Thai

For the data used in Figure 4, we use the H952 subset of the CEPH–HGDP panel [30 (link),31 (link),45 ] where some atypical samples and pairs of close relatives have been removed.
For the data used in Figure 5, we use an unpublished sample collected and genotyped by Dr. Jonathan Seidman and Dr. S. Sangwatanaroj. This consisted of 25 samples from Northern Thailand (after removing some individuals who are close relatives of people whose samples we retained) and 45 samples each from China and Japan (data drawn from the International Human Haplotype Map Project [32 ]). The Northern Thai samples were genotyped using an Affymetrix Xba chip. The dataset analyzed consisted of the overlap between the SNPs successfully genotyped in HapMap and the Affymetrix chip, and included 40,560 SNPs.
For the data of Mark Shriver and colleagues [5 (link)], we analyzed only autosomal data where no SNP had any missing data. We removed one individual who was a duplicate, two Burunge and Mbuti samples that represented close relatives of other samples, and nine Nasioi individuals who our data suggest are part of one or two extended families.

The PHQ-9 is a self-report measure, consisting of 9 questions based on the 9 DSM-IV criteria for major depressive episode. It refers to symptoms experienced by the patients during the two weeks prior to answering the questionnaires. After obtaining permission from the copy right holder, the PHQ-9 was translated following the guidelines for cross-cultural adaptation of self-report measures [18 (link)]. The process included two independent forward translations of the original PHQ-9 into Thai, consensus between translators on a forward translation, back-translation by a bilingual English teacher, and a review of the back-translation. Ten patients attending the out-patient department were invited to complete and to give comments on the pre-final version. Final modifications and adjustments were made accordingly.
Unlike many other questionnaires, the original PHQ-9 uses simple statements without culture-specific phrases, so there were only a few problems in the translation. Among the problems we encountered was the translation of particular words or phrases such as 'feeling down', 'fidgety' and 'restless'. After some discussions, the investigators were able to find phrases in Thai that conveyed approximately the same meanings.
Scores for each item in the PHQ-9 range from 0 (not at all), to 1 (several days), 2 (more than half of the days) and 3 (nearly every day), while summed scores range from 0 to 27. The PHQ-9 can be used as a screening tool with recommended cut-off scores of ten or greater for the diagnosis of major depression [11 (link)]. It can also be used to establish a diagnosis following a categorical algorithm. A major depressive disorder is diagnosed if 5 or more of the 9 symptoms have been present at least more than half the days of the past 2 weeks and 1 of these symptoms has been either depressed mood or anhedonia.

The PSS-10[8 ] measures the degree to which one perceives aspects of one's life as uncontrollable, unpredictable, and overloading. Participants are asked to respond to each question on a 5-point Likert scale ranging from 0 (never) to 4 (very often), indicating how often they have felt or thought a certain way within the past month. Scores range from 0 to 40, with higher composite scores indicative of greater perceived stress. The PSS-10 has demonstrated good internal consistency[8 ].
In this Thai version, the authors translated the original version into Thai language with cultural adaptations, and then this was back-translated by an English-Thai bilingual school teacher, who had no knowledge of the wording of the original English version of the PSS-10. The two versions were then compared item-by-item and minor discrepancies were addressed and corrected by a consensus of the authors and the school teacher. Thirty individuals including relatives of patients, psychiatric patients and students (in different courses and years), who were not participating in the study, were asked to complete the Thai version PSS for a pilot study. Additional grammatical errors and misspellings were subsequently corrected. The revising procedure was performed once with acceptable results before field-testing began.

The presence of AMR genes was scanned in the genomic assemblies of the outbreak strains clade using AMRFinderPlus v3.10.16 [49 (link)]. To assess penicillin susceptibility, the protein sequences of the penicillin binding proteins (PBPs) were extracted and screened for amino acid substitutions known to correlate with decreased penicillin susceptibility in streptococci [50 (link)]. In brief, the sequence of each PBP was aligned using muscle v3.8.1551 [51 (link)] and visualized in SEAVIEW v5.0.5 [52 (link)]. The prokka annotations of the Thai zoonotic clade were queried to identify acquired resistance genes in the outbreak strain using Panaroov1.2.9 [53 (link)]. In addition, the STC78 complete genome was scanned for integrative and conjugative elements (ICEs) and prophages using ICEFinder [54 (link)] and PHASTER [55 (link)], respectively. Acquired AMR genes and their genomic context were manually inspected using Artemis v18.1.0 [56 (link)]. PubMed was searched for primary research articles describing mobile genetic elements (MGEs) carrying the same AMR genes to identify putative homologous MGEs. The annotated MGEs were aligned using clinker and clustermap.js v.0.021 [57 (link)]. The plasmid acquired by the outbreak strain was visualized using ApE v3.0.8 [58 (link)] and a blastn [59 (link)] search was performed against bacterial reference genomes. To assess the presence of potential genes of interest, ABRicate (https://github.com/tseemann/abricate) was used with a custom database containing the sequences of 52 genes previously found to be putatively associated with zoonotic potential of S. suis strains [46 (link)].

