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Operating in Washington, DC from 1903 to 1960, the Washington Conservatory of Music was the first conservatory established by and for Black Americans in the United States. In this article we demonstrate the significant, continuing impact of the Washington Conservatory of Music through a study of the lives and contributions of the conservatory's first thirty graduates, from the inaugural classes of 1910 through 1914. As teachers, performers, composers, activists, and leaders, the graduates of the Washington Conservatory were cornerstones of musical communities across the United States. Graduates of the Washington Conservatory rarely achieved fame as concert artists, but their lack of fame ultimately says less about the extent of their accomplishments than it says about longstanding silences in music historiography that privileges composers and performers over educators and church musicians. Drawing on physical and digitized primary sources including newspapers, city directories, census records, and extensive archival material, we further the critical historical recovery undertaken by Doris McGinty, Sarah Schmalenberger, Maud Cuney-Hare, and Eileen Southern that answers basic--but frustratingly elusive--questions about a relatively quotidian matter: who did what in Black musical communities. We argue that Washington Conservatory alumni were not only anchors in musical networks that extend through the present but representative figures who built lasting institutions and who exemplify the ubiquity of Black classical musicianship through American music history. Keywords: Black classical musicians, American music, Washington Conservatory of Music, historiography, historical recovery, The Washington Conservatory of Music and School of Expression was the first conservatory founded by and for Black musicians in the United States. (1) Established in Washington, DC in 1903 [...]

Anthracnose and grain mold are two of the most significant diseases of sorghum, a versatile crop that plays an important part in the daily lives of millions of inhabitants, especially in the drier tropical regions. The aim of this study was to determine the influence of four agronomic traits in selected sorghum germplasms on the two diseases using Spearman’s ρ test to identify significant pairwise correlations. Both anthracnose and grain mold scores were significantly and negatively correlated with seed weight and germination rate. The grain mold infection score also demonstrated negative correlations with plant height (Spearman ρ = −0.61 and p-value = p-value = 0.0022). In this investigation, principal component analysis and clustering variables analysis revealed that seed weight and germination rate exhibited a directional alignment, suggesting a positive association. Similarly, panicle length and plant height clustered together, suggesting a shared variation pattern. Additionally, a support vector machine and random forest models effectively predicted the germination rate based on the studied traits, highlighting the potential of machine learning in understanding complex trait relationships in sorghum. This work provides insights into the relationship between agronomic traits and disease resistance, thus contributing to sorghum improvement efforts.

The Promise and Peril of Making Digital Maps Sing As geographer Mark Monmonier has noted in his popular textbook How to Lie with Maps, all maps necessarily reduce and distort [...]

Sorghum (Sorghum bicolor L.) ranks fifth as the most crucial cereal crop globally, yet its seed morphology remains relatively unexplored. This study investigated seed morphology in sorghum based on 115 mini-core and 130 Senegalese germplasms. Eight seed morphology traits encompassing size, shape, and color parameters were assessed. Statistical analyses explored potential associations between these traits and resistance to three major sorghum diseases: anthracnose, head smut, and downy mildew. Furthermore, genome-wide association studies (GWAS) were conducted using phenotypic data from over 24,000 seeds and over 290,000 publicly available single nucleotide polymorphisms (SNPs) through the Genome Association and Prediction Integrated Tool (GAPIT) R package. Significant SNPs associated with various seed morphology traits were identified and mapped onto the reference sorghum genome to identify novel candidate defense genes.

By using the quantum mechanics phenomenon, quantum computers provide a new dimension of computational power that drastically accelerates solving complex and resource-intensive problems. One of the most evolving but, due to the computation power of standard computers, reasonably limited application domain is natural language processing (NLP). NLP seeks to give interactive systems the ability to understand and manipulate human language. Making systems understand and manipulate human languages requires large amounts of data and computational power during learning as well as during the execution of NLP. For handling these amounts of data like text and audio recordings and the complexity of classical NLP algorithms, quantum computation has emerged as a promising solution. This work gives an extensive overview of this new field, known as quantum natural language processing (QNLP). Introducing the basics of quantum computing, we discuss its use in NLP by explaining the different proposed embedding models, quantum algorithms, and other methods of QNLP. As QNLP is still in its infancy, this comprehensive overview is the foundation that points to the upcoming research direction.

