Network_Vulnerability_Assessment
News Update on Vulnerability Research: April – 2019

Critical Infrastructure Vulnerability to Spatially Localized Failures with Applications to Chinese Railway System

This article studies a general variety of initiating events in vital infrastructures, referred to as spatially localized failures (SLFs), that are outlined because the failure of a collection of infrastructure elements distributed in an exceedingly spatially localized space because of harm sustained, whereas alternative elements outside the world don’t directly fail. These failures may be thought to be a special variety of intentional attack, like bomb or explosive assault, or a generalized modeling of the impact of localized natural hazards on large‐scale systems. this text introduces 3 SLFs models: node focused SLFs, district‐based SLFs, and circle‐shaped SLFs, and proposes a SLFs‐induced vulnerability analysis methodology from 3 aspects: identification of vital locations, comparisons of infrastructure vulnerability to random failures, topologically localized failures and SLFs, and quantification of infrastructure info worth. The planned SLFs‐induced vulnerability analysis methodology is finally applied to the Chinese railroad line and may be conjointly simply custom-made to investigate alternative vital infrastructures for valuable protection suggestions. [1]

Network efficiency and vulnerability analysis using the flow‐weighted efficiency measure

Analyzing the vulnerability of a network and distinctive its vital spots is of nice importance for today’s call manufacturers. The in‐depth information regarding the underlying network and its potency is key to adequate higher cognitive process. during this paper, the flow‐weighted potency live is introduced and exemplarily incontestible on a physical network—the underground network of city, Germany. This paper addresses the usability of the load values of a graph from associate degree potency purpose of read. The projected live calculates the flow‐weighted potency in a very subway network by computing the shortest route between each combine of stations and therefore the according bottleneck flow of the trains. Results show that the network potency is invariant over all schedules, whereas the flow‐weighted potency is considerably variable in keeping with the train schedules. [2]

Seismic Vulnerability Scenarios for Timisoara, Romania

Romania is an eu country with 2 major unstable zones, Vrancea and Banat. Timisoara is one amongst the most important cities in Balkan nation, set in Banat unstable space, characterised by shallow earthquakes, with depths between a pair of and twenty metric linear unit and necessary vertical forces. within the historical space of Timisoara there have been classified differing kinds of structures, victimization the HAZUS methodology (HAZUS 1999).

Seismic vulnerability analysis was done victimization totally different methodologies, Vulnerability Index, Tremuri, Vulnus and therefore the Romanian methodology in step with code P100-3/2013 so as to assess the behavior of historical buildings. supported the results obtained once applying the 3 methodologies, there’ll be more created fragility curves for buildings set within the three historic zones of Timisoara town. specifically the chance to possess in-plane or out-of-plane damages obtained by Vulnus is related to with the results of the nonlinear analysis created with Tremuri code considering totally different limit state. afterward, considering the everyday earthquakes in Banat space, it had been doable to outline the unstable response for 3 buildings, as a preview of unstable response of town and therefore the impact of the earthquake. this sort of research was created for the foremost frequent earthquake type. this text makes plain the primary step in estimating the hazard unstable situations for the analysis of the losses in terms of human life and money issues, giving the support for more bar and intervention methods. [3]

Characteristics of drought vulnerability for maize in the eastern part of Northwest China

Based on data distribution and diffusion methodology theory and combined with the standardized precipitation index and relative meteoric yield knowledge, meteoric factors and social factors were comprehensively thought of to assess the vulnerability of maize (Zea mays) to drought. The likelihood distribution curve of meteoric drought degree (MDD) and relative meteorological yield within the jap a part of Northwest China (Gansu, Ningxia and Shaanxi) from 1978 to 2016 were obtained, employing a two-dimensional traditional data diffusion methodology to construct the vulnerability relationship between MDD and relative meteoric yield. The drought vulnerability curve of maize within the study space was obtained. The likelihood distribution of MDD was increased by the fragility curve and summed to get the multi-year average risk. The MDD likelihood distribution curve showed that the probability of moderate drought in Shaanxi was comparatively high, followed by Kansu and Ningxia. The likelihood distribution of Kansu was additional separate. The likelihood of robust meteoric drought in Ningxia was high, followed by Shaanxi and Kansu. likelihood distribution of relative meteoric yield for maize in Kansu was extremely separate, with thick tailings, giant uncertainties, and additional extreme values, that were powerfully plagued by environmental condition, followed by Shaanxi and Ningxia. Taking meteoric drought because the cause and maize injury as the result, the vulnerability relationship between MDD and drought injury was obtained. With AN inflated MDD, the relative meteoric yield of maize bit by bit declined. From the typical price, once MDD was but −2.60, the relative meteoric yield of maize was reduced among 15%; once MDD was larger than −2.60, the relative meteoric yield of maize inflated among tenth. once the degree of meteoric drought exceeded −2.2, maize was most prone to drought in Shaanxi followed by Ningxia and Kansu. once meteoric drought was but −2.2, maize was most prone to drought in Shaanxi followed by Kansu and Ningxia. The expected values of relative meteoric production in Kansu, Ningxia, and Shaanxi were one.36%, 2.48%, and −1.76%, respectively; so, Shaanxi had the very best maize drought risk, followed by Kansu and Ningxia. This analysis had a transparent physical background and clear risk connotations. The results offer an information foundation and a theoretical basis for drought disaster reduction for maize within the study space. [4]

