Search

Mediation analysis has emerged as a versatile tool for answering mechanistic questions in microbiome research because it provides a statistical framework for attributing treatment effects to alternative causal pathways. Using a series of linked regressions, this analysis quantifies how complementary data relate to one another and respond to treatments. Despite these advances, existing software's rigid assumptions often result in users viewing mediation analysis as a black box. We designed the multimedia R package to make advanced mediation analysis techniques accessible, ensuring that statistical components are interpretable and adaptable. The package provides a uniform interface to direct and indirect effect estimation, synthetic null hypothesis testing, bootstrap confidence interval construction, and sensitivity analysis, enabling experimentation with various mediator and outcome models while maintaining a simple overall workflow. The software includes modules for regularized linear, compositional, random forest, hierarchical, and hurdle modeling, making it well-suited to microbiome data. We illustrate the package through two case studies. The first re-analyzes a study of the microbiome and metabolome of Inflammatory Bowel Disease patients, uncovering potential mechanistic interactions between the microbiome and disease-associated metabolites, not found in the original study. The second analyzes new data about the influence of mindfulness practice on the microbiome. The mediation analysis highlights shifts in taxa previously associated with depression that cannot be explained indirectly by diet or sleep behaviors alone. A gallery of examples and further documentation can be found at https://go.wisc.edu/830110., Importance: Microbiome studies routinely gather complementary data to capture different aspects of a microbiome's response to a change, such as the introduction of a therapeutic. Mediation analysis clarifies the extent to which responses occur sequentially via mediators, thereby supporting causal, rather than purely descriptive, interpretation. Multimedia is a modular R package with close ties to the wider microbiome software ecosystem that makes statistically rigorous, flexible mediation analysis easily accessible, setting the stage for precise and causally informed microbiome engineering.

Background: A lack of healthy red blood cells such as iron deficiency frequently consequences as anemia. In expectant women, iron deficiency increases the risk of developmental delays in the fetus. It is utilized to regenerate red blood cells (RBC), which determines the storage and transport of oxygen in the body. As for oral symptoms and indications, it has an inclusive of atrophic glossitis, oral mucosa atrophy, and oral mucosa tenderness or a sensation with burning in mouth. The objective is to determine RBC parameters for iron deficiency anemia (IDA) symptoms in oral disease patients., Methods: Normal and oral disease patients' blood is collected in a 2-mL EDTA vial for the hematology analyzer. With file number SRB/SDC/UG-2155/22/493, the institutional ethical approval committee approved this study: 10 controls (without anemia) and 10 patients with oral disease (with anemia)., Results: In oral disease patients with anemia, a decreased hemoglobin level is observed. In this study, nearly all anemic patients have IDA. In IDA, the RBC count decreases, the MCV decreases, the MCH decreases, and the RDW rises. We have analyzed various iron deficiency anemia indices. Ricercari and Eshani demonstrate a significant change in it., Conclusions: In predicting iron deficiency anemia, our results indicate that oral patients have significant differences in hematological parameters that are individual. It demonstrates that oral disease patients are developing iron deficiency anemia than controls (healthy). Additionally, the discrimination indices reveal that they do not have hemoglobinopathies but rather iron deficiencies. Oral manifestations are more prevalent in Iron deficiency anemia (IDA) patients than in healthy patients, according to our findings.

Mediation analysis has emerged as a versatile tool for answering mechanistic questions in microbiome research because it provides a statistical framework for attributing treatment effects to alternative causal pathways. Using a series of linked regressions, this analysis quantifies how complementary data relate to one another and respond to treatments. Despite these advances, existing software's rigid assumptions often result in users viewing mediation analysis as a black box. We designed the multimedia R package to make advanced mediation analysis techniques accessible, ensuring that statistical components are interpretable and adaptable. The package provides a uniform interface to direct and indirect effect estimation, synthetic null hypothesis testing, bootstrap confidence interval construction, and sensitivity analysis, enabling experimentation with various mediator and outcome models while maintaining a simple overall workflow. The software includes modules for regularized linear, compositional, random forest, hierarchical, and hurdle modeling, making it well-suited to microbiome data. We illustrate the package through two case studies. The first re-analyzes a study of the microbiome and metabolome of Inflammatory Bowel Disease patients, uncovering potential mechanistic interactions between the microbiome and disease-associated metabolites, not found in the original study. The second analyzes new data about the influence of mindfulness practice on the microbiome. The mediation analysis highlights shifts in taxa previously associated with depression that cannot be explained indirectly by diet or sleep behaviors alone. A gallery of examples and further documentation can be found at https://go.wisc.edu/830110., Competing Interests: CONFLICTS OF INTEREST The authors declare no conflict of interest

Background: Recent studies suggest that gut microbiota composition, abundance and diversity can influence many chronic diseases such as type 2 diabetes. Modulating gut microbiota through targeted nutrition can provide beneficial effects leading to the concept of personalized nutrition for health improvement. In this prospective clinical trial, we evaluated the impact of a microbiome-based targeted personalized diet on hyperglycaemic and hyperlipidaemic individuals. Specifically, BugSpeaks ® -a microbiome profile test that profiles microbiota using next generation sequencing and provides personalized nutritional recommendation based on the individual microbiota profile was evaluated., Methods: A total of 30 participants with type 2 diabetes and hyperlipidaemia were recruited for this study. The microbiome profile of the 15 participants (test arm) was evaluated using whole genome shotgun metagenomics and personalized nutritional recommendations based on their microbiota profile were provided. The remaining 15 participants (control arm) were provided with diabetic nutritional guidance for 3 months. Clinical and anthropometric parameters such as HbA1c, systolic/diastolic pressure, c-reactive protein levels and microbiota composition were measured and compared during the study., Results: The test arm (microbiome-based nutrition) showed a statistically significant decrease in HbA1c level from 8.30 (95% confidence interval (CI), [7.74-8.85]) to 6.67 (95% CI [6.2-7.05]), p < 0.001 after 90 days. The test arm also showed a 5% decline in the systolic pressure whereas the control arm showed a 7% increase. Incidentally, a sub-cohort of the test arm of patients with >130 mm Hg systolic pressure showed a statistically significant decrease of systolic pressure by 14%. Interestingly, CRP level was also found to drop by 19.5%. Alpha diversity measures showed a significant increase in Shannon diversity measure ( p < 0.05), after the microbiome-based personalized dietary intervention. The intervention led to a minimum two-fold (Log2 fold change increase in species like Phascolarctobacterium succinatutens, Bifidobacterium angulatum, and Levilactobacillus brevis which might have a beneficial role in the current context and a similar decrease in species like Alistipes finegoldii, and Sutterella faecalis which have been earlier shown to have some negative effects in the host. Overall, the study indicated a net positive impact of the microbiota based personalized dietary regime on the gut microbiome and correlated clinical parameters., Competing Interests: Gopalakrishna Kallapura, Anthony Surya Prakash, Kumar Sankaran, Prabhath Manjappa and Debojyoti Dhar are employed by Leucine Rich Bio Pvt Ltd and Sanjay Ambhore by Shreya Clinic., (© 2024 Kallapura et al.)

