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Understanding Built-up evolution and complexity over time is attained to exercise sustainable development and smart implementations in the future. The Spatiotemporal evolution of built-up analysis is carried out using the centroid shift model considering the geometry of the built-up from 1975 to 2019 (1975, 1990, 1999, 2009, and 2019). The centroid shift indicates the trajectory of the growth pattern and the future direction of growth. The study area experienced a unidirectional growth pattern till 2009, considering 1975 as the base year, and by 2019 the growth direction shifted by 3.52 degrees, and the overall centroid shift magnitude was 2.42 km2. The non-Euclidean geometric nature of the physical world puts forward fractal geometry to understand the complexity of built-up. The mean fractal dimension index shows the growth type in the study area; a directional approach towards the mean fractal dimension provides a critical understanding of the complexity around the built-up Centroid. Among all the eight directions considered, NW_W showed maximum built-up growth with infilling growth type, and the E_SE direction showed a significant jump in an area with external expansion by 2019. This direction indicates the future growth direction. The study concludes that complexity analysis critically enhances the spatiotemporal dynamics of built-up evolution.

Most of the small-molecule drugs approved for the treatment of cancer over the past 40 years are based on natural compounds. Bacteria provide an extensive reservoir for the development of further anti-cancer therapeutics to meet the challenges posed by the diversity of these malignant diseases. While identifying cytotoxic compounds is often easy, achieving selective targeting of cancer cells is challenging. Here we describe a novel experimental approach (the Pioneer platform) for the identification and development of 'pioneering' bacterial variants that either show or are conduced to exhibit selective contact-independent anti-cancer cytotoxic activities. We engineered human cancer cells to secrete Colicin M that repress the growth of the bacterium Escherichia coli, while immortalised non-transformed cells were engineered to express Chloramphenicol Acetyltransferase capable of relieving the bacteriostatic effect of Chloramphenicol. Through co-culturing of E. coli with these two engineered human cell lines, we show bacterial outgrowth of DH5α E. coli is constrained by the combination of negative and positive selection pressures. This result supports the potential for this approach to screen or adaptively evolve 'pioneering' bacterial variants that can selectively eliminate the cancer cell population. Overall, the Pioneer platform demonstrates potential utility for drug discovery through multi-partner experimental evolution.

Age-associated B cells (ABC) accumulate with age and in individuals with different immunological disorders, including cancer patients treated with immune checkpoint blockade and those with inborn errors of immunity. Here, we investigate whether ABCs from different conditions are similar and how they impact the longitudinal level of the COVID-19 vaccine response. Single-cell RNA sequencing indicates that ABCs with distinct aetiologies have common transcriptional profiles and can be categorised according to their expression of immune genes, such as the autoimmune regulator (AIRE). Furthermore, higher baseline ABC frequency correlates with decreased levels of antigen-specific memory B cells and reduced neutralising capacity against SARS-CoV-2. ABCs express high levels of the inhibitory FcγRIIB receptor and are distinctive in their ability to bind immune complexes, which could contribute to diminish vaccine responses either directly, or indirectly via enhanced clearance of immune complexed-antigen. Expansion of ABCs may, therefore, serve as a biomarker identifying individuals at risk of suboptimal responses to vaccination.

In the current investigation we formulated and evaluates matrix tablet using modified gum of Pistacia lantiscus gum (PLMG) as natural matrix forming agent used in various successively increasing concentration. The pre-compression study of the powder blends of drug, PLMG and other excipients were done by calculating bulk density, tapped density, angle of repose and carr’s index (% compressibility) and hausner’s ratio. The tablets using PLMG as matrix forming agent were prepared by direct compression method and prepared tablets were evaluated for thickness, hardness, weight uniformity, friability and content uniformity and were found according to the official guidelines by pharmacopoeia. The swelling behavior of prepared matrix tablets was studied using for 12 hours at 37 ±0.2 0C, it was fond that the drug to modified gum ratio of 1:2 was found to be optimum swelling with the sustained release of model drug up to 97.12 in matrix tablet formulation. The results revealed, that modified fraction of Pistacia lantiscus gum can be used as a drug release modifier to delay as the rate of drug release of which depended on the amount of gum composition, as the concentration of gum was increased there was sustain the drug release. Keywords: In the current investigation we formulated and evaluates matrix tablet using modified gum of Pistacia lantiscus gum (PLMG) as natural matrix forming agent used in various successively increasing concentration. The pre-compression study of the powder blends of drug, PLMG and other excipients were done by calculating bulk density, tapped density, angle of repose and carr’s index (% compressibility) and hausner’s ratio. The tablets using PLMG as matrix forming agent were prepared by direct compression method and prepared tablets were evaluated for thickness, hardness, weight uniformity, friability and content uniformity and were found according to the official guidelines by pharmacopoeia. The swelling behavior of prepared matrix tablets was studied using for 12 hours at 37 ±0.2 0C, it was fond that the drug to modified gum ratio of 1:2 was found to be optimum swelling with the sustained release of model drug up to 97.12 in matrix tablet formulation. The results revealed, that modified fraction of Pistacia lantiscus gum can be used as a drug release modifier to delay as the rate of drug release of which depended on the amount of gum composition, as the concentration of gum was increased there was sustain the drug release. Keywords: Pistacia lantiscus, tablet, modified gum, diclofenac potassium, hausner’s ratio. &nbsp

How cellular metabolic state impacts cellular programs is a fundamental, unresolved question. Here we investigated how glycolytic flux impacts embryonic development, using presomitic mesoderm (PSM) patterning as the experimental model. First, we identified fructose 1,6-bisphosphate (FBP) as an in vivo sentinel metabolite that mirrors glycolytic flux within PSM cells of post-implantation mouse embryos. We found that medium-supplementation with FBP, but not with other glycolytic metabolites, such as fructose 6-phosphate and 3-phosphoglycerate, impaired mesoderm segmentation. To genetically manipulate glycolytic flux and FBP levels, we generated a mouse model enabling the conditional overexpression of dominant active, cytoplasmic Pfkfb3 (cytoPfkfb3). Overexpression of cytoPfkfb3 indeed led to increased glycolytic flux/FBP levels and caused an impairment of mesoderm segmentation, paralleled by the downregulation of Wnt-signaling, reminiscent of the effects seen upon FBP-supplementation. To probe for mechanisms underlying glycolytic flux-signaling, we performed subcellular proteome analysis and revealed that cytoPfkfb3 overexpression altered subcellular localization of certain proteins, including glycolytic enzymes, in PSM cells. Specifically, we revealed that FBP supplementation caused depletion of Pfkl and Aldoa from the nuclear-soluble fraction. Combined, we propose that FBP functions as a flux-signaling metabolite connecting glycolysis and PSM patterning, potentially through modulating subcellular protein localization.