Community Vulnerability to Disasters in Botswana

Community vulnerability to varied hazards and connected risks complicates recovery, reconstruction, and adaptation to disaster shocks. Vulnerability results from many factors unmoving among the community requiring associate degree correct analysis of environmental threats. As such, vulnerability and capability assessments are essential within the analysis and higher comprehension of disasters and also the connected behaviour among the social surroundings. Hazard and vulnerability assessment diagnose situational crises and also the doubtless effects on individuals and the surroundings. A key result from the study on community resilience to disasters in Republic of Botswana shows that communities are vulnerable and are perpetually beneath disaster threat. though there are district disaster management committee, they’re solely active throughout emergency response and ignore the pre and post disaster activities. As such, communities, families, and people lack basic data, skills, and techniques necessary to boost their resilience to disasters. once reflective on problems that build people / or communities vulnerable, it’s crucial that communities develop measures to cut back vulnerabilities across teams within the community. Therefore, this paper seeks to draw the eye of people / or communities to disaster connected risks and to deliberately brace oneself for environmental hazards / risks and guarantee acceptable mitigation measures. [5]

Reference

[1] Ouyang, M., Tian, H., Wang, Z., Hong, L. and Mao, Z., 2019. Critical infrastructure vulnerability to spatially localized failures with applications to Chinese railway system. Risk Analysis39(1), pp.180-194. (Web Link)

[2] Nistor, M.S., Pickl, S., Raap, M. and Zsifkovits, M., 2019. Network efficiency and vulnerability analysis using the flow‐weighted efficiency measure. International Transactions in Operational Research26(2), pp.577-588. (Web Link)

[3] Apostol, I., Mosoarca, M., Chieffo, N. and Onescu, E., 2019. Seismic vulnerability scenarios for Timisoara, Romania. In Structural Analysis of Historical Constructions (pp. 1191-1200). Springer, Cham. (Web Link)

[4] Characteristics of drought vulnerability for maize in the eastern part of Northwest China

Ying Wang, Wen Zhao, Qiang Zhang & Yu-bi Yao
Scientific Reports volume 9, Article number: 964 (2019) (Web Link)

[5] Community Vulnerability to Disasters in Botswana

K. Maripe
Department of Social Work, University of Botswana, Botswana.

B. M. P. Setlalentoa
Faculty of Human and Social Sciences, North West University, South Africa. (Web Link)

Press Release on Water Consumption Research: April – 2019

Monthly blue water footprint caps in a river basin to achieve sustainable water consumption: The role of reservoirs

The blue water footprint (WF) measures the consumption of runoff during a basin. so as to confirm property water consumption, setting a monthly blue WF cap, that’s associate upper-limit to the blue WF during a basin every month, is an appropriate policy instrument. The blue WF cap during a basin depends on the precipitation that becomes runoff and therefore the have to maintain a minimum flow for sustaining ecosystems and livelihoods. Reservoirs on the watercourse usually sleek runoff variability and therefore raise the WF cap and scale back blue water scarceness throughout the season. Previous water scarceness studies, considering the quantitative relation of actual blue WF to the blue WF cap beneath natural background conditions, haven’t studied this result of reservoir storages. Here we tend to assess how water reservoirs influence blue WF caps over time and the way they have an effect on the variability of blue water scarceness during a basin. we tend to take the Huang He Basin over the amount Jan 2002–July 2006 as case study and contemplate information on ascertained storage changes in 5 giant reservoirs on the most stream. Results indicate that reservoirs distribute the blue WF cap and blue water scarceness levels over time. Monthly blue WF caps were usually down by reservoir storage throughout the flood season (July–October) and raised by reservoir releases over the amount of highest crop demand (March–June). However, with water storage prodigious twentieth of natural runoff in most rainy months, reservoirs contribute to “scarcity within the wet months”, which is to be understood as a scenario within which environmental flow needs associated with the incidence of natural peak flows aren’t any longer met. [1]

Green Smart Technology for Water (GST4Water): Life Cycle Analysis of Urban Water Consumption

The increasing inadequacy of water is encouraging methods in water saving and concrete water management systems dedicated to reducing resource consumption and environmental impact. At family and concrete scales, there’s Associate in Nursing increasing interest in onsite greywater and non-potable water use systems so as to enhance water convenience. during this framework, the project GST4Water funded by the eu Union (EU) beneath the POR-FESR 2014–2020 Program of Italian region Region, has been developed with the aim to implement water consumption observation systems, to outline solutions for greywater use, and to develop tools for environmental property analysis applied to water systems. this study focuses on this last goal, activity a life cycle assessment of the solutions optimized at a vicinity level. specifically, six totally different eventualities are compared, ranging from 2 models considering ancient installation along with or while not energy consumption associated with predicament generation, and 5 extra models connected with totally different assumptions in terms of greywater recovery systems, and energy and predicament production, at variable percentages of renewable and electrical phenomenon energy offer. Finally, Associate in Nursing analysis of the come back time of environmental investment is disbursed, supported the results obtained through the state of affairs analysis. [2]