Time series studies of microbiome interventions provide valuable data about microbial ecosystem structure. Unfortunately, existing models of microbial community dynamics have limited temporal memory and expressivity, relying on Markov or linearity assumptions. To address this, we introduce a new class of models based on transfer functions. These models learn impulse responses, capturing the potentially delayed effects of environmental changes on the microbial community. This allows us to simulate trajectories under hypothetical interventions and select significantly perturbed taxa with False Discovery Rate guarantees. Through simulations, we show that our approach effectively reduces forecasting errors compared to strong baselines and accurately pinpoints taxa of interest. Our case studies highlight the interpretability of the resulting differential response trajectories. An R package, mbtransfer, and notebooks to replicate the simulation and case studies are provided., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Sankaran, Jeganathan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Inclusivity is fundamental to progress in understanding and addressing the global phenomena of biological invasions because inclusivity fosters a breadth of perspectives, knowledge, and solutions. Here, we report on how the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) assessment on invasive alien species (IAS) prioritized inclusivity, the benefits of this approach, and the remaining challenges., Competing Interests: Declaration of interests M.A.N. is a member of the Editorial Board of TREE., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Piperine, a naturally occurring compound in black pepper ( Piper nigrum ), is known for its potential health benefits, including its reported enhancement of insulin sensitivity. However, the precise impact of piperine on hepatocyte nuclear factor 1 alpha (HNF-1α) and sterol regulatory element-binding protein 1c (SREBP-1c), transcription factors for insulin signaling and glucose metabolism in hepatocytes, remains unclear., Objective: This study aims to investigate the effect of piperine, compared to metformin, on blood glucose and insulin levels by modifying the expression of hepatic HNF-1α and SREBP-1c in high-fat-diet (HFD) and sucrose-induced type 2 diabetes mellitus (T2DM) rats and in human Chang liver cells., Methods: Adult male albino rats were categorized into four groups: group 1 as the control, group 2 as T2DM, group 3 as T2DM rats treated with piperine (40 mg), and group 4 as T2DM rats treated with metformin (50 mg). Fasting blood glucose (FBG) and serum insulin levels were measured using enzyme-linked immunosorbent assay (ELISA), while real-time polymerase chain reaction (RT-PCR) analysis was conducted to assess the mRNA expression of HNF-1α and SREBP-1c. Further, piperine was treated with normal and high glucose-induced Chang liver cells, and gene expression was analysed. Data analysis was performed using one-way analysis of variance (ANOVA), with a significance set at p<0.05., Results: Treatment with piperine led to a notable decrease in blood glucose levels and circulating insulin when compared with T2DM rats (group 2). Additionally, piperine administration resulted in the upregulation of HNF-1α mRNA expression and downregulation of SREBP-1c mRNA levels whose effects were found to be near that of the control and standard drug metformin's effects. In vitro study also confirmed that piperine improved the HNF-1α expression and reduced the expression of SREBP-1c in Chang liver cells., Conclusion: Our findings suggest that piperine treatment effectively regulates hyperglycemic and hyperinsulinemic insulin resistance in the liver by modulating the expression of HNF-1α and SREBP-1c. Consequently, piperine emerges as a promising candidate for therapeutic intervention in managing T2DM., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Zuvairiya et al.)

Introduction:  Glyphosate is a well-known broad-spectrum desiccant and herbicide. It is an active component used widely in popular weed control products like Roundup (BigHaat Agro Pvt Ltd, Bangalore, Karnataka, India), Rodeo (Corteva, Inc., Indianapolis, Indiana, United States), and PondMaster (PBI-Gordon Corporation, Shawnee, Kansas, United States). However, due to sustained presence, they tend to get deposited in the environmental resources and leach into the living system. It has been shown to develop various cancers and diabetes. However, its impact on GSK-3β (glycogen synthase kinase-3 beta) and FOXO-1 (forkhead box protein O1), both critical proteins involved in the regulation of glucose metabolism and insulin signaling, is unknown.  Objective: The primary objective of this study was to check whether antioxidant vitamins (C and E) can reduce hyperglycemia and hyperinsulinemia in response to glyphosate exposure and the secondary objective was to investigate whether antioxidant vitamins have the capacity to downregulate GSK-3β and FOXO-1-mediated oxidative stress in the liver of glyphosate induced rats.  Methods: We divided the experimental animals into three groups. Group 1 - control rats (animals were injected with olive oil (0.8ml) intraperitoneally), Group 2 - glyphosate-treated rats orally for ten weeks, Group 3 - glyphosate-treated rats received vitamin C and vitamin E. After 30 days of treatment, the animals were anesthetized, sera were separated and used for the biochemical analysis. Liver tissues from control and treated animals were dissected and stored at -20°C for further gene expression analysis. Fasting blood glucose (FBG) was assessed by calorimetric analysis, while serum insulin was measured by enzyme-linked immunosorbent assay (ELISA). Gene expression studies of specific genes (FOXO1 and GSK3) were analyzed by real-time reverse transcriptase-polymerase chain reaction (RT-PCR) analysis., Results: The expression level of FOX01 and GSK3β genes was higher in glyphosate-induced animals compared with the control group but was reduced significantly (p＜0.05) upon treatment with antioxidant vitamins (C and E). Other biochemical parameters, including FBG, serum insulin, and antioxidant enzyme assays, also showed that antioxidant vitamins reduce glyphosate-induced insulin resistance and type-2 diabetes.  Conclusion: The current study provides in vivo experimental evidence that antioxidant vitamins (C and E) reduce the glyphosate-mediated development of type-2 diabetes risk via the downregulation of FOX01 and GS-3β mRNA expression in the liver. Hence, vitamins C and E may be considered as therapeutics for the treatment of diabetes., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Chandran et al.)

