Enhanced ASR Robustness to Packet Loss with a Front-End Adaptation Network

In the realm of automatic speech recognition (ASR), robustness in noisy environments remains a significant challenge. Recent ASR models, such as Whisper, have shown promise, but their efficacy in noisy conditions can be further enhanced. This study is focused on recovering from packet loss to improve the word error rate (WER) of ASR models. We propose using a front-end adaptation network connected to a frozen ASR model. The adaptation network is trained to modify the corrupted input spectrum by minimizing the criteria of the ASR model in addition to an enhancement loss function. Our experiments demonstrate that the adaptation network, trained on Whisper's criteria, notably reduces word error rates across domains and languages in packet-loss scenarios. This improvement is achieved with minimal affect to Whisper model's foundational performance, underscoring our method's practicality and potential in enhancing ASR models in challenging acoustic environments.Comment: Accepted for publication at INTERSPEECH 202

Microsatellite polymorphism between and within broiler populations

Two independent broiler chicken populations were genotyped with microsatellite markers to determine genetic polymorphisms within and among broiler populations. Birds were genotyped with primers from the US Poultry Genome Mapping Kits 1 and 2. The 59 primer sets selected for this study provided wide genomic coverage. All 59 primer sets amplified a polymerase chain reaction product in Population L, whereas 57 primer sets produced a product in Population C. The average allele number per line per microsatellite was 2.8 and 2.9 for Populations L and C, respectively. Considering the 57 primer pairs generating product in both lines, 72.3% of the total alleles were unique to one or the other population. This study illustrates the high polymorphism level in broiler populations of microsatellites amplified from primers developed from Red Jungle Fowl or White Leghorn sequences

Detecting parent of origin and dominant QTL in a two-generation commercial poultry pedigree using variance component methodology

<p>Abstract</p> <p>Introduction</p> <p>Variance component QTL methodology was used to analyse three candidate regions on chicken chromosomes 1, 4 and 5 for dominant and parent-of-origin QTL effects. Data were available for bodyweight and conformation score measured at 40 days from a two-generation commercial broiler dam line. One hundred dams were nested in 46 sires with phenotypes and genotypes on 2708 offspring. Linear models were constructed to simultaneously estimate fixed, polygenic and QTL effects. Different genetic models were compared using likelihood ratio test statistics derived from the comparison of full with reduced or null models. Empirical thresholds were derived by permutation analysis.</p> <p>Results</p> <p>Dominant QTL were found for bodyweight on chicken chromosome 4 and for bodyweight and conformation score on chicken chromosome 5. Suggestive evidence for a maternally expressed QTL for bodyweight and conformation score was found on chromosome 1 in a region corresponding to orthologous imprinted regions in the human and mouse.</p> <p>Conclusion</p> <p>Initial results suggest that variance component analysis can be applied within commercial populations for the direct detection of segregating dominant and parent of origin effects.</p

Temporal transcriptome changes induced by MDV in marek's disease-resistant and -susceptible inbred chickens

