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Adjusting expectations in response to errors is a cornerstone of several learning theories (Rescorla & Wagner, 1972; Rumelhart et al., 1986). Grimmick (2019) shows that individuals deploy attention during cross-situational word learning based on the strength of the error signal. The current study introduced an equal number of accurate and inaccurate expectations about word-referent pairs. This study manipulated the difficulty of cross-situational word learning trials to examine whether the impact of errors differs depending on task demands. Individuals learned the initially accurate items better than the initially inaccurate ones. Manipulating the demands during word learning did not significantly impact the tendency to benefit from accuracy. This research is part of an ongoing project. This ongoing research explores how individual differences in vocabulary, inhibition, and working memory abilities interact with contextual factors, such as task difficulty, as individuals learn word-referent pairs that violate their expectations.

The Staphylococcus aureus Network Adaptive Platform (SNAP) trial is a multifactorial Bayesian adaptive platform trial that aims to improve the way that S. aureus bloodstream infection, a globally common and severe infectious disease, is treated. In a world first, the SNAP trial will simultaneously investigate the effects of multiple intervention modalities within multiple groups of participants with different forms of S. aureus bloodstream infection. Here, we formalise the trial structure, modelling approach, and decision rules that will be used for the SNAP trial. By summarising the statistical principles governing the design, our hope is that the SNAP trial will serve as an adaptable template that can be used to improve comparative effectiveness research efficiency in other disease areas.Trial registration NCT05137119 . Registered on 30 November 2021.

BackgroundThe Pediatric Emergency Medicine (PEM) Point-of-care Ultrasound (POCUS) Network (P2Network) was established in 2014 to provide a platform for international collaboration among experts, including multicenter research. The objective of this study was to use expert consensus to identify and prioritize PEM POCUS topics, to inform future collaborative multicenter research.MethodsOnline surveys were administered in a two-stage, modified Delphi study. A steering committee of 16 PEM POCUS experts was identified within the P2Network, with representation from the United States, Canada, Italy, and Australia. We solicited the participation of international PEM POCUS experts through professional society mailing lists, research networks, social media, and "word of mouth." After each round, responses were refined by the steering committee before being reissued to participants to determine the ranking of all the research questions based on means and to identify the high-level consensus topics. The final stage was a modified Hanlon process of prioritization round (HPP), which emphasized relevance, impact, and feasibility.ResultsFifty-four eligible participants (16.6%) provided 191 items to Survey 1 (Round 1). These were refined and consolidated into 52 research questions by the steering committee. These were issued for rating in Survey 2 (Round 2), which had 45 participants. At the completion of Round 2, all questions were ranked with six research questions reaching high-level consensus. Thirty-one research questions with mean ratings above neutral were selected for the HPP round. Highly ranked topics included clinical applications of POCUS to evaluate and manage children with shock, cardiac arrest, thoracoabdominal trauma, suspected cardiac failure, atraumatic limp, and intussusception.ConclusionsThis consensus study has established a research agenda to inform future international multicenter PEM POCUS trials. This study has highlighted the ongoing need for high-quality evidence for PEM POCUS applications to guide clinical practice.

According to prediction-based theories, prior learning creates expectations for subsequent learning events. As they learn words, individuals develop accurate and inaccurate expectations about word meaning. Existing research shows that people who shift their gaze to the referent of words more quickly have larger vocabularies. This shifting reflects the processing of accurate expectations about the referents of these words. Is the speed of processing inaccurate expectations also related to vocabulary size? To examine this question, adults learned eight novel word-object mappings during cross-situational word learning (CSWL). The mappings were either consistent or inconsistent with a prior familiarization phase. Early in CSWL, hearing inconsistent words violated expectations about the referent of those words. Shifting from a distractor to the target of an inconsistent word during CSWL was significantly associated with productive and receptive vocabulary. These findings are consistent with prediction-based theories, in which individuals use prediction errors to adjust their expectations.

BackgroundMajor advances in selection progress for cattle have been made following the introduction of genomic tools over the past 10-12 years. These tools depend upon the Bos taurus reference genome (UMD3.1.1), which was created using now-outdated technologies and is hindered by a variety of deficiencies and inaccuracies.ResultsWe present the new reference genome for cattle, ARS-UCD1.2, based on the same animal as the original to facilitate transfer and interpretation of results obtained from the earlier version, but applying a combination of modern technologies in a de novo assembly to increase continuity, accuracy, and completeness. The assembly includes 2.7 Gb and is >250× more continuous than the original assembly, with contig N50 >25 Mb and L50 of 32. We also greatly expanded supporting RNA-based data for annotation that identifies 30,396 total genes (21,039 protein coding). The new reference assembly is accessible in annotated form for public use.ConclusionsWe demonstrate that improved continuity of assembled sequence warrants the adoption of ARS-UCD1.2 as the new cattle reference genome and that increased assembly accuracy will benefit future research on this species.

