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Ketoprofen is a non-steroidal anti-inflammatory drug (NSAID) used to treat pain and inflammation in dogs. Despite having effective analgesic efficacy, prolonged oral administration has been associated with adverse effects. Transdermal delivery of ketoprofen has reduced the incidence of adverse effects in humans and could potentially be used in veterinary clinical medicine. A transdermal (TD) ketoprofen formulation was recently developed for use in dogs and this study aimed to determine the pharmacodynamic activity of this formulation using surgical castration as an acute inflammatory model. Twelve dogs were randomly assigned to either a Control group (n = 6) or a TD group (n = 6). All dogs were castrated using standard surgical protocols, administered with pre-medication, consisting of acepromazine (0.055 mg/kg) and methadone (0.5 mg/kg) intramuscularly (IM) 30 min prior to induction of general anaesthesia. All dogs were then anaesthetised by injecting alfaxalone (2 mg/kg IV) via a 20 G 3 cm catheter in the left cephalic vein and subsequently maintained using isoflurane. Along with that TD group dogs also received TD ketoprofen (10 mg/kg) 1 h before pre-medication. Bloods were collected at 0 - hour (pre-surgery), and 1 and 2-h post-surgery and analysed for circulating eicosanoids using liquid chromatography mass spectrometry (LCMS) methods. Measured levels of Thromboxane B 2 (TXB 2 ) at both 1 and 2 h and Prostaglandin E 2 (PGE 2 ) at 2 h post-surgery were higher in the Control group compared to the TD group, suggesting pre-operative application of TD ketoprofen has a possible inhibitory effect on systemic inflammation and could be used to treat pain and inflammation in dogs., Competing Interests: Declaration of competing interest None of the authors has any financial or personal relationships that could inappropriately influence or bias the content of the paper., (Crown Copyright © 2024. Published by Elsevier Ltd. All rights reserved.)

Development of artificial intelligence (AI) for medical imaging demands curation and cleaning of large-scale clinical datasets comprising hundreds of thousands of images. Some modalities, such as mammography, contain highly standardized imaging. In contrast, breast ultrasound imaging (BUS) can contain many irregularities not indicated by scan metadata, such as enhanced scan modes, sonographer annotations, or additional views. We present an open-source software solution for automatically processing clinical BUS datasets. The algorithm performs BUS scan filtering (flagging of invalid and non-B-mode scans), cleaning (dual-view scan detection, scan area cropping, and caliper detection), and knowledge extraction (BI-RADS Labeling and Measurement fields) from sonographer annotations. Its modular design enables users to adapt it to new settings. Experiments on an internal testing dataset of 430 clinical BUS images achieve >95% sensitivity and >98% specificity in detecting every type of text annotation, >98% sensitivity and specificity in detecting scans with blood flow highlighting, alternative scan modes, or invalid scans. A case study on a completely external, public dataset of BUS scans found that BUSClean identified text annotations and scans with blood flow highlighting with 88.6% and 90.9% sensitivity and 98.3% and 99.9% specificity, respectively. Adaptation of the lesion caliper detection method to account for a type of caliper specific to the case study demonstrates the intended use of BUSClean in new data distributions and improved performance in lesion caliper detection from 43.3% and 93.3% out-of-the-box to 92.1% and 92.3% sensitivity and specificity, respectively. Source code, example notebooks, and sample data are available at https://github.com/hawaii-ai/bus-cleaning., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Bunnell et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Specific lipid isomers are functionally critical, but their structural rigidity and usually minute geometry differences make separating them harder than other biomolecules. Such separations by ion mobility spectrometry (IMS) were recently enabled by new high-definition methods using dynamic electric fields, but major resolution gains are needed. Another problem of identifying many isomers with no unique fragments in ergodic collision-induced dissociation (CID) was partly addressed by the direct ozone-induced dissociation (OzID) that localizes the double bonds, but a low reaction efficiency has limited the sensitivity, dynamic range, throughput, and compatibility with other tools. Typically lipids are analyzed by MS as singly charged protonated, deprotonated, or ammoniated ions. Here, we explore the differential IMS (FAIMS) separations with OzID for exemplary lipids cationized by polyvalent metals. These multiply charged adducts have much greater FAIMS compensation voltages ( U C ) than the 1+ ions, with up to 10-fold resolution gain enabling baseline isomer separations even at a moderate resolving power of the SelexION stage. Concomitantly OzID speeds up by many orders of magnitude, producing a high yield of diagnostic fragments already in 1 ms. These capabilities can be ported to the superior high-definition FAIMS and high-pressure OzID systems to take lipidomic analyses to the next level.

Background: The purpose was to analyse the interrelations between planning and complexity metrics and gamma passing rates (GPRs) obtained from VMAT treatments and build the forecasting models for qualitative prediction (QD) of GPRs results., Materials and Method: 802 treatment arcs from the plans prepared for the head and neck, thorax, abdomen, and pelvic cancers were analysed. The plans were verified by portal dosimetry and analysed twice using the gamma method with 3%|2mm and 2%|2mm acceptance criteria. The tolerance limit of GPR was 95%. Red, yellow, and green QDs were established for GPR examination. The interrelations were examined, as well as the analysis of effective differentiation of QD. Three models for QD forecasting based on discriminant analysis (DA), random decision forest (RDF) methods, and the hybrid model (HM) were built and evaluated., Results: Most of the interrelations were small or moderate. The exception is correlations of the join function with the average number of monitor units per control point (R = 0.893) and the beam aperture with planning target volume (R = 0.897). While many metrics allow for the effective separation of the QDs from each other, the study shows that predicting the values of the QD is possible only through multi-component forecasting models, of which the HM is the most accurate (0.894)., Conclusion: Of the three models explored in this study, the HM, which uses DA methods to predict red QD and RDF methods to predict green and yellow QDs, is the most promising one., Competing Interests: Conflict of interests: Authors declare no conflict of interests, (© 2024 Greater Poland Cancer Centre.)

SWATH is a data acquisition strategy acclaimed for generating quantitatively accurate and consistent measurements of proteins across multiple samples. Its utility for proteomics studies in nonlaboratory animals, however, is currently compromised by the lack of sufficiently comprehensive and reliable public libraries, either experimental or predicted, and relevant platforms that support their sharing and utilization in an intuitive manner. Here we describe the development of the Veterinary Proteome Browser, VPBrowse (http://browser.proteo.cloud/), an on-line platform for genome-based representation of the Bos taurus proteome, which is equipped with an interactive database and tools for searching, visualization, and building quantitative mass spectrometry assays. In its current version (VPBrowse 1.0), it contains high-quality fragmentation spectra acquired on QToF instrument for over 36,000 proteotypic peptides, the experimental evidence for over 10,000 proteins. Data can be downloaded in different formats to enable analysis using popular software packages for SWATH data processing whilst normalization to iRT scale ensures compatibility with diverse chromatography systems. When applied to published blood plasma dataset from the biomarker discovery study, the resource supported label-free quantification of additional proteins not reported by the authors previously including PSMA4, a tissue leakage protein and a promising candidate biomarker of animal's response to dehorning-related injury., (© 2024 The Authors. PROTEOMICS published by Wiley‐VCH GmbH.)

Phthalate esters are known to be endocrine disrupting chemicals and are documented to pollute environments. Enzymatic degradation of PAEs is a potential bioremedial strategy to manage contamination. Thermostable bioremedial enzymes have advantages in enzyme manufacturing and storage. In this study, we identified, overexpressed, and characterised a moderately thermostable para-nitrobenzyl esterase from whole genome sequencing of a Bacillus velezensis NP05 from the Great Artesian Basin , capable of sequential 2-step hydrolysis of diisobutyl phthalate. The pnbA enzyme has a molecular weight of 55.14 kDa and pI of 5.31. It preferentially degrades para-nitrophenyl butanoate and has an optimal pH of 7-8. The pnbA esterase has an optimal temperature of 55 °C with a half-life of 4 h. Using HPLC we found that pnbA (0.122 U) can hydrolyse 0.83 mM of DIBP within 25 min. Lastly, pnbA is potentially a more economically viable candidate for enzymatic bioremediation of diisobutyl phthalate as a free enzyme., Competing Interests: None., (© 2024 The Authors. Published by Elsevier B.V.)

