text
string | predicted_class
string | confidence
float16 |
|---|---|---|
Next, the SU-8 3025 microstructures were added to the SU-8 3010 silicon wafer. SU-8 3025 photoresist was spin-coated on the wafer at 4500 rpm for 30 seconds. The coated wafer was soft baked at 65 °C for 1 minute, 95 °C for 4.5 minutes, and then 65 °C for 1 minute. The designed mask for the SU-8 3025 pattern was then aligned with the SU-8 3010 pattern and exposed for 55 seconds. After waiting for approximately 30 minutes, the wafer was developed using SU-8 developer (MicroChem). The finished structure was measured to be 18–20 µm in thickness.
|
study
| 99.06 |
Polyurethane-based plastic (Smooth-Cast ONYX SLOW, Smooth-On) was used to make replicas of the microstructures on the master silicon wafer, as previously described45. PDMS microfluidic devices were then fabricated from these molds using soft-lithography of RTV 615 PDMS (Momentive Performance Materials).
|
other
| 99.5 |
After baking, the cured microfluidic device was removed from its mold, and holes for the fluidic introduction ports including cross flow inlet (CFI) and sample inlet (SI) as well as the oscillation inlets (OSC1 and OSC2), were punched into it using a 0.5 mm outer diameter hole punch (Technical Innovations, Angleton, TX, USA). The outlets were punched using a 3 or 4 mm diameter puncher. The microfluidic device was then bonded by oxygen plasma (Model PDC-001, Harrick Plasma) to a blank layer of PDMS previously spin-coated onto a blank silicon wafer at 1500 rpm for 1 minute and then subsequently bonded to a standard microscope slide (50 × 75 mm, Fisher Scientific).
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other
| 87.8 |
Blood from healthy donors was obtained via venipuncture in 6 ml EDTA vacutainer tubes (BD, Canada) following informed consent. The approval was obtained from the University of British Columbia’s Clinical Research Ethics Board. The study was carried out in strict accordance with the guidelines and regulations of the University of British Columbia’s Clinical Research Ethics Board.
|
other
| 99.94 |
Whole blood (200 µl) was stained with Hoechst 33342 (Sigma Aldrich) to label the DNA of leukocytes. Hoechst (5 µg ml−1) was added at 1:100 (v/v) to the sample and incubated for 30 minutes at room temperature in the dark. Subsequently, the sample was washed three times in Phosphate Buffered Saline (PBS; CaCl2-free and MgSO4-free; Invitrogen) with 2% heat-inactivated fetal bovine serum (FBS). After the last wash, the supernatant was replaced with PBS containing 0.2% PluronicTM F-127 (Invitrogen) at 1:1 (v/v) ratio with the packed blood cells to obtain the similar hematocrit as whole blood.
|
study
| 99.94 |
Lymphocytes were stained with Alexa Fluor® 488 Mouse Anti-Human CD3, clone SP34-2 (557705, BD) and Alexa Fluor® 488 Mouse Anti-Human CD19, clone HIB19 (557697, BD). Granulocytes were stained with Alexa Fluor® 647 Mouse Anti-Human CD66b, clone G10F5 (561645, BD) and monocytes with Alexa Fluor® 488 Mouse Anti-Human CD14, clone M5E2 (557700, BD), all according to manufacturer’s instructions. After staining and washing, the supernatant was replaced with PBS containing 0.2% PluronicTM F-127 (Invitrogen) at 1:1 (v/v) ratio with the packed blood cells. Lymphocytes and neutrophils are observed at the outlets through fluorescence microscope. Multichannel images were taken as shown in Fig. 5G and H.
|
study
| 99.94 |
Historical baselines of ecological states can improve the interpretation of current anthropogenically induced change. Such baselines have already shown their value as a guide to modern day management and conservation . Records of fish landings and imports have, for example, been used to reconstruct past fish abundance and size [2–7]. Zoo-archaeological material of exploited animal populations may be particularly useful for reconstructing historical baselines as they provide a population level link between written historical sources and paleo-environmental data series.
|
other
| 99.9 |
Otoliths, a calcified structure in the inner ear of fish, are occasionally recovered during archaeological excavations . They are unique among zoo-archaeological material in that they simultaneously convey information on individual fish age , growth and reflect the environmental conditions that the fish encountered throughout life-history [12–14]. Otoliths from archaeological excavations have been used to examine historical changes in fish age [15, 16], trophic position and seasonality of human site occupation . Importantly, otoliths allow measures of annual growth, and thereby retrospective reconstructions of fish growth patterns. Growth reconstructions from archaeological otoliths have for example shown higher growth rate of Neolithic Baltic cod than in the modern population, particularly in the first year of growth . Conversely, research from the North Sea finds slightly slower growth rate of cod (as well as haddock and plaice) in early modern times than in recent times .
|
study
| 99.9 |
Fish growth trajectories are plastic and correlate with a number of environmental factors including food availability , temperature [21, 22] and acidification . Recent reductions in length at age have been noted for many exploited fish species [24–26]. The trend for reduced size is attributed to fisheries induced selection on fish life history traits, importantly on size at maturity, as fishing with common gear, such as trawls, favors individuals that mature at a smaller size [27–29]. Climate change may also result in evolutionary reductions in fish size through physiological adaptations . To facilitate interpretation of ongoing change in fish growth long term historical time series of growth trajectories are needed.
|
study
| 99.94 |
Archaeological excavations of historical fishing sites across the North Atlantic have unearthed high quantities of Atlantic cod bones [31, 32] and the species composition of bone assemblages suggests an early specialization on Atlantic cod fisheries [33–35]. The quantity and often good preservation of the zoo-archaeological material at these sites offer unparalleled opportunities of retrospective examinations of Atlantic cod biology, including estimation of historical growth trajectories. Previous research on Atlantic cod bones from archaeological excavations have suggested ecological changes before industrial fisheries, for example, a disruption in growth of north-east Arctic cod and loss of genetic diversity in Icelandic cod in the 16th century .
|
study
| 99.94 |
In the North Atlantic, the medieval and early modern periods were characterized by rapid increase in marine fisheries; as urbanization and globalization in western Europe drove increasing demand for stock fish; and multinational fishing fleets sought favorable fishing grounds for Atlantic cod . At the same time a cooling climate significantly affected societies across northern Europe, with the onset of the “little ice age” and subsequent temperature fluctuation; including a North Atlantic temperature minimum in the 17th century [39, 40]. Adult Atlantic cod are tolerant to a wide range of temperature and are known to migrate to areas with favorable temperature [41, 42]. However, age 0+ juveniles are dependent on shallow nearshore areas and may therefore be more affected by changes in sea temperature [43, 44].
|
study
| 99.6 |
In the current study, we analyze growth patterns of Atlantic cod otoliths from archaeological excavations of historical fishing sites in NW Iceland, dated to AD 970 –AD 1910. First, we examine the significance of change in otolith size, linear and quadric growth patterns across the millennium. Our initial hypothesis was based on faster growth in the medieval warm period followed by reductions in growth rate, particularly during the North Atlantic temperature minimum in the 17th century.
