old male visited the outpatient department of Acharya Vinoba Bhave Rural Hospital, Sawangi with chief complaints of swelling in the left lower jaw for 15 days. He had a past history of similar swelling on the right side of the jaw, for which he underwent intervention. The patient was referred from oral surgery to the Department of Radiodiagnosis for computed tomography scan of head and neck to know the extension of the lesion and underlying bony erosion. There was no significant family history or cutaneous manifestations. On computed tomography, there were multiple cystic lesions in the left hemimandible and maxilla. Histopathology revealed it to be Odontogenic keratocysts. Other findings included bilamellar falx cerebri and tentorium cerebelli calcifications, bridging sella, intracranial lipoma, spina bifida of C6 vertebra. The scalp was thickened with multiple foci of calcifications. Diagnosis of Gorlin-Goltz syndrome was made.


Inhibitors of the proteolytic activity of the 20S proteasome have transformed the treatment of multiple B-cell malignancies. These agents have also been employed with success in the treatment of patients with autoimmune diseases and immune-mediated disorders. However, new agents are needed to fully unlock the potential of proteasome inhibitors as immunomodulatory drugs. The discovery that selective inhibitors of the immunoproteasome possess broad anti-inflammatory activity in preclinical models has led to the progression of multiple compounds to clinical trials. This review focuses on the anti-inflammatory potential of immunoproteasome inhibition and the early development of KZR-616, the first selective inhibitor of the immunoproteasome to reach clinical testing. In 2004, Ciechanover, Hershko, and Rose were awarded the Nobel Prize in recognition of their seminal research that helped uncover and elucidate one of the most basic mechanisms of cellular function, the regulated turnover of intracellular proteins via the ubiquitin–proteasome system (UPS) . It was The notion that the proteasome regulates immune responses became evident soon after the discovery of the first inhibitors of catalytic activity . Using aWithin five years of regulatory approval of bortezomib for the treatment of multiple myeloma, the first case studies of clinical application of proteasome inhibition in immune-mediated disorders began to emerge. Everly et al. applied short courses of bortezomib treatment to patients undergoing antibody-mediated rejection (AMR) and acute cellular rejection (ACR) of their kidney transplants . In all The collective clinical and preclinical experience of bortezomib as an anti-inflammatory agent created the basis for the discovery and development of novel proteasome inhibitors as immunomodulatory agents. The three approved proteasome inhibitors have as their primary targets the β5 subunit of the constitutive proteasome and the LMP7 subunit of the immunoproteasome, referred to as the chymotrypsin-like (CT-L) subunits based on their similar substrate preferences . Cells eThe discovery of PR-957 (later ONX 0914) enabled researchers to probe the role of the immunoproteasome in therapeutically relevant in vitro and in vivo models of autoimmunity . ONX 091Armed with a tool compound that could selectively target the immunoproteasome in vitro and in vivo and assays that could translate subunit inhibition to the biologic outcome, researchers have built a strong case that selective targeting of the immunoproteasome could have a significant benefit in the treatment of autoimmune and immune-mediated disorders. This body of evidence is reviewed in more detail elsewhere in this issue and includes a review of the therapeutic activity of ONX 0914 seen in models of inflammatory arthritis, inflammatory bowel disease (IBD), SLE and lupus nephritis (LN), experimental autoimmune encephalomyelitis (EAE), and myasthenia gravis. In addition, salutary effects of immunoproteasome inhibition were seen both solid organ and allogeneic bone marrow transplant studies as well as acute inflammatory settings such as those seen with viral infection ,34. Ex vThough highly useful as a tool compound, ONX 0914 demonstrated poor pharmaceutical properties, in particular low solubility, that precluded development in the clinical setting . As a reIn addition to the preferred subunit inhibition profile of immunoproteasome active sites, KZR-616 was also optimized for pharmacokinetic and metabolic properties in order to facilitate clinical development as a potential chronic therapy. Utilization of the ketoepoxide pharmacophore was employed to reduce the risk of off-target activity. Using global proteomic approaches with both active site and chemical probes, carfilzomib and other peptide epoxyketones have been shown to have extremely low reactivity against other classes of hydrolases and a low overall chemical reactivity ,44. SecoKZR-616 entered clinical trials in 2016 with a study of single and repeat dosing in healthy volunteers . This stThe safety and clinical activity of KZR-616 is currently being evaluated in Phase 2 studies in patients with lupus nephritis (LN), polymyositis (PM), and dermatomyositis (DM). In the MISSION study (NCT03393013), weekly administration of KZR-616 is being tested in patients with active proliferative LN. This trial also contained an open-label, dose escalation Phase 1b portion in SLE patients with or without nephritis. The safety profile reported in 47 patients treated with weekly doses of KZR-616 at 45, 60, and 75 mg administered for up to 13 weeks with a 12-week follow-up period was similar to that reported in healthy volunteers . No clinVia impact on both the innate and adaptive immune response, selective inhibition of the immunoproteasome represents a novel and potentially broad approach to treating autoimmune diseases and immune-mediated disorders. In addition, the rapid immunomodulatory response to agents such as KZR-616 seen in patients along with a favorable safety and tolerability profile, suggest the use of such inhibitors in both the acute and chronic setting. These agents represent potentially significant improvements in both safety and/or efficacy relative to standard immunosuppressive therapies or other experimental therapeutics that target pathways with more limited molecular sequelae such as inhibitors of Bruton’s Tyrosine Kinase (BTK). KZR-616 represents the first selective inhibitor of the immunoproteasome to reach clinical testing in autoimmune diseases and early clinical results have faithfully replicated those seen in preclinical studies. Continued successful clinical trial results of KZR-616 will foster the expansion of the application of this agent to other immune-mediated disorders and may also foster the development of immunoproteasome inhibitors such as M358 in autoimmune diseases. Indeed, a drug discovery and development program based on reversible peptidomimetics that show selective inhibition of the immunoproteasome in vitro and therapeutic activity in mouse models of cardiac transplant has recently been initiated ,60. The


The push-pull technology (PPT) is considered as an alternative integrated pest management strategy for the control of fall armyworm and stemborer, among smallholder maize farmers in sub-Sahara African to conventional pesticides. However, the extent of PPT use in Rwanda where the technology was introduced in 2017 remains largely unexplored. This paper employed a fractional logit model to assess the factors influencing the intensity of adoption of PPT among smallholder maize farmers in Gatsibo and Nyagatare districts of Rwanda using survey data obtained from 194 PPT adopter households selected using a cluster sampling technique. While only 5 percent of smallholder farmers in Rwanda have adopted PPT as an integrated pest management strategy, on the average, these farmers cultivated 26 percent of their maize plots to the technology. Our results show that the perceived benefits of PPT, its perceived effectiveness in pest control, group membership, livestock ownership, and gender of the farmer had significant effects on the intensity of adoption of the PPT in Rwanda. These findings give compelling evidence to recommend that development initiatives should give emphasis on creating awareness on the perceived benefits of PPT adoption using group approaches that are gender disaggregated. Fall armyworm, Fractional logit model, Intensity of adoption, Push-pull technology, Stemborer. AbioticSpodoptera frugiperda) originated from the tropics and sub-tropics of America in early 2016 and spread to West and Central Africa in late 2016 and even later to other parts of Eastern, Northern, and Southern Africa (Chilo partellus) is a native of Asia that spread into Eastern, Southern, and Central Africa in the early 1930s and is now endemic in SSA  and stemborer pests remain the most important field pests in Eastern Africa owing to their negative economic impacts on maize production, the main food staple in the Eastern African region . The FAWn Africa . The motn Africa . Stemborc in SSA . The larc in SSA .According to Smallholder farmers in Rwanda and other parts of SSA have used many methods to control FAW and stemborer pest. These measures include handpicking, plant extracts, soil/ash and sawdust/pepper mixture, intercropping, and pesticide application . Pesticiicipe) and its allies established a habitant management strategy well-known as the push-pull technology (PPT)  . PPT is the pest . The secthe pest . This bithe pest .Introduced in 2017 in Rwanda, the PPT has been widely used in East and Southern Africa to control stemborer pests, Striga