Wild Beans (Phaseolus L.) of North America

The wild relatives of the five domesticated species of bean (Phaseolus L.) are widely distributed across the tropics and subtropics of the New World, with taxa extending from the Canadian border to Argentina, and on the Caribbean Islands, Bermuda, and the Galapagos Islands. Mesoamerica holds the largest concentration of species, particularly in the highlands of central Mexico, northward along the Sierra Madre Occidental, and south to Chiapas. The progenitors and close relatives of all five domesticates are also concentrated in this region. Plant breeding involving the use of wild relatives has almost entirely been directed toward the improvement of common bean (Phaseolus vulgaris L.), the most widely cultivated species, and successful contributions have mostly come from its progenitor (Phaseolus vulgaris L.) and a few other taxa. Wild relatives are considered to possess novel useful genetic variation that has not yet been fully explored. A number of wild Phaseolus are rare endemics that are threatened in their natural habitats and are insufficiently protected in situ. Significant ex situ collections of wild Phaseolus are maintained at the International Center for Tropical Agriculture (CIAT), the USDA-ARS National Plant Germplasm System, within the Sistema Nacional de Recursos Fitogenéticos para la Alimentación y la Agricultura (SINAREFI) Conservation Centers Network in Mexico, and at the Botanic Garden Meise, Belgium. Unfortunately, over 26% of Phaseolus taxa are not represented at all in these ex situ conservation facilities, and another 29% are represented by less than ten accessions, making over half of the species highly underrepresented in genebanks. Further efforts to enhance the protection of vulnerable species in their natural habitats, and further collecting to fill critical gaps in germplasm collections, are warranted

Oncogenic Gq/11 signaling acutely drives and chronically sustains metabolic reprogramming in uveal melanoma

Metabolic reprogramming has been shown to occur in uveal melanoma (UM), the most common intraocular tumor in adults. Mechanisms driving metabolic reprogramming in UM are poorly understood. Elucidation of these mechanisms could inform development of new therapeutic strategies for metastatic UM, which has poor prognosis because existing therapies are ineffective. Here, we determined whether metabolic reprogramming is driven by constitutively active mutant α-subunits of the heterotrimeric G proteins Gq or G11 (Gq/11), the oncogenic drivers in ∼90% of UM patients. Using PET-computed tomography imaging, microphysiometry, and GC/MS, we found that inhibition of oncogenic Gq/11 with the small molecule FR900359 (FR) attenuated glucose uptake by UM cells in vivo and in vitro, blunted glycolysis and mitochondrial respiration in UM cell lines and tumor cells isolated from patients, and reduced levels of several glycolytic and tricarboxylic acid cycle intermediates. FR acutely inhibited glycolysis and respiration and chronically attenuated expression of genes in both metabolic processes. UM therefore differs from other melanomas that exhibit a classic Warburg effect. Metabolic reprogramming in UM cell lines and patient samples involved protein kinase C and extracellular signal-regulated protein kinase 1/2 signaling downstream of oncogenic Gq/11. Chronic administration of FR upregulated expression of genes involved in metabolite scavenging and redox homeostasis, potentially as an adaptive mechanism explaining why FR does not efficiently kill UM tumor cells or regress UM tumor xenografts. These results establish that oncogenic Gq/11 signaling is a crucial driver of metabolic reprogramming in UM and lay a foundation for studies aimed at targeting metabolic reprogramming for therapeutic development

The Grizzly, October 6, 2016

How do UC Disability? • Ursinus\u27 Student Radio Renaissance • Get Ready to Vote, Ursinus! • Students Adjust to Philly Experience • CSCG Speaker Dissects Affordable Care Act • Schroeder Takes Love for American Studies Abroad • An Electronic Spin on Music • Opinion: Use Your Vote and Use it Wisely This November; Here\u27s What it\u27s Like to be a Republican at UC • Life of a Student Athlete at Ursinus: From Practice to Class • Kicking it Into Overdrivehttps://digitalcommons.ursinus.edu/grizzlynews/1651/thumbnail.jp

