Kajian Struktur Sosial Masyarakat Nelayan di Ekosistem Pesisir

Research aim is to analyse any social changes and dynamic of space capacity and critical point of social structure in coastal ecosystem. The main factor of structural change is external factor of structural formation (individu , system), or increase of community access to the change of local social environment, and external social environment. Dynamics of space capacity of social structure in coastal ecosystem of Karanggongso during periode of research can be explained through the two indicators, objective and subjective. There are a general critical point and a special critical point. This results explain that a evolution theory suggest a high possibility to be synthesized with any other theories, e.g. a conflict theory, an equilibrium theory, and a “timbul-tenggelam” theory. The synthesis process is an operational stage in a schematic mapping of theories by Appelbaum. Dynamics of social structure must be known bt any goverment and NGO, which have any development plans in the fisherman community

The Drosophila melanogaster host model

The deleterious and sometimes fatal outcomes of bacterial infectious diseases are the net result of the interactions between the pathogen and the host, and the genetically tractable fruit fly, Drosophila melanogaster, has emerged as a valuable tool for modeling the pathogen–host interactions of a wide variety of bacteria. These studies have revealed that there is a remarkable conservation of bacterial pathogenesis and host defence mechanisms between higher host organisms and Drosophila. This review presents an in-depth discussion of the Drosophila immune response, the Drosophila killing model, and the use of the model to examine bacterial–host interactions. The recent introduction of the Drosophila model into the oral microbiology field is discussed, specifically the use of the model to examine Porphyromonas gingivalis–host interactions, and finally the potential uses of this powerful model system to further elucidate oral bacterial-host interactions are addressed

Two-dimensional 1H NMR study of recombinant insect defensin A in water: resonance assignments, secondary structure and global folding.

International audienceA 500 MHz 2D 1H NMR study of recombinant insect defensin A is reported. This defense protein of 40 residues contains 3 disulfide bridges, is positively charged and exhibits antibacterial properties. 2D NMR maps of recombinant defensin A were fully assigned and secondary structure elements were localized. The set of NOE connectivities, 3JNH-alpha H coupling constants as well as 1H/2H exchange rates and delta delta/delta T temperature coefficients of NH protons strongly support the existence of an alpha-helix (residues 14-24) and of an antiparallel beta-sheet (residues 27-40). Models of the backbone folding were generated by using the DISMAN program and energy refined by using the AMBER program. This was done on the basis of: (i) 133 selected NOEs, (ii) 21 dihedral restraints from 3JNH-alpha H coupling constants, (iii) 12 hydrogen bonds mostly deduced from 1H/2H exchange rates or temperature coefficients, in addition to 9 initial disulfide bridge covalent constraints. The two secondary structure elements and the two bends connecting them involve approximately 70% of the total number of residues, which impose some stability in the C-terminal part of the molecule. The remaining N-terminal fragment forms a less well defined loop. This spatial organization, in which a beta-sheet is linked to an alpha-helix by two disulfide bridges and to a large loop by a third disulfide bridge, is rather similar to that found in scorpion charybdotoxin and seems to be partly present in several invertebrate toxins

Determination of disulfide bridges in natural and recombinant insect defensin A

The primary-structure comparison of natural insect defensin A from Phormia terranovae and recombinant insect defensin A from Saccharomyces cerevisiae has been accomplished using a combination of Edman degradation and liquid secondary ion mass spectrometry. The natural and recombinant proteins have the same primary structure with identical disulfide-bond designations (formula; see text) as determined from the peptides obtained after thermolysin digestion. The combined use of Edman degradation and mass spectometry allowed the disulfide-bridge structure to be determined with a total of only 40 micrograms (9.9 nmol) natural peptide. Mass spectrometry provides a rapid means of disulfide-bridge verification, requiring not more than 20 micrograms recombinant insect defensin A, which is compatible with use in batch analysis

