Motivation:
Planting urban trees is important for urban development. Foliage reflects, scatters, and absorbs incoming shortwave solar radiation, thus reducing the energy flow to the underlying ...surface, including streets, houses, and humans. Shaded areas, with primarily anthropogenic substrates, are not as heated as nonshaded areas. Subsequently, temperature, radiation and its influence on morbidity of fauna and flora are comparatively lower in these areas.
Materials and Methods:
Statistical methods were applied to assess the allometric functions of urban trees. The analyzed data were obtained from the tree cadaster of Magdeburg City, Germany, which contains information from 1936 to 2021. As of June 2022, the tree cadaster contained 89,766 trees. The tree species
Acer campestre
,
Acer platanoides
,
Malus
spp., and
Quercus robur
were subjected to investigation. Nine temperature optimum (TO)/precipitation optimum (PO) scenarios were considered, and their effects on the allometric relationships on the tree species were determined, respectively: 1. TO 17–19°C/PO 450–550 mm, 2. TO 19–21°C/PO 450–550 mm, 3. TO 21–23°C/PO 450–550 mm., 4. TO 17–19°C/PO 550–650 mm, 5. TO 19–21°C/PO 550–650 mm, 6. TO 21–23°C/PO 550–650 mm, 7. TO 17–19°C/PO 650–750 mm, 8. TO 19–21°C/PO 650–750 mm, 9. TO 21–23°C/PO 650–750 mm.
Results and Discussion:
In six of nine scenarios, a significant correlation was evident for temperature. Water uptake was found to be significant in scenario 6. No significant correlation for competition and breast head diameter growth could be determined in any scenario. Five of the nine scenarios were significantly comparable. While competition was evident as conditionally non-significant (
p
-value = 0.069 & 0.058), for the elevation forecast in scenarios 3 & 6 only, dependence was evident in all other scenarios. Regarding the crown diameter, temperature was significant in 3 of 9 scenarios. No significant relationship was found for water uptake and competition.
Conclusions:
No clear abiotic optimum could be identified. Thus, a continuous adjustment of the parameters is necessary to refine the growth functions. Moreover, the growth function adaptation according to the different age phases of trees might be considered on the long term.
Are there any correlations between land use and the associated prices charged for the soil? What is the significance of green infrastructure and what is the significance of public facilities and ...transport? For the analysis of the data, various methods of factor reduction and analysis were used to identify a multiple regression model that explained the price building. An effect was found between the pricing of the standard land reference value (SLRV), number of trees and distance to allotments. Summarizing the results, less than 231 trees in an SLRV zone causes an SLRV increase, the opposite is the case with a larger number of trees. The more accessible an allotment garden is (in terms of distance <421 m), the lower the SLV in the adjacent area. If the distance that must be covered to the allotment garden increases, the SLRV of the area increases. However, a more significant influence on the SLRV was concluded by the market economy variables. In summary, the present study indicates that (a) a uniform evaluation matrix for the SLRV should be created, and (b) the present subjective land assessments by the relevant experts should be complemented through targeting further training in the ecologically oriented planning context.
Natural products have long been a source of useful biological activity for the development of new drugs. Their macromolecular targets are, however, largely unknown, which hampers rational drug design ...and optimization. Here we present the development and experimental validation of a computational method for the discovery of such targets. The technique does not require three-dimensional target models and may be applied to structurally complex natural products. The algorithm dissects the natural products into fragments and infers potential pharmacological targets by comparing the fragments to synthetic reference drugs with known targets. We demonstrate that this approach results in confident predictions. In a prospective validation, we show that fragments of the potent antitumour agent archazolid A, a macrolide from the myxobacterium Archangium gephyra, contain relevant information regarding its polypharmacology. Biochemical and biophysical evaluation confirmed the predictions. The results obtained corroborate the practical applicability of the computational approach to natural product 'de-orphaning'.
As there are no clear on-target mechanisms that explain the increased risk for thrombosis and viral infection or reactivation associated with JAK inhibitors, the observed elevated risk may be a ...result of an off-target effect. Computational approaches combined with in vitro studies can be used to predict and validate the potential for an approved drug to interact with additional (often unwanted) targets and identify potential safety-related concerns. Potential off-targets of the JAK inhibitors baricitinib and tofacitinib were identified using two established machine learning approaches based on ligand similarity. The identified targets related to thrombosis or viral infection/reactivation were subsequently validated using in vitro assays. Inhibitory activity was identified for four drug-target pairs (PDE10A baricitinib, TRPM6 tofacitinib, PKN2 baricitinib, tofacitinib). Previously unknown off-target interactions of the two JAK inhibitors were identified. As the proposed pharmacological effects of these interactions include attenuation of pulmonary vascular remodeling, modulation of HCV response, and hypomagnesemia, the newly identified off-target interactions cannot explain an increased risk of thrombosis or viral infection/reactivation. While further evidence is required to explain both the elevated thrombosis and viral infection/reactivation risk, our results add to the evidence that these JAK inhibitors are promiscuous binders and highlight the potential for repurposing.