The combination of biopolymers with bioceramics plays vital role in development of artificial bone. Hydroxyapatite is extensively used as a material in prosthetic bone repair and replacement. In this paper synthesis of Hydroxyapatite- Polymethyl methacrylate – Zirconia (Hap-PMMA-ZrO2) composite by using powder metallurgy technique. The mechanical, morphological, In-vitro biocompatibility and tribological properties were characterized by universal testing machine, micro-vickers hardness tester, high resolution transmission electron microscope (HR-TEM), MTT assay and pin-on-disc setup. In-vitro cytotoxicity test on HeLa cell lines shows cell viability constant when doses concentration increases so material found non-toxic. Results show that micro Vickers hardness i.e. 520 approximately matches with natural human bone i.e. 400. Compressive strength is less as compared to human bone because of powder metallurgy route used for fabrication and is 74 MPa. Density of proposed composite artificial human bone i.e. 1.52 g/cc is less as compared to natural bone i.e. 2.90 g/cc. The Hap-PMMA-ZrO2 composite will be good biomaterials for bone repair and replacement work

Background: Direct laryngoscopy with Macintosh laryngoscope (ML) is the gold standard technique for tracheal intubation. However, visualization of the larynx and subsequent tracheal intubation may not be possible in difcult airways. TruView video laryngoscope (TVL) can provide good visualization of the glottis and visual conrmation of tracheal intubation in a difcult airway scenario. This manikin study was carried out to compare the skill acquisition for videolaryngoscopy using TVL and compare it with direct laryngoscopy using ML, in easy and difcult airway scenarios, in TVL novice participants. Material and methods: Thirty resident doctors were randomized to participate in a crossover, manikin study. Participants were asked to intubate a manikin in easy and difcult airway scenarios using TruView and Macintosh laryngoscope. Two trials were conducted at one-week interval and at the end of each trial parameters of Intubation time, Cormack-Lehane (CL) grading, success of intubation and ease of intubation which reect skill acquisition were recorded. All 30 participants completed the study. Results: In the easy scenario most results were comparable between the two devices. The TVL scored over ML in all aspects of difcult intubation with signicantly lesser time for intubation (23.27 ± 1.8sec, P

Pesticides have contributed significantly in increasing agricultural production and the farmers’ income globally. Responsible application of pesticides play a key role in agricultural input and help to protect seeds and safeguard crops from unwanted plants, insects, bacteria, fungi and rodents. Also, pesticides can have negative environmental impacts through contamination of soil, water and non-target plants and animals that can decrease biodiversity and, in some cases, reduce crop yield. Globally, total pesticides use in agriculture remained stable in 2020, at 2.7 million tonnes (Mt) of active ingredients. The worldwide application of pesticides per area of cropland was 1.8 kg/ha. On a per capita and per value of agricultural production basis, pesticides application was 0.69 kg/1000 international dollars and 0.37 kg/person, respectively. Total pesticides trade reached approximately 7.2 Mt of formulated products in 2020, with a value of USD 41.1 billion. Pesticides have contaminated every ecosystem on the earth since pesticide residues have been reported in water, air, soil, crops and non-target plants and animals. They can also be toxic to other organisms, including birds, fish, beneficial insects etc. The chemical residues of pesticides have impacted human health through the environment and food contamination. This review provides scientific information on pesticides about global scenario of production and utilization, present status in India, benefits from usage and use and application considerations. This study recommends use of bio-pesticides by the farmers to achieve more yield from agriculture with minimum effects on ecosystem. This review paper creates awareness among students, general public and scientific community for sustainable management practices for the use of pesticides to safeguard the natural resources and human health.

Type 1 and Type 2 diabetes are distinct genetic diseases of the pancreas which are defined by the abnormal level of blood glucose. Understanding the initial molecular perturbations that occur during the pathogenesis of diabetes is of critical importance in understanding these disorders. The inability to biopsy the human pancreas of living donors hampers insights into early detection, as the majority of diabetes studies have been performed on peripheral leukocytes from the blood, which is not the site of pathogenesis. Therefore, efforts have been made by various teams including the Human Pancreas Analysis Program (HPAP) to collect pancreatic tissues from deceased organ donors with different clinical phenotypes. HPAP is designed to define the molecular pathogenesis of islet dysfunction by generating detailed datasets of functional, cellular, and molecular information in pancreatic tissues of clinically well-defined organ donors with Type 1 and Type 2 diabetes. Moreover, data generated by HPAP continously become available through a centralized database, PANC-DB, thus enabling the diabetes research community to access these multi-dimensional data prepublication. Here, we present the computational workflow for single-cell RNA-seq data analysis of 258,379 high-quality cells from the pancreatic islets of 67 human donors generated by HPAP, the largest existing scRNA-seq dataset of human pancreatic tissues. We report various computational steps including preprocessing, doublet removal, clustering and cell type annotation across single-cell RNA-seq data from islets of four distintct classes of organ donors, i.e. non-diabetic control, autoantibody positive but normoglycemic, Type 1 diabetic, and Type 2 diabetic individuals. Moreover, we present an interactive tool, called CellxGene developed by the Chan Zuckerberg initiative, to navigate these high-dimensional datasets. Our data and interactive tools provide a reliable reference for singlecell pancreatic islet biology studies, especially diabetes-related conditions.

Existing neuroimaging studies have reported divergent structural alterations in insomnia disorder (ID). In the present study, we performed a large-scale coordinated meta-analysis by pooling structural brain measures from 1085 subjects (mean [SD] age 50.5 [13.9] years, 50.2% female, 17.4% with insomnia) across three international Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA)-Sleep cohorts. Two sites recruited patients with ID/controls: Freiburg (University of Freiburg Medical Center, Freiburg, Germany) 42/43 and KUMS (Kermanshah University of Medical Sciences, Kermanshah, Iran) 42/49, while the Study of Health in Pomerania (SHIP-Trend, University Medicine Greifswald, Greifswald, Germany) recruited population-based individuals with/without insomnia symptoms 75/662. The influence of insomnia on magnetic resonance imaging-based brain morphometry using an insomnia brain score was then assessed. Within each cohort, we used an ordinary least-squares linear regression to investigate the link between the individual regional cortical and subcortical volumes and the presence of insomnia symptoms. Then, we performed a fixed-effects meta-analysis across cohorts based on the first-level results. For the insomnia brain score, weighted logistic ridge regression was performed on one sample (Freiburg), which separated patients with ID from controls to train a model based on the segmentation measurements. Afterward, the insomnia brain scores were validated using the other two samples. The model was used to predict the log-odds of the subjects with insomnia given individual insomnia-related brain atrophy. After adjusting for multiple comparisons, we did not detect any significant associations between insomnia symptoms and cortical or subcortical volumes, nor could we identify a global insomnia-related brain atrophy pattern. Thus, we observed inconsistent brain morphology differences between individuals with and without insomnia across three independent cohorts. Further large-scale cross-sectional/longitudinal studies using both structural and functional neuroimaging are warranted to decipher the neurobiology of insomnia.