weed, and currently FAW pests . Previouicipe, in collaboration with the Government of Rwanda, introduced a PPT pilot project in 2017 to control FAW and stemborer pests  theory proposed by The theoretical bais of the AD model comprise an S-shaped diffusion curve that integrates the speed of penetration and a critical number of users in a two-step model of diffusion . The corThe UD model comprise of threefold important elements: (1) factors of UD model, (2) patterns of UD, and (3) outcomes of UD model. Factors that affect variety and rate of usage constitute UD factors . Combining rate and variety of usage (high/low) produces a four-way typology of usage , henceforth UD patterns . IntenseDifferent types of users have different experiences of the UD outcomes of the technology . In practice, the determinants of technology adoption are modeled using discrete choice , Tobit models, censored regressions, truncated regressions, and discriminant analysis techniques , 2012. TSeveral empirical models have been used to analyze the intensity of adoption of new technologies, including Poisson , Tobit  and fracThese methodologies, though, suffers from some constraints, specifically wherever the dispersion of the variable is restricted both below and above and a quantifiable share of the sample observations falls at one of the borders. The Fractional Response Models (FRM) offers a feasible option to addressing several of the econometric restrictions associated with nonlinear methods presently used in modelling continuous bounded dependent variables . The FRMThe FRM accounts for limitations of the existing methodologies for the statistical analysis of bounded dependent variables and provides a material number of corner observations. Given its simplicity in computation, FRM provides high levels of flexibility in its application to longitudinal, panel, and cross sectional data. Further, the FRM accounts for nonlinearity, and relaxes the numerous restricting assumptions that are necessary in traditional econometric results. The FRM extends the general linear models (GLM) to a class of functional forms that overcome the limitations of the outdated econometric models for variables that are bounded in nature .The approximation of the parameters in the model is grounded on a quasi-maximum likelihood method (QMLE), generating estimates that are proportionately efficient and entirely robust under the GLM circumstances . Accordi3.2ith household conditional on Yi denotes the intensity of adoption of PPT, Xi is a vector of farm, farmers and technology-specific attributes (Z(·) is a cumulative distribution function that follows a logistic distribution function representing a nonlinear link function satisfying E is the expectations operator.We employ a fractional logit model (FLM) to evaluate the intensity of adoption of PPT among smallholder maize farmers in Rwanda. The intensity of adoption of PPT is defined as the number of acres of maize under PPT per household divided by the total maize acreage per household, which is bounded between 0 and 1. Following tributes  and β a The QMLEs of 3.3We use survey data collected in 2018 from a sample of 194 PPT adopter households in the Nyagatare and Gatsibo districts in Rwanda. A cluster sampling procedure was used to select the respondents. In the first-stage, the two districts (Gatsibo and Nyagatare) were purposively selected since they were the pilot areas for the PPT project. Within each district, the pilot had been conducted in one sector, and thus, Gatunda and Nyagihanga sectors, from the Nyagatare and Gatsibo districts were selected, respectively. A simple random sampling technique was used in the second-stage to select 240 adopter households from a sampling frame of households who had participated in the pilot provided by the Rwanda Agricultural Board in the two districts. However, 46 of the selected households had stopped using the technology in the preceding 12 months and were therefore dropped from the sample resorting to a sample size of 194 adopters farming households who were interviewed with a pre-tested semi-structured questionnaire. The 194 adopter households selected comprised of 133 and 61 farmers from the Gatsibo and Nyagatare districts respectively, were then interviewed with a pre-tested semi-structured questionnaire. The data was then analyzed with Stata version 14.3.4how many acres of land have you set aside for maize production? The second question asked was: ii) of the total acreage you have set aside for maize production, how many acres have you allocated to push-pull technology (acres)?The use-diffusion theory and previous studies e.g.,  inform tFarmer's assessment of maize yields with PPT adoption as compared to the yields before PPT adoption was used to proxy for the perceived PPT benefits. Farmers were asked to compare their maize yields before and after the adoption of PPT. Their responses were grouped into a dummy outcome variable equivalent to 1 if household's perceive PPT increased yields and zero otherwise. Positive relationships have been reported between the perceived benefits of new technologies and their adoption . SimilarGroup membership was used as a representation for sources of information sharing on PPT, procuring inputs, and marketing output. It was measured as a dummy variable equivalent to one if a farmer was a member of an agricultural group and zero otherwise. Social networks such as groups or farmer associations facilitated the exchange and gathering of information related to PPT and provided platforms through which farmers accessed inputs and marketed output. Previous studies have revealed positive associations between group membership and PPT adoption .Off-farm income played a key role in providing financial resources necessary for investment in PPT and was measured as a dummy variable equivalent to one if a farmer had an off-farm income source and zero otherwise. Gender, education, age, livestock ownership, and family size were also used as control variables following preceding studies see .Gender was measured as a dummy variable equivalent to 1 if the household head was male and zero otherwise. The available literature on the influence of gender on the PPT adoption is mixed. Age was used as a proxy of farmers’ experience. Education was measured by the number of schooling years spent by the respondent, while age was measured in years. Several previous studies have shown positive relationships between age and education on one side and the adoption of PPT on the other . The num44.1Almost all households in the study area owned livestock with an average TLU of 1.91, which is understandable given the small land holding sizes. As expected with adopters of any technology, maize farmers in Rwanda had a positive perception of PPT use. Fifty-eight percent of the respondents perceived PPT use to increases maize yields, while 60 percent of the farmers perceived the technology to be effective in the control of FAW and stemborer relative to other methods. Moreover, 47 percent of the respondents undertook other off-farm income earning undertakings, which was used to complement farm incomes required to cover the initial labour costs for setting up the PPT plots that can be a hindrance to adoption.4.22 (1) = 0.01; Prob > chi2 = 0.907) ruling out the presence of heteroscedasticity. The Wald statistic was significantly at the one percent level signifying a high prediction power of the model  that disseminate information on PPT and focus on farmer groups especially those whose members own livestock. Furthermore, efforts to disseminate PPT information should target male farmers differently from female farmers given their different access to productive resources that are important drivers of technology adoption.Lastly, while the biological and societal background has been eloquently discussed, a direct comparison against other (or similar) models has not been elaborated. Moreover, despite the rigorous econometric methods validating the results on intensity of PPT adoption, the authors recognize the limitations in approximation. First, the study used cross-section data that does not capture the dynamics changes in integrated pest management used by smallholder maize farmers. Secondly, the study's limitation pertains to small sample size conducted when the technology was being disseminated among smallholder farmers. Thirdly, although our estimates demonstrate the factors influencing intensity of PPT adoption, the study did not take into account plot-varying characteristics and institution factors such as credit and extension accessibility. In view of overcoming this weakness, the study recommends future studies to include additional variables and years of sampling to validate the study's findings and get results that are more robust.Vincent Gadamba Misango: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.Jonathan Makau Nzuma; Patrick Irungu; Menale Kassie: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper.European Commission (Grant No. DCIFOOD/2018/402-634). the International Centre of Insect Physiology and Ecology (icipe), the Swedish International Development Cooperation Agency (Sida), the Swiss Agency for Development and Cooperation (SDC), Germany’s Federal Ministry for Economic Cooperation and Development (BMZ), the Federal Democratic Republic of Ethiopia and the Kenyan Government. Vincent Gadamba Misango was supported by the African Economic Research Consortium (AERC).This work was supported by the Data will be made available on request.The authors declare no conflict of interest.No additional information is available for this paper.


