Ecology

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To supplement the limited publicly available information on rabies risk, the US Centers for Disease Control and Prevention (CDC) performs an annual country-by-country qualitative assessment of rabies risks and protective factors. The results of this assessment are released annually in an open-access database of core metrics consisting of the presence of lyssaviruses (specifically canine or wildlife rabies virus variants, or other bat lyssaviruses), access to rabies immunoglobulins and vaccines, rabies surveillance capacity and canine rabies control capacity18. The analysis presented here builds upon the current CDC evaluation and specifically examines publicly available data to better inform the parameter of rabies surveillance capacity. This study found publicly available data regarding rabies animal testing by species, described testing practices in relation to the country’s human and dog populations, as well as by their stage of DMRVV control (defined by WHO), and used this data to calculate a surveillance testing threshold for DMRVV endemic countries.Data sources were categorized into four tiers, with the order reflecting the preference for selecting the most appropriate data for the purposes of this analysis. Tier 1 data sources were considered to be the preferential data source and included any official government data submitted to a Regional or International data repository. Official data repositories included the WHO GHO, Pan-American Health Organization Regional Information System for Epidemiologic Surveillance of Rabies (PAHO SIRVERA), and the European Rabies Bulletin. Tier 1 data sources also included official country reports found through literature search, so long as they were publicly available. Tier 2 data sources consisted of published reports in peer-reviewed literature or on a ministry of health or agriculture site that includes data from the entire country, as well as unofficial data repositories (e.g., Global Alliance on Rabies Control (GARC) Rabies Epidemiologic Bulletin). Tier 3 data consisted of one-time cross-sectional studies or studies describing sub-national testing activities and which could not be reliably extrapolated to an entire country. Tier 4 data sources include any resource not captured in the previous criteria that were obtained during literature searches. The primary data search was conducted in September 2021, with an update in September 2022. Only Tier 1 and Tier 2 data sources were included in the evaluation of animal testing rates. If multiple data sources contained conflicting testing rates, we prioritized data from surveillance repositories, then reports from ministries of health or agriculture, and, finally, peer-reviewed publications.For Tier 1 data (i.e., surveillance repository), data was included in this study if it described rabies testing conducted between the years 2010 and 2019. As political, economic, and epidemiologic factors directly influence the reliability and transparency of surveillance system data, we decided that a ten-year limit would capture any year-to-year variation in data and better characterize current passive surveillance practices. Additionally, the cutoff of 2019 was chosen so that the effects of the COVID-19 pandemic on rabies surveillance capacity would not affect this comprehensive evaluation and would account for lag time in reporting to Tier 1 data sources19,20. This study assumed data from these surveillance repositories is entered secondary to passive surveillance systems. If data was known to be from active surveillance activities, it was removed from analyses.For Tier 2 data (i.e., peer-reviewed publications), certain publications presented aggregated testing data that included years prior to the Tier 1 cutoff (i.e., 2010). To increase inclusivity of eligible data and keep the findings from this evaluation representative of current practices, eligible data must have had an end year ≥ 2012, regardless of the starting year of data (Table S1). The literature search was conducted on PubMed, Scopus, and Google for “rabies” AND “[country name]” from 2010 to December 2021. “Publicly available” was defined as any result appearing in PubMed or Scopus, or within the first three pages of a Google search. Exceptions to the first three pages were made for similar country names (e.g., Guinea, Congo). The first 10% of Spanish- and French-speaking countries were also searched for “rabia” and “raj,” respectively, to potentially capture any other sources of surveillance data. However, after no additional data was found, this was discontinued. If an article or resource quantifying animal testing capacity within these criteria was not found, the country was deemed to not have readily available data for analysis.For any countries