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IntroductionFluconazole is an important antifungal in the prevention and treatment of invasive Candida infections in neonates, even though its use in preterm infants is still off-label. Here, we performed a population pharmacokinetic study on fluconazole in preterm neonates in order to optimise dosing through the identified predictive patient characteristics.MethodsFluconazole concentrations obtained from preterm infants from two studies were pooled and analysed using NONMEM V.7.3. The developed model was used to evaluate current dosing practice. A therapeutic dosing strategy aiming to reach a minimum target exposure of 400 and 200 mg×hour/L per 24 hours for fluconazole-susceptible C. albicans meningitis and other systemic infections, respectively, was developed.ResultsIn 41 preterm neonates with median (range) gestational age 25.3 (24.0–35.1) weeks and median postnatal age (PNA) at treatment initiation 1.4 (0.2–32.5) days, 146 plasma samples were collected. A one-compartment model described the data best, with an estimated clearance of 0.0147 L/hour for a typical infant of 0.87 kg with a serum creatinine concentration of 60 µmol/L and volume of distribution of 0.844 L. Clearance was found to increase with 16% per 100 g increase in actual body weight, and to decrease with 12% per 10 µmol/L increase in creatinine concentration once PNA was above 1 week. Dose adjustments based on serum creatinine and daily dosing are required for therapeutic target attainment.ConclusionIn preterm neonates, fluconazole clearance is best predicted by actual body weight and serum creatinine concentration. Therefore, fluconazole dosing should not only be based on body weight but also on creatinine concentration to achieve optimal exposure in all infants.Ethics statementThe Erasmus MC ethics review board approved the protocol of the DINO Study (MEC-2014-067) and the Radboud UMC ethics review board waived the need for informed consent for cohort 2 (CMO-2021-8302). Written informed consent from parents/legal guardians was obtained prior to study initiation.

Chronically implanted brain-computer interfaces (BCIs) provide amazing opportunities to those living with disability and for the treatment of chronic disorders of the nervous system. However, this potential has yet to be fully realized in part due to the lack of stability in measured signals over time. Signal disruption stems from multiple sources including mechanical failure of the interface, changes in neuron health, and glial encapsulation of the electrodes that alter the impedance. In this study we present an algorithmic solution to the problem of long-term signal disruption in chronically implanted neural interfaces. Our approach utilizes a generative adversarial network (GAN), based on the original Unsupervised Image to Image Translation (UNIT) algorithm, which learns how to recover degraded signals back to their analogous non-disrupted (“clean”) exemplars measured at the time of implant. We demonstrate that this approach can reliably recover simulated signals in two types of commonly used neural interfaces: multi-electrode arrays (MEA), and electrocorticography (ECoG). To test the accuracy of signal recovery we employ a common BCI paradigm wherein a classification algorithm (neural decoder) is trained on the starting (non-disrupted) set of signals. Performance of the decoder demonstrates expected failure over time as the signal disruption accumulates. In simulated MEA experiments, our approach recovers decoder accuracy to >90% when as many as 13/ 32 channels are lost, or as many as 28/32 channels have their neural responses altered. In simulated ECoG experiments, our approach shows stabilization of the neural decoder indefinitely with decoder accuracies >95% over simulated lifetimes of over 1 year. Our results suggest that these types of neural networks can provide a useful tool to improve the long-term utility of chronically implanted neural interfaces.

Consideration is given to the stochastic problem of the coagulation of particles for the case of a size-independent coagulation kernel, and expressions are derived for the expectation value, variance and covariance of the cluster size distribution function, for both a discrete and a continuous spectrum of cluster sizes. The authors develop an asymptotic expansion in V-1 of these quantities (where V is the spatial volume), showing that as V to infinity the above expectation value tends to the deterministic result, and obtaining an explicit form for the first-order deviation from this expression for large (but finite) V. Analogous results are derived for the variance and covariance in the limit of large V. A discussion is given of the extent to which stochastic effects can produce significant changes to the deterministic results.

We give a new proof based on the recent very elegant argument of Marques Alves and Svaiter that the sub differential of a proper, convex lower semicontinuous function on a real Banach space is maximally monotone. We also show how the argument can be simplified in the reflexive case. © Heldermann Verlag.

In this paper, we show how convex analysis can be applied to the theory of sets that are "positive" with respect to a continuous quadratic form on a Banach space. Monotone sets can be considered as a special case of positive sets, and we show how our results lead to very efficient proofs of a number of results on monotone sets. One of the key techniques that we use is a generalization of the Fitzpatrick function from monotone set theory to an analogous function for positive sets. © Heldermann Verlag.

Values of triplet yields for dilute solutions of benzene in methylcylohexane, cyclohexane, methanol, ethanol, acetonitrile, perfluoro- n - hexane, and water, and also fluorescence yields and lifetimes in perfluoro- n -hexane are reported. The calculated rate constants for fluorescence and intersystem crossing are only slightly affected by solvent, except for water. The enhancement of “internal conversion” by increasing solvent polarity is interpreted as a result of an increase in the pre-exponential factor for a possible 1 (π,π * ) → 1 (σ,π * ) transition.

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This paper reviews some research studies on tillage methods influencing soil and moisture conservation in the eastern African countries of Kenya, Tanzania, Malawi and Ethiopia during the past four decades. Most of these studies were conducted in marginal rainfall (semi arid ) areas and on shallow soils of various textures (sandy clay loam, sandy clay, clay and loam). The studies were meant to establish the effects of tillage and residue management practices on physico-chemical soil properties (i.e. structure, bulk density, soil moisture and organic matter contents), runoff and infiltration. This review emphasizes the importance of appropriate tillage and residue management methods (contour bunds and terraces, minimum tillage, tied ridging, mulching and conventional tillage) in providing soil conditions favourable for soil moisture conservation and subsequent crop performance and yield on smallholder farms