bluuebunny/crossref_metadata_2025 huggingface dataset:
Parking Tracking System Using Ultrasonic Sensor HC-SR04 and NODEMCU ESP8266 Based IoT
Authors: Sulistyawan Vera Noviana , Salim Nur Azis , Abas Faizal Ghozali , Aulia Najma
Published: June 2023

Generally, many parking systems have not implemented a tracking intelligent system with the help of sensor technology and applications combined with the Internet of Things. This creates a lot of problems with the unexpected and irregular overflow of parking volumes. In overcoming the above problems, a study was made that discusses the parking tracking system design with the HC-SR04 ultrasonic sensor intelligent working system using a smartphone and an Internet of Things (IoT) based web application. This parking tracking system will use the main component, namely the ultrasonic sensor and several other components, namely the NODEMCU ESP8266. The Internet of Things will be used in this parking smart work system to provide information to parking users through a web system about the location of vacant parking slots. With this web system, users will be able to use the booking model and see which parking positions are available. The model used is a chip that is programmed and placed in each parking lot so that it can transmit digital information signals to a server that can be accessed in real-time by the user. With the planning of a parking tracking intelligent work system, it is hoped that it can help management of parking usage by users.
Application of multilayer perceptron (MLP) method for streamflow forecasting (case study: Upper Citarum River, Indonesia)
Authors: Enung  , Kasyanto Heri , Sari Risna Rismiana , Lubis Muhammad Fauzan
Published: June 2023

Flood forecasting is a critical component of flood early warning. The discharge that occurs is one of the parameters that can be used as a reference for predicting flooding. Various discharge forecasting models based on physically based models or data-driven models have been developed. One of the flood forecasting methods that can be considered for forecasting discharge on watersheds with limited physical data is the Artificial Neural Network (ANN). Furthermore, the ANN method allows the analysis process to be completed in less time and with fewer resources. One of the ANN models employed in this work is the multilayer perceptron (MLP). The MLP model was developed in this study to predict streamflow at the Citarum river, particularly at the Dayeuhkolot hydrological station at 2, 4, 6, 8, 10, 12, and 24 hours ahead. Two data input scenarios were used in the modeling scene. First, input data in the form of station rainfall data and discharge data. The second is regional rainfall and discharge data. Before predicting the discharge in the coming hours, the hyperparameters model is optimized using the GridSearchCV method. The model’s performance is assessed using the RMSE, NSE, and R2 values. The MLP method produced satisfactory results for both scenarios when predicting discharge in less than 4 hours with the NSE and R2 value higher than 0.9. Scenario 2 input data produces a slightly better prediction model than scenario 1. Based on NSE and R2 values, discharge prediction with a prediction time of more than 6 hours produces less accurate results.
Identification of Landslide Potential at UNNES Conservation Park Based on Rainfall and Soil Geology Parameters
Authors: Pambudi K.A. , Kusumawardani R.
Published: June 2023

Semarang city has a unique morphology with hilly in the south and lowland in the north area. UNNES Conservation Park as aa study area located at the hilly area has a potential of landslide susceptibility with the various level of slope and the type of soil. Furthermore, in the wet season, high intensity of rainfall in this area be considered as the factor for landslides triggering. This article focused analyse the potential for landslides in the study area. A cross section in north area which has steep slope was taken to be analysed. Comparison the manual method using Fellenius and Bishop and a numerical method for SF computation was applied for landslide potential identification. The results of analysis revealed the SF number was 1.746 with the Fellenius method and 1.786 with the Bishop method. Meanwhile, using the numerical method, the SF number is 1.440-1.471. From the result could be concluded the UNNES Park was categorized as stable class for Fellenius and Bishop method and critical class category for numerical method.
Design Criteria for Chicken Slaughterhouse Wastewater Treatment with Granular Activated Sludge in Sequencing Batch Reactor
Authors: Audelia P , Ratnaningsih  , Tazkiaturrizki
Published: June 2023

