Many nano/microparticles (n/µP), to which our body is exposed, have no physiological way of removal. Our immune system sense these “small particulate objects”, and tries to decrease their harmfulness. Since oxidation, phagocytosis and other methods of degradation do not work with small, chemically resistant, and hydrophobic nanoparticles (nP). This applies to soot from air pollution, nano-diamonds from cosmic impact, polishing and related machines, synthetic polymers, and dietary n/µP. Our body tries to separate these from the surrounding tissue using aggregates from neutrophil extracellular traps (NETs). This effectively works in soft tissues where n/µP are entrapped into granuloma-like structures and isolated. The interactions of hydrophobic nanocrystals with circulating or ductal patrolling neutrophils and the consequent formation of occlusive aggregated NETs (aggNETs) are prone to obstruct capillaries, bile ducts in gallbladder and liver, and many more tubular structures. This may cause serious health problems and often fatality. Here we describe how specific size and surface properties of n/µP can activate neutrophils and lead to aggregation-related pathologies. We discuss “natural” sources of n/µP and those tightly connected to unhealthy diets.  Environmental pollutants , artificial new materials , naturally-occurring crystals , or those generated during metabolism or defense  are continuously flooding the human body. A high exposure to n/µP concerns our immune system. All n/µP interact with tissues of our body in a different ways. Recent works demonstrate the growing importance of understanding how n/µP contribute to health, disease development, and prevention . Indeed,In the current manuscript we summarize new findings related to the role of neutrophils upon interaction with hydrophobic, non-degradable nP. We demonstrate the damage of dietary-born nanoparticles ) in the hepatobiliary system, effect of soot and related carbon particles in lung tissue, as well as effect of occlusions and obstruction caused by aggregated neutrophil extracellular traps (NETs) in different tissues of the body. In addition, we will discuss the resulting inflammatory responses.Studies involving animals, mice, including housing and care, method of euthanasia, and experimental protocols were approved by the Ethical committee of Danylo Halytsky Lviv National Medical University, protocols 20191216/10, 20180226/2, and 20130624/6, all experiments were designed to comply with principles of the 3Rs . Human tissue samples described in current study were obtained under approval of Ethical committee of Danylo Halytsky Lviv National Medical University, protocol 20180226/02.Human Jurkat T cells were cultured according to common laboratory practice techniques. When working with human monocytes derived from blood of healthy volunteers we used common best practices and criteria approved at ,3.Model for dietary-born nanoparticles. The model of murine NASH  using yoThe air pouch model was performed by injection of 5 mL of sterile air subcutaneously into the back of Balb/c mice [Polymorphonuclear neutrophilic granulocytes (PMN) and neutrophils are innate immune cells dealing with “particulate objects” found in our body. Neutrophils constitute 40% to 70% of all leukocytes, and their multisegmented nuclei easily allow them to extravasate (leave blood vessels) and patrol surfaces of mucosal linings, ducts, and wounds . Upon coThe formation of NETs is a heavy weapon used for effective immobilization and destruction of pathogens, for covering wound surfaces , and forOur recent data suggest that the surface properties and chemical composition has a dramatic effect on its interaction with tissues. In case a n/µP is made of biodegradable material and has a polar surface, it may be phagocytosed and degraded by lysosomal enzymes. In contrast, lipophilic surfaces of n/µP directly interact with lipid bilayer of plasma membranes . The n/µWhen eukaryotic cell senses plasma membrane leakage, it will immediately start the recycling machinery to determine, isolate, and remove the affected part of plasma membrane and engulf it into endosome . This enThe above-mentioned interaction of n/µP and plasma membrane is determined by universal characteristics of chemistry and cell structure. The latter response will differ between cell specifically. From our experience different cancer cells, primary human lymphocytes, macrophages, and RBCs lose cell integrity caused by an increased membrane permeability. Eventually they die after contacting small lipophilic n/µP ,20,21,22In neutrophils, specialized in immobilization and degradation of particulate matter, the engulfment of hydrophobic n/µP and the resulting lysosomal leakage activates further molecular cascades resulting in generation of ROS and initiation of NETs formation. It has been demonstrated that in acute kidney inflammation (AKI), crystal-induced necroptosis occurs, involving mitochondria permeability transition . After sNETs display a protective potential in our body. They immobilize and destruct fungal hyphae ; they isHowever, when NETs formation is exaggerated, and this may occur with chronic exposure to certain nP, excessive NETs formation and aggregation poses a threat to the host. NETs may aggregate and, consequently, sterically block ducts and vessels. Excessive NET formation reportedly contributed to the occlusion of blood vessels , and panUric acid is one of the major byproducts of the human catabolism, also being a strong antioxidant. The normal concentration range of uric acid (or hydrogen urate ion) in human blood is 25 to 80 mg/L for males and 15 to 60 mg/L for females ; the solNeutrophils directly ingest MSU crystals and initiate inflammasome-dependent NET-formation ,32. Thisluminae [Gallstones are mainly made of cholesterol, bile acids and calcium deposits and can easily rich few centimeters in diameter. Cholesterol crystals, the first known lipid crystals, have been discovered in Lviv University more than 150 years ago ; they arluminae . Neutropluminae . Cholestluminae . An autoluminae . Employiluminae , served luminae . Active luminae .Fructose is a highly soluble compound. Unfortunately, it is a cheap source of sugar in corn syrup, that is extensively used in soft drinks. Recently fructose has been linked to hepatopathology ; non-alcIn the experimental murine model for NASH we employed the C57BL/6N strain. When fed high lipid high cholesterol diet, the mice developed nano-alcoholic fatty liver disease and NASH within 4 and 6 weeks, respectively. The symptoms were accompanied by an increased NETs formation in the gall bladder and increased neutrophil elastase activity in the sera. Changing the dietary conditions from high-cholesterol to high-fructose (10% fructose in drinking water corresponding to extensive consumption of soft drinks), we have observed similarly increased levels of serum elastase activity as well as strong formation of NETs inside the gallbladder . BlockinCurrent lifestyle greatly promotes the junk food, which will be inevitably rich in cholesterol (due to frying stuff) and fructose (mainly due to soft drinks). It is prone to induce increased levels of either lipophilic n/µP or sharp n/µP both formed in the gastrointestinal tract and the hepatobiliary ducts. These particles stimulate the formation of NETs with a high tendency to aggregate.NETs allow our body to survive under the constant load of potentially dangerous n/µP and to sequester and isolate from surrounding tissues those that cannot be safely degraded. It is fair to assume, that any accumulation of foreign material in the body carries risk for the health. In this context, NETs can worsen or improve the situation.The initially formed NETs derived from sentinel cells and the content of neutrophil granules will attract further neutrophils to the area of contact with the nP. When they reach a critical concentration, enough to form aggregated NETs, the latter will shield the n/µP from viable areas of the tissue. The damaged cells will then cause local inflammation. Formation of aggregated NETs will be accompanied by the release of DNA-bound cytokine-degrading proteases  as well The beneficial role of n/µP aggregation is definitely in minimizing the area of contacting cells, which can be damaged, for example with soot nP. In patients with tuberculosis NETs effectively surround granulomas in the peritoneal cavity. In this way neutrophils collect and sequester pathogenic bacteria in a single area. This formed a shield and limited pathogen spread. Similar patterns have been observed when carbon particles were inhaled by humans. Contrary to the expected uniform distribution the dust particles were concentrated in few areas D,E. ManyUsing adjuvants, n/µP are intentionally injected into the organism, usually into soft tissues. NETs contribute to the controlled immune boost, needed to generate a sufficient immune response which is observed when vaccines are co-injected with particulate matter like aluminum oxide or hydroxide . The latTo track the ability of neutrophils to concentrate n/µP we have injected fluorescent nano-diamonds (fluorescent due to induced surface defects) intraperitoneally or intratracheally into mice. Then we followed distribution kinetics using in vivo fluorescent analysis of the whole animal. The signals gradually concentrated in limited areas in both abdominal cavities and lungs, correspondingly. After 5 days we observed diamond n/µP to be concentrated within granulomas, covered with DNA-rich material (NETs) A. In theAggregated NETs have a physical consistency of bubble gum; they are sticky, elastic, and insoluble, but highly absorptive due to the negative charge of the DNA. The most dangerous places in the bodies where these aggregates are prone to cause life-threatening conditions are vessels, ducts, and air spaces.In the circulation humans harbor an NET-resolving machinery, consisting of circulating DNase1 and DNase1L3 . The enzPresented data indicates the importance of the control of NET formation during systemic inflammation. Recent findings suggest possible NETs involvement in severe malaria , with heAs reported previously , in the On the surfaces of body cavities NETs tend to aggregate and sequester n/µP and cluster them in few spots. However, under flow conditions NETs upon contact with n/µP can either self-aggregate  or, like in gallstones, aggregate the n/µP and support the growth of the aggregates. If this happens in the microvasculature or in narrow ducts  this results in a permanent occlusion and mechanical damage of the organ. The importance of the site of NETs formation was demonstrated by the injection of cholesterol crystals into murine air pouches. In ducts NETs tend to agglomerate small cholesterol particles and were seen inside the crystals. In contrast, in the relatively stable environment of the air pouch NETs covered the whole cholesterol crystals C. Thus, Polar n/µP possess the permanent danger upon their contact with body tissues and our body utilize the developed protection mechanism by sequestering n/µP using neutrophils and neutrophil extracellular traps. This can have beneficial consequences resulting in the removal of the contact with dangerous agent with living cells and strong local immune response. Polar n/µP can also be detrimental as big nP-aggNET clusters can block or occlude vessels or ducts causing hypoxia and severe damage to the tissue and to the organism as a whole, respectively. Besides, phagocytosis of crystals such as silica, asbestos, and monosodium urate by neutrophils/macrophages may cause lysosomal damage thereby activating inflammasome complex and exacerbating inflammation and this complex question needs further investigation.


