jejuni has been well characterized, there is very little knowledge of the initial response INK 128 and adaptive mechanism of C. jejuni to Ery exposure. Transcriptomic analysis has been used to assess bacterial adaptive responses to antibiotic treatments. Three previous studies reported global gene expression patterns of Streptococcus pneumonia[12], Escherichia coli[13], and Haemophilus

influenzae[14] to sub-inhibitory doses of translation-inhibiting antibiotics. These reports demonstrated that exposure to these bacteriostatic antibiotics triggered the synthesis of a number of ribosomal proteins [12–14]. Other studies analyzed the transcriptional profiles of Staphlococcus aureus, E. coli, and Yersinia pestis under inhibitory doses of chloramphenicol, mupirocin, ampicillin, or ofloxacin [15–17], and a common observation of these studies was the repression of energy metabolism genes by these antibiotics. Although the transcriptomic response of C. jejuni to a fluoroquinolone

antibiotic has been reported [18], it remains unknown how this organism responds to macrolide treatment. In this study, the genome-wide transcriptional response of C. jejuni following exposure to both inhibitory and sub-inhibitory OSI-906 doses of Ery was assessed. Furthermore, contribution of several differentially expressed genes to antibiotic resistance, stress resistance, and host colonization was determined using isogenic gene knock-out mutants. Results Transcriptional responses of NCTC 11168 to an inhibitory dose of Ery To identify the adaptive response of Campylobacter to Ery treatment, microarray was used to analyze the

transcriptional changes in C. jejuni NCTC 11168 following exposure to Ery. After NCTC 11168 was exposed to an inhibitory dose of Ery (16× MIC) for 30 min, a total of 258 genes were shown to be differentially expressed, among which 139 were up-regulated and 119 were down-regulated (Additional file 1: Tables S1 and S2). Cluster of orthologous groups (COG) (http://​www.​ncbi.​nlm.​nih.​gov/​COG/​) analysis revealed changes Protein tyrosine phosphatase in multiple functional categories (Table 1). Among the up-regulated genes, the “cell motility” category showed the highest percentage (19.23%) of changes. For the down-regulated genes, the “Energy production and conversion” category showed the highest percentage (31.58%) of changes. Additionally, a number (85; 33%) of the differentially expressed genes were in the categories of “poorly characterized”/“function unknown”/”General function prediction only” (Table 1). Table 1 COG category of differentially-expressed genes in NCTC 11168 in response to treatment with an inhibitory dose of Ery COG category No. up-regulated (%)* No. down-regulated (%)* Total No. differentially expressed genes Amino acid transport and metabolism 14 (11.11%) 12 (9.52%) 26 Carbohydrate transport and metabolism 1 (2.94%) 4 (11.76%) 5 Cell cycle control, mitosis and meiosis 2 (14.29%) 2 (14.29%) 4 Cell motility 10 (19.23%) 2 (3.

Furthermore the supplement group had an increase in serum creatinine but not creatinine clearance suggesting no negative effect on renal function. Cornelissen et al [80] analyzed the effects

of 1 week loading protocol (3 X 5 g/d CM) followed by a 3 month maintenance period (5 g/d) on cardiac patients Captisol involved in an endurance and resistance training program. Although CM supplementation did not significantly enhance performance, markers of renal and liver function were within normal ranges indicating the safety of the applied creatine supplementation protocol. A retrospective study [81], that examined the effects of long lasting (0.8 to 4 years) CM supplementation on health markers and prescribed training benefits, suggested that

there is no negative health effects (including muscle cramp or injuries) caused by long term CM consumption. In addition, despite many anecdotal claims, it appears that creatine supplementation would have positive influences on muscle cramps and dehydration [82]. Creatine was found to increase total body water possibly by decreasing the risk of dehydration, reducing sweat rate, lowering core body temperature and exercising heart rate. Furthermore, creatine supplementation does not increase symptoms nor negatively affect hydration or thermoregulation status of athletes exercising in the heat [83, 84]. Additionally, CM ingestion has been shown to reduce the rate of perceived exertion when training in the heat [85]. It is prudent to note that creatine

