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Additional file 2: Supplementary Fig. 4. Heat map of comparisons of microglial burden between all groups and between grey and white matter within each group. a and b: CD68-positive microglia; c and d: CR3/43-positive microglia; e and f: Iba1-positive microglia. Frontal lobe: a, c, e. Temporal lobe: b, d, f. P values are presented in each box and represent results of comparisons between groups listed on corresponding vertical versus horizontal axes. The colour of each box represents the degree of statistical significance in the difference between groups, with red indicating a highly significant difference, blue a non-significant difference and white borderline (trend) or moderately significant difference, with gradations in between. FG = frontal grey matter; FW = frontal white matter; TG = temporal grey matter; TW = temporal white matter. Supplementary Fig. 5. Heat map of comparisons of microglial circularity between all groups and between grey and white matter within each group. a and b: CD68-positive microglia; c and d: CR3/43-positive microglia; e and f: Iba1-positive microglia. Frontal lobe: a, c, e. Temporal lobe: b, d, f. P values are presented in each box and represent results of comparisons between groups listed on corresponding vertical versus horizontal axes. The colour of each box represents the degree of statistical significance in the difference between groups, with red indicating a highly significant difference, blue a non-significant difference and white borderline (trend) or moderately significant difference, with gradations in between. FG = frontal grey matter; FW = frontal white matter; TG = temporal grey matter; TW = temporal white matter. Supplementary Fig. 6. Heat map of comparisons of microglial perimeter between all groups and between grey and white matter within each group. a and b: CD68-positive microglia; c and d: CR3/43-positive microglia; e and f: Iba1-positive microglia. Frontal lobe: a, c, e. Temporal lobe: b, d, f. P values are presented in each box and represent results of comparisons between groups listed on corresponding vertical versus horizontal axes. The colour of each box represents the degree of statistical significance in the difference between groups, with red indicating a highly significant difference, blue a non-significant difference and white borderline (trend) or moderately significant difference, with gradations in between. FG = frontal grey matter; FW = frontal white matter; TG = temporal grey matter; TW = temporal white matter. Supplementary Fig. 7. Heat map of comparisons of microglial dystrophy scores between all groups and between grey and white matter within each group. a frontal lobe; b temporal lobe. P values are presented in each box and represent results of comparisons between groups listed on corresponding vertical versus horizontal axes. The colour of each box represents the degree of statistical significance in the difference between groups, with red indicating a highly significant difference, blue a non-significant difference and white borderline (trend) or moderately significant difference, with gradations in between. FG = frontal grey matter; FW = frontal white matter; TG = temporal grey matter; TW = temporal white matter.

Additional file 1: Supplementary Table 1. Demographics and diagnoses of all individual cases and controls. Supplementary Fig. 1. Microglial burden compared between grey and white matter within each lobe for each group. Comparisons of the burden of CD68-positive (a, d, g, j, m, p), CR3/43-positive (b, e, h, k, n, q), and Iba1-positive (c, f, i, l, o, r) microglia for each group comparison level shown within Fig. 1 (numbers in coloured circles on left represent level of comparison). Graphs show median microglial burden (percentage area values) compared within lobes: frontal grey (FG) versus frontal white (FW) matter, and temporal grey (TG) versus temporal white (TW) matter. See legend in first graph on each row for bar colours. Error bars represent interquartile range. *p < 0.05; **p < 0.01; ***p ≤ 0.001; ****p ≤ 0.0001. Supplementary Fig. 2. Microglial circularity compared between grey and white matter within each lobe for each group. Comparisons of the circularity of CD68-positive (a, d, g, j, m, p), CR3/43-positive (b, e, h, k, n, q), and Iba1-positive (c, f, i, l, o, r) microglia for each group comparison level shown within Fig. 1 (numbers in coloured circles on left represent level of comparison). Graphs show median circularity values compared within lobes: frontal grey (FG) versus frontal white (FW) matter, and temporal grey (TG) versus temporal white (TW) matter. See legend in first graph on each row for bar colours. Error bars represent interquartile range. *p < 0.05; **p < 0.01; ***p ≤ 0.001; ****p ≤ 0.0001. Supplementary Fig. 3. Microglial perimeter compared between grey and white matter within each lobe for each group. Comparisons of the perimeter of CD68-positive (a, d, g, j, m, p), CR3/43-positive (b, e, h, k, n, q), and Iba1-positive (c, f, i, l, o, r) microglia for each group comparison level shown within Fig. 1 (numbers in coloured circles on left represent level of comparison). Graphs show median perimeter values compared within lobes: frontal grey (FG) versus frontal white (FW) matter, and temporal grey (TG) versus temporal white (TW) matter. See legend in first graph on each row for bar colours. Error bars represent interquartile range. *p < 0.05; **p < 0.01; ***p ≤ 0.001; ****p ≤ 0.0001

