Heuristic Algorithms for the Maximum Colorful Subtree Problem

In metabolomics, small molecules are structurally elucidated using tandem mass spectrometry (MS/MS); this computational task can be formulated as the Maximum Colorful Subtree problem, which is NP-hard. Unfortunately, data from a single metabolite requires us to solve hundreds or thousands of instances of this problem - and in a single Liquid Chromatography MS/MS run, hundreds or thousands of metabolites are measured. Here, we comprehensively evaluate the performance of several heuristic algorithms for the problem. Unfortunately, as is often the case in bioinformatics, the structure of the (chemically) true solution is not known to us; therefore we can only evaluate against the optimal solution of an instance. Evaluating the quality of a heuristic based on scores can be misleading: Even a slightly suboptimal solution can be structurally very different from the optimal solution, but it is the structure of a solution and not its score that is relevant for the downstream analysis. To this end, we propose a different evaluation setup: Given a set of candidate instances of which exactly one is known to be correct, the heuristic in question solves each instance to the best of its ability, producing a score for each instance, which is then used to rank the instances. We then evaluate whether the correct instance is ranked highly by the heuristic. We find that one particular heuristic consistently ranks the correct instance in a top position. We also find that the scores of the best heuristic solutions are very close to the optimal score; in contrast, the structure of the solutions can deviate significantly from the optimal structures. Integrating the heuristic allowed us to speed up computations in practice by a factor of 100-fold

MassFormer: Tandem Mass Spectrum Prediction with Graph Transformers

Mass spectrometry is a key tool in the study of small molecules, playing an important role in metabolomics, drug discovery, and environmental chemistry. Tandem mass spectra capture fragmentation patterns that provide key structural information about a molecule and help with its identification. Practitioners often rely on spectral library searches to match unknown spectra with known compounds. However, such search-based methods are limited by availability of reference experimental data. In this work we show that graph transformers can be used to accurately predict tandem mass spectra. Our model, MassFormer, outperforms competing deep learning approaches for spectrum prediction, and includes an interpretable attention mechanism to help explain predictions. We demonstrate that our model can be used to improve reference library coverage on a synthetic molecule identification task. Through quantitative analysis and visual inspection, we verify that our model recovers prior knowledge about the effect of collision energy on the generated spectrum. We evaluate our model on different types of mass spectra from two independent MS datasets and show that its performance generalizes. Code available at github.com/Roestlab/massformer.Comment: 14 pages (10 without bibliography), 5 figures, 3 table

MIST-CF: Chemical formula inference from tandem mass spectra

Chemical formula annotation for tandem mass spectrometry (MS/MS) data is the first step toward structurally elucidating unknown metabolites. While great strides have been made toward solving this problem, the current state-of-the-art method depends on time-intensive, proprietary, and expert-parameterized fragmentation tree construction and scoring. In this work we extend our previous spectrum Transformer methodology into an energy based modeling framework, MIST-CF, for learning to rank chemical formula and adduct assignments given an unannotated MS/MS spectrum. Importantly, MIST-CF learns in a data dependent fashion using a Formula Transformer neural network architecture and circumvents the need for fragmentation tree construction. We train and evaluate our model on a large open-access database, showing an absolute improvement of 10% top 1 accuracy over other neural network architectures. We further validate our approach on the CASMI2022 challenge dataset, achieving nearly equivalent performance to the winning entry within the positive mode category without any manual curation or post-processing of our results. These results demonstrate an exciting strategy to more powerfully leverage MS2 fragment peaks for predicting MS1 precursor chemical formula with data driven learning

