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opencc-zeroDec 2012View details →

Data from: Transcriptome analysis reveals novel patterning and pigmentation genes underlying Heliconius butterfly wing pattern variation

BACKGROUND: Heliconius butterfly wing pattern diversity offers a unique opportunity to investigate how natural genetic variation can drive the evolution of complex adaptive phenotypes. Positional cloning and candidate gene studies have identified a handful of regulatory and pigmentation genes implicated in Heliconius wing pattern variation, but little is known about the greater developmental networks within which these genes interact to pattern a wing. Here we took a large-scale transcriptomic approach to identify the network of genes involved in Heliconius wing pattern development and variation. This included applying over 140 transcriptome microarrays to assay gene expression in dissected wing pattern elements across a range of developmental stages and wing pattern morphs of Heliconius erato. RESULTS: We identified a number of putative early prepattern genes with color-pattern related expression domains. We also identified 51 genes differentially expressed in association with natural color pattern variation. Of these, the previously identified color pattern "switch gene" optix was recovered as the first transcript to show color-specific differential expression. Most differentially expressed genes were transcribed late in pupal development and have roles in cuticle formation or pigment synthesis. These include previously undescribed transporter genes associated with ommochrome pigmentation. Furthermore, we observed upregulation of melanin-repressing genes such as ebony and Dat1 in non-melanic patterns. CONCLUSIONS: This study identifies many new genes implicated in butterfly wing pattern development and provides a glimpse into the number and types of genes affected by variation in genes that drive color pattern evolution.

opencc-zeroDec 2017View details →

Data from: Taxonomic and evolutionary pattern revisions resulting from geometric morphometric analysis of Pennsylvanian Neognathodus conodonts, Illinois Basin

Conodont fossils are highly valuable for Paleozoic biostratigraphy and for interpreting evolutionary change, but identifying and describing conodont morphologies, and characterizing gradual shape variation remain challenging. We used geometric morphometrics (GM) to conduct the first landmark-based morphometric analysis of the biostratigraphically useful conodont genus Neognathodus. Our objective is to assess whether previously defined morphotype groups are reliably distinct from one another. As such, we reevaluate patterns of morphologic change in Neognathodus P1elements, perform maximum likelihood tests of evolutionary modes, and construct novel, GM-based biozonations through a Desmoinesian (Middle Pennsylvanian) section in the Illinois Basin. Our GM results record the entire spectrum of shape variability among Neognathodus morphotypes thus alleviating the problem of documenting and classifying gradual morphologic transitions between morphotypes. Statistically distinct GM groups support previously established classifications of N. bassleri, N. bothrops, and N. roundyi. Statistically indistinct pairs of GM groups do not support literature designations of N. medadultimus and N. medexultimus, and N. dilatus and N. metanodosus, and we synonymize each pair. Maximum likelihood tests of evolutionary modes provide the first statistical assessment of Neognathodus evolutionary models in the Desmoinesian. The most likely evolutionary models are an unbiased random walk or a general random walk. We name four distinct biozones through the Desmoinesian using GM results and these align with previous biozonation structure based on the Neognathodus Index (NI) illustrating that Neognathodus-based biostratigraphic correlations would not change between GM or NI methods. The structural similarity between both biozonations showcases that determining GM-based biozones is not redundant, as this comparison validates using landmark-based GM work to construct viable biozonations for subsequent stratigraphic correlations. Although this study is limited to the Illinois Basin, our quantitative methodology can be broadly applied to additional genera to test taxonomic designations, interpret statistically-robust evolutionary patterns, and construct valid biozones for this significant chordate group.

opencc-zeroDec 2017View details →

Data from: Postpartum family planning integration with maternal, newborn, and child health services: a cross-sectional analysis of client flow patterns in India and Kenya