Mycobacterium ulcerans is the causative agent of Buruli ulcer, a neglected tropical disease found in rural areas of West and Central Africa. Despite the ongoing efforts to tackle Buruli ulcer epidemics, the environmental reservoir of its pathogen remains elusive, underscoring the need for new approaches to improving disease prevention and management. In our study, we implemented a local-scale spatial clustering model and deciphered the genetic diversity of the bacteria in a small area of Benin where Buruli ulcer is endemic. Using 179 strain samples from West Africa, we conducted a phylogeographic analysis combining whole-genome sequencing with spatial scan statistics. The 8 distinct genotypes we identified were by no means randomly spread over the studied area. Instead, they were divided into 3 different geographic clusters, associated with landscape characteristics. Our results highlight the ability of M. ulcerans to evolve independently and differentially depending on location in a specific ecologic reservoir. Mycobacterium ulcerans (Buruli ulcer (BU) is a devastating necrotic human skin disease caused by M. ulcerans has potential primary environmental reservoirs in wetlands, rivers, and stagnant bodies of water , coupled with single-nucleotide polymorphism (SNP)–based genotyping, has led to major advances in M. ulcerans isolates, our objective was to identify and track on a local scale the genotypes circulating in the BU-endemic regions of Ouémé and Plateau in southeast Benin and in Ogun State in southwest Nigeria. We evaluated the presence of specific clusters according to the geographic localization of patients and performed local-scale clustering by using a phylogenetic analysis approach based on SNP typing, coupled with spatial scan statistics.In using a representative collection of 208 M. ulcerans strains isolated from patients diagnosed with and treated for BU during 2007–2016 at the Centre de Diagnostic et Traitement de la Lèpre et de l’Ulcère de Buruli (CDTLUB) in Pobè, Benin. We first sequenced and analyzed 179 strains; then, to perform validation of the model, we sequenced and analyzed a second set of 29 strains.We conducted WGS on 208 https://www.illumina.com) with Nextrera XT DNA preparation kit or Ion Torrent S5XL technology with IonXpress Plus Fragment library kit . We submitted generated reads to the National Center for Biotechnology Information Sequence Read Archive (BioProjectID no. PRJNA499075). . Images used were recorded on January 6, 2018, with a spatial resolution of 10 m. Land use and cover were recovered with a supervised classification using minimum distance algorithm. We created training samples on the basis of expert knowledge of West Africa topography and Google Earth.We acquired satellite imagery by using Sentinel-2 .We tested for statistical significance by using either Fisher exact test or 1-way analysis of variance. We validated the model by calculating accuracy and Matthews correlation coefficient on the confusion matrices . We perfWe analyzed 179 strains isolated from patients diagnosed with and treated for BU at CDTLUB during 2007–2016. Patients originated from regions of Ouémé (111 [52%]) and Plateau (47 [26%]) in southeast Benin and from Ogun State in southwest Nigeria (21 [12%]). The proportion of genomes selected was in accordance with the geographic origin of patients visiting CDTLUB . MoreoveAfter WGS, a total of 6,163 core genome SNPs were uncovered after mapping the 179 strains against the referent genome Agy99  Table 3aWe used an Eigenstrat-like principal component analysis approach to identify groups of genomes based on their SNP. We identified 8 groups with similar genotypic features and defined them as genotypes , panel AThe 8 genotypes were also identifiable in the phylogeny of the 174 strains identified as belonging to Mu_A1 lineage (West Africa lineage)  Figure 1To verify whether the 8 genotypes could be related to any of the basic characteristics of patients, we performed the Fisher exact test to analyze severity and sex and analysis of variance to analyze age. We found no significant association regarding severity, sex, or age (data not shown). We also considered finding an association between genotypes and presence of bone lesions. Our results showed no association between genotype and higher or lower incidence of osteomyelitis (data not shown). However, this lack of finding could be attributable to the limited amount of reported bone damage in our sampling (only 4 cases). We found no association between genotype and the year of strain isolation .M. ulcerans geographic origin, we used a multinomial spatial scan statistic  covered the area of Ogun State. This cluster was drastically different from those found in the Ouémé region of Benin. In this region of Nigeria, the RRs for infection with strains from genotype 1, 2, 3, or 6 were significantly higher than for any other genotype , southern Ouémé (9 patients), Plateau (4 patients), and Nigeria (5 patients). We identified 2 of the 29 strains as part of the MU_A2 lineage and added them to the outgroup to build a new phylogenetic tree. The 27 other strains were easily included in the phylogenetic tree  without M. ulcerans genotypes in some BU-endemic areas.For each of the 3 significant geographic clusters, we compared the observed repartition of new genomes to the expected distribution given by each cluster. The 2 clusters in Ouémé exhibited strong accuracy (91%) and a Matthews coefficient >0.6, corresponding to a strong relationship . The NigBU occurs in poor rural communities with little economic or political influence. A key epidemiologic feature of this disease is the distribution of cases in very well-delimited foci. However, in these areas, the precise zones of high-risk contamination in environments are not identified. As with other neglected tropical diseases, fighting BU will require integrated approaches to reduce transmission of the causative mycobacterium and ensure earlier patient management.M. ulcerans transmission, the precise route of M. ulcerans transmission to humans remains unclear. Molecular epidemiology studies conducted on a local scale can be adapted to elucidate the structure, diversity, evolution, dissemination, and life of the bacterial population.Socioeconomic factors, environmental changes, ecologic factors, and the conquest of new territories promote infections caused by pathogens with a wildlife origin  , whereas a Bayesian phylogenetic approach is suitable for studying temporal distribution of M. ulcerans genotypes in southeastern Benin and southwestern Nigeria. On this scale, our results showed a strong association between hydrologic drainage areas and M. ulcerans genotypes, because a clear difference was observed in the distribution of genotypes between BU patients living around Nigeria’s Yewa basin and Benin’s Ouémé basin. Our clustering revealed that bacteria evolved independently and differentially, depending on their specific ecologic reservoir. Moreover (and more surprisingly), we were able to detect clustering of M. ulcerans genotypes along a same drainage basin (in this case the Ouémé basin). Inside the main genotype (genotype 8), we were also able to detect subgenotypes with a similar clustering along the river, indicating dissemination of M. ulcerans on a local scale and then a persistence of M. ulcerans in independently endemic niches. These findings are consistent with previous scenarios in which M. ulcerans, once introduced into a new environment, expands instead of becoming a quiescent pathogen  or flying insects and might be present in aquatic niches for only the few months when wetlands exist just after the rainy seasons  regarding stable and local reservoirs of Mycobacterium ulcerans inferred from the nonrandom distribution of bacterial genotypes, Benin.Additional information (tables) regarding stable and local reservoirs of


Off-ladder alleles were seen at several loci, and these were identified by examining their underlying nucleotide sequences. Principal component analysis (PCA) and STRUCTURE showed no genetic structure within the Kuwaiti population. However, PCA revealed a correlation between geographic and genetic distance. Finally, phylogenetic trees demonstrated a close relationship between Kuwaitis and Middle Easterners at a global level, and a recent common ancestry for Kuwait with its northern neighbours of Iraq and Iran, at a regional level.This study evaluates the forensic utility of 23 autosomal short tandem repeat markers in 400 samples from the Kuwaiti population, of which four markers  are reported for the first time for Kuwait. All the markers were shown to exhibit no deviation from Hardy–Weinberg equilibrium, nor any linkage disequilibrium between and within loci, indicating that these loci are inherited independently, and their allele frequencies can be used to estimate match probabilities in the Kuwaiti population. The low combined match probability of 7.37 × 10 Currently, forensic DNA analysis in Kuwait is carried out by the Kuwaiti Identification DNA Laboratory (KIDL) using only short tandem repeat (STR) markers, including autosomal STRs, Y-chromosome STRs (Y-STRs) and X-chromosome STRs (X-STRs). Autosomal STRs are routinely used both for identification of individuals and paternity testing, whereas Y-STRs and X-STRs are used less frequently, and only for specific scenarios.The State of Kuwait is located on the Arabian Gulf in the northwest of the Asian continent and in the heart of the Middle East. Kuwait is bordered by the Kingdom of Saudi Arabia in the south, the Republic of Iraq in the north and west, and Iran in the east, across the Persian Gulf Sea. Kuwait’s population is about 4.8 million, which includes 1.4 million Kuwaiti nationals and 3.4 million foreign nationals, according to the 2019 census 3. Although these existing STRs are efficient for analysing cases of simple relationships, more STRs are increasingly required, particularly for complex paternity cases or to increase the discrimination power in cases of partial DNA profiles and DNA mixtures.To date, few papers have been published investigating the forensic utility and genetic diversity of autosomal STR markers in the Kuwaiti population. In 2008, Alenizi and colleagues reported the allele frequencies of 15 STR loci included in the AmpFℓSTR Identifiler kit 4, three additional autosomal STRs  to increase the power of discrimination, two sex chromosome markers (Amelogenin and DYS391), and two rapidly mutating Y-STRs (DYS570 and DYS576)5. The PowerPlex Fusion 6C kit was validated by multi-laboratory evaluation following SWGDAM guidelines5.Recently, Promega launched the PowerPlex Fusion 6C kit, a six-dye kit that can amplify 27 loci, including the 20 autosomal loci in the expanded CODIS set Before utilising this kit for criminal and relationship cases in Kuwait, population and forensic statistical data for the loci in the kit must be evaluated. In this study, we aim to increase the amount of genetic data available for the Kuwaiti population, using the 23 autosomal STRs in the PowerPlex Fusion 6C kit, of which four loci  have not been reported before for Kuwait. In addition, we aim to evaluate the forensic utility of these autosomal STRs in this underrepresented region, and to investigate the utility of these markers in population genetic differentiation by examining the genetic distance between the Kuwaiti population and other global populations for which data are available.GeneMapper ID-X software version 1.4 (Thermo Fisher Scientific).Blood samples were collected on Whatman FTA cards  from 400 unrelated Kuwaiti . DNA was amplified directly, without quantification, from a 1.2 mm FTA card punch, according to the directions in the PowerPlex Fusion 6C manual, using a SureCycler 8800 thermal cycler . Detection and separation of the DNA fragments were carried out using an Applied Biosystems 3500 Genetic Analyzer (Thermo Fisher Scientific) with the internal lane standard WEN ILS 500 and allelic ladder provided with the PowerPlex Fusion 6C kit. Genotype determination and allele calling for only the 23 autosomal loci were carried out using Arlequin statistical software version 3.5 was used to calculate allele frequencies, to test for linkage disequilibrium, and to test for deviation from the Hardy–Weinberg Equilibrium6. Forensic parameters, including the random match probability (RMP), discrimination power (DP), power of exclusion (PE), typical paternity index (TPI) and polymorphic information content (PIC), were calculated using STRAF (http://cmpg.unibe.ch/shiny/STRAF/), an online tool for STR data analysis7.Data analysis was carried out for the 23 autosomal loci only (the sex chromosomes are not included in this paper). 