Cornelia de Lange syndrome in diverse populations

Cornelia de Lange syndrome (CdLS) is a dominant multisystemic malformation syndrome due to mutations in five genes—NIPBL, SMC1A, HDAC8, SMC3, and RAD21. The characteristic facial dysmorphisms include microcephaly, arched eyebrows, synophrys, short nose with depressed bridge and anteverted nares, long philtrum, thin lips, micrognathia, and hypertrichosis. Most affected individuals have intellectual disability, growth deficiency, and upper limb anomalies. This study looked at individuals from diverse populations with both clinical and molecularly confirmed diagnoses of CdLS by facial analysis technology. Clinical data and images from 246 individuals with CdLS were obtained from 15 countries. This cohort included 49% female patients and ages ranged from infancy to 37 years. Individuals were grouped into ancestry categories of African descent, Asian, Latin American, Middle Eastern, and Caucasian. Across these populations, 14 features showed a statistically significant difference. The most common facial features found in all ancestry groups included synophrys, short nose with anteverted nares, and a long philtrum with thin vermillion of the upper lip. Using facial analysis technology we compared 246 individuals with CdLS to 246 gender/age matched controls and found that sensitivity was equal or greater than 95% for all groups. Specificity was equal or greater than 91%. In conclusion, we present consistent clinical findings from global populations with CdLS while demonstrating how facial analysis technology can be a tool to support accurate diagnoses in the clinical setting. This work, along with prior studies in this arena, will assist in earlier detection, recognition, and treatment of CdLS worldwide.Supplementary Table 1 Participants with photographs in Figures 2-5 from 10 countries. Supplementary Table 2. Geometric and texture feature comparison of Global (combined African descent, Asian, Latin American, Caucasian) CdLS individuals with normal controls using digital facial analysis technology. The ranges of the geometric linear features were normalized by the ear‐to‐ear distance. Geometric angle features are presented in degrees. Texture features were computed at three scales (r1, r2, and r3). Features are presented in order of their relevance for the diagnosis of CdLS. Supplementary Table 3. Geometric and texture feature comparison of African descent CdLS individuals with normal controls using digital facial analysis technology. The ranges of the geometric linear features were normalized by the ear‐to‐ear distance. Geometric angle features are presented in degrees. Texture features were computed at three scales (r1, r2, and r3). Features are presented in order of their relevance for the diagnosis of CdLS. Supplementary Table 4. Geometric and texture feature comparison of Asian CdLS individuals with normal controls using digital facial analysis technology. The ranges of the geometric linear features were normalized by the ear‐to‐ear distance. Geometric angle features are presented in degrees. Texture features were computed at three scales (r1, r2, and r3). Features are presented in order of their relevance for the diagnosis of CdLS. Supplementary Table 5. Geometric and texture feature comparison of Latin American CdLS individuals with normal controls using digital facial analysis technology. The ranges of the geometric linear features were normalized by the ear‐to‐ear distance. Geometric angle features are presented in degrees. Texture features were computed at three scales (r1, r2, and r3). Features are presented in order of their relevance for the diagnosis of CdLS. Supplementary Table 6. Geometric and texture feature comparison of Caucasian CdLS individuals with normal controls using digital facial analysis technology. The ranges of the geometric linear features were normalized by the ear‐to‐ear distance. Geometric angle features are presented in degrees. Texture features were computed at three scales (r1, r2, and r3). Features are presented in order of their relevance for the diagnosis of CdLS. Supplementary Figure 1. Global: Graph of area under the ROC curve (AUC), accuracy, sensitivity, and specificity versus the number of features selected. Supplementary Figure 2. African: Graph of area under the ROC curve (AUC), accuracy, sensitivity, and specificity versus the number of features selected. Supplementary Figure 3. Asian: Graph of area under the ROC curve (AUC), accuracy, sensitivity, and specificity versus the number of features selected. Supplementary Figure 4. Latin American: Graph of area under the ROC curve (AUC), accuracy, sensitivity, and specificity versus the number of features selected. Supplementary Figure 5. Caucasian: Graph of area under the ROC curve (AUC), accuracy, sensitivity, and specificity versus the number of features selectedPK and MM are supported by the Division of Intramural Research at the National Human Genome Research, NIH. Partial funding of this project was from a philanthropic gift from the Government of Abu Dhabi to the Children's National Health System. VS is supported by the Chulalongkorn Academic Advancement Into Its 2nd Century Project and the Thailand Research Fund. We would also like to acknowledge other clinicians who supported this work—MZ, JP, and GC. We would like to acknowledge that IDK, LD, MK, and SR are supported by the CdLS Center Endowed Funds at The Children's Hospital of Philadelphia and PO1 HD052860 from the NICHD. ES is supported by a fellowship from PKS Italia and PKSKids USA. LD was also supported by a postdoctoral training grant (T32 GM008638) from the NIGMS.http://wileyonlinelibrary.com/journal/ajmga2020-02-01hj2019Genetic

Cornelia de Lange syndrome in diverse populations.