Chicken Slaughterhouse (RPA) is a business in the field of slaughtering and processing chicken into parts that are ready to sell to customers, where the waste water which contains high levels of organic matter can pollute the environment. Therefore, it is necessary to treat wastewater that is efficient and easy to use such as Granunlar Activated Sludge (GAS) using Sequencing Batch Reactor (SBR). The study was conducted on a laboratory scale to determine the effect of aeration time and settling time using different aeration (7 L/min and 10 L/min). The formation of GAS was cultured by reacting the activated sludge with slow stirring for 28 days, after which it was acclimatized by 50%, 75%, and 100%, then entered into the main study. The results obtained from the processing results are the level of COD removal to 25, 66 mg/L with 97.49% efficiency; BOD became 107.61 mg/L with an efficiency of 85.29%; and removal of TSS to 64 mg/L with an efficiency of 60%. The optimum processing time is 1 hour aeration with 40 minutes of precipitation using an aeration rate of 10 L/min. The design criteria obtained were BOD Load 0.671 kg BOD/m3.day, F/M ratio 0.145 kg BOD/kg MLVSS.day, HRT 26 hours, and SRT 28 days.
Highest and Best Use (HBU) Analysis on Vacant Land in Jakarta Garden City, East Jakarta
Authors: Laena H , Raflis
Published: June 2023

Land availability in the capital of Indonesia, Jakarta, is dwindling. However, its population is growing. This growth affects population density and activity to rising, as well as the need for land, especially for housing purposes. Moreover, land prices are also escalated over time. Hence, to optimize the available land utilization and value, it is necessary to find the best designation and produce the highest land value. The Highest and Best Use Analysis is used to achieve this goal. By evaluating several aspects, namely, physical, legal, financial, and maximum productivity. This research was applied to an asset of vacant land in Asoca Street, Jakarta Garden City, Cakung, East Jakarta with an area of 3.725,68 m2. After the analysis, it is obtained that apartment is the best designation, producing the highest increase in land value of Rp 131.366.494/m2 and productivity after developed by 591,04%. It is also feasible to develop, because with an investment period of 10 years, the designation produces a net present value greater than zero, which is Rp 418.641.935.213, an internal rate of return greater than the minimum attractive rate of return of 7% which is 22,76%, and a payback period of 4 years, 3 months, and a day.
Redesign of Trans-Semarang Bus Stop at Prof. Soedarto Segment Using an Accessibility Approach
Authors: Pratiwi I , Wibowo A A , Savanti F , Yulianto D I , Setiyawan A
Published: June 2023

Bus rapid transit, which was established in the city of Semarang in 2009, is expected to be a solution for the transportation needs of the people of the city of Semarang. With good service and economical prices, bus rapid transit is quite a favorite form of transportation for the citizens of Semarang. However, the current design of the Trans-Semarang bus stop is still far from easy for its users to access. The need for public services in the form of easy accessibility of users, both disabled and elderly. There needs to be a review from the Semarang City government, regarding the fulfillment of bus shelter facilities and designs that are still less than standard. This study uses a descriptive method using qualitative techniques. The research subjects were bus stops and users of the Trans-Semarang bus stop. Data collection techniques used are observation and documentation. The results of the research obtained on the design of the Trans-Semarang bus stop indicate that there are several shortcomings in the design of the Trans-Semarang bus stop along the Prof. Soedarto. To achieve the standard of comfort in terms of accessibility, it is necessary to redesign the bus stop design following the standards of building design regulations. Some of these shortcomings have been improved by redesigning the Trans Semarang bus stop with an accessibility approach. In addition, it is necessary to add several facilities that are not yet available so that the function of the Trans-Semarang bus stop can be used optimally and sustainably by the residents of Semarang.
Creative Hub Planning in East Jakarta with Contemporary Tropical Architecture Approach
Authors: Wicaksono Dimas , Pratiwi Isna , Wibowo Ardiyan Adhi , Fadlillah Farah
Published: June 2023

The achievements of the world’s greatest economic performance and great prospects due to the rapid development of the Indonesian economy can be realized through the creative economy sector. Creative economy is an economic concept whose products are produced from creativity, ideas, and human knowledge as its main assets. The government also contributed by issuing PERPRES RI No. 6 of 2015, which calls for encouraging, strengthening, and promoting the creative economy in an effort to increase national economic growth. Based on these problems, it is important that there is a creative hub as a center for creative economic activities in each community activity center that is able to lift and develop economic potential in each region, including East Jakarta. As one of the cities with the largest area in DKI Jakarta, East Jakarta has various potentials that should be developed. In planning and designing this architecture, the primary data collection methods were observation and interviews, as well as secondary data collection by document/literature studies. The data obtained will then be processed by descriptive analysis and data synthesis is carried out to develop problem solving in the design of various aspects of the approach. The creative hub plan that will be made in the form of a single building on an area of 17,874 m2 in East Jakarta with a contemporary tropical architectural design approach. Contemporary tropical architecture is oriented to the tropical climate, but has freedom of expression in its design to keep up with the times. This approach aims to achieve user convenience that supports the productivity of the creative community. One application is by using sustainable materials such as wood, natural stone, brick, concrete, bamboo, and so on. Thus, the planned creative hub will be able to provide solutions to all community needs while at the same time having a good impact on the surrounding environment.
The Bottle Caps (Crown Cork) Waste’s Treatment Innovation as a Solonese Bridal Accessories Product to Supports the Creative Industry
Authors: Setyowati Erna , Nafisatun
Published: June 2023