Plant roots and the associated rhizosphere constitute a dynamic environment that fosters numerous intra- and interkingdom interactions, including metabolite exchange between plants and soil mediated by root exudates and the rhizosphere microbiome. These interactions affect plant fitness and performance, soil health, and the belowground carbon budget. Exploring and understanding the molecular mechanisms governing ecosystem responses via rhizosphere interactions allow the rational and sustainable design of future ecosystems. However, visualizing the plant root system architecture with spatially resolved root exudate and microbiome profiles along the root in its native state remains an ambitious grand challenge in rhizosphere biology. To address this challenge, we developed a three-dimensional (3D) root cartography platform to accurately visualize molecular and microbial constituents and their interactions in the root-rhizosphere zone. The rhizosphere, the soil layer tightly associated with a plant root, is one of the most dynamic interfaces on earth, harboring a multitude of intra- and interkingdom interactions. In addition, the root system architecture itself is highly plastic and responds to both biotic and abiotic influences in the rhizosphere. Plant roots exude a staggeringly diverse collection of small molecules, including secondary metabolites that shape the rhizosphere. The complexity and diversity of the root exudates speak to the intricacies of the chemical language that plants utilize to control rhizosphere interactions. Numerous chemical, physical, and biological factors in the rhizosphere can also influence the root exudate profiles . Modulat4–Brachypodium, Setaria, and Arabidopsis and model bacterial and fungal isolates are being used in EcoFABs that can then be customized to address specific scientific questions. A major downside to these approaches is that they do not mirror the true dynamics of the natural belowground environment in soil. Additional limitations pertain to the highly manipulated and constrained growing environments utilized by these systems, the minimal size of the units, and the short temporal scales used for experiments. Chemical imaging is another popular method for visualizing molecular-level details underlying plant-microbe interactions in the rhizosphere. For example, time of flight secondary ion mass spectrometry (ToF-SIMS)   and matroF-SIMS) , 18. GenoF-SIMS) . One advSorghum bicolor (L.) Moench]. The intact root system was first imaged using XCT. Subsequently, root segments with rhizosphere soil attached were excised from each quadrant of the rhizogrid layers, from the bottom to the top of the pot. The rhizosphere soil was recovered by washing the roots. Each rhizosphere sample was then screened for its associated microbiome and metabolites. High-resolution liquid chromatography-coupled mass spectrometry (LC-MS) was used for metabolite profiling  and in a setting that includes natural soil-level interactions occurring at the root-rhizosphere interface. Moreover, the rhizogrids can be customized to accommodate various root systems of interest as well as different growth substrates such as sand, vermiculite, and soils collected from agricultural fields and marginal lands. It is important to note that the growth medium should be tested to ensure enough contrast in XCT scans between the root system and the selected growth substrate to generate a robust 3D reconstruction of the root system architecture.Brachypodium distachyon (2 labeling with 13C can be employed in order to monitor the partitioning of recent photosynthates within the 3D root system and how this partitioning is affected by microbes and environmental perturbations. Our research with SIP has revealed that plants rapidly shift carbon allocation between aboveground versus belowground biomass and partitioning into different metabolites, including root exudates, in response to drought stress or in the presence versus the absence of microbes. Embedding this research to 3D root imaging will allow us to spatially resolve such shifts in the 3D root space. We also posit that the 3D root cartography platform that we present here offers a compelling opportunity to capitalize on recent developments in both sequencing technologies that enable the characterization of complex microbiomes with high throughput and advances in metabolomic approaches that allow the functional characterization of microbial pathways and the metabolic interactions of microbial communities with plants as they respond to diverse environmental conditions. To better visualize spatially resolved information and identify molecular and microbial hot spots along the 3D reconstructed root systems, we are currently developing a Python-based software package for data-type-agnostic visualization of features in XCT-derived 3D images.The presented example of 3D root cartography demonstrates the potential for utilizing this approach in visualizing spatially resolved molecular information on plant-microbe interactions belowground. These molecular interactions include specific metabolic interactions between microbes and root-exuded metabolites as well as colonization by and recruitment of specific microorganisms along the root. The metabolic versatility of the rhizosphere microbiome and the positive recruitment of specific microbial taxa can then be interpreted using correlation networks. The interactions themselves can be inferred from the various molecular data generated within the 3D root cartography platform to help visualize the specific interactions and the rhizosphere plant-microbe interactomes. This new approach has the potential to be extended to visualize how the native soil microbiome responds to abiotic perturbations such as drought and nutrient deficiencies and biotic stresses imposed by pathogens or the introduction of genetically engineered or other nonnative microbial strains. This approach can also be used to assess how specific members of the rhizosphere microbiome are recruited at different locations on the root by challenging the same root system with various native and synthetic microbiomes. We note that 3D visualization of microbial recruitment, colonization, and competition can be further enhanced by utilizing fluorescently labeled microbial strains coupled with cell sorting. The rhizogrid-based 3D root cartography platform can be expanded to include protein and transcript profiling. For example, targeted proteomics and metabolomics combined with quantitative transcript profiling of excised and barcoded root segments will inform on active metabolic pathways in different parts of the 3D root space and under different environmental settings, such as with or without associated microbes, and under drought or nondrought conditions. Complementary enzyme assays of the root segments will shed further light on metabolic processes. We also anticipate that direct 3D mapping of the chemical and physical properties of soil will become technically feasible and available within a 5-year time span. Coupling the properties of the surrounding soil to results from root and rhizosphere analyses will be necessary for a comprehensive understanding of biological processes in a plant-microbe-soil continuum. Much progress has been made in 2D soil investigations using a variety of optodes, and it is reasonable to assume that major inroads in 3D soil mapping will be made in the near future. Meanwhile, soil can be collected from rhizogrid coordinates and subjected to a multitude of approaches, such as near-edge X-ray spectromicroscopy, synchrotron X-ray absorption spectroscopy, and synchrotron-based microfluorescence spectroscopy . Universstachyon . The 3D The metabolome of an organism is closest to its final phenotype, is the first to respond to the surrounding environmental changes, and therefore plays an important role in defining the final phenotype . Rhizosp


Ecological fitness is the ability of individuals in a population to survive and reproduce. Individuals with increased fitness are better equipped to withstand the selective pressures of their environments. This paradigm pertains to all organismal life as we know it; however, it is also becoming increasingly clear that within multicellular organisms exist highly complex, competitive, and cooperative populations of cells under many of the same ecological and evolutionary constraints as populations of individuals in nature. In this review I discuss the parallels between populations of cancer cells and populations of individuals in the wild, highlighting how individuals in either context are constrained by their environments to converge on a small number of critical phenotypes to ensure survival and future reproductive success. I argue that the hallmarks of cancer can be distilled into key phenotypes necessary for cancer cell fitness: survival and reproduction. I posit that for therapeutic strategies to be maximally beneficial, they should seek to subvert these ecologically driven phenotypic responses. The facThe fitness parameter of reproduction in the context of cancer is proliferation by way of mitotic cell division. To divide, a cancer cell first needs to survive. However, while survival is a pre-requisite for this reproductive cell division, additional signals are also necessary to ensure reproductive success; as in nature, survival alone does not guarantee