MyD88 Dependent Signaling Contributes to Protective Host Defense against Burkholderia pseudomallei

Background: Toll-like receptors (TLRs) have a central role in the recognition of pathogens and the initiation of the innate immune response. Myeloid differentiation primary-response gene 88 (MyD88) and TIR-domain-containing adaptor protein inducing IFNb (TRIF) are regarded as the key signaling adaptor proteins for TLRs. Melioidosis, which is endemic in SE-Asia, is a severe infection caused by the gram-negative bacterium Burkholderia pseudomallei. We here aimed to characterize the role of MyD88 and TRIF in host defense against melioidosis. Methodology and Principal Findings: First, we found that MyD88, but not TRIF, deficient whole blood leukocytes released less TNFa upon stimulation with B. pseudomallei compared to wild-type (WT) cells. Thereafter we inoculated MyD88 knockout (KO), TRIF mutant and WT mice intranasally with B. pseudomallei and found that MyD88 KO, but not TRIF mutant mice demonstrated a strongly accelerated lethality, which was accompanied by significantly increased bacterial loads in lungs, liver and blood, and grossly enhanced liver damage compared to WT mice. The decreased bacterial clearance capacity of MyD88 KO mice was accompanied by a markedly reduced early pulmonary neutrophil recruitment and a diminished activation of neutrophils after infection with B. pseudomallei. MyD88 KO leukocytes displayed an unaltered capacity to phagocytose and kill B. pseudomallei in vitro. Conclusions: MyD88 dependent signaling, but not TRIF dependent signaling, contributes to a protective host respons

Sounds, Behaviour, and Auditory Receptors of the Armoured Ground Cricket, Acanthoplus longipes

The auditory sensory system of the taxon Hetrodinae has not been studied previously. Males of the African armoured ground cricket, Acanthoplus longipes (Orthoptera: Tettigoniidae: Hetrodinae) produce a calling song that lasts for minutes and consists of verses with two pulses. About three impulses are in the first pulse and about five impulses are in the second pulse. In contrast, the disturbance stridulation consists of verses with about 14 impulses that are not separated in pulses. Furthermore, the inter-impulse intervals of both types of sounds are different, whereas verses have similar durations. This indicates that the neuronal networks for sound generation are not identical. The frequency spectrum peaks at about 15 kHz in both types of sounds, whereas the hearing threshold has the greatest sensitivity between 4 and 10 kHz. The auditory afferents project into the prothoracic ganglion. The foreleg contains about 27 sensory neurons in the crista acustica; the midleg has 18 sensory neurons, and the hindleg has 14. The auditory system is similar to those of other Tettigoniidae

Anti-filarial Activity of Antibiotic Therapy Is Due to Extensive Apoptosis after Wolbachia Depletion from Filarial Nematodes

Filarial nematodes maintain a mutualistic relationship with the endosymbiont Wolbachia. Depletion of Wolbachia produces profound defects in nematode development, fertility and viability and thus has great promise as a novel approach for treating filarial diseases. However, little is known concerning the basis for this mutualistic relationship. Here we demonstrate using whole mount confocal microscopy that an immediate response to Wolbachia depletion is extensive apoptosis in the adult germline, and in the somatic cells of the embryos, microfilariae and fourth-stage larvae (L4). Surprisingly, apoptosis occurs in the majority of embryonic cells that had not been infected prior to antibiotic treatment. In addition, no apoptosis occurs in the hypodermal chords, which are populated with large numbers of Wolbachia, although disruption of the hypodermal cytoskeleton occurs following their depletion. Thus, the induction of apoptosis upon Wolbachia depletion is non-cell autonomous and suggests the involvement of factors originating from Wolbachia in the hypodermal chords. The pattern of apoptosis correlates closely with the nematode tissues and processes initially perturbed following depletion of Wolbachia, embryogenesis and long-term sterilization, which are sustained for several months until the premature death of the adult worms. Our observations provide a cellular mechanism to account for the sustained reductions in microfilarial loads and interruption of transmission that occurs prior to macrofilaricidal activity following antibiotic therapy of filarial nematodes

