Energy Resolved Mass Spectrometry for Interoperable Non-resonant Collisional Spectra in Metabolomics.

In untargeted metabolomics, the unambiguous identification of metabolites remains a major challenge. This requires high-quality spectral libraries for reliable metabolite identification, which is essential for translating metabolomics data into meaningful biological information. Several attempts have been made to generate reproducible product ion spectra (PIS) under a low collision energy (ELab) regime and nonresonant collisional conditions but have not fully succeeded. We examined the ERMS (energy-resolved mass spectrometry) breakdown curves of two lipo-amino acids and showed the possibility to highlight "singular points", called descriptors hereafter (linked to respective ELab depending on the instrument), for each of the monomodal product ion profiles. Using several instruments based on different technologies, the PIS recorded at these specific ELab sites shows remarkable similarities. The descriptors appeared as being independent of the fragmentation mechanisms and can be used to overcome the main instrumental effects that limit the interoperability of spectral libraries. This proof-of-concept study, performed on two particular lipo-amino acids, demonstrates the high potential of ERMS-derived information to determine the instrument-specific ELab at which PIS recorded in nonresonant conditions become highly similar and instrument-independent, thus comparable across platforms. This innovative but straightforward approach could help remove some of the obstacles to metabolite identification in nontargeted metabolomics, putting an end to a challenging chimera.

Interpreting Wide-Complex Tachycardia using Artificial Intelligence.

BACKGROUND: Adopting artificial intelligence in medicine may improve speed and accuracy in patient diagnosis. We sought to develop an artificial intelligence (AI) algorithm to interpret wide complex tachycardia (WCT) electrocardiograms (ECG) and compare its diagnostic accuracy to cardiologists. METHODS: Using 3330 WCT ECGs (2906 SVT and 424 VT), we created a training/validation (3131) and test set (199 ECGs). A convolutional neural network (CNN) structure using a modification of differentiable architecture search (DARTS), ZeroLess-DARTS, was developed to differentiate between SVT and VT. RESULTS: The mean accuracy of electrophysiology (EP) cardiologists was 92.5% with a sensitivity of 91.7%, specificity of 93.4%, positive predictive value of 93.7%, negative predictive value of 91.7%. NonEP cardiologists had an accuracy of 73.2 ± 14.4% with a sensitivity, specificity, positive and negative predictive value of 59.8 ± 18.2%, 93.8 ± 3.7%, 93.6 ± 2.3%, and 73.2 ± 14.4%, respectively. AI had superior sensitivity and accuracy (91.9% and 93.0%, respectively) than NonEP cardiologists, and had similar performance of EP cardiologists. Mean time to interpret each ECG varied between 10.1-13.8 seconds for EP cardiologists and 3.1 -16.6 seconds for NonEP cardiologists. Conversely AI required a mean of 0.0092 ± 0.0035 seconds for each ECG interpretation. CONCLUSIONS: AI appears to diagnose WCT with superior accuracy than Cardiologists and similar to those of Electrophysiologists. Using AI to assist with ECG interpretations may improve patient care.

Predictive modelling of deviation from expected milk yield in transition cows on automatic milking systems.