From infrared body temperature surveillance to lifeguarding, real-life visual search is usually continuous and comes with rare targets. Previous research has examined realistic search tasks involving separate slides (such as baggage screening and radiography), but search tasks that require continuous monitoring have generally received less attention. In this study, we investigated whether continuous visual search would display a target-rate effect similar to the low-prevalence effect (LPE) in regular visual search. We designed a continuous detection task for a target feature (e.g., a green color) among items of continuously and gradually changing features (e.g., other colors). In four experiments, we demonstrated target-rate effects in terms of slower hit response times (RTs) and higher miss rates when targets were rare. Similar to regular search, target-rate effects were also observed for relative frequencies across two target features. Taken together, these results suggest a target-rate effect in continuous visual search, and its behavioral characteristics are generally similar to those of the LPE in regular visual search.

Sweet sorghum is an attractive feedstock for the production of renewable chemicals and fuels due to the readily available fermentable sugars that can be extracted from the juice, and the additional stream of fermentable sugars that can be obtained from the cell wall polysaccharides in the bagasse. An important selection criterion for new sweet sorghum germplasm is resistance to anthracnose, a disease caused by the fungal pathogen Colletotrichum sublineolum. The identification of novel anthracnose-resistance sources present in sweet sorghum germplasm offers a fast track towards the development of new resistant sweet sorghum germplasm. We established a sweet sorghum diversity panel (SWDP) of 272 accessions from the USDA-ARS National Plant Germplasm (NPGS) collection that includes landraces from 22 countries and advanced breeding material, and that represents ~15% of the NPGS sweet sorghum collection. Genomic characterization of the SWDP identified 171,954 single nucleotide polymorphisms (SNPs) with an average of one SNP per 4,071 kb. Population structure analysis revealed that the SWDP could be stratified into four populations and one admixed group, and that this population structure could be aligned to sorghum’s racial classification. Results from a two-year replicated trial of the SWDP for anthracnose resistance response in Texas, Georgia, Florida, and Puerto Rico showed 27 accessions to be resistant across locations, while 145 accessions showed variable resistance response against local pathotypes. A genome-wide association study identified 16 novel genomic regions associated with anthracnose resistance. Four resistance loci on chromosomes 3, 6, 8 and 9 were identified against pathotypes from Puerto Rico, and two resistance loci on chromosomes 3 and 8 against pathotypes from Texas. In Georgia and Florida, three resistance loci were detected on chromosomes 4, 5, 6 and four on chromosomes 4, 5 (two loci) and 7, respectively. One resistance locus on chromosome 2 was effective against pathotypes from Texas and Puerto Rico and a genomic region of 41.6 kb at the tip of chromosome 8 was associated with resistance response observed in Georgia, Texas, and Puerto Rico. This publicly available SWDP and the extensive evaluation of anthracnose resistance represent a valuable genomic resource for the improvement of sorghum.

A newly documented pathotype 5 of the soil-borne fungus Sporisorium reilianum, causing head smut in sorghum, was tested against 153 unexplored Senegalese sorghum accessions. Among the 153 sorghum accessions tested, 63 (41%) exhibited complete resistance, showing no signs of infection by the fungus. The remaining 90 accessions (59%) displayed varying degrees of susceptibility. Sorghum responses against S. reilianum were explored to analyze the potential link with previously known seed morphology-related traits and new phenotype data from 59 lines for seed weight. A genome-wide association study (GWAS) screened 297,876 SNPs and identified highly significant associations (p < 1 × 10−5) with head smut resistance in sorghum. By mapping these significant SNPs to the reference genome, this study revealed 35 novel candidate defense genes potentially involved in disease resistance.