9. Newly presented markers therefore must be assessed for the presence of any population structure, to avoid calculation of forensic parameters using inaccurate allele frequencies taken from the total population, rather than the relevant subpopulation. Stratification also negatively impacts discrimination power, because the chance of random individuals possessing similar genotypes is higher within a subpopulation, than within the total population10. Two methods were therefore used to detect genetic structure in the population, principal component analysis (PCA), and a Bayesian-based method implemented in STRUCTURE version 2.3.413.Countries in the Arabian Peninsula, including Kuwait, have a high rate of consanguineous marriage, which causes differential distribution of alleles among families and tribes, resulting in population genetic stratificationK = 3) based on the donor’s surname. It has previously been found that the Kuwaiti population is mainly composed of settlers coming from three different regions: the Arabian Peninsula (from Saudi Arabia), the desert (representing nomadic tribes), and Persian countries (mainly from Iran)17. On this basis, the samples were categorised into three groups: KW-1 (n = 162) representing individuals originating from the Arabian Peninsula, KW-2 (n = 163), which consists of those coming from Persian countries and Iraq (north), and KW-3 (n = 75) composed of Bedouin individuals coming from nomadic tribes. PCA was carried out on allele frequencies at the 23 autosomal STR loci for the different population groups KW-1–KW-3 using R software33 and visualised using the factoextra package34.In order to demonstrate whether these two methods were able to cluster the samples into their real subpopulations, each sample was categorised into one of three ancestral subgroups (STRUCTURE (a Bayesian-based approach) takes a range of numbers of populations (K) in order to calculate the proportion of the genome of each individual in the sample originating from each inferred population11. STRUCTURE software calculates the likelihood of the data (X) for range of K values, and the true number of K is determined by the maximal value of Ln P(X|K). However, it was found by Evanno et al.18 that the maximal value does not always provide the correct number of K in the data. Instead, the maximal value of the rate of change (Delta K) in the Ln P(X|K) between successive K values accurately infers the true number of genetic clusters in the data18. As such, both Ln P(X|K) and Delta K at each K were calculated and reported. STRUCTURE was run without population information, as recommended in the STRUCTURE documentation, in order to check whether the results approximately agreed with the separation of samples into their subgroups. Thus, the predefined groups (KW-1 to KW-3) were only included as a population label rather than as prior information for the analysis. The parameters for the analysis were set as follows: ‘admixture’ and ‘correlated allele frequencies’ models using 100,000 Markov Chain Monte Carlo (MCMC) steps for each run, with the first 100,000 discarded as a burn-in, and the inferred number of K was set from 1 to 10. At each K, the analysis was repeated five times in order to test the results for consistency. The results were visualised using CLUMPAK 19, and the best K was calculated using STRUCTURE HARVESTER (available at http://taylor0.biology.ucla.edu/structureHarvester/)20.In contrast to PCA, which is an unsupervised clustering algorithm, popSTR (http://spsmart.cesga.es/popstr.php)23. Data from Lebanon (LEB) and an Indian (IND) population from Madhya Pradesh typed for the 23 autosomal loci24 were also included in the analysis. Genetic distance was also assessed at a regional level using allele frequencies for the 13 of the 23 autosomal loci  that are shared between the data reported in this study and other studies of Kuwait and neighbouring counties: Kuwait (KW13 and KW22), Iran (IRN25 and IRN12), Saudi Arabia (SA26 and SA127), Qatar (QAT28), Oman (OMN29), Yemen (YEM29), United Arab Emirates (UAE30), Bahrain (BAH31), and Iraq (IRQ32 and IRQ12). PCA analysis was conducted using R software33 and visualised using the factoextra package34.To assess the genetic distance between the Kuwaiti sample and other global populations, PCA was conducted based on allele frequencies of the 23 autosomal loci for 57 global populations grouped into seven continental regions: Africa (AFR), America (AMR), Central and South Asia (C_S_ASIA), the Middle East (ME), Europe (EUR) and East Asia (E_ASIA). Data for the global populations were obtained from the HGDP-CEPH Human Genome Diversity Panel (HGDP-CEPH) using the online forensic STR frequency browser, DA) based on Nei et al.35, which were calculated from the allele frequencies of the populations using POPTREE2 software36. The type of phylogenetic trees used were Neighbour-joining (NJ) trees, constructed using Mega X software version 10.0.537.In addition to the PCA, we studied the genetic relationship between the Kuwaiti samples and the other populations at both the continental and regional levels, using phylogenetic trees. These trees were constructed using pairwise genetic distances  was granted by the Department of Pure and Applied Chemistry Ethics Committee. Written, informed consent was obtained from all participants prior to sampling.39, SE33 was the most discriminative locus in the Kuwaiti population, having 45 different alleles (PIC = 0.945). In contrast, TPOX was the least discriminative locus, with only eight different alleles (PIC = 0.616). The calculated combined match probability (CMP) was 7.37 × 10–30, meaning that the probability of observing two identical profiles for the 23 autosomal loci in the Kuwaiti population was 1 in 1.36 × 1029 The TPI ranged between 1.439 (TPOX) and 8.333 (SE33), and the combined PE was > 99.9999%. These high values indicate the usefulness of the PowerPlex Fusion 6C kit for both human identification and paternity testing in the Kuwaiti population.Full PowerPlex Fusion 6C STR profiles were recovered from blood samples taken from 400 Kuwaiti individuals. Table p < 0.05). However, after Bonferroni correction of the significance level using the number of tests (0.05/253 = 0.000198), none of the pairs of loci showed significant linkage disequilibrium, indicating that all loci are statistically independent. Therefore, their allele frequencies can be multiplied together to estimate match probabilities in the Kuwaiti population.No significant deviation from the expectations of the Hardy–Weinberg Equilibrium was detected at any locus in the Kuwaiti genotypic data, therefore, the PowerPlex Fusion 6C autosomal STR alleles are independent and can be used to estimate allele frequencies from their genotype frequencies. Association between alleles at all possible pairwise combinations of loci was evaluated using the linkage disequilibrium test. Significant linkage disequilibrium was detected between 22  pairs of loci  alleles, and were observed in 13 samples. These samples were re-amplified for confirmation and all OL alleles were confirmed. OL alleles were observed at the PentaE , PentaD , D22S1045 , SE33 , and D18S51  loci. The samples were previously sequenced using the Verogen ForenSeq DNA Signature Prep kit (manuscript in preparation), and these data were examined to determine whether the undesignated alleles at the PentaE, PentaD, D22S1045 and SE33 loci could be identified; the repeat structure sequences from this dataset are shown in Table GeneMapper software.Alleles that could not be identified using the 40), except for the PentaD 11.2 allele, which is a novel allele not reported before in the literature.All of the identified alleles have been reported previously in the STRBase database 10. Despite the fact that, in this study, no significant deviation from the expectations of the Hardy–Weinberg Equilibrium was detected between the markers, indicating that there is no genetic stratification, it is useful to assess the markers to see if they reveal any genetic clusters within the data. To achieve this, PCA was carried out on the DNA profiles obtained from the Kuwaiti samples for the 23 autosomal PowerPlex Fusion 6C markers. PCA is an unsupervised clustering method that does not require any prior information about the ancestral origin of the samples. Simply, it clusters the samples based on their similarities to each other, forming homogenous clusters of individuals that can be seen on a PCA plot. As expected, the PCA plot  populations, and then uses this information to assign individuals into population subgroups18. The analysis was run without population information, and the mean log likelihood across five repeated runs of the analysis for each value of K (from 2 to 10) was estimated. The results showed inconsistency in estimating the log likelihood at K = 5 and over, which is indicated by the high standard deviation (SD), as presented in Supplementary Figure 18, the most likely inferred value of K was 7, as this is the number of populations at which the highest Delta K value was recorded , six American (N = 551), nine Central and South Asian (N = 202), four Middle Eastern (N = 160), 11 European (N = 2135), and 17 East Asian (N = 227) populations. An Indian population N = 374) and a Lebanese population (N = 505) were also typed for the 23 loci, thus were added to the analysis. Both PCA and phylogenetic analyses were carried out, and the resulting plots characterise the genetic differentiation between populations. The distribution of the populations on the PCA plot . It is therefore not possible to determine whether sampling from different sub-populations could explain why, in contrast to our sample, these two Kuwaiti samples cluster more closely with the Saudi Arabian sample than the samples from Iran and Iraq. Overall, it can be seen that the allele frequencies of the 23 autosomal markers in the PowerPlex Fusion 6C kit can be successfully used to separate both geographically distant global populations and closely related populations on the basis of their genetic distance, making them a good choice for detecting genetic differentiation between populations.In this study, 30% of individuals declared their origins as being from the north (Iraq and Iran), 39% from the south region , and 24% had parents of different origin (admixed). Therefore, the closer genetic relationship of our samples to the northern region might be due to the presence of these individuals. There is no information available about the population of origin for the samples collected in the two previous Kuwaiti studies (KW1STRUCTURE and PCA analysis show no signature of genetic structuring of the Kuwaiti population into subpopulations. Comparison of the Kuwaiti population to other global populations indicates that Kuwait clusters with other Middle Eastern populations, and shows a close relationship with Iran and Iraq, suggesting that they may share common ancestry.This study evaluated the forensic utility of the 23 autosomal STR loci included in the Promega PowerPlex Fusion 6C kit for the Kuwaiti population. Among these loci, D10S1248, D22S1045, D2S441 and SE33 are reported for the first time for Kuwait. The genetic data indicate that these 23 autosomal STRs are highly polymorphic in the Kuwaiti population and are of high value for human identification and paternity testing. Supplementary Information


Obesity causes a wide range of systemic diseases and is associated with mood and anxiety disorders. It is also associated with dopaminergic reward system function. However, the relationships between microstructural properties of the dopaminergic system and body mass index (BMI) have not been investigated. In this study, we investigated the associations of BMI with mean diffusivity (MD), diffusion tensor imaging measure in areas of the dopaminergic system (MDDS) in 435 healthy young adults with mild obesity and without obesity (BMI < 40). We detected the association between greater BMI and lower MD of the right globus pallidus and the right putamen. These results suggest that the property of the dopaminergic system is associated with BMI among young adults with mild obesity and without obesity. Not only that, obesity is associated with mood and anxiety disorders, and these associations appear to be bidirectional2. Further, it has been pointed out that the brain reward system is involved in regulating food intake3, and obesity and addiction, as well as conditions of the diminished capacity for enjoying the reward, are suggested to have overlapping neural mechanisms, according to multiple reviews4, and these conditions involve addictive, impulsive, and compulsive behaviors.In the modern age, obesity is increasingly common across the globe, and this has caused a substantial increase in a wide range of diseases and mortality1 with a wide range of neural mechanisms. First, the psychological measure of condition of the diminished capacity for enjoying the reward is associated with a deficiency of dopamine receptor (particularly that of D2)5, and the sensitivity to reward is associated with dopamine availability or the sensitivity of dopamine pathway6. Greater responsivity in reward processing regions such as the caudate, putamen, and orbitofrontal cortex (OFC) and other networks in response to palatable food cues is associated with an increased risk for obesity7. Individuals with mild obesity show greater responsivity in these regions to palatable food cues compared with lean controls8. Exposure to food cues increases DA in the striatum and this response is associated with the desire to eat the