Academic literature on the topic 'Sea code (Senat Al-Bahar)'

Abstract: Fisheries management in Oman faces many challenges due to the current management. There are conflicts among fishermen in using different fishing gears and fishing in the same grounds. Also, the government does not consider the fishermen in setting the policies of fisheries management. In addition, there is unrestricted access to new technologies to the sector and insufficient management of the fisheries resource. This study is based on a survey covered 376 respondents in Oman. Fishermen and non fishermen were targeted and selected randomly. The study aimed to investigate their opinions on the present fisheries management and their opinion on the implementation of Co-management. The figures showed that 32% of fishermen disagreed, while 43.3% of non fishermen undecided on the performance of present fisheries management. However, 33.2% of fishermen and 35% of non fishermen strongly agreed and undecided respectively on the introduction of fisheries co-management. These findings disclose the need to review of the present fisheries management policies and involve the fishermen in the management. This attitude will lead to improve the fisheries management in general and will improve the socioeconomic conditions of fishermen.

A diverse and productive mosaic of highly important ecosystems border the coastline of the Arabian Gulf, providing invaluable goods and services to coastal populations and representing the most biodiverse habitats in a region better known for its arid deserts. Recently, however, these ecosystems have come under escalating pressure from urbanization, fisheries activity, and global climate change. Artificial reefs have been in use for centuries in the Gulf region, where they were inherited through family lines and regulated under the senat al-bahar (the ‘code of the sea’). Today, regional marine managers and policymakers are increasingly promoting artificial reefs as a tool to mitigate the ongoing impacts on Gulf ecosystems and fisheries. Artificial reefs may support some goals of marine managers and policymakers, but they are not a panacea and involve many risks. Without appropriate design, regulation, and management, artificial reefs can exacerbate existing problems or inadvertently create new issues that add to management burdens in coastal areas.

Authors: Kai Wang & Maja Bucan ### INTRODUCTION High-density single nucleotide polymorphism (SNP) genotyping arrays recently have been used for copy number variation (CNV) detection and analysis, because the arrays can serve a dual role for SNP- and CNV-based association studies. They also can provide considerably higher precision and resolution than traditional techniques. Here we describe PennCNV, a computational protocol designed for CNV detection from high-density SNP genotyping data. This protocol extracts allele-specific signal intensities from genotyping arrays, and then integrates information on SNP spacing and SNP allele frequencies to generate CNV calls by a hidden Markov model (HMM) algorithm. Analyses of CNVs from SNP genotyping arrays will provide a more comprehensive view of genome variation, and complement current genome-wide association studies in identifying disease susceptibility loci. ### OVERVIEW CNV refers to genomic segments of at least one kb in size, for which copy number differences have been observed in comparison to reference genome assemblies (Feuk et al. 2006; Freeman et al. 2006). Multiple large-scale studies have reported prevalent CNVs in humans, suggesting that they may account for a significant portion of phenotypic variation (Iafrate et al. 2004; Sebat et al. 2004; Tuzun et al. 2005; Conrad et al. 2006; McCarroll et al. 2006; Redon et al. 2006). The precise and comprehensive identification of CNVs would greatly benefit the functional analysis of human genome variation, and complement current genome-wide association studies that use SNPs. In recent years, several techniques have been developed to detect CNVs in human and other mammalian genomes (Carter 2007). Traditionally, large chromosome rearrangements have been detected with G-banded karyotypic analyses or fluorescence in situ hybridization (FISH) using fluorescent probes that bind to part of the chromosomes. With the development of microarray technology, array-comparative genomic hybridization (CGH) platforms can be used to detect gains and losses of genomic segments (Pinkel et al. 1998), especially in tumor tissues. This technique involves labeling a reference genome and a testing genome with different fluorescence markers, hybridizing to the microarray with genomic clones, and then analyzing the intensity of the