Abstract Colletotrichum sublineola is a destructive fungal pathogen that causes anthracnose in sorghum. Senegalese sorghum germplasm is currently being considered as an option of sources for genetic resistance. In a recent study, Senegalese sorghum accessions were evaluated for response to a mixture of Texas isolates of C. sublineola at the 8-leaf stage in the greenhouse. As a comparison, 159 Senegalese sorghum accessions at the 1-leaf developmental stage were evaluated against a single Texas isolate of C. sublineola (FSP53) using an excised-leaf assay. A genome-wide association study (GWAS) was conducted based on the phenotypic data acquired to discover genetic variation associated with response to C. sublineola using 193,727 single nucleotide polymorphisms (SNPs) throughout the genome. Sorghum seedlings tended to be more resistant when compared with sorghum plants inoculated at the 8-leaf stage in the greenhouse in previous experiments. Based on the highest score evaluated in the 1-leaf developmental stage excised leaf assay for each accession, 16 accessions were labeled as susceptible. GWAS identified the SNP locus S01_72868925 that is associated with protein kinase domain // Leucine rich repeat N-terminal domain at a level of confidence that surpassed Bonferroni correction. Along with the SNP locus S01_72868925, other top SNP loci were also associated with genes that are known to play critical roles in plant defense or plant stress responses.

Anthracnose and grain mold are two of the most significant diseases of sorghum, a versatile crop that plays an important part in the daily lives of millions of inhabitants, especially in the drier tropical regions. The aim of this study was to determine the influence of four agronomic traits in selected sorghum germplasms on the two diseases using Spearman's ρ test to identify significant pairwise correlations. Both anthracnose and grain mold scores were significantly and negatively correlated with seed weight and germination rate. The grain mold infection score also demonstrated negative correlations with plant height (Spearman ρ = −0.61 and p-value = <0.0001) and panicle length (Spearman ρ = −0.27 and p-value = 0.0022). In this investigation, principal component analysis and clustering variables analysis revealed that seed weight and germination rate exhibited a directional alignment, suggesting a positive association. Similarly, panicle length and plant height clustered together, suggesting a shared variation pattern. Additionally, a support vector machine and random forest models effectively predicted the germination rate based on the studied traits, highlighting the potential of machine learning in understanding complex trait relationships in sorghum. This work provides insights into the relationship between agronomic traits and disease resistance, thus contributing to sorghum improvement efforts. [ABSTRACT FROM AUTHOR]

Sporisorium reilianum, the causal agent of sorghum (Sorghum bicolor (L.) Moench) head smut, is present in most sorghum-producing regions. This seed replacement fungal disease can reduce yield by up to 80% in severely infected fields. Management of this disease can be challenging due to the appearance of different pathotypes within the pathogenic population. In this research, the genetic variability and pathogenicity of isolates collected from five Texas Counties was conducted. Due to the lack of available space, 21 out of 32 sequenced isolates were selected and evaluated for virulence patterns on the six sorghum differentials, Tx7078, BTx635, SC170-6-17 (TAM2571), SA281 (Early Hegari), Tx414, and BTx643. The results reveal the occurrence of a new pathotype, 1A, and four previously documented US pathotypes when the 21 isolates were evaluated for virulence patterns on the differentials. The most prevalent was pathotype 5, which was recovered from Brazos, Hidalgo, Nueces, and Willacy Counties, Texas. This pathotype was followed by 1A and 6 in frequency of recovery. Pathotype 4 was identified only from isolates collected from Hidalgo County, while pathotype 1 was from Burleson County, Texas. It appeared that the previous US head smut pathotypes (2 and 3) are no longer common, and the new pathotypes, 1A, 5, and 6, are now predominant. The phylogenetic tree constructed from the single-nucleotide polymorphism (SNP) data through the neighbor-joining method showed high genetic diversity among the tested isolates. Some of the diverse clades among the tested isolates were independent of their sampled locations. Notably, HS37, HS49, and HS65 formed a clade and were classified as 1A in the virulence study, while HS 61 and HS 66, which were collected from Nueces County, were grouped and identified as pathotype 5.

Nadia Boulanger and Her World, edited by Jeanice Brooks. Chieago: University of Chicago Press and the Bard Music Festival, 2020. xvi, 364 pp. Nadia Boulanger's name has long served as [...]