|
study
| 100.0 |
The archaeological excavations were carried out at the historical fishing sites; Breiðavík (BRV, 24°24'45.98”W, 65°32'38.13"N) and Kollsvík (KOV, 24°21´6.19”W, 65°36´36.07”N) in north-western Iceland (Fig 1). In July 2012 we excavated two trenches; one in Breiðavík (50cm x 50cm) and one in Kollsvík (1m x 80cm). In July 2015 we again excavated two trenches; both in Breiðavík (the first 2m x 50cm, the second 1m x 50cm).
|
other
| 99.9 |
Archaeological units, i.e. individual cultural deposits, were identified, recorded and excavated in reversed order, starting with the youngest. Each deposit was sieved with a 4mm mesh to retrieve bones, otoliths and finds. During the post excavation work all identifiable bones were identified to a species level and all Atlantic cod otoliths were removed from the bone assemblage for further analysis. The deposits were initially dated by their context, i.e. stratigraphical sequence or finds, and ultimately by 14C dating (Scottish Universities Environment Research Centre). Mean 14C age, quoted in years AD, was used in analysis. The error around the mean (Table 1), is expressed at the one sigma level of confidence, including components from the counting statistics on the sample, modern reference standard and blank and the random machine error. Five deposits could not be dated by 14C and were assigned an “informed mean date” based on their stratigraphical sequence and 14C dates of the adjacent deposits formed the error around the informed mean date (Table 1).
|
study
| 100.0 |
Sample sizes of otoliths from each archaeological deposit. Dating information is given as mean 14C dates and the associated error and for deposits with no 14C information as informed estimates and range (see text for details). Age represents fish otolith age within deposits.
|
other
| 99.94 |
We used a total of 220 archaeological specimens for the current analysis. Sample numbers can be found in S1 File. The archaeological excavations were permitted by the Icelandic Cultural Heritage Agency: Permit no: 21505–0060. The samples used in this study were deposited at the National Museum of Iceland: Conservation no: 2015–33.
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other
| 99.9 |
A total of 220 otoliths were used for aging and growth determination, n = 57 from the 2012 excavation and n = 163 from the 2015 excavation (Table 1). All otoliths used were well preserved, although broken otoliths were used for age determination and growth estimates when it was possible to clearly identify all annual rings. Age determination and growth measures were done “blinded”, that is, without information on 14C dating.
|
study
| 99.75 |
The sagittae otoliths were sectioned along the transverse axis (cross-sectioning), this involves embedding the otoliths in black resin blocks, removing a thin section from the transverse midplane. Images were taken of each otolith again using Leica IC80 HD (Digital Camera Module by Leica Microsystems) under a stereomicroscope at 10× magnification using a reflected light setting, with resolution 2048 x 1536 pixels. The age determination and annotation of cod otoliths in this study were carried out by highly experienced otolith reader. The otolith sizes at the mid-point of respective annual translucent zones were then marked along a transect from the core to the outer distal edge of the otolith, with the transect being approximately perpendicular to the width of the otolith (following Li et al. , Fig 2). Only the first five years of growth were measured. Fish growth slows with age and accurate estimates of annuli growth along the distal axis became increasing difficult with age, particularly in the very old individuals.
|
study
| 100.0 |
We did not attempt back calculations of fish size as archaeological otoliths are known to be subject to shrinkage and estimates of life fish length based on archaeological otoliths may be underestimated . We therefore used otolith increment measures directly in subsequent statistical analysis. Note that this equates to not consider the biological intercept model , that is, the non-linear relationship between somatic and otolith growth. Both the data and figures presented should be interpreted with this in mind.
|
study
| 99.9 |
First, we examined if growth patterns differed between archaeological deposits using the first five years of growth of 220 otoliths, a total of 1100 growth measures (Table 1). A second-order polynomial (Eq 1) was fitted using a generalized linear mixed model (GLMM) .
|
study
| 100.0 |
Where Yij is the otolith size observation of individual i at Time j, β0i is the intercept, β1i is the linear slope and β2i the quadratic curvature, εij is the residual error and T is time of the observation (fish age in years). The mean estimated date (AD) for each archaeological deposit (Table 1) formed a fixed effect on both the linear and quadric term and fish ID and fish age at mortality were set as random effects. We used the deposit closest to modern times as the reference point (AD 1910). Statistical significance (p-values) for parameter estimates was assessed using the normal approximation (i.e., treating the t-value as a z-value). Models were fitted using the lme4 package v. 1.1–12 in R version 3.0.2 .
|
study
| 100.0 |
Second, as there were often few otoliths in each archaeological deposit, we divided the otoliths into three non-overlapping temporal groups, the first represented samples dated to before AD 1499 (Medieval period), the second group included samples dated to AD 1500—AD 1784 (Early Modern period) and finally samples dated to post AD 1785 (Modern period). This classification represents a common historical designation of periods. We then repeated the GLMM, as described above, but replacing archaeological deposit with period as a fixed effect.
|
study
| 99.94 |
Where Yij is the otolith size observation of individual i at Time j and T is time of the observation (fish age in years). L∞ is the maximum length (in the current analysis of otolith increment), K is the relative growth rate, t0 is the theoretical age for time at which length is zero. The vBGF is widely used in fisheries biology [49, 50] and our aim was to facilitate comparison of historical Atlantic cod growth rate to previous studies as well as to test for parameter differences between periods. Two models were fitted, 1) a model assuming the same parameters values for all 220 otoliths and 2) a model allowing all parameters to differ between the three periods. vBGF models 1 and 2 were then compared using a likelihood ratio test. Models were implemented in R version 3.0.2 using the package FSA . The current growth data is based on otolith increments and within individual measures are expected to be non-independent. Therefore, we attempted to fit a non-linear mixed model with a user defined vBGF using nlme in the lme4 package v. 1.1–12 . However, vBGF models including random effects did not converge (data not shown). Code for statistical analysis can be found in S2 File.
|
study
| 100.0 |
The results support consistent growth patterns of Atlantic cod through the millennium, that is, repeated fluctuations but no long term shifts in growth patterns. Fig 3 depicts the otolith growth data and fitted growth patterns for each archaeological deposit examined.
|
other
| 98.8 |
The figure depicts five-year otolith increment growth (mean and SE), as well as the fitted quadric growth patterns for each of the seventeen archaeological deposits. Archaeological deposits are represented by mean 14C or estimated mean date AD. The color represents designated historical periods, red = Medieval, blue = Early modern and green = Modern.
|
other
| 99.9 |
In the first generalized linear mixed model, comparing across all archaeological deposits, the intercept, the linear term and the quadratic term were all significant (Table 2) showing that both linear and quadratic growth curves represent the observed growth pattern (note that this does not indicate any difference between archaeological deposits). The effects of archeological deposit as a fixed effect on otolith increment size was significant for AD 1680 (using AD 1910 as a base for comparison) (Table 2). There were significant interaction effects of the linear term and the archaeological deposits dated to AD 1680 and AD 1621, showing significantly lower linear growth rate in AD 1680 and higher linear growth rate in AD 1621. Finally, there was significant interaction of the quadric term the archeological deposit dated to AD 1570, representing increased quadric curvature, that is, relatively slower growth at age 3+ and age 4+.