Objectives: Maternal, newborn, and child health (MNCH) services represent opportunities to integrate postpartum family planning (PPFP). Objectives were to determine levels of MNCH-family planning (FP) integration and associations between integration, client characteristics, and service delivery factors in facilities that received programmatic PPFP support. Design and setting: Cross-sectional client flow assessment conducted May–July 2014, over 5 days at 10 purposively selected public sector facilities in India (four hospitals) and Kenya (two hospitals, four health centers). Participants: 2,158 client visits tracked (1,294 India; 864 Kenya). Women aged 18 or older accessing services while pregnant and/or with a child under 2 years. Interventions: PPFP/postpartum intrauterine device—Bihar, India (2012–2013); Jharkhand, India (2010–2014); Embu, Kenya (2008–2012). Maternal, infant, and young child nutrition/FP integration—Bondo, Kenya (2011–2013). Primary outcome measures: Proportion of visits where clients received integrated MNCH-FP services, client characteristics as predictors of MNCH-FP integration, and MNCH-FP integration as predictor of length of time spent at facility. Results: Levels of MNCH-FP integration varied widely across facilities (5.3% to 63.0%), as did proportion of clients receiving MNCH-FP integrated services by service area. Clients traveling 30–59 minutes were half as likely to receive integrated services versus those traveling under 30 minutes (odds ratio [OR] 0.5, 95% confidence interval [CI] 0.4–0.7, p<.001). Clients receiving MNCH-FP services (versus MNCH services only) spent an average of 10.5 minutes longer at the facility (95% CI −0.1–21.9, not statistically significant). Conclusions: Findings suggest importance of focused programmatic support for integration by MNCH service area. FP integration was highest in areas receiving specific support. Integration does not seem to impose an undue burden on clients in terms of time spent at the facility. Clients living furthest from facilities are least likely to receive integrated services.

opencc-zeroDec 2018View details →

Data from: Seasonal dynamics and co-occurrence patterns of honey bee pathogens revealed by high-throughput RT-qPCR analysis

The health of the honey bee Apis mellifera is challenged by introduced parasites that interact with its inherent pathogens and cause elevated rates of colony losses. To elucidate co-occurrence, population dynamics and synergistic interactions of honey bee pathogens, we established an array of diagnostic assays for a high-throughput qPCR platform. Assuming that interaction of pathogens requires co-occurrence within the same individual, single worker bees were analyzed instead of collective samples. Eleven viruses, four parasites and three pathogenic bacteria were quantified in more than one thousand single bees sampled from sixteen disease-free apiaries in Southwest Germany. The most abundant viruses were Black Queen Cell Virus (84%), Lake Sinai Virus 1 (42%), and Deformed Wing Virus B (35%). Forager bees from asymptomatic colonies were infected with two different viruses in average, and simultaneous infection with four to six viruses was common (14%). Also the intestinal parasites Nosema ceranae (96%) and Crithidia mellificae/Lotmaria passim (52%) occurred very frequently. These results indicate that low-level infections in honey bees are more common than previously assumed. All viruses showed seasonal variation, while N. ceranae did not. The foulbrood bacteria Paenibacillus larvae and Melissococcus plutonius were regionally distributed. Spearman's correlations and multiple regression analysis indicated possible synergistic interactions between the common pathogens, particularly for Black Queen Cell Virus. Beyond its suitability for further studies on honey bees, this targeted approach may be, due to its precision, capacity and flexibility, a viable alternative to more expensive, sequencing-based approaches in non-model systems.

FIGURE 7 in Intrapopulational variation in color pattern of Trichomycterus davisi (Haseman, 1911) (Siluriformes: Trichomycteridae) corroborated by morphometrics and molecular analysis

FIGURE 7. Box plot of color pattern classes (Phenotypes I, II and III) by standard length (a, b, and c referring to significant differences as eVidenced by ANOVA).

FIGURE 8 in Intrapopulational variation in color pattern of Trichomycterus davisi (Haseman, 1911) (Siluriformes: Trichomycteridae) corroborated by morphometrics and molecular analysis

FIGURE 8. Median-joining networks between haplotypes (A) and Bayesian phylogenetic tree (B) of the COI gene to phenotypes of Trichomycterus davisi from the Ribeirão João Pinheiro and Rio Iguaçu. and T. iheringi. In A) each circle represents a unique haplotype with circle sizes being proportional to their frequencies. Each color is corresponding to a phenotype, and the numbers in parentheses represent the mutation steps between haplotypes. In B) the numbers on the nodes represent posterior probability higher than 95%.