Background: Artificial intelligence (AI) large language models (LLMs) such as ChatGPT have demonstrated the ability to pass standardized exams. These models are not trained for a specific task, but instead trained to predict sequences of text from large corpora of documents sourced from the internet. It has been shown that even models trained on this general task can pass exams in a variety of domain-specific fields, including the United States Medical Licensing Examination. We asked if large language models would perform as well on a much narrower subdomain tests designed for medical specialists. Furthermore, we wanted to better understand how progressive generations of GPT (generative pre-trained transformer) models may be evolving in the completeness and sophistication of their responses even while generational training remains general. In this study, we evaluated the performance of two versions of GPT (GPT 3 and 4) on their ability to pass the certification exam given to physicians to work as osteoporosis specialists and become a certified clinical densitometrists. The CCD exam has a possible score range of 150 to 400. To pass, you need a score of 300., Methods: A 100-question multiple-choice practice exam was obtained from a 3rd party exam preparation website that mimics the accredited certification tests given by the ISCD (International Society for Clinical Densitometry). The exam was administered to two versions of GPT, the free version (GPT Playground) and ChatGPT+, which are based on GPT-3 and GPT-4, respectively (OpenAI, San Francisco, CA). The systems were prompted with the exam questions verbatim. If the response was purely textual and did not specify which of the multiple-choice answers to select, the authors matched the text to the closest answer. Each exam was graded and an estimated ISCD score was provided from the exam website. In addition, each response was evaluated by a rheumatologist CCD and ranked for accuracy using a 5-level scale. The two GPT versions were compared in terms of response accuracy and length., Results: The average response length was 11.6 ±19 words for GPT-3 and 50.0±43.6 words for GPT-4. GPT-3 answered 62 questions correctly resulting in a failing ISCD score of 289. However, GPT-4 answered 82 questions correctly with a passing score of 342. GPT-3 scored highest on the "Overview of Low Bone Mass and Osteoporosis" category (72 % correct) while GPT-4 scored well above 80 % accuracy on all categories except "Imaging Technology in Bone Health" (65 % correct). Regarding subjective accuracy, GPT-3 answered 23 questions with nonsensical or totally wrong responses while GPT-4 had no responses in that category., Conclusion: If this had been an actual certification exam, GPT-4 would now have a CCD suffix to its name even after being trained using general internet knowledge. Clearly, more goes into physician training than can be captured in this exam. However, GPT algorithms may prove to be valuable physician aids in the diagnoses and monitoring of osteoporosis and other diseases., Competing Interests: Conflicts of Interest The authors declare no conflict of interest., (Copyright © 2024 The International Society for Clinical Densitometry. Published by Elsevier Inc. All rights reserved.)

Background: Body shape, an intuitive health indicator, is deterministically driven by body composition. We developed and validated a deep learning model that generates accurate dual-energy X-ray absorptiometry (DXA) scans from three-dimensional optical body scans (3DO), enabling compositional analysis of the whole body and specified subregions. Previous works on generative medical imaging models lack quantitative validation and only report quality metrics., Methods: Our model was self-supervised pretrained on two large clinical DXA datasets and fine-tuned using the Shape Up! Adults study dataset. Model-predicted scans from a holdout test set were evaluated using clinical commercial DXA software for compositional accuracy., Results: Predicted DXA scans achieve R 2 of 0.73, 0.89, and 0.99 and RMSEs of 5.32, 6.56, and 4.15 kg for total fat mass (FM), fat-free mass (FFM), and total mass, respectively. Custom subregion analysis results in R 2 s of 0.70-0.89 for left and right thigh composition. We demonstrate the ability of models to produce quantitatively accurate visualizations of soft tissue and bone, confirming a strong relationship between body shape and composition., Conclusions: This work highlights the potential of generative models in medical imaging and reinforces the importance of quantitative validation for assessing their clinical utility., (© 2024. The Author(s).)

An analysis of placental chorionic villous and decidual basalis tissue immunoreactivity in patients after cesarean section due to a placenta accreta spectrum disorder and elective cesarean section followed by a depressed mood., Research Background: Over the past few years, interest in investigating immune dysfunction in patients with psychiatric disorders has increased. B7-H4 is a molecule with immunosuppressive properties that seems to play a key role in establishing maternal tolerance against fetal antigens. The aim of this study was to compare the B7-H4 immunoreactivity levels in patients after cesarean section., Methods: Placental and decidual tissue samples were obtained from 49 women who delivered at Bielański Hospital in Warsaw between 2009 and 2015. Fifteen of the patients developed postpartum depression and 14 had a diagnosis of placenta accreta spectrum. The control group consisted of 20 healthy patients on whom cesarean section was performed due to breech presentation at term., Results: The highest levels of B7-H4 immunoreactivity were found in the placental chorionic villous and decidual basalis tissue samples of the patients who later developed postpartum depression, while the lowest levels were found in the samples of those patients with a placenta accreta spectrum disorder. The difference between the B7-H4 immunoreactivity levels of these two groups was statistically significant. The B7-H4 expression levels were statistically significantly higher in the women in the postpartum depression group than in the control group., Conclusion: Postpartum depression follows a disturbance of the suppressive milieu responsible for rebalancing the maternal immune system after the initial cytotoxic activation during labor., (© 2023 Japan Society of Obstetrics and Gynecology.)

The development of biomarkers of fertility could provide benefits for the genetic improvement of dairy cows. Circulating small extracellular vesicles (sEVs) show promise as diagnostic or prognostic markers since their cargo reflects the metabolic state of the cell of origin; thus, they mirror the physiological status of the host. Here, we employed data-independent acquisition mass spectrometry to survey the plasma and plasma sEV proteomes of two different cohorts of Young (Peripubertal; n = 30) and Aged (Primiparous; n = 20) dairy cows ( Bos taurus ) of high- and low-genetic merit of fertility and known pregnancy outcomes (ProteomeXchange data set identifier PXD042891). We established predictive models of fertility status with an area under the curve of 0.97 (sEV; p value = 3.302e-07) and 0.95 (plasma; p value = 6.405e-08). Biomarker candidates unique to high-fertility Young cattle had a sensitivity of 0.77 and specificity of 0.67 (* p = 0.0287). Low-fertility biomarker candidates uniquely identified in sEVs from Young and Aged cattle had a sensitivity and specificity of 0.69 and 1.0, respectively (*** p = 0.0005). Our bioinformatics pipeline enabled quantification of plasma and circulating sEV proteins associated with fertility phenotype. Further investigations are warranted to validate this research in a larger population, which may lead to improved classification of fertility status in cattle.

Scope: Milk and milk products such as infant formula (IF) play a fundamental role in serving the nutritional needs of the developing infant. Extracellular vesicles (EVs) in human (HM) and cow milk (CM) contain molecular cargo such as proteins and micro(mi)RNAs that serve as functional messengers between cells and may be of importance to infant health. Most IF is derived from a CM protein base, however differences between HM and CM EV molecular cargo have not been extensively studied., Methods and Results: This study develops a pipeline using advanced proteomics and transcriptomics to enable cross-species comparison of milk and IF EVs. The number of nanoparticles per mL of IF is significantly reduced compared to unprocessed CM. 130 proteins and 514 miRNAs are differentially abundant between HM and CM EVs. While 90% of CM EV miRNAs are also identified in IF EVs, only 20% of CM EV proteins are identified in IF EVs., Conclusions: This workflow identifies key species-specific differences that can be used to optimize IF recipes and enhance infant nutrition. Improved preservation of EV functional molecular cargo in IF products is of critical importance to retaining molecular drivers of good health and should be the focus of future investigations., (© 2023 The Authors. Molecular Nutrition & Food Research published by Wiley-VCH GmbH.)