The transition period is a pivotal time in the production cycle of the dairy cow. It is estimated that between 30% and 50% of all cows experience metabolic or infectious disease during this time. One of the most common and economically consequential effects of disease during the transition period is a reduction in early lactation milk production. This has led to the utilisation of deviation from expected milk yield in early lactation as a proxy measure for transition health. However, to date, this analysis has been used exclusively for the retrospective assessment of transition cow health. Statistical models capable of predicting deviations from expected milk yield may allow producers to proactively manage animals predicted to suffer negative deviations in early lactation milk production. The objective of this retrospective cohort study was first, to explore the accuracy with which cow-level production and behaviour data collected on automatic milking systems (AMS) from 1-3 days in milk (DIM) can predict deviation from expected 30-day cumulative milk yield in multiparous cows. And second, to assess the accuracy with which predicted yield deviations can classify cows into groups which may facilitate improved transition management. Production, rumination, and physical activity data from 31 commercial AMS were accessed. A 3-step analytical procedure was then conducted. In Step 1, expected cumulative yield for 1-30 DIM for each individual cow-lactation was calculated using a mixed effect linear model. In Step 2, 30-Day Yield Deviation (YD) was calculated as the difference between observed and expected cumulative yield. Lactations were then assigned to one of three groups based on their YD, RED Group (0% YD). In Step 3, yield, rumination, and physical activity data from days 1-3 in lactation were used to predict YD using machine learning models. Following external validation, YD was predicted across the test data set with a mean absolute error of 9%. Categorisation of animals suffering large negative deviations (RED group) was achieved with a specificity of 99%, sensitivity of 35%, and balanced accuracy of 67%. Our results suggest that milk yield, rumination and physical activity patterns expressed by dairy cows from 1-3 DIM have utility in the prediction of deviation from expected 30-day cumulative yield. However, these predictions currently lack the sensitivity required to classify cows reliably and completely into groups which may facilitate improved transition cow management.

Adherent skin barrier drape use is associated with a reduced risk of cardiac implantable device infection: Results from a prospective study of 14,225 procedures.

Background: Cardiac implantable electronic device (CIED) infection is a costly and highly morbid complication. Perioperative interventions, including the use of antibiotic pouches and intensified perioperative antibiotic regimens, have demonstrated marginal efficacy at reducing CIED infection. Additional research is needed to identify additional interventions to reduce infection risk. Objective: We sought to evaluate whether adherent skin barrier drape use is associated with a reduction in CIED infection. Methods: A prospective registry of all CIED implantation procedures was established at our institution in January 2007. The registry was established in collaboration with our hospital infection prevention team with a specific focus on prospectively identifying all potential CIED infections. All potential CIED infections were independently adjudicated by 2 physicians blinded to the use of an adherent skin barrier drape. Results: Over a 13-year period, 14,225 procedures were completed (mean age 72 ± 14 years; female 4,918 (35%); new implants 10,005 (70%); pulse generator changes 2585 (18%); upgrades 1635 (11%). Of those, 2469 procedures (17.4%) were performed using an adherent skin barrier drape. There were 103 adjudicated device infections (0.73%). The infection rate in patients in the barrier use groups was 8 of 2469 (0.32%) as compared with 95 of 11,756 (0.8%) in the nonuse group (P = .0084). In multivariable analysis, the use of an adherent skin barrier drape was independently associated with a reduction in infection (odds ratio 0.32; 95% confidence interval 0.154-0.665; P = .002). Conclusion: The use of an adherent skin barrier drape at the time of cardiac device surgery is associated with a lower risk of subsequent infection.

Quantification of the effect of in-utero events on lifetime resilience in dairy cows.

Currently, the dairy industry is facing many challenges that could affect its sustainability, including climate change and public perception of the industry. As a result, interest is increasing in the concept of identifying resilient animals, those with a long productive lifespan, good reproductive performance and milk yield. There is much evidence that events in utero, i.e., the Developmental Origins of Health and Disease (DOHaD), alter life-course health of offspring and we hypothesized that these could alter resilience in calves, where resilience is identified using lifetime data. The aim of this study was to quantify lifetime resilience scores (LRS) using an existing scoring system based on longevity with secondary corrections for age at first calving and calving interval and to quantify the effects of in-utero events on the LRS using 2 data sets. The first was a large data set of cattle in 83 farms in Great Britain born from 2006 to 2015 and the second was a smaller, more granular data set of cattle born between 2003 and 2015 in the Langhill research herd at Scotland's Rural College. Events during dam's pregnancy included health events (lameness, mastitis, use of an antibiotic or anti-inflammatory medication), the impact of heat stress as measured by temperature-humidity index and perturbations in milk yield and quality (somatic cell count, percentage fat, percentage protein and fat:protein ratio). Daughters born to dams that experienced higher temperature-humidity indexes while they were in-utero during the first and third trimesters of pregnancy had lower LRS. Daughter LRS scores were also lower where milk yields or median fat percentages in the first trimester were low, and when milk yields were high in the third trimester. Dam LRS was positively associated with LRS of their offspring, however, as parity of the dam increased, LRS of their calves decreased. Similarly, in the Langhill herd, dams of a higher parity produced calves with lower LRS. Additionally, dams which recorded a high max locomotion score in the third trimester of pregnancy were negatively associated with lower calf LRS in the Langhill herd. Our results suggest that events that occur during pregnancy have lifelong consequences for the calf's lifetime performance. However, experience of higher temperature-humidity indexes, higher dam LRS scores and mothers in higher parities explained a relatively small proportion of variation in offspring LRS, which suggests that other factors play a substantial role in determining calf LRS scores. While 'big data' can contain a considerable amount of noise, similar findings between the 2 data sets indicate it is likely these findings are real.