Communications biology 6(1), 705 (2023). doi:10.1038/s42003-023-05073-w, Functional connectivity (FC) refers to the statistical dependencies between activity of distinct brain areas. To study temporal fluctuations in FC within the duration of a functional magnetic resonance imaging (fMRI) scanning session, researchers have proposed the computation of an edge time series (ETS) and their derivatives. Evidence suggests that FC is driven by a few time points of high-amplitude co-fluctuation (HACF) in the ETS, which may also contribute disproportionately to interindividual differences. However, it remains unclear to what degree different time points actually contribute to brain-behaviour associations. Here, we systematically evaluate this question by assessing the predictive utility of FC estimates at different levels of co-fluctuation using machine learning (ML) approaches. We demonstrate that time points of lower and intermediate co-fluctuation levels provide overall highest subject specificity as well as highest predictive capacity of individual-level phenotypes., Published by Springer Nature, London

NeuroImage 273, 120083 - (2023). doi:10.1016/j.neuroimage.2023.120083, Naturalistic viewing (NV) is currently considered a promising paradigm for studying individual differences in functional brain organization. While whole brain functional connectivity (FC) under NV has been relatively well characterized, so far little work has been done on a network level.Here, we extend current knowledge by characterizing the influence of NV on FC in fourteen meta-analytically derived brain networks considering three different movie stimuli in comparison to resting-state (RS). We show that NV increases identifiability of individuals over RS based on functional connectivity in certain, but not all networks. Furthermore, movie stimuli including a narrative appear more distinct from RS. In addition, we assess individual variability in network FC by comparing within- and between-subject similarity during NV and RS. We show that NV can evoke individually distinct NFC patterns by increasing inter-subject variability while retaining within-subject similarity. Crucially, our results highlight that this effect is not observable across all networks, but rather dependent on the network-stimulus combination. Our results confirm that NV can improve the detection of individual differences over RS and underline the importance of selecting the appropriate combination of movie and cognitive network for the research question at hand., Published by Academic Press, Orlando, Fla.

NeuroImage 270, 119947 - (2023). doi:10.1016/j.neuroimage.2023.119947, The difference between age predicted using anatomical brain scans and chronological age, i.e., the brain-age delta, provides a proxy for atypical aging. Various data representations and machine learning (ML) algorithms have been used for brain-age estimation. However, how these choices compare on performance criteria important for real-world applications, such as; (1) within-dataset accuracy, (2) cross-dataset generalization, (3) test-retest reliability, and (4) longitudinal consistency, remains uncharacterized. We evaluated 128 workflows consisting of 16 feature representations derived from gray matter (GM) images and eight ML algorithms with diverse inductive biases. Using four large neuroimaging databases covering the adult lifespan (total N = 2953, 18–88 years), we followed a systematic model selection procedure by sequentially applying stringent criteria. The 128 workflows showed a within-dataset mean absolute error (MAE) between 4.73–8.38 years, from which 32 broadly sampled workflows showed a cross-dataset MAE between 5.23–8.98 years. The test-retest reliability and longitudinal consistency of the top 10 workflows were comparable. The choice of feature representation and the ML algorithm both affected the performance. Specifically, voxel-wise feature spaces (smoothed and resampled), with and without principal components analysis, with non-linear and kernel-based ML algorithms performed well. Strikingly, the correlation of brain-age delta with behavioral measures disagreed between within-dataset and cross-dataset predictions. Application of the best-performing workflow on the ADNI sample showed a significantly higher brain-age delta in Alzheimer's and mild cognitive impairment patients compared to healthy controls. However, in the presence of age bias, the delta estimates in the patients varied depending on the sample used for bias correction. Taken together, brain-age shows promise, but further evaluation and improvements are needed for its real-world application., Published by Academic Press, Orlando, Fla.

Objectives: In the era of globalization, a sedentary lifestyle is highly linked with obesity and neurobehavioral complications such as depression. While depression is associated with dopamine dysfunction in the ventral tegmental area (VTA), ghrelin enhances the dopaminergic activity in the VTA. Therefore, the present study aimed to assess the effect of ghrelin on depression-like behaviour in rats subjected to a high-fat diet (HFD) and disturbed diurnal rhythm (DDR) for 45 days. Methods: The neurobehavioral deficits resulting from HFD and DDR in rats, and the behaviour modulation by intra-VTA administration of ghrelin, alone or in combination with ghrelin receptor antagonist were confirmed by evaluation of behavioural parameters in the elevated plus-maze, forced swim test, open field test, and rotarod assessment. Further, the levels of pro-inflammatory cytokines such as tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β) and IL-6, oxidative stress marker malondialdehyde (MDA), and antioxidants enzymes like superoxide dismutase (SOD), reduced glutathione (GSH), and catalase (CAT) were measured. Results: The levels of TNF-α, IL-1β, IL-6, and MDA were increased in the brain of HFD and DDR exposed rats, while that of SOD, GSH, and CAT were reduced. Intra-VTA ghrelin administration from day 41-45 to the HFD and DDR exposed rats improved cognitive behaviour and physical activity confirming the antidepressant effect. Moreover, ghrelin restored the levels of SOD, GSH and CAT efficiently, and reduced that of MDA, TNF-α, IL-1β and IL-6, which signifies its protective effect. Conclusion: Overall, this study confirmed the ameliorative effect of ghrelin in HFD- and DDR-induced depression-like behaviour.

Publisher Copyright: © 2022 The Authors. Published under the terms of the CC BY 4.0 license. Adaptive evolution under controlled laboratory conditions has been highly effective in selecting organisms with beneficial phenotypes such as stress tolerance. The evolution route is particularly attractive when the organisms are either difficult to engineer or the genetic basis of the phenotype is complex. However, many desired traits, like metabolite secretion, have been inaccessible to adaptive selection due to their trade-off with cell growth. Here, we utilize genome-scale metabolic models to design nutrient environments for selecting lineages with enhanced metabolite secretion. To overcome the growth-secretion trade-off, we identify environments wherein growth becomes correlated with a secondary trait termed tacking trait. The latter is selected to be coupled with the desired trait in the application environment where the trait manifestation is required. Thus, adaptive evolution in the model-designed selection environment and subsequent return to the application environment is predicted to enhance the desired trait. We experimentally validate this strategy by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds, and confirm the predicted flux-rerouting using genomic, transcriptomic, and proteomic analyses. Overall, model-designed selection environments open new opportunities for predictive evolution.

