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Transition cow diseases can negatively impact animal welfare and reduce dairy herd profitability. Transition cow disease incidence has remained relatively stable over time despite monitoring and management efforts aimed to reduce the risk of developing diseases. Dairy cattle disease risk is monitored by assessing multiple factors, including certain biomarker test results, health records, feed intake, body condition score, and milk production. However, these factors, which are used to make herd management decisions, are often reviewed separately without considering the correlation between them. In addition, the biomarkers that are currently used for monitoring may not be representative of the complex physiological changes that occur during the transition period. Predictive modeling, which uses data to predict future or current outcomes, is a method that can be used to combine the most predictive variables and their interactions efficiently. The use of an effective predictive model with relevant predictors for transition cow diseases will result in better targeted interventions, and therefore lower disease incidence. This review will discuss predictive modeling methods and candidate variables in the context of transition cow diseases. The next step is to investigate novel biomarkers and statistical methods that are best suited for the prediction of transition cow diseases.
Antibiotics are widely used by all specialties in the hospital setting. We evaluated previously defined high-risk antibiotic use in relation to Clostridioides difficile infections (CDIs).
We analyzed 2016–2017 data from 171 hospitals. High-risk antibiotics included second-, third-, and fourth-generation cephalosporins, fluoroquinolones, carbapenems, and lincosamides. A CDI case was a positive stool C. difficile toxin or molecular assay result from a patient without a positive result in the previous 8 weeks. Hospital-associated (HA) CDI cases included specimens collected >3 calendar days after admission or ≤3 calendar days from a patient with a prior same-hospital discharge within 28 days. We used the multivariable Poisson regression model to estimate the relative risk (RR) of high-risk antibiotic use on HA CDI, controlling for confounders.
The median days of therapy for high-risk antibiotic use was 241.2 (interquartile range [IQR], 192.6–295.2) per 1,000 days present; the overall HA CDI rate was 33 (IQR, 24–43) per 10,000 admissions. The overall correlation of high-risk antibiotic use and HA CDI was 0.22 (P = .003), and higher correlation was observed in teaching hospitals (0.38; P = .002). For every 100-day (per 1,000 days present) increase in high-risk antibiotic therapy, there was a 12% increase in HA CDI (RR, 1.12; 95% CI, 1.04–1.21; P = .002) after adjusting for confounders.
High-risk antibiotic use is an independent predictor of HA CDI. This assessment of poststewardship implementation in the United States highlights the importance of tracking trends of antimicrobial use over time as it relates to CDI.
Identifying risk factors of individuals in a clinical-high-risk state for psychosis are vital to prevention and early intervention efforts. Among prodromal abnormalities, cognitive functioning has shown intermediate levels of impairment in CHR relative to first-episode psychosis and healthy controls, highlighting a potential role as a risk factor for transition to psychosis and other negative clinical outcomes. The current study used the AX-CPT, a brief 15-min computerized task, to determine whether cognitive control impairments in CHR at baseline could predict clinical status at 12-month follow-up.
Baseline AX-CPT data were obtained from 117 CHR individuals participating in two studies, the Early Detection, Intervention, and Prevention of Psychosis Program (EDIPPP) and the Understanding Early Psychosis Programs (EP) and used to predict clinical status at 12-month follow-up. At 12 months, 19 individuals converted to a first episode of psychosis (CHR-C), 52 remitted (CHR-R), and 46 had persistent sub-threshold symptoms (CHR-P). Binary logistic regression and multinomial logistic regression were used to test prediction models.
Baseline AX-CPT performance (d-prime context) was less impaired in CHR-R compared to CHR-P and CHR-C patient groups. AX-CPT predictive validity was robust (0.723) for discriminating converters v. non-converters, and even greater (0.771) when predicting CHR three subgroups.
These longitudinal outcome data indicate that cognitive control deficits as measured by AX-CPT d-prime context are a strong predictor of clinical outcome in CHR individuals. The AX-CPT is brief, easily implemented and cost-effective measure that may be valuable for large-scale prediction efforts.