Anthracnose, incited by Colletotrichum sublineola, is the most destructive foliar disease of sorghum and, under severe conditions, yield losses can exceed 80% on susceptible cultivars. The hyper-variable nature of the pathogen makes its management challenging despite the occurrence of several resistant sources. In this study, the genetic variability and pathogenicity of 140 isolates of C. sublineola, which were sequenced using restriction site-associated sequencing (RAD-Seq), resulted in 1244 quality SNPs. The genetic relationship based on the SNP data showed low to high genetic diversity based on isolates’ origin. Isolates from Georgia and North Carolina were grouped into multiple clusters with some level of genetic relationships to each other. Even though some isolates from Texas formed a cluster, others clustered with isolates from Puerto Rico. The isolates from Puerto Rico showed scattered distribution, indicating the diverse nature of these isolates. A population structure and cluster analysis revealed that the genetic variation was stratified into eight populations and one admixture group. The virulence pattern of 30 sequenced isolates on 18 sorghum differential lines revealed 27 new pathotypes. SC748-5, SC112-14, and Brandes were resistant to all the tested isolates, while BTx623 was susceptible to all. Line TAM428 was susceptible to all the pathotypes, except for pathotype 26. Future use of the 18 differentials employed in this study, which contains cultivars/lines which have been used in the Americas, Asia, and Africa, could allow for better characterization of C. sublineola pathotypes at a global level, thus accelerating the development of sorghum lines with stable resistance to the anthracnose pathogen.

A new method allows researchers to automatically assign cells into different cell types and tissues, a step which is critical for understanding complex organisms.

Deriving the detailed synaptic connections of an entire nervous system is the unrealized goal of the nascent field of connectomics. For the fruit fly Drosophila, in particular, we need to dissect the brain, connectives, and ventral nerve cord as a single continuous unit, fix and stain it, and undertake automated segmentation of neuron membranes. To achieve this, we designed a protocol using progressive lowering of temperature dehydration (PLT), a technique routinely used to preserve cellular structure and antigenicity. We combined PLT with low temperature en bloc staining (LTS) and recover fixed neurons as round profiles with darkly stained synapses, suitable for machine segmentation and automatic synapse detection. Here we report three different PLT-LTS methods designed to meet the requirements for FIB-SEM imaging of the Drosophila brain. These requirements include: good preservation of ultrastructural detail, high level of en bloc staining, artifact-free microdissection, and smooth hot-knife cutting to reduce the brain to dimensions suited to FIB-SEM. In addition to PLT-LTS, we designed a jig to microdissect and pre-fix the fly’s delicate brain and central nervous system. Collectively these methods optimize morphological preservation, allow us to image the brain usually at 8 nm per voxel, and simultaneously speed the formerly slow rate of FIB-SEM imaging.

Automatic Speech Recognition (ASR) is an active field of research due to its large number of applications and the proliferation of interfaces or computing devices that can support speech processing. However, the bulk of applications are based on well-resourced languages that overshadow under-resourced ones. Yet, ASR represents an undeniable means to promote such languages, especially when designing human-to-human or human-to-machine systems involving illiterate people. An approach to design an ASR system targeting under-resourced languages is to start with a limited vocabulary. ASR using a limited vocabulary is a subset of the speech recognition problem that focuses on the recognition of a small number of words or sentences. This paper aims to provide a comprehensive view of mechanisms behind ASR systems as well as techniques, tools, projects, recent contributions, and possible future directions in ASR using a limited vocabulary. This work consequently provides a way forward when designing an ASR system using limited vocabulary. Although an emphasis is put on limited vocabulary, most of the tools and techniques reported in this survey can be applied to ASR systems in general. AbbreviationsACC: Accuracy; AM: Acoustic Model; ASR: Automatic Speech Recognition; BD-4SK-ASR: Basic Dataset for Sorani Kurdish Automatic Speech Recognition; CER: Character Error Rate; CMU: Carnegie Mellon University; CNN: Convolutional Neural Network; CNTK: CogNitive ToolKit; CUED: Cambridge University Engineering Department; DCT:Discrete Cosine Transformation; DL: Deep Learning; DNN: Deep Neural Network; DRL: Deep Reinforcement Learning; DWT: Discrete Wavelet Transform; FFT: Fast Fourier Transformation; GMM: Gaussian Mixture Model; HMM: Hidden Markov Model; HTK: Hidden Markov Model ToolKit; JASPER: Just Another Speech Recognizer; LDA: Linear Discriminant Analysis; LER: Letter Error Rate; LGB: Light Gradient Boosting Machine; LM:Language Model; LPC: Linear Predictive Coding; LVCSR: Large Vocabulary Continuous Speech Recognition; LVQ: Learning Vector Quantization Algorithm; MFCC: Mel-Frequency Cepstrum Coefficient; ML: Machine Learning; PCM:Pulse-Code Modulation; PPVT: Peabody Picture Vocabulary Test; RASTA: RelAtive SpecTral; RLAT: Rapid Language Adaptation Toolkit; S2ST: Speech-to-Speech Translation; SAPI: Speech Application Programming Interface; SDK: Software Development Kit; SVASR:Small Vocabulary Automatic Speech Recognition; WER: Word Error Rate