Bottle cap waste’s (crown/cork) pollutes the environment, a breakthrough solution is needed for recycling innovation to become a Solonese women’s bridal accessories product that has aesthetic value and adds its selling value. The objective of the study was to determine the innovation of bottle cap waste treatment as a product of the Solonese women’s bridal accessories. Data collection techniques using observation, documentation and online questionnaires. The design of this research use the R&D the descriptive analysis with product validity and feasibility tests. The results of the validity test of all accessories innovation products in the form of (cunduk mentul, cunduk comb, centung, earrings, brooches, rings, men’s necklaces, tretes bracelets, panetep, tretes necklaces) show an average of 98.46% with very valid criteria, and the preference test results for ten accessories products is obtained by an average of 96.61% with the criteria of very much likeness. The Solonese women’s bridal accessories product as an innovation for bottle cap’s treatment waste is very feasible and can be developed as a home industry to support the creative craft commodities.
Simultaneous imaging of luminescence and prompt x-rays during irradiation with spot-scanning proton beams at clinical dose level
Authors: Yamamoto Seiichi , Yamashita Tomohiro , Kobashi Yusuke , Yabe Takuya , Akagi Takashi , Yamaguchi Mitsutaka , Kawachi Naoki , Kamada Kei , Yoshikawa Akira , Kataoka Jun
Published: June 2023

Prompt x-ray imaging is a promising method for observing the beam shape from outside a subject. However, its distribution is different from dose distribution, and thus a comparison with the dose is required. Meanwhile, luminescence imaging of water is a possible method for imaging the dose distribution. Consequently, we performed simultaneous imaging of luminescence and prompt x-rays during irradiation by proton beams to compare the distributions between these two different imaging methods. Optical imaging of water was conducted with spot-scanning proton beams at clinical dose level during irradiation to a fluorescein (FS) water phantom set in a black box. Prompt x-ray imaging was also conducted simultaneously from outside the black box using a developed x-ray camera during proton beam irradiation to the phantom. We measured images of the luminescence of FS water and prompt x-rays for various types of proton beams, including pencil beams, spread-out Bragg peak (SOBP) beams, and clinically used therapy beams. After the imaging, ranges were estimated from FS water and prompt x-rays and compared with those calculated with a treatment planning system (TPS). We could measure the prompt x-ray and FS water images simultaneously for all types of proton beams. The ranges estimated from the FS water and those calculated with the TPS closely matched, within a difference of several mm. Similar range difference was found between the results estimated from prompt x-ray images and those calculated with the TPS. We confirmed that the simultaneous imaging of luminescence and prompt x-rays were possible during irradiation with spot-scanning proton beams at a clinical dose level. This method can be applied to range estimation as well as comparison with the dose for prompt x-ray imaging or other imaging methods used in therapy with various types of proton beams at a clinical dose level.
HMMPolish: a coding region polishing tool for TGS-sequenced RNA viruses
Authors: Yu Runzhou , Abdullah Syed Muhammad Umer , Sun Yanni
Published: June 2023

Access to accurate viral genomes is important to downstream data analysis. Third-generation sequencing (TGS) has recently become a popular platform for virus sequencing because of its long read length. However, its per-base error rate, which is higher than next-generation sequencing, can lead to genomes with errors. Polishing tools are thus needed to correct errors either before or after sequence assembly. Despite promising results of available polishing tools, there is still room to improve the error correction performance to perform more accurate genome assembly. The errors, particularly those in coding regions, can hamper analysis such as linage identification and variant monitoring. In this work, we developed a novel pipeline, HMMPolish, for correcting (polishing) errors in protein-coding regions of known RNA viruses. This tool can be applied to either raw TGS reads or the assembled sequences of the target virus. By utilizing profile Hidden Markov Models of protein families/domains in known viruses, HMMPolish can correct errors that are ignored by available polishers. We extensively validated HMMPolish on 34 datasets that covered four clinically important viruses, including HIV-1, influenza-A, norovirus, and severe acute respiratory syndrome coronavirus 2. These datasets contain reads with different properties, such as sequencing depth and platforms (PacBio or Nanopore). The benchmark results against popular/representative polishers show that HMMPolish competes favorably on error correction in coding regions of known RNA viruses.
COmic: convolutional kernel networks for interpretable end-to-end learning on (multi-)omics data
Authors: Ditz Jonas C , Reuter Bernhard , Pfeifer Nico
Published: June 2023