Economic development and residential water consumption in Chile

A better understanding of the relative importance of things associated with global climate change and to changes related to economic process would serve to tell water policy and to focus scarce public resources on anticipated issues arising from distinct sources of changes in water demand. this text investigates the determinants of residential water consumption in Chile, a developing country that has seen noteworthy changes in incomes, unit size, financial condition rates and levels of urbanization, and that is projected to expertise vital climatical however varied changes, betting on the region of the country. Panel information for 1998-2010 at the municipal level is employed to research the sensitivity of residential water demand to climate and development-related factors. within the case of Chile, the result on water consumption of those development-related changes is calculable to be many times that of the changes related to climate projections for fifty to eighty years within the future. [3]

Effect of unboiled water consumption data on sensitivity analysis in quantitative microbial risk assessment

Quantitative microbic risk assessment of drinkable is mostly followed by sensitivity analysis for examining the relative importance of variables of the simulation model on the end result. This study investigated the impact of the applied math strategies applied to unboiled water consumption knowledge on sensitivity analysis. The sensitivity analysis for concentration of E. coli (E. coli) in treated water showed utterly totally different results from the analysis for E. coli dose. This was thanks to the appliance of a Poisson model to the water consumption, that instructed that twenty seven of the folks failed to drink H2O. Our study then applied a unique model—an exponential distribution—to the water consumption knowledge. additionally, incidental water intake was assigned  to non-consumers within the Poisson model. The results of sensitivity analyses for these cases were terribly totally different from those obtained from the primary analysis. This study thus incontestible that the applied math strategies wont to analyze water consumption knowledge have nice impacts on sensitivity analysis, though they are doing not have an effect on the yearly risk of infection. Specifically, applied math strategies might devalue sensitivity analysis. To avoid this downside, it’s desirable to use endless model like the exponential model, instead of a separate one like the Poisson model, to explain the variability in water consumption. [4]

The Nexus between the Sleeping Time, Water Consumption and the Body Mass Index

Aims: A healthy Body Mass Index (BMI) is widely regarded as important for overall health that helps to avert and control many adverse health effects. It is also known that the sleep deprivation and dehydration have a strong impact on healthy life and sleep deprivation is common among university students and has been associated with poor academic performance. We aim to study the relationship between sleep deprivation and dehydration with BMI.

Study Design: Data collection and statistical analysis.

Place and Duration of Study: University of Peradeniya, Sri Lanka, between September 2017 and March 2018.

Methodology: We examined the association of daily sleeping time and daily water consumption with the Body Mass Index among 452 university students of age 24-26 years and 326 females, and 126 males consented to participate in the study. A cross-tabulation analysis was used to identify the relationship between water consumption and daily sleeping time with BMI.

Results: Results of the chi-square test show that there is a significant association between BMI and sleeping time of the students as the calculated chi-square value of 13.771 was significant as p is 0.008 at 4 degrees of freedom. Results of phi and Cramer’s V measures of association show that the correlation coefficient between BMI and sleeping time is 0.175 with a p-value of 0.008. Also between BMI and water consumption of the students, the calculated chi-square value of 11.538 was significant as P is 0.021 (<0.05) at 4 degrees of freedom. Results of phi and Cramer’s V measures of association show that the correlation coefficient between BMI and water consumption as 0.160 with a p-value of 0.021.

Conclusion: The phi measure of symmetric coefficient shows a significant positive association; that is the students who are consuming more water are prone to fall in higher BMI category while students consume less water falls to lower BMI. The phi measure of symmetric coefficient shows a significant positive association; that is the student who gets less sleep are prone to fall in higher BMI category while students take long sleep a day falls to lower BMI. [5]

 

Reference

[1] Zhuo, L., Hoekstra, A.Y., Wu, P. and Zhao, X., 2019. Monthly blue water footprint caps in a river basin to achieve sustainable water consumption: The role of reservoirs. Science of the total environment650, pp.891-899. (Web Link)

[2] Bonoli, A., Di Fusco, E., Zanni, S., Lauriola, I., Ciriello, V. and Di Federico, V., 2019. Green Smart Technology for Water (GST4Water): Life Cycle Analysis of Urban Water Consumption. Water11(2), p.389. (Web Link)

[3] Fercovic, J., Foster, W. and Melo, O., 2019. Economic development and residential water consumption in Chile. Environment and Development Economics, 24(1), pp.23-46. (Web Link)

[4] Effect of unboiled water consumption data on sensitivity analysis in quantitative microbial risk assessment

Sadahiko Itoh & Liang Zhou
npj Clean Watervolume 1, Article number: 18 (2018) (Web Link)

[5] The Nexus between the Sleeping Time, Water Consumption and the Body Mass Index