Sequential window acquisition of all theoretical mass spectra-mass spectrometry underpinned by advanced bioinformatics offers a framework for comprehensive analysis of proteomes and the discovery of robust biomarkers. However, the lack of a generic sample preparation platform to tackle the heterogeneity of material collected from different sources may be a limiting factor to the broad application of this technique. We have developed universal and fully automated workflows using a robotic sample preparation platform, which enabled in-depth and reproducible proteome coverage and characterization of bovine and ovine specimens representing healthy animals and a model of myocardial infarction. High correlation ( R 2 = 0.85) between sheep proteomics and transcriptomics datasets validated the developments. The findings suggest that automated workflows can be employed for various clinical applications across different animal species and animal models of health and disease.

Background: Bronchial thermoplasty (BT) is an interventional endoscopic treatment for severe asthma leading to the clinical improvement, but morphologic changes of bronchial wall related to the procedure and predictors of a favorable response to BT remain uncertain. The aim of the study was to validate an endobronchial ultrasound (EBUS) in assessing the effectiveness of BT treatment., Methods: Patients with severe asthma who met the clinical criteria for BT were included. In all patients clinical data, ACT and AQLQ questionnaires, laboratory tests, pulmonary function tests and bronchoscopy with radial probe EBUS and bronchial biopsies were collected. BT was performed in patients with the thickest bronchial wall L 2 layer representing ASM. These patients were evaluated before and after 12 months of follow-up. The relationship between baseline parameters and clinical response was explored., Results: Forty patients with severe asthma were enrolled to the study. All 11 patients qualified to BT successfully completed the 3 sessions of bronchoscopy. BT improved asthma control ( P =0.006), quality of life ( P =0.028) and decreased exacerbation rate ( P =0.005). Eight of the 11 patients (72.7%) showed a clinically meaningful improvement. BT also led to a significant decrease in the thicknesses of bronchial wall layers in EBUS (L 1 decreased from 0.183 to 0.173 mm, P =0.003; L 2 from 0.207 to 0.185 mm, P = 0.003; and L 3-5 from 0.969 to 0.886 mm, P =0.003). Median ASM mass decreased by 61.8% ( P =0.002). However, there was no association between baseline patient characteristics and the magnitude of clinical improvement after BT., Conclusion: BT was associated with a significant decrease in the thickness of the bronchial wall layers measured by EBUS including L 2 layer representing ASM and ASM mass reduction in bronchial biopsy. EBUS can assess bronchial structural changes related to BT; however, it did not predict the favorable clinical response to therapy., Competing Interests: The authors report no conflicts of interest in this work., (© 2023 Soja et al.)

Chlamydia is the most common bacterial sexually transmitted infection worldwide and it is widely acknowledged that controlling the rampant community transmission of this infection requires vaccine development. In this study, for the first time, we elucidate the long-term response to male mouse chlamydial vaccination with chlamydial major outer membrane protein (MOMP) and ISCOMATRIX (IMX) both prophylactically and in a novel therapeutic setting. Vaccination significantly reduced and, in some cases, cleared chlamydial burden from the prostates, epididymides, and testes, which correlates with high IgG and IgA tires in tissues and serum. Important markers of sperm health and fertility were protected including sperm motility and proteins associated with fertility in men. Within splenocytes, expression of IFNγ, TNFα, IL17, IL13, IL10, and TGFβ were changed by both infection and vaccination within CD4 and CD8 T cells and regulatory T cells. Within the testicular tissue, phenotypic and concentration changes were observed in macrophages and T cells (resident and transitory). This revealed some pathogenic phenotypes associated with infection and critically that vaccination allows maintenance of testicular homeostasis, likely by preventing significant influx of CD4 T cells and promoting IL10 production. Finally, we demonstrated the testes contained immature (B220+) B cells and mature (CD138+) Chlamydia-specific plasma cells. Thus, through vaccination, we can maintain the healthy function of the testes, which is vital to protection of male fertility., (© The Author(s) 2023. Published by Oxford University Press behalf of Society for the Study of Reproduction.)

Pichia pastoris (Komagataella phaffii) is widely used for industrial production of heterologous proteins due to high secretory capabilities but selection of highly productive engineered strains remains a limiting step. Despite availability of a comprehensive molecular toolbox for construct design and gene integration, there is high clonal variability among transformants due to frequent multi-copy and off-target random integration. Therefore, functional screening of several hundreds of transformant clones is essential to identify the best protein production strains. Screening methods are commonly based on deep-well plate cultures with analysis by immunoblotting or enzyme activity assays of post-induction samples, and each heterologous protein produced may require development of bespoke assays with multiple sample processing steps. In this work, we developed a generic system based on a P. pastoris strain that uses a protein-based biosensor to identify highly productive protein secretion clones from a heterogeneous set of transformants. The biosensor uses a split green fluorescent protein where the large GFP fragment (GFP1-10) is fused to a sequence-specific protease from Tobacco Etch Virus (TEV) and is targeted to the endoplasmic reticulum. Recombinant proteins targeted for secretion are tagged with the small fragment of the split GFP (GFP11). Recombinant protein production can be measured by monitoring GFP fluorescence, which is dependent on interaction between the large and small GFP fragments. The reconstituted GFP is cleaved from the target protein by TEV protease, allowing for secretion of the untagged protein of interest and intracellular retention of the mature GFP. We demonstrate this technology with four recombinant proteins (phytase, laccase, β-casein and β-lactoglobulin) and show that the biosensor directly reports protein production levels that correlate with traditional assays. Our results confirm that the split GFP biosensor can be used for facile, generic, and rapid screening of P. pastoris clones to identify those with the highest production levels., (© 2023. The Author(s).)

Background: Streptococcus agalactiae , referred to as Group B Streptococcus (GBS), is a prominent bacterium causing life-threatening neonatal infections. Although antibiotics are efficient against GBS, growing antibiotic resistance forces the search for alternative treatments and/or prevention approaches. Antimicrobial photodynamic inactivation (aPDI) appears to be a potent alternative non-antibiotic strategy against GBS., Methods: The effect of rose bengal aPDI on various GBS serotypes, Lactobacillus species, human eukaryotic cell lines and microbial vaginal flora composition was evaluated., Results: RB-mediated aPDI was evidenced to exert high bactericidal efficacy towards S. agalactiae in vitro (>4 log 10 units of viability reduction for planktonic and >2 log 10 units for multispecies biofilm culture) and in vivo (ca. 2 log 10 units of viability reduction in mice vaginal GBS colonization model) in microbiological and metagenomic analyses. At the same time, RB-mediated aPDI was evidenced to be not mutagenic and safe for human vaginal cells, as well as capable of maintaining the balance and viability of vaginal microbial flora., Conclusions: aPDI can efficiently kill GBS and serve as an alternative approach against GBS vaginal colonization and/or infections.

Microbes are found in nearly every habitat and organism on the planet, where they are critical to host health, fitness, and metabolism. In most organisms, few microbes are inherited at birth; instead, acquiring microbiomes generally involves complicated interactions between the environment, hosts, and symbionts. Despite the criticality of microbiome acquisition, we know little about where hosts' microbes reside when not in or on hosts of interest. Because microbes span a continuum ranging from generalists associating with multiple hosts and habitats to specialists with narrower host ranges, identifying potential sources of microbial diversity that can contribute to the microbiomes of unrelated hosts is a gap in our understanding of microbiome assembly. Microbial dispersal attenuates with distance, so identifying sources and sinks requires data from microbiomes that are contemporary and near enough for potential microbial transmission. Here, we characterize microbiomes across adjacent terrestrial and aquatic hosts and habitats throughout an entire watershed, showing that the most species-poor microbiomes are partial subsets of the most species-rich and that microbiomes of plants and animals are nested within those of their environments. Furthermore, we show that the host and habitat range of a microbe within a single ecosystem predicts its global distribution, a relationship with implications for global microbial assembly processes. Thus, the tendency for microbes to occupy multiple habitats and unrelated hosts enables persistent microbiomes, even when host populations are disjunct. Our whole-watershed census demonstrates how a nested distribution of microbes, following the trophic hierarchies of hosts, can shape microbial acquisition.