Impacts of selected Ecological Focus Area options in European farmed landscapes on climate regulation and pollination services: a systematic map protocol

Background: This systematic map protocol responds to an urgent policy need to evaluate key environmental benefits of new compulsory greening measures in the European Union’s Common Agricultural Policy (CAP), with the aim of building a policy better linked to environmental performance. The systematic map will focus on Ecological Focus Areas (EFAs), in which larger arable farmers must dedicate 5% of their arable land to ecologically beneficial habitats, landscape features and land uses. The European Commission’s Joint Research Centre has used a software tool called the ‘EFA calculator’ to inform the European Commission about environmental benefits of EFA implementation. However, there are gaps in the EFA calculator’s coverage of ecosystem services, especially ‘global climate regulation’, and an opportunity to use systematic mapping methods to enhance its capture of evidence, in advance of forthcoming CAP reforms. We describe a method for assembling a database of relevant, peer-reviewed research conducted in all agricultural landscapes in Europe and neighbouring countries with similar biogeography, addressing the primary question: what are the impacts of selected EFA features in agricultural land on two policy-relevant ecosystem service outcomes—global climate regulation and pollination? The method is streamlined to allow results in good time for the current, time-limited opportunity to influence reforms of the CAP greening measures at European and Member State level. Methods: We will search four bibliographic databases in English, using a predefined and tested search string that focuses on a subset of EFA options and ecosystem service outcomes. The options and outcomes are selected as those with particular policy relevance and traction. Only articles in English will be included. We will screen search results at title, abstract and full text levels, recording the number of studies deemed non-relevant (with reasons at full text). A systematic map database that displays the meta-data (i.e. descriptive summary information about settings and methods) of relevant studies will be produced following full text assessment. The systematic map database will be published as a MS-Excel database. The nature and extent of the evidence base will be discussed, and the applicability of methods to convert the available evidence into EFA calculator scores will be assessed

Artificial Intelligence in Supply Chain Operations Planning: Collaboration and Digital Perspectives