Introduction Cancer continues to pose a significant challenge in medical research. Phytochemicals derived from plants have emerged as a promising avenue for pioneering drug discovery due to their potential for reduced toxicity. The phosphatidylinositol 3-kinase (PI3K) pathway has gained recognition as a pivotal signaling pathway with implications across multiple facets of cancer initiation and progression. This study focuses on the virtual screening of phytochemicals from Schinus molle , evaluating their potential as inhibitors of PI3K, a crucial target in cancer therapy. Methods and materials The present study involved a comprehensive in silico screening of phytochemicals derived from S. molle. The screening process encompassed various parameters, such as drug-likeness, pharmacokinetics, molecular docking, toxicity analysis, bioavailability assessment, and molecular target exploration. The primary objective of this systematic approach was to identify potential lead compounds. The study aimed to provide a detailed understanding of the molecular properties of the phytochemicals and their potential as drug candidates. Results Upon analyzing 18 compounds, two compounds were noteworthy. Beta-spathulene and kaempferol demonstrated significant affinity for PI3K and favorable attributes concerning drug-likeness, pharmacokinetics, and bioavailability. Conclusion While our computational investigation lays a promising foundation, it is essential to emphasize that further experimental studies, including in vitro and in vivo experiments, are imperative to validate the action of these lead compounds., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Kumar et al.)

Background Metastatic colorectal cancer (mCRC) continues to rank as the second deadliest cancer on the global scale. CRC diagnosed at metastatic (stage IV) makes treatment strategies more challenging. Even though there are numerous therapeutic options available, the side effects of these treatments threaten the human health. Therefore, we are in the phase of searching new molecules that are less harmful and cost-effective. The common source of many pharmaceutical medications is plants. This study focuses on virtually screening phytochemicals from Conium maculatum as potential inhibitors of the epidermal growth factor receptor (EGFR), a crucial target in cancer therapy. Methods and materials C. maculatum was selected due to its phytochemicals and prior indications of its anticancer properties. In silico investigations encompass druglikeness screening, pharmacokinetics assessment, molecular docking, toxicity prediction, molecular target screening, and molecular dynamics simulations. A comprehensive analysis led to the identification of promising lead compounds. Results A total of 25 compounds exhibited favorable pharmacokinetic and drug-like characteristics. Among them, 12 compounds displayed a high affinity for EGFR as determined by molecular docking experiments. Further safety assessment using ProTox-II revealed that seven compounds had no anticipated toxicity, affirming their safety profiles.  Conclusion These findings collectively predicted the efficacy of seven phytochemicals from C. maculatum as EGFR inhibitors in mCRC. Further experimental investigations and optimization of the identified leads were needed to validate the efficacy and safety of identified lead compounds and explore their therapeutic potential in CRC., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Venkateswaran et al.)

Topic modeling is a popular method used to describe biological count data. With topic models, the user must specify the number of topics $K$. Since there is no definitive way to choose $K$ and since a true value might not exist, we develop a method, which we call topic alignment, to study the relationships across models with different $K$. In addition, we present three diagnostics based on the alignment. These techniques can show how many topics are consistently present across different models, if a topic is only transiently present, or if a topic splits into more topics when $K$ increases. This strategy gives more insight into the process of generating the data than choosing a single value of $K$ would. We design a visual representation of these cross-model relationships, show the effectiveness of these tools for interpreting the topics on simulated and real data, and release an accompanying R package, alto., (© The Author 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The present study was aimed to evaluate the anti-adipogenic activity of piperine (PIP) and epigallocatechin gallate (EGCG) in 3T3-L1 cells. In cytotoxicity studies, PIP and EGCG showed IC 50 values of 260 and 218 µM respectively and in combination (20 µM each) did not show cytotoxicity. Treatment with PIP and EGCG (20 µM each) significantly ( p <.01) inhibited cell differentiation, lipid droplets deposition and enhanced glycerol release in 3T3-L1 cells. The secreted level of leptin was decreased but adiponectin level was increased in treated 3T3-L1 cells than untreated cells. In molecular expression studies, key adipogenic genes PPAR-γ, SREBP-1c, FAS, Fab-4, C/EBP-α and HMG-CoA reductase were markedly down-regulated but UCP-1 was up-regulated intreated 3T3-L1 cells and the same trend was observed in expression levels of selected proteins. In conclusion, our results demonstrated a combination of PIP and EGCG exhibited strong anti-adipogenic and lipid lowering effect than individual treatments due to synergism.

Introduction: Oral inflammation, often triggered by infections, injuries, or immune responses, can compromise treatment outcomes, delay healing, and contribute to patient discomfort. The development of green nanoparticle synthesis methods is receiving attention due to their potential advantages over existing approaches. These procedures use commonly available, affordable, and environmentally friendly natural plant extracts. Due to their numerous uses in various industries, titanium oxide nanoparticles (TiO 2 NPs) have attracted the most attention among the nanoparticles. In this study, we present the green synthesis of Myristica fragrans (mace) extract as a reductant and stabilizer for the production of curcumin-functionalized TiO 2 NPs (CTN). We additionally evaluated the effectiveness of these nanoparticles as anti-inflammatory agents., Objective: In this study, we aim to develop biogenic TiO 2 NPs using Myristica fragrans as a natural capping agent and functionalized with curcumin for effectively managing oral inflammation in dental applications., Methods: The nanoparticles were synthesized using the green synthesis method and characterized using various characterization techniques. Biocompatibility was evaluated using hemolytic assays, and the bioactivity of the nanoparticles was assessed using anti-inflammatory assays., Results: Curcumin-coated M-TiO 2 NPs (MCTN) were successfully synthesized and characterized by various techniques, confirming their morphology, crystallinity, functionalization, elemental composition, size, and stability. In vitro bioactivity studies revealed that MCTN exhibited significant anti-inflammatory activity, as evidenced by the inhibition of protein denaturation with minimal hemolytic potential. These findings highlight the potential of MCTN as a promising candidate for anti-inflammatory applications., Conclusion: Our results suggest that MCTN exhibits promising anti-inflammatory and anti-hemolytic properties. However, further in-depth in vivo analysis is required to fully understand their efficacy and toxicity., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, A.S. et al.)