P–756 Predictive factors influencing multiple live births in cumulative IVF cycles: retrospective analysis of over 265000 embryo transfer procedures from the national French registry

Abstract Study question What are the factors that could predict the number of embryos to be transferred in order to diminish risk of multiple pregnancies? Summary answer Single embryo transfer (SET) is advisable for &amp;lt;38 year-old women in fresh cycles and for &amp;lt;35 year-old women in FET whatever the IVF number attempts. What is known already Multiple pregnancies are associated to increased maternal and perinatal complications. Risks associated to multiple implantations are significantly reduced with SET policy. However, while SET is more assertive with a lesser negative impact in younger patients (&amp;lt;35 years), its feasibility is less evident for the older population, whom oocyte quality is likely compromised. A double embryo transfer (DET) could improve chances of implantation and shorten their time to pregnancy. Identification of risk factors for multiple pregnancies could help in decision making for a double or SET and reduce chances for multiple gestations without reducing the chances to achieve pregnancy. Study design, size, duration A retrospective study from the national French data registry provided and approved by the Agence de la Biomédecine was performed. A total of 196530 fresh and 68913 frozen cycles from women aged 18–43 year-old were included (2014–2017). Risk factors assessed included women’s age, number of attempts, number of oocytes, fertilization rate, embryo stage, number of embryos transferred, number of supernumerary embryos frozen. Secondary infertility, oocyte donor, oocyte freezing, PGT, freeze-all and IVM cycles were excluded. Participants/materials, setting, methods Cumulative cycles derived from 65% of ICSI, 32% of IVF and 3,2% IVF/ICSI. The distribution of patients age at oocyte retrieval was 60% &amp;lt; 35, 21% &amp;lt; 38, 11% &amp;lt; 40, and 8% ≥ 40 years old. Multivariable logistic regression was conducted to calculate adjusted odds ratios with 95% confidence intervals for live birth chance and multiple live birth risk associated with each risk factor. Main results and the role of chance The chances of obtaining a cumulative live birth decreases with increased patients age (OR 0.71 for 35–38 years and 0.47 for 38–40 years, p &amp;lt; 0.00001), with increased number of attempts (from OR 0.87 for attempt = 2 to OR 0.74 for attempt ≥ 4, p &amp;lt; 0.00001), and for frozen embryos transferred (OR 0.14, p &amp;lt; 0.00001). The chances of live birth increases with the increased number of oocytes (from OR 1.33 for 4–12 to OR 1.52 for &amp;gt; 18, p &amp;lt; 0.00001 in all cases), with a fertilisation rate &amp;gt;40% (OR 1.29, p &amp;lt; 0.00001), with blastocyst transfer (OR 1.29, p &amp;lt; 0.00001), with the increase on the number of frozen embryos (OR 7.37 for &amp;gt;1, OR 13.08 for &amp;gt;2, and OR 16.92 for &amp;gt;6, p &amp;lt; 0.00001 in all cases) and number of embryos transferred (OR 1.42 for 2 embryos and OR 1.39 for &amp;gt;2 embryos, p &amp;lt; 0.00001 in all cases). In case of live birth, the risks of multiple births when two embryos were transferred decreases in patients aged &amp;gt;38 years (OR 0.50, p &amp;lt; 0.00001) and for frozen embryos transferred (OR 0.65, p &amp;lt; 0.00001). The risk increases with a fertilisation rate &amp;gt;60% (OR 1.30, p &amp;lt; 0.00001), with blastocysts transfer (OR 1.34, p &amp;lt; 0.00001) and when at least one supernumerary embryo is frozen (OR &amp;gt; 1.30, p &amp;lt; 0.00001). Limitations, reasons for caution This study is limited in only providing a risk-benefit balance for multiples on the choice of transferring one or two embryos. Clinical data such as stimulation protocols and doses of gonadotropins were not considered in this evaluation. Wider implications of the findings: This study provides help to develop a strategy for the medical staff in the decision making for the number of embryos to be transferred. It may also serve as a patient’s information aid and help to improve their chances of achieving a health singleton if pregnant. Trial registration number Not applicable </jats:sec