RAFF-5 Study to Improve the Quality and Safety of Care for Patients Seen in the Emergency Department With Acute Atrial Fibrillation and Flutter.

BACKGROUND: We sought to improve the immediate and subsequent care of emergency department (ED) patients with acute atrial fibrillation (AF) and flutter (AFL) by implementing the principles of the Canadian Association of Emergency Physicians AF/AFL Best Practices Checklist. METHODS: This cohort study included 3 periods: before (7 months), intervention introduction (1 month), and after (7 months), and was conducted at a major academic centre. We included patients who presented with an episode of acute AF or AFL and used multiple strategies to support ED adoption of the Canadian Association of Emergency Physicians checklist. We developed new cardiology rapid-access follow-up processes. The main outcomes were unsafe and suboptimal treatments in the ED. RESULTS: We included 1108 patient visits, with 559 in the before and 549 in the after period. In a comparison of the periods, there was an increase in use of chemical cardioversion (20.6% vs 25.0%; absolute difference [AD], 4.4%) and in electrical cardioversion (39.2% vs 51.2%; AD, 12.0%). More patients were discharged with sinus rhythm restored (66.9% vs 75.0%; AD, 8.1%). The proportion seen in a follow-up cardiology clinic increased from 24.2% to 39.9% (AD, 15.7%) and the mean time until seen decreased substantially (103.3 vs 49.0 days; AD, -54.3 days). There were very few unsafe cases (0.4% vs 0.7%) and, although there was an increase in suboptimal care (19.5% vs 23.1%), overall patient outcomes were excellent. CONCLUSIONS: We successfully improved the care for ED patients with acute AF/AFL and achieved more frequent and more rapid cardiology follow-up. Although cases of unsafe management were uncommon and patient outcomes were excellent, there are opportunities for physicians to improve their care of acute AF/AFL patients. GOV IDENTIFIER: NCT05468281.

Unveiling the Role of Ge in CZTSSe Solar Cells by Advanced Micro-To-Atom Scale Characterizations.

Kesterite is an earth-abundant energy material with high predicted power conversion efficiency, making it a sustainable and promising option for photovoltaics. However, a large open circuit voltage Voc deficit due to non-radiative recombination at intrinsic defects remains a major hurdle, limiting device performance. Incorporating Ge into the kesterite structure emerges as an effective approach for enhancing performance by manipulating defects and morphology. Herein, how different amounts of Ge affect the kesterite growth pathways through the combination of advanced microscopy characterization techniques are systematically investigated. The results demonstrate the significance of incorporating Ge during the selenization process of the CZTSSe thin film. At high temperature, the Ge incorporation effectively delays the selenization process due to the formation of a ZnSe layer on top of the metal alloys through decomposition of the Cu-Zn alloy and formation of Cu-Sn alloy, subsequently forming of Cu-Sn-Se phase. Such an effect is compounded by more Ge incorporation that further postpones kesterite formation. Furthermore, introducing Ge mitigates detrimental "horizontal" grain boundaries by increasing the grain size on upper layer. The Ge incorporation strategy discussed in this study holds great promise for improving device performance and grain quality in CZTSSe and other polycrystalline chalcogenide solar cells.