Background: Mortality research has identified biomarkers predictive of all-cause mortality risk. Most of these markers, such as body mass index, are predictive cross-sectionally, while for others the longitudinal change has been shown to be predictive, for instance greater-than-average muscle and weight loss in older adults. And while sometimes markers are derived from imaging modalities such as DXA, full scans are rarely used. This study builds on that knowledge and tests two hypotheses to improve all-cause mortality prediction. The first hypothesis is that features derived from raw total-body DXA imaging using deep learning are predictive of all-cause mortality with and without clinical risk factors, meanwhile, the second hypothesis states that sequential total-body DXA scans and recurrent neural network models outperform comparable models using only one observation with and without clinical risk factors., Methods: Multiple deep neural network architectures were designed to test theses hypotheses. The models were trained and evaluated on data from the 16-year-long Health, Aging, and Body Composition Study including over 15,000 scans from over 3000 older, multi-race male and female adults. This study further used explainable AI techniques to interpret the predictions and evaluate the contribution of different inputs., Results: The results demonstrate that longitudinal total-body DXA scans are predictive of all-cause mortality and improve performance of traditional mortality prediction models. On a held-out test set, the strongest model achieves an area under the receiver operator characteristic curve of 0.79., Conclusion: This study demonstrates the efficacy of deep learning for the analysis of DXA medical imaging in a cross-sectional and longitudinal setting. By analyzing the trained deep learning models, this work also sheds light on what constitutes healthy aging in a diverse cohort., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2022.)

Assessment of pain responses and inflammation during animal surgery is difficult because traditional methods, such as visual analogue scores, are not applicable while under anaesthesia. Acute phase proteins (APPs), such as C-reactive protein and haptoglobin, that are typically monitored in veterinary research, do not show a significant change until at least 2 h post-surgery and therefore, immediate pathophysiological changes are uncertain. The current study used sequential window acquisition of all theoretical mass spectra (SWATH-MS) to investigate plasma proteome changes that occur immediately following surgery in dogs and also to assess the efficacy of a novel transdermal ketoprofen (TK) formulation. Castration was chosen as surgical model in this study. The procedure was performed on twelve dogs (n = 6 in two groups) and blood samples were collected at 0 h, 1 and 2 h after surgery for proteomic analysis. Following surgery, there was a general downregulation of proteins, including complement C- 3, complement factor B, complement factor D, transthyretin, and proteins associated with lipid, cholesterol, and glucose metabolisms, reflecting the systemic response to surgical trauma. Many of these changes were diminished in the transdermal group (TD) since ketoprofen, a non-steroidal anti-inflammatory drug (NSAID), inhibits prostanoids and the associated chemotactic neutrophil migration to site of tissue injury. SIGNIFICANCE: SWATH-MS Proteomic analysis revealed significant changes in plasma proteins, predominantly involved in early acute phase and inflammatory response at 1 & 2 h after surgery in castrated dogs. Pre-operative application of transdermal ketoprofen formulation had reduced the systemic immune response, which was confirmed by negligible alteration of proteins in transdermal treated group. A key outcome of this experiment was studying the efficacy of a novel transdermal NSAID formulation in dogs., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background: Sentinel lymph nodes (SLNs) are both the first site where breast cancer (BC) metastases form and where anti-tumoral immunity develops. Despite being the most potent antigen-presenting cells, dendritic cells (DCs) located in a nodal tissue can both promote or suppress immune response against cancer in SLNs., Methods: In SLNs excisions obtained from 123 invasive BC patients, we performed immunohistochemistry (IHC) for CD1a, CD1c, DC-LAMP, and DC-SIGN to identify different DCs populations. Then we investigated the numbers of DCs subsets in tumor-free, micrometastatic, and macrometastatic SLNs with the use of a light microscope., Results: We observed that CD1c+ and DC-SIGN+ DCs were more numerous in SLNs with a larger tumor size. More abundant intratumoral DC-LAMP+ population was related to a higher number of metastatic lymph nodes. Conversely, more abundant CD1a+ DCs were associated with a decreasing nodal burden in SLNs and a lower number of involved lymph nodes. Moreover, densities of the investigated DC populations differed with respect to tumor grade, HER2 overexpression, hormone receptor status, and histologic type of BC., Conclusions: According to their subtype, DCs are associated with either lower or higher nodal burden in SLNs from invasive BC patients. These relationships appear to be dependent not only on the maturation state of DCs but also on the histological and biological characteristics of the tumor.

Proteomic analysis of small extracellular vesicles (sEVs) poses a significant challenge. A 'gold-standard' method for plasma sEV enrichment for downstream proteomic analysis is yet to be established. Methods were evaluated for their capacity to successfully isolate and enrich sEVs from plasma, minimise the presence of highly abundant plasma proteins, and result in the optimum representation of sEV proteins by liquid chromatography tandem mass spectrometry. Plasma from four cattle (Bos taurus) of similar physical attributes and genetics were used. Three methods of sEV enrichment were utilised: ultracentrifugation (UC), size-exclusion chromatography (SEC), and ultrafiltration (UF). These methods were combined to create four groups for methodological evaluation: UC + SEC, UC + SEC + UF, SEC + UC and SEC + UF. The UC + SEC method yielded the highest number of protein identifications (IDs). The SEC + UC method reduced plasma protein IDs compared to the other methods, but also resulted in the lowest number of protein IDs overall. The UC + SEC + UF method decreased sEV protein ID, particle number, mean and mode particle size, particle yield, and did not improve purity compared to the UC + SEC method. In this study, the UC + SEC method was the best method for sEV protein ID, purity, and overall particle yield. Our data suggest that the method and sequence of sEV enrichment strategy impacts protein ID, which may influence the outcome of biomarker discovery studies.

Mass spectrometry-based plasma proteomics offers a major advance for biomarker discovery in the veterinary field, which has traditionally been limited to quantification of a small number of proteins using biochemical assays. The development of foundational data and tools related to sequential window acquisition of all theoretical mass spectra (SWATH)-mass spectrometry has allowed for quantitative profiling of a significant number of plasma proteins in humans and several animal species. Enabling SWATH in dogs enhances human biomedical research as a model species, and significantly improves diagnostic and disease monitoring capability. In this study, a comprehensive peptide spectral library specific to canine plasma proteome was developed and evaluated using SWATH for protein quantification in non-depleted dog plasma. Specifically, plasma samples were subjected to various orthogonal fractionation and digestion techniques, and peptide fragmentation data corresponding to over 420 proteins was collected. Subsequently, a SWATH-based assay was introduced that leveraged the developed resource and that enabled reproducible quantification of 400 proteins in non-depleted plasma samples corresponding to various disease conditions. The ability to profile the abundance of such a significant number of plasma proteins using a single method in dogs has the potential to accelerate biomarker discovery studies in this species.

Pain assessment in farm animals has primarily relied on a combination of behavioral and physiological responses, although these are relatively subjective and difficult to quantify. It is essential to develop more effective biomarkers of pain in production animals since they are frequently exposed to routine surgical husbandry procedures. More effective biomarkers of pain would improve welfare, limit the loss of productivity associated with pain and permit better assessment of analgesics. This study aimed to investigate the use of a modern mass spectrometry data independent acquisition strategy, termed Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS), to detect candidate protein biomarkers that are known to associate with nociceptive and inflammatory processes in cattle, which could then be used to assess the efficacy of potential analgesics. Calves were randomly divided into two groups that were either surgically dehorned or subjected to restraint stress, without provision of anaesthesia or analgesia in accordance with current industry standards. Samples were analysed before and after dehorning at multiple timepoints. Significant changes in protein concentrations were detected predominantly at 24 and 96 h following dehorning, including kininogens, proteins associated with the coagulation and complement cascades and serine protease inhibitors. Gene ontology analysis revealed that the identified candidate biomarkers were associated with stress, wound healing, immune response, blood coagulation and the inflammatory and acute phase responses, which could be expected following surgical damage to tissues, but can now be more objectively assessed. These results offer more definitive and quantitative monitoring of response to tissue injury induced pain and inflammation.