Cornelia de Lange syndrome (CdLS) is a dominant multisystemic malformation syndrome due to mutations in five genes-NIPBL, SMC1A, HDAC8, SMC3, and RAD21. The characteristic facial dysmorphisms include microcephaly, arched eyebrows, synophrys, short nose with depressed bridge and anteverted nares, long philtrum, thin lips, micrognathia, and hypertrichosis. Most affected individuals have intellectual disability, growth deficiency, and upper limb anomalies. This study looked at individuals from diverse populations with both clinical and molecularly confirmed diagnoses of CdLS by facial analysis technology. Clinical data and images from 246 individuals with CdLS were obtained from 15 countries. This cohort included 49% female patients and ages ranged from infancy to 37 years. Individuals were grouped into ancestry categories of African descent, Asian, Latin American, Middle Eastern, and Caucasian. Across these populations, 14 features showed a statistically significant difference. The most common facial features found in all ancestry groups included synophrys, short nose with anteverted nares, and a long philtrum with thin vermillion of the upper lip. Using facial analysis technology we compared 246 individuals with CdLS to 246 gender/age matched controls and found that sensitivity was equal or greater than 95% for all groups. Specificity was equal or greater than 91%. In conclusion, we present consistent clinical findings from global populations with CdLS while demonstrating how facial analysis technology can be a tool to support accurate diagnoses in the clinical setting. This work, along with prior studies in this arena, will assist in earlier detection, recognition, and treatment of CdLS worldwide

Cornelia de Lange Syndrome in Diverse Populations.