As the urgency to achieve net-zero emissions by 2050 intensifies, industries face an imperative to reimagine their role in the fight against climate change. One promising avenue arises from the realization that industrial emissions, often deemed pollutants, can be the building blocks of a circular economy strategy. By directly utilizing these carbon emissions as raw materials, we can produce net-zero or low-carbon fuels, carbonates, polymers, and chemicals. At the heart of this paradigm shift lies the production of carbon-neutral methanol from industrial flue gas-a technically viable approach that has gained significant momentum in recent years. The conditions under which such a circular economy model for producing renewable methanol becomes commercially sustainable based on realistic constraints, however, are not sufficiently explored in the existing literature. This paper fills this gap by investigating if and when net-zero methanol production from industrial flue gas will be a sustainable long-term strategy. Using detailed technoeconomic modeling of integrated hydrogen and methanol production ecosystems for two production capacities, I will evaluate 32 practical production scenarios using realistic regulatory, economic, and market conditions. Even though renewable methanol from industrial emissions can be a viable technical solution to address climate change and global warming, I will show why this strategy will be commercially feasible only under favorable economic, regulatory, and market conditions. Furthermore, I will demonstrate how the market price of methanol and the cost of carbon-free electricity critically influence the commercial feasibility of this approach. When these two parameters are unfavorable, I will show why other factors, namely, carbon credits and byproduct (oxygen) sales, will not be sufficient to create an economically sustainable circular economy of renewable methanol from industrial emissions. Finally, I will provide arguments on why one has to think through stakeholder cooperation and public-private partnerships to mitigate various project risks. Despite the importance of this topic, it is not sufficiently covered in the available scientific literature. To advance policy and regulatory frameworks in this area, I strongly believe that further research and development is needed. I will also share perspectives on regulatory derisking mechanisms, which can help align regulations with private investors' preferences. With the analyses and arguments showcased in this paper, I will firmly assert that without favorable conditions, strong partnerships, and stakeholder cooperation, the production of renewable net-zero methanol from industrial emissions risks becoming a dead-end strategy., Competing Interests: The author declares no competing financial interest., (© 2023 The Author. Published by American Chemical Society.)

Purpose study: Alzheimer is a type of dementia that usually affects older adults by creating memory loss due to damaged brain cells. The damaged brain cells lead to shrinkage in the size of the brain and it is very difficult to extract the grey matter (GM) and white matter (WM). The segmentation of GM and WM is a challenging task due to its homogeneous nature between the neighborhood tissues. In this proposed system, an attempt has been made to extract GM and WM tissues using optimization-based segmentation techniques. Materials and methods: The optimization method is considered for the classification of normal and alzheimer disease (ad) through magnetic resonance images (mri) using a modified cuckoo search algorithm. Gray Level Co-Occurrence Matrix (GLCM) features are calculated from the extracted GM and WM. Principal Component Analysis (PCA) is adopted for selecting the best features from the GLCM features. Support Vector Machine (SVM) is a classifier which is used to classify the normal and abnormal images. Results: The proposed optimization algorithm provides most promising and efficient level of image segmentation compared to fuzzy c means (fcm), otsu, particle swarm optimization (pso) and cuckoo search (cs). The modified cuckoo yields high accuracy of 96%, sensitivity of 97% and specificity of 94% than other methods due to its powerful searching potential for the proper identification of gray and WM tissues. Conclusions: The results of the classification process proved the effectiveness of the proposed technique in identifying alzheimer affected patients due to its very strong optimization ability. The proposed pipeline helps to diagnose early detection of AD and better assessment of the neuroprotective effect of a therapy.

Treponema denticola is a gram-negative bacteria that is associated with periodontal diseases. Literature derived, six indole based oxadiazole derivatives are docked with the target Factor H binding protein (fHbp) protein. Results show better docking interaction compared to clinically proven drugs and all compounds obey Lipinski's rule of five. Hence, the compounds were inferred to be potential inhibitors for factor H binding protein of Treponema denticola ., (© 2023 Biomedical Informatics.)

Beta arrestins are a family of adaptor proteins that help in the regulation of signaling and trafficking of various G protein coupled receptors (GPCRs). Six oxadiazole derivatives taken from literature are analyzed for anti-cancer properties. The toxicity profiles of all the drugs were similar to Tamoxifen used as control. Data shows that compounds 2, 4, and 6 exhibited comparably significant molecular interactions with the cancerous protein for further consideration., (© 2023 Biomedical Informatics.)

Diabetes is characterized by elevated blood sugar and insulin resistance. In poorly controlled or uncontrolled diabetes, persistent hyperglycemia causes oral and systemic problems. Therefore, it is of interest to evaluate biochemical indicators for oral health and diabetes using a fully automatic biochemistry analyzer which separates patient serum from blood samples. Data shows that diabetic oral complex patients showed high RBS, HbA1c, FBS, and PBSS. Thus, dental condition is linked to diabetes., Competing Interests: None declared, (© 2023 Biomedical Informatics.)

The high biological activity and interesting optical properties of the aza compounds is known. Therefore, it is of interest to document the molecular docking analysis data of Aza compounds with the heme-binding protein from an anaerobic, Gram-negative bacterium Tannerella Forsythia . Hence, we report the optimal binding features of Aza compounds with the heme-binding protein from Tannerella Forsythia for further consideration in drug discovery against the pathogen., Competing Interests: The authors declare that there is no conflict of interests., (© 2023 Biomedical Informatics.)

Treponema Denticola has a virulent protein called cystalysin, which causes periodontitis. Therefore, it is of interest to design efficient drug that may have fewer side effects than the present clinical drugs, considering most of them are multidrug resistant. The molecular docking analysis show that the selected thiazo derivatives (1-6) show better binding energies and amino acid interactions compared to the clinically proven drugs proving to be potential inhibitors against the protein., (© 2023 Biomedical Informatics.)

It is of interest to document the molecular docking analysis of protein Filamin-A with thioazo compounds. The compounds 1, 3, 5, and 6 showed best molecular docking interaction as compared to the drug doxorubicin. Among the selected ligands (1-6), compound 3 shows better interaction score than doxorubicin and follows Lipinski's rule of five. Hence, it could be considered as a potential lead molecule for inhibiting protein filamin A in the treatment of oral cancer., (© 2023 Biomedical Informatics.)