Multifunctional Surface Treatment against Imperfections and Halide Segregation in Wide-Band Gap Perovskite Solar Cells.

Mixed-halide wide-band gap perovskites (WBPs) still suffer from losses due to imperfections within the absorber and the segregation of halide ions under external stimuli. Herein, we design a multifunctional passivator (MFP) by mixing bromide salt, formamidinium bromide (FABr) with a p-type self-assembled monolayer (SAM) to target the nonradiative recombination pathways. Photoluminescence measurement shows considerable suppression of nonradiative recombination rates after treatment with FABr. However, WBPs still remained susceptible to halide segregation for which the addition of 25% p-type SAM was effective to decelerate segregation. It is observed that FABr can act as a passivating agent of the donor impurities, shifting the Fermi-level (Ef) toward the mid-band gap, while p-type SAM could cause an overweight of Ef toward the valence band. Favorable band bending at the interface could prevent the funneling of carriers toward I-rich clusters. Instead, charge carriers funnel toward an integrated SAM, preventing the accumulation of polaron-induced strain on the lattice. Consequently, n-i-p structured devices with an optimal MFP treatment show an average open-circuit voltage (VOC) increase of about 20 mV and fill factor (FF) increase by 4% compared with the control samples. The unencapsulated devices retained 95% of their initial performance when stored at room temperature under 40% relative humidity for 2800 h.

Cd-Free Pure Sulfide Kesterite Cu2 ZnSnS4 Solar Cell with Over 800 mV Open-Circuit Voltage Enabled by Phase Evolution Intervention.

The Cd-free Cu2 ZnSnS4 (CZTS) solar cell is an ideal candidate for producing low-cost clean energy through green materials owing to its inherent environmental friendliness and earth abundance. Nevertheless, sulfide CZTS has long suffered from severe open-circuit voltage (VOC ) deficits, limiting the full exploitation of performance potential and further progress. Here, an effective strategy is proposed to alleviate the nonradiative VOC loss by manipulating the phase evolution during the critical kesterite phase formation stage. With a Ge cap layer on the precursor, premature CZTS grain formation is suppressed at low temperatures, leading to fewer nucleation centers at the initial crystallization stage. Consequently, the CZTS grain formation and crystallization are deferred to high temperatures, resulting in enhanced grain interior quality and less unfavorable grain boundaries in the final film. As a result, a champion efficiency of 10.7% for Cd-free CZTS solar cells with remarkably high VOC beyond 800 mV (63.2% Schockley-Queisser limit) is realized, indicating that nonradiative recombination is effectively inhibited. This strategy may advance other compound semiconductors seeking high-quality crystallization.

Improvement of mammary gland health in 81 'sentinel herds' in England and Scotland between 2012 and 2021.

BACKGROUND: Achieving a reduction in mastitis in dairy cows is a common industry goal, but there is no recent peer-reviewed record of progress in the UK. METHODS: A convenience sample of 125 herds in England and Scotland was recruited based on the quality of records in 2016, willingness to participate and representative geographical distribution. Individual cow somatic cell counts and clinical mastitis data from 2012 to 2021 were summarised annually, and temporal changes were analysed. Eighty-one herds had sufficient data for comparison between 2012 and 2021, for one or more parameters. RESULTS: Over this period, the median incidence rate of clinical mastitis decreased from 40.0 to 21.0 cases per 100 cows per year (p < 0.001), with improvement in both lactation and dry period indicators. Lactation new infection rate calculated from individual cow somatic cell counts fell from 8.75% to 5.95% (p < 0.001), dry period new infection rate fell from 16.8% to 14.1% (p < 0.05) and proportion of cows over 200,000 cells/mL fell from 20.0% to 14.3% (p < 0.001). LIMITATIONS: Data were necessarily from herds with good records and do not provide absolute values for the industry. CONCLUSION: The findings reflect good progress over a 10-year period in a cohort of well-recorded herds and align with other national datasets.

Do barrier dressings reduce cardiac implantable device infection: Protocol for a randomized controlled trial (BARRIER-PROTECT).