<p>Abstract</p> <p>Background</p> <p>Marek's disease (MD) is a lymphoproliferative disease in chickens caused by Marek's disease virus (MDV) and characterized by T cell lymphoma and infiltration of lymphoid cells into various organs such as liver, spleen, peripheral nerves and muscle. Resistance to MD and disease risk have long been thought to be influenced both by genetic and environmental factors, the combination of which contributes to the observed outcome in an individual. We hypothesize that after MDV infection, genes related to MD-resistance or -susceptibility may exhibit different trends in transcriptional activity in chicken lines having a varying degree of resistance to MD.</p> <p>Results</p> <p>In order to study the mechanisms of resistance and susceptibility to MD, we performed genome-wide temporal expression analysis in spleen tissues from MD-resistant line 6₃, susceptible line 7₂and recombinant congenic strain M (RCS-M) that has a phenotype intermediate between lines 6₃and 7₂after MDV infection. Three time points of the MDV life cycle in chicken were selected for study: 5 days post infection (dpi), 10dpi and 21dpi, representing the early cytolytic, latent and late cytolytic stages, respectively. We observed similar gene expression profiles at the three time points in line 6₃and RCS-M chickens that are both different from line 7₂. Pathway analysis using Ingenuity Pathway Analysis (IPA) showed that MDV can broadly influence the chickens irrespective of whether they are resistant or susceptible to MD. However, some pathways like cardiac arrhythmia and cardiovascular disease were found to be affected only in line 7₂; while some networks related to cell-mediated immune response and antigen presentation were enriched only in line 6₃and RCS-M. We identified 78 and 30 candidate genes associated with MD resistance, at 10 and 21dpi respectively, by considering genes having the same trend of expression change after MDV infection in lines 6₃and RCS-M. On the other hand, by considering genes with the same trend of expression change after MDV infection in lines 7₂and RCS-M, we identified 78 and 43 genes at 10 and 21dpi, respectively, which may be associated with MD-susceptibility.</p> <p>Conclusions</p> <p>By testing temporal transcriptome changes using three representative chicken lines with different resistance to MD, we identified 108 candidate genes for MD-resistance and 121 candidate genes for MD-susceptibility over the three time points. Genes included in our resistance or susceptibility genes lists that are also involved in more than 5 biofunctions, such as <it>CD8α</it>, <it>IL8</it>, <it>USP18</it>, and <it>CTLA4</it>, are considered to be important genes involved in MD-resistance or -susceptibility. We were also able to identify several biofunctions related with immune response that we believe play an important role in MD-resistance.</p

Wrong–Site Surgery: Does That Really Happen?

It’s going to be a busy day in the operating room (OR). The orthopedic group has a full caseload, neurosurgery is performing four spinal cases. The new general surgeon has two gall bladder cases and anesthesia is doing a half-dozen pain management injections in the block room. The first case of the day has been delayed, as an auto accident on the freeway has the surgeon stuck in traffic. The preoperative care unit is filled with anxious patients and their significant others. Transport personnel are arriving with patients from the nursing units as staff are busy starting intravenous lines and initiating preop orders. All in a coordinated effort, preparing for surgical procedures. What could possibly go wrong

Wrong-Site Surgery in Pennsylvania During 2015–2019: A Study of Variables Associated With 368 Events From 178 Facilities

Wrong-site surgery (WSS) is a well-known type of medical error that may cause a high degree of patient harm. In Pennsylvania, healthcare facilities are mandated to report WSS events, among other patient safety concerns, to the Pennsylvania Patient Safety Reporting System (PA-PSRS) database. In the study we identified instances of WSS events (not including near misses) that occurred during 2015–2019 and were reported to PA-PSRS. During the five-year period, we found that 178 healthcare facilities reported a total of 368 WSS events, which was an average of 1.42 WSS events per week in Pennsylvania. Also, we revealed that 76% (278 of 368) of the WSS events contributed to or resulted in temporary harm or permanent harm to the patient. Overall, the study shows that the frequency of WSS varied according to a range of variables, including error type (e.g., wrong side, wrong site, wrong procedure, wrong patient); year; facility type; hospital bed size; hospital procedure location; procedure; body region; body part; and clinician specialty. Our findings are aligned with some of the previous research on WSS; however, the current study also addresses many gaps in the literature. We encourage readers to use the visuals in the manuscript and appendices to gain new insight into the relation among the variables associated with WSS. Ultimately, the findings reported in the current study help to convey a more complete account of the variables associated with WSS, which can be used to assist staff in making informed decisions about allocating resources to mitigate risk.</jats:p

Online Supplement to "Wrong-Site Surgery in Pennsylvania During 2015–2019: A Study of Variables Associated With 368 Events From 178 Facilities"

This supplementary material has been provided by the authors to give readers additional information about their work.</jats:p