Cold-start problems in data-driven prediction of drug-drug interaction effects

Combining drugs, a phenomenon often referred to as polypharmacy, can induce additional adverse effects. The identification of adverse combinations is a key task in pharmacovigilance. In this context, in silico approaches based on machine learning are promising as they can learn from a limited number of combinations to predict for all. In this work, we identify various subtasks in predicting effects caused by drug–drug interaction. Predicting drug–drug interaction effects for drugs that already exist is very different from predicting outcomes for newly developed drugs, commonly called a cold-start problem. We propose suitable validation schemes for the different subtasks that emerge. These validation schemes are critical to correctly assess the performance. We develop a new model that obtains AUC-ROC =0.843 for the hardest cold-start task up to AUC-ROC =0.957 for the easiest one on the benchmark dataset of Zitnik et al. Finally, we illustrate how our predictions can be used to improve post-market surveillance systems or detect drug–drug interaction effects earlier during drug development

The Hyperdimensional Transform: a Holographic Representation of Functions

Integral transforms are invaluable mathematical tools to map functions into spaces where they are easier to characterize. We introduce the hyperdimensional transform as a new kind of integral transform. It converts square-integrable functions into noise-robust, holographic, high-dimensional representations called hyperdimensional vectors. The central idea is to approximate a function by a linear combination of random functions. We formally introduce a set of stochastic, orthogonal basis functions and define the hyperdimensional transform and its inverse. We discuss general transform-related properties such as its uniqueness, approximation properties of the inverse transform, and the representation of integrals and derivatives. The hyperdimensional transform offers a powerful, flexible framework that connects closely with other integral transforms, such as the Fourier, Laplace, and fuzzy transforms. Moreover, it provides theoretical foundations and new insights for the field of hyperdimensional computing, a computing paradigm that is rapidly gaining attention for efficient and explainable machine learning algorithms, with potential applications in statistical modelling and machine learning. In addition, we provide straightforward and easily understandable code, which can function as a tutorial and allows for the reproduction of the demonstrated examples, from computing the transform to solving differential equations

The Hyperdimensional Transform for Distributional Modelling, Regression and Classification

Hyperdimensional computing (HDC) is an increasingly popular computing paradigm with immense potential for future intelligent applications. Although the main ideas already took form in the 1990s, HDC recently gained significant attention, especially in the field of machine learning and data science. Next to efficiency, interoperability and explainability, HDC offers attractive properties for generalization as it can be seen as an attempt to combine connectionist ideas from neural networks with symbolic aspects. In recent work, we introduced the hyperdimensional transform, revealing deep theoretical foundations for representing functions and distributions as high-dimensional holographic vectors. Here, we present the power of the hyperdimensional transform to a broad data science audience. We use the hyperdimensional transform as a theoretical basis and provide insight into state-of-the-art HDC approaches for machine learning. We show how existing algorithms can be modified and how this transform can lead to a novel, well-founded toolbox. Next to the standard regression and classification tasks of machine learning, our discussion includes various aspects of statistical modelling, such as representation, learning and deconvolving distributions, sampling, Bayesian inference, and uncertainty estimation

Complete mesocolic excision does not increase short-term complications in laparoscopic left-sided colectomies : a comparative retrospective single-center study

Background: Since the implementation of total mesorectal excision (TME) in rectal cancer surgery, oncological outcomes improved dramatically. With the technique of complete mesocolic excision (CME) with central vascular ligation (CVL), the same surgical principles were introduced to the field of colon cancer surgery. Until now, current literature fails to invariably demonstrate its oncological superiority when compared to conventional surgery, and there are some concerns on increased morbidity. The aim of this study is to compare short-term outcomes after left-sided laparoscopic CME versus conventional surgery. Methods: In this retrospective analysis, data on all laparoscopic sigmoidal resections performed during a 3-year period (October 2015 to October 2018) at our institution were collected. A comparative analysis between the CME group-for sigmoid colon cancer-and the non-CME group-for benign disease-was performed. Results: One hundred sixty-three patients met the inclusion criteria and were included for analysis. Data on 66 CME resections were compared with 97 controls. Median age and operative risk were higher in the CME group. One leak was observed in the CME group (1/66) and 3 in the non-CME group (3/97), representing no significant difference. Regarding hospital stay, postoperative complications, surgical site infections, and intra-abdominal collections, no differences were observed. There was a slightly lower reoperation (1.5% versus 6.2%, p = 0.243) and readmission rate (4.5% versus 6.2%, p = 0.740) in the CME group during the first 30 postoperative days. Operation times were significantly longer in the CME group (210 versus 184 min, p < 0.001), and a trend towards longer pathological specimens in the CME group was noted (21 vs 19 cm, p = 0.059). Conclusions: CME does not increase short-term complications in laparoscopic left-sided colectomies. Significantly longer operation times were observed in the CME group