Background: Cardiac implantable electronic device (CIED) procedures can be associated with serious complications, including infection with significant mortality and morbidity, necessitating removal of the device and prolonged hospitalization. One potential pathophysiological mechanism is pocket contamination at the time of device implantation. Therefore, steps taken to prevent contamination at this stage can potentially reduce CIED infections.The barrier dressing, an adhesive material applied to the skin, has the potential to reduce the colonization of the surgical site with host flora that can predispose to infection. There are a limited number of randomized prospective studies on barrier dressing use during various surgeries, but it has never been systematically studied in CIED implantation. Objectives: Do Barrier Dressings Reduce Cardiac Implantable Device Infection? (BARRIER-PROTECT trial; NCT04591366) is a single-centre, prospective, double-armed, single-blinded, randomized controlled trial designed to evaluate the use of an intra-operative adhesive barrier dressing to reduce the risk of end-of-procedure pocket swab positivity. We hypothesize that adhesive draping during implant procedures will reduce the risk of contamination from the skin flora. Also, we aim to investigate if the end-of-procedure pocket swab culture positivity can be used as a potential surrogate marker of CIED infection. Methods and Design: Patients undergoing a second or later procedure on the same device pocket (pulse generator change, lead/pocket revision or upgrade) will be enrolled. Eligible and consenting patients will be equally randomized to the use of barrier dressing or not using an automated web-based system. Patients, but not the operator, will be blinded to the arm. The person performing the pocket swabs will also be blinded. The primary endpoint is the end-of-procedure pocket swab culture positivity. The main secondary endpoint is the CIED infection rate. Discussion: This is the first randomized controlled trial to assess the effectiveness of using a barrier adhesive draping on reducing the end-of-procedure pocket swab culture positivity. In this study, we are exploring a low-cost intervention that may significantly reduce CIED infection. Also, having a valid surrogate marker for CIED infection at the time of implant will facilitate design of future clinical trials.

The use of machine learning to predict somatic cell count status in dairy cows post-calving.

Udder health remains a priority for the global dairy industry to reduce pain, economic losses, and antibiotic usage. The dry period is a critical time for the prevention of new intra-mammary infections and it provides a point for curing existing intra-mammary infections. Given the wealth of udder health data commonly generated through routine milk recording and the importance of udder health to the productivity and longevity of individual cows, an opportunity exists to extract greater value from cow-level data to undertake risk-based decision-making. The aim of this research was to construct a machine learning model, using routinely collected farm data, to make probabilistic predictions at drying off for an individual cow's risk of a raised somatic cell count (hence intra-mammary infection) post-calving. Anonymized data were obtained as a large convenience sample from 108 UK dairy herds that undertook regular milk recording. The outcome measure evaluated was the presence of a raised somatic cell count in the 30 days post-calving in this observational study. Using a 56-farm training dataset, machine learning analysis was performed using the extreme gradient boosting decision tree algorithm, XGBoost. External validation was undertaken on a separate 28-farm test dataset. Statistical assessment to evaluate model performance using the external dataset returned calibration plots, a Scaled Brier Score of 0.095, and a Mean Absolute Calibration Error of 0.009. Test dataset model calibration performance indicated that the probability of a raised somatic cell count post-calving was well differentiated across probabilities to allow an end user to apply group-level risk decisions. Herd-level new intra-mammary infection rate during the dry period was a key driver of the probability that a cow had a raised SCC post-calving, highlighting the importance of optimizing environmental hygiene conditions. In conclusion, this research has determined that probabilistic classification of the risk of a raised SCC in the 30 days post-calving is achievable with a high degree of certainty, using routinely collected data. These predicted probabilities provide the opportunity for farmers to undertake risk decision-making by grouping cows based on their probabilities and optimizing management strategies for individual cows immediately after calving, according to their likelihood of intra-mammary infection.

Barrier Strategy for Strain-Free Encapsulation of Perovskite Solar Cells.