hybridization signal for each clone. Further variations of the array-CGH technique have been developed to detect CNVs in human populations (Iafrate et al. 2004; Sebat et al. 2004; Locke et al. 2006; Redon et al. 2006). Genome-wide oligonucleotide arrays use short oligonucleotides spotted to arrays by inkjet technology or by photolithography. They offer higher resolution than large-insert clone array platforms and can achieve complete genome coverage. Furthermore, paired-end mapping (Korbel et al. 2007) and clone-end resequencing (Eichler et al. 2007) have also been implemented for CNV detection, and can identify many more small-scale CNVs. Finally, whole-genome resequencing (Service 2006) could soon prove cost-effective for the most comprehensive identification of CNVs in the human genome. In addition to the techniques mentioned above, high-density SNP genotyping arrays have become more popular for copy number detection and analysis, because the arrays can serve a dual role for both SNP-based and CNV-based association studies. In high-density SNP genotyping platforms, a signal intensity measure is summarized for each allele of a given SNP marker. Analysis of signal intensities across the genome can then be used to identify regions with multiple SNPs that support deletions or duplications (Komura et al. 2006; Peiffer et al. 2006). However, there are limitations to the use of SNP genotyping arrays for CNV detection: SNPs in these arrays are not uniformly distributed across the genome and are sparse in regions with segmental duplications or complex CNVs (Carter 2007). To overcome these limitations, the new generations of SNP genotyping arrays from Affymetrix and Illumina have now incorporated additional nonpolymorphic (NP) markers to provide more comprehensive coverage of the human genome. We describe here a computational protocol designed for CNV detection using data from high-density SNP genotyping arrays. ### SIGNAL PREPROCESSING PROCEDURE FOR SNP GENOTYPING ARRAYS We have developed the PennCNV algorithm for high-resolution CNV detection from whole-genome SNP genotyping data (Wang et al. 2007); the software is available at [http://www.neurogenome.org/cnv/penncnv](http://www.neurogenome.org/cnv/penncnv). This algorithm was developed originally using the Illumina HumanHap550 arrays, but can be extended readily to similar SNP genotyping platforms. The PennCNV algorithm uses two measures of signal intensity at each SNP. The Log R Ratio (LRR) is a normalized measure of the total signal intensity for two alleles of the SNP. The B Allele Frequency (BAF) is a normalized measure of the allelic intensity ratio of two alleles. The combination of LRR and BAF can be used to infer copy number changes in the genome (Fig. 1). For example, in the presence of a deletion, there is a decrease in LRR values and a lack of heterozygotes in BAF values; in the presence of a duplication, there is an increase in LRR values and a splitting of heterozygous genotype clusters into two clusters. To derive the LRR and BAF values, a signal preprocessing procedure is necessary for SNP genotyping platforms (Fig. 2).  **Figure 1.** An illustration of the BAF (upper panel) and LRR (lower panel) values in chromosome 20 of a person with large (>1 Mb) deletion and duplication. The BAF is a measure of the allelic intensity ratio: When a deletion CNV is present, BAF values cluster around 0 or 1 but are absent around 0.5; when duplication is present, BAF values cluster around 0, 0.33, 0.67, and 1, reflecting the AAA, AAB, ABB, and BBB genotypes, respectively. The LRR is a normalized measure of total signal intensity: When a deletion CNV is present, LRR values for SNP markers in this region decrease; when duplication is present, LRR values increase.  **Figure 2**. A flowchart of the data processing procedure for deriving the LRR and BAF values used in CNV detection. The Illumina BeadStudio software can be used to calculate LRR and BAF values from raw array image data. For the Affymetrix platform, first extract signal intensity values for all markers using the Affymetrix Power Tools software, and then apply helper scripts for calculating LRR and BAF values. The LRR and BAF values for all markers, together with other relevant information, can then be integrated into an HMM in the PennCNV software for CNV detection. The first step of signal preprocessing is the extraction of allele-specific signal intensity values. With the Illumina platform, this step can be performed by the BeadStudio software developed by Illumina as a framework for data analysis and visualization of several types of arrays. The software takes a raw image scan of SNP genotyping arrays and performs a five-step