reproduction . Indeed, disseminated cancer cells have been shown to remain undetectable for decades . The reaIctidomys tridecemlineatus) exhibits a significant upregulation in phospho-p38 during torpor and a low phospho-ERK: phospho-p38 ratio  ERK:phospho-p38 signaling . For exa38 ratio . Likewis38 ratio . Hiberna38 ratio . In the An ecological niche is the interaction between an organism and its environment. While this interaction is most often discussed from the perspective of the influence of the environment on the organism, the concept of a niche also includes the influence of the organism on its environment. The ability of the organism to re-shape its environment to create a more optimal niche is referred to as niche construction . This coLike beavers, cancer cells also substantially modify their environments in my, and in doing so influence their selection. For instance, tumor cells alter the geography of their environments through deposition and proteolytic cleavage and clearance of extracellular matrix components . These pNiche construction is not restricted to the local environment of the tumor. Systemic dissemination of signals also primes the pre-metastatic niche toward a cancer cell-permissive environment . SignaliThe most effective way to prevent the building of a dam is to remove the beavers before they cut down any trees. In the same way, early detection of cancer has been one of the most effective ways to improve cancer survival . DespiteTherapy substantially modifies the cancer cell population heterogeneity, fitness landscape, and ecology of the tumor . WhetherIn addition, the reshaping of the cancer cell population structure by therapeutic challenge can also alter the fitness landscape of the cell population in which removal of a drug-sensitive population allows drug-resistant cells to repopulate a newly vacant ecological niche . This ecWhile the goal of systemic therapy is to target the cancer cells, the therapy can also have unintended consequences on non-malignant cells within the ecological system, some of which can promote further aggressive features of the cancer cells. For example, treatment-induced damage to cells within the tumor microenvironment has been shown to release secreted factors that enhance cancer cell survival . LikewisViewing cancer from an ecological perspective can impact treatment paradigms. For instance, adaptive therapy in which treatment doses and schedules are adjusted based on the differential fitness of resistant and sensitive populations in the context of a drug has significantly prolonged tumor control in preclinical models of breast and ovarian cancers , with onWhile the predominant focus of adaptive therapy has been on differential fitness and competition within cancer cell populations, other benefits of adaptive therapy may exist that are related to the tumor microenvironment. Lower drug doses may prevent toxicity to immune predatory cells. In the context of dying tumor cells, reduced lymphopenia may improve systemic immune response to localized and disseminated cancer cells. Similarly, lower drug concentrations within the tumor microenvironment may reduce the induction of a migratory/invasive phenotype in response to drug-mediated resource depletion.In addition to adaptive therapy, strategies to target the tumor microenvironment and alter tissue ecology could be leveraged for novel combinations to inhibit both cancer cell-intrinsic and cell-extrinsic survival signals , alter tsolutions for maintaining fitness within the ecological niche of the body. Ecologically informed therapeutic strategies can take advantage of these phenotypic responses required for fitness by using novel treatment approaches. To do this, the landscape of fitness parameters for each patient should be defined in order to identify rationale combinations or targets that control multiple aspects of cancer cell fitness. Beyond genetic drivers alone, therapeutic strategies should also consider the following in defining the fitness landscape of each patient: (1) identifying key resources, (2) defining the reproduction vs. dormancy/survival axis for tumors, (3) characterizing population heterogeneity, (4) quantifying dispersal likelihood, and (5) defining the predator/commensal state of the immune system. Broader partnership between ecologists and cancer researchers/physicians will help inform these strategies and could lead to further breakthroughs and innovation that capitalize on advances in spatially resolved genomics, measurements of the temporal dynamics of cancer cell populations, and an emerging arsenal of therapies that target both cancer cells and their habitats. Coupling these emerging technologies with ecologically informed models of cancer may enhance our ability to treat cancer as a chronic, but controllable illness that will substantially prolong the lives of cancer patients.The range of possible genetic solutions available to cancer cells in order to ensure survival and proliferation is vast. These innumerable possible solutions are constrained by the ecology of the individual patient, as well as the fundamental needs for survival and proliferation under stress, resulting in a set of phenotypic hallmarks. The hallmarks, at their essence, represent the phenotypic


Pinus sylvestris L. growing in a moisture-sensitive habitat of the forest-steppe of Southern Siberia (Russia) to investigate and eliminate legacy effect from cell production in tracheid traits. Anatomical parameters were calculated to describe the results of the three main subsequent stages of conifer xylem tracheid development, namely, cell number per radial file in the ring, mean and maximum cell radial diameter, and mean and maximum cell-wall thickness. Although tree-ring width was almost directly proportional to cell number, non-linear relationships with cell number were revealed in tracheid measurements. They exhibited a stronger relationship in the areas of narrow rings and stable anatomical structure in wider rings. The exponential models proposed in this study demonstrated these relationships in numerical terms with morphometric meaning. The ratio of anatomical measurements to their modeled values was used to develop long-term anatomical chronologies, which proved to retain information about climatic fluctuations independent of tree-ring width (cell number), despite decreased common signal.Dendroclimatic research offers insight into tree growth–climate response as a solution to the forward problem and provides reconstructions of climatic variables as products of the reverse problem. Methodological developments in dendroclimatology have led to the inclusion of a variety of tree growth parameters in this field. Tree-ring traits developed during short time intervals of a growing season can potentially provide a finer temporal scale of both dendroclimatic applications and offer a better understanding of the mechanisms of tree growth reaction to climatic variations. Furthermore, the transition from classical dendroclimatic studies based on a single integral variable (tree-ring width) to the modern multitude of quantitative variables  adds a lot of complexity, which mainly arises from intrinsic feedbacks between wood traits and muddles seasonality of registered climatic signal. This study utilized life-long wood anatomical measurements of 150- to 280-year-old trees of  Climate has a profound effect on the growth and distributional range of tree species, and therefore, improved understanding of the tree growth–climate relationship will be helpful in their better management and conservation under changing climate scenarios  to ascertain the separation of climatic responses in these “refined” anatomical chronologies.Recent improvements in the methods of wood sectioning and technological advancement in measurement (semiautomatic and automatic) of cell anatomical traits have opened new possibilities in dendroclimatology , Scots pine (Pinus sylvestris L.), and silver birch (Betula pendula Roth.) in different proportions occupies most of the ridge, but open forest stands alternating with steppe vegetation are found on the drier sites of southern and southeastern slopes, particularly in the foothills.The study was carried out in the eastern parts of the Bateni Ridge of Kuznetsky Alatau mountain range in southern Siberia . The althttp://meteo.ru/data).The region experiences a sharp continental climate Alisov,  characteTRW) series of each sample were measured using LINTAB-5 system and TSAP-win program  were collected from the vicinity of the Bograd village located at the northeastern tip of the ridge . The initial sampling was performed in 1989, which was subsequently repeated during 2012–2018. A total of 86 trees had been sampled during the whole period to find out the oldest pine trees in the area. Tree core samples were collected from healthy undamaged adult trees of the upper canopy level within the forest stand (tree height ~10–12 m), throughout slopes facing southern, southwestern, and southeastern aspects, where higher insolation increases the sensitivity of trees to moisture deficit and strengthens the climatic signal. Sample collection was performed using an increment borer at breast height (~1.3 m) perpendicularly to the slope, i.e., mainly in the west–east direction. The collected samples were transported to the laboratory and prepared for measurements as