The performance loss caused by encapsulation has been an obstacle to guarantee the excellent power conversion efficiency of perovskite solar cells (PSCs) in practical application. This work revealed that the encapsulation-induced performance loss is highly related to the tensile strains imposed on the functional layers of the device when the PSC is exposed directly to the deformed encapsulant. A barrier strategy is developed by employing a nonadhesive barrier layer to isolate the deformed encapsulant from the PSC functional layer, achieving a strain-free encapsulation of the PSCs. The encapsulated device with a barrier layer effectively reduced the relative performance loss from 21.4% to 5.7% and dramatically improved the stability of the device under double 85 environment conditions. This work provides an effective strategy to mitigate the negative impact of encapsulation on the performance of PSCs as well as insight into the underlying mechanism of the accelerated degradation of PSCs under external strains.

The Prevalence and Characteristics of Arrhythmic Mitral Valve Prolapse in Patients With Unexplained Cardiac Arrest.

BACKGROUND: There is growing evidence that mitral valve prolapse (MVP) is associated with otherwise unexplained cardiac arrest (UCA). However, reports are hindered by the absence of a systematic ascertainment of alternative diagnoses. OBJECTIVES: This study reports the prevalence and characteristics of MVP in a large cohort of patients with UCA. METHODS: Patients were enrolled following an UCA, defined as cardiac arrest with no coronary artery disease, preserved left ventricular ejection fraction, and no apparent explanation on electrocardiogram. A comprehensive evaluation was performed, and patients were diagnosed with idiopathic ventricular fibrillation (IVF) if no cause was found. Echocardiography reports were reviewed for MVP. Patients with MVP were divided into 2 groups: those with IVF (AMVP) and those with an alternative diagnosis (nonarrhythmic MVP). Patient characteristics were then compared. The long-term outcomes of AMVP were reported. RESULTS: Among 571 with an initially UCA, 34 patients had MVP (6%). The prevalence of definite MVP was significantly higher in patients with IVF than those with an alternative diagnosis (24 of 366 [6.6%] vs 5 of 205 [2.4%]; P = 0.03). Bileaflet prolapse was significantly associated with AMVP (18 of 23 [78%] vs 1 of 8 [12.5%]; P = 0.001; OR: 25.2). The proportion of patients with AMVP who received appropriate implantable cardioverter-defibrillator therapies over a median follow-up of 42 months was 21.1% (4 of 19). CONCLUSIONS: MVP is associated with otherwise UCA (IVF), with a prevalence of 6.6%. Bileaflet prolapse appears to be a feature of AMVP, although future studies need to ascertain its independent association. A significant proportion of patients with AMVP received appropriate implantable cardioverter-defibrillator therapies during follow-up.

The 2023 Canadian Cardiovascular Society Clinical Practice Update on Management of the Patient With a Prolonged QT Interval.

A prolonged QT interval on the electrocardiogram is associated with an increased risk of the torsades de pointes form of ventricular arrhythmia resulting in syncope, sudden cardiac arrest or death, or misdiagnosis as a seizure disorder. The cause of QT prolongation can be congenital and inherited as an autosomal dominant variant, or it can be transient and acquired, often because of QT-prolonging drugs or electrolyte abnormalities. Automated measurement of the QT interval can be inaccurate, especially when the baseline electrocardiogram is abnormal, and manual verification is recommended. In this clinical practice update we provide practical tips about measurement of the QT interval, diagnosis, and management of congenital long QT syndrome and acquired prolongation of the QT interval. For congenital long QT syndrome, certain ß-adrenergic-blocking drugs are highly effective, and implantable defibrillators are infrequently required. Many commonly prescribed drugs such as antidepressants and antibiotics can prolong the QT interval, and recommendations are provided on their safe use.

Unravelling transmission of Mycobacterium avium subspecies paratuberculosis to dairy calves: results of a lifelong longitudinal study.

