Leiden University Scholary Publications
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Reassembling quarried landscapes through non-destructive X-ray fluorescence: the decorated metates from Central Nicaragua
Full text link Between 300 CE and 1550 CE, the Isthmo-Colombian Area had one of the highest concentrations of stone artisans. This is reflected in the decorated metates that extend from Honduras to northern Colombia. The Chontales department, in central Nicaragua, plays an important role due to its geographic location between different cultural regions. In fact, archaeological investigations point to a strong tendency towards a local ethnic identity due to the style of its standing stone sculptures. However, the production of decorated metates appears to share similarities with broader regional styles from northern areas of the Isthmo-Colombian Area. Considering that most of the decorated metates were collected throughout the early half of the 20th century and belong to museums where no or limited information on their original context is contained, the study of these materials must rely mostly on alternative lines of evidence such as archaeometric studies. In this research, we explore the relationship between artisanal production and the selection of (volcanic) raw material sources in the Chontales archaeological landscape. In this paper, we present the (1) non-destructive characterization of the collection and (2) explore the correlation between geochemical sources and stylistic characteristics. Contrary to previous assumptions, the study finds that a wide variety of volcanic material sources were used in decorated metate manufacturing and may, in fact, have been widely circulated. Furthermore, the study suggests that specific sources were used for particular tasks. These results may provide insight into our understanding of persistent crafting traditions and intergroup interactions in Central Nicaragua. NWOVI.C.221.093Archaeological Science
On the generalization ability of probabilistic neural networks for hyperspectral remote sensing of absorption properties across optically complex waters
Full text linkMachine learning models have steadily improved in estimating inherent optical properties (IOPs) from remote sensing observations. Yet, their generalization ability when applied to new water bodies, beyond those they were trained on, is not well understood. We present a novel approach for assessing model generalization across various scenarios, including interpolation within in situ observation datasets, extrapolation beyond the training scope, and application to hyperspectral observations from the PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite involving atmospheric correction. We evaluate five probabilistic neural networks (PNNs), including novel architectures like recurrent neural networks, for their ability to estimate absorption at 443 and 675 nm from hyperspectral reflectance. The median symmetric accuracy (MdSA) worsens from 25% in interpolation scenarios to 50% in extrapolation scenarios, and reaches 80% when applied to PRISMA satellite imagery. Across all scenarios, models produce uncertainty estimates exceeding 40%, often reflecting systematic underconfidence. PNNs show better calibration during extrapolation, suggesting an intrinsic awareness of retrieval constraints. To address this miscalibration, we introduce an uncertainty recalibration method that only withholds 10% of the training dataset, but improves model calibration in 86% of PRISMA evaluations with minimal accuracy trade-offs. Resulting well-calibrated uncertainty estimates enable reliable uncertainty propagation for downstream applications. IOP retrieval uncertainty is predominantly aleatoric (inherent to the observations). Therefore, increasing the number of measurements from the same distribution or selecting a different neural network architecture trained on the same dataset does not enhance model accuracy. Our findings indicate that we have reached a predictability limit in retrieving IOPs using purely data-driven approaches. We therefore advocate embedding physical principles of IOPs into model architectures, creating physics-informed neural networks capable of surpassing current limitations.Environmental Biolog
Recombination of localized quasiparticles in disordered superconductors
Full text linkQuantum Matter and Optic
In Vivo phage display for the identification of muscle homing peptides to improve the delivery of phosphorodiamidate morpholino oligomers for duchenne muscular dystrophy therapy
Full text linkThe severe X-linked degenerative neuromuscular disease Duchenne muscular dystrophy (DMD) is caused by the loss of dystrophin through reading frame disruptive mutations in the DMD gene. Dystrophin protein is crucial for the stability of the muscle. Targeting specific exons with antisense oligonucleotides (ASO) will prevent inclusion of the exon during pre-mRNA splicing, which can restore the reading frame, facilitating the production of partially functional dystrophin proteins. For DMD, four ASOs of the phosphorodiamidate morpholino oligomer (PMOs) chemistry are FDA approved. It is anticipated that improved delivery to skeletal muscle and heart will lead to larger therapeutic results. With our research, we sought to identify muscle-homing peptides that can achieve increased delivery of ASOs to muscle or heart when conjugated to PMOs. We applied in vivo phage display biopanning mouse models for DMD to identify muscle-homing peptides while simultaneously negatively selecting peptides that home to unwanted organs, such as the kidney and liver. After confirmation of the muscle homing ability in vitro, we conjugated selected candidate peptides to PMOs to be tested in vivo, where we found that conjugation of one specific muscle homing peptide led to significantly improved delivery to muscle, with a small improvement in exon skipping and dystrophin restoration.Functional Genomics of Muscle, Nerve and Brain Disorder