Cattle ticks pose a significant threat to the health and profitability of cattle herds globally. The investigation of factors leading to natural tick resistance in cattle is directed toward targeted breeding strategies that may combat cattle tick infestation on the genetic level. Exosomes (EXs), small extracellular vesicles (EVs) of 50 to 150 nm diameter, are released from all cell types into biofluids such as blood plasma and milk, have been successfully used in diagnostic and prognostic studies in humans, and can provide essential information regarding the overall health state of animals. Mass spectrometry (MS) is a highly sensitive proteomics application that can be used to identify proteins in a complex mixture and is particularly useful for biomarker development. In this proof of principle study, EXs were isolated from the blood plasma of cattle (Bos taurus) with high (HTR) and low tick resistance (LTR) (n = 3/group). Cattle were classified as HTR or LTR using a tick scoring system, and EXs isolated from the cattle blood plasma using an established protocol. EXs were subjected to MS analysis in data-dependent acquisition mode and protein search performed using Protein Pilot against the B. taurus proteome. A total of 490 unique proteins were identified across all samples. Of these, proteins present in all replicates from each group were selected for further analysis (HTR = 121; LTR = 130). Gene ontology analysis was performed using PANTHER GO online software tool. Proteins unique to HTR and LTR cattle were divided by protein class, of which 50% were associated with immunity/defense in the HTR group, whereas this protein class was not detected in EXs from LTR cattle. Similarly, unique proteins in HTR cattle were associated with B-cell activation, immunoglobins, immune response, and cellular iron ion homeostasis. In LTR cattle, unique exosomal proteins were associated with actin filament binding, purine nucleotide binding, plasma membrane protein complex, and carbohydrate derivative binding. This is the first study to demonstrate that MS analysis of EXs derived from the blood plasma of HTR and LTR cattle can be successfully applied to profile the systemic effects of tick burden., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society of Animal Science.)

The collection of blood plasma is minimally invasive, and the fluid is a rich source of proteins for biomarker studies in both humans and animals. Plasma protein analysis by mass spectrometry (MS) can be challenging, though modern data acquisition strategies, such as sequential window acquisition of all theoretical fragment ion spectra (SWATH), enable reproducible quantitation of hundreds of proteins in non-depleted plasma from humans and laboratory model animals. Although there is strong potential to enhance veterinary and translational research, SWATH-based plasma proteomics in non-laboratory animals is virtually non-existent. One limitation to date is the lack of comprehensively annotated genomes to aid protein identification. The current study established plasma peptide spectral repositories for sheep and cattle that enabled quantification of over 200 proteins in non-depleted plasma using SWATH approach. Moreover, bioinformatics pipeline was developed to leverage inter-species homologies to enhance the depth of baseline libraries and plasma protein quantification in bovids. Finally, the practical utility of using bovid libraries for SWATH data extraction in taxonomically related non-domestic ungulate species (giraffe) has been demonstrated. SIGNIFICANCE: Ability to quickly generate comprehensive spectral libraries is limiting the applicability of data-independent acquisition, such as SWATH, to study proteomes of non-laboratory animals. We describe an approach to obtain relatively shallow foundational plasma repositories from domestic ruminants and employ homology searches to increase the depth of data, which we subsequently extend to unsequenced ungulates using SWATH method. When applied to cross-species proteomics, the number of proteins quantified by our approach far exceeds what is traditionally used in plasma protein tests., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background The ability of deep learning (DL) models to classify women as at risk for either screening mammography-detected or interval cancer (not detected at mammography) has not yet been explored in the literature. Purpose To examine the ability of DL models to estimate the risk of interval and screening-detected breast cancers with and without clinical risk factors. Materials and Methods This study was performed on 25 096 digital screening mammograms obtained from January 2006 to December 2013. The mammograms were obtained in 6369 women without breast cancer, 1609 of whom developed screening-detected breast cancer and 351 of whom developed interval invasive breast cancer. A DL model was trained on the negative mammograms to classify women into those who did not develop cancer and those who developed screening-detected cancer or interval invasive cancer. Model effectiveness was evaluated as a matched concordance statistic (C statistic) in a held-out 26% (1669 of 6369) test set of the mammograms. Results The C statistics and odds ratios for comparing patients with screening-detected cancer versus matched controls were 0.66 (95% CI: 0.63, 0.69) and 1.25 (95% CI: 1.17, 1.33), respectively, for the DL model, 0.62 (95% CI: 0.59, 0.65) and 2.14 (95% CI: 1.32, 3.45) for the clinical risk factors with the Breast Imaging Reporting and Data System (BI-RADS) density model, and 0.66 (95% CI: 0.63, 0.69) and 1.21 (95% CI: 1.13, 1.30) for the combined DL and clinical risk factors model. For comparing patients with interval cancer versus controls, the C statistics and odds ratios were 0.64 (95% CI: 0.58, 0.71) and 1.26 (95% CI: 1.10, 1.45), respectively, for the DL model, 0.71 (95% CI: 0.65, 0.77) and 7.25 (95% CI: 2.94, 17.9) for the risk factors with BI-RADS density (b rated vs non-b rated) model, and 0.72 (95% CI: 0.66, 0.78) and 1.10 (95% CI: 0.94, 1.29) for the combined DL and clinical risk factors model. The P values between the DL, BI-RADS, and combined model's ability to detect screen and interval cancer were .99, .002, and .03, respectively. Conclusion The deep learning model outperformed in determining screening-detected cancer risk but underperformed for interval cancer risk when compared with clinical risk factors including breast density. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Bae and Kim in this issue.

Background: While breast imaging such as full-field digital mammography and digital breast tomosynthesis have helped to reduced breast cancer mortality, issues with low specificity exist resulting in unnecessary biopsies. The fundamental information used in diagnostic decisions are primarily based in lesion morphology. We explore a dual-energy compositional breast imaging technique known as three-compartment breast (3CB) to show how the addition of compositional information improves malignancy detection., Methods: Women who presented with Breast Imaging-Reporting and Data System (BI-RADS) diagnostic categories 4 or 5 and who were scheduled for breast biopsies were consecutively recruited for both standard mammography and 3CB imaging. Computer-aided detection (CAD) software was used to assign a morphology-based prediction of malignancy for all biopsied lesions. Compositional signatures for all lesions were calculated using 3CB imaging and a neural network evaluated CAD predictions with composition to predict a new probability of malignancy. CAD and neural network predictions were compared to the biopsy pathology., Results: The addition of 3CB compositional information to CAD improves malignancy predictions resulting in an area under the receiver operating characteristic curve (AUC) of 0.81 (confidence interval (CI) of 0.74-0.88) on a held-out test set, while CAD software alone achieves an AUC of 0.69 (CI 0.60-0.78). We also identify that invasive breast cancers have a unique compositional signature characterized by reduced lipid content and increased water and protein content when compared to surrounding tissues., Conclusion: Clinically, 3CB may potentially provide increased accuracy in predicting malignancy and a feasible avenue to explore compositional breast imaging biomarkers., Competing Interests: Competing interestsK.D. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: receives royalties from Hologic. Other relationships: disclosed no relevant relationships. M.G. Activities related to the present article: disclosed no relevant relationships. Activities related to the present article: is a stockholder in R2/Hologic; is a co-founder in Quantitative Insights (now advisor to Qlarity Imaging); receives royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverain Medical, Mitsubishi, and Toshiba; receives royalties through institution is a licensee on patents. Other relationships: disclosed no relevant relationships. J.S. Activities related to the present article: in kind equipment support from Hologic and iCAD. Activities not related to the present article: investigator-initiated grant from Hologic. Other relationships: disclosed no relevant relationships. All other authors have no competing interests to declare., (© The Author(s) 2021.)

The reproductive status of dairy cows remains a challenge for dairy farmers worldwide, with impaired fertility linked to a significant reduction in herd profitability, due in part to impaired immunity, increased metabolic pressure, and longer postpartum anestrous interval (PPAI). Exosomes are nanovesicles released from a variety of cell types and end up in circulation, and carry proteins, bioactive peptides, lipids, and nucleic acids specific to the place of origin. As such, their role in health and disease has been investigated in humans and animals. This review discusses research into exosomes in the context of reproduction in dairy herds and introduces recent advances in mass-spectrometry (MS) based proteomics that have a potential to advance quantitative profiling of exosomal protein cargo in a search for early biomarkers of cattle fertility.