Bernstein Conference 2022 abstract. http://bernstein-conference.de

Machine learning (ML) methods are increasingly being used to predict pathologies and biological traits using neuroimaging data. Here controlling for confounds is essential to get unbiased estimates of generalization performance and to identify the features driving predictions. However, a systematic evaluation of the advantages and disadvantages of available alternatives is lacking. This makes it difficult to compare results across studies and to build deployment quality models. Here, we evaluated two commonly used confound removal schemes–whole data confound regression (WDCR) and cross-validated confound regression (CVCR)–to understand their effectiveness and biases induced in generalization performance estimation. Additionally, we study the interaction of the confound removal schemes with Z-score normalization, a common practice in ML modelling. We applied eight combinations of confound removal schemes and normalization (pipelines) to decode sex from resting-state functional MRI (rfMRI) data while controlling for two confounds, brain size and age. We show that both schemes effectively remove linear univariate and multivariate confounding effects resulting in reduced model performance with CVCR providing better generalization estimates, i.e., closer to out-of-sample performance than WDCR. We found no effect of normalizing before or after confound removal. In the presence of dataset and confound shift, four tested confound removal procedures yielded mixed results, raising new questions. We conclude that CVCR is a better method to control for confounding effects in neuroimaging studies. We believe that our in-depth analyses shed light on choices associated with confound removal and hope that it generates more interest in this problem instrumental to numerous applications.

Alternative energy sources are becoming increasingly popular in today's environmentally concerned society. As a back-up plan in case of a scarcity of traditional power sources, solar energy output in India is increasing significantly (coal, oil, gas etc.). For example, solar panels may create and convert solar energy into electrical energy that can subsequently be stored in batteries for usage in many home and business applications. Design modifications for the SBCS or Solar Battery Charging Station are being discussed in this project. The goal of this initiative is to provide low-cost and environmentally friendly power to urban and rural communities, while also reducing the strain on the national grid. Electric rickshaws, which may be operated by the batteries charged throughout the process, would be beneficial for elderly or physically challenged rickshaw drivers. Two 200W solar panels will be used to charge 48-volt battery sets at various times of the day in the execution of the project. In addition, if the solar irradiance is too low to charge the batteries within a specific time constraint, the station will switch to backup power to charge the batteries automatically. Detailed explanations of both solar panel and national grid charging are provided in this document.

Cholecystitis can be a serious complication following endoscopic retrograde cholangiopancreatography (ERCP). Though the concept of early post-ERCP cholecystitis (PEC) is well known, studies that analysed the predictors, occurrence and outcomes of early PEC are rare. This is an analysis of 11 cases developing severe early post-ERCP cholecystitis.

Deterioration in working memory capacity (WMC) has been associated with normal aging, but it remains unknown how age affects the relationship between WMC and connectivity within functional brain networks. We therefore examined the predictability of WMC from fMRI-based resting-state functional connectivity (RSFC) within eight meta-analytically defined functional brain networks and the connectome in young and old adults using relevance vector machine in a robust cross-validation scheme. Particular brain networks have been associated with mental functions linked to WMC to a varying degree and are associated with age-related differences in performance. Comparing prediction performance between the young and old sample revealed age-specific effects: In young adults, we found a general unpredictability of WMC from RSFC in networks subserving WM, cognitive action control, vigilant attention, theory-of-mind cognition, and semantic memory, whereas in older adults each network significantly predicted WMC. Moreover, both WM-related and WM-unrelated networks were differently predictive in older adults with low versus high WMC. These results indicate that the within-network functional coupling during task-free states is specifically related to individual task performance in advanced age, suggesting neural-level reorganization. In particular, our findings support the notion of a decreased segregation of functional brain networks, deterioration of network integrity within different networks and/or compensation by reorganization as factors driving associations between individual WMC and within-network RSFC in older adults. Thus, using multivariate pattern regression provided novel insights into age-related brain reorganization by linking cognitive capacity to brain network integrity. (DIPF/Orig.)