supplementation has been shown to reduce the body’s endogenous H 89 in vitro production of creatine, however levels return to normal after a brief period of time when supplementation ceases [1, 6]. Despite this creatine supplementation has not been studied/supplemented with for a relatively long period. Due to this, long term effects Rebamipide are unknown, therefore safety cannot be guaranteed. Whilst the long term effects of creatine supplementation remain unclear, no definitive certainty of either a negative or a positive effect upon the body has been determined for many health professionals and national agencies [19, 78]. For example the French Sanitary Agency has banned the buying of creatine due to the unproven allegation that a potential effect of creatine supplementation could be that of mutagenicity and carcinogenicity from the production of heterocyclic amines [78]. Long term and epidemiological data should continue to be produced and collected to determine the safety of creatine in all healthy individuals under all conditions [78]. Conclusion and practical recommendations The above review indicates that creatine supplementation has positive effects on: Amplifying the effects of resistance training for enhancing strength and hypertrophy [5, 22, 28]. Improving the quality and benefits of high intensity intermittent speed training [21]. Improving aerobic endurance performance in trials lasting more than 150s [7].

Proc Natl Acad Sci USA 1970, 65:737–744.PubMedCrossRef 27. Lakaye B, Makarchikov AF, Antunes AF, Zorzi W, Coumans B, De Pauw E, Wins P, Grisar T, Bettendorff L: Molecular characterization of a specific thiamine triphosphatase widely expressed in mammalian tissues. J Biol Chem 2002, 277:13771–13777.PubMedCrossRef 28. Peterson GL: A simplification of the protein assay method of Lowry et al. which is more generally applicable. Anal Biochem 1977, 83:346–356.PubMedCrossRef 29. Bettendorff L, Peeters M, Jouan C, Wins P, Schoffeniels E: Determination

of thiamin and its phosphate esters in cultured neurons and astrocytes using an ion-pair reversed-phase high-performance liquid chromatographic method. Anal Biochem 1991, 198:52–59.PubMedCrossRef 30. Gangolf M, FK228 Wins P, Thiry M, El Moualij B, Bettendorff L: Thiamine triphosphate www.selleckchem.com/products/Thiazovivin.html synthesis in the rat brain is mitochondrial and coupled

to the respiratory chain. J Biol Chem 2010, 285:583–594.PubMedCrossRef Authors’ contributions TG made most of the experimental work. BL and PW participated in the design of the study and the interpretation of the data. BEM and WZ contributed to the interpretation of the data and were responsible for the respiratory experiments. LB was the project leader. The manuscript was written by LB and PW. All authors read and approved the study.”
“Background Porcine reproductive and respiratory syndrome virus (PRRSV) is recognized as one of the major infective agents in the pig industry worldwide else since its appearance in the 1980s. It

was first diagnosed in the USA in 1987 [1], immediately found in Europe, soon disseminated to the rest of the world [2]. The disease is characterized by reproductive failure in pregnant sows and respiratory distress particularly in suckling piglets, thereupon getting its name. PRRSV is a single-stranded positive RNA virus and a member of the family Arteriviridae in the order of Nidovirales [3]. Based on phylogenetic analyses of different virus isolates around the world, PRRSV can be differentiated into two genotypes: Type I, represented by the European prototype Lelystad strain LV, and Type II, the prototype being the Northern American ATCC strain VR2332. Chinese isolates were assigned as members of the genotype II [4]. Extensive molecular studies show that PRRSV is highly variable in antigenicity, virulence and sequence diversity [5, 6]. PRRSV is a small, enveloped, single positive-stranded RNA virus including a genome of about 15 kb, encoding nine ORFs [2, 7, 8]. The PRRSV genome is comprised of two polymerase genes, ORF1a and 1b, and seven structural genes, ORF2a, 2b, 3, 4, 5, 6, and 7 [9]. ORF1a and ORF1b constitutes approximately 75% of the viral genome, and are characterized by a process of ribosomal frame shifting translated into a large polyprotein; which by self-cleavage gives rise to the non-structural proteins (NSPs) including the RNA-dependent RNA polymerase [10].