per the standard protocols laid down by Cook and Kairiukstis . Tree riam Rinn, . The croam Rinn, . FinallyTRW correlations with the local chronology  out of the full sample collection. The cores were selected on the basis of maximum cambial age (the number of tree rings from bark to pith), high ronology , and theronology , if any N), their radial diameter (D), and cell wall thickness (CWT), were measured  were normalized by compressing/expanding them to the same number of cells as averaged over five measured radial rows  and average (Dmean) radial diameter, and maximum (CWTmax) and average (CWTmean) wall thickness, were calculated for each ring from normalized averaged tracheidograms  = 0.85, r = 0.92, r = 0.89, r = 0.97; Zhirnova et al., Dmax and CWTmax can be interpreted as easily derived replacements of zonal characteristics, and they should register the environmental signal in more short-term intervals than Dmean and CWTmean, despite strong relationships between them.Microphotographs of cross-sections were obtained using a digital camera  mounted on an optical microscope with 200x magnification . Among the considered tree rings, numerous intraannual density fluctuations (IADF) have been observed in various positions within the ring. Some of the narrow rings showed thinner cell walls in latewood (light rings). Frost rings and blue rings were not observed. Anatomical traits of all the rings in the selected samples were measured using semiautomatic program Lineyka 2.01 Silkin, . Tree riVaganov, . Due to idograms . Robustnidograms . Mean vap < 0.1 were observed either in the total series or in the earliest 50-year period. Therefore, detrending of anatomical chronologies was not performed. The individual TRW series were calculated as the sum of D. They were used to crossdate the anatomical measurement series with the preliminary LINTAB measurements of the same cores. The subseries of anatomically derived TRW for each core piece were separately crossdated with full TRW series measured on LINTAB (r > 0.9), and then, all the dated subseries were combined into one series. Additionally, these TRW series were crossdated with local TRW chronology (r > 0.7) developed from cores of all 86 trees collected at the sampling site , Dmean(N), CWTmax(N), and CWTmean(N) using equation and numerical terms aiming for better statistical fitness of models (maximizing R2) and for easy ecological interpretation. The models were derived using a unified form of the equation, whereas numerical terms were developed separately for each considered tree due to expected differences in requirements to water conductivity and mechanical strength of xylem owing to the allometry of trees, genetic predisposition, and microconditions  of temperature and precipitation, which were calculated using daily data from the Shira station. Analysis was performed for the entire cover period of climatic data and for moving 30-year intervals (1-year step) to assess the temporal stability of response.Climatic response of averaged local chronologies of TRW(N)  exhibited the least variability of tree-ring width and cell numbers (TRW(N) on the local scale too. The majority of rings (95%) were found in the range of TRW < 1,400 μm and N < 45.The length of the individual series of anatomical measurements ranged from 150 to 280 years spanning over the period 1739–2018 CE . The claTRW(N) . The rel numbers . NeverthD and CWT per tree ring was observed in narrow rings synchronously to an increase in N but gradually turned to stable values of cell characteristics in wider rings with large N. After selecting the numerical parameters for all exponential models . Furthermore, the average correlations of individual series with respective local chronologies were recorded as 0.67–0.70 before indexing for all cell characteristics, 0.64–0.66 for indexed D, and 0.51–0.55 for indexed CWT.It was noted that the initial average inter-serial correlations (0.36–0.40) between the anatomical chronologies of seven individual trees substantially decreased after indexing , where humid conditions during May to June favored the formation of large cells, and temperatures of this period displayed a significant negative relationship with Dmean. From mid-June to the end of July, a negative influence of both climatic variables has been detected on cell size, which was more pronounced in the case of Dmean. Moreover, CWT revealed a dependence on precipitation from mid-June to early August, whereas a negative association of air temperature was more pronounced with CWTmax and observed during July–August. However, a positive link between CWTmean and temperatures in May has added an interesting fact in the case of the tree growth–climate relationship of Scots pine.Tree growth–climate relationship for local indexed chronologies was analyzed using daily data of temperature (1966–2008) and precipitation (1936–2008) generalized with a window of 21 days. Significant relationships have been observed between local indexed chronologies and climate variables during the season from 90 to 270 day of the year, i.e., March 30–September 26 . The TRWTRW chronology  with a shift in negative response to temperature to the later dates.Temporal stability analysis of the climatic response of Scots pine ronology  exhibiteTRW(N) function from direct proportionality  were caused by the variability of the average D in the tree ring. This variability is made up of several components: (i) age-size trends in juvenile wood , (iii) impact of current climatic fluctuations during cell expansion, and (iv) stochastic component. Consequently, TRW(N) function exhibits a slight curvature in the area of narrow rings . Although weather fluctuations affect the anatomical structure and functional traits of the xylem produced during the ensuing stages of tracheid differentiation after the end of cell division, their effect was not practically reflected in the TRW.The tree-ring width as the sum of the cell radial diameters assimilates the results of two successive stages in tracheid differentiation, namely, cell division in the cambial zone and their subsequent growth by expansion during the season of growth Larson, . Therefoow rings . NeverthAnatomical traits revealed lesser variability with a limited range of values as compared to cell production, which is mainly linked with the functional limitations of the xylem as a mechanical and water-conducting tissue Fromm, . HoweverCWT(N) dependencies. All cells of the nascent xylem at the time of their production presented the size and the primary CWT similar to those of the cambium, which provided the opportunity to describe the revealed dependencies in the form of exponential functions with numerical coefficients of explicit values. Extreme external conditions suppress the rates of all growth processes (cell division and differentiation) in the xylem simultaneously. Assuming a common zero point for these rates of growth processes, the zero cell division will correspond to zero expansion and zero secondary wall deposition. Therefore, at the points of the intersection of functions with the vertical axis , tracheids should have the same measurements as initial cambial cells not passed through the processes of differentiation. The asymptotic values Das and CWTas to which the curve tends in the region of wider rings (N → max) characterized the balance between efficiency and safety of optimal hydraulic architecture (lumen size and tracheid strength) for each tree. The numerical coefficient (a) regulates the range of substantial dependence of anatomical characteristics on cell production.In wide rings, both the average and maximum estimates of considered cell parameters fluctuate around values that probably reflect optimal functionality of the xylem for every individual tree. The differences in these values between trees express the genetic or phenotypic  setting of a particular tree and micro conditions of habitat. The assumption about partial genetic conditionality of the xylem structure has been confirmed by scales of species and provenances by several studies, e.g., quantitative analysis of anatomical traits for pines of different provenances sharing habitat  TRW as an indicator of the impact of environmental conditions on cell production in the cambial zone; (2) Dmax and Dmean, explaining climatic conditions during the process of cell expansion in the middle of earlywood zone and the entire ring, respectively; (3) CWTmax and CWTmean, indicating climate variability during the process of deposition of the secondary cell wall in the typical latewood tracheids and the entire ring, respectively.Indexed chronologies have been developed with a lower amount of common signal due to the elimination of variation inherited from cell production and extrinsic (climate) processes recorded in it. However, significant relationships of these chronologies were still observed with climatic factors. The independence of the indexed anatomical chronologies from cambial activity processes has been reflected by drastic differences in their response to climate as compared with climatic signals registered in TRW climatic response was more focused on earlywood than latewood, and seasonal interval of significant correlations  has been consistent with the production of earlywood tracheids. The differences in the seasonality of climatic response of earlywood and latewood have been