Ascorbate (vitamin C) is an essential multifunctional molecule for both plants and mammals. In plants, ascorbate is the most abundant water-soluble antioxidant that supports stress tolerance. In humans, ascorbate is an essential micronutrient and promotes iron (Fe) absorption in the gut. Engineering crops with increased ascorbate levels have the potential to improve both crop stress tolerance and human health. Here, rice ( Oryza sativa L.) plants were engineered to constitutively overexpress the rice GDP-L-galactose phosphorylase coding sequence (35S- OsGGP ), which encodes the rate-limiting enzymatic step of the L-galactose pathway. Ascorbate concentrations were negligible in both null segregant (NS) and 35S- OsGGP brown rice (BR, unpolished grain), but significantly increased in 35S- OsGGP germinated brown rice (GBR) relative to NS. Foliar ascorbate concentrations were significantly increased in 35S- OsGGP plants in the vegetative growth phase relative to NS, but significantly reduced at the reproductive growth phase and were associated with reduced OsGGP transcript levels. The 35S- OsGGP plants did not display altered salt tolerance at the vegetative growth phase despite having elevated ascorbate concentrations. Ascorbate concentrations were positively correlated with ferritin concentrations in Caco-2 cells - an accurate predictor of Fe bioavailability in human digestion - exposed to in vitro digests of NS and 35S- OsGGP BR and GBR samples., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Broad, Bonneau, Beasley, Roden, Sadowski, Jewell, Brien, Berger, Tako, Glahn, Hellens and Johnson.)

Rationale: Eicosanoids are short-lived bio-responsive lipids produced locally from oxidation of polyunsaturated fatty acids (FAs) via a cascade of enzymatic or free radical reactions. Alterations in the composition and concentration of eicosanoids are indicative of inflammation responses and there is strong interest in developing analytical methods for the sensitive and selective detection of these lipids in biological mixtures. Most eicosanoids are hydroxy FAs (HFAs), which present a particular analytical challenge due to the presence of regioisomers arising from differing locations of hydroxylation and unsaturation within their structures., Methods: In this study, the recently developed derivatization reagent 1-(3-(aminomethyl)-4-iodophenyl)pyridin-1-ium (4-I-AMPP + ) was applied to a representative set of HFAs including bioactive eicosanoids. Photodissociation (PD) mass spectra obtained at 266 nm of 4-I-AMPP + -modified HFAs exhibit abundant product ions arising from photolysis of the aryl-iodide bond within the derivative with subsequent migration of the radical to the hydroxyl group promoting fragmentation of the FA chain and facilitating structural assignment., Results: Representative polyunsaturated HFAs (from the hydroxyeicosatetraenoic acid and hydroxyeicosapentaenoic acid families) were derivatized with 4-I-AMPP + and subjected to a reversed-phase liquid chromatography workflow that afforded chromatographic resolution of isomers in conjunction with structurally diagnostic PD mass spectra., Conclusions: PD of these complex HFAs was found to be sensitive to the locations of hydroxyl groups and carbon-carbon double bonds, which are structural properties strongly associated with the biosynthetic origins of these lipid mediators., (© 2020 John Wiley & Sons, Ltd.)

Environmental temperature has effects on sperm quality with differences in susceptibility between cattle subspecies and breeds, but very little is known about the seminal plasma protein (SPP) changes resulting from testicular heat stress. Scrotal insulation (SI) for 48 hr was applied to Brahman (Bos indicus) bulls. Semen was collected at 3-day intervals from before, until 74 days post-SI. The changes in sperm morphology and motility following SI were comparable to previously reported and differences were detected in measures of sperm chromatin conformation as early as 8 days post-SI. New proteins spots, in the SPP two-dimensional (2-D) gels, were apparent when comparing pre-SI with 74 days post-SI, and SPP identified as associated with mechanisms of cellular repair and protection. Similar trends between 2-D gel and Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS) data was observed, with SWATH-MS able to quantify individual SPP that otherwise were not resolved on 2-D gel. The SPP assessment at peak sperm damage (21-24 days) showed a significant difference in 29 SPP (adjusted p < .05), and identified six proteins with change in abundance in the SI group. In conclusion both spermatozoa and SPP composition of bulls are susceptible to temperature change incurred by SI, and SPP markers for testicular heat insults may be detected., (© 2020 Wiley Periodicals, Inc.)

Introduction : Immunoglobulin E is an important modulator of the inflammatory reaction in allergic asthma. It also contributes to airway remodeling in the course of the disease. The authors evaluated airway structural changes in severe allergic asthma during the omalizumab therapy. Patients and methods : The study included 13 patients with severe allergic asthma treated with omalizumab for at least one year. In each patient clinical, laboratory, and spirometry parameters were evaluated before and after the treatment. In addition, bronchoscopy with bronchial mucosa biopsy and bronchoalveolar lavage was performed. The basal lamina thickness, inflammatory cell infiltration, fibronectin, as well as type I and III collagen accumulation were assessed in bronchial mucosa specimens, together with the assessment of bronchoalveolar lavage cellularity. Results : The omalizumab therapy led to a decrease in the basal lamina thickness ( p  = 0.002), and to a reduction in fibronectin ( p  = 0.02), but not collagen deposits in the bronchial mucosa. The decrease in fibronectin accumulation was associated with an improvement in asthma control and quality of life ( p  = 0.01, both), and a diminished dose of systemic corticosteroids ( p  = 0.001). It was also associated with a tendency towards reduction of the eosinophil count in the peripheral blood, bronchoalveolar lavage fluid, and bronchial mucosa specimens. Conclusion : Our study has shown that omalizumab, effective in the treatment of severe allergic asthma, may also decrease unfavorable structural airway changes in allergic asthmatics, at least with respect to the fibronectin deposit and an increased thickness of the basal lamina. However, more extensive observational studies are needed to verify the above hypothesis.

Saliva has become one of the more attractive body fluids for protein biomarker discovery studies because of its ease of collection, non-invasiveness and multiple samples can be collected from an individual at a single time point. With the development of modern data acquisition strategies, such as Sequential Window Acquisition of all Theoretical Mass Spectrometry (SWATH-MS), the quality of saliva sample preparation has become a crucial factor for a successful protein identification and quantification. Several sample preparation methods have been proposed, but there has been no systematic evaluation conducted to date that compared each of these methods. We have therefore, performed an extensive assessment using technical and biological repeats to evaluate the number of protein IDs and repeatability of three most commonly used techniques, in-solution digestion, filter-aided sample preparation (FASP) and in stage-tip (iST) digestion. We discovered that in the case of human saliva sample, FASP provided the highest number of proteins (human and microbial) identifiable from a pool saliva sample, and there were no significant differences in terms of repeatability among the three methods investigated., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Learning in physical neural systems must rely on learning rules that are local in both space and time. Optimal learning in deep neural architectures requires that non-local information be available to the deep synapses. Thus, in general, optimal learning in physical neural systems requires the presence of a deep learning channel to communicate non-local information to deep synapses, in a direction opposite to the forward propagation of the activities. Theoretical arguments suggest that for circular autoencoders, an important class of neural architectures where the output layer is identical to the input layer, alternative algorithms may exist that enable local learning without the need for additional learning channels, by using the forward activation channel as the deep learning channel. Here we systematically identify, classify, and study several such local learning algorithms, based on the general idea of recirculating information from the output layer to the hidden layers. We show through simulations and mathematical derivations that these algorithms are robust and converge to critical points of the global error function. In most cases, we show that these recirculation algorithms are very similar to an adaptive form of random backpropagation, where each hidden layer receives a linearly transformed, slowly-varying, version of the output error., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Random backpropagation (RBP) is a variant of the backpropagation algorithm for training neural networks, where the transpose of the forward matrices are replaced by fixed random matrices in the calculation of the weight updates. It is remarkable both because of its effectiveness, in spite of using random matrices to communicate error information, and because it completely removes the taxing requirement of maintaining symmetric weights in a physical neural system. To better understand random backpropagation, we first connect it to the notions of local learning and learning channels. Through this connection, we derive several alternatives to RBP, including skipped RBP (SRPB), adaptive RBP (ARBP), sparse RBP, and their combinations (e.g. ASRBP) and analyze their computational complexity. We then study their behavior through simulations using the MNIST and CIFAR-10 bechnmark datasets. These simulations show that most of these variants work robustly, almost as well as backpropagation, and that multiplication by the derivatives of the activation functions is important. As a follow-up, we study also the low-end of the number of bits required to communicate error information over the learning channel. We then provide partial intuitive explanations for some of the remarkable properties of RBP and its variations. Finally, we prove several mathematical results, including the convergence to fixed points of linear chains of arbitrary length, the convergence to fixed points of linear autoencoders with decorrelated data, the long-term existence of solutions for linear systems with a single hidden layer and convergence in special cases, and the convergence to fixed points of non-linear chains, when the derivative of the activation functions is included.