Full list of authors: Price-Whelan, Adrian M.; Lim, Pey Lian; Earl, Nicholas; Starkman, Nathaniel; Bradley, Larry; Shupe, David L.; Patil, Aarya A.; Corrales, Lia; Brasseur, C. E.; Noethe, Maximilian; Donath, Axel; Tollerud, Erik; Morris, Brett M.; Ginsburg, Adam; Vaher, Eero; Weaver, Benjamin A.; Tocknell, James; Jamieson, William; van Kerkwijk, Marten H.; Robitaille, Thomas P.; Merry, Bruce; Bachetti, Matteo; Gunther, H. Moritz; Aldcroft, Thomas L.; Alvarado-Montes, Jaime A.; Archibald, Anne M.; Bodi, Attila; Bapat, Shreyas; Barentsen, Geert; Bazan, Juanjo; Biswas, Manish; Boquien, Mederic; Burke, D. J.; Cara, Daria; Cara, Mihai; Conroy, Kyle E.; Conseil, Simon; Craig, Matthew W.; Cross, Robert M.; Cruz, Kelle L.; D'Eugenio, Francesco; Dencheva, Nadia; Devillepoix, Hadrien A. R.; Dietrich, Jorg P.; Eigenbrot, Arthur Davis; Erben, Thomas; Ferreira, Leonardo; Foreman-Mackey, Daniel; Fox, Ryan; Freij, Nabil; Garg, Suyog; Geda, Robel; Glattly, Lauren; Gondhalekar, Yash; Gordon, Karl D.; Grant, David; Greenfield, Perry; Groener, Austen M.; Guest, Steve; Gurovich, Sebastian; Handberg, Rasmus; Hart, Akeem; Hatfield-Dodds, Zac; Homeier, Derek; Hosseinzadeh, Griffin; Jenness, Tim; Jones, Craig K.; Joseph, Prajwel; Kalmbach, J. Bryce; Karamehmetoglu, Emir; Kaluszynski, Mikolaj; Kelley, Michael S. P.; Kern, Nicholas; Kerzendorf, Wolfgang E.; Koch, Eric W.; Kulumani, Shankar; Lee, Antony; Ly, Chun; Ma, Zhiyuan; MacBride, Conor; Maljaars, Jakob M.; Muna, Demitri; Murphy, N. A.; Norman, Henrik; O'Steen, Richard; Oman, Kyle A.; Pacifici, Camilla; Pascual, Sergio; Pascual-Granado, J.; Patil, Rohit R.; Perren, Gabriel, I; Pickering, Timothy E.; Rastogi, Tanuj; Roulston, Benjamin R.; Ryan, Daniel F.; Rykoff, Eli S.; Sabater, Jose; Sakurikar, Parikshit; Salgado, Jesus; Sanghi, Aniket; Saunders, Nicholas; Savchenko, Volodymyr; Schwardt, Ludwig; Seifert-Eckert, Michael; Shih, Albert Y.; Jain, Anany Shrey; Shukla, Gyanendra; Sick, Jonathan; Simpson, Chris; Singanamalla, Sudheesh; Singer, Leo P.; Singhal, Jaladh; Sinha, Manodeep; Sipocz, Brigitta M.; Spitler, Lee R.; Stansby, David; Streicher, Ole; Sumak, Jani; Swinbank, John D.; Taranu, Dan S.; Tewary, Nikita; Tremblay, Grant R.; De Val-Borro, Miguel; Vasovic, Zlatan; Van Kooten, Samuel J.; Verma, Shresth; Cardoso, Jose Vinicius de Miranda; Williams, Peter K. G.; Wilson, Tom J.; Winkel, Benjamin; Wood-Vasey, W. M.; Xue, Rui; Yoachim, Peter; Zhang, Chen; Zonca, Andrea; Astropy Project Contributors; TARDIS Collaboration; Astropy Coordination Comm.--This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited., The Astropy Project supports and fosters the development of open-source and openly developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package astropy, which serves as the foundation for more specialized projects and packages. In this article, we summarize key features in the core package as of the recent major release, version 5.0, and provide major updates on the Project. We then discuss supporting a broader ecosystem of interoperable packages, including connections with several astronomical observatories and missions. We also revisit the future outlook of the Astropy Project and the current status of Learn Astropy. We conclude by raising and discussing the current and future challenges facing the Project. © 2022. The Author(s). Published by the American Astronomical Society., We acknowledge the Gordon and Betty Moore Foundation for their continued financial support. This work is partially supported by NASA under grant No. 80NSSC22K0347 issued through the NASA ROSES program. This work is partially supported by the international Gemini Observatory, a program of NSF’s NOIRLab, which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation, on behalf of the Gemini partnership of Argentina, Brazil, Canada, Chile, the Republic of Korea, and the United States of America. We also thank NumFOCUS and the Python Software Foundation for financial support. J.A.A.-M. acknowledges funding support from Macquarie University through the International Macquarie University Research Excellence Scholarship (“iMQRES”). A.B. was supported by the Lendület Program of the Hungarian Academy of Sciences, project No. LP2018-7, and the KKP-137523 “SeismoLab” Élvonal grant of the Hungarian Research, Development and Innovation Office (NKFIH). M.B. gratefully acknowledges support from the ANID BASAL project FB210003 and the FONDECYT regular grant 1211000. F.D.E. acknowledges funding through the H2020 ERC Consolidator grant 683184, the ERC Advanced grant 695671 “QUENCH” and support from the Science and Technology Facilities Council (STFC). S.G. made contributions to Astropy as part of the Google Summer of Code 2021. S.G. acknowledges some intellectual support from the CCAD-UNC-NOVA. Funding for the Stellar Astrophysics Centre is provided by The Danish National Research Foundation (grant agreement No.: DNRF106). N.K. acknowledges support from the MIT Pappalardo fellowship. K.A.O. acknowledges support from the European Research Council (ERC) through Advanced Investigator grant to C.S. Frenk, DMIDAS (GA 786910). C.P. is supported by the Canadian Space Agency under a contract with NRC Herzberg Astronomy and Astrophysics. S.P. has been supported by Spanish MINECO-FEDER grant RTI2018-096188-B-I00 J.P.G. acknowledges funding support from Spanish public funds for research from project PID2019-107061GB-C63 from the “Programas Estatales de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i y de I+D+i Orientada a los Retos de la Sociedad,” as well as from the State Agency for Research of the Spanish MCIU through the “Center of Excellence Severo Ochoa” award to the Instituto de Astrofísica de Andalucía (SEV-2017-0709). N.S. acknowledges support from the National Science Foundation through the Graduate Research Fellowship Program under grant 1842402. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Supported by the international Gemini Observatory, a program of NSF’s NOIRLab, which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation, on behalf of the Gemini Observatory partnership: the National Science Foundation (United States), National Research Council (Canada), Agencia Nacional de Investigación y Desarrollo (Chile), Ministerio de Ciencia, Tecnología e Innovación (Argentina), Ministério da Ciência, Tecnologia, Inovações e Comunicações (Brazil), and Korea Astronomy and Space Science Institute (Republic of Korea). Parts of this research were supported by the Australian Research Council Centre of Excellence for All-Sky Astro-physics in 3 Dimensions (ASTRO 3D), through project number CE170100013. D.S. is supported by STFC grant ST/S000240/1. N.S. acknowledges the support of the Science and Engineering Research Council of Canada (NSERC) Canadian Graduate Scholarship—Doctoral Program, [funding reference numbers CGSD547212020].

Machine learning (ML) approaches to data analysis are now widely adopted in many fields including epidemiology and medicine. To apply these approaches, confounds must first be removed as is commonly done by featurewise removal of their variance by linear regression before applying ML. Here, we show this common approach to confound removal biases ML models, leading to misleading results. Specifically, this common deconfounding approach can leak information such that what are null or moderate effects become amplified to near-perfect prediction when nonlinear ML approaches are subsequently applied. We identify and evaluate possible mechanisms for such confound-leakage and provide practical guidance to mitigate its negative impact. We demonstrate the real-world importance of confound-leakage by analyzing a clinical dataset where accuracy is overestimated for predicting attention deficit hyperactivity disorder (ADHD) with depression as a confound. Our results have wide-reaching implications for implementation and deployment of ML workflows and beg caution against na\"ive use of standard confound removal approaches., Comment: Revised Introduction, added CoI, results unchanged

Inferring linear relationships lies at the heart of many empirical investigations. A measure of linear dependence should correctly evaluate the strength of the relationship as well as qualify whether it is meaningful for the population. Pearson's correlation coefficient (PCC), the \textit{de-facto} measure for bivariate relationships, is known to lack in both regards. The estimated strength $r$ maybe wrong due to limited sample size, and nonnormality of data. In the context of statistical significance testing, erroneous interpretation of a $p$-value as posterior probability leads to Type I errors -- a general issue with significance testing that extends to PCC. Such errors are exacerbated when testing multiple hypotheses simultaneously. To tackle these issues, we propose a machine-learning-based predictive data calibration method which essentially conditions the data samples on the expected linear relationship. Calculating PCC using calibrated data yields a calibrated $p$-value that can be interpreted as posterior probability together with a calibrated $r$ estimate, a desired outcome not provided by other methods. Furthermore, the ensuing independent interpretation of each test might eliminate the need for multiple testing correction. We provide empirical evidence favouring the proposed method using several simulations and application to real-world data.