|
study
| 100.0 |
Estimates of vBGF parameters did not differ significantly between periods, that is, the model allowing all vBGF parameters to differ between periods was not a significantly better fit than a model with the same parameter values for all otoliths (df = 6, difference in log likelihood = -0.61, χ2 = 1.23, p = 0.98). L∞ estimates for the three periods varied between 2.203 and 2.441, estimates of K varied between 0.204 and 0.248 and estimates of t0 varied between -0.018 and 0.124 (Table 3). Any comparison of these parameter values should acknowledge the shrinkage of archeological otoliths .
|
study
| 100.0 |
Examination of growth trajectories across AD 970 to AD 1910 showed significantly slower growth, as well as smaller total otolith increment lengths, in the late 17th century signaling reduced growth of age 0+ juveniles. Other notable changes in the polynomial growth model include negative estimates of the quadratic term in AD 1570, suggesting slower growth of age 3+ and age 4+ juveniles, and finally steeper linear growth in AD 1621 (Table 2, Fig 3). Despite these variations between the archaeological deposits we highlight that no consistent or long term shifts in Atlantic cod growth patterns growth trajectories were noted between periods, as may have been expected, for example, between the medieval warm period and modern times (Fig 3, S2 Table).
|
study
| 100.0 |
Atlantic cod growth has been examined across 20th century time series that have shown considerable short term and inter-annual fluctuations in growth [52, 53]. Archaeological datasets do not capture intra-annual or between cohort variation as cohorts and multiple years are inevitable pooled within a single archaeological deposit. The current results may therefore underestimate temporal fluctuations in growth and this is further indicated by the loss of any significant effects when the otoliths were pooled to three historical periods (S2 Table). However, the current data signals a notable decline in juvenile growth the 17th century. This is consistent with the results of Geffen et al., that showed decreased growth of north-east Arctic cod between the early 16th century and the 18th century. The current results add to those previously reported as they provide a second geographically distinct dataset and the temporal resolution of the current data allows further deductions on the timing and extent of growth shifts in Atlantic cod.
|
study
| 100.0 |
The reduction in growth in the late 17th century appears to represent slower growth of age 0+ juveniles (Fig 3). The North Atlantic cooled in the 17th century and historical documents report harsh winters and inshore conditions e.g. icebergs and ice covered fjords around Iceland [39, 40]. Juvenile Atlantic cod nursery areas are in inshore waters and age 0+ juveniles are particularly likely to be found in shallow nearshore waters [43, 44]. Atlantic cod age 0+ juveniles may therefore be more effected by local climate effects than older cod that can seek favorable temperature and foraging conditions [41, 42]. Previous research has shown that ocean temperature was not a primary source of otolith growth variation in juvenile Atlantic cod . However, lower sea temperatures in the 17th century may also have affected food availability. Juvenile cod feed predominantly on zooplankton and are dependent on phenological matching of zooplankton blooms [54–56]. This matching may be disrupted by climate effects suggesting that food limitation could also explain slower growth of age 0+ juvenile Atlantic cod in the 17th century.
|
study
| 100.0 |
The current data suggests that Atlantic cod growth was not more rapid in the warmer period preceding the 17th century sea temperature minimum. In fact, growth in the 4th and 5th year of life was less rapid in AD 1570 (Table 2, Fig 3). This result may be consistent with previous research that show that adult growth is maximized at cooler sea temperatures [41, 57–59], as well as studies from the 20th century that have shown that warmer periods in Icelandic waters negatively impact cod, primarily through northward migrations of capelin; favored forage fish . Finally, the current growth reconstructions support that the large sized fish described in the medieval and early modern periods by anecdotes and archaeological reconstruction [1, 31] is not likely to represent a shift in growth patterns but the higher age of the pristine cod populations. Higher mean age of historical Atlantic cod populations has been found in previous studies [31, 37] and the current study (Table 1).
|
study
| 100.0 |
As any fisheries samples, archaeological fish remains can be biased, for example; by season, fishing methods and market preferences, all of which could affect the size of the landed fish. A particular consideration for interpreting growth patterns based on Atlantic cod otoliths from archaeological sites is that growth trajectories differ between populations of Atlantic cod, importantly, between migratory and coastal ecotypes [61, 62]. Any shifts in population distributions or in the frequency of populations or ecotypes in the catch could result in concurrent signals of change in growth patterns. Ólafsdóttir et al., reported lower incident of PanI genotypes, representative of migratory Atlantic cod, in archaeological samples dated to post AD 1600. Therefore, we suggest that further research is needed to conclude on historical growth trajectories of migratory and coastal ecotypes.
|
study
| 99.94 |
To conclude, the current results provide a high resolution chronological record of consistent growth patterns of north-east Atlantic cod on a millennium scale; a potentially valuable baseline for modern day studies of environmental effects on Atlantic cod growth. We moreover propose that further study on otoliths from archaeological excavations has the potential to increase understanding on environmental effects on fish growth trajectories.
|
study
| 99.7 |
Yeast strains belonging to diverse species produce and secrete proteins or glycoproteins, known as killer toxins (KTs), that are lethal to susceptible strains . This property, which offers a competitive advantage to self-immune killer yeasts in their ecological niches, has found several applications in the biological control of plant pathogens and spoiling yeasts in the food and fermentation industries . In the medical field, KTs have been used for the biotyping of pathogenic microorganisms, in epidemiological studies, and for the identification of novel cellular targets in microbial cells and the development of new antimicrobials .
|
review
| 99.9 |
Some KTs, such as K1 and K28 from Saccharomyces cerevisiae, have a narrow spectrum of activity, limited to susceptible strains of the same species, while other KTs show a wide killing spectrum . In particular, some Wickerhamomyces anomalus (formerly Pichia anomala) KTs proved to be active against a wide range of microorganisms, including other yeast species, filamentous fungi, bacteria, and protozoan parasites .
|
review
| 91.94 |
Killer strains of W. anomalus have been isolated from different sources, including plants and food products , arthropods such as the crab Portunus trituberculatus , and mosquitoes of the species Anopheles stephensi . Recently, the isolation and characterization of a W. anomalus strain displaying the killer phenotype was reported from specimens of the sand fly Phlebotomus perniciosus .
|
study
| 99.94 |
The ability to inhibit harmful microorganisms in a variety of habitats and the wide killing spectrum of the produced KTs have prompted the use of W. anomalus as a bio-control agent , since it could be classed as a low risk microorganism, rarely traced in human samples . In addition, W. anomalus KTs activity against dermatophytes and pathogenic yeasts, especially Candida spp., led to the hypothesis that could be applied in medical mycology as alternative antifungal compounds .