[EN] Digital transformation provide supply chains (SCs) with extensive accurate data that should be combined with analytical techniques to improve their management. Among these techniques Artificial Intelligence (AI) has proved their suitability, memory and ability to manage uncertain and constantly changing information. Despite the fact that a number of AI literature reviews exist, no comprehensive review of reviews for the SC operations planning has yet been conducted. This paper aims to provide a comprehensive review of AI literature reviews in a structured manner to gain insights into their evolution in incorporating new ICTs and collaboration. Results show that hybrization man-machine and collaboration and ethical aspects are understudied.This research has been funded by the project entitled NIOTOME (Ref. RTI2018-102020-B-I00) (MCI/AEI/FEDER, UE). The first author was supported by the Generalitat Valenciana (Conselleria de Educación, Investigación, Cultura y Deporte) under Grant ACIF/2019/021.Rodríguez-Sánchez, MDLÁ.; Alemany Díaz, MDM.; Boza, A.; Cuenca, L.; Ortiz Bas, Á. (2020). Artificial Intelligence in Supply Chain Operations Planning: Collaboration and Digital Perspectives. IFIP Advances in Information and Communication Technology. 598:365-378. https://doi.org/10.1007/978-3-030-62412-5_30S365378598Lezoche, M., Hernandez, J.E., Alemany, M.M.E., Díaz, E.A., Panetto, H., Kacprzyk, J.: Agri-food 4.0: a survey of the supply chains and technologies for the future agriculture. Comput. Ind. 117, 103–187 (2020)Stock, J.R., Boyer, S.L.: Developing a consensus definition of supply chain management: a qualitative study. Int. J. Phys. Distrib. Logistics Manag. 39(8), 690–711 (2009)Min, H.: Artificial intelligence in supply chain management: theory and applications. Int. J. Logistics Res. Appl. 13(1), 13–39 (2010). https://doi.org/10.1080/13675560902736537Hariri, R.H., Fredericks, E.M., Bowers, K.M.: Uncertainty in big data analytics: survey, opportunities, and challenges. J. Big Data 6(1), 1–16 (2019). https://doi.org/10.1186/s40537-019-0206-3Duan, Y., Edwards, J.S., Dwivedi, Y.K.: Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. Int. J. Inf. Manage. 48(2019), 63–71 (2019). https://doi.org/10.1016/j.ijinfomgt.2019.01.021McCarthy, J., Minsky, M.L., Rochester, N., Shannon, C.E.: A proposal for the dartmouth summer research project on artificial intelligence. AI Mag. 27(4), 12–14 (2006)Barr, A., Feigenbaum, E.A.: The Handbook of Artificial Intelligence, vol. 2. Heuristech: William Kaufmann, Pitman (1982)High-Level Expert Group on Artificial Intelligence, European Commission. A definition of AI: main capabilities and disciplines (2019)Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A., De Felice, F.: Artificial intelligence and machine learning applications in smart production: progress, trends, and directions. Sustainability (Switzerland) 12(2) (2020). https://doi.org/10.3390/su12020492Cheng, L., Yu, T.: A new generation of AI: a review and perspective on machine learning technologies applied to smart energy and electric power systems. Int. J. Energy Res. 43(6), 1928–1973 (2019). https://doi.org/10.1002/er.4333Duan, Y., Edwards, J.S., Dwivedi, Y.K.: Artificial intelligence for decision-making in the era of big data. Evolution, challenges and research agenda. Int. J. Inf. Manag. 48, 63–71 (2019)Varshney, S., Jigyasu, R., Sharma, A., Mathew, L.: Review of various artificial intelligence techniques and its applications. IOP Conf. Ser. Mater. Sci. Eng. 594(1) (2019)Cheng, L., Yu, T.: A new generation of AI: a review and perspective on machine learning technologies applied to smart energy and electric power systems. Int. J. Energy Res. 43, 1928–1973 (2019)Seuring, S., Müller, M.: From a literature review to a conceptual framework for sustainable supply chain management. J. Clean. Prod. 16(15), 1699–1710 (2008). https://doi.org/10.1016/j.jclepro.2008.04.020Metaxiotis, K.S., Askounis, D., Psarras, J.: Expert Systems In Production Planning And Scheduling: A State-Of-The-Art Survey. J. Intell. Manuf. 13(4), 253–260 (2002). https://doi.org/10.1023/A:1016064126976Power, Y., Bahri, P.A.: Integration techniques in intelligent operational management: a review. Knowl. Based Syst. 18(2–3), 89–97 (2005). https://doi.org/10.1016/j.knosys.2004.04.009Shen, W., Hao, Q., Yoon, H.J., Norrie, D.H.: Applications of agent-based systems in intelligent manufacturing: an updated review. Adv. Eng. Inform. 20(4), 415–431 (2006). https://doi.org/10.1016/j.aei.2006.05.004Kobbacy, K.A.H., Vadera, S., Rasmy, M.H.: AI and OR in management of operations: history and trends. J. Oper. Res. Soc. 58(1), 10–28 (2007). https://doi.org/10.1057/palgrave.jors.2602132Zhang, W.J., Xie, S.Q.: Agent technology for collaborative process planning: a review. Int. J. Adv. Manuf. Technol. 32(3), 315–325 (2007). https://doi.org/10.1007/s00170-005-0345-xIbáñez, O., Cordón, O., Damas, S., Magdalena, L.: A review on the application of hybrid artificial intelligence systems to optimization problems in operations management. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds.) HAIS 2009. LNCS (LNAI), vol. 5572, pp. 360–367. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02319-4_43Kobbacy, K.A.H., Vadera, S.: A survey of AI in operations management from 2005 to 2009. J. Manuf. Technol. Manag. 