Competing magnetic interactions in low-dimensional quantum magnets can lead to the exotic ground state with fractionalized excitations. Herein, we present our results on an S = 5/2 quasi-one-dimensional spin system Bi3FeMo2O12. The structure of Bi3FeMo2O12 consists of very well separated, infinite zig-zag S = 5/2 spin chains. The observation of a broad maximum around 10 K in the magnetic susceptibility suggests the presence of short-range spin correlations. Magnetic susceptibility data do not fit to S=5/2 uniform spin chain model due to the presence of 2nd nearest-neighbor coupling (J2) along with the 1st nearest-neighbor coupling J1 of the zig-zag chain. The electronic structure calculations infer that the value of J1 is comparable with J2 (J2/J1~1.1) with a negligible inter-chain interaction (J'/J ~ 0.01), implying that Bi3FeMo2O12 is a highly frustrated triangular chain system. The absence of magnetic long-range ordering down to 0.2 K is seen in the heat capacity data, despite a relatively large antiferromagnetic Curie-Weiss temperature of -40 K. The magnetic heat capacity follows nearly a linear behavior at low temperatures indicating that the S = 5/2 anisotropic triangular chain exhibits the gapless excitations., 7 pages, 6 figures, 1 table

Bhise, Mahendra R., Patil, Chandrahas R., Salunkhe, Chandrakant B., Kambhar, Sidanand V. (2021): New species of Asterina and Balladyna (black mildew fungi) from Mahabaleshwar, Maharashtra, India. Phytotaxa 511 (3): 283-288, DOI: 10.11646/phytotaxa.511.3.7, URL: http://dx.doi.org/10.11646/phytotaxa.511.3.7

Diabetic nephropathy is the main cause of morbidity and mortality in patients with diabetes mellitus. The results of many clinical studies shows variation in the correlation of HbA1c and microalbuminuria in type 2 diabetes mellitus. In the present study we tried to correlate the HbA1c levels and microalbuminuria with respect to duration in type 2 diabetes mellitus cases and also studied microalbuminuria as a marker of nephropathy in type 2 diabetes mellitus. Materials and Methods: The study was conducted in the Department of Biochemistry at Raichur Institute of Medical Sciences. 100 subjects were recruited based on the inclusion and exclusion criteria, 50 were healthy controls and 50 were type 2 DM patients. FBS, PPBS, blood urea, serum creatinine, HbA1c, serum sodium, serum potassium and urinary micro albumin were analyzed. Statistical analysis was done by using student ‘t’ test and Chi square test. Results: A statistically significant difference was observed in values of FBS, PPBS, blood urea, serum creatinine, HbA1c, serum sodium, serum potassium and urinary microalbumin levels in cases compared to controls. In our study the mean HbA1c values were 5.1321.11% in controls and 7.5121.19% in cases & mean HbA1c value in patients without microalbuminuria is 7.130.84 and in patients with microalbuminuria is 8.121.44 which is statistically significant (p=0.005). Conclusion: The present study concluded that estimating glycosylated hemoglobin as an indicator of glycemic control and microalbuminuria in random urine sample for renal involvement in diabetic subjects provide a convenient method for early diagnosis and intervention. Thus the study suggests microalbuminuria as a nephropathic marker in type 2 diabetes mellitus. The possibility, delay or reverse the progression of diabetic nephropathy can be achieved only by perfect long term metabolic control. Keywords: Diabetic Nephropathy, HbA1c, Microalbuminuria, Type 2 Diabetes Mellitus.

The neural underpinnings of individual identity reflected in cognition, behavior, and disease remain elusive. Functional connectivity profiles have been used as a “fingerprint” with which an individual can be identified in a dataset. These established functional connectivity fingerprints generally show high accuracy but are still sensitive to mental states. A truly unique, and especially state-independent, neural fingerprint will shed light on fundamental intra-individual brain organization. Moreover, a fingerprint that also captures inter-individual differences in brain-behavior associations will provide the necessary ingredients for the development of biomarkers for precision medicine. With resting-state and task fMRI-data of the Human Connectome Project and the enhanced Nathan Kline Institute sample, we show that the local functional fingerprint, and especially regional homogeneity (ReHo), is 1) a highly accurate neural fingerprint, 2) more stable within an individual regardless of their mental state (compared to the baseline functional connectome fingerprint), and 3) captures specific inter-individual differences. Our findings are replicable across parcellations as well as resilient to confounding effects. Further analyses showed that the attention networks and the Default Mode Network contributed most to individual “uniqueness”. Moreover, with the OpenNeuro.ds000115 sample, we show that ReHo is also stable in individuals with schizophrenia and that its stability relates to intelligence subtest scores. Altogether, our findings show the potential of the application of local functional fingerprints in precision medicine.

Additional file 2: Supplementary Table 1. Percentage nucleotide and amino acid similarity for each gene of Nipah virus with respect to the Nipah virus retrieved from human sequence of Bangladesh, India, 2004 (Accession Number: AY988601.1) outbreak.