|
study
| 99.75 |
Candida spp. are the most frequently isolated yeasts in clinical specimens. The frequency of invasive opportunistic fungal infections caused by species of this genus has significantly increased in recent years, particularly in immunosuppressed individuals and patients with indwelling medical devices . Worldwide, the prevalent cause of invasive candidiasis remains C. albicans, although the epidemiology of candidal infections has gradually shifted towards non-albicans species, such as C. glabrata and C. krusei . Increasing concern is rising in view of growing reports of resistance to antifungal drugs, with particular reference to resistance to azoles in non-albicans Candida species .
|
review
| 99.9 |
In the aim of searching for new molecules, potentially effective against strains resistant to conventional antifungal drugs, we investigated the in vitro activity of a KT produced by the recently isolated W. anomalus strain 1F1 (Wa1F1-KT) against both susceptible and azole-resistant clinical isolates as well as laboratory strains of C. albicans and C. glabrata displaying known mutations .
|
study
| 100.0 |
The production of Wa1F1-KT by the strain W. anomalus 1F1 was analyzed over time. The activity of the concentrated culture supernatant obtained at different time periods was determined against the reference C. lusitaniae strain on solid medium and quantified by Arbitrary Units/mL (1 Arbitrary Unit (AU) is defined as the amount of KT producing an inhibition zone of 1 mm2). As shown in Figure 1, the results indicate that the activity of the Wa1F1-KT sample obtained after 24 h of incubation was low (559 AU/mL), although it increased after 48 h of incubation (1815 AU/mL), and reached a maximum after 72 h (2326 AU/mL). The killing activity decreased steeply thereafter.
|
study
| 100.0 |
Western blot analysis was carried out on crude extracts from 72-h cultures of the W. anomalus strains 1F1 and ATCC 96603 (KT-producing, positive control strain). As a negative control, a crude extract from W. anomalus UM3 (KT-nonproducing strain) culture and 50-fold concentrated YPD medium were used. Bands were detected with mAbKT4, a monoclonal antibody directed against a KT produced by the reference ATCC 96603 strain (Wa96603-KT) and shown to cross-react with KTs produced by other W. anomalus strains and other killer yeasts . The results showed that mAbKT4 reacts with high-molecular mass proteins in samples obtained from W. anomalus 1F1 and ATCC 96603, but not from controls (Figure 2). A single band of approximately 220 kDa was revealed in extracts from the reference strain, while a single band with a lower molecular mass (approximately 160–170 kDa) was detected in the extract from 1F1 strain, indicating the secretion of KTs with a common epitope, although with some structural differences.
|
study
| 100.0 |
The concentrated extract from W. anomalus 1F1 culture was analyzed by size exclusion chromatography and eluted fractions were assayed for killing activity against the reference C. lusitaniae strain. In agreement with the immunoblot data, the results indicated the elution of active Wa1F1-KT in fractions 34–39 of the size exclusion chromatogram (Figure 3), corresponding to the highest molecular mass separation zone.
|
study
| 100.0 |
Wa1F1-KT was tested by an agar diffusion assay against the reference C. lusitaniae NEQAS 6208 strain and clinical isolates and laboratory strains of C. albicans and C. glabrata susceptible or resistant to fluconazole. The results showed that, when tested at 25 °C, Wa1F1-KT was active towards both fluconazole-susceptible and -resistant strains of C. glabrata, with some quantitative differences (Table 1). Wa1F1-KT showed no effect against the reference C. albicans SC5314 strain and against two fluconazole-susceptible clinical isolates (DSY347 and DSY544) and the DSY544-derived fluconazole-resistant mutant strain (DSY775) of C. albicans. On the contrary, a good effect was detected against the DSY347-derived fluconazole-resistant mutant C. albicans DSY289 strain. No effect against any of the tested strains was detected when the assay was carried out at 30 and 37 °C.
|
study
| 100.0 |
To investigate if Wa1F1-KT could act on susceptible yeast strains by hydrolyzing major cell wall components, its ability to digest the soluble β-1,3-glucan laminarin was assayed in comparison with Wa96603-KT, the KT produced by the Williopsis saturnus var. mrakii MUCL 41968 (Wm41968-KT), with known β-glucanase activity , and laminarinase. The results showed that all KTs could hydrolyze laminarin to an end product presenting similar relative mobility to the product of the laminarinase reaction (Figure 4).
|
study
| 100.0 |
Mucosal and invasive candidiasis are the most common mycoses in humans . Invasive candidiasis and candidemia, in particular, are an emerging health problem, especially in hospitalized and immunosuppressed individuals and patients with indwelling medical devices . Additionally, the diffusion of antifungal drugs resistance in Candida spp., particularly non-albicans, makes it necessary to look for new treatments against these infections .
|
review
| 57.3 |
In the present study, with the aim of searching for alternative antifungal agents, a KT produced by the newly isolated W. anomalus 1F1 strain was assayed in vitro against clinical isolates and laboratory strains of C. albicans and C. glabrata displaying known mutations and different susceptibility to fluconazole .
|
study
| 99.94 |
W. anomalus may produce different KTs with variable molecular mass (8–300 kDa), structural characteristics, pH and temperature optima, and antimicrobial activity range . KTs from W. anomalus and other killer yeasts have been shown to exert a β-1,3-glucanase activity, and may cause damage to the cell wall of susceptible yeasts as a result of degradation of the main β-glucans cell wall components .
|
study
| 100.0 |
Our results showed that Wa1F1-KT shares at least one epitope with the KT produced by W. anomalus ATCC 96603, although the molecular mass of the two toxins is slightly different, indicating that they are related but not identical. Both Wa96603-KT and Wa1F1-KT degraded the soluble β-glucan laminarin in a manner similar to laminarinase, an endo-1,3(4)-β-glucanase, as did Wm41968-KT, whose glucanase activity had been already suggested .
|
study
| 100.0 |
The spectrum of activity of Wa96603-KT and Wa1F1-KT, however, appeared to be different, as the latter proved to be active against the C. glabrata isolates but did not affect the majority of C. albicans strains tested in this study, while Wa96603-KT was previously shown to kill different clinical C. albicans isolates . Selective killing of non-albicans species by W. anomalus KTs with β-glucanase activity has previously been reported . This differential spectrum may be explained with differences in the specificity of β-glucanase activity of KTs, which may selectively recognize different glycosidic linkages and glucan receptors on target yeast cells .
|
study
| 100.0 |
Although some KTs from W. anomalus display high stability at 37 °C and even higher temperatures , many KTs show lower thermostability . We found that the optimal temperature for Wa1F1-KT activity was lower than the physiological value in the human body. Nevertheless, the absence of β-glucans on mammalian cells suggests its potential application against fungal infections at skin and mucosal membrane levels, as has been demonstrated with other KTs . Further characterization of Wa1F1-KT enzymatic activity and the cloning of its encoding gene may represent the next step to investigate the feasibility to produce molecules with broader therapeutic activity, possibly including systemic infections.
|
study
| 100.0 |
The mechanism of action of Wa1F1-KT on C. glabrata appeared to be independent from the fluconazole-resistance pathway, as only slightly different effects were observed against the susceptible clinical isolate (DSY562) or its mutant derivative strains (SFY93, SFY105, SFY115, SFY116). This phenomenon underlies the potential of Wa1F1-KT as a universally active anti-C. glabrata tool, likely not affected by drug-resistance phenotypes.