M. A. D. Priyadarshani
Department of Mathematics, University of Peradeniya, 20400, Sri Lanka.

J. A. Weliwita
Department of Mathematics, University of Peradeniya, 20400, Sri Lanka.

S. M. M. Lakmali
Department of Mathematics, University of Peradeniya, 20400, Sri Lanka

S. Witharana
Higher Colleges of Technology, Abu-Dhabi, United Arab Emirates. (Web Link)

Peer Review History: The Production of Biogas from Cow Dung for Powering a Motor Vehicle Tyre Tube

Biogas production from cow dung for powering a tyre tube was investigated, using a 20litre capacity prototype biogas digester, fabricated in the Lagos state university (LASU) research laboratory. The experiment was batch operated and daily gas yield from the digester was monitored for 21 days. The ambient and slurry temperatures, PH were also monitored. Two important aspects of the biogas itself is digester and starter. This Research aim to fabricate a digester that can be used to convert a cow dung into methane for powering a tyre tube and the objective to determine whether cow dung from ranch can produce methane. In this study a mini biogas digester fabricated and tested for organic waste, thirty-five (35) kg of the cow dung, water and starter was used. (The starter that used in this study is silica gel). The cow dung inserted into the digester through the inlet pipe, and kept for three weeks. The ratio of cow dung water is 1:2. The result obtained from the biogas production showed that methane has 67.9%. This result show that these waste could be a source of a renewable gas if managed properly since the waste sluggishly continued gas production after 21 days retention time.

Source: http://www.journaljenrr.com/index.php/JENRR/article/view/29733

wireless-networks
Peer Review History: Current Trend in Wireless Networks

In this study, a general overview of the current trend wireless network has been presented the explosive parallel growth of both the Internet and cellular telephone services are becoming the two most important phenomena that impact the required modern feasible and secure telecommunications. A wireless service technology has become well-known in technology markets. WLAN implemented the development of the wired LAN. Industries produce the required components, which lead to the fast developments in designing and implementing of such networks. The merging of wireless network deemed to be a solution to some of the problems of the wired networks. In this survey study, we will try to discuss the advantages of wireless devices and indicate the challenges involved in this technology from the point of view of its foundation, architecture, requirements, its components, and protocols.

Source: http://www.journalajrcos.com/index.php/AJRCOS/article/view/30077

Peer Review History: Degradative Effect of I.R Radiations on the Constituents of Bitumen

Sample of natural bitumen were taken from bitumen well in Agbabu town in Odigbo Local Government of Ondo State. These samples were separately irradiated with infrared radiations for a period of seven hours. Part of the sample was withdrawn at interval of One, Three and Seven hours. The withdrawn sample was later separated into maltene and asphaltene fractions. The maltene fraction was further separated into saturated, aromatic and polar fraction. The saturated and aromatic fractions were subjected to gas chromatography analysis. The Saturated and aromatic profiles of the bitumen were found to vary with the period of irradiation. The Chemical composition of both the saturated and aromatic compounds in the bitumen decreased with the period of irradiation. Thus, decrease in the chemical composition of bitumen as a result of irradiation cause aging of bitumen. Therefore, I.R radiations were found to have a degradative effect on the composition of bitumen.

Source: http://www.journalajacr.com/index.php/AJACR/article/view/30074

Peer Review History: Surgical Management of Subtrochanteric Fracture with Intramedullary Nailing in Osteopetrosis – A Rare Case Report

Background: Osteopetrosis, also called as “Osteosclerosis”, “Marble bone disease” or “Albers-Schonberg disease, is an extremely rare inherited sclerotic bone disorder. The primary defect in osteopetrosis is due to mutation in CLCN-7 gene. Osteopetrosis is marked by increased bone density due to the defect in bone reabsorption by osteoclasts which leads to accumulation of bone with defective architecture, making them brittle and susceptible to fracture.

Case Report: We reported a 36 years old normotensive and non-diabetic female with type 2 adult type of osteopetrosis with subtrochanteric fracture of right femur and highlighted the surgical management with intramedullary interlocking nailing and technical difficulties encountered during the surgery. The classical features of osteopetrosis associated with this case and past history of left trochanteric fracture & its surgical management, iatrogenic fracture associated with surgical implant removal has been enlightened in this article to bring about the awareness among the readers. The patient has been explained about the natural history of disease and counselled for genetic screening to evaluate the mutant alleles. Due to lack of facilities, genetic testing could not be done.

Conclusion: We recommend intramedullary interlocking nailing is the best surgical modality of choice for subtrochanteric fracture of femur in a case of osteopetrosis.

Source: http://www.journalajorr.com/index.php/AJORR/article/view/30092

Peer Review History: The African Paleotropical Influence on the Biogeography of the Flora of Jazan, KSA

Aims: To put all selected species in their proper place as in the phytogeographical affinities for each region.

Study Design: Field and jazan herbarium design was used in this study.

Place and Duration of Study: A total of 201 plant species were selected from Jazan of Saudi Arabia.