FIGURE 5 in Intrapopulational variation in color pattern of Trichomycterus davisi (Haseman, 1911) (Siluriformes: Trichomycteridae) corroborated by morphometrics and molecular analysis

FIGURE 5. Scatter plot of indiVidual scores from the combined samples of Trichomycterus davisi phenotypes of the Ribeirão João Pinheiro in the first three axes of the Principal Component Analysis (PCA). Phenotype I (open squares), Phenotype II (filled squares), and Phenotype III (crosses).

FIGURE 6 in Intrapopulational variation in color pattern of Trichomycterus davisi (Haseman, 1911) (Siluriformes: Trichomycteridae) corroborated by morphometrics and molecular analysis

FIGURE 6. Frequencies (%) of the number of specimens by color pattern classes (Phenotypes I, II, and III).

FIGURE 4 in Intrapopulational variation in color pattern of Trichomycterus davisi (Haseman, 1911) (Siluriformes: Trichomycteridae) corroborated by morphometrics and molecular analysis

FIGURE 4. Type-specimens of T. davisi. A) FMNH 60309, holotype, 41.3 mm SL, b) FMNH 54242, paratype, 43.5 mm SL, and C) FMNH 54242, paratype, 23.2 mm SL.

FIGURE 3 in Intrapopulational variation in color pattern of Trichomycterus davisi (Haseman, 1911) (Siluriformes: Trichomycteridae) corroborated by morphometrics and molecular analysis

FIGURE 3. Color pattern Variation in T. davisi of Ribeirão João Pinheiro, Telêmaco Borba, state of Paraná, Brazil. MZUEL 11776, Phenotype I (specimens A, 84.56 mm SL, and B, 43.91 mm SL), Phenotype II (specimens C, 68.24 mm SL, D, 49.28 mm SL and E, 73.56 mm SL) and Phenotype III (specimens F, 53.11 mm SL, and G, 34.83 mm SL). Scale bars represent 10 mm.

FIGURE 1 in Intrapopulational variation in color pattern of Trichomycterus davisi (Haseman, 1911) (Siluriformes: Trichomycteridae) corroborated by morphometrics and molecular analysis

FIGURE 1. Location of sampling area in the Ribeirão João Pinheiro, 24°16'41"S, 050°35'12"w, Fazenda Monte Alegre Ecological ReserVe, Telêmaco Borba (yellow diamond) and Ribeirão Macaquinho, 25°38'34"S, 049°35'24"w, Serrinha, Município de Contenda, near the type locality of Trichomycterus davisi (yellow star), state of Paraná, Brazil.

opencc-zeroOct 2023View details →

Distribution pattern of rocky desertification in southwest China and analysis of its main driving factors based on GIS and Geodetector

Rocky desertification, a pressing environmental concern in Southwest China, significantly impacts local living conditions and regional sustainability. Employing remote sensing on a macro scale, this study focuses on identifying and analyzing the spatial distribution and driving factors of rocky desertification. Conducted in Southwest China, using Landsat data from Google Earth Engine for 2020, the research quantitatively extracts information on rocky desertification patches through traditional methods. Excluding unlikely areas using land use data, spatial distribution features and driving factors are examined via GIS spatial analysis and a geodetector model. The main conclusions are as follows. Rocky desertification covers 217,530.4 km2 (accounting for 15.6% of Southwest China), with areas of slight, moderate, and severe rocky desertification at 81.3%, 7.1%, and 11.6%, respectively. Spatially, rocky desertification primarily occurs in areas where lithology is carbonate rock between clastic rocks and continuous limestone, slope exceeds 15°, elevation ranges is 1000–2000 m, land use types are grassland and woodland, precipitation is 80–120 mm, and population density is below 50 people/km2. Human activities have minimal influence. Geodetector analysis identifies lithology, land use type, and slope as primary driving factors, with interactive effects of lithology and land use type and slope and land use type jointly influencing rocky desertification formation in Southwest China.