|
study
| 100.0 |
On the contrary, the fact that Wa1F1-KT was active only on C. albicans DSY289 implies that the mechanism of action of the toxin may be dependent upon the specific mutations that confer resistance to fluconazole in this strain. C. albicans DSY289 was derived from the fluconazole-susceptible clinical strain DSY347 by mutations that confer combined resistance to azoles . In particular, the point mutations S405F/Y132H in the ERG11 gene encoding the enzyme lanosterol 14-α-sterol demethylase, which is involved in converting lanosterol into ergosterol, an essential component of the fungal cell membrane, are associated with a conformational change of the target enzyme and reduced interaction or binding of azoles . The gain-of-function A736V mutation in the transcriptional activator TAC1 causes the overexpression of the ATP binding cassette (ABC)-transporters CDR1 and CDR2, decreasing the concentration of azoles within the fungal cell . It is not clear how this mutation may affect Wa1F1-KT activity, but it may be speculated that the overexpression of transport systems and associated extracellular loops could possibly alter the recognition of the cell surface target by the toxin or that the transcriptional activator TAC1 is involved in the regulation of transcription of toxin receptors.
|
study
| 100.0 |
Further studies aimed at the biochemical characterization of properly purified Wa1F1-KT, the elucidation of its mechanism of action, and the reasons for its differential killing ability against different mutant strains can provide important information for developing new strategies to combat infections caused by azole-resistant Candida strains.
|
study
| 100.0 |
Strains belonging to the species W. anomalus, W. saturnus var. mrakii, C. lusitaniae, C. albicans, and C. glabrata were used in this study. W. anomalus 1F1 isolated from P. perniciosus , W. anomalus ATCC 96603 (a KT-producing strain formerly referred to as UP25F) , and Williopsis saturnus var. mrakii MUCL 41968 were used for the production of Wa1F1-KT, Wa96603-KT, and Wm41986-KT, respectively. The KT non-producing, KT-susceptible, W. anomalus UM3 strain was also used in this study as a negative control for KT expression .
|
study
| 100.0 |
The activity of Wa1F1-KT was tested against the reference C. albicans strain SC5314, two wild-type C. albicans clinical isolates (DSY544 and DSY347) , two C. albicans mutant strains resistant to fluconazole (DSY775, derived from DSY544, and DSY289, derived from DSY347) , two wild-type C. glabrata clinical isolates susceptible (DSY562) and resistant (DSY565) to fluconazole , and four C. glabrata fluconazole-resistant strains derived from DSY562 by mutations in the gene CgPDR1 (SFY93, SFY105, SFY115, SFY116) . The reference strain C. lusitaniae NEQAS 6208, known to be susceptible to the activity of Wa1F1-KT , was also used as a positive control for KT activity.
|
study
| 100.0 |
KT-producing strains were grown in YPD medium (1% yeast extract, 2% peptone, and 2% dextrose), then subcultured for KT production in YPD medium with 15% glycerol, buffered at pH 4.6 with 0.1 M citric acid and 0.2 M Na2HPO4. For the KT activity assay, YPD medium was added with 3% agar, and 0.003% methylene blue, and adjusted to pH 4.6 with 0.1 M citric acid and 0.2 M Na2HPO4.
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study
| 100.0 |
For the production of crude extracts, a seed culture of the W. anomalus and W. saturnus var. mrakii strains was incubated at 20 °C for 24 h with shaking at 150 rpm in YPD medium. Flasks (500-mL volume) containing 100 mL of YPD buffered at pH 4.6, with 15% glycerol, were inoculated with 1 mL of the seed culture and incubated at 20 °C for 72 h with shaking (150 rpm). After this period, the cells were removed by centrifugation (5000× g, 10 min, 4 °C); the supernatant was filtered through 0.45 µm pore size membranes (Merck Millipore, Darmstadt, Germany) and concentrated (50-fold) through an Amicon Ultra-15 (10-kDa cutoff) filter unit (Merck Millipore) by centrifugation at 4000× g, 4 °C. Accordingly, a concentrated extract of YPD medium used for KT production was prepared. The concentrated crude extracts were stored at 4 °C until use.
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study
| 100.0 |
The crude extracts from W. anomalus ATCC 96603, UM3, and 1F1 were analyzed by non-continuous denaturing sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS–PAGE) in 7% polyacrylamide gel, at 100 Volts for 2 h in a minigel system (Bio-Rad Laboratories, Hercules, CA, USA). Concentrated YPD medium was also run as a control.
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study
| 99.94 |
Proteins were electrically transferred to a polyvinylidene difluoride (PVDF) membrane at 100 Volts for 1 h. The total protein content of the crude extracts was estimated by Ponceau staining prior to immunodetection. The PVDF membrane was then incubated for 1 h at room temperature with 5% bovine serum albumin in Tris-buffered saline (TBS) at pH 7.5 and 0.5% tween-20 (TBS-T). Subsequently, the membrane was incubated overnight at 4 °C with a 1:500 dilution in TBS-T of the monoclonal W. anomalus ATCC 96603 KT-neutralizing antibody mAbKT4 , known to cross-react with KTs from other Wickerhamomyces and Williopsis strains. After washing three times in TBS-T, the membrane was incubated for 1 h at room temperature with a secondary, peroxidase-conjugated anti-mouse antibody. The membrane was thoroughly washed with TBS-T, incubated for 1 min with the proper substrate (BM Chemiluminescence blotting substrate, Roche, Basel, Switzerland), and detected by ChemiDoc 2000R (Kodak, Rochester, NY, USA).
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study
| 99.94 |
The crude Wa1F1-KT was dialyzed against 0.01 M citric acid-Na2HPO4 buffer (pH 4.5) for 24 h at 4 °C using a membrane with a molecular mass cut-off of 10 kDa. Analytical gel filtration was performed on a HiPrep Sephacryl S-200 prepacked column (GE Healthcare Life Sciences, Marlborough, MA, USA), characterized by bed dimensions of 16 × 600 mm and an exclusion limit (for globular proteins) of about 400 kDa, connected to an AKTA purifier system (GE Healthcare Life Sciences). Dialyzed crude extract was applied to the column, equilibrated, and eluted with 1.2 column volume of citric acid-Na2HPO4 buffer, pH 4.5. Eluted fractions (1 mL) were combined according to chromatogram peaks, lyophilized, and re-solubilized in 1 mL of sterile distilled water. Total protein content was quantified with an infrared-based spectrometry system (Direct Detect™, Merck Millipore) and the concentrated fractions were assayed for killing activity against the C. lusitaniae NEQAS 6208 susceptible strain (see below).
|
study
| 100.0 |
The activity of Wa1F1-KT was tested against the reference C. lusitaniae NEQAS 6208 strain and fluconazole-susceptible or -resistant clinical isolates as well as laboratory strains of C. albicans and C. glabrata. Each test strain, grown overnight on SDA plates, was resuspended in water to a final concentration of 0.5 McFarland and spread (100 µL) on the surface of YPD agar plates. Crude extracts from W. anomalus 1F1 were poured into wells of 8 mm (40 µL per well) cut into the agar plates. The plates were incubated for 48 h at 25, 30, or 37 °C and the diameter of the area of growth inhibition was measured.