Small, dense, wireless sensor networks are beginning to revolutionize our understanding of the physical world by providing fine resolution sampling of the surrounding environment. The ability to have many small devices streaming real-time data physically distributed near the objects being sensed brings new opportunities to observe and act on the world which could provide significant benefits to mankind. While wireless sensor-net systems are beginning to be fielded in applications today on the ground, underwater sensor nets remain quite limited by comparison. Still, a large portion of ocean research is conducted by placing sensors (that measure current speeds, temperature, salinity, pressure, bioluminescence, chemicals, etc.) into the ocean and later physically retrieving them to download and analyze their collected data. Real-time underwater wireless sensor networks that do exist are often sparsely deployed over wide areas. The existence of small, dense wireless sensor networks on land was made possible by the advent of low-cost radio platforms. These radio platforms cost a few hundred U.S. dollars enabling researchers to purchase many nodes with a fixed budget allowing for dense, short-range deployment. The aquatic counterpart to the terrestrial radio is the underwater acoustic modem. Existing underwater acoustic modems' power consumption, ranges, and price points are all designed for sparse, long-range, expensive systems rather than for small, dense, and inexpensive sensor-nets. It is widely recognized that an aquatic counterpart to inexpensive terrestrial radio would be required to enable deployment of small dense underwater wireless sensor networks for advanced underwater ecological analyses. This thesis describes the full design of an underwater acoustic modem for small, dense wireless sensor networks starting with the most critical component from a cost perspective - the transducer. The design replaces a commercial underwater transducer with a homemade underwater transducer using inexpensive piezoceramic material and builds the rest of the modem's components around the properties of the homemade transducer to extract as much performance as possible. By building the modem from inexpensive components, the design provides a low-cost, low-power alternative to existing commercial modems for use in short-range networks

Frontotemporal Lobar Degeneration (FTLD) encompasses certain related neurodegenerative disorders which alter behaviour, personality and language. Heterogeneous ribonuclear proteins (hnRNPs) maintain RNA metabolism and changes in their function may underpin the pathogenesis of FTLD. Immunostaining for hnRNP A1, A2/B1 and A3 was performed on sections of temporal cortex with hippocampus from 61 patients with FTLD, stratified by pathological hallmarks into FTLD-tau and FTLD-TDP type A, B and C subtypes, and by genetics into patients with C9orf72 expansions, MAPT or GRN mutations, or those without known mutation. Four patients with Motor Neurone Disease (MND) with C9orf72 expansions and 10 healthy controls were also studied. Semi-quantitative analysis assessed hnRNP staining intensity in dentate gyrus (DG) and CA4 region of hippocampus, and temporal cortex (Tcx) in the different pathological and genetic groups. Immunostaining for hnRNP A1, A2/B1 and A3 revealed no consistent changes in pattern or amount of physiological staining across any of the pathological or genetic groups. No immunostaining of any inclusions resembling TDP-43 immunoreactive neuronal cytoplasmic inclusions or dystrophic neurites, was seen in either Tcx or DG of the hippocampus in any of the FTLD cases investigated for hnRNP A1, A2/B1 and A3. However, immunostaining for hnRNP A3 showed that inclusion bodies, resembling those TDP-43 negative, p62-immunopositive structures containing dipeptide repeat proteins (DPR) were variably observed in hippocampus and cerebellum. The proportion of cases showing hnRNP A3-immunoreactive DPR, and the number of hnRNP A3-positive inclusions within cases, was significantly greater in DG than in cells of CA4 region and cerebellum, but the latter was significantly less in all three regions compared to that detected by p62 immunostaining. Electronic supplementary material The online version of this article (doi:10.1186/s40478-017-0437-5) contains supplementary material, which is available to authorized users.

Conventional time domain symbol synchronizer uses static predefined threshold to evaluate the level of environment noise and extract synchronization signal, but is less feasible to apply in underwater acoustic channel because of the great delay and noise variance make the threshold definition inaccurate. The paper provides a new hardware scheme using real time orthogonal sliding correlation as adaptive threshold to build a symbol synchronizer suitable for an underwater FSK acoustic receiver. The design has been implemented and evaluated on FPGA board. Both the simulation and real data experimental results show that the design provides accurate synchronization which consumes 9% of resources slices.