Maternal systemic inflammation during pregnancy may restrict embryo−fetal growth, but the extent of this effect remains poorly established in undernourished populations. In a cohort of 653 maternal−newborn dyads participating in a multi-armed, micronutrient supplementation trial in southern Nepal, we investigated associations between maternal inflammation, assessed by serum α1-acid glycoprotein and C-reactive protein, in the first and third trimesters of pregnancy, and newborn weight, length and head and chest circumferences. Median (IQR) maternal concentrations in α1-acid glycoprotein and C-reactive protein in the first and third trimesters were 0.65 (0.53–0.76) and 0.40 (0.33–0.50) g/l, and 0.56 (0.25–1.54) and 1.07 (0.43–2.32) mg/l, respectively. α1-acid glycoprotein was inversely associated with birth size: weight, length, head circumference and chest circumference were lower by 116 g (P = 2.3 × 10−6), and 0.45 (P = 3.1 × 10−5), 0.18 (P = 0.0191) and 0.48 (P = 1.7 × 10−7) cm, respectively, per 50% increase in α1-acid glycoprotein averaged across both trimesters. Adjustment for maternal age, parity, gestational age, nutritional and socio-economic status and daily micronutrient supplementation failed to alter any association. Serum C-reactive protein concentration was largely unassociated with newborn size. In rural Nepal, birth size was inversely associated with low-grade, chronic inflammation during pregnancy as indicated by serum α1-acid glycoprotein.
The rise of additive manufacturing (AM) has enabled the rapid production of complex part geometries across multiple material domains. To date, however, AM of inorganic semiconductor materials has not been fully realized due to the difficulty of forming single-crystal materials with traditional AM processes. Here, we demonstrate a novel semiconductor synthesis method using a combination of liquid and gas precursors to additively print gallium nitride. Growth rates of 1–2 µm/min are demonstrated in printed regions while maintaining epitaxial alignment with the substrate. We also outline critical variables for the future development, improvement, and implementation of the proposed process.
The primary objective of this study was to examine the impact of an electronic medical record (EMR)–driven intensive care unit (ICU) antimicrobial stewardship (AMS) service on clinician compliance with face-to-face AMS recommendations. AMS recommendations were defined by an internally developed “5 Moments of Antimicrobial Prescribing” metric: (1) escalation, (2) de-escalation, (3) discontinuation, (4) switch, and (5) optimization. The secondary objectives included measuring the impact of this service on (1) antibiotic appropriateness, and (2) use of high-priority target antimicrobials.
A prospective review was undertaken of the implementation and compliance with a new ICU-AMS service that utilized EMR data coupled with face-to-face recommendations. Additional patient data were collected when an AMS recommendation was made. The impact of the ICU-AMS round on antimicrobial appropriateness was evaluated using point-prevalence survey data.
For the 202 patients, 412 recommendations were made in accordance with the “5 Moments” metric. The most common recommendation made by the ICU-AMS team was moment 3 (discontinuation), which comprised 173 of 412 recommendations (42.0%), with an acceptance rate of 83.8% (145 of 173). Data collected for point-prevalence surveys showed an increase in prescribing appropriateness from 21 of 45 (46.7%) preintervention (October 2016) to 30 of 39 (76.9%) during the study period (September 2017).
The integration of EMR with an ICU-AMS program allowed us to implement a new AMS service, which was associated with high clinician compliance with recommendations and improved antibiotic appropriateness. Our “5 Moments of Antimicrobial Prescribing” metric provides a framework for measuring AMS recommendation compliance.
Experiments were performed within Sandia National Labs’ Multiphase Shock Tube to measure and quantify the shock-induced dispersal of a shock/dense particle curtain interaction. Following interaction with a planar travelling shock wave, schlieren imaging at 75 kHz was used to track the upstream and downstream edges of the curtain. Data were obtained for two particle diameter ranges (
) across Mach numbers ranging from 1.24 to 2.02. Using these data, along with data compiled from the literature, the dispersion of a dense curtain was studied for multiple Mach numbers (1.2–2.6), particle sizes (
) and volume fractions (9–32 %). Data were non-dimensionalized according to two different scaling methods found within the literature, with time scales defined based on either particle propagation time or pressure ratio across a reflected shock. The data show that spreading of the particle curtain is a function of the volume fraction, with the effectiveness of each time scale based on the proximity of a given curtain’s volume fraction to the dilute mixture regime. It is seen that volume fraction corrections applied to a traditional particle propagation time scale result in the best collapse of the data between the two time scales tested here. In addition, a constant-thickness regime has been identified, which has not been noted within previous literature.
