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opencc-by-4.0Feb 2018View details →

Fig. 8 in Two new species of deep-water Calcigorgia gorgonians (Anthozoa: Octocorallia) from the Kurile Islands, Sea of Okhotsk, with a review of distinctive characters of the known species of the genus

Fig. 8. Calcigorgia simushiri sp. nov., holotype (MIMB 20721), sclerites from the upper part of the body wall of polyp. A. Short warty clubs. B. Longer clubs with curved handles. C. Spindles. D. Ovals. E. Capstans ornamented with girdled warts. F. Club-like spindles. Scale bar: 0.1 mm.

opencc-by-4.0Feb 2018View details →

Fig. 1 in Two new species of deep-water Calcigorgia gorgonians (Anthozoa: Octocorallia) from the Kurile Islands, Sea of Okhotsk, with a review of distinctive characters of the known species of the genus

Fig. 1. Calcigorgia matua sp. nov. A. Holotype (MIMB 20722), Kurile Islands, Sea of Okhotsk. B. Paratype (MIMB 20724), Kurile Islands, Sea of Okhotsk. Scale bar: 10 mm.

opencc-by-4.0Feb 2018View details →

Fig. 5 in Two new species of deep-water Calcigorgia gorgonians (Anthozoa: Octocorallia) from the Kurile Islands, Sea of Okhotsk, with a review of distinctive characters of the known species of the genus

Fig. 5. Calcigorgia matua sp. nov., paratype (MIMB 20724), sclerites. A. Leafy clubs of the tentacles. B. Warty clubs from the tentacles. C. Spindles from the tentacles. D. Leafy clubs from the polyp body wall. E. Warty club from polyp body wall. F. Capstans and 8-radiate bodies of the polyp body wall. G. Leafy clubs from the coenenchyme. H. Capstans and 8-radiate bodies from the coenenchyme. I. Warty club from the coenenchyme. J. Spindle from coenenchyme. Scale bar: 0.1 mm.

Fig. 1 in Review of the Palaearctic species of Ismaridae Thomson, 1858 (Hymenoptera: Diaprioidea)

Fig. 1. Ismarus spp., ♀♀ (A–G. Habitus; H–I. Mesosoma in lateral view). A. I. apicalis Kolyada & Chemyreva, 2016. B. I. dorsiger (Haliday, 1831). C, H. I. halidayi Förster, 1850. D. I. grandis Alekseev, 1978. E. I. rugulosus Förster, 1850. F. I. spinalis Kolyada & Chemyreva, 2016. G, I. I. flavicornis (Thomson, 1858).

Fig. 2 in Review of the Palaearctic species of Ismaridae Thomson, 1858 (Hymenoptera: Diaprioidea)

Fig. 2. Ismarus spp., ♂♂. (A–G, I. Habitus; H. Antennae). A. I. apicalis Kolyada & Chemyreva, 2016. B. I. dorsiger (Haliday, 1831). C. I. flavicornis (Thomson, 1858). D. I. grandis Alekseev, 1978. E. I. halidayi Förster, 1850. F. I. multiporus Kolyada & Chemyreva. G. I. rugulosus Förster, 1850. H–I. I. spinalis Kolyada & Chemyreva, 2016.

Fig. 5 in Review of the Palaearctic species of Ismaridae Thomson, 1858 (Hymenoptera: Diaprioidea)

Fig. 5. Ismarus excavatus Kim & Lee sp. nov. (A–D. Holotype, ♀; E–F. Allotype, ♂). A. Antenna. B. Habitus in lateral view. C. Head in dorsal view. D. Mesosoma in dorsal view. E. Antenna (A3–A5). F. Habitus in lateral view.

Fig. 4 in Review of the Palaearctic species of Ismaridae Thomson, 1858 (Hymenoptera: Diaprioidea)

Fig. 4. Ismarus distinctus Kim, Notton & Ødegaard sp. nov. (A, D–E. Holotype, ♀; B–C. Allotype, ♂). A. Habitus in lateral view. B. Antenna (A3–A5). C. Habitus in lateral view. D. Habitus in dorsal view. E. Head, Mesosoma in dorsal view.

Fig. 3 in Review of the Palaearctic species of Ismaridae Thomson, 1858 (Hymenoptera: Diaprioidea)

Fig. 3. Ismarus brevis Kim & Lee sp. nov., holotype, ♂. A. Antenna. B. Habitus in lateral view. C. Head in dorsal view. D. Mesosoma in dorsal view.