Porphyromonas gingivalis is known to produce major virulence factor, Gingipain R that could penetrate the gingivae and cause tissue destruction. In this research we aim to target the gingipain R protein with imidazole quinoline derivatives (1-6) via insilico means. Molecular docking results show, compounds (1-6) have better affinity and amino acid interactions compared to the standard clinically proven drugs used as control group, and they obey Lipinski's rule of five and can be used as potential drug candidates to inhibit gingipain R., Competing Interests: The authors declare that there is no conflict of interests., (© 2023 Biomedical Informatics.)

It is of interest to identify the JAK STAT 3 signaling inhibitors to abrogate tumorigenesis in oral cancer. Hence, molecular docking was performed with known oxazole compounds (1-5) and the 3D crystal structure of JAK-1 protein from Homo sapiens (PDB ID: 3EYG). The results show that the oxo-azo derivatives showed better interactions within the binding site of proteins. We report that compounds 1, 4 and 5 optimal binding features with JAK STAT 3., Competing Interests: The authors declare that there is no conflict of interests., (© 2023 Biomedical Informatics.)

Healthcare, sports, the military, location monitoring and wireless body networks are emerging as technology of major relevance. As a result of the widespread usage of biomedical sensor networks in medical applications, it is essential that data packets containing vital signs be reliably and effectively supplied to the medical center. Because of its mobility, real-time monitoring, cheap cost, and real-time feedback, it may be used in a broad variety of applications. Effective data transport and a limited energy supply are the main challenges in WBAN. Uses genetic heuristics to enhance routing algorithm efficiency. Low-energy adaptive clustering hierarchy (LEACH) and distributed energy efficiency clustering (DEC) are two kinds of clustering algorithms (DEEC). A clustering-based routing protocol may be optimized using this study's optimization approach so that the network's lifetime can be extended.. The cluster heads (CHs) in sensor nodes are picked with the least amount of overhead grading possible. The cluster is being balanced. Passive clustering based on Bioinspired Particle Swarm Optimization (BPSO) should be used for clustering purposes. Routing messages efficiently means sending them quickly and efficiently without using a lot of bandwidth. Using constraints such as distance and residual power, the optimal path for the cluster may be determined with the help of iterative and heuristic chicken swarm optimization (IHCSO) for short. An evaluation of the packet distribution allocation, capacity, and average end-to-end latency illustrates the practicability of the proposed system in research concerning its efficiency. According to the findings of the research, following the technique that was proposed leads to much better outcomes., Competing Interests: Declaration of Competing Interest None., (Copyright © 2022 IPEM. Published by Elsevier Ltd. All rights reserved.)

Bacterial secondary metabolites are a major source of antibiotics and other bioactive compounds. In microbial communities, these molecules can mediate interspecies interactions and responses to environmental change. Despite the importance of secondary metabolites in human health and microbial ecology, little is known about their roles and regulation in the context of multispecies communities. In a simplified model of the rhizosphere composed of Bacillus cereus , Flavobacterium johnsoniae , and Pseudomonas koreensis , we show that the dynamics of secondary metabolism depend on community species composition and interspecies interactions. Comparative metatranscriptomics and metametabolomics reveal that the abundance of transcripts of biosynthetic gene clusters (BGCs) and metabolomic molecular features differ between monocultures or dual cultures and a tripartite community. In both two- and three-member cocultures, P. koreensis modified expression of BGCs for zwittermicin, petrobactin, and other secondary metabolites in B. cereus and F. johnsoniae, whereas the BGC transcriptional response to the community in P. koreensis itself was minimal. Pairwise and tripartite cocultures with P. koreensis displayed unique molecular features that appear to be derivatives of lokisin, suggesting metabolic handoffs between species. Deleting the BGC for koreenceine, another P. koreensis metabolite, altered transcript and metabolite profiles across the community, including substantial up-regulation of the petrobactin and bacillibactin BGCs in B. cereus , suggesting that koreenceine represses siderophore production. Results from this model community show that bacterial BGC expression and chemical output depend on the identity and biosynthetic capacity of coculture partners, suggesting community composition and microbiome interactions may shape the regulation of secondary metabolism in nature.

The improper and excessive growth of brain cells may lead to the formation of a brain tumor. Brain tumors are the major cause of death from cancer. As a direct consequence of this, it is becoming more challenging to identify a treatment that is effective for a specific kind of brain tumor. The brain may be imaged in three dimensions using a standard MRI scan. Its primary function is to examine, identify, diagnose, and classify a variety of neurological conditions. Radiation therapy is employed in the treatment of tumors, and MRI segmentation is used to guide treatment. Because of this, we are able to assess whether or not a piece that was spotted by an MRI is a tumor. Using MRI scans, this study proposes a machine learning and medically assisted multimodal approach to segmenting and classifying brain tumors. MRI pictures contain noise. The geometric mean filter is utilized during picture preprocessing to facilitate the removal of noise. Fuzzy c-means algorithms are responsible for segmenting an image into smaller parts. The identification of a region of interest is facilitated by segmentation. The GLCM Grey-level co-occurrence matrix is utilized in order to carry out the process of dimension reduction. The GLCM algorithm is used to extract features from photographs. The photos are then categorized using various machine learning methods, including SVM, RBF, ANN, and AdaBoost. The performance of the SVM RBF algorithm is superior when it comes to the classification and detection of brain tumors., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 L. Anand et al.)

Breast cancer is the second leading cause of death among women, behind only heart disease. However, despite the high incidence and mortality rates associated with breast cancer, it is still unclear as to what is responsible for its development in the first place. The prevention of breast cancer is not possible with any of the current available methods. Patients who are diagnosed and treated for breast cancer at an early stage have a better chance of having a successful treatment and recovery. In the field of breast cancer detection, digital mammography is widely acknowledged to be a highly effective method of detecting the disease early on. We may be able to improve early detection of breast cancer with the use of image processing techniques, thereby boosting our chances of survival and treatment success. This article discusses a breast cancer image processing and machine learning framework that was developed. The input data set for this framework is a sequence of mammography images, which are used as input data. The CLAHE approach is then utilized to improve the overall quality of the photographs by means of image processing. It is called contrast restricted adaptive histogram equalization (CLAHE), and it is an improvement on the original histogram equalization technique. This aids in the removal of noise from photographs while simultaneously improving picture quality. The segmentation of images is the next step in the framework's development. An image is divided into distinct portions at this point because the pixels are labeled at this step. This assists in the identification of objects and the delineation of boundaries. To categorize these preprocessed images, techniques such as fuzzy SVM, Bayesian classifier, and random forest are employed, among others., Competing Interests: The authors declare that they have no conflict of interest., (Copyright © 2022 Sushovan Chaudhury et al.)