food9. The involvement of dopamine in food reward is associated with “wanting” food, and this effect likely involves the dorsal striatum3. Mice that do not synthesize dopamine die of starvation due to lack of motivation to eat, whereas restoring dopamine neurotransmission in the dorsal striatum rescues these animals10. Neuroimaging studies showed an availability of dopamine receptor D2 is positively correlated with BMI in the striatal regions among individuals with mild obesity and without obesity11; however, this association was negative among individuals with morbid obesity (BMI > 40)1. Further, a wide range of previous studies have also investigated the associations of BMI with regional gray matter volume, cortical thickness, and white matter structural property (integrity) measured by fractional anisotropy of diffusion tensor imaging (DTI) as well as mean diffusivity (MD) measures of DTI in white matter areas13. Further, although obesity is suggested to downregulate dopaminergic function, hedonic eating is known to temporally increase dopamine release and suppress anxiety and depression, whereas stress, anxiety, and depression are suggested to lead to hedonic eating14.A wide range of neuroscientific studies have revealed that dopaminergic system plays a key role in these neural mechanisms involving reward and behaviors associated with obesity15). As summarized in our previous study16, the MD derived from DTI is considered a reflection of water diffusivity17. Greater tissue density, due to greater numbers of cellular structures, may prevent the free diffusion of water molecules, thereby decreasing the MD value20. As we reviewed previously21, MD in areas of the dopaminergic system (MDDS), particularly in subcortical areas, such as the putamen, caudate, and globus pallidus, is associated with several conditions related to alterations of the dopaminergic system. MD in areas of the dopaminergic system, such as the caudate and putamen, negatively correlated with dopamine synthesis capacity, as measured by positron emission tomography 22. MDDS has been shown to be a more sensitive index to detect the pathology of the dopaminergic system (Parkinson’s disease) than other magnetic resonance imaging (MRI) measures and positron emission tomography (PET)24. MDDS is negatively correlated with extraversion and novelty seeking, which have been associated with the dopaminergic activity21. In addition, MD and MDDS have been shown to be sensitive detectors of neural plasticity, including very rapid changes and changes caused by pharmacological and cognitive interventions related to dopamine26.So far, the associations between microstructural properties (MD) of the dopaminergic system and BMI have not been investigated. Previous studies have examined the association between obesity and MD in the white matter of the brain in a relatively small sample, but reported inconsistent results. Some studies reported an increase in MD, while other studies reported a decrease or no difference in MD .27. All subjects were university students, postgraduates, or university graduates of less than one year’s standing. For further subject information, see Overall, 435 healthy, right-handed individuals  participated in this study. The mean age of subjects was 20.8 years . All subjects had normal vision and none had a history of neurological or psychiatric illness. Handedness was evaluated using the Edinburgh Handedness InventoryWe calculated the BMI of each participant by dividing weight in kilograms by the square of height in meters. Height was measured by a stadiometer and body weight by an electronic scale.3 voxels, 60 slices, SENSE reduction factor = 2, number of acquisitions = 1). The diffusion weighting was isotropically distributed along 32 directions . Additionally, 3 images with no diffusion weighting  were acquired using a spin-echo EPI sequence . Further, there are acquisitions for phase correction and signal stabilization. These were not used as reconstructed images. FA and MD maps were calculated from the images using a commercially available diffusion tensor analysis package on an MR console which is provided by Philips . In this package calculations were performed according to a previously proposed method28. The FA and MD maps generated by this method showed results consistent with those of previous studies29. The descriptions from this subsection are mostly reproduced from our previous study using the exact same methods30. Apparent motion-induced artifacts were checked by visual inspection, and none were removed.MRI data were acquired using a 3 T Philips Achieva scanner. Diffusion-weighted data were acquired using a spin-echo EPI sequence -based registration process which utilizes the information of both FA images and MD images , we segmented the FA images and MD images of the subjects. Then, using the diffeomorphic anatomical registration through exponentiated lie algebra registration process implemented in SPM8, (1) raw FA images, (2) raw MD images, (3) the regional gray matter density map [rGMD map], (4) the regional white matter density map [rWMD map], and (5) the regional cerebral spinal fluid (CSF) density map [rGMD map] from the second new segmentation process were normalized to yield images with 1.5 × 1.5 × 1.5 mm3 voxels. Subsequently, from the normalized images of the (a) MD map, (b) rGMD map, (c) rWMD map, and (d) rCSFD map, areas that were not strongly likely to be gray matter or white matter in our custom template (defined by “gray matter tissue probability + white matter tissue probability < 0.99” in the template) were removed to exclude the strong effects of CSF on MD. These images were then smoothed by convolving with an isotropic Gaussian kernel of 8-mm full width at half maximum. For additional details on these procedures, see Preprocessing of the structural data was performed using Statistical Parametric Mapping software  implemented in Matlab . The descriptions from this subsection are mostly reproduced from our previous study32, volume-level framewise displacement (FD) during the diffusion-weighted imaging scan (motion during the scan) and BMI. FD during the diffusion-weighted imaging scan was calculated based on the method used by Power et al.33 from the obtained all diffusion weighted images using SPM8’s extension software Data Processing Assistant for Resting-State fMRI (DPARSF) [part of the toolbox for Data Processing and Analysis of Brain Imaging (DPABI) (https://rfmri.org/dpabi)34].We investigated associations of regional MD with individual differences in BMI. The statistical analyses of imaging data were performed with SPM8. Multiple regression analysis was performed, which included sex, age, TIV  nonparametric testing using the TFCE toolbox (https://dbm.neuro.uni-jena.de/tfce/). We applied a threshold of FWE corrected at P < 0.025 .A multiple comparison correction was performed using threshold-free cluster enhancement (TFCE)https://www.fmri.wfubmc.edu/cms/software#PickAtlas)37. The mask images of the ROIs were generated using the Brodmann area option in the PickAtlas Tool. Subsequently, the mean MD and rGMD values of these images were extracted from the aforementioned normalized images. Here, values were extracted only from areas with “gray matter tissue probability + white matter tissue probability > 0.999” in the custom template mentioned above. The ROIs were anatomically defined to avoid “double dipping procedures”38 and overfitting effects of the whole brain analyses39.After identifying associations of MDDS with BMI see “”, we empThe associations of MD values of anatomical ROI of the right putamen and right globus pallidus with BMI were tested by multiple regression analyses using SPSS 22.0 . The dependent variable was the mean MD in one of these ROIs and the independent variables comprised BMI, age, sex, TIV, FD and the mean rGMD value of the corresponding ROIs.P < 0.05 were considered to be statistically significant after correcting for the false discovery rate (FDR) using the classical one stage method40, were considered as statistically significant.Among all ROI analyses, results with a threshold of 41 (BMI: 23–25) was 16.5% (44/266) for males and 10.7% (18/169) for females. The proportion of participants who were classified as obese according to the Asian standards (BMI ≧ 25), but were classified as overweight according to the Western standards (BMI: 25–30) was 8.6% (23/266) for males and 5.3% (9/169) for females. The proportion of participants who were classified as obese according to the Western standards (BMI ≧ 30) was 1.1% (3/266) for males and 0.6% (1/169) for females. Mean and SD values of BMI were 21.75 ± 2.61 in males and 21.16 ± 2.15 in females. Although the sample included both males and females, we did not investigate sex differences in the neural correlates of BMI because of the limited numbers of participants.Figure P = 0.016, corrected for multiple comparison , 379 voxels below the threshold of P < 0.025, corrected]. There were no other significant relationships. These analyses did not consider the effects of rGMD differences on MD because we used nonparametric statistical tests that could not consider these effects. However, we carefully removed the CSF in the preprocessing stages, and the following ROI analyses that considered these effects yielded similar results.Whole-brain multiple regression analysis, after controlling for sex, age, and TIV, showed that BMI was significantly and negatively correlated with MD in the anatomical cluster spread around the right globus pallidus, right putamen, and right posterior insula [Fig. P < 0.05, corrected for multiple comparisons).We then investigated the associations between BMI and MD of the right globus pallidus and right putamen, regions that were anatomically defined after correcting for the effects of ROI rGMD as well as age, sex, FD, and TIV. The results showed that BMI significantly and negatively correlated with MD of the right globus pallidus and the right putamen  height and weight from all project participants. However, the 43 for whom self-reported height and weight data was available, replicated the significant associations between greater BMI and lower MD in the right putamen and the right globus pallidus  the sensitivity to reward conferred by the increased dopamine receptor D2 in the striatum leads to increased hedonic eating behaviors in adults with mild obesity and without obesity, resulting in an increase in the BMI and obesity, (2) but this association is reversed in the sample with morbid obesity because of downregulation of D2 receptors. Further, MDDS is strongly and negatively correlated with the availability of D2 receptors22.The physiological mechanisms for the associations of MDDS with BMI are not clear; however, we suggest a possible speculative mechanism from the previously suggested notion. As described in the Introduction, it has been suggested that like individuals with drug addiction, individuals with obesity have lower reward sensitivity that arises from a primary deficiency of dopamine receptors (the reward deficiency hypothesis), resulting in a compensatory overconsumption of the reward49. Unlike a previous study investigating the association between dopamine receptor availability and BMI1, the current study did not include individuals with high obesity (BMI > 40) as well as subjects of anorexia nervosa, although, in Japan obesity is defined as BMI > 25, and not 30 unlike in western countries50 and the present sample include some samples with obesity. As a result, although previous studies suggested a non-linear association between obesity and mood or dopaminergic activity, we could only investigate the associations among the sample with subjects without severe obesity (BMI < 35)6. In addition, previous studies revealed ethnic differences in the effects of BMI on mood states, and young white females showed stronger associations between depressed mood and obesity than young Hispanic and black females51. A previous study revealed sex differences in the associations among BMI, mood, and brain structures52, but the present study could not investigate these differences because of the small sample size. Future studies are needed to confirm whether the present findings can be generalized to the broader population and possible sex differences of associations of BMI with MD.One limitation of the present study is that the sampling was limited; it included only young, healthy, and well educated adults. The limited sampling is a common limitation of many studies using a college cohortIn conclusion, among subjects without severe obesity, lower MDDS is associated with greater BMI. These results suggest that, among the adults with mild obesity and without obesity, the dopaminergic system is associated with BMI. Our results are in line with the previously suggested notion that hyperfunction of the dopaminergic system is associated with a tendency of obesity.All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.This study was approved by the Ethics Committee of Tohoku University.Informed consent was obtained from all individual participants included in the study.Supplementary Information.