normalization procedure to derive X and Y values for each SNP marker, representing signal intensities for the A and B alleles, respectively. These steps include outlier removal, background estimation, rotational estimation, shear estimation, and scaling estimation (for details, refer to the technical documentation available at [http://www.illumina.com](http://www.illumina.com/)). The Affymetrix platform uses a set of command line tools (the Affymetrix Power Tools) to perform data normalization and allele-specific signal extraction from raw CEL files generated in genotyping experiments. This step uses a self-normalization algorithm that only requires information contained within the arrays themselves, without relying on any data model built on external reference samples. The next step in data preprocessing is to derive LRR and BAF from X and Y values for each SNP, based on “canonical genotype clusters” compiled from a set of standard external reference samples (e.g., HapMap samples). The transformation from R and θ into LRR and BAF values adjusts for different chemical characteristics of each SNP, such that values for different SNPs can be compared more readily. This step is designed for and implemented by the Illumina platform (Peiffer et al. 2006), but it can be adapted to the Affymetrix platform as well. Briefly, it involves the calculation of R and θ values, and then the transformation of R and θ into LRR and BAF, respectively. Although referred to as “polar transformation” (Peiffer et al. 2006), the procedure for R value calculation is not a typical polar transformation. Instead, R is calculated as the total signal intensity of two alleles, or R = X + Y. As a normalized measure of total signal intensity, the LRR value for each SNP is then calculated as LRR = log2(Robserved/Rexpected), in which Rexpected is computed from linear interpolation of canonical genotype clusters (Peiffer et al. 2006) obtained from a set of reference samples. The θ value measures the relative allelic intensity ratio of two alleles and is calculated as θ = arctan(Y/X)/(π/2), which ranges from 0 to 1. The BAF refers to a normalized measure of relative signal intensity ratio of the B and A alleles:  in which θAA, θAB, and θBB are the θ values for three canonical genotype clusters generated from external reference samples (such as HapMap samples). The transformation procedure described above can be applied to both Illumina and Affymetrix platforms, allowing signal intensity data from these arrays to be analyzed by the PennCNV software. This method has been tested extensively using the Illumina platform for large-scale studies. For example, PennCNV was used for CNV calling in a study that analyzed genome diversity and variations in worldwide human populations by Illumina SNP arrays, and a false positive rate of 0.7% was estimated from duplicated samples (Jakobsson et al. 2008). On the other hand, the signal transformation procedure on the Affymetrix platform has not yet been evaluated as rigorously. To provide better coverage of known CNV regions and to fill in gaps that are not covered by SNPs, several recently developed SNP genotyping arrays (including the Illumina HumanHap 1M array and the Affymetrix genome-wide 5.0 and 6.0 arrays) contain NP markers on the chip. These markers can be handled in a fashion similar to SNPs for copy number inference, but there are some differences. First, the R-value is calculated as the signal intensity of the NP marker rather than the sum of two alleles. Also, the θ and BAF values cannot be derived for NP markers. Consequently, they are not used in the likelihood calculation. Finally, owing to the use of fewer probes, the variance of LRR values for NP markers may be different from SNP markers. Therefore, the likelihood model parameters for LRR are slightly different between NP markers and SNP markers. ### HIDDEN MARKOV MODEL FOR CNV DETECTION The PennCNV algorithm is an HMM-based algorithm that analyzes signal patterns across the genome and identifies consecutive markers with copy number changes. In the algorithm, the probability of observing a particular copy number state at a particular marker is dependent on the state at the previous marker, so it provides a natural framework for modeling dependence structures between nearby SNPs. As in other studies (Colella et al. 2007), six different possible copy number states are assigned for each marker, representing copy numbers of zero to four, as well as copy-neutral loss of heterozygosity. The signal intensity patterns (LRR and BAF) for five or more copies cannot be readily differentiated from four copies (Wang et al. 2007), so these rare situations are considered to be the same