The computer-assisted design of new chemical entities has made a leap forward with the development of machine learning models for automated molecule generation. The overarching goal of this ...conceptual approach is to augment the creativity of medicinal chemists with a machine intelligence. In this Perspective we highlight prospective applications of "de novo" drug design and target prediction, aiming to generate natural product-inspired bioactive compounds from scratch. A virtual chemist transforms pharmacologically active natural products into new, easily synthesizable small molecules with desired properties and activity. Computational activity prediction and automated compound generation offer the possibility to systematically transfer the wealth of pharmaceutically active natural products to synthetic small molecule drug discovery. We present selected prospective examples and dare a forecast into the future of natural product-inspired drug discovery.
Flow cytometry is a widely used technique for the analysis of cell populations in the study and diagnosis of human diseases. It yields large amounts of high-dimensional data, the analysis of which ...would clearly benefit from efficient computational approaches aiming at automated diagnosis and decision support. This article presents our analysis of flow cytometry data in the framework of the DREAM6/FlowCAP2 Molecular Classification of Acute Myeloid Leukemia (AML) Challenge, 2011. In the challenge, example data was provided for a set of 179 subjects, comprising healthy donors and 23 cases of AML. The participants were asked to provide predictions with respect to the condition of 180 patients in a test set. We extracted feature vectors from the data in terms of single marker statistics, including characteristic moments, median and interquartile range of the observed values. Subsequently, we applied Generalized Matrix Relevance Learning Vector Quantization (GMLVQ), a machine learning technique which extends standard LVQ by an adaptive distance measure. Our method achieved the best possible performance with respect to the diagnoses of test set patients. The extraction of features from the flow cytometry data is outlined in detail, the machine learning approach is discussed and classification results are presented. In addition, we illustrate how GMLVQ can provide deeper insight into the problem by allowing to infer the relevance of specific markers and features for the diagnosis.
The cyclodepsipeptide doliculide is a marine natural product with strong actin‐polymerizing and anticancer activities. Evidence for doliculide acting as a potent and subtype‐selective antagonist of ...prostanoid E receptor 3 (EP3) is presented. Computational target prediction suggested that this membrane receptor is a likely macromolecular target and enabled immediate in vitro validation. This proof‐of‐concept study demonstrates the in silico deorphanization of phenotypic screening hits as a viable concept for future natural‐product‐inspired chemical biology and drug discovery efforts.
Computational adoption services: Macromolecular targets of the marine natural product doliculide were successfully identified using a chemocentric computer‐based prediction method. This study reveals this cyclodepsipeptide as a potent antagonist of the human prostanoid receptor EP3.
Context:
Adrenal tumors have a prevalence of around 2% in the general population. Adrenocortical carcinoma (ACC) is rare but accounts for 2–11% of incidentally discovered adrenal masses. ...Differentiating ACC from adrenocortical adenoma (ACA) represents a diagnostic challenge in patients with adrenal incidentalomas, with tumor size, imaging, and even histology all providing unsatisfactory predictive values.
Objective:
Here we developed a novel steroid metabolomic approach, mass spectrometry-based steroid profiling followed by machine learning analysis, and examined its diagnostic value for the detection of adrenal malignancy.
Design:
Quantification of 32 distinct adrenal derived steroids was carried out by gas chromatography/mass spectrometry in 24-h urine samples from 102 ACA patients (age range 19–84 yr) and 45 ACC patients (20–80 yr). Underlying diagnosis was ascertained by histology and metastasis in ACC and by clinical follow-up median duration 52 (range 26–201) months without evidence of metastasis in ACA. Steroid excretion data were subjected to generalized matrix learning vector quantization (GMLVQ) to identify the most discriminative steroids.
Results:
Steroid profiling revealed a pattern of predominantly immature, early-stage steroidogenesis in ACC. GMLVQ analysis identified a subset of nine steroids that performed best in differentiating ACA from ACC. Receiver-operating characteristics analysis of GMLVQ results demonstrated sensitivity = specificity = 90% (area under the curve = 0.97) employing all 32 steroids and sensitivity = specificity = 88% (area under the curve = 0.96) when using only the nine most differentiating markers.
Conclusions:
Urine steroid metabolomics is a novel, highly sensitive, and specific biomarker tool for discriminating benign from malignant adrenal tumors, with obvious promise for the diagnostic work-up of patients with adrenal incidentalomas.
De novo drug design aims to generate molecules from scratch that possess specific chemical and pharmacological properties. We present a computational approach utilizing interactome-based deep ...learning for ligand- and structure-based generation of drug-like molecules. This method capitalizes on the unique strengths of both graph neural networks and chemical language models, offering an alternative to the need for application-specific reinforcement, transfer, or few-shot learning. It enables the "zero-shot" construction of compound libraries tailored to possess specific bioactivity, synthesizability, and structural novelty. In order to proactively evaluate the deep interactome learning framework for protein structure-based drug design, potential new ligands targeting the binding site of the human peroxisome proliferator-activated receptor (PPAR) subtype gamma are generated. The top-ranking designs are chemically synthesized and computationally, biophysically, and biochemically characterized. Potent PPAR partial agonists are identified, demonstrating favorable activity and the desired selectivity profiles for both nuclear receptors and off-target interactions. Crystal structure determination of the ligand-receptor complex confirms the anticipated binding mode. This successful outcome positively advocates interactome-based de novo design for application in bioorganic and medicinal chemistry, enabling the creation of innovative bioactive molecules.
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