22(6), 706–733 (2011). https://doi.org/10.1108/17410381111149602Guo, Z.X., Wong, W.K., Leung, S.Y.S., Li, M.: Applications of artificial intelligence in the apparel industry: a review. Text. Res. J. 81(18), 1871–1892 (2011). https://doi.org/10.1177/0040517511411968Priore, P., Gómez, A., Pino, R., Rosillo, R.: Dynamic scheduling of manufacturing systems using machine learning: an updated review. Artif. Intell. Eng. Des. Anal. Manuf. AIEDAM 28(1), 83–97 (2014). https://doi.org/10.1017/S0890060413000516Renzi, C., Leali, F., Cavazzuti, M., Andrisano, A.: A review on artificial intelligence applications to the optimal design of dedicated and reconfigurable manufacturing systems. Int. J. Adv. Manuf. Technol. 72(1–4), 403–418 (2014). https://doi.org/10.1007/s00170-014-5674-1Ngai, E.W.T., Peng, S., Alexander, P., Moon, K.K.L.: Decision support and intelligent systems in the textile and apparel supply chain: an academic review of research articles. Expert Syst. Appl. 41(1), 81–91 (2014). https://doi.org/10.1016/j.eswa.2013.07.013Rooh, U.A., Li, A., Ali, M.M.: Fuzzy, neural network and expert systems methodologies and applications - a review. J. Mob. Multimedia 11, 157–176 (2015)Bello, O., Teodoriu, C., Yaqoob, T., Oppelt, J., Holzmann, J., Obiwanne, A.: Application of artificial intelligence techniques in drilling system design and operations: a state of the art review and future research pathways. In: Society of Petroleum Engineers - SPE Nigeria Annual International Conference and Exhibition (2016)Arvitrida, N.I.: A review of agent-based modeling approach in the supply chain collaboration context. IOP Conf. Ser. Mater. Sci. Eng. 337(1) (2018). https://doi.org/10.1088/1757-899x/337/1/012015Zanon, L.G., Carpinetti, L.C.R.: Fuzzy cognitive maps and grey systems theory in the supply chain management context: a literature review and a research proposal. In: IEEE International Conference on Fuzzy Systems, July 2018, pp. 1–8 (2018). https://doi.org/10.1109/fuzz-ieee.2018.8491473Burggräf, P., Wagner, J., Koke, B.: Artificial intelligence in production management: a review of the current state of affairs and research trends in academia. In: 2018 International Conference on Information Management and Processing, ICIMP 2018, January 2018, pp. 82–88 (2018). https://doi.org/10.1109/icimp1.2018.8325846Diez-Olivan, A., Del Ser, J., Galar, D., Sierra, B.: Data fusion and machine learning for industrial prognosis: trends and perspectives towards Industry 4.0. Inf. Fusion 50, 92–111 (2019). https://doi.org/10.1016/j.inffus.2018.10.005Ni, D., Xiao, Z., Lim, M.K.: A systematic review of the research trends of machine learning in supply chain management. Int. J. Mach. Learn. Cybernet. 11(7), 1463–1482 (2019). https://doi.org/10.1007/s13042-019-01050-0Ning, C., You, F.: Optimization under uncertainty in the era of big data and deep learning: when machine learning meets mathematical programming. Comput. Chem. Eng. 125, 434–448 (2019). https://doi.org/10.1016/j.compchemeng.2019.03.034Okwu, M.O., Nwachukwu, A.N.: A review of fuzzy logic applications in petroleum exploration, production and distribution operations. J. Petrol. Explor. Prod. Technol. 9(2), 1555–1568 (2018). https://doi.org/10.1007/s13202-018-0560-2Weber, F.D., Schütte, R.: State-of-the-art and adoption of artificial intelligence in retailing. Digit. Policy Regul. Gov. 21(3), 264–279 (2019). https://doi.org/10.1108/DPRG-09-2018-0050Giri, C., Jain, S., Zeng, X., Bruniaux, P.: A detailed review of artificial intelligence applied in the fashion and apparel industry. IEEE Access 7, 95376–95396 (2019). https://doi.org/10.1109/ACCESS.2019.2928979Leo Kumar, S.P.: Knowledge-based expert system in manufacturing planning: State-of-the-art review. Int. J. Prod. Res. 57(15–16), 4766–4790 (2019). https://doi.org/10.1080/00207543.2018.1424372Barua, L., Zou, B., Zhou, Y.: Machine learning for international freight transportation management: a comprehensive review. Res. Transp. Bus. Manag. (2020). https://doi.org/10.1016/j.rtbm.2020.100453Chai, J., Ngai, E.W.T.: Decision-making techniques in supplier selection: recent accomplishments and what lies ahead. Expert Syst. Appl. 140 (2020). https://doi.org/10.1016/j.eswa.2019.112903Usuga Cadavid, J.P., Lamouri, S., Grabot, B., Pellerin, R., Fortin, A.: Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0. J. Intell. Manuf. 31(6), 1531–1558 (2020). https://doi.org/10.1007/s10845-019-01531-7Ekramifard, A., Amintoosi, H., Seno, A.H., Dehghantanha, A., Parizi, R.M.: A systematic literature review of integration of blockchain and artificial intelligence. In: Choo, K.-K.R., Dehghantanha, A., Parizi, R.M. (eds.) Blockchain Cybersecurity, Trust and Privacy. AIS, vol. 79, pp. 147–160. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-38181-3_8Vrbka, J., Rowland, Z.: Using artificial intelligence in company management. In: Ashmarina, S.I., Vochozka, M., Mantulenko, V.V. (eds.) ISCDTE 2019. LNNS, vol. 84, pp. 422–429. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-27015-5_51Leslie, D.: Understanding artificial intelligence ethics and safety: a guide for the responsible design and implementation of AI systems in the public sector. The Alan Turing Institute (2019)Queiroz, M.M., Ivanov, D., Dolgui, A., et al.: Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Ann Oper Res (2020). https://doi.org/10.1007/s10479-020-03685-