that were part of the surveillance threshold calculation for DMRVV endemic countries, the preferred tiered data was compared to all other data sources. For one country (i.e., Brazil), there was a notable lack of dog testing data and known discrepancies in data reporting between their two reporting systems (i.e., SINAN, SIRVERA)21. In this situation, a median rate was calculated between a Tier 1 and Tier 3 data source. No other such discrepancies were noted. The type of surveillance (active or passive) was noted for each data source; we assumed passive surveillance with Tier 1 data unless compelling evidence existed to display that this was not the case. A strictly active surveillance program was excluded from all analyses. A summary of overall testing practices was performed and standardized according to the number of years each data source contained.As evaluations of rabies testing rates spanned over multiple years, population estimates were obtained to reflect the most recent year in the available data. Three separate testing rates were calculated and standardized based on the human population within the country: [1] All animal, [2] Domestic animal, and [3] Wildlife. There are different social and cultural behaviors that affect the human to dog ratio and interactions between people and animals. These differences can impact the susceptibility of dogs to rabies virus infection and the likelihood of human interactions with rabid animals. Therefore, we additionally calculated country testing rates standardized by the estimated dog population, to provide an additional indicator value of adequate surveillance capacity. Estimated dog populations were obtained from a previous study22. This resulted in up to four calculated rabies testing rates per country, depending upon available data.Equation 1: All-animal per human testing rate (AAHR)$$frac{Average,number,of,all,animals,tested/year}{{Estimated,human,population}} times 100,000$$
(1)
Equation 2: Domestic animal per human testing rate (DAHR)$$frac{Average, number, of, domestic, animals, tested/year}{{Estimated, human, population}} times 100,000$$
(2)
Equation 3: Domestic animal per dog testing rate (DADR)$$frac{Average, number, of, domestic ,animals, tested/year}{{Estimated ,dog, population}} times 100,000$$
(3)
Equation 4: Wildlife per human testing rate (WHR)$$frac{Average, number ,of, wildlife, animals, tested/year}{{Estimated ,human, population}} times 100,000$$
(4)
The WHO rabies epidemiologic Status is divided into five categories in escalating levels of dog rabies control: [1] Endemic dog-transmitted human rabies, [2] Endemic dog rabies, [3] Sporadic dog-transmitted rabies, [4] Controlled dog rabies, and [5] No dog rabies. The WHO Status was established based on existing data and expert knowledge to help better define the level of rabies control for each country23. In addition to these five WHO Statuses, countries in Status [5] were further sub-categorized into [5a] (rabies virus free), and [5b] (wildlife rabies enzootic) based on CDC’s wildlife rabies status; the CDC rabies status was also used for any country without a WHO Status (n = 11)24. Average testing rates for the aforementioned equations were calculated for each WHO Rabies Status category, treating each country as an equally weighted value in the rate calculation. Only descriptive analyses were conducted to describe surveillance and testing data, as data quality was not deemed acceptable for multi-variable statistical analysis and testing rates were heavily left-skewed. Data is presented as median and IQR as the data was noted to not reflect a parametric distribution.Ethics approvalThis activity was reviewed by CDC and was conducted consistent with applicable federal law and CDC policy. (See e.g., 45 C.F.R. part 46, 21 C.F.R. part 56; 42 U.S.C. §241(d); 5 U.S.C. §552a; 44 U.S.C. §3501 et seq.) The views and opinions of the manuscript are of the authors alone and do not represent those of CDC or any other federal agency. More

Summary of SCTLD microbiome studiesInitially, datasets were acquired from 17 SCTLD studies, but one study [24] did not pass quality filtering and was removed from the analysis, resulting in 16 SCTLD studies used in this meta-analysis. In addition, one Acropora spp. rapid tissue loss (RTL) disease study was included for comparison of bacteria which may be associated more generally with coral tissue loss diseases (Supplementary Table 1). The combined dataset included 2425 samples, representing various coral species and environments described below. A total of 63 miscellaneous samples such as lab controls were included in this total (Supplementary Table 1). Samples from the studies were sequenced using five primer pairs: CS1-515F/CS2-806R [31] with additional 5’ linker sequences [32] (n = 79), 515FY [33]/806RB [34] (n = 