The recent international attention paid to the formalization of the informal economy finds reflection in ILO Recommendation No. 204 concerning the transition from the informal to the formal economy and the Sustainable Development Goals (Target 8.3). There is great diversity within the categories of the informal sector, informal employment, and informal economy in India. This paper examines the category of the 'informal economy' as understood in international instruments as well as in international statistics and maps these onto legal categories recognized within Indian law. The categories of 'employed', 'engaged', and 'work arrangement' used in Indian laws, and their interpretation by the courts, are useful to understand the links between the concepts of work, employment, and livelihoods. The paper also focuses on the diversity of the informal economy, focusing on wage employment, self-employment, including the diverse forms of own-account work and contributing (unpaid) family labour. The categorization of gig and platform workers as own-account or waged workers continues to pose a normative challenge. The regulatory responses for formalization of each segmented category of informal workers and informal enterprises cannot be uniform, and neither do they need to be linked to any particular domain of the law. Moving beyond the extension of social security coverage as the key vehicle for formalization, the paper suggests various entry points through which law and policy can improve conditions of work and protect the livelihood of those in the informal economy as measures to achieve formalization., Competing Interests: Conflict of interestThere is no conflict of interest., (© The Author(s), under exclusive licence to Indian Society of Labour Economics 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Pterocarpus santalinus has huge demand owing to its commercial and medicinal value. However, there are limited research studies on its therapeutic activity against obesity and obesity-induced inflammation and underlying mechanism of action. Therefore, in the present study, chloroform bioactive fraction of P. santalinus (CFP) was isolated and evaluated for its activity against adipogenesis and adipogenesis-induced inflammation in 3T3-L1 cell culture model. LC-MS/MS analysis of CFP was performed to identify the compounds present. CFP-treated 3T3-L1 cells (50, 100 and 200 μg/ml) have significantly ( p  < 0.01 or < 0.05) enhanced glycerol release and adiponectin level, but reduced lipid accumulation and leptin, and MTT assay demonstrated CFP was non-toxic till a dose of 300 µg/ml at 24 and 48 h. A considerable reduction in tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) levels was witnessed in lipopolysaccharide (LPS)-induced 3T3-L1 cells with CFP treatment in dose-dependent manner. Gene expression studies demonstrated down-regulation of mRNA expression of peroxisome proliferator-activated receptor-γ (PPAR-γ), sterol regulatory element-binding protein-1c (SREBP-1c), leptin, TNF-α and IL-6 but up-regulation of adiponectin and uncoupling protein-1 (UCP-1) and the same trend was observed in protein expression also. In conclusion, it is suggested that CFP could be beneficial to treat obesity and associated inflammation., Competing Interests: Conflict of interestThe authors have no conflict of interest to declare., (© King Abdulaziz City for Science and Technology 2021.)

The unique and ubiquitous bacterial lipoprotein biosynthesis pathway is an attractive new antibiotic target. Crystal structures of its three biosynthetic enzymes have been solved recently. The first enzyme, Phosphatidylglycerol:proLipoprotein diacylglyceryl Transferase (Lgt), which initiates the post-translational modification at the metabolic interface of protein biosynthesis, phospholipid biosynthesis, protein secretion and lipid modification was reported to be a seven-transmembrane helical structure with a catalytic periplasmic head. Its complete solubilization in water or mild detergent in a fully active state, its chromatographic behaviour as an active monomer in the absence of detergent and recovery of active whole-length protein after proteolytic treatment of spheroplasts cast serious doubts about its proposed membrane association and orientation. Rather, it could be a seven-helical bundle partially embedded in the inner membrane's inner leaflet aided by hydrophobic interaction. In fact, there are examples where originally reported seven-transmembrane proteins were later shown to be seven-helical peripheral membrane proteins based on solubilization criterion and re-analysis. Validated computational tool, Membrane Optimal Docking Area (MODA), also predicted a weaker association of Lgt's helices with the membrane compared to typical transmembrane proteins. This insight is crucial to Lgt-based antibiotic design.

The human microbiome is a complex ecological system, and describing its structure and function under different environmental conditions is important from both basic scientific and medical perspectives. Viewed through a biostatistical lens, many microbiome analysis goals can be formulated as latent variable modeling problems. However, although probabilistic latent variable models are a cornerstone of modern unsupervised learning, they are rarely applied in the context of microbiome data analysis, in spite of the evolutionary, temporal, and count structure that could be directly incorporated through such models. We explore the application of probabilistic latent variable models to microbiome data, with a focus on Latent Dirichlet allocation, Non-negative matrix factorization, and Dynamic Unigram models. To develop guidelines for when different methods are appropriate, we perform a simulation study. We further illustrate and compare these techniques using the data of Dethlefsen and Relman (2011, Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proceedings of the National Academy of Sciences108, 4554-4561), a study on the effects of antibiotics on bacterial community composition. Code and data for all simulations and case studies are available publicly., (© The Author 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The simultaneous study of multiple measurement types is a frequently encountered problem in practical data analysis. It is especially common in microbiome research, where several sources of data-for example, 16s-rRNA, metagenomic, metabolomic, or transcriptomic data-can be collected on the same physical samples. There has been a proliferation of proposals for analyzing such multitable microbiome data, as is often the case when new data sources become more readily available, facilitating inquiry into new types of scientific questions. However, stepping back from the rush for new methods for multitable analysis in the microbiome literature, it is worthwhile to recognize the broader landscape of multitable methods, as they have been relevant in problem domains ranging across economics, robotics, genomics, chemometrics, and neuroscience. In different contexts, these techniques are called data integration, multi-omic, and multitask methods, for example. Of course, there is no unique optimal algorithm to use across domains-different instances of the multitable problem possess specific structure or variation that are worth incorporating in methodology. Our purpose here is not to develop new algorithms, but rather to 1) distill relevant themes across different analysis approaches and 2) provide concrete workflows for approaching analysis, as a function of ultimate analysis goals and data characteristics (heterogeneity, dimensionality, sparsity). Towards the second goal, we have made code for all analysis and figures available online at https://github.com/krisrs1128/multitable&#95;review.