repeatedly noted by different researchers in their studies of earlywood and latewood widths  via redistribution of available resources from cell expansion to wall deposition. This phenomenon during the most severe droughts leads to the formation of IADF in the form of a band of latewood-like tracheids in earlywood (Lautner, A positive response of the Lautner, . HoweverCWT in the latewood expressed a positive reaction to precipitation and negative feedback with temperatures toward the end of the season, as reflected in both CWTmax and CWTmean chronologies.In contrast to pine stands growing in the center of the valley, completely within the arid steppe zone with possible genetic adaptation to frequent droughts, the sampled population of mountain forest in this study could be adapted to any particular climatic extreme only through phenotypic plasticity, due to the mixing of the genetic pool between trees growing at a variety of elevations and slopes (cf. Alden and Loopstra, TRW chronology, as revealed from the temporal stability analysis of climatic responses. D chronologies indicated a clear earlier beginning of xylogenesis, whereas the ending point of significant dendroclimatic correlations for CWT shifted to the later calendar dates. At the same time, patterns of response in the middle of the season representing a transition from earlywood to latewood staid “glued” to the same calendar date in the second half of June, which adds evidence for daylength being the main driver of this switch for Scots pine. Overall, stable intensity and uniform long-term shifts of climatic signal in the indexed anatomical chronologies support their usage as fine-scale climatic proxies, despite lowering of the common signal during indexation.Regional warming in the study region has obviously increased the length of the season with temperatures appropriate for wood formation, both in spring and in autumn (see Rogers and Mosely-Thompson, In this study, while tree-ring width exhibited linearity of almost direct proportion to cell numbers, cellular traits deciphered non-linearity of hydraulic and mechanical functions of xylem with regard to cell production and displayed a stronger relationship in the areas of narrow rings and stable anatomical structure of wider rings. These relationships were described by a negative exponential function. Furthermore, climate–growth relations revealed that standardized indexed chronologies of wood traits are strongly influenced by climatic factors, especially moisture availability. This reflects the applicability of this study on plant growth and wood quality in changing climate scenarios, particularly droughts. However, specific mechanisms and the causal nature of the observed climatic reactions need to be further explored in terms of tree physiology of this and other species to obtain a more robust database to make a strong observation. The temporal stability of revealed relationships also has to be explored on a centennial scale, as the length of the presented dataset allows such investigation. Regardless of the direction of future research, the obtained climate-sensitive anatomical chronologies in this study offer an important trait of distinct temporal intraseasonal separation of the climatic signal. Increasing the cover period for chronologies of used anatomical datasets constitutes them as promising proxies for the inverse dendroclimatic task, which can lead to the reconstruction of important climatic factors for relatively short intraseasonal intervals.The original contributions presented in the study are included in the article/EV conceived and designed the research. EB supervised and coordinated study in general. EV and EB provided financial support. EV, EB, DZ, and LB performed fieldwork. DZ supervised and participated in the processing of samples, and supervised anatomical measurements. DD and MZ under guidance of LB performed calculations, data analysis, and created graphics. LB, KU, and ST finalized the manuscript and later revised it. All authors contributed to interpretation of results and writing of the first draft.This research was funded by the Ministry of Science and Higher Education of the Russian Federation, scientific topic code FSRZ-2020-0010, and the Russian Foundation for Basic Research, grant number 20-016-00049. The funding sources had no involvement in study design, in the collection, analysis, interpretation of data, in the writing, and in the decision to submit the article for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The incidence of cancer in older adults is strongly increasing due to the ageing of the population . In receIn order to improve individualized decision making in older adults with cancer, predictive tools can be used in daily clinical practice . SeveralAnother way to further improve the prediction of outcomes in older adults with cancer is to study predictive biomarkers. The study by Di Donato and colleagues showed that metabolomics are highly predictive of prognosis in older adults with metastatic colorectal cancer . Berben Furthermore, several studies investigated the use of predictive models in different settings. Souwer et al. developed a prediction model to predict postoperative complications in older adults with early-stage colorectal cancer . AdditioAnother important aspect in improving outcomes in older patients with cancer is the organization of health care systems. Soto-de-Perez-Calis and colleagues validated a Spanish version of the Geriatric assessment in order to improve health care for older Spanish-speaking patients . The revFinally, several studies described the outcomes of older patients with cancer in different settings. De Glas et al. showed that older patients with metastatic melanoma who were treated with immunotherapy had similar chances of response and toxicity compared to younger trial populations . CoudercIn conclusion, this Special Issue included a broad range of papers enhancing the field of Geriatric Oncology, mainly aiming at further individualizing the treatment of older adults with cancer. As the population of older adults with cancer will continue to rise, these types of studies are important to address the specific needs of this heterogeneous population. We would, therefore, like to thank all participating authors and reviewers for their contribution to this Geriatric Oncology Special Issue.


The preponderance of neuronal doctrine stimulated a retaliatory response from gliobiologists, some of whom proclaimed neuroglia as the primary seat of intelligence2.The human brain  is a complex multiscale hierarchical structure that spans from individual molecules to organelles, from organelles to cells, which are organised into nuclei and layers, which further form gyri and sulci, lobes, parts, and hemispheres thus making the whole organ covered by meninges and floating in the cerebrospinal fluid within the skull. The nervous tissue is made from the neural cells  and non-neural cells that include endotheliocytes, smooth muscle cells and pericytes. All these cells are surrounded by the extracellular space containing extracellular matrix. There are roughly 100 billion neurons in the human brain, ∼ 85 billion of glia, and maybe 5–10 billion endotheliocytes. However, cellular physiology of the brain tends to see every cell population in isolation, with neurons being regarded as the main elements while other cells being subservient to neuronal demands. This is the neuronal doctrine generally associated with Santiago Ramon y Cajal, who, in reality, hold other brain cells in high esteem and never declared neuronal superiority3  or form of brain activity  requires interactions of all elements of the active milieu. We define several principles of interaction in the active milieu. First, the elements  interact9; this structure is very much different from leaflets covering synapses in the neocortex. The functional expression of receptors is another variable that defines functional interactions between elements. Finally, pathology acts by reshaping the active milieu through, for example, cellular reactivity or by the invasion of blood-born factors10, which alter morpho-functional interactions and thus affect the operation of the nervous tissue.Active milieus are many, and they are region specific reflecting different properties of cells in different parts of the brain. For example, in the cerebellum, radial Bergmann astrocytes create distinct perisynaptic covers organised into an array of appendages emanating from the main processesin vivo experiments that simultaneously interrogate different cell types requires a different conceptual framework, which considers multiple interactions among brain cell types within the multicellular physiological environment. The concept of the brain active milieu may redefine the experimental design to functional links of all cellular and non-cellular elements of the nervous tissue by probing multiple interaction routes in which each target element is involved.Description of brain function based on organisation and plasticity of the neuronal circuit provided useful conceptual framework for testable hypothesises. These hypotheses have been routinely examined in reduced systems such as neuronal cultures or brain slices. The development of techniques for This work was supported by the Russian Foundation for Basic Research (RFBR), Grant No. 21-54-53018.