BackgroundMajor advances in selection progress for cattle have been made following the introduction of genomic tools over the past 10-12 years. These tools depend upon the Bos taurus reference genome (UMD3.1.1), which was created using now-outdated technologies and is hindered by a variety of deficiencies and inaccuracies.ResultsWe present the new reference genome for cattle, ARS-UCD1.2, based on the same animal as the original to facilitate transfer and interpretation of results obtained from the earlier version, but applying a combination of modern technologies in a de novo assembly to increase continuity, accuracy, and completeness. The assembly includes 2.7 Gb and is >250× more continuous than the original assembly, with contig N50 >25 Mb and L50 of 32. We also greatly expanded supporting RNA-based data for annotation that identifies 30,396 total genes (21,039 protein coding). The new reference assembly is accessible in annotated form for public use.ConclusionsWe demonstrate that improved continuity of assembled sequence warrants the adoption of ARS-UCD1.2 as the new cattle reference genome and that increased assembly accuracy will benefit future research on this species.

Errors are usually viewed as detrimental to learning. Yet, recent proposals suggest that errors may create desirable difficul-ties and thereby improve learning. We evaluated these proposals in the context of cross-situational word learning. Duringeach learning trial, adults saw two images and heard two words. In the Error1 condition, the first word was unexpectedbased on prior experience and the second was expected. The referent of the unexpected word could only be establishedafter hearing the expected word. In the Error2 condition, the expected word came first, which made it easier to learn themapping of the subsequent unexpected word. There was no difference between the conditions; however, expected wordswere only learned significantly better than unexpected words in the Error2 condition. This suggests that the structure ofthe learning environment modulates the impact of errors.

How do individuals learn language when there are so many possible potential referents for each word? Prediction-basedtheories of language learning propose that predictions enable individuals to learn from implicit negative evidence bycomparing the predictions to outcomes. However, the role of prediction errors for learning has yet to be established.Traditionally, prediction errors have been believed to hinder learning. Recently though, prediction errors have been as-sociated with improved novel word acquisition in cross situational learning. This present study used a cross-situationalword learning task to examine the relation between prediction error-based processes during word learning and vocabularysize. The results showed that learners who switched their gaze more quickly from the non-target to the target image whenthey had to detect and correct prediction errors had higher productive vocabularies. This research supports the theory thatproductive vocabulary is strongly tied to predictive processes.

The rumen is a complex ecosystem composed of anaerobic bacteria, protozoa, fungi, methanogenic archaea and phages. These microbes interact closely to breakdown plant material that cannot be digested by humans, whilst providing metabolic energy to the host and, in the case of archaea, producing methane. Consequently, ruminants produce meat and milk, which are rich in high-quality protein, vitamins and minerals, and therefore contribute to food security. As the world population is predicted to reach approximately 9.7 billion by 2050, an increase in ruminant production to satisfy global protein demand is necessary, despite limited land availability, and whilst ensuring environmental impact is minimized. Although challenging, these goals can be met, but depend on our understanding of the rumen microbiome. Attempts to manipulate the rumen microbiome to benefit global agricultural challenges have been ongoing for decades with limited success, mostly due to the lack of a detailed understanding of this microbiome and our limited ability to culture most of these microbes outside the rumen. The potential to manipulate the rumen microbiome and meet global livestock challenges through animal breeding and introduction of dietary interventions during early life have recently emerged as promising new technologies. Our inability to phenotype ruminants in a high-throughput manner has also hampered progress, although the recent increase in "omic" data may allow further development of mathematical models and rumen microbial gene biomarkers as proxies. Advances in computational tools, high-throughput sequencing technologies and cultivation-independent "omics" approaches continue to revolutionize our understanding of the rumen microbiome. This will ultimately provide the knowledge framework needed to solve current and future ruminant livestock challenges.