Populations of wild boar were widespread across both logged and primary forests, whereas sambar and muntjac occupancy was lower in logged forest (48.4% and 19.2% respectively), with gaur showing no significant difference. Subsequent modelling revealed the importance of conserving lower elevation habitat in both habitat types, particularly <1,000 m asl, for which occupancies of sambar, muntjac and gaur were typically higher. This finding is important because 75 percent  of Peninsular Malaysia’s Main Range Forest (Banjaran Titiwangsa) is under 1,000 m asl and therefore at risk of being converted to industrial timber plantations, which calls for renewed thinking around forest management planning.Across the tropics, large-bodied mammals have been affected by selective logging in ways that vary with levels of timber extraction, collateral damage, species-specific traits and secondary effects of hunting, as facilitated by improved access through logging roads. In Peninsular Malaysia, 3.0 million hectares or 61 percent of its Permanent Reserved Forests is officially assigned for commercial selective logging. Understanding how wildlife adapts and uses logged forest is critical for its management and, for threatened species, their conservation. In this study, we quantify the population status of four tropical ungulate species in a large selectively logged forest reserve and an adjacent primary forest protected area. We then conduct finer scale analyses to identify the species-specific factors that determine their occurrence. A combined indirect sign-camera trapping approach with a large sampling effort  covering a wide area (560 km The worldwide trade in tropical timber from managed natural forests is estimated at US$11.2 billion a year , even whTragulus spp.) and yellow muntjac (Muntiacus atherodes) [Rusa unicolor) and gaur (Bos gaurus) [The impacts of logging vary among mammalian species. For example, some species experience negative impacts from logging including the loss of fruiting trees on mouse deer (herodes)  or the dherodes) . In cont gaurus) . Such di gaurus) .In Peninsular Malaysia, about three million hectares or 61 percent of its Permanent Reserved Forest is primarily assigned for commercial selective logging through its designation as Production Forest. The Forestry Department of Malaysia requires the use of reduced-impact logging practices that include pre-harvest planning, which includes identification and demarcation of sensitive areas such as road and stream buffer zones. It also includes minimising skidding distances, directional felling and post-harvest site closure . ComparePanthera pardus) and dhole  [Muntiacus muntjac) and wild boar (Sus scrofa), listed as Least Concern. Yet, the population status and ecology of these ungulates under different forest management regimes is poorly understood but greatly needed for better management.Malaysia is a biodiversity-rich country and given its prominence, Permanent Reserved Forest should play an important role for much of Peninsular Malaysia’s threatened wildlife. For example, this land use type represents 85 percent of Malaysia’s presumed tiger habitat, with co-occurring populations of leopard  . These falpinus) , such asIn this study we aim to conduct the first occupancy-based assessments of a variety of threatened and non-threatened ungulate species living in a Permanent Reserved Forest and an adjacent primary forest protected area in the northern Peninsular Malaysia state of Perak. We use a combined dataset from indirect sign surveys and camera traps, to compare the efficiency of these two methods in sampling difficult-to-detect species. In addition, we used results from an interview survey conducted from May-September 2010 with 190 Orang Asli respondents from both study areas (92 in the logged-over Permanent Reserved Forest and 98 in the primary forest protected area) to supplement our ecological findings .2 RBSP was officially gazetted in 2007 as a strictly protected area with only non-exploitive commercial activities permitted, such as limited tourism. The altitudinal range of 260 m to 1,533 m above sea level (asl) supports lowland dipterocarp (5.6%), hill dipterocarp (71.5%), upper dipterocarp (20.9%) and montane (2.0%) forest types. Being located by the southern Thai border, which has a long-running history of insurgency, there has been a Malaysian army presence in RBSP since the late 1940s. In contrast, we compared the unlogged forest of RSBP with the 1,489 km2 TFR, which was officially gazetted in 1991 as a Permanent Reserved Forest and has been undergoing selective logging since the 1970s. The altitudinal range of 260 m to 2,160 m asl supports lowland dipterocarp (4.2%), hill dipterocarp (34.4%), upper dipterocarp (41.7%) and montane (19.7%) forest types. The TFR has no specific wildlife management plan or prescriptions and an estimated 5,000 indigenous people reside within 24 settlements in and around the area, whereas RBSP has an estimated 740 indigenous people (Department of Orang Asli Affairs unpublished data).The two study areas the Royal Belum State Park (RBSP) and Temengor Forest Reserve (TFR) are both located in the state of Perak in Peninsular Malaysia  in each study area in which both sign and camera trapping surveys were conducted , such as forest trails, ridges, sand beds, river banks, saltlicks and logging roads. Signs of focal species and weather condition (rain or no rain within a 24-hour period) were recorded within each sampling unit.Species detection through three indirect sign surveys over nine months was conducted by six teams, each of two personnel (a field biologist and an indigenous guide). Each team was rotated in sequence to survey each sampling unit once, thereby minimising observer bias between temporal replicates and to avoid introducing heterogeneity into detection probabilities. Each team was required to survey a minimum of 1 km per 1 km2 in each study area. A 140 single camera traps were placed at locations considered as most likely to record prey (i.e. trails such as on ridges or old logging roads) within the 4 km2 km grid system. Single traps for prey occupancy were moved once, during the third or fourth month, within the 4 km2 grid cell to increase spatial trapping coverage. Custom-built passive-sensor digital camera trap units , programmed to take photographs at ten second intervals between detections, were used. Camera traps were set to be operational for 24 hours each day with no break in monitoring, except during events of malfunction or damage. All camera traps were mounted on trees at a height of approximately 50 cm from the ground, depending on the slope of the trail. Camera traps were checked every 2–3 months to retrieve data and replace batteries.Camera trapping was first conducted for approximately nine months in TFR and subsequently another nine months in RBSP, with a sampling area of 280 kmAs the home range sizes of gaur, sambar, wild boar and muntjac in Malaysia are not known, a scaling model that expresses the relationship between body mass and home range size was used to estimate these sizes . This thM) in kilograms was multiplied by the observed scaling relationship per individual area used (m), taken as 2.06 for herbivores. This value was then raised to the power of a calculated scaling exponent (h), which is 1.02 for herbivores, resulting in the species-specific home range size estimates. The values for m and h were obtained from Jetz et al. (2004) [M from Kawanishi and Sunquist (2004) [M for wild boar that was obtained from Ickes et al. (2001) [2, 3.0 km2, 1.4 km2 and 0.4 km2. Sampling units of 16, 4, 4 and 1 km2 were used to define a sampling unit that was larger than the approximate home range values for gaur, sambar, wild boar and muntjac, respectively. This definition of a larger arbitrary sampling unit size is therefore an additional criterion to ensure that occupancy and not habitat use was being measured. Four 4 km2 grid cells were combined to create a sampling unit for gaur (16 km2) yielding 22 cells in TFR and 20 in RBSP. The 4 km2 grid cell was divided into four sampling units for muntjac (1 km2) yielding 280 cells per study area, whereas for sambar and wild boar the original number of 4 km2 sites per study area (n = 70) were retained.The average body mass (. 2004)  and M frt (2004) , with th04  and Mψ), defined as the probability of occurrence within a sampling unit, was estimated from a combination of camera trap and indirect sign survey data within a likelihood-based sampling approach. Individual prey species detection histories (H) were constructed for each site (sampling unit). This included 10 four-week sampling occasions for camera trapping and three temporal occasions consisting of one independent sign survey each, similar to a multiple method approach used by Long et al. (2011) [2 grid cells being merged for gaur sampling), detections were aggregated to produce a single detection history for a particular sampling unit based on the corresponding calendar dates. Though this approach will inevitably cause the effort applied in each sampling occasion to be uneven, this is dealt with by incorporating a trap effort covariate to account for possible variation in detection probability. Species detection histories from both indirect sign surveys and camera trapping were used to produce probabilities in which detection probability (p) was varied in the design matrix to be conditional upon each method.Occupancy (. (2011) . For eac2 sampling units.Data from the sampling units for the two study areas were combined. The two main disturbances to the habitat within each study area are logging (in TFR) and permanent human presence . In terms of the habitat topography, the altitudinal range in TFR is greater than in RBSP. The proximity of the road bisecting TFR and RBSP in relation to the sampling blocks was deemed as being too far away (>7 km) to directly influence the occupancy of prey species within the study areas  and was P < 0.05) non-independence between most of the continuous variables across the three sampling unit scales  but the correlation coefficients (rs) were all <0.6, except for two variables at two scales; logging intensity index  and mean NDVI . Although NDVI can be used as a proxy to describe the structural vegetation disturbance caused by logging, a logging intensity index was used instead because it measures disturbances caused by logging on a temporal scale (years logged since 2010) and a spatial scale (size of area logged according to year).To test for collinearity between the continuous covariates, a Spearman’s rank correlation was performed. Covariates were considered to be highly correlated if their corresponding coefficients were >0.6. This test identified significant  + (1/2 x 0.1)) = 0.25’. For RBSP, the logging intensity index was zero for all sampling units because it represents unlogged forest. At the 1 km2 scale, the correlation coefficient was close to 0.6 (rs = -0.58) between logging intensity and mean NDVI. Hence, in order to standardize the use of covariates across all prey species at all scales, mean NDVI was not used in the analysis.Obtaining detailed information on the volume or number of logged trees was not possible. Instead, we used information provided by the State Forestry Department of Perak to calculate a logging intensity index for each sampling unit, whereby the size of the logged area was multiplied by the reciprocal number of years logged