The primary focus in this paper is on bipedal robot development and controlling it for the different surface conditions by using an AI based approach. Pressure sensor, servomotor and software-driven microprocessors are the core components of the system. The two-path communication among robot and ground by methods for pressure detecting information for different ground surface conditions is fundamental. In our machine, we're imposing the robot which may be managed by means of AI. The movement of two-legged gadget is called as taking walks. Strolling can be statically or dynamically solid. Going for walks is usually dynamically solid. Airborne time for ASIMO is 0.08 sec. Keep foot flat at the floor (fully actuated). Estimate hazard of foot roll by means of measuring ground reaction forces. Cautiously design desired trajectories thru optimization. Keep knees bent (avoid singularity). The adaptive trajectory monitoring manages (high remarks profits). The maximum critical undertaking inside the development of biped robot is robotic mechanical structure. Stiffness and compliance consist with biped determine with flexibility of shape. The primary goal is to design a biped shape that may effortlessly control and capable of dealing with entire situation like humans. A normal man can weigh up to ten kg of load without difficulty and panting with accuracy. Even as designing we ought to remember such a lot of parameters inside the proposed machine, we're interfacing the flexi force sensor with Arduino mega. And measuring the foot stress of robotic after that send that fee to raspberry pi primarily based on AI robot will controlled mechanically., {"references":["Bhattacharya, S., Maiti, T. K., Dutta, S., Luo, A., Miura-Mattausch, M., &Mattausch, H. J. System Simulation for Robot Control Based on AI Approach. In 2018 14th IEEE International Conference on Solid- State and Integrated Circuit Technology (ICSICT), 1-4. IEEE.","Sinnet, R. W., Powell, M. J., Shah, R. P., & Ames, A. D. (2011). A humaninspired hybrid control approach to bipedal robotic walking. IFAC Proceedings Volumes, 44(1), 6904- 6911.","Lee, H. W., Yang, J. L., Zhang, S. Q., & Chen, Q. (2018, July). Research on the Stability of Biped Robot Walking on Different Road Surfaces. In 2018 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII) 54-57. IEEE.","Wilamowski, B. M., & Yu, H. (2010). Improved computation for Levenberg–Marquardt training. IEEE transactions on neural networks, 21(6), 930-937.","Allgeuer, P., & Behnke, S. (2018, November). Bipedal walking with corrective actions in the tilt phase space. In 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids) . 1-9. IEEE.","http://ijsrst.com/archive.php?v=9&i=4 8&pyear=2020","https://dataaspirant.com/2017/01/13/s upport-vector-machine-algorithm/","https://www.tekscan.com/productssolutions/ embedded-force-sensors","https://www.researchgate.net/profile/ HJ_Mattausch","https://en.wikipedia.org/wiki/Humano id_robot"]}

The Cu2O thin film was developed using an electrodeposition approach for resistive memory application. The impact of the deposition voltage (1V, 2V, 3V, and 4V) on resistive switching (RS)/memristive properties of Cu2O thin films was studied. The XRD spectrum reveals that deposited Cu2O has a cubic crystal structure. The bipolar RS in Al/Cu2O/FTO device was clearly observed during the current-voltage (I-V) measurement. The basic memristive properties were calculated from I-V data. The charge transport studies suggested that the SCLC mechanism was responsible for device conduction, and RS was due to filamentary effect. The result suggested that the electrodeposition technique is useful to fabricate a memristive device for various applications.

BACKGROUND: Disentangling psychopathological heterogeneity in schizophrenia is challenging, and previous results remain inconclusive. We employed advanced machine learning to identify a stable and generalizable factorization of the Positive and Negative Syndrome Scale and used it to identify psychopathological subtypes as well as their neurobiological differentiations. METHODS: Positive and Negative Syndrome Scale data from the Pharmacotherapy Monitoring and Outcome Survey cohort (1545 patients; 586 followed up after 1.35 +/- 0.70 years) were used for learning the factor structure by an orthonormal projective non-negative factorization. An international sample, pooled from 9 medical centers across Europe, the United States, and Asia (490 patients), was used for validation. Patients were clustered into psychopathological subtypes based on the identified factor structure, and the neurobiological divergence between the subtypes was assessed by classification analysis on functional magnetic resonance imaging connectivity patterns. RESULTS: A 4-factor structure representing negative, positive, affective, and cognitive symptoms was identified as the most stable and generalizable representation of psychopathology. It showed higher internal consistency than the original Positive and Negative Syndrome Scale subscales and previously proposed factor models. Based on this representation, the positive-negative dichotomy was confirmed as the (only) robust psychopathological subtypes, and these subtypes were longitudinally stable in about 80% of the repeatedly assessed patients. Finally, the individual subtype could be predicted with good accuracy from functional connectivity profiles of the ventromedial frontal cortex, temporoparietal junction, and precuneus. CONCLUSIONS: Machine learning applied to multisite data with cross-validation yielded a factorization generalizable across populations and medical systems. Together with subtyping and the demonstrated ability to predict subtype membership from neuroimaging data, this work further disentangles the heterogeneity in schizophrenia.

Despite the availability of multiple therapeutic agents, the search for novel pain management of neuropathic pain is still a challenge. Oxidative stress and inflammatory signaling are prominently involved in clinical manifestation of neuropathic pain. Toxicodendron pubescens, popularly known as Rhus Tox (RT) is recommended in alternative medicines as an anti-inflammatory and analgesic remedy. Earlier, we reported anti-inflammatory, anti-arthritic and immunomodulatory activities of Rhus Tox. In continuation, we evaluated antinociceptive efficacy of Rhus Tox in the neuropathic pain and delineated its underlying mechanism. Initially, in-vitro assay using LPS-mediated ROS-induced U-87 glioblastoma cells was performed to study the effect of Rhus Tox on reactive oxygen species (ROS), anti-oxidant status and cytokine profile. Rhus Tox decreased oxidative stress and cytokine release with restoration of anti-oxidant systems. Chronic treatment with Rhus Tox ultra dilutions for 14 days ameliorated neuropathic pain revealed as inhibition of cold, warm and mechanical allodynia along with improved motor nerve conduction velocity (MNCV) in constricted nerve. Rhus Tox decreased the oxidative and nitrosative stress by reducing malondialdehyde (MDA) and nitric oxide (NO) content, respectively along with up regulated glutathione (GSH), superoxide dismutase (SOD) and catalase activity in sciatic nerve of rats. Notably, Rhus Tox treatment caused significant reductions in the levels of tumor necrosis factor (TNF-α), interleukin-6 (IL-6) and interleukin-1β (IL-1β) as compared with CCI-control group. Protective effect of Rhus Tox against CCI-induced sciatic nerve injury in histopathology study was exhibited through maintenance of normal nerve architecture and inhibition of inflammatory changes. Overall, neuroprotective effect of Rhus Tox in CCI-induced neuropathic pain suggests the involvement of anti-oxidative and anti-inflammatory mechanisms.