The gene hrpXv (hrpX of X. campestris pv. vesicatoria) was characterized NVP-BSK805 and its function was determined. The amino acid sequence deduced indicated similarity with proteins of the AraC family, which act in the regulation of gene expression. Mutations at position 1,335 of that gene stopped

the resulting mutant from inducing disease symptoms in susceptible pepper and tomato plants and HR in resistant plants. Complementation with fragments of that gene showed that only 580 bp after the initiator codon is enough to produce a functional polypeptide. The cell concentration of hrpX mutants in planta revealed that the mutant had 105 times less bacteria than the wild type genotype [18]. These results described in previous studies of the genes hrpB4 and hrpX corroborate the results we obtained for the mutants 02H02 and 03C01, which carry mutations Angiogenesis inhibitor in the genes hrpB4 and hrpXct, respectively. These two mutants caused no disease and their growth in citrus leaves was much lower than the Xcc isolate 306 (Fig. 2). In Xcv, HrpXv acts as a transcriptional activator for genes of the group hrp. HrpXv is necessary for transcriptional activation of five hrp genes (loci hrpB to hrpF) [18]. The protein HrpB4 is necessary for the complete functionality of TTSS, since hrpB4 mutants are not able to secrete AvrBs3 or HrpB2 proteins in Xcv [20]. Therefore, it can be assumed

that these ZD1839 two mutants, 02H02 and 03C01, lost their virulence because of their inability to take

TTSS factors to the host cell, which are necessary for growth in planta, since when these mutants are reactivated in culture media, cellular multiplication is similar to that of wild type. Another non-pathogenic mutant had mutated ORF XAC3980, which has similarity with the Xyllela fastidiosa gene htrA (high temperature requirement). First identified in E. coli, the locus htrA encodes a serine protease HtrA (also called DegP) that contains a catalytic triad (His105-Asp135-Ser210) required for proteolytic activity and two PDZ domains responsible for oligomerization of the protein complex, substrate recognition and substrate binding. Besides proteolytic activity, E. coli HtrA shows chaperone activity in vitro at low temperatures, where a conformational change of the protein masks the proteolytic residues. At high temperatures, the catalytic residues are accessible and the proteolytic activity of HtrA prevails. The HtrA proteases identified in E. coli are required for growth at 42°C and for the degradation of abnormally folded proteins in the periplasm. It was later demonstrated that HtrA degrades heat-denatured proteins, in vivo and in vitro. The very small amount of substrate for HtrA catalytic activity found in vivo suggests that the main biological role of the protein is the removal of nonnative, abnormally folded proteins from inside the cellular envelope. In E.

Time can be interpreted as a proxy for time-varying causal factors of long-term sickness absence, such as the commitment to the organization, psychosocial factors, medical follow-up and sickness benefits. Given the difficulty of measuring these theoretically important concepts over time, time-dependent parametric models are useful for modelling the changes in the hazard rate over time. Based on our results, we recommend that future sickness absence studies address the issue of time-dependence of return to work using parametric models.

The shape of the baseline hazard may give clues for the ideal moment of intervention programmes aimed at reducing long-term sickness absence. According to the Gompertz–Makeham model of return to work, the probability of success of an intervention to stimulate return to work decreases with the duration Y-27632 concentration of sickness absence. Joling et al. (2006) tested several types of Weibull models of duration dependence for sickness absence. They found positive duration dependence: the return to work rate increased over time. We found negative duration dependence: the return to work rate decreased monotonically over time. The difference is probably

due to the fact that Joling et al. analyzed both short term absences and long-term absences, while we focused on sickness absence lasting longer than 6 weeks. Using the appropriate model, it is possible to estimate how many employees are still absent any point in time after their sickness notice. By adding predictors to the model, it is possible to investigate the presence of variable ML323 manufacturer duration dependence across workers. Early interventions could be targeted

to the type stiripentol of workers most likely to be subject to negative duration dependence (Joling et al. 2006). The Gompertz–Makeham model of return to work has three parameters (A, B and C) to which covariates can be linked. Covariates in the B-term have an impact on the return to work rate. Covariates in the C-term test whether these effects increase or decrease with absence duration. The importance and direction of the influence of covariates on return to work “in the long run” is assessed by linking covariates to the A-term. About 27% of the long-term absentees had two or more long-term absence episodes. The units of analysis in survival analysis are episodes and this lowers the standard error of covariate estimates, as compared to an analysis based on independent observations, increasing the possibility of finding significant effects of covariates. There are techniques to deal with this dependence. For example, a model accommodating multiple spells can be applied. It is also possible to add a time-invariant unobserved hazard rate constant specific for each individual (‘frailty models’). It summarizes the impact of ‘omitted’ variables on the hazard rate and can be regarded as person characteristics, for example someone’s health status. Christensen et al. (2007) and Joling et al.