The rumen is a complex ecosystem composed of anaerobic bacteria, protozoa, fungi, methanogenic archaea and phages. These microbes interact closely to breakdown plant material that cannot be digested by humans, whilst providing metabolic energy to the host and, in the case of archaea, producing methane. Consequently, ruminants produce meat and milk, which are rich in high-quality protein, vitamins and minerals, and therefore contribute to food security. As the world population is predicted to reach approximately 9.7 billion by 2050, an increase in ruminant production to satisfy global protein demand is necessary, despite limited land availability, and whilst ensuring environmental impact is minimized. Although challenging, these goals can be met, but depend on our understanding of the rumen microbiome. Attempts to manipulate the rumen microbiome to benefit global agricultural challenges have been ongoing for decades with limited success, mostly due to the lack of a detailed understanding of this microbiome and our limited ability to culture most of these microbes outside the rumen. The potential to manipulate the rumen microbiome and meet global livestock challenges through animal breeding and introduction of dietary interventions during early life have recently emerged as promising new technologies. Our inability to phenotype ruminants in a high-throughput manner has also hampered progress, although the recent increase in "omic" data may allow further development of mathematical models and rumen microbial gene biomarkers as proxies. Advances in computational tools, high-throughput sequencing technologies and cultivation-independent "omics" approaches continue to revolutionize our understanding of the rumen microbiome. This will ultimately provide the knowledge framework needed to solve current and future ruminant livestock challenges.

Puberty is a complex physiological event by which animals mature into an adult capable of sexual reproduction. In order to enhance our understanding of the genes and regulatory pathways and networks involved in puberty, we characterized the transcriptome of five reproductive tissues (i.e. hypothalamus, pituitary gland, ovary, uterus, and endometrium) as well as tissues known to be relevant to growth and metabolism needed to achieve puberty (i.e., longissimus dorsi muscle, adipose, and liver). These tissues were collected from pre- and post-pubertal Brangus heifers (3/8 Brahman; Bos indicus x 5/8 Angus; Bos taurus) derived from a population of cattle used to identify quantitative trait loci associated with fertility traits (i.e., age of first observed corpus luteum (ACL), first service conception (FSC), and heifer pregnancy (HPG)). In order to exploit the power of complementary omics analyses, pre- and post-puberty co-expression gene networks were constructed by combining the results from genome-wide association studies (GWAS), RNA-Seq, and bovine transcription factors. Eight tissues among pre-pubertal and post-pubertal Brangus heifers revealed 1,515 differentially expressed and 943 tissue-specific genes within the 17,832 genes confirmed by RNA-Seq analysis. The hypothalamus experienced the most notable up-regulation of genes via puberty (i.e., 204 out of 275 genes). Combining the results of GWAS and RNA-Seq, we identified 25 loci containing a single nucleotide polymorphism (SNP) associated with ACL, FSC, and (or) HPG. Seventeen of these SNP were within a gene and 13 of the genes were expressed in uterus or endometrium. Multi-tissue omics analyses revealed 2,450 co-expressed genes relative to puberty. The pre-pubertal network had 372,861 connections whereas the post-pubertal network had 328,357 connections. A sub-network from this process revealed key transcriptional regulators (i.e., PITX2, FOXA1, DACH2, PROP1, SIX6, etc.). Results from these multi-tissue omics analyses improve understanding of the number of genes and their complex interactions for puberty in cattle.

Puberty is a complex physiological event by which animals mature into an adult capable of sexual reproduction. In order to enhance our understanding of the genes and regulatory pathways and networks involved in puberty, we characterized the transcriptome of five reproductive tissues (i.e. hypothalamus, pituitary gland, ovary, uterus, and endometrium) as well as tissues known to be relevant to growth and metabolism needed to achieve puberty (i.e., longissimus dorsi muscle, adipose, and liver). These tissues were collected from pre- and post-pubertal Brangus heifers (3/8 Brahman; Bos indicus x 5/8 Angus; Bos taurus) derived from a population of cattle used to identify quantitative trait loci associated with fertility traits (i.e., age of first observed corpus luteum (ACL), first service conception (FSC), and heifer pregnancy (HPG)). In order to exploit the power of complementary omics analyses, pre- and post-puberty co-expression gene networks were constructed by combining the results from genome-wide association studies (GWAS), RNA-Seq, and bovine transcription factors. Eight tissues among pre-pubertal and post-pubertal Brangus heifers revealed 1,515 differentially expressed and 943 tissue-specific genes within the 17,832 genes confirmed by RNA-Seq analysis. The hypothalamus experienced the most notable up-regulation of genes via puberty (i.e., 204 out of 275 genes). Combining the results of GWAS and RNA-Seq, we identified 25 loci containing a single nucleotide polymorphism (SNP) associated with ACL, FSC, and (or) HPG. Seventeen of these SNP were within a gene and 13 of the genes were expressed in uterus or endometrium. Multi-tissue omics analyses revealed 2,450 co-expressed genes relative to puberty. The pre-pubertal network had 372,861 connections whereas the post-pubertal network had 328,357 connections. A sub-network from this process revealed key transcriptional regulators (i.e., PITX2, FOXA1, DACH2, PROP1, SIX6, etc.). Results from these multi-tissue omics analyses improve understanding of the number of genes and their complex interactions for puberty in cattle.