Over the last two decades, technological advancements in the field of proteomics have advanced our understanding of the complex biological systems of living organisms. Techniques based on mass spectrometry (MS) have emerged as powerful tools to contextualise existing genomic information and to create quantitative protein profiles from plasma, tissues or cell lines of various species. Proteomic approaches have been used increasingly in veterinary science to investigate biological processes responsible for growth, reproduction and pathological events. However, the adoption of proteomic approaches by veterinary investigators lags behind that of researchers in the human medical field. Furthermore, in contrast to human proteomics studies, interpretation of veterinary proteomic data is difficult due to the limited protein databases available for many animal species. This review article examines the current use of advanced proteomics techniques for evaluation of animal health and welfare and covers the current status of clinical veterinary proteomics research, including successful protein identification and data interpretation studies. It includes a description of an emerging tool, sequential window acquisition of all theoretical fragment ion mass spectra (SWATH-MS), available on selected mass spectrometry instruments. This newly developed data acquisition technique combines advantages of discovery and targeted proteomics approaches, and thus has the potential to advance the veterinary proteomics field by enhancing identification and reproducibility of proteomics data., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

In a physical neural system, learning rules must be local both in space and time. In order for learning to occur, non-local information must be communicated to the deep synapses through a communication channel, the deep learning channel. We identify several possible architectures for this learning channel (Bidirectional, Conjoined, Twin, Distinct) and six symmetry challenges: (1) symmetry of architectures; (2) symmetry of weights; (3) symmetry of neurons; (4) symmetry of derivatives; (5) symmetry of processing; and (6) symmetry of learning rules. Random backpropagation (RBP) addresses the second and third symmetry, and some of its variations, such as skipped RBP (SRBP) address the first and the fourth symmetry. Here we address the last two desirable symmetries showing through simulations that they can be achieved and that the learning channel is particularly robust to symmetry variations. Specifically, random backpropagation and its variations can be performed with the same non-linear neurons used in the main input-output forward channel, and the connections in the learning channel can be adapted using the same algorithm used in the forward channel, removing the need for any specialized hardware in the learning channel. Finally, we provide mathematical results in simple cases showing that the learning equations in the forward and backward channels converge to fixed points, for almost any initial conditions. In symmetric architectures, if the weights in both channels are small at initialization, adaptation in both channels leads to weights that are essentially symmetric during and after learning. Biological connections are discussed., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Background: Unlike humans, there is currently no publicly available reference mass spectrometry-based circulating acellular proteome data for sheep, limiting the analysis and interpretation of a range of physiological changes and disease states. The objective of this study was to develop a robust and comprehensive method to characterise the circulating acellular proteome in ovine serum., Methods: Serum samples from healthy sheep were subjected to shotgun proteomic analysis using nano liquid chromatography nano electrospray ionisation tandem mass spectrometry (nanoLC-nanoESI-MS/MS) on a quadrupole time-of-flight instrument (TripleTOF® 5600+, SCIEX). Proteins were identified using ProteinPilot™ (SCIEX) and Mascot (Matrix Science) software based on a minimum of two unmodified highly scoring unique peptides per protein at a false discovery rate (FDR) of 1% software by searching a subset of the Universal Protein Resource Knowledgebase (UniProtKB) database (http://www.uniprot.org). PeptideShaker (CompOmics, VIB-UGent) searches were used to validate protein identifications from ProteinPilot™ and Mascot., Results: ProteinPilot™ and Mascot identified 245 and 379 protein groups (IDs), respectively, and PeptideShaker validated 133 protein IDs from the entire dataset. Since Mascot software is considered the industry standard and identified the most proteins, these were analysed using the Protein ANalysis THrough Evolutionary Relationships (PANTHER) classification tool revealing the association of 349 genes with 127 protein pathway hits. These data are available via ProteomeXchange with identifier PXD004989., Conclusions: These results demonstrated for the first time the feasibility of characterising the ovine circulating acellular proteome using nanoLC-nanoESI-MS/MS. This peptide spectral data contributes to a protein library that can be used to identify a wide range of proteins in ovine serum.

Both the American College of Rheumatology (ACR) and European League Against Rheumatism (EULAR) guidelines recommend the use of methotrexate (MTX) for the treatment of rheumatoid arthritis (RA) when there is no contraindication. While MTX is the foundation of RA therapy (Singh et al. in Arthritis Care Res 64:625-639,2012), absorption saturation compromises its oral bioavailability (BA). Differences in the relative BA of oral versus subcutaneous (SC) MTX demonstrate the need for guidance on successful dose-conversion strategies. This study was designed to compare MTX PK profiles as a result of MTX administration via three different treatment administrations: oral, SC MTX administered via an auto-injector (MTXAI) into the abdomen (MTXAI ab ) and into the thigh (MTXAI th ). In this paper, we establish a dose-conversion method based on the BA of MTX from oral and SC administration. SC administration provided higher exposure of MTX than the same dose given orally. Unlike the exposure limitations of oral MTX, dose-proportional exposure was seen with SC MTX.

In a physical neural system, where storage and processing are intimately intertwined, the rules for adjusting the synaptic weights can only depend on variables that are available locally, such as the activity of the pre- and post-synaptic neurons, resulting in local learning rules. A systematic framework for studying the space of local learning rules is obtained by first specifying the nature of the local variables, and then the functional form that ties them together into each learning rule. Such a framework enables also the systematic discovery of new learning rules and exploration of relationships between learning rules and group symmetries. We study polynomial local learning rules stratified by their degree and analyze their behavior and capabilities in both linear and non-linear units and networks. Stacking local learning rules in deep feedforward networks leads to deep local learning. While deep local learning can learn interesting representations, it cannot learn complex input-output functions, even when targets are available for the top layer. Learning complex input-output functions requires local deep learning where target information is communicated to the deep layers through a backward learning channel. The nature of the communicated information about the targets and the structure of the learning channel partition the space of learning algorithms. For any learning algorithm, the capacity of the learning channel can be defined as the number of bits provided about the error gradient per weight, divided by the number of required operations per weight. We estimate the capacity associated with several learning algorithms and show that backpropagation outperforms them by simultaneously maximizing the information rate and minimizing the computational cost. This result is also shown to be true for recurrent networks, by unfolding them in time. The theory clarifies the concept of Hebbian learning, establishes the power and limitations of local learning rules, introduces the learning channel which enables a formal analysis of the optimality of backpropagation, and explains the sparsity of the space of learning rules discovered so far., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Berardinelli-Seip congenital lipodystrophy 2 (BSCL2) is caused by loss-of-function mutations in SEIPIN, a protein implicated in both adipogenesis and lipid droplet expansion but whose molecular function remains obscure. Here, we identify physical and functional interactions between SEIPIN and microsomal isoforms of glycerol-3-phosphate acyltransferase (GPAT) in multiple organisms. Compared to controls, GPAT activity was elevated in SEIPIN-deficient cells and tissues and GPAT kinetic values were altered. Increased GPAT activity appears to underpin the block in adipogenesis and abnormal lipid droplet morphology associated with SEIPIN loss. Overexpression of Gpat3 blocked adipogenesis, and Gpat3 knockdown in SEIPIN-deficient preadipocytes partially restored differentiation. GPAT overexpression in yeast, preadipocytes, and fly salivary glands also formed supersized lipid droplets. Finally, pharmacological inhibition of GPAT in Seipin -/- mouse preadipocytes partially restored adipogenesis. These data identify SEIPIN as an evolutionarily conserved regulator of microsomal GPAT and suggest that GPAT inhibitors might be useful for the treatment of human BSCL2 patients., (Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.)