Fischerella muscicola UTEX 1829 [GenBank: AB075984], Fischerella sp. PCC 9339 [IMG Gene ID: 2517062088], Fischerella

sp. ATCC 43239 [GenBank: KJ768872], Fischerella ambigua UTEX 1930 [GenBank: KJ768871], Fischerella muscicola SAG 1427-1 [GenBank: AB075985], Fischerella sp. PCC 9431 [IMG Gene ID: 2512976007], Hapalosiphon welwitschii UH strain IC-52-3 [GenBank: KJ767019], Westiella intricata UH strain HT-29-1 [GenBank: KJ767016], Hapalosiphon hibernicus BZ-3-1 [GenBank: EU151900], Fischerella sp. CENA 19 [GenBank: AY039703], Fischerella sp. JSC-11 [GenBank: HM636645], Fischerella thermalis PCC 7521 [GenBank: AB075987], Fischerella muscicola PCC 7414 [GenBank: AB075986], Chlorogloeopsis fritschii PCC 6912 [GenBank: AB093489], Stem Cells inhibitor Chlorogloeopsis fritschii PCC 9212 [GenBank: AB075982], Fischerella sp. PCC 9605 [IMG Gene ID: 2516144612], Mastigocladopsis repens PCC 10914 [GenBank: AJ544079], Mastigocoleus testarum BC 008 [IMG Gene ID: 2264826627] and Synechocystis sp. PCC 6803 [GenBank: NR_074311]. *indicates hpi/amb/wel gene cluster was identified in these strains. ^ indicates these strains are known producers of hapalindole-family of natural products. Synechocystis sp.

PCC 6803 was used as the outgroup. Phylogenetic trees were constructed using the Geneious Selleck Ibrutinib Tree Builder program, using the neighbour-joining method. Numbers at each branch point are the bootstrap values for percentages of 100 replicate check details trees. Tryptophan biosynthesis Five of the six essential genes required for the biosynthesis of L-tryptophan from chorismate, which are paralogues of trpABCDE (T1-5), were identified in all nine biosynthetic gene clusters [14]. The sixth gene, trpF, a phosphoribosylanthranilate isomerase gene, is located outside of the gene cluster consistently

in all strains analyzed. Analysis of the genomes sequenced in this study revealed some cyanobacterial strains also contain a second set of genes which encode for tryptophan biosynthesis, however, other strains only contain the tryptophan genes within the gene cluster for tryptophan biosynthesis. Another gene common to all nine gene clusters is C2, a DAHP (3-deoxy-D-arabinoheptulosonate-7-phosphate) synthase gene, which encodes an enzyme regulating the biosynthesis of DAHP from the condensation of PEP (phosphoenolpyruvate) and erythrose-4-phosphate, the first enzymatic step of aromatic amino acid synthesis [15]. Indole-isonitrile biosynthesis A signature chemical feature of the hapalindole family of alkaloids is the presence of an isonitrile functional group.

By immunohistochemical GS1101 analysis and molecular studies, the intracellular expression and distribution of LEF-1 and HBsAg, cyclin D1 and c-myc gene expression were compared between HBsAg positive and negative HCC tissues, peritumor tissues and normal liver tissues. The possible roles of HBsAg in HCC development are discussed. Methods Human liver tissues Thirty surgical resected HCC tissues from different individuals were provided by Shanghai Cancer Institute. Tissue samples were categorized as tumorous (T) or matched

adjacent peritumorous liver tissues (pT) by hematoxylin and eosin (HE) stained sections under the microscope. The size and regions of the resection of the tumorous and peritumorous tissues were decided by the surgeons based on each individual case under the regulation of the ethics committee. All these HCCs were associated with HBV infection as defined by serum HBsAg positive.