Fig. 7 in Review of the Palaearctic species of Ismaridae Thomson, 1858 (Hymenoptera: Diaprioidea)

Fig. 7. Ismarus tripotini Kim & Lee sp. nov., holotype, ♀. A. Habitus in lateral view. B. Hind tibia. C. Head in dorsal view. D. Mesosoma in dorsal view.

Fig. 6 in Review of the Palaearctic species of Ismaridae Thomson, 1858 (Hymenoptera: Diaprioidea)

Fig. 6. Ismarus similis Kim, Notton & Lee sp. nov., holotype, ♀. A. Habitus in lateral view. B. Head in dorsal view. C. Mesosoma in dorsal view. D. Head, mesosoma in lateral view. E. Petiole, T1 in dorsal view.

opencc-by-sa-4.0Apr 2018View details →

Review of Emissions from Smouldering Peat Fires

The file contains two table compilations of up-to-date inter-study of peat fire gas and particle emission factors (EFs) found in the scientific literature, both from laboratory and field studies. According to the geographical origins of the peat used in fire emission studies, we classified the samples into two categories: boreal and temperate peat (we merge these two climate zones into one category owing to the limited sampling location information reported in the literature), and tropical peat. By doing this,  the best estimate peat fire EFs were calculated and compared between the two peat categories for the first time. It is hoped that the complied peat fire EFs can be used to improve the estimation of the total peat fire emission.  This data was analysed in our journal paper paper: Y. Hu, N. Fernandez-Anez, T. E. L. Smith, G. Rein, Review of Emissions from Smouldering Peat Fires and Their Contribution to Regional Haze Episodes, International Journal of Wildland Fire, 2018 (in press), DOI:10.1071/WF17084.

Review of Recent Trends in Measuring the Computing Systems Intelligence-Figure 3. Intelligence of different living creature (accessed 01.11.2017). 3.1. A painting elephant (http://www.wittyfacts.com/suda-the-painting-elephant/); 3.2. A common octopus (https://en.wikipedia.org/wiki/Octopus). 3.3. An African grey parrot (https://en.wikipedia.org/wiki/Grey_parrot)

Many observations proved that octopus species have an impressive spatial learning capacity, advanced navigational abilities, and advanced predatory techniques. The dexterity is important for using and manipulating tools. Zullo, Sumbre, Agnisola, Flash, & Hochner, (2009) studied the successful dexterity of octopuses. They have highly sensitive suction cups and prehensile arms, squid, and cuttlefish. This allows them to hold and manipulate objects. The motor skills of octopuses (Figure 3.2) do not seem to depend upon mapping their body. Some species of parrots are able to mimic very well the human speech. There were performed many studies with parrots that shown that some individuals are able to associate words with their meanings. Another observed ability is to form simple sentences. It has been shown that some grey parrots perform at the cognitive level of a 3-year-old child in some tasks. Pepperberg (2006) proved that some parrots can count up to 6. Figure 3.3 presents a frequently studied species of parrots, called African grey parrot.

Review of Recent Trends in Measuring the Computing Systems Intelligence-igure 2. Intelligence of different living creature (accessed 01.11.2017). 2.1. A crow solving a complex task (https://www.disclose.tv/spooky-genius-crow-had-to-be-removed-from-scientific-experiment- 314886). 2.2. A group of dolphins with a social behaviour (http://www.sciencemag.org/news/2012/04/teamwork-builds-big-brains); 2.3. An orangutan that use a spear to fish (https://primatology.net/2008/04/29/orangutan-photographed-using-tool-as-spear-to-fish)

Some species of birds have been shown capable of using different tools. Many studies consider the crows as very intelligent. Smirnova, Lazareva, and Zorina (2000) suggested that crows have some kind of numerical ability. Figure 2.1 presents a crow that uses a tool, a small stone in order to catch a worm from a glass of water.The dolphins in many studies are considered intelligent at the individual level. An advanced ability of dolphins is the self-awareness. Marten and Psarakos (1995) presented an interesting study based on self-view television to distinguish between self-examination and social behavior in the Bottlenose dolphin. The most well-known abilities of dolphins are to teach, learn and cooperate. Dolphins have a complex communication and social behaviour. Figure 2.2 presents the image of a common group of dolphins. Some studies prove that primates are one of the most intelligent in the class of animals (Reader, Hager, & Laland, 2011). Orangutans are one of the most intelligent primates. The ability of orangutans to use different types of tools in order to perform tasks is well-known. Figure 2.3 presents an orangutan that uses a spear to catch fish. The orangutans can be considered intelligent at individual level.