There is an increasing need in biology and clinical medicine to robustly and reliably measure tens to hundreds of peptides and proteins in clinical and biological samples with high sensitivity, specificity, reproducibility, and repeatability. Previously, we demonstrated that LC-MRM-MS with isotope dilution has suitable performance for quantitative measurements of small numbers of relatively abundant proteins in human plasma and that the resulting assays can be transferred across laboratories while maintaining high reproducibility and quantitative precision. Here, we significantly extend that earlier work, demonstrating that 11 laboratories using 14 LC-MS systems can develop, determine analytical figures of merit, and apply highly multiplexed MRM-MS assays targeting 125 peptides derived from 27 cancer-relevant proteins and seven control proteins to precisely and reproducibly measure the analytes in human plasma. To ensure consistent generation of high quality data, we incorporated a system suitability protocol (SSP) into our experimental design. The SSP enabled real-time monitoring of LC-MRM-MS performance during assay development and implementation, facilitating early detection and correction of chromatographic and instrumental problems. Low to subnanogram/ml sensitivity for proteins in plasma was achieved by one-step immunoaffinity depletion of 14 abundant plasma proteins prior to analysis. Median intra- and interlaboratory reproducibility was <20%, sufficient for most biological studies and candidate protein biomarker verification. Digestion recovery of peptides was assessed and quantitative accuracy improved using heavy-isotope-labeled versions of the proteins as internal standards. Using the highly multiplexed assay, participating laboratories were able to precisely and reproducibly determine the levels of a series of analytes in blinded samples used to simulate an interlaboratory clinical study of patient samples. Our study further establishes that LC-MRM-MS using stable isotope dilution, with appropriate attention to analytical validation and appropriate quality control measures, enables sensitive, specific, reproducible, and quantitative measurements of proteins and peptides in complex biological matrices such as plasma., (© 2015 by The American Society for Biochemistry and Molecular Biology, Inc.)

We introduce the model of generalized open quantum walks on networks using the Transition Operation Matrices formalism. We focus our analysis on the mean first passage time and the average return time in Apollonian networks. These results differ significantly from a classical walk on these networks. We show a comparison of the classical and quantum behaviour of walks on these networks.

The Higgs boson is thought to provide the interaction that imparts mass to the fundamental fermions, but while measurements at the Large Hadron Collider (LHC) are consistent with this hypothesis, current analysis techniques lack the statistical power to cross the traditional 5σ significance barrier without more data. Deep learning techniques have the potential to increase the statistical power of this analysis by automatically learning complex, high-level data representations. In this work, deep neural networks are used to detect the decay of the Higgs boson to a pair of tau leptons. A Bayesian optimization algorithm is used to tune the network architecture and training algorithm hyperparameters, resulting in a deep network of eight nonlinear processing layers that improves upon the performance of shallow classifiers even without the use of features specifically engineered by physicists for this application. The improvement in discovery significance is equivalent to an increase in the accumulated data set of 25%.

Motivation: Animal models are widely used in biomedical research for reasons ranging from practical to ethical. An important issue is whether rodent models are predictive of human biology. This has been addressed recently in the framework of a series of challenges designed by the systems biology verification for Industrial Methodology for Process Verification in Research (sbv IMPROVER) initiative. In particular, one of the sub-challenges was devoted to the prediction of protein phosphorylation responses in human bronchial epithelial cells, exposed to a number of different chemical stimuli, given the responses in rat bronchial epithelial cells. Participating teams were asked to make inter-species predictions on the basis of available training examples, comprising transcriptomics and phosphoproteomics data., Results: Here, the two best performing teams present their data-driven approaches and computational methods. In addition, post hoc analyses of the datasets and challenge results were performed by the participants and challenge organizers. The challenge outcome indicates that successful prediction of protein phosphorylation status in human based on rat phosphorylation levels is feasible. However, within the limitations of the computational tools used, the inclusion of gene expression data does not improve the prediction quality. The post hoc analysis of time-specific measurements sheds light on the signaling pathways in both species., Availability and Implementation: A detailed description of the dataset, challenge design and outcome is available at www.sbvimprover.com. The code used by team IGB is provided under http://github.com/uci-igb/improver2013. Implementations of the algorithms applied by team AMG are available at http://bhanot.biomaps.rutgers.edu/wiki/AMG-sc2-code.zip., Contact: meikelbiehl@gmail.com., (© The Author 2014. Published by Oxford University Press.)

Collisions at high-energy particle colliders are a traditionally fruitful source of exotic particle discoveries. Finding these rare particles requires solving difficult signal-versus-background classification problems, hence machine-learning approaches are often used. Standard approaches have relied on 'shallow' machine-learning models that have a limited capacity to learn complex nonlinear functions of the inputs, and rely on a painstaking search through manually constructed nonlinear features. Progress on this problem has slowed, as a variety of techniques have shown equivalent performance. Recent advances in the field of deep learning make it possible to learn more complex functions and better discriminate between signal and background classes. Here, using benchmark data sets, we show that deep-learning methods need no manually constructed inputs and yet improve the classification metric by as much as 8% over the best current approaches. This demonstrates that deep-learning approaches can improve the power of collider searches for exotic particles.

Dropout is a recently introduced algorithm for training neural network by randomly dropping units during training to prevent their co-adaptation. A mathematical analysis of some of the static and dynamic properties of dropout is provided using Bernoulli gating variables, general enough to accommodate dropout on units or connections, and with variable rates. The framework allows a complete analysis of the ensemble averaging properties of dropout in linear networks, which is useful to understand the non-linear case. The ensemble averaging properties of dropout in non-linear logistic networks result from three fundamental equations: (1) the approximation of the expectations of logistic functions by normalized geometric means, for which bounds and estimates are derived; (2) the algebraic equality between normalized geometric means of logistic functions with the logistic of the means, which mathematically characterizes logistic functions; and (3) the linearity of the means with respect to sums, as well as products of independent variables. The results are also extended to other classes of transfer functions, including rectified linear functions. Approximation errors tend to cancel each other and do not accumulate. Dropout can also be connected to stochastic neurons and used to predict firing rates, and to backpropagation by viewing the backward propagation as ensemble averaging in a dropout linear network. Moreover, the convergence properties of dropout can be understood in terms of stochastic gradient descent. Finally, for the regularization properties of dropout, the expectation of the dropout gradient is the gradient of the corresponding approximation ensemble, regularized by an adaptive weight decay term with a propensity for self-consistent variance minimization and sparse representations.

Predicting the 3D structures of small molecules is a common problem in chemoinformatics. Even the best methods are inaccurate for complex molecules, and there is a large gap in accuracy between proprietary and free algorithms. Previous work presented COSMOS, a novel data-driven algorithm that uses knowledge of known structures from the Cambridge Structural Database and demonstrates performance that was competitive with proprietary algorithms. However, dependence on the Cambridge Structural Database prevented its widespread use. Here, we present an updated version of the COSMOS structure predictor, complete with a free structure library derived from open data sources. We demonstrate that COSMOS performs better than other freely available methods, with a mean RMSD of 1.16 and 1.68 Å for organic and metal-organic structures, respectively, and a mean prediction time of 60 ms per molecule. This is a 17% and 20% reduction, respectively, in RMSD compared to the free predictor provided by Open Babel, and it is 10 times faster. The ChemDB Web portal provides a COSMOS prediction Web server, as well as downloadable copies of the COSMOS executable and library of molecular substructures.