Fig. 7 in Morphology-based phylogenetic analysis of South American Sericini chafers (Coleoptera, Scarabaeidae) contrasts patterns of morphological disparity and current classification

Fig. 7. Patterns of disparity derived from discrete morphological data: plots of axis 1 and 2 from principal coordinate analysis.

Fig. 6 in Morphology-based phylogenetic analysis of South American Sericini chafers (Coleoptera, Scarabaeidae) contrasts patterns of morphological disparity and current classification

Fig. 6. Single most parsimonious tree from implied weighting (K = 71.62) on the reduced data set (run 9), part 2. Support values (bootstrap/symmetric resampling) below 50 not shown.

Fig. 5 in Morphology-based phylogenetic analysis of South American Sericini chafers (Coleoptera, Scarabaeidae) contrasts patterns of morphological disparity and current classification

Fig. 5. Single most parsimonious tree from implied weighting (K = 71.62) on the reduced data set (run 9), part 1. Support values (bootstrap/symmetric resampling) below 50 not shown.

Fig. 4 in Morphology-based phylogenetic analysis of South American Sericini chafers (Coleoptera, Scarabaeidae) contrasts patterns of morphological disparity and current classification

Fig. 4. Characters illustrated: male genitalia. A-H) Aedeagus, dorsal view. A) Symmela beskei; B) S. varians; C) Astaena exquisita; D) A. leechi; E) A. longicornis; F) A. schnebli; G) A. sparsetosa; H) S. capixaba. Scale bars: A H) 0.5 mm.

Fig. 2 in Morphology-based phylogenetic analysis of South American Sericini chafers (Coleoptera, Scarabaeidae) contrasts patterns of morphological disparity and current classification

Fig. 2. Characters illustrated: pronotum and elytra. A-I) Pronotum, dorsal view; J-L) Elytra, dorsal view; M O) Elytra, lateral view. A) Astaena baroni; B) A. explaniceps; C, M) A. fuscipennis; D) A. peruana; E) A. producta; F) A. pygidiallis; G) A. ruficollis; H) A. suturalis; I) Symmela flavimana; J) A. longula; K) A. pilosa; L) A. pinguins; N) A. marginicollis; O) A. tarsalis. Scale bars: A-O) 1 mm.

Fig. 3 in Morphology-based phylogenetic analysis of South American Sericini chafers (Coleoptera, Scarabaeidae) contrasts patterns of morphological disparity and current classification

Fig. 3. Characters illustrated: Abdomen and legs. A) Abdomen, lateral view; B, C) Abdomen, ventral view; D F) Prolegs, dorsal view; G, H) Protarsi, lateral view; I,J) Metacoxa, lateral view; K,L) Metatibia, lateral face; M) Metatibia, interior face; N) Metatibia, dorsal view; O,P) Metatarsi, dorsal view; Q) Metatarsi, ventral view. A) Astaena fuscipennis; B) A. tarsalis; C) Symmela opaca; D) A. montivaga; E) S. jatahyensis; F) S. mutabilis; G) Raysymmela boliviensis; H) Parasymmela amazonica; I, L, O) A. aequatorialis; J) A. rosettae; K) Astaena heterophylla; M) A. andina; N) A. semiopaca; P) A. longicornis; Q) A. boliviana. Scale bars: A-F, I-Q) 1 mm; G, H) 0.3 mm.

Fig. 8. A in Morphology-based phylogenetic analysis of South American Sericini chafers (Coleoptera, Scarabaeidae) contrasts patterns of morphological disparity and current classification

Fig. 8. A - Unrooted single most parsimonious tree from implied weighting (K = 71.62) on the reduced data set (run 9); B - Unrooted single tree from distancebased clustering.