since 2010. For example, if a sampling unit contained two patches of forest that were respectively logged in 2010 and 2009 and covered 0.2 and 0.1 km2 grid cells were combined to create a sampling unit for gaur, it was not possible to incorporate detection probability covariates for rain and observer bias due to the survey design, where for each survey each observer surveyed different 4 km2 grid cells; a 16 km2 grid cell was also typically surveyed over 3–4 days with varying levels of rainfall. Therefore, only survey effort  and a study area covariate were used to account for gaur detection probability. Continuous covariates were transformed into standardized z-scores prior to analysis.To account for uneven detection probability in all species except gaur, five covariates were tested: (i) survey effort per grid cell ; (ii) number of trap-nights a camera trap or multiple camera traps  were operational within a site; (iii) a binary variable for whether it had rained (1) or not (0) within a sampling unit 24-hours prior to the survey (which may influence indirect sign detection); (iv) categorical variables (1–5) assigned for each observer (excluding the reference observer) to account for potential bias between observers; and (v) a binary study area covariate. As four 4 kmp) was modelled where the parameter was either assumed constant or allowed to vary with individual or additively combined covariates. For each model, a global model for the probability of occupancy (ψ) was maintained. Subsequently, the influence of covariates on occupancy was modeled where the parameter was either assumed constant or allowed to vary with individual or additively combined covariates, whilst maintaining the top ranked model for detection probability as derived from the first step.The combination of covariates that best explained occupancy for each prey species and detection probabilities were investigated using PRESENCE v4.0 software  under thCandidate models were ranked using the small-sample correction to Akaike’s information criterion (AICc)  by changwas used .wi) of models that contained a particular covariate; where Ʃwi > 0.50 were considered indicative of a strong habitat use response to a covariate [wi >90%, occupancy estimates were model averaged and corresponding standard errors calculated [Covariates that were likely to affect detection probability and occupancy probabilities were identified based on the covariates that were contained in the top ranked model and relative summed Akaike weights No. surveys(K) for each prey species according to each method was determined by individually analysing each method, where estimates of p were derived from constant models, ψ(.)p(.). These estimates were compared between methods for each species, whereby probabilities greater than 0.15 were considered as unacceptable to make an inference on occupancy patterns for a population [The occupancy analysis was conducted with data combined from indirect sign surveys and camera trapping, which were expected to increase the probability of species detection. To compare the effectiveness of the two survey methods to detect the focal prey species, a single dataset for both study areas combined was used to generate comparative naive detection probabilities. The detection efficiency was assessed based on the naive estimates; higher naive occupancy estimates indicate a higher number of unique detections. Next, the probability of a false absence (1- pulation . Even thpulation .K), to obtain occupancy estimates with a specified improved precision (i.e. Standard Error (SE); Variance, Based on the comparisons of naive occupancies and false absences, the survey method that performed better in terms of detection efficiency was further evaluated to provide recommendations in terms of the number of sites that would be required to be surveyed for future sampling. Recommendations developed by MacKenzie and Royle (2005)  were use. (2006) . All necψ(.)p(zone) model was used to subsequently investigate differences in detection probabilities between methods and study areas. The estimated wild boar detection probability from sign surveys and camera trapping showed variation in detection probability between study sites and methods in which detection probability was significantly higher in RBSP ; and RBSP . Based on these data, detections and naive occupancies of all species were found to be higher in RBSP than in TFR . The higp(.) model performed poorly for sambar (ΔAICc = 145.3), muntjac (ΔAICc = 296.7), and was not highly ranked for gaur (ΔAICc = 3.3). The percentage of relative summed model weights for covariates influencing detection probability showed a high level of support for covariates contained within the top ranked model for all species, in which gaur detection probabilities were highly affected by sign survey effort , for sambar it was zone, rain and camera trapping effort (trap-nights), and for muntjac it was zone, observer sign detectability variation, camera trapping effort and sign survey effort .Model averaged occupancy estimates were higher in the primary forests of RBSP compared to the selectively logged forest of TFR for sambar (1.9 times higher) and muntjac p(.) and estimates from sign surveys (2), 69 (1 km2), 107 (4 km2) and 93 (16 km2) sites would be required to be surveyed for wild boar, muntjac, sambar and gaur, respectively, in order to obtain precise estimates of occupancy with a standard error of 0.05.Naive occupancy estimates for wild boar, muntjac, sambar and gaur were consistently higher with the use of indirect sign surveys compared to camera trap surveys, as indicated by a higher number of unique detections . Detecti surveys , a prediThe combined sign and camera trap sampling approach used in our study sheds new and important light on Malaysia’s under threat ungulates. It provides the first set of robust occupancy estimates from Permanent Reserved Forest that is primarily classified as Production Forests and highlights the importance of this forest type in supporting populations of threatened wildlife, which in this case supports a guild of threatened large carnivores ,34. HoweSus barbatus) [Considering each of the target ungulate species individually, the widespread (near 100% occupancy) finding for wild boar was unsurprising because as a non-ruminant omnivore it is a habitat generalist ,35. Howearbatus) . Beardedψ = 1.0 at 500 m asl when > 15 km from settlements and ψ < 0.1 at 1,200 m asl when < 4 km), a similar finding to a study from Thailand where sambar sign was two-fold higher in lowland forest than montane forest [For muntjac, occupancy decreased as elevation increased , but with a strong preference for lowland-hill (0–750 m asl) dipterocarp forest, where occupancy was 0.99, as also found in a muntjac habitat suitability study from India . Sambar e forest .Ficus sp.) abundance—a key food item of ruminant frugivorous species in rainforest >700 m asl; and, the quality of foliage and browse materials [The influence of elevation is in part explained by its association with biophysical parameters such as temperature and precipitation that change with altitude to shape the local diversity of plants and habitat structure, which influence mammal density, distribution and diversity ,40. For aterials ,41,42.For sambar, an additional explanatory factor was identified, with lower occupancies recorded closer to human habitation, which follows similar patterns from India and Indonesia ,37. The Bos javanicus), may benefit from selective logging practices that result in grassy clearings and bamboo patches [For gaur, the predicted probability of occupancy reduced from 0.5 at 1,000 m asl to 0.0 at 1,800 m asl, indicating an altitudinal range limit for the species and a general preference to occupy lowland-hill dipterocarp forest. From three radio-tracked gaur in the Ulu Lepar Forest of Pahang state, Peninsular Malaysia, habitat below 61 m asl was found to be intensively used in a study area that ranged up to 1079 m asl . In the The combined use of camera trap and indirect sign survey data increased the detection of multiple prey species. The sign surveys outperformed the camera trap surveys in obtaining a greater number of unique detections for each of the focal species, and consequently yielded higher detection probabilities. This finding is important because low detection probabilities make it difficult for the occupancy models to distinguish reliably between sites with a low species detection probability (i.e. <0.15) and sites where a species is truly absent . Taking 2 grid cells that were next to each other and may not be proportionate to the home range size of the species, this might lead occupancy to being interpreted as ‘intensity of use’ [2 were reduced or combined to be larger than the largest home range sizes for each species. The definition of a larger arbitrary sampling unit size than the approximate home range values for gaur, sambar, wild boar and muntjac, is an additional criterion to satisfy the closure assumption at the species level, as suggested by MacKenzie et al. (2006) [The issue of scale in the interpretation of the effects of home range size and the use of arbitrary grid cells for occupancy needs careful consideration as it serves an important criterion to satisfy the corresponding assumptions. A basic assumption of the occupancy estimation method is that sites are closed to changes in occupancy during the repeat surveys  and give of use’ . To redu. (2006) .Another important issue that influences the choice of survey method is cost. Assuming that the costs for basic field equipment  are equal for both survey methods, camera trapping would incur an additional cost of US$42,000, as the 70 camera traps under the current design cost US$600 each. Even if camera trapping were to reduce the time spent in the field as opposed to conducting sign surveys, the overall costs are still expected to be much higher due to the high initial equipment cost of camera traps. Additionally, if camera trap damage and theft or malfunction is high, this will inevitably result in a reduced data set and incur additional costs for replacement camera units or for repairing damaged cameras, which may not necessarily be cost efficient for a long-term approach. Sign surveys would be less expensive to conduct under the current survey design, but unlike camera traps would only record data on the target species.Prior to our study, ungulate occupancy surveys were conducted in Endau-Rompin National Park, which straddles Johor and Pahang states , and SunReconciling the need for economic development and biodiversity conservation is a global challenge . Our stuS1 Table(DOCX)Click here for additional data file.S2 Tablei = AICc model weight, K = Number of parameters within the model. Models for (ψ): Set = Nearest distance of a settlement to the centre of a sampling unit, Elev = Mean elevation for a sampling unit, Lg = Logging intensity index for a sampling unit. Zone = Binary study area covariate. Models for (p): Zone = Binary study area covariate, Obs = Categorical observer based covariate, Rain = Binary based covariate on whether it rained or not, Dist = Total distance walked within a sampling unit according to each sign survey, TN = Total number of trap-nights for a given sampling unit according to each sampling occasion.Note: ΔAICc = Difference in AICc values between each model and the model with the lowest AICc. w(DOCX)Click here for additional data file.