Beneficial effects of physical activity in an HIV-infected woman with lipodystrophy: a case report

<p>Abstract</p> <p>Introduction</p> <p>Lipodystrophy is common in patients infected with human immunodeficiency virus receiving highly active antiretroviral therapy, and presents with morphologic changes and metabolic alterations that are associated with depressive behavior and reduced quality of life. We examined the effects of exercise training on morphological changes, lipid profile and quality of life in a woman with human immunodeficiency virus presenting with lipodystrophy.</p> <p>Case presentation</p> <p>A 31-year-old Latin-American Caucasian woman infected with human immunodeficiency virus participated in a 12-week progressive resistance exercise training program with an aerobic component. Her weight, height, skinfold thickness, body circumferences, femur and humerus diameter, blood lipid profile, maximal oxygen uptake volume, exercise duration, strength and quality of life were assessed pre-exercise and post-exercise training. After 12 weeks, she exhibited reductions in her total subcutaneous fat (18.5%), central subcutaneous fat (21.0%), peripheral subcutaneous fat (10.7%), waist circumference (WC) (4.5%), triglycerides (9.9%), total cholesterol (12.0%) and low-density lipoprotein cholesterol (8.6%). She had increased body mass (4.6%), body mass index (4.37%), humerus and femur diameter (3.0% and 2.3%, respectively), high-density lipoprotein cholesterol (16.7%), maximal oxygen uptake volume (33.3%), exercise duration (37.5%) and strength (65.5%). Quality of life measures improved mainly for psychological and physical measures, independence and social relationships.</p> <p>Conclusions</p> <p>These findings suggest that supervised progressive resistance exercise training is a safe and effective treatment for evolving morphologic and metabolic disorders in adults infected with HIV receiving highly active antiretroviral therapy, and improves their quality of life.</p

Neuropeptide Signaling Differentially Affects Phase Maintenance and Rhythm Generation in SCN and Extra-SCN Circadian Oscillators