Sequence analysis included 80 nucleotide sequences of PfGARP from Thai isolates, one clinical isolate from Guinea (isolate MDCU32) and 18 publicly available complete gene sequences whose isolate names, country of origins and their GenBank accession numbers are as follows: 3D7 (Netherlands from West Africa, AL844501), CD01 (Congo, LR129686), Dd2 (Indochina, LR131290), FC27 (Papua New Guinea, J03998), FCC1/HN (Hainan in China, AF251290), GA01 (Gambia, LR131386), GB4 (Ghana, LR131402), KH1 (Cambodia, LR131418), KH2 (Cambodia, LR131306), HB3 (Honduras, LR131338), IGH-CR14 (India, GG6656811), IT (Brazil, LR131322), KE01 (Kenya, LR131354), ML01 (Mali, LR131481), SD01 (Sudan, LR131466), SN01 (Senegal, LR131434), TG01 (Togo, LR131450), and UGT5.1 (Vietnam, KE124372). Of these, the 3D7, FC27and FCC1/HN sequences were determined by Sanger dideoxy-chain termination method whereas the remaining isolates were assembled sequences from next-generation sequencing platforms (Supplemental Table S1). Sequence alignment was performed by using the CLUSTAL_X program, taken into account appropriate codon match in the coding region by manual adjustment to maintain the reading frame. The sequence from the FC27 strain was used as a reference^{6 (link)}. Searching for nucleotide repeats was performed by using the Tandem Repeats Finder version 4.0 program with the default option. Nucleotide diversity (π), the rate of synonymous substitutions per synonymous site (d_S) and the rate of nonsynonymous substitutions per nonsynonymous site (d_N) were determined from the average values of sequence differences in all pairwise comparison of each taxon and the standard error was computed from 1000 bootstrap pseudoreplicates implemented in the MEGA 6.0 program^{41 (link)}. Haplotype diversity and its sampling variance were computed by taking into account the presence of gaps in the aligned sequences using the DnaSP version 5.10 program^{42 (link)}. Natural selection on codon substitution was determined by using fast unconstrained Bayesian approximation (FUBAR) method in the Datamonkey Web-Server^{43 (link),44 (link)}. Neighbor-joining phylogenetic tree based on nucleotide sequences was constructed by using maximum composite likelihood parameter whereas maximum likelihood tree was built using Tamura-Nei model with the rate variation model allowed for some sites to be evolutionarily invariable. The Arlequin 3.5.2.2 software was deployed to determine genetic differentiation between populations, the fixation index (F_ST), using analysis of molecular variance approach (AMOVA) akin to the Weir and Cockerham’s method but taken into account the number of mutations between haplotypes^{45 (link)}. One hundred permutations were deployed to determine the significance levels of the fixation indices. Prediction of linear B cell epitopes in PfGARP was performed by using a sequence similarity to known experimentally verified epitopes from the Immune Epitope DataBase (IEDB) implemented in the BepiBlast Web Server^{11 (link)}. Furthermore, linear B cell epitopes were also predicted based on protein language models implemented in BepiPred-3.0^{12 (link)}. Potential HLA-class II-binding peptides were analyzed by using the IEDB recommended 2.22 algorithm with a default 12–18 amino acid residues option. The predicted HLA-class II-binding peptides were predicted based on the percentile rank < 10 and the IC₅₀ threshold for HLA binding affinity ≤ 1000 nM^{14 (link)}. The analysis mainly concerned the common HLA class II haplotypes among Thai populations with allele frequency > 0.1^{13 (link)}.

The study was a prospective, cross-sectional field study to evaluate the performance of the two diagnostic assays, qPCR and iELISA, on serum and OF samples in Vietnam. Samples were collected from dates 2019 to 2021. Selection of farms was not researcher-driven, but part of ongoing ASFV regulatory activities by the Vietnamese veterinary services. Farms were from 17 provinces (Bac Giang, Bac Ninh, Dong Nai, Ha Nam, Ha Noi, Ha Tay, Hai Duong, Hoa Binh, Hung Yen, Nam Dinh, Nghe An, Phu Tho, Son La, Thai Binh, Thai Nguyen, Vinh Phuc, and Yen Bai). Sample collection was performed on farms throughout Vietnam using outbreaks detected/reported by the farm's veterinarian and farm owner. ASF-acutely affected, chronically affected, and unaffected herds were targeted. Acutely affected farms were defined as those with pigs with severe clinical symptoms of ASF, chronically affected farms as those with pigs which had developed mild clinical symptoms of ASF for a period of time (~6 weeks−2 months), and unaffected farms as those with no clinical or laboratory history of ASF at the farm level. On farm, pigs were selected by the farm's veterinarian, and on ASF-affected farms specifically, animals exhibiting clinical signs consistent with ASF were targeted for sampling. All pigs on farms were eligible for sampling. Paired individual serum and OF samples were collected from 100 pigs on 30 acutely ASF-affected farms, 98 pigs on 37 chronically affected farms, and 200 pigs on 20 non-affected farms, for a total of 398 paired samples from 87 farms. The number of samples taken per acute or chronic farm ranged from 1 to 10, while 10 samples were consistently collected on each unaffected farm. Farm information was recorded at the time of sampling including the farm's province, farm type, animals per barn and pen, brief history of ASF on the farm, overall health status of the pigs, and general vaccination status. Each sampled pig's age group category was also recorded.