Methodology: About 201 plant species (seven species of Pteridophytes, one species of Gymnosperms and 193 species of Angiosperms) related to 59 families were recorded from a total of 524 species previously recorded in 2013 from Jazan region of Saudi Arabia. The selected plant species were revealed a distribution relationships between the three African paleotropical floristic regions and showed 9 African paleotropical floristic elements.

Results: The chorological analysis revealed the highest percentages of 51.24% was inhabiting in Afromontane archipelago-like regional center of endemism (AF) of the total recorded species. The distribution relationships among the African paleotropical floristic elements was subjected to numerical analysis which showed the similarity and dissimilarity between the elements based on the UPGMA dendrogram software.

Conclusion: The program  was constructed two main groups, the first group (I) were included with Afromontane archipelago-like regional center of endemism (AF) flowed by the Guineo-Congolian regional center of endemism (GC). The second group (II) in which Sahara Regional Subzone (SS1) was recognized in a separated in a single level.

Source: http://www.journalajsspn.com/index.php/AJSSPN/article/view/26584

Peer Review History: Risk Assessment of “Other Substances” –Eicosapentaenoic Acid, Docosapentaenoic Acid and Docosahexaenoic Acid

The Norwegian Scientific Committee for Food Safety (Vitenskapskomiteen for mattrygghet, VKM) has, at the request of the Norwegian Food Safety Authority (Mattilsynet; NFSA), assessed the risk of “other substances” in food supplements and energy drinks sold in Norway. VKM has assessed the risk of doses given by NFSA. These risk assessments will provide NFSA with the scientific basis while regulating the addition of “other substances” to food supplements and other foods.

“Other substances” are described in the food supplement directive 2002/46/EC as substances other than vitamins or minerals that have a nutritional or physiological effect. The substance is added mainly to food supplements, but also to energy drinks and other foods. VKM has not in this series of risk assessments of “other substances” evaluated any potential beneficial effects from these substances, only possible adverse effects.

The present report is a risk assessment of eicosapentaenoic acid (EPA), docosapentaenoic acid (DPA) and docosahexaenoic acid (DHA) in food supplements, and is based on previous risk assessments and a literature search.

It is emphasised that this risk assessment concerns the single fatty acids EPA, DPA or DHA separately and not mixtures of these as found in e.g. fish oil/cod liver oil. For risk assessment of combined mixtures of n-3 LCPUFAs in e.g. fish oil/cod liver oil, see the EFSA opinion from 2012 or the VKM assessment from 2011 (EFSA, 2012; VKM, 2011). In the reviewed literature of this risk assessment, no studies investigating ratios between EPA, DPA, DHA or other fatty acids in mixtures have been identified.

EPA, DPA and DHA are long chain n-3 polyunsaturated fatty acids (n-3 LCPUFA) and in food these fatty acids are incorporated in triacylglycerols (TAGs) and phospholipids (PLs). Dietary sources are fatty fish, cod liver-, seal-, whale-, fish- and krill oils and human milk, containing various ratios of these fatty acids in combination. EPA can be metabolised to eicosanoids such as prostaglandins, prostacyclins and leukotrienes, all groups are biologically active substances. The eicosanoids participate in the regulation of blood pressure, renal function, blood coagulation, inflammatory and immunological reactions. DHA is an essential structural component of the brain, skin, sperm, testicles and retina. DPA can be retro-converted to EPA or converted to DHA. Still little is known of the biological effects of DPA.

Humans have a limited capacity to synthesise EPA, DPA and subsequently DHA from the precursor alpha-linolenic acid (ALA), and this endogenous production is negligible in comparison to the doses used in supplementation studies.

According to information from the NFSA, EPA, DPA and DHA are food supplement ingredients in Norway, and NFSA has requested a risk assessment of these fatty acids in the following doses in food supplements:

EPA: 1500, 1750 and 1825 mg/day

DPA: 100, 125 and 150 mg/day

DHA: 1050 and 1290 mg/day

Children below 10 years were not included in the terms of reference.

Information about intake of EPA, DPA and DHA from the diet is scarce, but calculations performed in the Norwegian Mother and Child Cohort Study indicate a mean total intake (SD) from food and supplements of EPA around 330 (340) mg/day, DPA 43 (30) mg/day and DHA 430 (380) mg/day among pregnant women (2002 to 2008). Mean combined intake of EPA, DPA and DHA from fish oil/ cod liver oil in adults participating in a nationally representative dietary survey was 735 mg/day (VKM, 2014).

The major concerns with high intake of EPA and DHA have been increased bleeding time, adverse effects related to immune function, lipid peroxidation and glucose homeostasis. EFSA concluded in 2012 that long-term supplemental intakes of 5 g/day of the n-3 LCPUFA do not raise safety concerns for adults with regard to an increased risk of spontaneous bleeding episodes or bleeding complications, or affect glucose homeostasis, immune function or lipid peroxidation. In 2011, VKM concluded that an intake n-3 LCPUFA up to 6.9 g/day was not associated with increased risk of any serious adverse events.