Circadian rhythms in physiology and behavior are coordinated by the brain's dominant circadian pacemaker located in the suprachiasmatic nuclei (SCN) of the hypothalamus. Vasoactive intestinal polypeptide (VIP) and its receptor, VPAC2, play important roles in the functioning of the SCN pacemaker. Mice lacking VPAC2 receptors (Vipr2−/−) express disrupted behavioral and metabolic rhythms and show altered SCN neuronal activity and clock gene expression. Within the brain, the SCN is not the only site containing endogenous circadian oscillators, nor is it the only site of VPAC2 receptor expression; both VPAC2 receptors and rhythmic clock gene/protein expression have been noted in the arcuate (Arc) and dorsomedial (DMH) nuclei of the mediobasal hypothalamus, and in the pituitary gland. The functional role of VPAC2 receptors in rhythm generation and maintenance in these tissues is, however, unknown. We used wild type (WT) and Vipr2−/− mice expressing a luciferase reporter (PER2::LUC) to investigate whether circadian rhythms in the clock gene protein PER2 in these extra-SCN tissues were compromised by the absence of the VPAC2 receptor. Vipr2−/− SCN cultures expressed significantly lower amplitude PER2::LUC oscillations than WT SCN. Surprisingly, in Vipr2−/− Arc/ME/PT complex (Arc, median eminence and pars tuberalis), DMH and pituitary, the period, amplitude and rate of damping of rhythms were not significantly different to WT. Intriguingly, while we found WT SCN and Arc/ME/PT tissues to maintain a consistent circadian phase when cultured, the phase of corresponding Vipr2−/− cultures was reset by cull/culture procedure. These data demonstrate that while the main rhythm parameters of extra-SCN circadian oscillations are maintained in Vipr2−/− mice, the ability of these oscillators to resist phase shifts is compromised. These deficiencies may contribute towards the aberrant behavior and metabolism associated with Vipr2−/− animals. Further, our data indicate a link between circadian rhythm strength and the ability of tissues to resist circadian phase resetting

Reconstructing grassland fire history using sedimentary charcoal: Considering count, size and shape

Citation: Leys, B. A., Commerford, J. L., & McLauchlan, K. K. (2017). Reconstructing grassland fire history using sedimentary charcoal: Considering count, size and shape. Plos One, 12(4), 15. doi:10.1371/journal.pone.0176445Fire is a key Earth system process, with 80% of annual fire activity taking place in grassland areas. However, past fire regimes in grassland systems have been difficult to quantify due to challenges in interpreting the charcoal signal in depositional environments. To improve reconstructions of grassland fire regimes, it is essential to assess two key traits: (1) charcoal count, and (2) charcoal shape. In this study, we quantified the number of charcoal pieces in 51 sediment samples of ponds in the Great Plains and tested its relevance as a proxy for the fire regime by examining 13 potential factors influencing charcoal count, including various fire regime components (e.g. the fire frequency, the area burned, and the fire season), vegetation cover and pollen assemblages, and climate variables. We also quantified the width to length (W: L) ratio of charcoal particles, to assess its utility as a proxy of fuel types in grassland environments by direct comparison with vegetation cover and pollen assemblages. Our first conclusion is that charcoal particles produced by grassland fires are smaller than those produced by forest fires. Thus, a mesh size of 120 mu m as used in forested environments is too large for grassland ecosystems. We recommend counting all charcoal particles over 60 mu m in grasslands and mixed grass-forest environments to increase the number of samples with useful data. Second, a W: L ratio of 0.5 or smaller appears to be an indicator for fuel types, when vegetation surrounding the site is before composed of at least 40% grassland vegetation. Third, the area burned within 1060m of the depositional environments explained both the count and the area of charcoal particles. Therefore, changes in charcoal count or charcoal area through time indicate a change in area burned. The fire regimes of grassland systems, including both human and climatic influences on fire behavior, can be characterized by long-term charcoal records