Normal liver tissues (NL) from liver transplantation donors (n = 9) were obtained from Shanghai Cancer Institute and First Affiliated Hospital, Zhejiang University School of Medicine (kindly provided by Dr. Shusen Zheng). All samples collected followed the regulations of the ethics committees of both hospitals. Immunohistochemical staining Resected liver tissue samples were immediately immersed in 4% formalin and fixed for 18 to 24 h and paraffin-embedded. Immunohistochemical staining was carried out on tissue sections selleck screening library by using anti-LEF-1 polyclonal rabbit antibody (1:50, Abcam, Cambridge, UK) or anti-HBsAg monoclonal antibody (1:50, Changdao Sodium butyrate Biotech, Shanghai, China) to detect the expression of LEF-1 and HBsAg respectively. Reverse transcription and real-time PCR After treated with 10 U DNase

I (TaKaRa, Dalian, China) at 37°C for 30 min, 2 μg total RNA was reverse transcribed into cDNA by SuperScript II reverse transcriptase (Invitrogen, Carisbad CA, USA) according to the manufacturer’s protocol. Quantitative real-time PCR was carried out using specific primer pairs designed by PrimerBank [11]. For real-time PCR, 2 μl of 10-fold dilutions of the cDNA products were assayed using the Premix Ex Taq Perfect Real Time PCR kit (TaKaRa, Dalian, China). To assess the association of HBsAg and LEF-1 isoforms in HCC tissues, two pairs of primers were designed to detect different LEF-1 isoforms. Primers LP1 and LP2 were designed to target the β-catenin binding domain, which could differentiate the 38 kDa truncated LEF-1 isoform from the 55 kDa full-length LEF-1 [12]. Another pair of primers LP3 and LP4 was targeted to the 3′ UTR region of LEF-1 mRNA, and thus could detect both the full length and the isoforms. The house keeping gene GAPDH was used as an internal control. All experiments were performed twice independently. Primers used in this study are listed in Table 1.

In general, one will only find those SNPs that exist among the genomic samples used in the comparisons and novel SNPs will remain undiscovered [21]. This discovery bias can strongly affect taxonomic interpretation of results [22, 23].

Although discovery bias is often less consequential for genotyping efforts, the effects of our choice of strains for SNP discovery are clearly apparent in our phylogenetic tree. The discovery strains are distinguished by their positions at terminal branches in the phylogeny. There is greater diversity observed in B. abortus simply because two strains were part of the selleckchem discovery panel. Furthermore, although isolates on a branch will be grouped by the SNPs they share (or do not share), additional structure exists in the “true” phylogeny that is not apparent in the

genotype tree. Branch lengths are also highly affected by the SNP discovery process. Species that are basal within this phylogeny, such as B. ceti B. pinnipedialis B. ovis, and B. neotomae have short branch lengths merely because these genomes were not part of SNP discovery. It must also be noted that B. suis biovar 5 is part of this basal group. SNPs that should group it with the rest of the B. suis clade were not present in our MIP assay, which is not surprising since this branch is extremely short, even with whole genome analysis [JTF unpubl. data, [24]. We did not observe differentiation of these and the other Brucella species, nor Saracatinib order did we expect it because genomes from these groups were not a part of SNP discovery. Whole genome resequencing at the Broad Institute of MIT/Harvard recently generated genomes for over 100 additional Brucella strains and these genomes should provide a broad basis for future genotyping efforts, with canonical SNPs developed for each of the important isolates and clades. Future genotyping

efforts should include SNPs from all of the recognized species and biovars. Comparative work using some of these genomes has already been fruitful, demonstrating the emergence of Tideglusib the marine Brucella from within the terrestrial Brucella and showing a methodology for whole genome analysis [24]. A trade-off exists in current genotyping efforts between throughput and genomic sampling. Does one aim for a maximum amount of potentially informative loci through approaches such as whole genome sequencing but having to sacrifice the number of isolates that can be evaluated? Or does one aim for more complete sampling of large numbers of isolates but with a limited set of loci using individual SNP assays such as CUMA? Of course the ultimate answer depends on your research interest or clinical application as well as the amount of resources at hand. MIP assays provide phylogenetic resolution for an intermediate number of samples and intermediate number of SNPs.