|
study
| 100.0 |
The ability of Wa1F1-KT to hydrolyze the soluble β-1,3-glucan laminarin (Sigma-Aldrich, St. Louis, MO, USA) was assayed in comparison to Wa96603-KT, Wm41968-KT (with recognized β-glucanase activity ), and laminarinase (Sigma-Aldrich). The reaction mixtures contained 20 µL of 50-fold concentrated crude KTs or 10 µL of laminarinase (4.5 U/mL) and 2 mg/mL laminarin in 100 µL of citric acid-Na2HPO4 buffer (0.01 M, pH 4.5). After incubation at 25 °C for 2 and 4 h, the reaction was stopped by heating at 100 °C for 15 min. The activity on laminarin was estimated through observation of the end products of laminarin hydrolysis by thin layer chromatography . Reaction mixtures containing crude KTs inactivated by heating at 100 °C for 15 min were used as controls.
|
study
| 100.0 |
Functional near-infrared spectroscopy (fNIRS) is a technique capable of measuring concentration changes of oxygenated and deoxygenated hemoglobin from the variations of absorbed near-infrared light during its transportation in tissues1. The employment of fNIRS to assess brain activity has increased over the last few years due to its advantages over functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) mainly concerning its robustness to artifacts due to motion2, thus enabling a greater gamut of naturalistic experiments3–8.
|
review
| 99.8 |
While fMRI is capable of measuring whole-brain functional activity along with the structural image of the individual, fNIRS experiments are designed with a limited number of sources and detectors (optodes). The optodes are positioned on selected portion(s) of the scalp with an expectation to assess the activity of a set of brain cortical regions that are relevant to the designed experiment. However, the translation of regions of interest to the placement of optodes on a measuring cap (shown in Fig. 1) remains a challenge for experimental design with fNIRS.Figure 1Challenge commonly faced when designing an fNIRS experiment to assess a set of regions of interest expected to be activated according to a study hypothesis: the translation to an fNIRS optode layout by choosing appropriate sources and detectors positions to maximize anatomical specificity to regions of interest. Illustrated are Brodmann areas 4, 9 and 19 and 21 and the fNIRS cap layout with corresponding color-coded channels.
|
study
| 100.0 |
Challenge commonly faced when designing an fNIRS experiment to assess a set of regions of interest expected to be activated according to a study hypothesis: the translation to an fNIRS optode layout by choosing appropriate sources and detectors positions to maximize anatomical specificity to regions of interest. Illustrated are Brodmann areas 4, 9 and 19 and 21 and the fNIRS cap layout with corresponding color-coded channels.
|
other
| 89.9 |
A few recent studies have suggested approaches to overcome this challenge, e.g. for epileptic discharges9, or based on iterative probe geometry modifications10, which was extended to the voxel-space for an image-based approach11. Herein, we propose an alternative approach to automatically decide optodes positions based on 10–10 and 10–5 systems12 according to a set of brain regions of interest. This method is based on the sensitivity profile from photon transport simulations run on two head atlases (Methods section). The results were compiled into a toolbox to facilitate the definition of optodes positions, the fNIRS Optodes’ Location Decider (fOLD).
|
study
| 100.0 |
As different tissues of the human head present different optical properties (e.g. absorption and scattering)13,14, it is necessary to segment the atlases to be used for photon transport simulations. The segmentation results in discrimination of five tissues: scalp, skull, cerebrospinal fluid (CSF), and gray and white matter.
|
study
| 92.25 |
Briefly, the segmentation procedure in SPM12 returns probability maps for each of the five tissues of our interest. For each tissue, a NIfTI file is generated with an image with same size and origin as in the input file (e.g. Colin27 atlas) and each voxel is set to the probability of being part of a given tissue. And the sum of the probabilities of a given voxel along head tissues plus air is 1. For example, voxel (x = 57, y = 126, z = 144) of Colin27 atlas corresponding to MNI coordinate (x = −34, y = 0, z = 72) presented probability 86.27% for skull and 13.73% for CSF.
|
study
| 99.94 |
To create a single image file for the final tissue segmentation, we have defined each voxel to be part of a given tissue if its probability was higher than all other tissues and if it was greater than 0.2. The latter was particularly important for boundary voxels e.g. between scalp and air. Voxels corresponding to scalp were assigned the value 1, skull 2, CSF 3, gray matter 4 and white matter 5. All voxels that did not fulfill the comparisons for any of tissue was assigned value 0 (air).
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study
| 99.94 |
As this procedure could potentially result in unitary holes inside the tissue (i.e. tissue voxels assigned to air), the resulting image was smoothed in SPM12 with full width at half maximum (FWHM) of [2 mm, 2 mm, 2 mm]. The values in the smoothed image were rounded in Matlab2017a. The resulting segmentation of Colin27 is depicted in Fig. 2A.Figure 2Axial view of the tissue segmentation of (A) Colin27 template and (B) SPM12 tissue probability maps (TPM.nii). This resulted on five layers: scalp (blue), skull (cyan), cerebrospinal fluid (CSF, yellow), gray matter (red) and white matter (black).
|
study
| 100.0 |
In addition to the Colin27 head model, which is based on 27 averages of a single subject, we have considered a second head atlas based on the tissue probability maps provided in SPM12. These maps have been generated based on 549 subjects from the IXI dataset18. The specific subjects used to compute TPM.nii can be found in spm_templates.man19. We spatially resampled20 the TPM.nii image from 1.5 × 1.5 × 1.5 mm3 to 2 × 2 × 2 mm3 and followed the same procedure as for Colin27 to obtain the segmented image (herein called “SPM12”), illustrated in Fig. 2B.
|
study
| 100.0 |
We initially considered the 10–10 international system as primary reference for optodes placement as its positions can be identified from a set of fiducial points (e.g. nasion, inion and left/right preauricular points)12, which may be visually determined in head models.
|
other
| 99.7 |
First, we converted the image file of the tissue segmentation into mesh by using the function ‘v2s’ provided in the iso2mesh toolbox21,22. We also corrected the coordinates to MNI space by accounting for the voxel size and origin of the image. After plotting the corrected mesh in Matlab2017a, we visually identified four fiducial points (depicted in Fig. 3A) whose spatial identification followed the definition provided by Jurcak et al.12 for nasion, inion and preauricular points.Figure 3(A) MNI space localization (in mm) of fiducial points on mesh of Colin27. (B) Left view of sources (red) and detectors (green) positions on the 10–10 international system that have been initially considered for photon transport simulation (Methods section).
|
study
| 100.0 |
(A) MNI space localization (in mm) of fiducial points on mesh of Colin27. (B) Left view of sources (red) and detectors (green) positions on the 10–10 international system that have been initially considered for photon transport simulation (Methods section).