Review of Recent Trends in Measuring the Computing Systems Intelligence-Figure 1. Intelligence of different simple living creature (accessed 01.11.2017). 1.1. A carnivorous plants catching an insect (https://phys.org/news/2016-05-colombia-peace-reveal-jungle-species.html); 1.2. A colony of ants solving a very complex task (https://mappingignorance.org/2016/05/27/rafting-ants); 1.3. The collective behaviour of a school of fish (https://simple.wikipedia.org/wiki/Shoaling_and_schooling)

The biological intelligence of different life forms, ranging from very simple (such as plants) to very complex (such as humans) is the subject of many studies and a large amount of research. Frequent studies related to different kind of biological intelligence include: the intelligence of horses (Krueger, & Heinze, 2008; Krueger, Farmer, & Heinze, 2014; Schuetz, Farmer, & Krueger, 2016), intelligence of pigs (Broom, Sena, & Moynihan, 2009), intelligence of dogs (Coren, 1995), intelligence of primates (Reader, Hager, & Laland, 2011) and so one. Figures 1, 2, and 3 present some biological life forms that are frequently considered intelligent. Trewavas (2002; 2005) considered that plants intelligence should be based on principles such as their ability to adjust their morphology, and phenotype accordingly to ensure self- preservation and reproduction. Figure 1.1 presents an intelligent plant (carnivorous) that uses a strategy for catching very fast flying insects. In order to eat the insect, it makes a movement. Figure 1.1 presents the catching of an insect by a carnivorous plant. The intelligence of colonies of ants, termites and other insects that live in large colonies is considered at the colony level (Brady, Fisher, Schultz, & Ward, 2014; Johnson, Borowiec, Chiu, Lee, Atallah, & Ward, 2013). Figure 1.2 presents the coherent intelligent surviving behaviour of a colony of a species of ants. The ants make a structural reorganization in order to move on the surface of the water. Figure 1.3 presents a very large school of fish with an intelligent coherent collective feeding and self-protecting behaviour. Each individual fish has a very simple behavior. Based on this it cannot be considered intelligent. The intelligence in large schools of fish emerges at the collective level (Shaw, 1978; Parrish, Viscedo, & Grunbaum, 2002).

Review of Recent Trends in Measuring the Computing Systems Intelligence-Figure 4. Intelligent robots (accessed 01.11.2017). 4.1. Erica, a humanoid robot (https://www.tech-review.com/erica-is-the-latest-japanese-robot-with-human-appearance.html). 4.2. Atlas, a bipedal humanoid robot developed by Boston Dynamics (https://en.wikipedia.org/wiki/Atlas_(robot))

One of the most highly quoted and interesting definitions of machine intelligence was presented by Alan Turing (1950). Turing considered a computing system intelligent if a human assessor could not decide the nature of the system (being human or artificial) based on questions asked from a room hidden from a human assessor. Until recently there were performed different discussions and comments on the Turing test. Hernández-Orallo (2000) presents an interesting study related to the Turing Test. Dowe and Hajek, (1998) propose a computational extension of the Turing Test. The design and development of intelligent systems are historically very recent. But, even if the advance of hardware and software is very fast, it will take a longer time until the artificial computing systems will attain a similar intelligence with the humans. Based on this fact, we consider that is not appropriate to formulate the problem of the direct comparison at a general level of human intelligence with the machine intelligence. Different definitions were proposed for the intelligence of the agents (Russell, & Norvig, 2003; Iantovics, & Zamfirescu, 2013). Many authors (Russell, & Norvig, 2003; Iantovics, 2005) argue that the intelligence of the agents cannot be defined universally. The impossibility to give a universal definition to the human intelligence is based mostly on the enormous complexity of the human brain and complexity of the human thinking and decision making. Similarly, we may consider the impossibility of universal definition of intelligence of the agents based on the very large variety (by type and complexity) of intelligent agents. The machine intelligence frequently is defined based on different abilities such as (Iantovics, 2005; Sharkey, 2006): autonomous learning, self-adaptation, and evolution. These principles of considering the intelligence are inspired by biological life forms able to learn autonomously during their life cycle, to adapt to the environment and to evolve during more generations. We would like to outline that not all the designed agents are intelligent. There is not a required property of an agent to be intelligent.