Flexible Sensor Network Reprogramming for Logistics

Besides the currently realized applications, Wireless Sensor Networks can be put to use in logistics processes. However, doing so requires a level of flexibility and safety not provided by the current WSN software platforms. This paper discusses a logistics scenario, and presents SensorScheme, a runtime environment used to realize this scenario, based on semantics of the Scheme programming language. SensorScheme is a general purpose WSN platform, providing dynamic reprogramming, memory safety (sandboxing), blocking I/O, marshalled communication, compact code transport. It improves on the state of the art by making better use of the little available memory, thereby providing greater capability in terms of program size and complexity. We illustrate the use of our platform with some application examples, and provide experimental results to show its compactness, speed of operation and energy efficiency

Fine-scale spatial genetic structure in the frankincense tree Boswellia papyrifera (Del.) Hochst. and implications for conservation

The fine-scale genetic structure and how it varies between generations depends on the spatial scale of gene dispersal and other fundamental aspects of species’ biology, such as the mating system. Such knowledge is crucial for the design of genetic conservation strategies. This is particularly relevant for species that are increasingly fragmented such as Boswellia papyrifera. This species occurs in dry tropical forests from Ethiopia, Eritrea and Sudan and is an important source of frankincense, a highly valued aromatic resin obtained from the bark of the tree. This study assessed the genetic diversity and fine-scale spatial genetic structure (FSGS) of two cohorts (adults and seedlings) from two populations (Guba-Arenja and Kurmuk) in Western Ethiopia and inferred intra-population gene dispersal in the species, using microsatellite markers. The expected heterozygosity (HE) was 0.664–0.724. The spatial analyses based on kinship coefficient (Fij) revealed a significant positive genetic correlation up to a distance of 130 m. Spatial genetic structure was relatively weak (Sp = 0.002–0.014) indicating that gene dispersal is extensive within the populations. Based on the FSGS patterns found, we estimate indirectly gene dispersal distances of 103 and 124 m for the two populations studied. The high heterozygosity, the low fixation index and the low Sp values found in this study are consistent with outcrossing as the (predominant) mating system in B. papyrifera. We suggest that seed collection for ex situ conservation and reforestation programmes of B. papyrifera should use trees separated by distances of at least 100 m but preferably 150 m to limit genetic relatedness among seeds from different trees

Core Connections: Stitching Together the Heart of Atlanta through the Redevelopment of Underground Atlanta.

The heart of Atlanta, a prosperous tourist destination brimming with life, has found itself containing areas that have been commercially cut off from inner-city connections in contradiction to being in a heavy transit area. Atlanta’s longstanding history of inhabiting smaller sub-cities inside a larger context that houses a constantly growing population has become overshadowed by traffic, underutilized spaces that create massive voids, and fragmentation which prevents the city’s unification. Core Connections focuses on creating a mixed-use development comprised of parking, retail, and office that reconnects the surrounding contents of Downtown Atlanta and repairs the area as the city’s core. Core Connections will highlight the product of a strategic balance in prioritizing pedestrian flow by creating an environmentally rich layout that allows pedestrians to escape the hustle and bustle of Downtown Atlanta and draws visitors into the area. Creating a mixed-use development with pedestrian access and flow as its primary focus elevates the surrounding contexts of the chosen site to meet the existing urban expectations of Downtown Atlanta on an equal level. The intersectionality between the mixed-use development and Atlanta’s existing infrastructure will fill the hole that has gone unchecked in the city’s center for years. The objectives for the proposal shall occur at Underground Atlanta, directly across from the Five Points Marta Station. Redesigning the area will extend the existing commercial retail market to meet the street, develop an open-air plaza for leisure activity, and reconstruct the infrastructure for pedestrian walkability. These changes will effectively redefine Underground Atlanta’s flow, giving the area a much-needed uplift to Atlanta’s rich culture and history. Core Connections brings together social practices to achieve equity, equality, diversity, and inclusiveness at the highest levels of urbanization. This proposal will ultimately answer the questions concerning downtown Atlanta’s land configuration, the fading of downtown Atlanta’s rich history due to exterior conditions, the distinguishing factors which led to Underground Atlanta’s decline, and the existing conditions that determine if Underground Atlanta will remain an influential location to visit and socialize