Fornix, Brain

Three classes of statistical tests were used to assess metabolic variability across the human microbiome. First, pathways and modules differentially abundant in at least one of the seven analyzed body sites were determined by the LEfSe system for metagenomic biomarker discovery [23] (link). These differences were summarized into overall patterns of variation using principal component analysis on a matrix of average module abundances per body site, Winsorized at 20% (a robust arithmetic mean [30] ), filtered at a minimum of 0.01% in at least one site, and normalized to z-scores. Since LEfSe is not appropriate for HUMAnN's binary pathway coverage scores, we determined site-enriched or underenriched pathways and modules as follows: a module was in aggregate present at a site if it occurred with coverage ≥0.9 in ≥90% of the site's samples; absent if it occurred with coverage ≤0.1 in ≥90% of samples; and differential if it was present in at least one site and absent in at least one other. Pathways were analyzed identically using a ≥0.5 coverage criterion, since no large pathways consistently had coverage ≥0.9.
The third test described here associated pathway and module abundance not with human microbiome body sites, but with one or more of the subject clinical metadata variables described by the HMP [9] . These included continuous descriptors of each sample (e.g. subject age, body mass index, vaginal introitus and posterior fornix pH for women, etc.) as well as categorical variables (e.g. gender or location, see Supplemental Table S1). Pathway and module abundances were associated with these metadata first by stratifying by body site. Within each body site, each pathway/metadata pair present above 0.01% in at least 10% of samples was independently tested using Spearman's ρ for continuous metadata and the Kruskal-Wallis nonparametric ANOVA for categorical, after removing any outliers outside of the upper or lower inner fences. The resulting p-values were corrected using the Benjamini-Hochberg method within each body site and thresholded at a minimum FDR q<0.1.

The set of ground truth long-range fiber bundles was designed to cover the whole human brain and features many of the relevant spatial configurations, such as crossing, kissing, twisting and fanning fibers, thus representing the morphology of the major known in vivo fiber bundles. The process to obtain these bundles consisted of three steps. First, a whole-brain global tractography was performed on a high-quality in vivo diffusion-weighted image. Then, 25 major long-range bundles were manually extracted from the resulting tractogram. In the third step, these bundles were refined to obtain smooth and well-defined bundles. Each of these steps is detailed in the following paragraphs.
We chose one of the diffusion-weighted data sets included in the Q3 data release of the HCP^{39 (link)}, subject 100307, to perform whole-brain global fiber tractography^{52 , 68 (link)}. Among other customizations, the HCP scanners are equipped with a set of high-end gradient coils, enabling diffusion encoding gradient strengths of 100 mT m⁻¹. By comparison, most standard magnetic resonance scanners feature gradient strengths of about 30 to 40 mT m⁻¹. This hardware setup allows the acquisition of data sets featuring exceptionally high resolutions (1.25 mm isotropic, 270 gradient directions) while maintaining an excellent SNR. All data sets were corrected for head motion, eddy currents and susceptibility distortions and are, in general, of very high quality^{69 –73 (link)}. Detailed information regarding the employed imaging protocols as well as the data sets themselves may be found on http://humanconnectome.org.
Global fiber tractography was performed using MITK Diffusion^{74 (link)} with the following parameters: 900,000,000 iterations, a particle length of 1 mm, a particle width of 0.1 mm, and a particle weight of 0.002. Furthermore, we repeated the tractography six times and combined the resulting whole-brain tractograms into one large data set consisting of over five million streamlines. The selected parameters provided for a very high sensitivity of the tractography method. The specificity of the resulting tractogram was of lesser concern since the tracts of interest were extracted manually in the second step.
Bundle segmentation was performed by an expert radiologist using manually placed inclusion and exclusion regions of interest (ROI). We followed the concepts introduced in ref. ⁴⁰ for the ROI placement and fiber extraction. Twenty-five bundles were extracted, covering association, projection, and commissural fibers across the whole brain (Fig. 1): CC, left and right cingulum (Cg), Fornix (Fx), anterior commissure (CA), left and right optic radiation (OR), posterior commissure (CP), left and right inferior cerebellar peduncle (ICP), middle cerebellar peduncle (MCP), left and right superior cerebellar peduncle (SCP), left and right parieto-occipital pontine tract (POPT), left and right cortico-spinal tract (CST), left and right frontopontine tracts (FPT), left and right ILF, left and right UF, and left and right SLF. As mentioned in the “Discussion” section, the IFOF, the MdLF, as well as the middle and inferior temporal projections of the AF were not included.
After manual extraction, the individual long-range bundles were further refined to serve as ground truth for the image simulation as also shown in Fig. 1. The original extracted tracts featured a large number of prematurely ending fibers and the individual streamlines were not smooth. To obtain smooth tracts without prematurely ending fibers, we simulated a diffusion-weighted image from each original tract individually using Fiberfox (www.mitk.org33). Since no complex fiber configurations, such as crossings, were present in the individual tract images and no artifacts were simulated, it was possible to obtain very smooth and complete tracts from these images with a simple tensor-based streamline tractography. Supplementary Fig. 7 illustrates the result of this refining procedure on the left CST.