thuringiensis bacterium itself. Previously, we demonstrated that B. thuringiensis toxin had substantially reduced ability to kill gypsy moth and three other species of lepidopteran larvae that had been treated with antibiotics, and that ingestion of an enteric-derived learn more bacterium significantly increased lethality of subsequent ingestion of B. thuringiensis [30, 31]. We observed that the enteric

bacterium, Enterobacter sp. NAB3, grew to high population densities in vitro in hemolymph extracted from live gypsy moth larvae, whereas B. thuringiensis was rapidly cleared, which is inconsistent with the model of B. thuringiensis bacteremia as a cause of larval death. However, these results did not distinguish between the possibilities that gut bacteria contribute to B. thuringiensis-induced lethality by bacteremia or by another mechanism. There is increasing recognition that an important feature of gut microbiota of both invertebrates and vertebrates is their ability to shape and modulate the host immune response [32–36]. In certain circumstances this effect can become deleterious to the host. For instance, uncontrolled

activation of the immune response by enteric bacteria leads to chronic infection and pathogenesis in both invertebrates and vertebrates [37–39]. Interestingly, some recent studies have also linked activation of the immune response of Lepidoptera to ingestion Tryptophan synthase of non-lethal doses of B. thuringiensis. For example, ingestion of low doses of B. thuringiensis Sepantronium solubility dmso by Galleria mellonella larvae increased both oxidative stress levels in the gut [40] and the phagocytic activity of hemocytes [41].

In Trichoplusia ni larvae, exposure to B. thuringiensis reduced both the numbers of hemocytes and components of the humoral immune response (antimicrobial peptides and phenoloxidase activity) [42]. It remains unclear what effectors trigger this immune modulation, and the contribution of enteric bacteria to this response is not known. Modulation of the host immune response could be an indirect mechanism by which gut microbiota alter susceptibility to B. thuringiensis. As an initial step to distinguish between a direct or host-mediated role of gut microbiota in larval death following the ingestion of B. thuringiensis, we examined the possible association between the host immune response and larval susceptibility to B. thuringiensis. Results Effects of intra-hemocoelic injection of B. thuringiensis and Enterobacter sp. NAB3 on larval hemolymph Injections of greater than 107 cells of an over-night culture of either B. thuringiensis or Enterobacter sp. NAB3 into the hemocoel of gypsy moth larvae led to a pronounced cellular and humoral immune response (Figure 1). In hemolymph sampled from larvae 24 h after injection of Enterobacter sp.

Dated records are important as scientists attempt to document range shifts; Selleckchem GSK3235025 e.g. tapir, Sumatran rhinoceros and orangutans were more widely distributed until recently (Meijaard 2003; Tougard and Montuire 2006; Earl of Cranbrook 2009). Some of the impediments to developing regional public databases for conservation managers are discussed by Srikwan et al. (2006) and Webb et al. (2010). Patterns of distribution There are many biogeographic patterns within Southeast Asia including temperate—tropical gradients in species richness, a peninsula effect at the tip of the Thai-Malay peninsula, and numerous examples of the species-area effect. The latter

are important to conservationists as the rise in sea level (discussed

below) will result in more species losses on smaller islands (Okie and Brown 2009). Other patterns of interest include the location of biodiversity hotspots, centers of endemism and refugia. Although defining hotspots as congruent with whole biogeographic subregions (Fig. 1: Indochina, Sundaic, Philippine and Wallacea), as done by Conservation International (2007), may be too broad-scale for some purposes, the identification of smaller areas of endemism or species richness can guide the location of protected areas, e.g., the Mentawi islands with their find protocol 17 species of endemic mammals (Corlett 2009a), numerous isolated karst mountains (Clements et al. 2006, 2008), IUCN’s Key Biodiversity Areas (Brooks et al. 2008), and BirdLife International’s Important Bird Areas (Chan et al. 2004). Understanding the history of today’s hotspots is necessary to establish whether they are ancient and geographically fixed, or whether they have moved in response to past climatic change? Hotspots of freshwater biota are

also known: the mid- and lower-Mekong River has probably the second richest fish fauna in the world (Rainboth et al. 2010) and also harbors a very diverse mollusc fauna. Unfortunately, both the basic documentation of this fauna and the still confused history of the region’s rivers make it difficult to delimit aquatic hotspots. Although terrestrial Carbohydrate biotas may be conserved by protecting hotspots (fortress conservation) this approach is less useful for river and wetland biotas whose conservation typically requires watershed level management. If hotspots capture areas of great species richness today, Pleistocene refugia are thought to have enabled these species to survive environmental challenges in the past. Several workers have argued that during cooler glacial conditions rainforest retreated to the hills of peninsula Malaysia, western Sumatra, the Mentawi Islands, and the center of Borneo, and that during hypothermal periods the rainforest was replaced by savanna woodland or grassland on the emerged Sunda plains and elsewhere (Heaney 1991; Morley 2000, 2007).