|
other
| 99.9 |
After the fiducial points have been located, we used the functions provided within the tool Mesh2EEG23,24, which returned as output the MNI coordinates of 329 positions of the 10–5 international system. From these, we initially considered 74 positions within the 10–10 system, as illustrated in Fig. 3B.
|
other
| 99.25 |
The assignment of each position to a source or a detector was empirically done with the goal to maximize the number of channels, while also achieving a similar number of resulting sources and detectors. We proceeded by alternating sources and detectors on neighboring positions, which resulted on 38 sources and 36 detectors.
|
other
| 99.9 |
To assess the migration of photons within the head tissues and identify the areas of the brain that can potentially be measured by each measuring channel (i.e. a source-detector pair), we have performed simulations of photon transport from each optode position with Monte Carlo Extreme (MCX, v2017.3), a software that accelerates simulations with Graphics Processing Units, as described in refs25,26.
|
study
| 99.94 |
As input for MCX, we provided an *.inp text file with relevant information for the simulation, as exemplified in ref.25. We defined the number of photons to be launched to 108. The optode position has been set in coordinates in voxel space. The original direction of the photon was placed towards the center of the image, which we defined as voxel coordinates corresponding to the origin (0, 0, 0) in MNI space. The binary volume file (*.bin) has been created from the segmented NIfTI file of interest (Colin27 or SPM12). Both time gate’s step and end were set to 5 ns to result on a single flux distribution. Voxel size and dimensions were defined according to the atlas of interest. Optodes were modelled as pencil beam (default setting in MCX). Finally we considered five different media (tissues described in Methods section), whose optical properties were set in accordance to Strangman et al.27 (Table 1).Table 1Optical properties provided as input for Monte Carlo simulations.TissueScattering (1/mm)AnisotropyAbsorption (1/mm)RefractionScalp0.720.010.0172751Skull0.920.010.0119251CSF0.010.010.0025001Gray1.100.010.0195001White1.350.010.0169001
|
study
| 100.0 |
For each optode simulation, the MCX binary was called along with the following settings: (i) enable automatic thread and block configuration to maximize speed (-A); (ii) disable the solution normalization (-U 0); (iii) save the flux field (-S 1); and (iv) divide photons into 100 groups (-r 100). The latter was required to avoid “kernel launch time-out” error due to non-dedicated graphics card28. Simulations were performed in an Ubuntu 16.04.02 LTS (Xenial Xerus) with Intel Xeon E5 2650 v3 2.3 GHz, GeForce Gtx 770 and CUDA 8.0.
|
other
| 99.75 |
The flux field solution of each optode simulation was normalized as described in Boas et al.29 and following its implementation in AtlasViewer30 by dividing the output by the number of simulated photons and correcting the resulting photon fluence to respect energy conservation (all photons launched should either exit the media or be absorbed by it).
|
study
| 99.7 |
The sensitivity for each fNIRS channel (source-detector pair) was calculated as the voxel-wise product of the corrected photon fluence obtained for the source and the detector (adjoint field)29. This was normalized by the sum of sensitivity of all voxels, similarly as described by Brigadoi and Cooper, 201531, so each voxel was represented by a percentage sensitivity in respect to the whole volume. Figure 4A depicts the normalized sensitivity result for channel formed by source Fz and detector AFz.Figure 4(A) Illustration of a single-channel photon transport simulation after sensitivity normalization. (B) Normalized sensitivity results for all channels considered based on sources and detectors positions depicted in Fig. 3B. In both cases, the color scale has been set from 10−6 (black) to 3*10−3 (white) and the head atlas was Colin27.
|
study
| 100.0 |
(A) Illustration of a single-channel photon transport simulation after sensitivity normalization. (B) Normalized sensitivity results for all channels considered based on sources and detectors positions depicted in Fig. 3B. In both cases, the color scale has been set from 10−6 (black) to 3*10−3 (white) and the head atlas was Colin27.
|
study
| 99.94 |
The normalized sensitivity was calculated for each fNIRS channel based on 38 sources and 36 detectors (Methods section) positioned on each head atlas (Methods section). 130 fNIRS channels have been formed by neighboring sources and detectors. The resulting normalized sensitivities for all channels on Colin27 left hemisphere are illustrated on Fig. 4B.
|
study
| 99.94 |
After computing the normalized sensitivity (normSens), as described in Methods section, the brain sensitivity (brainSens) of a given channel (ch) with respect to a head atlas (h) is calculated. This is done by summing normSens from all voxels (i) classified as gray and white matter during tissue segmentation27, as follows:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$brainSens(ch)=\sum _{i}^{brain}normSens(ch,i)$$\end{document}brainSens(ch)=∑ibrainnormSens(ch,i)To better assess the influence of a given region of interest (i.e. set of voxels) inside the brain in respect to brainSens(ch), we define “specificity” as follows:2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Specificit{y}_{ROI}(ch)=100\times \sum _{j}^{ROI}\frac{normSens(ch,j)}{brainSens(ch)}$$\end{document}SpecificityROI(ch)=100×∑jROInormSens(ch,j)brainSens(ch)Equation 2 can be translated as a weighted mean of the voxels (j) within a given region of interest (ROI), in which the weight corresponds to the normalized sensitivity (normSens), normalized by the sensitivity to the brain (brainSens) to percentage. Thus, it provides the anatomical specificity of a channel (ch) to the region-of-interest (ROI).
|
study
| 100.0 |
Finally, we define the coordinates in the MNI space of a given channel as the weighted mean of the MNI coordinates of the voxels (k) within the brain that can be reached by a given channel. Similarly, the weight is the normalized sensitivity:3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MN{I}_{x,y,z}(ch)=\sum _{k}^{brain}\frac{normSens(ch,k)\times MN{I}_{x,y,z}(k)}{brainSens(ch)}$$\end{document}MNIx,y,z(ch)=∑kbrainnormSens(ch,k)×MNIx,y,z(k)brainSens(ch)According to equation (3), each fNIRS channel can be spatially represented with a set of MNI coordinates (x, y, z) in respect to the normalized sensitivity and brain sensitivity results obtained from the head atlas of interest (Colin27 or SPM12).
|
study
| 100.0 |
To assist on the experimental design of fNIRS in terms of definition of regions of interest, we considered five available parcellation atlases: (a) Automated Anatomical Labeling (AAL2)32,33, (b) Atlas of Intrinsic Connectivity of Homotopic Areas (AICHA)34, (c) Brodmann Areas35, (d) Jülich histological (cyto- and myelo-architectonic) atlas (Anatomy Toolbox)36–38, and (e) LONI Probabilistic Brain Atlas (LPBA40)39.