In Senegal, sorghum ranks third after millet and maize among dryland cereal production and plays a critical role in the daily lives of millions of inhabitants. Yet, the crop’s productivity and profitability are hampered by biotic stresses, including Exserohilum turcicum, causing leaf blight. A total of 101 sorghum accessions collected from Niger and Senegal, SC748-5 and BTx623, were evaluated in three different environments (Kaymor, Kolda, and Ndiaganiao) in Senegal for their reactions against the leaf blight pathogen. The results showed that 11 out of the 101 accessions evaluated exhibited 100% incidence, and the overall mean incidence was 88.4%. Accession N15 had the lowest incidence of 50%. The overall mean severity was 31.6%, while accessions N15, N43, N38, N46, N30, N28, and N23 from Niger recorded the lowest severity levels, ranging from 15.5% to 25.5%. Accession N15 exhibited both low leaf blight incidence and severity, indicating that it may possess genes for resistance to E. turcicum. Also, the accessions evaluated in this study were sequenced. A GWAS identified six novel single-nucleotide polymorphisms (SNPs) associated with an average leaf blight incidence rate. The candidate genes were found in chromosomes 2, 3, 5, 8, and 9. Except for SNP locus S05_48064154, all five SNPs associated with the leaf blight incidence rate were associated with the plant defense and stress responses. In conclusion, the candidate genes identified could offer additional options for enhancing plant resistance against E. turcicum through plant breeding or gene editing.

Abstract Anthracnose caused by the fungal pathogen C. sublineola is an economically important constraint on worldwide sorghum production. The most effective strategy to safeguard yield is through the introgression of resistance alleles. This requires elucidation of the genetic basis of the different resistance sources that have been identified. In this study, 223 recombinant inbred lines (RILs) derived from crossing anthracnose-differentials QL3 (96 RILs) and IS18760 (127 RILs) with the common susceptible parent PI609251 were evaluated at four field locations in the United States (Florida, Georgia, Texas, and Puerto Rico) for their anthracnose resistance response. Both RIL populations were highly susceptible to anthracnose in Florida and Georgia, while in Puerto Rico and Texas they were segregating for anthracnose resistance response. A genome scan using a composite linkage map of 982 single nucleotide polymorphisms (SNPs) detected two genomic regions of 4.31 and 0.85 Mb on chromosomes 4 and 8, respectively, that explained 10–27% of the phenotypic variation in Texas and Puerto Rico. In parallel, a subset of 43 RILs that contained 67% of the recombination events were evaluated against anthracnose pathotypes from Arkansas (2), Puerto Rico (2) and Texas (4) in the greenhouse. A genome scan showed that the 7.57 Mb region at the distal end of the short arm of chromosome 5 is associated with the resistance response against the pathotype AMP-048 from Arkansas. Comparative analysis identified the genomic region on chromosome 4 overlaps with an anthracnose resistance locus identified in another anthracnose-differential line, SC414-12E, indicating this genomic region is of interest for introgression in susceptible sorghum germplasm. Candidate gene analysis for the resistance locus on chromosome 5 identified an R-gene cluster that has high similarity to another R-gene cluster associated with anthracnose resistance on chromosome 9.