Biodiversity monitoring in urban community gardens using proximal sensing and drone remote sensing
Full text linkIn urban community gardens, artificially managed ground cover types, including vegetative and non-vegetative ground components, are both critical to ecological functioning. Yet, how these non-vegetative components influence spectral diversity in ways that are different from natural systems has not been addressed. This study investigated the potential of combining spectral and structural diversity variables, corresponding to the Spectral Variation and Height Variation Hypotheses, respectively, to monitor plant and ground cover diversity. These variables were derived from in situ hyperspectral measurements, drone-based multispectral imagery, and three-dimensional canopy height models. We examined four biodiversity variables, including plant species richness, total plant abundances, ground cover entropy, and ground cover richness, across five urban community gardens over two years. Spectral diversity was calculated based on the Coefficient of Variation (CV), Spectral Angle Mapper (SAM), and Shannon's Entropy (Entropy) indices at multiple spectral ranges. Structural diversity variables, including canopy height variation and image texture features. Our results showed that Red-Edge and Near-infrared (NIR) bands effectively captured compositional variation in ground cover, while visible wavelengths better reflected subtle differences in vegetative components. Texture features and height-based structural variables provided valuable insights into canopy complexity, particularly improving predictions of plant abundance and ground cover entropy. Finally, we found that integrating spectral and structural diversity variables further enhanced predictive performance due to considering canopy biochemical and structural differences. This multi-metric approach outperformed single-source analyses, underscoring the value of combining complementary remote sensing data for better interpreting urban garden biodiversity. Our findings highlight the importance of characterizing canopy structural heterogeneity in advancing biodiversity monitoring within these complex urban ecosystems.Global Challenges (FGGA
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A novel cyclopropenyl fatty acid library reveals tissue-specific preferences for regulatory T cell uptake through click-chemistry
Full text linkNWOChemical Biology & Immunolog
Distribution of acyclovir in central nervous system compartments: a porcine pharmacokinetic model
Full text linkHerpes simplex virus (HSV) encephalitis is a severe infection with high mortality and neurological sequelae if untreated. Intravenous acyclovir (ACV) is the standard treatment, but its central nervous system (CNS) penetration is not fully understood. To evaluate the distribution of ACV in various CNS compartments in a porcine pharmacokinetic model, 12 female pigs were divided into two groups: group I receiving a single ACV dose (10 mg/kg) and group II receiving three doses over 24 h. Microdialysis sampled unbound ACV concentrations in cortical and subcortical extracellular fluid (ECF), ventricular cerebrospinal fluid (CSF), and cisternal CSF. The ACV target concentration was defined as peak concentration (fCmax) > inhibitory concentration 50% (IC50) for HSV-1 at 0.56 µg/mL. Pharmacokinetic parameters, including fCmax, time above IC50 (T > IC50), and area under the curve (AUC), were analyzed. The target ACV concentration (fCmax > 0.56 µg/mL) was achieved in all ECF and CSF compartments during the second and third dosing intervals. The T > IC50 and AUC increased from the first to the third dose and were consistent across compartments. Intracerebral penetration ratios (fAUCtissue/fAUCplasma) during the third dose ranged from 0.18 to 0.32 within the CNS compartments. In conclusion, ACV administered intravenously at 10 mg/kg every eighth hour achieved therapeutic levels in porcine CNS compartments after the second dose, suggesting that current dosing regimens are effective in treating HSV encephalitis. However, the first dose may not reach therapeutic levels, suggesting that higher initial dosages or prolonged infusions should be considered. Further studies under inflammatory conditions are warranted to extrapolate these findings.Pharmacolog
Reciprocity evolves more readily in competitive than cooperative socio-ecologies
No full textTracking what others did and matching other’s expected actions is seen across a range of biological systems. As reciprocal matching rewards and reinforces cooperators and punishes and discourages non-cooperators, reciprocal matching can help communal living. The strength of reciprocity as a social strategy also comes from its success in protecting the individual against the risk of exploitation by punishing defectors. Although often overlooked, this feature carries a strong weight when exploitation risk is high. Here, we use evolutionary agent-based simulations to examine how reciprocal matching evolves across competitive socio-ecologies with a high risk of exploitation and cooperative socio-ecologies with a lower risk of exploitation. Results show that reciprocal matching as a social strategy for communal living evolves more readily in more competitive socio-ecologies where the risk of exploitation is high. Results also hold in standard Prisoner’s Dilemmas with its equilibrium in single strategies (i.e. unconditional non-cooperation), for specific forms of reciprocity (i.e. tit-for-tat) and the likelihood of repeated interactions. Because reciprocal matching requires some capacity for social perception and memory, these findings suggest that such capacities for social cognition likewise serve to protect against exploitation and evolved in more competitive socio-ecologies as well.Social, Cognitive, and Affective Decision Makin