Some adverse health effects related to gastrointestinal function, including abdominal cramps, flatulence, eructation, vomiting and diarrhea have been reported, but seem to be associated with intake of an oily substance and not related specifically to EPA, DPA and/or DHA.

EPA:

In the report from 2012, EFSA concluded that 1.8 g/day of supplemental EPA does not raise safety concerns in adults. None of the included studies from our literature searches limited to 2012 and onwards have investigated bleeding complications. The dosages of EPA in the three included studies in this report range from 1.8 to 3.8 g/day for 12 weeks. The main endpoints in the studies included lipid peroxidation, inflammation biomarkers of cardiovascular diseases and no serious adverse events were found related to the main endpoints. In general, adverse events were described as gastrointestinal discomforts and not related to dosage.

Studies of longer duration are necessary before an intake above 1.8 g of EPA can be considered safe.

The Norwegian Scientific Committee for Food Safety (VKM) concludes that the specified doses of 1500, 1750, 1825 mg/day of EPA in food supplements are unlikely to cause adverse health effects in adults (≥18 years).

In 2012, EFSA did not make conclusions for children or adolescents for EPA. No new studies with EPA supplementation have been identified in children or adolescents after 2012, and therefore no risk assessment can be made for children (≥10 years) or adolescents.

DPA:

No dosage of DPA in food supplements can be evaluated due to lack of data.

DHA:

EFSA concluded that 1 g/day of supplemental DHA does not raise safety concerns for the general population (including children and adolescents). The dosages of DHA in the included trials in this report range from 1.0 to 3.6 g/day and the duration from five weeks to four years. Six out of seven studies have used dosages from 1 to 2 g DHA/day. The last study included up to 3.6 g DHA/day for four years and the age spanned from 7 to 31 years. The main endpoints in all studies included lipid peroxidation, inflammation, cognitive performance, blood pressure and biomarkers of cardiovascular diseases and no serious adverse events were found related to the main endpoints. In general, adverse events were described as gastrointestinal discomforts and not related to dosage. VKM therefore considers that the specified daily doses of DHA that moderately exceed 1 g per day (1050 and 1290 mg/day) are unlikely to cause adverse health effects in the general population including children ≥10 years and adolescents.

VKM concludes that the specified doses of 1050 and 1290 mg/day of DHA in food supplements are unlikely to cause adverse health effects in the general population including children (≥10 years), adolescents and adults (≥18 years).

Source: http://www.journalejnfs.com/index.php/EJNFS/article/view/27086

Inhibitory effect of Milk thistle seed extract on cadmium chloride induced DNA damage in liver cells

Abstract:

Cadmium (Cd) is a biologically non-essential but economically valuable metal. Its increase in the environment has been linked to various diseases such as diabetes, organs failure, bone damage, endocrine hormone imbalance and cancer in humans and animals. Cd mainly accumulates in the liver and lead to its malfunction. In the present paper, we investigated the inhibitory effect of milk thistle (Silybum marianumseed (MTS) extract on cell death and DNA damage induced by cadmium chloride (CdCl2) in rat liver cells. The alkaline and neutral comet assays were used to evaluate double and single breaks in DNA. The treatment of the cells with CdClalone resulted in dose dependent decrease in cell viability compared to control cells; while cells co-treated with MTS extract resulted in increased cell viability. Furthermore, treatment of the cells with CdClalone caused increase in DNA damage as shown by the % DNA in the tail and olive tail moment in both alkaline and neutral comet assays. Co-treatment with MTS extract inhibited the Cd-induced DNA damage as shown by the decreased % DNA in the tail and olive tail moment. Our results clearly showed the inhibitory effect of MTS extract on Cd-induced DNA damage in rat liver cells.

Key words: Cadmium, Milk thistle, inhibition, comet assay, DNA damage.

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Modeling of retention behaviors of ecotoxicity of anilines and phenols by chemometrics models

Abstract

Environmental hazard is the risk of damage to the environment eg air pollution, water pollution, toxins, and radioactivity. We performed studies upon an extended series of 65 toxic compounds anilines and phenols with chromatographic retention (log k) using quantitative structure–retention relationship (QSRR) methods that imply analysis of correlations and representation of models. A suitable set of molecular descriptors was calculated and the genetic algorithm (GA) was employed to select those descriptors that resulted in the best-fit models. The partial least squares (PLS), kernel partial least squares PLS (KPLS) and Levenberg- Marquardt artificial neural network (L-M ANN) were utilized to construct the linear and nonlinear QSRR models. The proposed methods will be of importance in this research, and could be expected to apply to other similar research fields.\

