**<IP_ADDRESS> Language issues**

Modern languages for real-time imaging must provide an easy interface to hardware devices and provide a framework for maintainability, portability and reliability, among many other features. Many programming languages are commonly used to implement Real-Time Colour Imaging Systems, including C, C++, C#, Java, Visual Basic, Fortran, assembly language, and even BASIC. Poor coding style is frequently the source of performance deterioration in real-time imaging systems. In many cases, the negative effects are due to performance penalties associated with object composition, inheritance, and polymorphism in object-oriented languages. But object-oriented languages are rapidly displacing the lower-level languages like C and assembly language in real-time colour imaging systems, and it is probably a good thing because of the accompanying benefits.

Understanding the performance impact of various language features, particularly as they relate to image storage and manipulation, is essential to using the most appropriate construct for a particular situation. There is no clear answer, and experimentation with the language compiler in conjunction with performance measurement tools can be helpful in obtaining the most efficient implementations.

The following list summarizes key issues when implementing real-time imaging systems in a high-level language:

Finally, be wary of code that evolved from non-object-oriented languages such as C into object-oriented version in C++ or Java. Frequently, these conversions are made hastily and incorporate the worst of both the object-oriented and non-object-oriented paradigms simultaneously.

Java is an object-oriented language, with a syntax that is similar to C++ and C#, and to a lesser extent, C. Besides, modern object-oriented languages such as C++ and C# have quite a lot in common with Java (Lukac & Plataniotis, 2007).


**2.2. DNA preparation**

Genomic DNA (gDNA) was isolated from peripheral blood leukocytes using the Wizard® Genomic DNA purification kit (Promega, Alexandria, NSW, Australia). DNA quality and concentration were assessed using the Nanodrop™ 1000 spectrophotometer (Thermo Scien‐ tific, Scoresby, Vic, Australia) that measures the purity of DNA by the ratio of absorbance of molecules at 260 and 280 nm. Samples with ratios between 1.8 and 2.0 were accepted for analysis whilst ratios lower than this may represent the presence of contaminants and these samples were not processed further [26]. At least, 250 ng of input gDNA was prepared per sample.

#### **2.3. Candidate gene identification and gene panel design**

An extensive literature search using public databases was performed to assemble an initial candidate gene list of all genes reasonably hypothesized to have an impact on platelet number and size (*n* = 173). A final list of candidate genes (*n* = 19) was derived by including those genes in which mutations were known to be definitively associated with IPNDs (predominantly, macrothrombocytopenia) and by excluding genes, which although known to result in throm‐ bocytopenia, could easily be identified by conventional and clinical methods characterized by distinct clinical phenotypes.

A TruSeq custom amplicon (TruSeq® Custom Amplicon Kit, Illumina Inc., Scoresby, Vic, Australia) specific for the target regions of the selected 19 genes (Table 1, *ACTN1, CD36, ETS1, F2R, FLI1, GATA1, GFI1B, GP1BA, GP1BB, GP6, GP9, ITGA2, ITGA2B, ITGB1, ITGB3, MYH9, NBEAL2, P2RY12, RUNX1, TUBB1*) was designed as an entire custom pool using the webbased software tool, Illumina Design Studio (Illumina Inc.). This generated 201 gene targets that were either exons or gene regions that were split into 632 amplicons, each of approximately 250 base pairs (bps). There were no undesignable targets and a total coverage of 91% was predicted for the panel.

Following informed written consent, 20 ml of blood was taken from an antecubital vein and collected into EDTA tubes. This blood was easily transported, in some cases, over 1,000 km

A total of 95 patient DNA samples were analyzed. This included two internal controls for

32 male patients (mean age 37.4 years, range 18–92 years) and 44 female patients (mean age 38.7 years, range 18–79 years) were included in the NGS assay. The mean age of the cohort was 38.1 years (range 18–92 years). Sixteen de-identified DNA samples were received from

Phenotypic testing data were available for 59 (62.1%) individuals. This included platelet functional analysis (PFA) (*n* = 25, 26.0% of the cohort), light transmission aggregometry / whole blood impedance aggregometry (LTA/WBIA) (*n* = 39, 41.3% of the cohort), flow cytometry (*n* = 45, 47.8% of the cohort) and electron microscopy (*n* = 12, 13% of the cohort). These phenotypic test results suggested a diagnosis to a "pathway level", that is, a description to the level of the suspected defective biochemical pathway, in only 11 cases. Pathway orientated defects included, storage pool disorders (*n* = 3), platelet glycoprotein deficiency (*n* = 3), platelet signaling defects (*n* = 2), platelet secretion defects (*n* = 2) as well as α-granule disorder (*n* = 1).