The ophthalmologists performed ocular examinations at the DED subspecialty outpatient clinic at Keio University Hospital (Y.O., M.U., M.K., and E. S.). The examination included the best corrected visual acuity (BCVA) measurement and anterior and posterior segment examinations. Clinical ocular parameters, based on our DED clinic consensus algorithms, such as the tear-film breakup time (TFBUT), corneal fluorescein staining score (CFS score; 0–9), corneoconjunctival lissamine green staining score (LG; 0–9), degree of fibrosis (0–2), degree of filamentary keratitis (0–2), and degree of hyperemia (0–2), were documented^{68 (link)}. Schirmer’s test (Sterilized Tear Production Measuring Strips, 4,987,896,590,227; Ayumi Pharmaceutical Corporation, Tokyo, Japan) and the CTT (Zone-Quick Phenol Red Thread Tear Test; 2,564,187, Showa Yakuhin Kako Co., Ltd., Tokyo, Japan) were performed before ocular evaluations to avoid the effect of the fluorescein/lissamine green dye solution. For CTT, a phenol red threat was placed in the lateral lower fornix for 15 s. When the phenol red comes in contact with the tear film, it changes color from yellow to red. The thread was removed after 15 s, and the length of the red portion was measured from the tip regardless of the fold^{50 (link)}. The tear secretion was measured in both eyes, and the lower value was used for the analysis. Visual acuity was measured using a standard Snellen chart.

As previously described, the home visit included written informed consent, a questionnaire, genital self-sampling (cervical and vaginal), and collection of a urine specimen
^{9 (link)}
. There were no restrictions on the timing of urine self-sample collection, and 69.5% (419/603) of the total BILHIV study samples were performed between 9:00 and 14:00
^{9 (link)}
. Enrolled women who were not currently menstruating were then invited to attend Livingstone Central Hospital cervical cancer clinic, where midwives collected CVL. After speculum insertion, normal saline (10 mL) was flushed across the cervix and vaginal walls for one minute with a bulb syringe and CVL fluid was collected from the posterior fornices.

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Medical Research and Ethics Committee of Universiti Kebangsaan Malaysia (FF-2015-037). All eligible pregnant women were informed about the study and they were provided with a patient information leaflet. Written consent was obtained. A detailed history, physical examination, and sterile speculum examination were performed either in the antenatal clinic or patient admission centre of the Department of Obstetrics and Gynaecology, Universiti Kebangsaan Malaysia Medical Centre. All the details were recorded on a proforma.
Vaginal fluid was collected during speculum examination in the following manner. Women were positioned in the dorsal position. A Cusco speculum was inserted without any lubricant. Characteristics of the vaginal discharge were noted. Three samples were then obtained from the posterior vaginal fornix using sterile cotton-tipped swabs. The first swab was sent for culture and sensitivity to look for any organism that might cause the vaginal discharge such as Candida spp or Group B Streptococcus. The second sample was obtained for BV® Blue testing to diagnose BV.
For the BV® Blue test, the swab was immersed into the BV® Blue Testing Vessel that contained a chromogenic substrate of bacterial sialidase at room temperature (24–32 °C). The vessel was left standing for 10 min. The test vessel was checked to ensure it contained only colourless fluid without sediment. Subsequently, one drop of BV® Blue Developer Solution was added into the testing vessel and swirled. Then, the result was read immediately. A positive result was interpreted as a change of colour to blue or green while yellow was a negative result. If the result was not blue/green or yellow, then the test was repeated. For those with BV® Blue tested positive, the third swab would be sent to the laboratory for detection of GV using the PCR method. For those who tested negative, the third swab for GV was discarded. To reduce interpersonal data interpretation errors, these tests were performed by a single operator.