Developmental and adult/ageing neurotoxicity is an area needing alternative methods for chemical risk assessment. The formulation of a strategy to screen large numbers of chemicals is highly relevant due to potential exposure to compounds that may have long-term adverse health consequences on the nervous system, leading to neurodegeneration. Adverse Outcome Pathways (AOPs) provide information on relevant molecular initiating events (MIEs) and key events (KEs) that could inform the development of computational alternatives for these complex effects. We propose a screening method integrating multiple Quantitative Structure–Activity Relationship (QSAR) models. The MIEs of existing AOP networks of developmental and adult/ageing neurotoxicity were modelled to predict neurotoxicity. Random Forests were used to model each MIE. Predictions returned by single models were integrated and evaluated for their capability to predict neurotoxicity. Specifically, MIE predictions were used within various types of classifiers and compared with other reference standards  to benchmark their predictive capability. Overall, classifiers based on MIE predictions returned predictive performances comparable to those based on chemical descriptors and structural fingerprints. The integrated computational approach described here will be beneficial for large-scale screening and prioritisation of chemicals as a function of their potential to cause long-term neurotoxic effects. The human brain is exceptionally sensitive to injury, and several neurodevelopmental processes have been shown to be highly vulnerable to external factors ,2. Such Overall, genetic factors seem to account for about 30–40% of all cases of DNT disorders . EvidencComputational toxicology has been shown to be a cost- and time-efficient alternative to traditional toxicity testing methods . QuantitToxicology has recently undergone a paradigm shift towards the use of alternative testing methods based on knowledge of the biological modes of action and pathways that are responsible for adverse effects, defined as adverse outcome pathways (AOPs). An AOP is a logical construct that connects an upstream molecular initiating event (MIE)  to a downstream adverse outcome (AO), progressing through a series of key events (KEs) ,18. AccoIn the present manuscript, we present an integrated computational system to predict neurotoxic potential that relies on the identification of the MIEs activated by chemicals. MIEs upstream of neurotoxicity were identified from recently published AOP networks, then QSAR models were developed for the prediction of each MIE. Predictions from QSARs for individual MIEs were evaluated for their capability to discriminate neurotoxic and non-neurotoxic compounds as part of an integrated computational prediction system. MIEs were used as independent variables in various machine learning approaches and compared for their predictive power with other widely used methods for chemical description, i.e., fingerprints and molecular descriptors. The predictions returned by QSARs presented here may represent an effective first tier of an Integrated Approaches to Testing and Assessment (IATA) to rapidly screen many chemicals, providing information regarding potential MIEs and associated mechanisms of toxicity and thus, helping to prioritise chemicals for additional and better-targeted screening/testing, e.g., in vitro testing ,28.Performance statistics from external validation for the QSARs for MIEs are reported in The statistics of the balanced random forest (BRF) models for MIEs were extremely good and confirmed the high quality of the information included in ChEMBL database and its suitability as a source of data for modelling the interactions of ligands with molecular targets upstream of biological pathways  ,30. One External validation returned BA values in the range of 0.84–0.99. The statistics were balanced between sensitivity (SEN) and specificity (SPE); although SEN was higher in most cases, slightly lower values for the inactive class were most common in all datasets. As for Matthews Correlation Coefficient (MCC) values, several of the models showed values below the average, such as the BRFs for CYP2E1, KAR, and TTR. MCC has beenproposed for the evaluation of classification between two very unbalanced categories; however, it was observed that this parameter can sometimes be biased by high unbalance datasets on terms of the categories  . Indeed,The relative importance of MIEs for neurotoxicity prediction was evaluated using the methods described in Thyroid elements seem to be relevant  to neurotoxicity; in particular, the exclusion of TTR always leads to a reduction in BA average. TTR is the fourth MIE in terms of variable importance. THRs, TPO and TTR are involved in the biosynthesis, metabolism, and transportation of the thyroid hormone, respectively. Among the two isoforms of thyroid receptors, THRβ is consistently the most important (first descriptor for variable importance), while the removal of THRα did not negatively affect performance. Among ionotropic glutamate receptors, AMPAR and KAR seem to be more linked to neurotoxicity than NMDAR. AMPA/kainite receptor-mediated neurotoxicity was reported to possibly play a role in neuronal neurodegeneration in amyotrophic lateral sclerosis  and in tIn the present work, a new integrated computational system was proposed for predicting the neurotoxic potential of chemicals as a function of their capability to trigger MIE  upstream of neurotoxicity. QSARs were developed to predict MIE induction while BRFs were applied to handle the unbalanced training data. In the last part of the manuscript, MIE predictions were used to classify neurotoxic and non-neurotoxic compounds and compared for their predictivity with other approaches to describe the structure of chemicals, namely, chemical descriptors and extended fingerprints.Overall, in two out of three cases MIEs perform comparably with chemical descriptors and better than fingerprints, which are considered the gold standard for describing chemical structures within QSARs . The onlDespite this, one of the key advantages afforded by the use of MIE responses in place of the classical structural representation of molecules is the interpretability of the predictions. Indeed, a complete profile of the neuronal receptors and enzymes that are activated is given together with the overall neurotoxicity outcome, providing insights into the possible mode of action of a predicted neurotoxic chemical. This aspect was further verified by predicting a series of known neurotoxicants with the models predicting MIEs. The mode of action for these neurotoxicants was reported in a review by Masjosthusmann and coworkers  who gathIn addition to increased biological relevance, MIE predictions simplify models to a reduced number of variables , while in the case of descriptors and fingerprints several hundred variables may be included in the models.The RFs developed here for the prediction of single MIEs returned satisfactory predictive performance and were confirmed to be a valuable method in the field of computational toxicology. The statistical performance of the models presented here confirmed our previous findings that using RFs with internal balancing of categories (BRF) represents one of the best methods for handling the unbalanced distribution typical of biological data ,38.In the present manuscript, the use of biological information  instead of the classical structural description of molecules was proposed for the development of QSARs. The use of biological data  utilised as input variables to develop QSARs has been increasingly explored in the recent literature . This stThe development of new machine learning and artificial intelligence-based approaches is highly desirable, as it allows for the detection of chemicals with potential neurotoxicity and DNT effects in a more time- and resource-efficient way compared to traditional in vivo testing. In addition, data from in silico screenings based on AOPs can provide a scientifically sound rationale to make decisions relating to assessment of the safety of chemicals. The mechanistic nature of AOPs provides knowledge to guide the design of new IATAs to meet regulatory needs ,42. In p2+ influx, with consequent cell death, memory problems, and convulsions [Glutamate ionotropic receptors, i.e., N-methyl-D-aspartate (NMDAR), alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionate (AMPAR) and kainate (KAR) are