Introduction

Fathead minnows (Pimephales promelas) are found in every drainage in Minnesota. It is the most common species of minnow in the state. They live in many kinds of lakes and streams, but are especially common in shallow, weedy lakes; bog ponds; low-gradient, turbid (cloudy) streams; and ditches. These habitats often have no predators and low oxygen levels. Fatheads are noted for their ability to withstand low oxygen levels. Fatheads commonly occur with white suckers, bluntnose minnows, common shiners, northern redbelly dace, creek chubs, and young-of-the-year black bullheads. Fathead minnows are considered an opportunist feeder. They eat just about anything that they come across, such as algae, protozoa (like amoeba), plant matter, insects (adults and larvae), rotifers, and copepods. In lakes and deeper streams, fatheads are common prey for crappies, rock bass, perch, walleyes, largemouth bass, and northern pike. They also are eaten by snapping turtles, herons, kingfishers, and terns. Eggs of the fathead are eaten by painted turtles and certain large leeches. Although humans do not eat fatheads, they harvest them as bait [1]. Environmental hazard is a generic term for any situation or state of event

 

which poses a threat to the surrounding natural environment and adversely affects people’s health. This term incorporates topics like pollution and natural disasters such as storms and earthquakes. Hazards can be categorized in five types: Chemical, Physical, Mechanical, Biological and Psychosocial. Environmental hazard and risk assessment of chemical substances requires comprehensive information on the exposure, fate and ecotoxicology of the contaminants; however, complete data sets are rarely available. One reason for these deficiencies is that testing capacities are limited, which impedes the thorough experimental investigation of all the existing and new chemicals [2].

To fill at least some of the data gaps, mathematical modeling techniques are used to provide sufficiently accurate substitutes. The models can be used to estimate the parameters related to the fate and effects of chemicals and hence to identify contaminants of special environmental concern and to obtain a ranking of potentially hazardous pollutants. In

this way, the priority compounds can then be subjected to detailed testing and the limited resources for experimental investigations can be directed effectively to the chemicals that are most likely to have an environmental impact Attention in mathematical modeling techniques also arises from their application as absolute alternatives to animal experiments, in the interests of time-effectiveness, cost-effectiveness and animal welfare [3].

Alternative methods assist the policy of the “Three Rs” (replacement, reduction and refinement of the use of laboratory animals) and several regulatory organizations have been established to investigate and promote alternative methods.  Chemical modeling techniques are based on the premise that the structure of a compound determines all its properties. The study of the type of chemical structure of a foreign substance which will interact to a living system and produce a well-defined biological endpoint is commonly referred to as quantitative structure-retention relationships QSRR [4-5]. The use of QSRR for toxicity estimation of new chemicals or to regulatory toxicological assessment is increasing, especially in aquatic toxicology. Alternatively to QSRR models quantitative retention relationships QRRR, represent other kind of modeling techniques, in which chromatographic retention parameters are used as descriptor and/or predictor variables of a given biological response of chemicals. QSRR models using retention factors (log k) obtained using conventional RP-HPLC, micellar liquid chromatography (MLC) and biopartitioning micellar chromatography (BMC) have been reported [6-10].

The aim of the present study is estimation of ability optimal descriptors calculated with linear regression (the partial least squares (PLS)) and non-linear regressions (the kernel partial least squares (KPLS) and Levenberg- Marquardt artificial neural network (L-M ANN)) in QSRR analysis of logarithm of the retention factor in BMC (log k) for toxicity to Fathead Minnows of anilines and phenols. The stability and predictive power of these models were validated using Leave-Group-Out Cross-Validation (LGO CV) and external test set.

Results and Discussion

Linear model

Results of the GA-PLS model

The best model is selected on the basis of the highest square correlation coefficient leave-group-out cross validation (R2), the least root mean squares error (RMSE) and relative error (RE). These parameters are probably the most popular measure of how well a model fits the data. The best GA-PLS model contains 23 selected descriptors in 11 latent variables space. The R2 and mean RE for training and test sets were (0.788, 0.709) and (15.49, 22.88), respectively. The predicted values of log k are plotted against the experimental values for training and test sets in Figure 1. For this in general, the number of components (latent variables) is less than the number of independent variables in PLS analysis. The PLS model uses higher number of descriptors that allow the model to extract better structural information from descriptors to result in a lower prediction error.

Figure 1: Plots of predicted retention time against the experimental values by GA-PLS model.

Nonlinear model

Results of the GA-KPLS model

In this paper a radial basis kernel function, k(x,y)= exp(||x-y||2/c), was selected as the kernel function with where r is a constant that can be determined by considering the process to be predicted (here r was set to be 1), m is the dimension of the input space and  is the variance of the data [11-12]. It means that the value of c depends on the system under the study. The 16 descriptors in 9 latent variables space chosen by GA-KPLS feature selection methods were contained. The R2 and mean RE for training and test sets were (0.811, 0.754) and (13.08, 19.70), respectively. It can be seen from these results that statistical results for GA-KPLS model are superior to GA-PLS method. Figure 2 shows the plot of the GA-KPLS predicted versus experimental values for log k of all of the molecules in the data set. 

Results of the L-M ANN model

With the aim of improving the predictive performance of nonlinear QSRR model, L-M ANN modeling was performed. The networks were generated using the sixteen descriptors appearing in the GA-KPLS models as their inputs and log k as their output. For ANN generation, data set was separated into three groups: calibration and prediction (training) and test sets. All molecules were randomly placed in these sets. A three-layer network with a sigmoid transfer function was designed for each ANN. Before training the networks the input and output values were normalized between -1 & 1.