1219), S-D-Bact-0341-b-S-17/S-D-Bact-0785-a-A-21 [35] (n = 31), 515F/806R [31] (n = 49), and 515F [31]/Arch806R [36] (n = 984; Fig. 1A). Although five primer pairs were used across studies, only the forward reads were evaluated in this analysis (see “Methods”). A description of the differences between 515F primers can be found in detail [34].Fig. 1: The number of aquaria and field samples for each coral species.A small subunit (SSU) rRNA gene primer sets, B sample type, and C disease state. NAs in (A, B) represent sediment and seawater samples. Coral species codes represent the following: Acropora cervicornis (ACER), Acropora palmata (APAL), Colpophyllia natans (CNAT), Diploria labyrinthiformis (DLAB), Dichocoenia stokesii (DSTO), Montastraea cavernosa (MCAV), Meandrina meandrites (MMEA), Orbicella annularis (OANN), Orbicella faveolata (OFAV), Orbicella franksi (OFRA), Porites astreoides (PAST), Pseudodiploria clivosa (PCLI), Pseudodiploria strigosa (PSTR), Stephanocoenia intersepta (SINT), and Siderastrea siderea (SSID).Full size imageSamples were collected throughout Florida and the U.S. Virgin Islands (USVI). Field samples totaled 1274, representing 40 sites, and a further 1088 samples were from aquaria (i.e., laboratory-based experiments; Fig. 1). Thirteen SCTLD-susceptible coral species were included, with Montastraea cavernosa (MCAV; n = 543) and Orbicella faveolata (OFAV; n = 357) most represented and Pseudodiploria clivosa (PCLI; n = 6) and Orbicella franksi (OFRA; n = 7) least represented (Fig. 1). Coral samples (n = 2031) were from three compartments: mucus only (n = 393), mucus and surface tissue (tissue slurry; n = 1585), and skeleton samples with embedded coral tissue (tissue slurry skeleton; n = 53). Seawater (n = 198) and sediment (n = 133) samples from both the field and aquaria experiments also were included to evaluate potential sources of transmission of disease-associated bacteria (Fig. 1B). For seawater from aquaria experiments, 18 L samples were collected [27], while in the field between 60 mL and 1 L samples were collected [11, 25]. In sediment aquaria experiments, 2 mL samples were collected [12], and in the field, approximately 5 mL samples were collected (of the 5 mL, DNA was extracted from 0.25 g sediment [11]). Coral samples represented three SCTLD health states: apparently healthy colonies (AH), which was the most represented (n = 1021), followed by lesions on diseased colonies (DL; n = 661), and unaffected areas on diseased colonies (DU; n = 349; Fig. 1C). AH represents grossly normal tissue, DU grossly normal tissue on diseased colonies, and DL grossly abnormal tissue.Differences in the microbial composition were found in AH corals among zones (vulnerable, endemic, and epidemic)Differences in alpha-diversity were tested among three SCTLD zones: vulnerable (i.e., locations where the disease had not been observed/reported), endemic (i.e., locations where a disease outbreak had moved through the reef and no or few colonies had active lesions), and epidemic (i.e., locations where the outbreak was active and prevalent). For alpha-diversity, for AH field-sourced samples, after filtering, 41,504 amplicon sequence variants (ASVs) remained, which were reduced to 15,021 following rarefaction. Among the filtered AH samples, Shannon (alpha) diversity from the vulnerable zone was slightly higher (estimated marginal means (emmean) = 3.95) compared to the epidemic zone (emmean = 3.70), but this was not significant (Supplementary Fig. 1). For beta-diversity, both within and between-group differences were tested using a filtered counts table. Within-group beta-diversity (variation in microbial composition or dispersion) was not different between zones, but was significant for all comparisons between zones (PERMANOVA, P-adjusted (Padj) More

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Scanning electron microscopy and energy spectrum analysisThe SEM images show the morphological structures of DA and Al-DA before and after adsorption (Fig. 1). DA and Al-DA have disk-like microstructures29 with sur-faces containing both large and small pores, that is, DA and Al-DA have unique multi-level pore structures. The main component of DA and Al-DA is silica, which has a large specific surface area, good thermal stability, and is a natural green material for use as a water treatment agent with a porous structure31. The micrographs show that before adsorption, the DA surface is smooth with a distinct pore structure, whereas modification with aluminium hydroxide makes DA coarse and loose because of the formation of amorphous aluminium hydroxide colloids32. After adsorption, the surface pore structure is covered over for DA and completely covered over for Al-DA, which indicates that F− reacts with Al3+ to form nanoscale precipitates22. The results