Review of Recent Trends in Measuring the Computing Systems Intelligence-Figure 5. What machine intelligence is (accessed 01.11.2017) http://www.ibmbigdatahub.com/blog/measuring-artificial-intelligence-quotient)

There are many developed cooperative systems composed of very simple agents that at the system’s level are considered intelligent. Yang, Galis, Guo, and Liu (2003) presented an intelligent cooperative mobile multiagent system composed of simple reactive agents. The mobile agents are specialized in a computer network administration. They are endowed with knowledge retained as a set of rules which describe network administration tasks. The multiagent system could be considered intelligent based on the fact that it simulates the behavior of a human network administrator. In some cooperative systems, the member agents can organize themselves into cooperative coalitions/groups. Each coalition being able to solve cooperatively problems. Iantovics and Zamfirescu (2013) presented such an adaptive cooperative multiagent system, able to reorganize autonomously the coalitions in order to solve more intelligently problems. The biological and artificial intelligence are by a completely different type (Figure 5). Recently, the biological intelligence is the source of inspiration for the development of many intelligent artificial systems and different problem-solving algorithms.

opencc-by-4.0Jun 2018View details →

Supplementary material for "Business Process Simulation: A Systematic Literature Review"

This material containing a list of the bibliographical data of the final sample and the sample of 300 publications before excluding publications outside of our focus supplements the following literature review: Rosenthal, Kristina; Ternes, Benjamin; Strecker, Stefan, (2018). “Business Process Simulation: A Systematic Literature Review”. In: Proceedings of the 26th European Conference on Information Systems (ECIS), Portsmouth, UK, June 23–28, 2018.

opencc-by-4.0Jun 2018View details →

Data for: A systematic review and meta-analysis of Drosophila short-term-memory genetics: robust reproducibility, but little independent replication

All the data, code, analyses, and figures used in the study entitled: "A systematic review and meta-analysis of Drosophila short-term-memory genetics: robust reproducibility, but little independent replication" (doi: https://doi.org/<a href="http://bb2sz3ek3z.search.serialssolutions.com/?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&__char_set=utf8&rft_id=info:doi/10.1101/247650&rfr_id=info:sid/libx&rft.genre=article">10.1101/247650</a>) Abstract Geneticists have long used olfactory conditioning techniques in Drosophila to identify the neurons and genes that mediate learning. While this method has characterized an abundance of memory-related genes, little is known about how these genes induce short-term memory (STM) via signaling pathways; characterizing these networks will be essential to developing mechanistic models of memory formation. Here, we investigated why elucidating the STM pathways has been relatively slow. One possibility is that the STM evidence base is weak due to publication of poorly reproducible results, as has been observed in other fields. We examined this hypothesis by performing a systematic review and subsequent meta-analysis of the STM genetics field. Using several metrics to quantify the variation between discovery articles and follow-up studies, we found that seven genes were highly replicated, showed no publication bias, and had generally high reproducibility. However, the remaining ~80% memory genes have not been replicated since their initial discovery. Although we observed only a few studies that investigated gene interactions, the reviewed genes could together account for >1000% memory. This large summed effect size indicates either that some of the gene findings are not reproducible, that many memory genes participate in shared pathways, or that current protocols lack the specificity needed to identify core plasticity memory genes. Mechanistic theories of memory and cognition will require the convergence of evidence from system, circuit, cellular, molecular, and genetic experiments. As this study demonstrates, systematic data synthesis is an essential tool for this integrated brain science.

opencc-by-4.0Jul 2018View details →

Literature Review 13/02/2018: Genetic risk of Parkinson's disease dementia due to APOE4 or MAPT

Literature review assessing genetic risk of dementia due to APOE4 or MAPT in Parkinson's disease, performed on the 13th February 2018. All studies had to fulfil three a priori inclusion criteria: 1) Case control studies using clinically diagnosed or pathologically confirmed PD and PDD. 2) Time between motor diagnosis and experimental assessment could be defined or estimated. 3) Genotype information supplied, allowing the odds ratio (OR) and confidence intervals (CI) to be calculated that aligned with the genotype categories used in this work. For MAPT, a PubMed search for the term “MAPT Parkinson’s dementia” identified 105 potential matches, of which 9 met the inclusion criteria. For APOE4, a PubMed search for the term “APOE Parkinson’s dementia” identified 188 potential matches, of which 19 met the inclusion criteria. Note, the review includes several publications arising from the CamPaIGN cohort; As we were interested in genetic risk as a function of time from diagnosis, we included each unique study time-point.