Johne's disease (JD) is a chronic disease of ruminants endemic in the UK and other countries and responsible for large economic losses for the dairy sector. JD is caused by Mycobacterium avium subspecies paratuberculosis (MAP), which typically infects calves that remain latently infected during a long period, making early detection of infection challenging. Cow to calf transmission can occur in-utero, via milk/colostrum or faecal-orally. Understanding of the different transmission routes to calves is important in informing control recommendations. Our aim in this longitudinal study was to measure the association between the transmission routes via the dam and the environment on a calf subsequently testing serologically positive for MAP. The study population comprised of 439 UK dairy calves from 6 herds enrolled between 2012 and 2013. These calves were followed up from birth until 2023. At birth individual calf data was captured. During follow-up, individuals entering the milking herd were quarterly tested for the presence of MAP antibodies using milk ELISA. Cox regression models were used to measure the association between exposure from the dam (in-utero and/or colostrum) or from the environment (long time in dirty yard) and time to first detection of MAP infection. An association between calves born to positive dams and probability of having a MAP positive test result remained after excluding potential MAP transmission via colostrum (Hazard ratio: 2.24; 95% CI: 1.14 - 4.41). Calves unlikely to be infected with MAP via the in-utero or colostrum route, had 3.68 (95% CI: 3.68 1.45-9.33) higher hazard of a positive test result when they stayed longer in a dirty calving area. The effect of the dam infection status on transmission to calves precedes the dam's seroconversion and persists after excluding the potential role of transmission via colostrum. The association between time spent in a dirty calving area and probability of a MAP positive test result highlights the role of environmental contamination as a source of infection in addition to the dam.

Incorporating heterogeneity in farmer disease control behaviour into a livestock disease transmission model.

Human behaviour is critical to effective responses to livestock disease outbreaks, especially with respect to vaccination uptake. Traditionally, mathematical models used to inform this behaviour have not taken heterogeneity in farmer behaviour into account. We address this by exploring how heterogeneity in farmers vaccination behaviour can be incorporated to inform mathematical models. We developed and used a graphical user interface to elicit farmers (n = 60) vaccination decisions to an unfolding fast-spreading epidemic and linked this to their psychosocial and behavioural profiles. We identified, via cluster analysis, robust patterns of heterogeneity in vaccination behaviour. By incorporating these vaccination behavioural groupings into a mathematical model for a fast-spreading livestock infection, using computational simulation we explored how the inclusion of heterogeneity in farmer disease control behaviour may impact epidemiological and economic focused outcomes. When assuming homogeneity in farmer behaviour versus configurations informed by the psychosocial profile cluster estimates, the modelled scenarios revealed a disconnect in projected distributions and threshold statistics across outbreak size, outbreak duration and economic metrics.

Accurate prediction of calving in dairy cows by applying feature engineering and machine learning.

Prediction of calving is key to dairy cow management. Current trends of increasing herd sizes globally can directly impact the time that farmers spend monitoring individual animals. Automated monitoring on behavioural and physiological changes prior to parturition can be used to develop machine learning solutions for calving prediction. In this study, we developed a machine learning algorithm for the prediction of calving in dairy cows. We demonstrated that temperature and activity index information retrieved from a commercial reticuloruminal bolus sensor can accurately predict calving from 1-day to 5-days in advance. The best prediction solution using data from 82 dairy cows, achieved up to 87.81 % in accuracy, 92.99 % in specificity, 75.84 % in sensitivity, 82.99 % in positive predictive value (PPV), 78.85 % in F-score, and 90.02 % in negative predictive value (NPV) on the test dataset when using information from 2-days in advance and all the subsets of feature characteristics (temperature + drinking + activity). The performance only decreased by 2.45 % points in accuracy, 0.74 % points in specificity, 6.41 % points in sensitivity, 2.45 % points in positive predictive value, 4.91 % points in F-score, and 2.44 % points in negative predictive value on the test dataset when using all feature characteristics and 5-days in advance information compared to using all features and information from 2-days in advance. Full evaluation of the performance of the prediction showed an improvement when using all the different subsets of feature characteristics together (temperature, activity, and drinking) compared to using temperature features only. When adding activity and drinking to the subset of temperature features, an average increase of 2.70, 1.52, 5.40, 4.39, 5.02, 2.13 % points in accuracy, specificity, sensitivity, PPV, F-score, and NPV, respectively, was obtained. Notably, evaluation of feature importance (i.e., relative weight of any given feature in relation to model prediction) showed that 3-5 (depending on the selected days in advance model) of the top ten features were derived from drinking behaviour, showing the relevance that this behaviour can have in the prediction of calving. This algorithm can provide a useful tool for automated calving prediction in dairy cows which has potential for improvement of health, welfare, and productivity in the dairy industry.