The image restoration has emerged as a very vital investigation technique in the domain of the image processing. The underlying motive behind the image restoration is devoted to the augmentation of the perceived visual impact of image so as to make it almost identical to the original image. A host of exploration approaches are now in vogues which are intended to steer clear of the noise, thereby regaining the images with original quality. In our earlier research, two distinct noise elimination methods like the (OGHP) and SURE shrinkage were effectively employed for the purpose of denoising, though the relative PSNR and SSIM efficiencies did not come up to the desired level. In the innovative approach envisaged in the document, at the outset, the noise is included by means of two processes like the salt and pepper and impulse noise. Subsequently, the pre-processing methods are performed with the able assistance of two novel filters such as the adaptive median filter and adaptive fuzzy switching. Thereafter, the preprocessed image is furnished to the succeeding function of noise elimination like the (OGHP) and SURE shrinkage. In the course of the OGHP noise elimination technique, the GHP constraints are optimized by employing the Cuckoo Search Algorithm. Thereafter, the noise-eliminated image is effectively estimated with the help of the Discrete Wavelet Transform (DWT). The consequential noiseless images are subjected to the image restoration procedure by efficiently employing the AGA approach. The cheering performance outcomes chant the success stories of the novel image restoration method, highlighting its superlative efficiency. Moreover, the efficacy of the innovative approach is assessed by means of a set of noise-polluted images and contrasted with the modern noiseless image restoration technique.

Serovars of Salmonella enterica are common food-borne bacterial pathogens. Salmonella typhi, which causes typhoid, is the most dangerous of them. Though detailed molecular pathogenesis studies reveal many virulence factors, inability to identify their biochemical functions hampers the development of diagnostic methods and therapeutic leads. Lack of quicker diagnosis is an impediment in starting early antibiotic treatment to reduce the severe morbidity and mortality in typhoid. In this study, employing bioinformatic prediction, biochemical analysis, and recombinantly cloning the active region, we show that extracellularly secreted virulence-associated protein, small intestinal invasion factor E (SiiE), possesses a sulfite oxidase (SO) domain that catalyzes the conversion of sodium sulfite to sodium sulfate using tungsten as the cofactor. This activity common to Salmonella enterica serovars seems to be specific to them from bioinformatic analysis of available bacterial genomes. Along with the ability of this large non-fimbrial adhesin of 600 kDa binding to sialic acid on the host cells, this activity could aid in subverting the host defense mechanism by destroying sulfites released by the immune cells and colonize the host gastrointestinal epithelium. Being an extracellular enzyme, it could be an ideal candidate for developing diagnostics of S. enterica, particularly S. typhi.

The gut microbiome has been linked to host obesity; however, sex-specific associations between microbiome and fat distribution are not well understood. Here we show sex-specific microbiome signatures contributing to obesity despite both sexes having similar gut microbiome characteristics, including overall abundance and diversity. Our comparisons of the taxa associated with the android fat ratio in men and women found that there is no widespread species-level overlap. We did observe overlap between the sexes at the genus and family levels in the gut microbiome, such as Holdemanella and Gemmiger; however, they had opposite correlations with fat distribution in men and women. Our findings support a role for fat distribution in sex-specific relationships with the composition of the microbiome. Our results suggest that studies of the gut microbiome and abdominal obesity-related disease outcomes should account for sex-specific differences.

Background: Mechanical ventilation (MV) causes discomfort but whether it causes pain remains controversial. Around the world neonatal intensive care units (NICU) often utilize narcotics and/or sedatives during MV of vulnerable infants yet the association with adverse neonatal outcomes has not been adequately addressed., Objective: Test for associations between the use of narcotics/sedatives during MV and mortality/morbidity in preterm infants in a large infant cohort in Canada., Design/methods: Preterm infants born <35 weeks gestational age (GA) requiring MV for >24 hrs were identified retrospectively from the Canadian Neonatal Network database, 2010-12. Infants were categorized according to whether they received narcotics/sedatives for greater than 24 hours concurrently with MV. Infants were excluded if moribund on admission, had major congenital anomalies, diagnoses where narcotic administration is routine and suspected seizures. Multivariable logistic and linear regression analysis tested for association of narcotics/sedatives use during MV with mortality/morbidity (nosocomial infections, BPD, ROP, IVH) and length of MV., Results: After exclusions the cohort included 2672 infants; 467(17%) exposed only to narcotics 101(4%) only to sedatives and 299(11%) to both. All models were adjusted for GA, gender, small for GA, SNAP-II score >20, multiple births, delivery mode, outborn, PDA status, MV type, use of high flow, muscle relaxant use, indwelling lines, caffeine and surfactant therapy. The composite mortality/morbidity, and MV days were significantly higher for MV infants exposed to narcotics, sedatives or both compared to infants not exposed., Conclusion: Mounting evidence of the adverse short and long-term impacts of narcotics/sedatives during MV supports the need for further work in alternative therapies.

The conformational analysis of long chain phosphates poses a serious challenge due to the presence of rotationally flexible multiple alkyl groups. Tri- sec-butyl phosphate (TsBP) is an interesting example, in which branching can be expected to influence the conformational landscape. To solve the conformational problem of TsBP systematically, the conformations of model dimethyl- sec-butyl phosphate (DMsBP), a molecule possessing a single secondary butyl strand, were analyzed. On the basis of the analysis of the energy profile of DMsBP, a few conformational bunches were eliminated. The presence of branched methyl group appears to completely influence the conformational space of TsBP and as a result, the number of conformations is drastically reduced in comparison to its structural isomer, tri- n-butyl phosphate (TBP). B3LYP level of theory in association with 6-311++G(d,p) basis set was used for computing all the conformer geometries. Experimentally, the conformations of TsBP were studied using infrared spectroscopy by trapping the molecule in N 2 and Ar matrixes at low temperatures, which were correlated well with the computational results.

Our investigations on extracellular biochemical events to find readily and sensitively detectable/measurable molecular targets for developing easier, simpler, and quicker diagnostic methods and tools for bacterial pathogens led to the observation that bacteria grown in the presence of glucose produced a compound capable of quenching fluorescein. Under the experimental conditions, among various sugars, glucose was found to induce maximum amount of the quencher when Escherichia coli was grown in presence of 50 mM glucose in rarified LB. The release of quencher closely following bacterial growth significantly from fourth hour after moderate inoculation. This fluorescein-quencher was purified using TLC and HPLC and identified using GC-MS as 13-docosenamide or erucamide, originally known as plant lipid, is a neuroactive compound in human and animals. Fluorescence and UV-absorption spectral analysis showed that the compound formed stable adduct with fluorescein in the ground state. Commercial 13-docosonamide enabled quantitation of the compound produced in micromolar quantities during glucose utilization from the medium. Twenty-seven different commonly encountered bacteria, pathogens or otherwise, could produce the quencher. A simple microplate-based growth monitoring method was developed exploiting quenching as an easily and readily measurable signal, either using a reader or an imager. While 13-docosenamide release by bacteria may be relevant in host-bacteria interactions, especially when growing under conditions that provide glucose, the new approach with inexpensive reagents can provide a new antibiogram technique.