Sorghum is considered the fifth most important crop in the world. Despite the potential value of Senegalese germplasm for various traits, such as resistance to fungal diseases, there is limited information on the study of sorghum seed morphology. In this study, 162 Senegalese germplasms were evaluated for seed area size, length, width, length-to-width ratio, perimeter, circularity, the distance between the intersection of length & width (IS) and center of gravity (CG), and seed darkness and brightness by scanning and analyzing morphology-related traits with SmartGrain software at the USDA-ARS Plant Science Research Unit. Correlations between seed morphology-related traits and traits associated with anthracnose and head smut resistance were analyzed. Lastly, genome-wide association studies were performed on phenotypic data collected from over 16,000 seeds and 193,727 publicly available single nucleotide polymorphisms (SNPs). Several significant SNPs were found and mapped to the reference sorghum genome to uncover multiple candidate genes potentially associated with seed morphology. The results indicate clear correlations among seed morphology-related traits and potential associations between seed morphology and the defense response of sorghum. GWAS analysis listed candidate genes associated with seed morphologies that can be used for sorghum breeding in the future.

Due to advances in electron microscopy and deep learning, it is now practical to reconstruct a connectome, a description of neurons and the chemical synapses between them, for significant volumes of neural tissue. Smaller past reconstructions were primarily used by domain experts, could be handled by downloading data, and performance was not a serious problem. But new and much larger reconstructions upend these assumptions. These networks now contain tens of thousands of neurons and tens of millions of connections, with yet larger reconstructions pending, and are of interest to a large community of non-specialists. Allowing other scientists to make use of this data needs more than publication—it requires new tools that are publicly available, easy to use, and efficiently handle large data. We introduce neuPrint to address these data analysis challenges. Neuprint contains two major components—a web interface and programmer APIs. The web interface is designed to allow any scientist worldwide, using only a browser, to quickly ask and answer typical biological queries about a connectome. The neuPrint APIs allow more computer-savvy scientists to make more complex or higher volume queries. NeuPrint also provides features for assessing reconstruction quality. Internally, neuPrint organizes connectome data as a graph stored in a neo4j database. This gives high performance for typical queries, provides access though a public and well documented query language Cypher, and will extend well to future larger connectomics databases. Our experience is also an experiment in open science. We find a significant fraction of the readers of the article proceed to examine the data directly. In our case preprints worked exactly as intended, with data inquiries and PDF downloads starting immediately after pre-print publication, and little affected by formal publication later. From this we deduce that many readers are more interested in our data than in our analysis of our data, suggesting that data-only papers can be well appreciated and that public data release can speed up the propagation of scientific results by many months. We also find that providing, and keeping, the data available for online access imposes substantial additional costs to connectomics research.

Abstract Basal leaf angle distribution was surveyed in twenty-one Johnsongrass cultivars near the end of the vegetative stage. The angles increased from the top to the bottom leaves, and compared to cultivated grain sorghums, the average angle was larger in Johnsongrass. When basal leaf angle distribution data were correlated with pathogenicity test data from excised-leaf assays for three isolates of Colletotrichum sublineola, the results showed a weak positive correlation between basal leaf angle and pathogenicity level in Johnsongrass. In order to investigate a protective role of leaf thickness to C. sublineola, leaf thickness was measured in three sorghum cultivars and one Johnsongrass cultivar at the 8-leaf-stage. Leaf thickness near the apex, near the base, and half-way between the two points were measured in the top four leaves of each plant. Thickness of leaf blade and midrib were recorded separately. Using an excised-leaf-assay, the three points were inoculated with C. sublineola, and pathogenicity level was recorded 4-days-post-inoculation. Results showed strong negative correlations between leaf midrib thickness and pathogenicity level in sorghum and Johnsongrass but not in leaf blades.

Streptomyces bacteria produce metabolites with diverse biological activities. Here, Ho et al. show that some of these metabolites are volatile terpenes that attract fruit flies, while other metabolites can kill the insect’s larvae upon ingestion of bacterial spores.