|
study
| 99.94 |
Briefly, AAL was created from a parcellation of the high-resolution T1 volume of Colin2716 and its second and most recent version (AAL2) implemented a new parcellation of the orbitofrontal cortex. AICHA presents 192 functional regions as identified from resting-state data collected with fMRI from 281 individuals. Brodmann areas was acquired from MRIcron17, developed by Chris Rorden, with parcellation to the Colin27 atlas. Jülich histological atlas has been obtained from the SPM Anatomy Toolbox, based on post-mortem brains from ten subjects and normalized to Colin27 atlas. LPBA40 was created based on 40 healthy subjects and spatially normalized using different algorithms, from which we selected the SPM5 unified segmentation method40. The atlases are illustrated in Fig. 5.Figure 5Illustration of brain parcellation atlases’ results incorporated in the toolbox: (A) Automated Anatomical Labeling (AAL2)32,33, (B) Atlas of Intrinsic Connectivity of Homotopic Areas (AICHA)34, (C) Brodmann35, (D) Jülich (SPM Anatomy Toolbox)36–38, (E) LONI Probabilistic Brain Atlas (LPBA40)39. (A,C,D) were overlaid on Colin27. And (B,E) overlaid on head atlas generated from tissue probability maps of SPM12.
|
study
| 100.0 |
Illustration of brain parcellation atlases’ results incorporated in the toolbox: (A) Automated Anatomical Labeling (AAL2)32,33, (B) Atlas of Intrinsic Connectivity of Homotopic Areas (AICHA)34, (C) Brodmann35, (D) Jülich (SPM Anatomy Toolbox)36–38, (E) LONI Probabilistic Brain Atlas (LPBA40)39. (A,C,D) were overlaid on Colin27. And (B,E) overlaid on head atlas generated from tissue probability maps of SPM12.
|
study
| 78.3 |
As AAL2, Brodmann and Jülich atlases were registered to Colin27; we used it as reference head atlas for the anatomical labeling procedure. AICHA and LPBA40 were then considered within the head atlas generated from the tissue probability maps available in SPM12, as described in Methods section. The alignment of each parcellation atlas was compared with the head atlas of reference in SPM12. We used SPM12 function “Coregister: Estimate”41 to improve the alignment of Jülich parcellation atlas with the Colin27 head atlas that was obtained from MRIcron.
|
study
| 100.0 |
After alignment corrections between parcellation atlas and reference atlas, we computed the specificity of each landmark for a given channel according to the definition of equation 2, for which each ROI was defined as a landmark available in the parcellation method of interest. Remaining voxels of the reference atlas within the brain volume that did not present any overlap with the parcellation was classified as “Brain_Outside”. At the end of this, anatomical landmarks and respective specificity are assigned to each channel. For example, the results for channel formed by detector AFz and source Fz are presented in Table 2.Table 2Example of anatomical landmarks and specificity results for channel AFz-Fz and Brodmann parcellation method. Results related to coverage <10% have been omitted.Anatomical landmarkSpecificity (%)9 - Dorsolateral prefrontal cortex61.7710 - Frontopolar area20.268 - Includes Frontal eye fields12.15
|
study
| 100.0 |
As the goal of the present method and toolbox is to retrieve channels as output of selected regions of interest, results have been grouped by landmarks, thus enabling look-up tables from anatomical landmarks of interest. An example is shown in Table 3 for landmark “Precentral_L” as defined in AAL2 and resulting channels information (source, detector and MNI coordinates).Table 3Example of channels with at least 10% specificity of “Precentral_L” (AAL2).SourceDetectorSpecificity (%)Distance (mm)XYZC3FC354.1237−50−350C3C149.3840−42−2062FC5FC342.4436−551234FC1C135.8839−26−568CzC131.8939−17−2074FC1FC321.1036−381255FC5C516.6633−62−323CP1C115.6439−27−3671The inter-optode distances according to positions on Colin27 are provided. MNI spatial coordinates of each channel obtained based on rounded results from Equation 3.
|
study
| 100.0 |
The method proposed so far is expected to provide a broad range of channels over the cortex and thus cover most regions that can be reached with fNIRS. However, as these channels are formed by sources and detectors placed on 10–10 international system, users interested in running EEG-fNIRS multi-modal measures would not be capable of using the toolbox, as EEG placement by standard is based on 10–20 and 10–10 international systems12.
|
other
| 99.8 |
To extend the possible optodes positions to 10–5 international system positions regarding layouts available for EEG caps, we have considered as a reference a cap with 130 positions in total. With this new design, one has the possibility to use either 32 positions (Fig. 6A) or 64 positions for EEG electrodes (Fig. 6B), while the locations for fNIRS optodes do not overlap with the 10–10 system.Figure 6(A) Expansion of the method described for 10–10 international system to (B) the 10–5 system to allow for multi-modal measurements with EEG, either 32 or 64 electrodes. EEG and fNIRS positions are based on a layout accommodating 130 positions in total. EEG 1–32 electrodes positions are depicted in green, while the complimentary 33–64 are in yellow; fNIRS sources positions are in red and the detectors are in blue.
|
study
| 99.94 |
(A) Expansion of the method described for 10–10 international system to (B) the 10–5 system to allow for multi-modal measurements with EEG, either 32 or 64 electrodes. EEG and fNIRS positions are based on a layout accommodating 130 positions in total. EEG 1–32 electrodes positions are depicted in green, while the complimentary 33–64 are in yellow; fNIRS sources positions are in red and the detectors are in blue.
|
other
| 99.7 |
We have visually assigned sources and detectors to 10–5 system positions with the goal to maximize the number of possible channels considering adjacent optodes. This resulted on the layout illustrated in Fig. 6, which presents 28 sources positions and 28 detectors positions over the scalp. From these positions, we considered 89 possible channels in total.
|
other
| 99.8 |
Once the positions have been assigned and their coordinates have been retrieved for both head atlases (Methods section), we proceeded with the methods described in the Methods section. Therefore, we ran the photon transport simulations and computed the normalized sensitivity, ROIs specificity and channels coordinates, and obtained the anatomical landmarks results of each parcellation atlas.
|
study
| 99.94 |
From the methods described in Methods section and the derived results for both 10–10 and 10–5 extended positions for all brain parcellation methods considered and based on the two head atlases of reference, we developed, in Matlab2017a App Designer42, the toolbox “fNIRS Optodes’ Location Decider” (fOLD). The graphical user interface displayed upon software initialization is illustrated in Fig. 7.Figure 7Graphical user interface of the fNIRS Optodes’ Location Decider (fOLD). Depicted is the blank 10–10 layout as displayed upon toolbox initialization.
|
other
| 97.5 |
On the top left corner, there is a list of parcellation atlases available (Methods section). The list of anatomical landmarks corresponds to the chosen atlas (in the figure, AAL2) that can be reached by any of the fNIRS channels resulted from the methods described in Methods section.
|
other
| 99.6 |
Under the anatomical landmarks list, there is a specificity threshold that can be set either numerically or with a sliding bar to define the lower specificity limit to the selected landmarks that a channel must present to be included in the fNIRS optodes arrangement (Methods section). The “force symmetry” checkbox allows the user to force the algorithm of optode positions selection to always generate symmetric locations (in terms of placed optodes) between the left and the right hemisphere.
|
other
| 99.9 |
The button “Save” allows the user to store the current arrangement settings to load in a posterior session by clicking on “Load”. “Export” button allows saving the most relevant information on the current positions as text and Excel files (*.xls). “Help” will display yellow text boxes with complimentary information about important features.
|
other
| 99.94 |
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