Cornelia de Lange syndrome (CdLS) is a dominant multisystemic malformation syndrome due to mutations in five genes—NIPBL, SMC1A, HDAC8, SMC3, and RAD21. The characteristic facial dysmorphisms include microcephaly, arched eyebrows, synophrys, short nose with depressed bridge and anteverted nares, long philtrum, thin lips, micrognathia, and hypertrichosis. Most affected individuals have intellectual disability, growth deficiency, and upper limb anomalies. This study looked at individuals from diverse populations with both clinical and molecularly confirmed diagnoses of CdLS by facial analysis technology. Clinical data and images from 246 individuals with CdLS were obtained from 15 countries. This cohort included 49% female patients and ages ranged from infancy to 37 years. Individuals were grouped into ancestry categories of African descent, Asian, Latin American, Middle Eastern, and Caucasian. Across these populations, 14 features showed a statistically significant difference. The most common facial features found in all ancestry groups included synophrys, short nose with anteverted nares, and a long philtrum with thin vermillion of the upper lip. Using facial analysis technology we compared 246 individuals with CdLS to 246 gender/age matched controls and found that sensitivity was equal or greater than 95% for all groups. Specificity was equal or greater than 91%. In conclusion, we present consistent clinical findings from global populations with CdLS while demonstrating how facial analysis technology can be a tool to support accurate diagnoses in the clinical setting. This work, along with prior studies in this arena, will assist in earlier detection, recognition, and treatment of CdLS worldwide.Supplementary Table 1 Participants with photographs in Figures 2-5 from 10 countries. Supplementary Table 2. Geometric and texture feature comparison of Global (combined African descent, Asian, Latin American, Caucasian) CdLS individuals with normal controls using digital facial analysis technology. The ranges of the geometric linear features were normalized by the ear‐to‐ear distance. Geometric angle features are presented in degrees. Texture features were computed at three scales (r1, r2, and r3). Features are presented in order of their relevance for the diagnosis of CdLS. Supplementary Table 3. Geometric and texture feature comparison of African descent CdLS individuals with normal controls using digital facial analysis technology. The ranges of the geometric linear features were normalized by the ear‐to‐ear distance. Geometric angle features are presented in degrees. Texture features were computed at three scales (r1, r2, and r3). Features are presented in order of their relevance for the diagnosis of CdLS. Supplementary Table 4. Geometric and texture feature comparison of Asian CdLS individuals with normal controls using digital facial analysis technology. The ranges of the geometric linear features were normalized by the ear‐to‐ear distance. Geometric angle features are presented in degrees. Texture features were computed at three scales (r1, r2, and r3). Features are presented in order of their relevance for the diagnosis of CdLS. Supplementary Table 5. Geometric and texture feature comparison of Latin American CdLS individuals with normal controls using digital facial analysis technology. The ranges of the geometric linear features were normalized by the ear‐to‐ear distance. Geometric angle features are presented in degrees. Texture features were computed at three scales (r1, r2, and r3). Features are presented in order of their relevance for the diagnosis of CdLS. Supplementary Table 6. Geometric and texture feature comparison of Caucasian CdLS individuals with normal controls using digital facial analysis technology. The ranges of the geometric linear features were normalized by the ear‐to‐ear distance. Geometric angle features are presented in degrees. Texture features were computed at three scales (r1, r2, and r3). Features are presented in order of their relevance for the diagnosis of CdLS. Supplementary Figure 1. Global: Graph of area under the ROC curve (AUC), accuracy, sensitivity, and specificity versus the number of features selected. Supplementary Figure 2. African: Graph of area under the ROC curve (AUC), accuracy, sensitivity, and specificity versus the number of features selected. Supplementary Figure 3. Asian: Graph of area under the ROC curve (AUC), accuracy, sensitivity, and specificity versus the number of features selected. Supplementary Figure 4. Latin American: Graph of area under the ROC curve (AUC), accuracy, sensitivity, and specificity versus the number of features selected. Supplementary Figure 5. Caucasian: Graph of area under the ROC curve (AUC), accuracy, sensitivity, and specificity versus the number of features selectedPK and MM are supported by the Division of Intramural Research at the National Human Genome Research, NIH. Partial funding of this project was from a philanthropic gift from the Government of Abu Dhabi to the Children's National Health System. VS is supported by the Chulalongkorn Academic Advancement Into Its 2nd Century Project and the Thailand Research Fund. We would also like to acknowledge other clinicians who supported this work—MZ, JP, and GC. We would like to acknowledge that IDK, LD, MK, and SR are supported by the CdLS Center Endowed Funds at The Children's Hospital of Philadelphia and PO1 HD052860 from the NICHD. ES is supported by a fellowship from PKS Italia and PKSKids USA. LD was also supported by a postdoctoral training grant (T32 GM008638) from the NIGMS.http://wileyonlinelibrary.com/journal/ajmga2020-02-01hj2019Genetic

Using trained dogs and organic semi-conducting sensors to identify asymptomatic and mild SARS-CoV-2 infections: an observational study

Background A rapid, accurate, non-invasive diagnostic screen is needed to identify people with SARS-CoV-2 infection. We investigated whether organic semi-conducting (OSC) sensors and trained dogs could distinguish between people infected with asymptomatic or mild symptoms, and uninfected individuals, and the impact of screening at ports-of-entry. Methods Odour samples were collected from adults, and SARS-CoV-2 infection status confirmed using RT-PCR. OSC sensors captured the volatile organic compound (VOC) profile of odour samples. Trained dogs were tested in a double-blind trial to determine their ability to detect differences in VOCs between infected and uninfected individuals, with sensitivity and specificity as the primary outcome. Mathematical modelling was used to investigate the impact of bio-detection dogs for screening. Results About, 3921 adults were enrolled in the study and odour samples collected from 1097 SARS-CoV-2 infected and 2031 uninfected individuals. OSC sensors were able to distinguish between SARS-CoV-2 infected individuals and uninfected, with sensitivity from 98% (95% CI 95–100) to 100% and specificity from 99% (95% CI 97–100) to 100%. Six dogs were able to distinguish between samples with sensitivity ranging from 82% (95% CI 76–87) to 94% (95% CI 89–98) and specificity ranging from 76% (95% CI 70–82) to 92% (95% CI 88–96). Mathematical modelling suggests that dog screening plus a confirmatory PCR test could detect up to 89% of SARS-CoV-2 infections, averting up to 2.2 times as much transmission compared to isolation of symptomatic individuals only. Conclusions People infected with SARS-CoV-2, with asymptomatic or mild symptoms, have a distinct odour that can be identified by sensors and trained dogs with a high degree of accuracy. Odour-based diagnostics using sensors and/or dogs may prove a rapid and effective tool for screening large numbers of people