state as four copies. There are two main components in the HMM for the inference of the most likely path of copy number states: emission probability and transition probability. Emission probability refers to the likelihood of observing the signal intensity patterns (LRR and BAF values) at each marker, whereas transition probability refers to the probability of having copy number changes between adjacent markers. The challenge of the HMM algorithm lies in the inference of the most likely copy number state at each genotyped marker, because this state depends not only on the signal intensity data at this marker, but also on the transition probability from the previous marker. The emission probability of each marker is a function of LRR and BAF values (except for NP markers), which are assumed to be conditionally independent given the copy number state at that marker in the likelihood calculation. As shown in Figure 1, different copy number states have distinct patterns of LRR and BAF values. The transition probability is dependent on the previous and the current copy number states, as well as the distance between the two markers. Intuitively, copy number states are unlikely to change between nearby markers but more likely to change between sparsely spaced markers (Marioni et al. 2006). The details of the mathematical formula for modeling the likelihood of LRR and BAF are presented in Wang et al. (2007). Briefly, we use the Viterbi algorithm (Viterbi 1967) to infer the most likely states at each SNP efficiently, and then generate CNV calls by identifying stretches of states that are different from the normal state. ### FUNCTIONAL ANNOTATION, INTERPRETATION, AND INHERITANCE ANALYSIS OF CNVs After generating CNV calls using the PennCNV algorithm, one can annotate them functionally and predict their possible effects. CNVs can affect genome function by deleting or duplicating entire genes, parts of genes (such as exons), or important intergenic/intragenic regulatory elements (Feuk et al. 2006). The PennCNV software package contains auxiliary programs to annotate CNVs, such as identifying overlapping or nearby genes and conserved genomic elements for CNV calls. These functional annotations can help formulate new hypotheses on the functional consequences of particular CNVs. Because the precision of CNV calls is dependent on the technical platform used, their appropriate interpretation requires an understanding of the particular technology used for CNV analysis. Although the “actual CNV” is a stretch of sequence that can start and end at any base pair, the detectable size of a CNV call typically depends on the resolution of the experimental platform. For example, CNV calls could be composed of one or a few bacterial artificial chromosome (BAC) clones (in an array-CGH platform), a stretch of adjacent SNPs (in a high-density genotyping platform), or a stretch of NP markers (in the SNP genotyping arrays with NP markers, or in ultrahigh-density whole genome tiling arrays). Low-resolution CNV calls may cover functionally important genomic elements, but the actual CNVs may not have any functional consequences (Hegele 2007). Likewise, CNV calls from high-density SNP genotyping arrays underestimate slightly the sizes of CNV regions. Furthermore, it is important to recognize that most of these technical platforms can only detect simple duplications or deletions, but are unable to infer more complex structural variations, such as inversions or translocations that can also affect genome function (Tuzun et al. 2005). Many current CNV studies have examined individuals from the same families (e.g., HapMap families or Centre d’Etude du Polymorphism Humain [CEPH] families), so Mendelian inheritance can be used to evaluate CNV detection accuracy and generate more confident CNV calls. CNV calls made in both an offspring and his/her parents increase the confidence of the calls, whereas the fraction of CNV calls in the offspring but not detected in the parents (CNV-NDPs) can be used as a composite measure to evaluate CNV calling algorithms (Wang et al. 2007). In addition, family information can also be used directly in CNV calling algorithms for more accurate inference of CNVs. The PennCNV package includes a posterior validation procedure that can incorporate family information to validate CNV calls and resolve boundary discordances between related individuals. Through appropriate prior probability specification, this posterior validation procedure also takes into account possible de novo events, which may be important for human diseases. ### SUMMARY CNVs in the human genome can be detected from high-density SNP genotyping data with well-designed computational algorithms. Further development of methods that better model signal intensity patterns, that handle samples with low-quality signals, and that use more available information, will improve the accuracy of CNV detection and complement current genome-wide association studies in identifying novel disease susceptibility loci. Previous Section ### REFERENCES 1. Carter N.P. (2007) [Methods and strategies for analyzing copy number variation using DNA microarrays.](http://cshprotocols.cshlp.org/external-ref?access_num=10.1038/ng2028&link;_type=DOI) *Nat. Genet*. 