Alteration of organic remains during the transition from the bio- to lithosphere is affected strongly by biotic processes of microbes influencing the potential of dead matter to become fossilized or vanish ultimately. If fossilized, bones, cartilage, and tooth dentine often display traces of bioerosion caused by destructive microbes. The causal agents, however, usually remain ambiguous. Here we present a new type of tissue alteration in fossil deep-sea shark teeth with in situ preservation of the responsible organisms embedded in a delicate filmy substance identified as extrapolymeric matter. The invading microorganisms are arranged in nest- or chain-like patterns between fluorapatite bundles of the superficial enameloid. Chemical analysis of the bacteriomorph structures indicates replacement by a phyllosilicate, which enabled in situ preservation. Our results imply that bacteria invaded the hypermineralized tissue for harvesting intra-crystalline bound organic matter, which provided nutrient supply in a nutrient depleted deep-marine environment they inhabited. We document here for the first time in situ bacteria preservation in tooth enameloid, one of the hardest mineralized tissues developed by animals. This unambiguously verifies that microbes also colonize highly mineralized dental capping tissues with only minor organic content when nutrients are scarce as in deep-marine environments. Preservation of soft tissue body fossils (e.g. skin) requires specific conditions of abiotic  and biotic  factors, as well as marginal fluctuations in the continuum of processes during sedimentation, fossilization, lithification and preservation through geological time5. Paradoxically, microbial activity can be both preserving and destructive. One of the most famous preserving effects of microbial activity is documented by fossils from the Eocene Messel oil shale, which exhibit a unique preservation of soft tissues. Closer examination of the "skin shadows" preserved in these fossils reveals an accumulation of lithified bacterial colonies3, mirroring the original contour. However, the preservative effects of bacteria are, definitely, rare phenomena requiring specific conditions. Generally, microorganisms are predominantly responsible for destructive processes, removing digestible soft tissues of carcasses preceding diagenesis. Additionally, some organisms, like the bone-eating worm Osedax, literally invade bones to obtain nutrients6 when food supply is limited, as in bathyal marine settings.Teeth and bones are often the only evidence of ancient vertebrate life because of the mineralized nature of tissues. There are numerous possibilities for chemical alteration during the transition from the bio- to the lithosphere of which bacterial catabolysis of these tissues and organic matter within the carcass is an important example7. Studies of decay processes in both modern and fossil deep-sea environments such as, whale-falls, show that they represent important nutrition supplies for deep-sea organisms. However, these previous studies focused only rarely on bacteria or archaea, which are at the base of the food webs8. Nevertheless, limitations in nutrients are also to be expected in ancient deep marine environments with similar microbial alteration of skeletal material, as in comparable modern habitats. Although microbial alteration of bones (bioerosion) is known from aquatic environments, equivalent alteration in teeth has only rarely been reported. Documented bioerosion patterns of dental tissues include endolithic macro- and microborings of fossil teeth and small tubules within dentine represented by traces and holes of endolithic bivalves, clionaid sponges, serpulid worms, and routes of microbial intrusion10. Here we review the fossil record of bioerosion and document for the first time in situ bacteria within the highly mineralized and organic-poor tooth enameloid of an extinct deep-water shark. This finding represents a hitherto unrecognized bioerosion type for teeth and nutrition source in deep-sea environments.Decay of bony material in modern deep-sea environments is dominated by anaerobic microbial decomposition of the large lipid reservoirs within bones11, several teeth of the extinct shark Cretacladoides noricum were studied in detail, of which only two out of 40 examined teeth display internal in situ microbial alteration of the superficial enameloid 3F) and corresponds exactly to the pattern present in extant shark teeth12. Two of the teeth exhibit aggregates of mineralized, regularly shaped, coccoidal to short rod-shaped structures ranging from 0.5 to 1 µm in length and 0.4–0.5 µm in diameter. They are organized in chains that are arranged parallel to each other between the enameloid bundles , and F (4.96 wt%). After conversion to mol% and correction of oxygen valence by one fluorine, an apatite formula Ca5.03P2.98O11.98F1.35 close to the ideal composition is obtained. The chemical analysis of the coccoidal aggregates , Al2O3 (9.57 wt%), FeO (5.45 wt%), MgO (5.01 wt%), and Na2O (0.75 wt%), in addition to CaO (27.56 wt%), P2O5 (23.60 wt%), and F (1.60 wt%).EDS analysis of the highly mineralized enameloid bundle of one of the teeth at 15 keV at the NHM Vienna Fig. A shows d13, approximately 2 µm penetration depth must be considered at an average X-ray energy of 2 keV, a density of ~ 3 g/cm3 and 15 keV acceleration voltage. Thus, the results confirm the fluorapatite composition of the matrix but the Na, Mg, Al, Si and Fe peaks also indicate the presence of an aluminosilicate mineral [Si4O10] ∙ Na0.25 is obtained, which compares well with a clay mineral between montmorillonite, ~ (Al1.67Mg0.33)[(OH)2|Si4O10] ∙ Na0.33(H2O)4, and saponite, Mg3[(OH)2|4O10] ∙ x(H2O)y14. Note that the 5 keV EDS analysis  of the X-ray radiation Fig. B. Using 5.07P3.00O12.13F0.81 which is close to the ideal formula of apatite (see above). Carbon concentrations in enameloid were between 10.08 and 11.92 wt% in three spots. For comparison, two spots on the bacterial remnants resulted in 11.16 and 11.51 wt%. At 10 keV carbon contents on enameloid were scattered strongly between 6.54 and 16.90 wt%, whereas the coccoids gave 9.41 and 10.58 wt%. At 5 keV the carbon signal was strongly corrupted by the strong EDS zero peak and gave 31.47 wt% C in enameloid and 15.12 and 19.40 wt% in the bacterial structures. Thus, none of these measurements confirmed an excess of carbon from relics of organic material in the coccoids.At 15 keV conditions, analysing for C, O, F, P, Ca independently (oxygen not by stoichiometry), the enameloid composition resulted in a formula of ~ Ca15.Contamination with recent bacteria can be excluded here because of the chemical fingerprint of extant bacteria, in which the three elements C, N, and O constitute 80–90 wt%16. Both extant and fossilized bacteria exhibit a size range between 0.5 and 4 µm17, which coincides with the size of the mineralized bodies interpreted as fossilized bacteria herein. Bacteria and cyanobacteria are the predominant bioerosion-causing organisms, however, both causal agents differ significantly in the type of traces they produce. Cyanobacteria are known to infest shells of marine bivalves in suitable habitats within the photic zone18. These phototrophic organisms create branching tunnels but do not produce traces with localized demineralization or cuffing of redeposited mineral, which is typical for terrestrial or freshwater bacteria20. Thus, the taphonomic alteration of the bacteriomorph invaders of the herein described shark teeth differs significantly from bacteria occupying terrestrial or freshwater environments. Nevertheless, the distinct morphology and size of the fossilized bodies point towards bacteriomorph microorganism but exclude cyanobacteria due to their phototrophic lifestyle.The regular size and morphology of the coccoidal/rod-shaped structures in our study, as well as their specific distribution in linear chains in the cavities between the parallel bundles of tooth enameloid, is suggestive of microorganisms, such as bacteria. Indeed, they strongly resemble fossilized microorganisms associated with decaying macro-organisms elsewhere, such as the phosphatised bacteria of the “skin shadow” of fossil vertebrates in the Enspel oilshale22.Given the presence of bacteria, the observed filmy substance linking the individual bacteriomorph structures together, as well as the enameloid crystallites, are thus interpreted here as microbial extrapolymeric substances (EPS). EPS is a common exudate of microbes, used for attachment to substrates and as a control of the external physico-chemical conditionsAs noted above, microbial degradation of organic substances is very common; what is not so common, however, is the physical preservation and mineralization of the degrading heterotrophs for which specific physico-chemical conditions are necessary, specifically, a micro-scale anaerobic environment . Indeed, only two out of 40 tooth specimens exhibit this phenomenon in our study. The small number of affected teeth, however, indicates either very specific conditions under which invasion of bacteria into the hypermineralized tooth enameloid was feasible or lack of potential for fossilization of colonising microbes on or in other teeth due to diverging time intervals of tooth shedding, pH conditions, or other influencing factors.It is not possible to establish the exact composition of the fossilized bacteria owing to their small size. Nevertheless, the morphology of the tiny flaky minerals attached to the surfaces of many of the coccoids/rods forming their immediate substrate is reminiscent of phyllosilicates replacing or formed on EPS-like film, as is also suggested by the elements typical for aluminosilicates in spectrum B (15 keV) and B′ (5 keV) of Fig. 23. During decomposition and diagenesis, the composition and concentrations of elements in the surrounding fluids control the type and composition of minerals replacing organic substrates. These elements come either from seawater and/or from elements released by the degradation of an organo-mineral substrate . Release of cell/EPS-bound elements is also influenced by the metabolic activity of microbes. For example, an increase in local alkalinity due to heterotroph degradation (by sulphate reducers) of primary photosynthetic mats releases Ca2+ ions into the fluid medium, which then combine with CO2 in seawater to form Ca carbonate24. In the case of the shark teeth in a deep-water environment, the organic substrate would be provided by the organic matrix (collagen and other proteins) of the teeth itself. If the replacing mineral was carbonate, the latter would have been enriched in transition elements, such as Fe and Mn (the latter not present at any detectable levels here), or Mg, resulting from the degraded substrate. In the case of phyllosilicate formation, Na-Fe3+ phyllosilicates have been experimentally produced on microbial EPS25. Indeed, EPS plays an important role in the biosynthesis of different types of clays26. Reactive sites on the surfaces of microbial cells act as loci for the nucleation of clay minerals in the poorly crystalline state27. Subsequent diagenesis and aging transforms these poorly crystalline materials into crystalline phases.Whatever the exact composition of the replacement minerals is, they are likely to have formed as the result of microbially influenced changes of the immediate environment leading to enrichment in certain elements with consequent precipitation onto functional groups of the degrading microbial structures5. Normally, bacteria are only found on surfaces or within the pulp cavity, which is easily accessible. However, they also gain access to tissues such as dentine through tooth surface lesions or due to previous bioerosion of other organisms. It is thus not surprising to find their fossilized remains associated with skeletal structures such as shark teeth and even within dental tissues. However, the presence of bacteria within the hypermineralized capping tissues of teeth, such as enamel or enameloid providing only very small amounts of severe accessible organic matter as a possible nutrition source, has not been documented up to now.The presence of bacteria associated with teeth is, during the lifetime of an animal, normal