Figure 2: Plots of predicted log k versus the experimental values by GA-KPLS model.

The network was then trained using the training set by the back propagation strategy for optimization of the weights and bias values. The proper number of nodes in the hidden layer was determined by training the network with different number of nodes in the hidden layer. The root-mean-square error (RMSE) value measures how good the outputs are in comparison with the target values. It should be noted that for evaluating the over fitting, the training of the network for the prediction of log k must stop when the RMSE of the prediction set begins to increase while RMSE of calibration set continues to decrease. Therefore, training of the network was stopped when overtraining began. All of the above mentioned steps were carried out using basic back propagation, conjugate gradient and Levenberge-Marquardt weight update functions. It was realized that the RMSE for the training and test sets are minimum when three neurons were selected in the hidden layer. Finally, the number of iterations was optimized with the optimum values for the variables. It was realized that after 18 iterations, the RMSE for prediction set were minimum. The R2 and mean relative error for calibration, prediction and test sets were (0.976, 0.945, 0.887) and (4.14, 5.21, 8.39), respectively. Comparison between these values and other statistical parameter reveals the superiority of the L-M ANN model over other model. The key strength of neural networks, unlike regression analysis, is their ability to flexible mapping of the selected features by manipulating their functional dependence implicitly. The statistical parameters reveal the high predictive ability of L-M ANN model. The whole of these data clearly displays a significant improvement of the QSRR model consequent to nonlinear statistical treatment. Plot of predicted log k versus experimental log k values by L-M ANN for training and test sets are shown in Figure.3a and 3b. Obviously, there is a close agreement between the experimental and predicted log k and the data represent a very low scattering around a straight line with respective slope and intercept close to one and zero. As can be seen in this section, the L-M ANN is more reproducible than other models for modeling the log k of compounds.

Figure 3: Plot of predicted log k obtained by L-M ANN against the experimental values (a) for training set and (b) test set.

Model validation and statistical parameters

 

The accuracy of proposed models was illustrated using the evaluation techniques such as leave group out cross-validation (LGO-CV) procedure, validation through an external test set. In addition, chance correlation procedure is a useful method for investigating the accuracy of the resulted model, by which one can make sure if the results were obtained by chance or not. Cross validation is a popular technique used to explore the reliability of statistical models. Based on this technique, a number of modified data sets are created by deleting in each case one or a small group (leave-some-out) of objects. For each data set, an input–output model is developed, based on the utilized modeling technique. Each model is evaluated, by measuring its accuracy in predicting the responses of the remaining data (the ones or group data that have not been utilized in the development of the model). In particular, the LGO-CV procedure was utilized in this study. A QSRR model was then constructed on the basis of this reduced data set and subsequently used to predict the removed data. This procedure was repeated until a complete set of predicted was obtained. The data set should be divided into three new sub-data sets, one for calibration and prediction (training), and the other one for testing. The calibration set was used for model generation. The prediction set was applied deal with over fitting of the network, whereas test set which its molecules have no role in model building was used for the evaluation of the predictive ability of the models for external set [13].

In the other hand by means of training set, the best model is found and then, the prediction power of it is checked by test set, as an external data set. In this work, 60% of the database was used for calibration set, 20% for prediction set and 20% for test set [14], randomly (in each running program, from all 65 components, 39 components are in calibration set, 13 components are in prediction set and 13 components are in test set). The result clearly displays a significant improvement of the QSRR model consequent to non-linear statistical treatment and a substantial independence of model prediction from the structure of the test molecule. In the above analysis, the descriptive power of a given model has been measured by its ability to predict log k of unknown compounds. For the constructed models, two general statistical parameters were selected to evaluate the prediction ability of the model for log k values. For this case, the predicted log k of each sample in the prediction step was compared with the experimental log k. The root mean square error of prediction (RMSE) is a measurement of the average difference between predicted and experimental values, at the prediction stage. The RMSE can be interpreted as the average prediction error, expressed in the same units as the original response values. The RMSEP was obtained using the following formula:

The second statistical parameter was the relative error of prediction (RE) that shows the predictive ability of each component, and is calculated as:

Where yi is the experimental log k value of the anilines and phenols in the sample i, represents the predicted log k value in the sample i, is the mean of experimental log k values in the prediction set and n is the total number of samples used in the test set [15].

Conclusion

The GA-PLS, GA-KPLS and L-M ANN models was applied for the prediction of the log k values of ecotoxicity of anilines and phenols. High correlation coefficients and low prediction errors confirmed the good predictability of models. All methods seemed to be useful, although a comparison between these methods revealed the slight superiority of the L-M ANN over other models. Application of the developed model to a testing set of 13 compounds demonstrates that the new model is reliable with good predictive accuracy and simple formulation. The QSRR procedure allowed us to achieve a precise and relatively fast method for determination of log k of different series of these compounds to predict with sufficient accuracy the log k of new substituted compounds.

Source: peerscientist.com