39(Suppl):S16–S21. - Colella S., Yau C., Taylor J.M., Mirza G., Butler H., Clouston P., Bassett A.S., Seller A., Holmes C.C., Ragoussis J. 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Shortly after returning to the London Hospital from Uganda in 1974, and still jet-lagged and culture-shocked, I had an unexpected call from a Dr Kumar at the Maudsley Hospital whose name was then unfamiliar to me. Stephen Wolkind (Child Psychiatrist at the London Hospital working with Professor Pond) had informed him that I had completed a study of postnatal depression in East Africa and had used Goldberg’s Standardised Psychiatric Interview (SPI) translated into Luganda. Could we meet, and could I advise him on the use of the SPI? I was surprised, flattered and motivated by this request. We met in Turner St, London E1. This was the beginning of a friendly and mutually respectful collaboration, which facilitated the later development of the Edinburgh Postnatal Depression Scale (EPDS) (Cox et al. 1987), helped launch the 1980 meeting in Manchester, when the Marcé Society was founded, and motivated Phase One of the International Transcultural Postnatal Depression Study. Channi Kumar was a fine team player, and as a leader had that knack of making you feel respected and at ease. His greeting ‘Come in dear boy and have a seat’ when he ushered you to a chair piled high with research papers, was characteristic of his style and productivity. We would then talk, not only about screening scales, but about College matters and the Perinatal Special Interest Group (which later became a Specialty Section), as well as our ‘Blue Skies’ research programmes. My interest in perinatal psychiatry began when, as an impressionable medical student, I first met Brice Pitt at Claybury Hospital. He was carrying out a study of ‘atypical’ postnatal depression and was devising a self-report questionnaire to detect increases in depression scores after birth. This early experience, together with a postgraduate seminar some years later, must surely have been on my mind when I was asked by Allen German on my arrival in Uganda, what research I was planning to do. I replied that I wished to replicate Assael’s finding (1972) that a quarter of pregnant women at Kasangati had mental health problems, and I was curious to know whether African women experienced depression as described by Pitt (1968)—and if not, what were the differences.

Abstract Since 2000, cast iron-liners have been replaced in several engine projects by Fe-based thermally sprayed coatings in the bores of a light metal crankcase. In contrast to cast in liners the linerless versions of these Al-crankcases are very demanding with regard to the porosity and tensile strength in the areas around the bores. The casting porosity has to be diminished to maximum pores smaller than 1mm² due to the roughening procedure, either mechanical roughening (MR) or high power water jet roughening (WR), in order to prevent either tool failure (MR) or widened pores (WR). At Nemak Dillingen these challenges are met by the Core Package Process (CPS), offering the advantages of a highly flexible casting design and a nearly unlimited choice of the cast alloy. These boundaries enable the production of lightweight crankcases made of the strong and creep resistant Al-Si-Cu based secondary alloy A319. The high quality of the cylinder bore surface is achieved by a carefully designed thermal household of the solidifying casting. The cylinder chill form a stable and sound shell in the very beginning of solidification, whereas feeding takes place from the sidewall structure of the crankcase. At the same time, specially designed chills for the bearing seat enable a very short solidification time, the resulting properties are crucial for highly loaded diesel engines. After casting and machining, the crankcases have been mechanically roughened and coated with 0.8 % C-Steel. The coatings and the interface between the coating and the casted Al-substrate have been investigated by means of light microscopy regarding the interlock between coating and substrate and the near-surface porosity of the cast metal.