Predicting lameness in dairy cattle using untargeted liquid chromatography-mass spectrometry-based metabolomics and machine learning.

Lameness in dairy cattle is a highly prevalent condition that impacts on the health and welfare of dairy cows. Prompt detection and implementation of effective treatment is important for managing lameness. However, major limitations are associated with visual assessment of lameness, which is the most commonly used method to detect lameness. The aims of this study were to investigate the use of metabolomics and machine learning to develop novel methods to detect lameness. Untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) alongside machine learning models and a stability selection method were utilized to evaluate the predictive accuracy of differences in the metabolomics profile of first-lactation dairy cows before (during the transition period) and at the time of lameness (based on visual assessment using the 0-3 scale of the Agriculture and Horticulture Development Board). Urine samples were collected from 2 cohorts of dairy heifers and stored at -86°C before analysis using LC-MS. Cohort 1 (n = 90) cows were recruited as current first-lactation cows with weekly mobility scores recorded over a 4-mo timeframe, from which newly lame and nonlame cows were identified. Cohort 2 (n = 30) cows were recruited within 3 wk before calving, and lameness events (based on mobility score) were recorded through lactation until a minimum of 70 d in milk (DIM). All cows were matched paired by DIM ± 14 d. The median DIM at lameness identification was 187.5 and 28.5 for cohort 1 and 2, respectively. The best performing machine learning models predicted lameness at the time of lameness with an accuracy of between 81 and 82%. Using stability selection, the prediction accuracy at the time of lameness was 80 to 81%. For samples collected before and after calving, the best performing machine learning model predicted lameness with an accuracy of 71 and 75%, respectively. The findings from this study demonstrate that untargeted LC-MS profiling combined with machine learning methods can be used to predict lameness as early as before calving and before observable changes in gait in first-lactation dairy cows. The methods also provide accuracies for detecting lameness at the time of observable changes in gait of up to 82%. The findings demonstrate that these methods could provide substantial advancements in the early prediction and prevention of lameness risk. Further external validation work is required to confirm these findings are generalizable; however, this study provides the basis from which future work can be conducted.

Bifacial and Semitransparent Sb2 (S,Se)3 Solar Cells for Single-Junction and Tandem Photovoltaic Applications.

Thin-film solar cells are expected to play a significant role in the space industry, building integrated photovoltaic (BIPV), indoor applications, and tandem solar cells, where bifaciality and semitransparency are highly desired. Sb2 (S,Se)3 has emerged as a promising new photovoltaic (PV) material for its high absorption coefficient, tunable bandgap, and nontoxic and earth-abundant constituents. However, high-efficiency Sb2 (S,Se)3 solar cells exclusively employ monofacial architectures, leaving a considerable gap toward large-scale application in aforementioned fields. Here, a bifacial and semitransparent Sb2 (S,Se)3 solar cell and its extended application in tandem solar cells are reported. The transparent conductive oxides (TCOs) and the ultrathin inner n-i-p structure provide high long-wavelength transmittance. Despite the MnS/ITO Schottky junction, power conversion efficiencies (PCEs) of 7.41% and 6.36% are achieved with front and rear illumination, respectively, contributing to a great bifaciality of 0.86. Consequently, the reported device gains great enhancement in PV performance by exploiting albedo of surroundings and shows exceptional capability in absorbing tilt incident light. Moreover, an Sb2 (S,Se)3 /Si tandem solar cell with a PCE of 11.66% is achieved in preliminary trials. These exciting findings imply that bifacial and semitransparent Sb2 (S,Se)3 solar cells possess tremendous potential in practical applications based on their unique characteristics.