responsible for excitatory synaptic transmission and synaptic plasticity, which are fundamental for learning and memory . Sustainvulsions  and incrProtein adduct formation is the covalent interaction between an electrophilic chemical and the nucleophilic part of a protein, and may lead to damage of the protein and the potential loss of its function. This may affect thiol- and seleno-containing proteins, which offer antioxidant protection . The bin+/I− symporter (NIS) is critical for the physiological production and maintenance of thyroid hormone levels in the serum, as it mediates the transport of iodide into thyroid cells. Its inhibition (MIE F) results in decreased thyroid hormone synthesis, with effects on neurocognitive function in children [The function of the Nachildren ,52.Acetylcholinesterase (AChE) is an enzyme present in both central and peripheral nervous systems and in muscular motor plaques. It is responsible for the enzymatic cleavage of the neurotransmitter acetylcholine . Inhibit2+ channels (RyR) contribute to neurotransmission and synaptic plasticity. Polychlorinated biphenyl (PCB) exposure has been reported to alter intracellular Ca2+ levels and to interfere with normal neuronal dendritic growth and plasticity in a RyR-dependent manner (MIE L) [Ryanodine-sensitive Ca (MIE L) .Thyroid hormone receptors α and β (THRα and THRβ) mediate the effects of thyroid hormones, while thyroperoxidase (TPO) and deionidase are involved in the biosynthesis/catabolism of thyroid hormones. Transtyretrin serum binding protein (TTR), monocarboxylate transporters 8 and 10, and the solute carrier organic anion transporter family member 1C14 (OATP1C1) are involved in the transportation of thyroid hormones at various levels . InterfeThe complexes of the respiratory chain play a pivotal role in neuronal and glial cell survival and cell death, as they regulate both energy metabolism and apoptotic/necrotic pathways. The interaction of xenobiotics with these enzymes can interfere in various ways with their normal functionality, e.g., inhibiting the production of ATP (MIE M) or interfering with the redox cycle (MIE N and O), with consequent increased production of ROS and oxidative stress. Oxidative stress contributes to a loss of function of hippocampal neural progenitor cells and a decline in learning and memory performance . MoreoveVoltage-gated sodium channels (VGSC) are the primary molecules responsible for the control of the electrophysiological potentials of electrically excitable cells. Various isoforms exist, with isoforms 1, 2, 3, and 6 reported to be mainly expressed in the central nervous system . NeurotoIonotropic GABA receptors (GABAR) are ligand-gated ion channels which play important roles in inhibitory neurotransmission . InterfeMIEs linked to neurotoxicity were identified from the AOP networks  publisheHomo sapiens were considered. For each target, only bioactivities with pChEMBL values were chosen. This term refers to all the comparable measures of half-maximal responses  on a negative logarithmic scale [Bos taurus data were used, as human data were not available. pCheMBL data distributions were heavily skewed in the majority of cases towards positive values.Data relative to each of the molecular targets identified in ic scale . DiffereIn order to prevent skew towards positive values, which is different from the natural distribution of biological data , each dataset was further enriched with the chemicals included in the datasets of the remaining endpoints. These chemicals were treated as ‘decoys’ and assumed to be inactive. Due to the very large number of data available, AChE data were not used to enrich inactive samples of other endpoints to avoid the creation of datasets excessively unbalanced towards inactives. A semi-automated curation procedure  was applhttps://cdk.github.io/ (accessed on 7 March 2022)) and used as input for QSAR modelling. The BRF [ChEMBL datasets from  The BRF  implemen The BRF . The numModels were validated by splitting each data set into a training (80%) and a test set (20%) by applying a stratification sampling to the activity classes. The splitting procedure was repeated 100 times using different random splits, ensuring that each chemical in the datasets was included in the test set the same number of times in order to avoid bias due to the molecules present in the different sets. The performance using the test set was calculated for each iteration, then the final performance of each model was calculated by averaging the statistical parameters obtained using the test sets relative to each of the 100 iterations.As no data were found for TPO inhibition from ChEMBL, the QSAR model for predicting TPO inhibition proposed by Gadaleta et al.  was usedMIEs D (Binding and SH/SeH proteins involved in protection against oxidative stress) and H (Protein Adduct Formation) do not refer to an interaction with a specific receptor/enzyme; rather, they describe non-specific covalent binding to biological macromolecules . Because this type of binding refers to the intrinsic reactivity of molecules, SMARTS compiled by Enoch et al.  describiPredictions using single QSARs for MIEs of neurotoxicity were evaluated for their capability to predict the neurotoxic potential of chemicals. Neurotoxicity data were retrieved from Kosnik et al. , who lisThe final list of 69 compounds along with their neurotoxic classification is reported in the The MIEs for the 69 chemicals with data for neuroactivity were predicted with the 15 BRFs developed from the entire datasets in The predictions for the 69 chemicals from Kosnik et al.  were repThe predictions generated by the MIE models for the 69 chemicals were used as independent variables to develop new integrated QSAR models for predicting the neurotoxicity of chemicals.K-Nearest Neighbours (k-NN) : EuclideRandom Forest (RF) : the numMulti-linear Perceptron–Artificial Neural Networks (NNET) ,81: one Three different classifiers able to naturally handle a high number of independent variables were trained based on the neurotoxicity data. Five different settings were applied for the various classifiers, as implemented in KNIME .K-NeareIn order to verify the capability of MIE prediction to discriminate neuroactive and non-neuroactive compounds, QSARs based on MIEs were compared with other models developed with the same algorithms  and different independent variables MIEs were iteratively removed, then QSARs for neurotoxicity were developed with the remaining features, as described in (2)Variable importance was calculated for each MIE within RF models. A score was calculated based on the attribute usage statistics in the RF for each descriptor by counting how many times it was selected for a split (#split) and at which rank  among all available attributes (#candidates) in the trees of the ensemble:The relative importance of the various MIEs on the neurotoxicity predictions was evaluated in two ways.Variable importance calculated in this way was averaged among the various modelling iterations.In the present manuscript, a new take on the traditional QSAR methodology was proposed to predict neurotoxicity by employing the biological information associated with chemicals (in the form of ligand-based predictions of MIE activation data) in place of the traditional structural data. The main advantage of this approach is that it can both return a prediction of the adverse outcome and provide insights into the specific mechanisms and molecular events that trigger toxicity. Emphasising the specific mechanisms of action behind neurotoxicity will increase the confidence of scientists and regulators in the predictions returned by these models. Having information about the activated molecular targets that are responsible for an apical effect may, in some cases, provide indications to chemists of possible modifications to the structure of hazardous chemicals, allowing for the designing of safe alternatives.Despite their increasing usage, the application of the AOP framework in computational toxicology remains hampered by numerous and serious challenges. In general, an AOP is always a simplification of more complex and articulated biological pathways. Indeed, for certain biological processes, there are gaps in definitive knowledge of all responsible molecular determinants and mechanisms. In the case of neurotoxicity, there is a lack of understanding of all the MIEs involved in the alteration of downstream KEs as well as the occurrence of the AOs . Several