A preliminary field trial to compare control techniques for invasive Berberis aquifolium in Belgian coastal dunes

Non-native Berberis aquifolium is an invasive species in Belgian coastal dunes. With its strong clonal growth through suckers, this evergreen shrub outcompetes native species and affects dune succession. To prevent further secondary spread and mitigate its impact, there was an urgent need for knowledge on the effectiveness of control measures, both at the plant and habitat level. Here, we report on a first control experiment. Individual B. aquifolium clones were subjected to one of four treatments (manual uprooting, foliar herbicide application, stem cutting followed by herbicide or salt application), with regrowth being measured up to one year after treatment. We analyzed the relationship between kill rate, treatment, dune area, plant volume and number of plant stems using a generalized linear model. Berberis aquifolium plants proved most susceptible to foliar herbicide application (5% glyphosate solution), resulting in 88% (64%-97%) of the clones dying after treatment. The predicted kill rate decreased with an increasing number of stems under all treatments. We discuss the limitations of our experiment and the potential for actual field application of the different treatments. We present some guidelines for future control that may become further refined as experience builds up and we provide some recommendations for tackling invasive alien species in Atlantic dune ecosystems

Detailed analysis of the composition of selected plastic packaging waste products and its implications for mechanical and thermochemical recycling

Plastic packaging typically consists of a mixture of polymers and contains a whole range of components, such as paper, organic residue, halogens, and metals, which pose problems during recycling. Nevertheless, until today, limited detailed data are available on the full polymer composition of plastic packaging waste taking into account the separable packaging parts present in a certain waste stream, nor on their quantitative levels of (elemental) impurities. This paper therefore presents an unprecedented indepth analysis of the polymer and elemental composition, including C, H, N, S, O, metals, and halogens, of commonly generated plastic packaging waste streams in European sorting facilities. Various analytical techniques are applied, including Fourier transform infrared (FTIR) spectroscopy, differential scanning calorimetry (DSC), polarized optical microscopy, ion chromatography, and inductively coupled plasma optical emission spectrometry (ICP-OES), on more than 100 different plastic packaging products, which are all separated into their different packaging subcomponents (e.g., a bottle into the bottle itself, the cap, and the label). Our results show that certain waste streams consist of mixtures of up to nine different polymers and contain various elements of the periodic table, in particular metals such as Ca, Al, Na, Zn, and Fe and halogens like CI and F, occurring in concentrations between 1 and 3000 ppm. As discussed in the paper, both polymer and elemental impurities impede in many cases closed-loop recycling and require advanced pretreatment steps, increasing the overall recycling cost

Scale-sensitive governance in forest and landscape restoration : a systematic review

Building on different bodies of the governance literature, we propose a conceptual framework specifying nine scale-sensitive governance arrangements that aim to (1) create cross-scale fit between the governance and ecological scales, and/or (2) foster cross-level alignment between different governance levels. To understand how scale-sensitive governance has played out in practice, our systematic review builds on 84 peer-reviewed empirical journal articles, which represent 84 cases of forest and landscape restoration governance. In the case studies, we identified eight out of nine scale-sensitive governance arrangements: moving tasks to other governance levels; task-specific organisations; polycentric governance; multilevel coordination; multilevel collaboration; multilevel learning; bridging organisations; and multilevel networks. These arrangements constitute important elements of the multilevel environmental governance landscape, and we analysed their role in promoting forest and landscape restoration. By using the proposed conceptual framework, a better understanding is created of how different scale-sensitive governance arrangements can support existing and future restoration efforts that are implemented as part of the UN Decade on Ecosystem Restoration