Trabajo presentado en la 12ª Reunión de la Red Española de Levaduras, celebrada en El Escorial (España), del 11 al 13 Diciembre 2019, The use of species alternative to Saccharomyces cerevisiae in winemaking has been expanded during the last decade. These yeasts contribute to the diversity and fullness of wine sensory features, and are typically used in combination with S. cerevisiae, in order to ensure complete fermentation. This practice has drawn the interest in interactions between different oenological yeasts. However, yeast-yeast interactions might also be relevant in spontaneous and conventional fermentations, or in the vineyard. The field is relatively young and, although several interactions have been described and some mechanisms have been suggested, the possible involvement of extracellular vesicles has not yet been considered. In this work, production of EVs by six wine yeast species, growing in synthetic grape must, is shown. Proteomic analysis of EVs from S. cerevisiae, and Torulaspora delbrueckii showed a significant enrichment in glycolytic enzymes and cell-wall related proteins, as described for some other yeasts, as well as for S. cerevisiae under other growth conditions. The most abundant protein found in S. cerevisiae, T. delbrueckii, and Lachancea thermotolerans was the enzyme exo-1,3-ß-glucanase. Fractions enriched in EVs from a T. delbrueckii strain interfere with the growth of other wine yeast strains; but exo-1,3-ß-glucanase was not required for this activity. These findings suggest EVs may play a role in fungal interactions during wine fermentation and other aspects of wine yeast biology.

Trabajo presentado en la 7th Conference on Physiology of Yeast and Filamentous, celebrado en Milán (Italia), del 24 al 27 de junio de 2019, Extracellular vesicles (EVs) are membranous structures with a broad range of sizes (20-500 nm), and are ubiquitous in nature. EVs are often able to cross the cell wall and release different molecules such as proteins, lipids, nucleic acids, polysaccharides and pigments (1) into the recipient cells. In fungi, they have been attributed to diverse functions such as cell-to-cell communication, response to nutrient availability, cell defense, RNA export, morphological transition and prion transmission (2). However, EVs have not been previously studied from wine yeast species neither under winemaking conditions. We hypothesize that wine yeasts produce EVs as part of an interspecific interaction mechanism. For the first time, we have isolated wine yeast EVs from the culture supernatant of synthetic grape must. These EVs produced by six yeast species were characterized by transmission electron microscopy, including two different strains of Saccharomyces cerevisiae and five non-Saccharomyces isolates. A comparative proteomic analysis was run for three fractions of S. cerevisiae EC1118, cell content, EVs and vesicle-free secretome. In addition, we have compared the proteome of the EVs and vesicle-free secretome fractions of S. cerevisiae and Torulaspora delbruekii. Finally, we have shown induction of cell mortality by EVs from some strains on different yeast isolates., We acknowledge funding by MINECO (AGL2015-63629-R and PhD training contract for AM, BES-2016-077557).

Trabajo presentado en Congreso Nacional de Biotecnología (BIOTEC 2019), celebrado en Vigo (España), del 10 al 13 de junio de 2019, Fungal extracellular vesicles (EVs) are membranous structures able to go across the cell wall and release different molecules to the outer space. EVs have been attributed functions in several biological processes like cell-to-cell communication, response to nutrient availability, morphological transitions, or modulation of the host defence mechanisms. Here we show extracellular vesicle production from several wine yeasts in synthetic grape must. We characterized these EVs by EM, and both EVs and vesiclefree supernatant (VFS) by proteomic analysis. Torulaspora delbrueckii EVs can influence growth and viability from other wine yeast strains, We acknowledge funding by MINECO (AGL2015-63629-R and PhD training contract for AM, BES-2016-077557).

INTRODUCTION:Millions of children in India still suffer from poor health and under-nutrition, despite substantial improvement over decades of public health programmes. The Anganwadi centres under the Integrated Child Development Scheme (ICDS) provide a range of health and nutrition services to pregnant women, children

The hippocampus displays a complex organization and function that is perturbed in many neuropathologies. Histological work revealed a complex arrangement of subfields along the medial-lateral and the ventral-dorsal dimension, which contrasts with the anterior-posterior functional differentiation. The variety of maps has raised the need for an integrative multimodal view. We applied connectivity-based parcellation to 1) intrinsic connectivity 2) task-based connectivity, and 3) structural covariance, as complementary windows into structural and functional differentiation of the hippocampus. Strikingly, while functional properties (i.e., intrinsic and task-based) revealed similar partitions dominated by an anterior-posterior organization, structural covariance exhibited a hybrid pattern reflecting both functional and cytoarchitectonic subdivision. Capitalizing on the consistency of functional parcellations, we defined robust functional maps at different levels of partitions, which are openly available for the scientific community. Our functional maps demonstrated a head-body and tail partition, subdivided along the anterior-posterior and medial-lateral axis. Behavioral profiling of these fine partitions based on activation data indicated an emotion-cognition gradient along the anterior-posterior axis and additionally suggested a self-world-centric gradient supporting the role of the hippocampus in the construction of abstract representations for spatial navigation and episodic memory.

Trabajo presentado en Congreso Nacional de Biotecnología (BIOTEC 2019), celebrado en Vigo (España), del 10 al 13 de junio de 2019, Excessive acetic acid production is a key problem in winemaking processes aiming to reduce ethanol content of wine (1). It is related to a boost in acetate yields observed for Saccharomyces cerevisiae and some other yeasts under aerobic fermentation conditions. However, the biochemical mechanisms underlying this problem are unclear. We therefore used a genomic-scale modelling approach, combined with experimentally determined uptake/secretion rates, to gain insights into the metabolic rewiring of S. cerevisiae in response to oxygen under conditions relevant for winemaking. The model was also used to identify gene deletion targets affecting the production of acetic acid under aerobic conditions. Several gene knock-out with expected reduction in acetic acid production were thus identified as targets using MoMa and MiMBl algorithms. The predictions were tested using strains from the prototrophic yeast knockout collection for the fermentation of natural grape must under aerobic conditions. Although many of the prediction did not show any reduction in acetic acid yield, and increased yield was observed in some cases, significant acetate reduction was obtained for three gene deletion strains, ald6¿, mdh3¿ and rip1¿. The involvement of ALD6 on aerobic acetic acid production could be expected from results by other authors. However, the results for MDH3 and RIP1 could not be easily anticipated and thus provide new insights into yeast metabolism concerning acetic acid production, a major concern for the wine industry., We acknowledge funding by MINECO (AGL2015-63629-R and PhD training contract for AJR.

Launching of Rocket is a complex phenomenon. There is high chance of failure in such missions. Hence it is necessary to predict the launch path or Trajectory, External disturbances and various other parameters. Numerical Simulation is one such way of predicting launch parameters which helps in taking precautions preventing failure. This Paper presents a platform for simulation of Rocket launch vehicle and control Law validation. The analysis is performed during the Lift-off phase in atmosphere using a mathematical nonlinear dynamic model. Set of six degrees of freedom equations of motion with necessary reference frames and transformations in between them is developed. The Mathematical Model is applied in MATLAB and Simulink simulation which can be easily adjusted for any type of launch vehicle or rocket.