but they also contribute to microbial degradation of soft-tissues after death during decompositionC. carcharias)28 indicating a variable content of collagen, proteins, and other organic structures (e.g. tubular vesicles) in enameloid depending on species and tooth maturation29. It has been demonstrated that the fluorapatite crystallites are devoid of any organic matter, while the crystallite bundles are encased by an organic matrix that generally has a smooth, sometimes also a fibrous appearance12. Additionally, the collagen fibres within the enameloid of an extant salmon shark (L. ditropis) are arranged in a regular pattern and the fibres cross each other30.The resistant hypermineralized outermost layer of enameloid of shark teeth consists of fluorapatite. The fluorapatite crystals are embedded in an organic matrix of about 4.5 wt%, as documented for a tooth of a great white shark  and generate an acidic microenvironment, which has the ability to demineralise the enamel38 and bacteria invade the bone through haversian canals in order to attain this valuable nutrient supply39. While they subsequently follow the collagen fibrils, they are unable to cross the cement lines of secondary osteons19. Similarly, microbes can invade shark teeth via the nutritive foramina in the root and ascend apically using dentinal canals, resulting in a chain-like arrangement of bacteria. Moreover, bacteria can migrate into the enameloid because the enameloid/dentine boundary is not an insuperable separating layer in chondrichthyans, as is the enamel/dentine boundary in mammals, but is penetrated by dentinal tubes extending into the enameloid. This would form possible pathways for bacteria from the dentine into the enameloid corresponding to the orientation, arrangement and location of the fossilized bacteria between the enameloid bundles.Accordingly, the organic matrix between the fluorapatite bundles inside the teeth would have provided a nutrient supply for invading microbes. In bones, microbial degradation of collagen fibrils using collagenases provides a high-energy yieldHowever, invasion of enameloid by means of external bioerosion of the tooth surfaces, which is observable in numerous teeth of different species deriving from the same faunal assemblage Fig. H–J, enabOsedax, which hosts microbial symbionts to benefit from their collagenolytic enzyme activity40. The symbionts comprise primarily Oceanospirillales and Epsilonproteobacteria, which colonies the root tissues of the small worms enabling the degradation of different types of collagen during intrusion of the worm into bones of a whale fall40. Indirect evidence provided by trace fossils (i.e. boreholes) in fossil whale bones, document that this highly specialized polychaete worm has a fossil record since the Oligocene (~ 30 million years)6, but most likely already originated in the Cretaceous41.Numerous incredible symbiotic relationships are known in the biosphere, resulting in a benefit of both involved parties. An obvious example of a remarkable symbiosis of a comparative, marine habitat represents the bone-eating worm Osedax species (e.g. O. rubiplumus) and ancient traces measured on fossil whale bones a possible trend in borehole size increasing from the Oligocene (0.1–0.45 mm) to today (0.1–2.0 mm) is recognized6. Thus, the slightly smaller (~ 0.08 mm) boreholes in teeth of this fauna , possess the ability to digest different types of collagen40. Despite the fact that these are only a few examples of microbial species, which are able to obtain nourishment from mineralized collagen, they show that some specialists adapted to this food source and also survived in extreme habitats.It is unclear, which organism is responsible for the bioerosion patterns (boreholes) present in the fossil teeth, such as a pycnodont fish tooth Fig. J from th18, but their phototrophic lifestyle precludes them as candidate organisms for degradation of shark teeth in a deep-sea setting. Additionally, cyanobacteria normally form branching tunnels rather than creating chain-like arranged globular structures. Consequently, considering different modes of life and especially environmental limitations, we argue for a heterotroph lifestyle of the herein described bacteria.Cyanobacteria also produce collagenases to tunnel into marine shellsAporrhais pespelecani) and in a Cretaceous fish scale (Beryx ornatus)43. However, subsequent studies of the Viennese Pathologist Carl Wedl, focusing on tunnel-like structures in human teeth, had received much more publicity44. Kölliker attributed these tunnels to a fungal attack, while Wedl is not specific about the causal agents and described them either as parasitic plants, microscopic parasites, or as fungi44. Further investigations by Wedl additionally demonstrated the occurrence of these microstructures in a horse bone, in teeth of fossil cartilaginous fishes (Hemipristis and Myliobates), and one bony fish (Pycnodus)44. However, the interpreted fungi do not penetrate the enamel, being limited to the spongy bone and tooth cementum45. Following investigations on bone  and cartilage  in various groups, Roux46 described the bone-penetrating fungus, Mycelites ossifragus. Subsequent investigations led Bernhauser47 to conclude that the structures described by Wedl44 and Roux46 belong to an inchnogenus rather than a distinct fungal species.Bioerosion results in the loss of information and thus plays a crucial role affecting the structure and consequently the potential for preservation of hard tissues like bones and teeth. Nevertheless, microbial alteration of skeletal structures provides much evidence about taphonomic conditions. Bioerosion caused by microbes is a phenomenon that is known since at least the nineteenth century when the anatomist and histologist Rudolf Albert von Kölliker described meandering tunnels in e.g. a fossil gastropod  and three non-Wedl-types  of structure. Of these four types of bioerosion structures, only the Wedl-type is supposed to originate via fungal colonization and the other three types are interpreted to be the product of bacterial activities.Hackett44, Roux46, and Hackett48, numerous studies have dealt with histological microstructures of exhumed teeth and bones of ancient humans51, marine and terrestrial vertebrates8 and marine invertebrates53. The focus of some studies was on causal agents48, others, conversely, examined the influence of different environmental settings and the subsequent impact of the alteration of the tissue by specific micro-organisms55. However, the identity of the microorganisms responsible for bioerosion patterns in teeth and bones usually remains ambiguous54.Since the fundamental contributions of Wedl58. In rare cases, fossilized in situ colonies are documented, e.g., in various Eocene vertebrate fossils with soft tissue preservation from the Messel pit16, in an Early Cretaceous pterosaur head crest from Brazil59, and in the dentine of a historic human tooth45. Another example of soft tissue preservation replicated by microbial biofilms is the conservation of muscle fibres in a Jurassic horseshoe crab60. However, in situ bacteria invading tooth enameloid, one of the hardest and most highly mineralized biogenic tissues developed by an animal and lacking significant cavities or lacunae in contrast to dentine, have not been reported up to now.Fossil bacteria are well known throughout the rock record and are the oldest known preserved traces of life, the latter occurring as silicified remainsCretacladoides noricum. The 0.5–1 µm-sized organisms, associated in linear, chain-like colonies occur in the enameloid of the shark teeth between the parallel bundles of the fluorapatite crystallites.We identified fossilized microorganisms inhabiting the hypermineralized outermost layer of enameloid of teeth in the extinct, Early Cretaceous shark, A delicate filmy substance coating and linking the fossilized bacteriomorph bodies was replaced by a Fe–Mg rich phyllosilicate, most probably a clay mineral close to smectite (montmorillonite) and saponite. However, limitations of the analytical conditions preclude definitive identification of the mineral phase replacing the microfossils.The microorganisms were likely fossilized during decay and diagenesis of the shark teeth since microbial invasion of bones and teeth (reservoirs of proteins) is a known behaviour of heterotrophs in the oligotrophic deep-sea environment. This observation is particularly significant because it is the first time that fossilized microorganisms have been observed in the highly mineralized enameloid of shark teeth. Moreover, it shows that microbes obviously not only colonize less mineralized skeletal structures such as bones or dentine, but also target the scarce organic matter (collagen and other proteins) in highly mineralized tissues such as enameloid if the boundary is permeable or the surface is damaged. Even though the organic content in the enameloid is rather low, it seemingly nevertheless provides an additional, high-energy source of nutrient matter in an otherwise nutrient-poor environment.Cretacladoides noricum11 and were found in association with the published Early Cretaceous deep-water chondrichthyan assemblage reported in Fuchs et al.61 and Feichtinger et al.62.The bacteria-bearing teeth forming the focus of this study belong to the extinct, Early Cretaceous shark species, 2O, cleaned with an ultrasonic bath for 5 min, and dried by 60 degrees for 2 h. Tooth NHMW 2017/0055/0028 was then examined with a FEI Quanta 3D FEG at the Department of Lithospheric Research at the University of Vienna, without any coating with an excitation energy of the electron beam of 5 kV. Quantitative energy dispersive spectrometry (EDS) analysis of the enameloid bundle and aggregates of tooth NHMW 2017/0055/0028 were obtained by a JEOL “Hyperprobe” JXA 8530-F field-emission electron microprobe (FE-EPMA) in combination with an online JEOL quantitative ZAF-correction program at the Central Research Laboratories of the Natural History Museum Vienna (NHMW). For the EDS analyses, the sample was coated with a 10 nm carbon film. An accelerating voltage of 15 and 5 keV, a beam current of 5 nA, and fully focused electron beam (with an estimated beam diameter of ~ 70–80 nm) were used. The Count Rate was 1055.00 CPS. The comparative EDS studies of enameloid and coccoids were performed with a FEI Inspect-S scanning electron microscope with an EDAX Apollo XV SDD EDS detector at 15, 10 and 5 keV acceleration voltage. Spectra were acquired for 30–90 s to obtain a good signal to noise ratio, and intensities were corrected with the ZAF algorithm. The teeth (NHMW 2020/0042/0001 and NHMW 2017/0055/0028) are housed in the Geological—Palaeontological Department of the Natural History Museum Vienna, Austria (NHMW). The brightness and contrast of the images were adjusted using Adobe Photoshop Elements 8.0. Ink.The teeth were extracted from the limestone matrix using 12% acetic acid . The residual sediment was screen washed using different mesh sizes  and dried by 60 °C for 24 h. The extraction process was made on two, separate samples in order to exclude contamination. The teeth were separated from the residual sediment using a fine preparation needle and were mounted in resin  in longitudinal direction, subsequently wet-ground with siliciumcarbid 1000 (~ 4 µm grain size) and the resulting surface  hint to open marine conditions and deeper depositional environments for the condensed shark tooth layer. Condensation took place in deep marine areas by deep-water currents and winnowing of sediment, leading to condensation and enrichment of bioclastic material. The assumed deep-water environment is also mirrored by the presence of the microfossil group of calpionellids typical for pelagic to hemipelagic sedimentation. The facies and fossil assemblage from macro- and microfossils observed in thin sections is also characteristic for deep-water pelagic deposits and basinal settings from 200 to 1000 m in the Tethyan Lower Cretaceous64.The teeth described here derived from the KB1-A section that consists of bioclastic wacke- to packstones of the so-called Steinmühl Formation occurring in the northern tectonic units of the Northern Calcareous Alps in Upper Austria Fig. A–C. The Supplementary Informations.