In a recent paper (J. Fluid Mech., vol. 861, 2019, pp. 328–348), Benilov derived equations governing a laminar liquid sheet (a curtain) that emanates from a slot whose centreline is inclined to the vertical. The equations are valid for slender sheets whose characteristic length scale in the direction of flow is much larger than its cross-sectional thickness. For a liquid that leaves a slot with average speed,
, volumetric flow rate per unit width,
, surface tension,
, and density,
, Benilov obtains parametric equations that predict steady-state curtain shapes that bend upwards against gravity provided
. Benilov’s parametric equations are shown to be identical to those derived by Finnicum, Weinstein, and Ruschak (J. Fluid Mech., vol. 255, 1993, pp. 647–665). In the latter form, it is straightforward to deduce an alternative solution of Benilov’s equations where a curtain falls vertically regardless of the slot’s orientation. This solution is consistent with prior experimental and theoretical results that show that a liquid curtain can emerge from a slot at an angle different from that of the slot centreline.
We present a case of sudden cardiac arrest in the field with complete neurological recovery in an 18-year-old athlete with phenotypic Noonan syndrome. Evaluation revealed interventricular septal thickness of 18 mm without left ventricular outflow tract obstruction and no other identifiable structural, electrophysiologic, or genetic abnormality except isolated heterozygous variant for desmoplakin DSP variant p.Lys2103Glu, with unknown clinical significance.
A survey of Antarctic toothfish (Dissostichus mawsoni) was conducted in the northern Ross Sea region during the winter of 2016 to document the timing and location of spawning activity, to collect biological information about reproductive status during the spawning season and to look for temporal signals in biological data from D. mawsoni that may indicate a spawning migration of mature toothfish from the continental slope region to the northern Ross Sea region. The 58 day survey showed that spawning of D. mawsoni began on some seamounts by early July. No changes were detected between winter and summer in length, age, sex ratio or condition factor distributions for D. mawsoni in the northern Ross Sea as hypothesized following a spawning migration from the slope to the northern Ross Sea region. These results suggest that the distribution of D. mawsoni in the Ross Sea is mainly accomplished through ontogenetic migration and not annual return spawning migrations.
As demonstrated by neuroimaging data, the human brain contains systems that control responses to threat. The revised Reinforcement Sensitivity Theory of personality predicts that individual differences in the reactivity of these brain systems produce anxiety and fear-related personality traits. Here we discuss some of the challenges in testing this theory and, as an example, present a pilot study that aimed to dissociate brain activity during pursuit by threat and goal conflict. We did this by translating the Mouse Defense Test Battery for human fMRI use. In this version, dubbed the Joystick Operated Runway Task (JORT), we repeatedly exposed 24 participants to pursuit and goal conflict, with and without threat of electric shock. The runway design of JORT allowed the effect of threat distance on brain activation to be evaluated independently of context. Goal conflict plus threat of electric shock caused deactivation in a network of brain areas that included the fusiform and middle temporal gyri, as well as the default mode network core, including medial frontal regions, precuneus and posterior cingulate gyrus, and laterally the inferior parietal and angular gyri. Consistent with earlier research, we also found that imminent threat activated the midbrain and that this effect was significantly stronger during the simple pursuit condition than during goal conflict. Also consistent with earlier research, we found significantly greater hippocampal activation during goal conflict than pursuit by imminent threat. In conclusion, our results contribute knowledge to theories linking anxiety disorders to altered functioning in defensive brain systems and also highlight challenges in this research domain.
be a configuration of
. Each pair of points has a Euclidean distance in the configuration. Given some graph
vertices, we measure the point-pair distances corresponding to the edges of
. In this paper, we study the question of when a generic
dimensions will be uniquely determined (up to an unknowable Euclidean transformation) from a given set of point-pair distances together with knowledge of
. In this setting the distances are given simply as a set of real numbers; they are not labeled with the combinatorial data that describes which point pair gave rise to which distance, nor is data about
given. We show, perhaps surprisingly, that in terms of generic uniqueness, labels have no effect. A generic configuration is determined by an unlabeled set of point-pair distances (together with
) if and only if it is determined by the labeled distances.