of the EDS analysis (Fig. 2) show that the content of elemental Al increased from 3.96 to 12.74% after DA was modified with aluminium hydroxide, indicating that Al adhered effectively to the modified DA surface. After adsorption, the content of elemental Al decreased from 3.96 to 1.36% for DA and from 12.74 to 2.03% for Al-DA, which fully confirmed that fluorine preferentially combined with Al to form aluminium precipitates during adsorption, thereby decreasing the Al content.Figure 1SEM images of DA and Al-DA before and after adsorption. (A) Before DA adsorption. (B) After DA adsorption. (C) Before Al-DA adsorption. (D) After Al-DA adsorption.Full size imageFigure 2EDS graphs of DA and Al-DA before and after adsorption. (A) Before DA adsorption. (B) After DA adsorption. (C) Before Al-DA adsorption. (D) After Al-DA adsorption.Full size imageXRD analysisThe surface mineral composition and crystallinity of the materials before and after adsorption were analyzed by XRD (Fig. 3). In the DA and Al-DA patterns, the wide diffraction peaks at approximately 22.0°, 26.0°, and 50.0° mainly correspond to amorphous SiO2, and the diffraction peak at approximately 35° mainly corresponds to amorphous Al2O3, indicating that the material is polycrystalline29. It has been re-ported that amorphous materials may be good adsorbents because of a large specific surface area and numerous active sites33. Many Al(OH)3 peaks and NaCl peaks appear in the XRD pattern of Al-DA, indicating the successful modification of DA by aluminium hydroxide. After adsorption, Na3AlF6 peaks appear in the DA pattern, and Na3AlF6 and AlF3 peaks appear in the Al-DA pattern, whereas the characteristic peaks of NaCl are absent in the Al-DA pattern, which indicates the participation of NaCl in the adsorption process. It has been demonstrated that in the presence of excess sodium fluoride in the reaction solution, the generated aluminium fluoride combines with sodium fluoride to form a NaAlF4 intermediate, which is subsequently converted to cryolite complexes by further adsorption of sodium fluoride34. This result confirms the XRD mapping results.Figure 3XRD patterns of DA and Al-DA before and after adsorption.Full size imageInfrared analysisFigure 4 shows the FTIR spectra of DA and Al-DA before and after adsorption: peaks at 3418, 1635, 1096, 791, and 538 cm−1 appear in the spectrum of DA spectrum before adsorption, and peaks at 3630, 3449, 1637, 1094, 913, 793, and 538 cm−1, appear in the Al-DA spectrum before adsorption. The strong and broad band centered at 3418 cm−1 is due to the stretching vibration of the adsorbed water hydroxyl group (O–H) and the surface hydroxyl group, the vibrational peak at approximately 1635 cm−1 is probably from bound water or the surface hydroxyl group; the peaks at 1096 cm−1 and 538 cm−1 correspond to siloxane groups (Si–O–Si–) and an Al–O absorption band, respectively; and the strong oscillations at 791 cm−1 may be attributed to inorganic Al salts35,36,37. The original absorption peak in the DA spectrum is shifted in the spectrum of DA modified with aluminium hydroxide, confirming the successful modification of DA. The shift of the band at 3418 cm−1 in the DA spectrum to a higher frequency at 3623 cm−1 in the DA spectrum after fluoride absorption is caused by fluoride bonding and has been previously reported38. Another noticeable change in the spectra of DA and Al-DA before and after adsorption is the increase or decrease in the intensity of bending vibrations of specific peaks because the highly electronegative fluoride may have an inductive effect on the respective groups that leads to a blueshift, and the formation of hydrogen bonds leads to a redshift and broadening of the spectral band. The shifts and changes of these peaks indicate the interaction of fluoride with the respective groups29. The new peak at approximately 1170 cm−1 in the spectra of DA and Al-DA with adsorbed fluoride may be due to the formation of Al-F bonds6. The IR spectra show that the formation of a new bonding electronic structure by surface complexation with F− is one of the main mechanisms for the adsorption of F−.Figure 4FTIR spectra of DA and Al-DA before and after adsorption.Full size imageZeta potential analysisThe zeta potential of the material surface plays a very key role in the adsorption process, which reflects the surface charge properties of the material under different pH conditions, and also reflects the surface properties of the material. To obtain the zero charge point of the material, we studied the potential change of the material under different pH values. The results are shown in Fig. 5. In the range of pH 3–11, the zeta potential of the two adsorbents decreased linearly with the increase in pH, and the pHzpc of DA and Al-DA were 9.84 and 10.61, respectively. When pH  More