In ELISA, a popular analytical diagnostic tool, the stable non-covalent immobilization (coating) of hydrophilic proteins/peptides on to hydrophobic polystyrene surface has remained a major common challenge. Recombinant bacterial lipid modification of proteins in Escherichia coli system has been shown in this study to solve this problem owing to the hydrophobic anchorage provided by three fatty acyl groups in N-acyl-S-diacylglyceryl Cys at the N-terminus. Exploiting this first post-translational protein engineering, the most abundantly expressed white spot syndrome viral protein ICP11 was lipid-modified and tested as a new target in a new ELISA method useful to shrimp farming. The lipid served as a potent adjuvant to enhance the titer (16 times) of higher affinity antibodies where amino terminal lipoamino acid N-acyl-S-diacylglyceryl cysteine of bacterial lipoproteins induce inflammatory responses through TLR and stimulate humoral immune responses without additional adjuvant and also aided in the immobilization of even a few nanograms of ICP11. Competition between the immobilized and the free antigen from the sample provided a sensitive measure of antigen in the infected shrimp tissues. The detection limit for ICP11 protein using competitive ELISA was 250 pg and the linear range of the assay was 15-240 ng.

Uranyl tris nitrato i.e. [UO 2 (NO 3 ) 3 ] - was formed by adding tetramethylammonium nitrate to uranyl nitrate in acetonitrile medium. The luminescence features of this complex in acetonitrile are very sensitive to water content, which could lead to the use of it as a luminescent probe for water present in acetonitrile. The luminescence intensity ratio of 507 to 467 nm peak of uranyl tris nitrato showed a linear response in the range 0-5% (v/v) water content in acetonitrile. The present method was applied for three synthetic samples of acetonitrile for water detection and the results obtained were compared using Karl Fischer titration. There was a good agreement in the values obtained by both the methods., (Copyright © 2018 John Wiley & Sons, Ltd.)

The lytic enzyme, endolysin, is encoded by bacteriophages (phages) to destroy the peptidoglycan layer of host bacterial cells. The release of phage progenies to start the new infection cycle is dependent on the cell lysis event. Endolysin encoded by DLP12 cryptic prophage is a SAR endolysin which is retained by the bacterium presumably due to the benefit it confers. The structure of DLP12 endolysin (Id: 4ZPU) determined at 2.4 Å resolution is presented here. The DLP12 endolysin structure shows a modular nature and is organized into distinct structural regions. One of the monomers has the loops at the active site in a different conformation. This has led to a suggestion of depicting possibly active and inactive state of DLP12 endolysin. Comparison of DLP12 endolysin structure and sequence with those of related endolysins shows the core three-dimensional fold is similar and the catalytic triad geometry is highly conserved despite the sequence differences. Features essential for T4 lysozyme structure and function such as the distance between catalytic groups, salt bridge and presence of nucleophilic water are conserved in DLP12 endolysin and other endolysins analyzed., (© 2017 Wiley Periodicals, Inc.)

We introduce methods for visualization of data structured along trees, especially hierarchically structured collections of time series. To this end, we identify questions that often emerge when working with hierarchical data and provide an R package to simplify their investigation. Our key contribution is the adaptation of the visualization principles of focus-plus-context and linking to the study of tree-structured data. Our motivating application is to the analysis of bacterial time series, where an evolutionary tree relating bacteria is available a priori. However, we have identified common problem types where, if a tree is not directly available, it can be constructed from data and then studied using our techniques. We perform detailed case studies to describe the alternative use cases, interpretations, and utility of the proposed visualization methods.

Study of product diversity by companies and corporations led to the discovery of a new statistical distribution, the (1/f) distribution, assessed according to the Herfindahl-Hirschman index. Products of human creative thinking and decision-making seem to obey the index very widely or universally. This article explores examples of its applications in business and economics as evidence for a new scientific conjecture concerning the physiological basis of Mind: that mind functions from critical instabilities in the brain physiology. Instabilities result in (1/f) distributions of physiological responses, so the two sources of (1/f) distribution present interesting parallels. Without comprehensively reviewing all Herfindahl-Hirschman index applications, many similar examples of (1/f) distributions are considered: those in numbers of divisions and subdivisions within big corporations, and numbers of corporations in industrial sectors. Evidently, this kind of distribution derives from individual and collective creative work. The paper reasons that it reveals important evidence about the conjectured basis of mind and its creative capacities. A previous Special Issue paper showed that many of the mind's properties, including self-awareness, result from critical instabilities being the loci of control of biological systems. A consequence of such instability is that when faced by several identical stimuli, the physiology offers a (1/f) distribution of responses. Studies described here showing that creative thinking produces (1/f) distributions of results may thus be considered a first round of direct evidence for the mind functioning from critical instability. Physical systems producing (1/f) distributions do not have that implication. Other cybernetic systems may behave differently., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Spectroscopy under isolated conditions at low temperatures is an excellent tool to characterize the aggregates stabilized through weak interactions. Within the framework of weak interactions, the π-stacking interactions are considered unconventional with the limited experimental proofs, wherein the bonding associates are either aromatic and heterocyclic compounds or their combinations. Besides aromatic compounds, π-stacking networks can even be realized with molecules possessing electron rich π-clouds. In this work, the N 2 molecule as a possible π-bonding partner is explored for the first time in which hetero π-stacking was achieved between pyrrole and N 2 precursors. The matrix isolation experiments performed by seeding pyrrole and N 2 mixtures in an Ar matrix at low temperatures with subsequent infrared spectral characterization revealed the generation of adducts stabilized through a π(pyrrole)···π(N 2 ) interaction. Under identical conditions with the likelihood of two competing π-stacking and hydrogen-bonding interactions in pyrrole-N 2 associates, π-stacking dominates energetically over hydrogen-bonding interaction.