Genomic DNA (gDNA) was isolated from peripheral blood leukocytes using the Wizard® Genomic DNA purification kit (Promega, Alexandria, NSW, Australia). DNA quality and concentration were assessed using the Nanodrop™ 1000 spectrophotometer (Thermo Scien‐ tific, Scoresby, Vic, Australia) that measures the purity of DNA by the ratio of absorbance of molecules at 260 and 280 nm. Samples with ratios between 1.8 and 2.0 were accepted for analysis whilst ratios lower than this may represent the presence of contaminants and these samples were not processed further [26]. At least, 250 ng of input gDNA was prepared per

An extensive literature search using public databases was performed to assemble an initial candidate gene list of all genes reasonably hypothesized to have an impact on platelet number and size (*n* = 173). A final list of candidate genes (*n* = 19) was derived by including those genes in which mutations were known to be definitively associated with IPNDs (predominantly, macrothrombocytopenia) and by excluding genes, which although known to result in throm‐ bocytopenia, could easily be identified by conventional and clinical methods characterized by

A TruSeq custom amplicon (TruSeq® Custom Amplicon Kit, Illumina Inc., Scoresby, Vic, Australia) specific for the target regions of the selected 19 genes (Table 1, *ACTN1, CD36, ETS1, F2R, FLI1, GATA1, GFI1B, GP1BA, GP1BB, GP6, GP9, ITGA2, ITGA2B, ITGB1, ITGB3, MYH9, NBEAL2, P2RY12, RUNX1, TUBB1*) was designed as an entire custom pool using the webbased software tool, Illumina Design Studio (Illumina Inc.). This generated 201 gene targets that were either exons or gene regions that were split into 632 amplicons, each of approximately

which DNA-based diagnosis had previously been established by Sanger sequencing.

referring institutions for which no additional laboratory data were available.

**2.3. Candidate gene identification and gene panel design**

between diagnostic sites in Australia.

390 Next Generation Sequencing - Advances, Applications and Challenges

**2.2. DNA preparation**

distinct clinical phenotypes.

sample.

**Table 1.** Candidate gene list. OMIM, online Mendelian inheritance in man; AR, autosomal recessive; AD, autosomal dominant; XL, X-linked; nd, not defined, \*In progress (OMIM)


*4.4. Comparison of Urban Heat Island and Climate Scenario Contributions on Heat Stress*

Urban heat islands, as well as the projections of the climate scenarios for 2050, show significantly raised temperatures compared to rural areas in the current climate (see Figure 7). More specifically, rural areas in the WL and WH scenarios showed approximately the same number of tropical nights for the center of The Hague in the current climate. Nevertheless, climate change appears to create more significant heat stress than the imposed urbanization by the applied building assignments. This applies especially to The Hague Southwest. The neighborhoods 1 and 3 in the CID have large building assignments, and the increase in the number of tropical nights falls within the single climate-change effect of the GL and GH scenarios. In particular, the higher minimum temperatures showed a large increase in the climate scenarios. For the 5% warmest nights, an increase in night minima of 0.7 ◦C is foreseen for the GL scenario, and up to 2.2 ◦C for the WH scenario (1.2 ◦C and 2.7 ◦C compared to the reference climate period of 1981–2010). This climate-change effect is about the same for rural and urban areas, which is discussed in the next paragraph. In contrast, the impact of urban planning strategies is smaller, but still relevant. The largest relative temperature differences among the urban planning strategies were found in The Hague Southwest. The preserving green strategy led to a 42% lower increase in UHImax in the 95th percentile than the building on green spaces strategy, i.e., 0.33 ◦C and 0.57 ◦C, respectively. For the CID, the building assignment was much larger, and the increased vegetation fraction strategy noted an increase of 1.09 ◦C compared to 1.36 ◦C for the preserving green strategy (difference of 20%). Tropical nights in urban areas were generally related to a lower UHImax percentile, which corresponds to a lower UHImax, and consequently, the numbers increased by 25%.

The climate-change effect is nearly the same for rural and urban areas since the UHI will be very similar in the future climate. In the diagnostic equation for UHImax (Equation (1)), this is represented by an offset between a projected decrease in DTR and a projected increase in incoming short-wave radiation, except for the WL scenario. This scenario shows the largest decrease in DTR with an unchanged amount of sunshine. However, this results in a minor reduction in UHI by 2–3%. In July and August, the UHI slightly increased (2–3%) for the H-scenarios, according to the increase in DTR. Most other studies confirmed the small or non-significant average UHI changes for mid-latitude cities in western Europe for the future climate [58–60]. There are climate models even hinting at a decrease in UHImax and UHITMIN due to larger projected precipitation deficits and associated drier soils in the summer time [57,59]. In such situations, rural temperatures increase, because less energy is used for evapotranspiration. Hamdi et al. [59] found that, for a coupled urban and regional climate model, the higher percentiles of UHITMIN could decrease by 1 ◦C. For our study area, the effect of soil dryness does not play a substantial role in the UHI, because the land is below sea level and ground water levels are controlled.