Under the United Nations Charter, the U.N. Security Council has several important functions and powers, not least with regard to taking binding actions to maintain international peace and security. The ten elected members have the opportunity to influence this area and others during their two-year terms on the Council. In this paper, we aim to illustrate some of these opportunities, identify potential guidance from prior elected members’ experiences, and outline the key procedures that incoming elected members should be aware of as they prepare to join the Council. In doing so, we seek in part to summarize the current state of scholarship and policy analysis in an effort to make this material more accessible to States and, particularly, to States’ legal advisers. We drafted this paper with a view towards States that have been elected and are preparing to join the Council, as well as for those States that are considering bidding for a seat on the Council. As a starting point, it may be warranted to dedicate resources for personnel at home in the capital and at the Mission in New York to become deeply familiar with the language, structure, and content of the relevant provisions of the U.N. Charter. That is because it is through those provisions that Council members engage in the diverse forms of political contestation and cooperation at the center of the Council’s work. In both the Charter itself and the Council’s practices and procedures, there are structural impediments that may hinder the influence of elected members on the Security Council. These include the permanent members’ veto power over decisions on matters not characterized as procedural and the short preparation time for newly elected members. Nevertheless, elected members have found creative ways to have an impact. Many of the Council’s “procedures” — such as the “penholder” system for drafting resolutions — are informal practices that can be navigated by resourceful and well-prepared elected members. Mechanisms through which elected members can exert influence include the following: Drafting resolutions; Drafting Presidential Statements, which might serve as a prelude to future resolutions; Drafting Notes by the President, which can be used, among other things, to change Council working methods; Chairing subsidiary bodies, such as sanctions committees; Chairing the Presidency; Introducing new substantive topics onto the Council’s agenda; and Undertaking “Arria-formula” meetings, which allow for broader participation from outside the Council. Case studies help illustrate the types and degrees of impact that elected members can have through their own initiative. Examples include the following undertakings: Canada’s emphasis in 1999–2000 on civilian protection, which led to numerous resolutions and the establishment of civilian protection as a topic on which the Council remains “seized” and continues to have regular debates; Belgium’s effort in 2007 to clarify the Council’s strategy around addressing natural resources and armed conflict, which resulted in a Presidential Statement; Australia’s efforts in 2014 resulting in the placing of the North Korean human rights situation on the Council’s agenda for the first time; and Brazil’s “Responsibility while Protecting” 2011 concept note, which helped shape debate around the Responsibility to Protect concept. Elected members have also influenced Council processes by working together in diverse coalitions. Examples include the following instances: Egypt, Japan, New Zealand, Spain, and Uruguay drafted a resolution that was adopted in 2016 on the protection of health-care workers in armed conflict; Cote d’Ivoire, Kuwait, the Netherlands, and Sweden drafted a resolution that was adopted in 2018 condemning the use of famine as an instrument of warfare; Malaysia, New Zealand, Senegal, and Venezuela tabled a 2016 resolution, which was ultimately adopted, condemning Israeli settlements in Palestinian territory; and A group of successive elected members helped reform the process around the imposition of sanctions against al-Qaeda and associated entities (later including the Islamic State of Iraq and the Levant), including by establishing an Ombudsperson. Past elected members’ experiences may offer some specific pieces of guidance for new members preparing to take their seats on the Council. For example, prospective, new, and current members might seek to take the following measures: Increase the size of and support for the staff of the Mission to the U.N., both in New York and in home capitals; Deploy high-level officials to help gain support for initiatives; Partner with members of the P5 who are the informal “penholder” on certain topics, as this may offer more opportunities to draft resolutions; Build support for initiatives from U.N. Member States that do not currently sit on the Council; and Leave enough time to see initiatives through to completion and continue to follow up after leaving the Council.