O-124 A new artificial intelligence (AI) system in the block: impact of clinical data on embryo selection using four different time-lapse incubators

Abstract Study question Can AI algorithms assist embryologists in evaluating embryos from any time-lapse system (TLS) along with clinical data to better predict pregnancy outcomes and reduce time-to-pregnancy? Summary answer Our algorithm (Embryoly) significantly increases accuracy in predicting clinical pregnancy by 26.9% amongst embryos deemed of fair and good quality when clinical data is included. What is known already Embryologists routinely use defined morpho-kinetic criteria to decide which embryo to transfer, and yet, many embryos deemed of good quality fail to lead to a pregnancy. Thus, AI algorithms to assist embryologists in objectively selecting the most promising embryos are in demand. To date, several reports indicate that AI algorithms are capable of predicting pregnancy clinical outcomes but to the best of our knowledge they only consider visual data (or together with a small set of clinical features) from individual TLI systems to generate their predictions.  Study design, size, duration A dataset of 6790 embryos (97.82% known clinical pregnancy outcome, 31.47% frozen transfers) from 2519 patients from 11 European fertility centers recorded with 4 different TLS (GERI-Merck, Embryoscope &amp; EmbryoscopePlus-Vitrolife and MIRI-Esco) was used to train and validate Embryoly. Nine out of 93 clinical factors were identified as being the most predictive, including woman age, woman and man BMI and AMH levels. Performances were evaluated on a separate test dataset (393 videos). Participants/materials, setting, methods Clinical pregnancy outcome was predicted using a 3D convolutional neural network that analyzed up to 5 days of embryo development. The output score was further analyzed considering the clinical features to generate a second clinical score. Both predictions were compared to those of 10 senior embryologists made on the same test dataset (with and without clinical features). Embryo quality was assessed as: poor, fair, good. Unless specified otherwise, McNemar test was used for statistical tests. Main results and the role of chance Overall accuracy of embryologists in predicting clinical pregnancy  based on videos alone was 57.25% (CI 95% : 52.34% - 62.16%) compared to 60.56% (CI 95% : 55.71% - 65.41%) for Embryoly (p = 0.35). When videos were analyzed together with the clinical factors, overall accuracy of embryologists was significantly lower than Embryoly (60.05% [CI 95% : 55.19% - 64.91%] vs 68.19% [CI 95% : 63.57% - 72.82%], p-value=0.015, respectively). Clinical factors significantly increased our accuracy by 7.63% (p-value=0.030). More specifically, Embryoly algorithms fared better in terms of detecting false positives (31.30% vs 19.34%) compared to embryologists, with a specificity of 74.4% vs. 58.6%, respectively. If we consider only embryos of fair and good quality (71.50% of our test dataset) Embryoly’s accuracy was 13.52% higher than that of embryologists. This translates into AI having an even better ability to detect false positives for embryos that could be seen as good candidates for transfer (20.28% false positives against 42.70% for the embryologists). Embryoly performs differently across selected TLS when analyzing videos alone, but not when clinical data was also considered (chi2 test, p &amp;lt; 0.001 and 0.5, respectively). Further work will investigate these discrepancies across TLS. Limitations, reasons for caution As of today, Embryoly’s accuracy in predicting the outcome of poor-quality embryos is not different to that of embryologists (79.46% vs 84.96%; p-value=0.19). We are improving this by exposing Embryoly to more “poor quality” embryos, so as to also identify poor quality embryos with unexpected potential for implantation. Wider implications of the findings Our pioneering findings support the use of AI for a standardized and couple-centered care in clinical embryology, integrating male and female factors with embryo development analyses from multiple TLS. Our approach has the potential to cost-effectively reduce time to pregnancy and is another step toward a personalized embryo transfer strategy. Trial registration number Not applicable </jats:sec