Motivation

The size of available omics datasets is steadily increasing with technological advancement in recent years. While this increase in sample size can be used to improve the performance of relevant prediction tasks in healthcare, models that are optimized for large datasets usually operate as black boxes. In high-stakes scenarios, like healthcare, using a black-box model poses safety and security issues. Without an explanation about molecular factors and phenotypes that affected the prediction, healthcare providers are left with no choice but to blindly trust the models. We propose a new type of artificial neural network, named Convolutional Omics Kernel Network (COmic). By combining convolutional kernel networks with pathway-induced kernels, our method enables robust and interpretable end-to-end learning on omics datasets ranging in size from a few hundred to several hundreds of thousands of samples. Furthermore, COmic can be easily adapted to utilize multiomics data.

Results

We evaluated the performance capabilities of COmic on six different breast cancer cohorts. Additionally, we trained COmic models on multiomics data using the METABRIC cohort. Our models performed either better or similar to competitors on both tasks. We show how the use of pathway-induced Laplacian kernels opens the black-box nature of neural networks and results in intrinsically interpretable models that eliminate the need for post hoc explanation models.

Availability and implementation

Datasets, labels, and pathway-induced graph Laplacians used for the single-omics tasks can be downloaded at https://ibm.ent.box.com/s/ac2ilhyn7xjj27r0xiwtom4crccuobst/folder/48027287036. While datasets and graph Laplacians for the METABRIC cohort can be downloaded from the above mentioned repository, the labels have to be downloaded from cBioPortal at https://www.cbioportal.org/study/clinicalData?id=brca\_metabric. COmic source code as well as all scripts necessary to reproduce the experiments and analysis are publicly available at https://github.com/jditz/comics.
PlasmoFAB: a benchmark to foster machine learning for Plasmodium falciparum protein antigen candidate prediction
Authors: Ditz Jonas C , Wistuba-Hamprecht Jacqueline , Maier Timo , Fendel Rolf , Pfeifer Nico , Reuter Bernhard
Published: June 2023

Motivation

Machine learning methods can be used to support scientific discovery in healthcare-related research fields. However, these methods can only be reliably used if they can be trained on high-quality and curated datasets. Currently, no such dataset for the exploration of Plasmodium falciparum protein antigen candidates exists. The parasite P.falciparum causes the infectious disease malaria. Thus, identifying potential antigens is of utmost importance for the development of antimalarial drugs and vaccines. Since exploring antigen candidates experimentally is an expensive and time-consuming process, applying machine learning methods to support this process has the potential to accelerate the development of drugs and vaccines, which are needed for fighting and controlling malaria.

Results

We developed PlasmoFAB, a curated benchmark that can be used to train machine learning methods for the exploration of P.falciparum protein antigen candidates. We combined an extensive literature search with domain expertise to create high-quality labels for P.falciparum specific proteins that distinguish between antigen candidates and intracellular proteins. Additionally, we used our benchmark to compare different well-known prediction models and available protein localization prediction services on the task of identifying protein antigen candidates. We show that available general-purpose services are unable to provide sufficient performance on identifying protein antigen candidates and are outperformed by our models that were trained on this tailored data.

Availability and implementation

PlasmoFAB is publicly available on Zenodo with DOI 10.5281/zenodo.7433087. Furthermore, all scripts that were used in the creation of PlasmoFAB and the training and evaluation of machine learning models are open source and publicly available on GitHub here: https://github.com/msmdev/PlasmoFAB.
ArkDTA: attention regularization guided by non-covalent interactions for explainable drug–target binding affinity prediction
Authors: Gim Mogan , Choe Junseok , Baek Seungheun , Park Jueon , Lee Chaeeun , Ju Minjae , Lee Sumin , Kang Jaewoo
Published: June 2023

Motivation

Protein–ligand binding affinity prediction is a central task in drug design and development. Cross-modal attention mechanism has recently become a core component of many deep learning models due to its potential to improve model explainability. Non-covalent interactions (NCIs), one of the most critical domain knowledge in binding affinity prediction task, should be incorporated into protein–ligand attention mechanism for more explainable deep drug–target interaction models. We propose ArkDTA, a novel deep neural architecture for explainable binding affinity prediction guided by NCIs.

Results

Experimental results show that ArkDTA achieves predictive performance comparable to current state-of-the-art models while significantly improving model explainability. Qualitative investigation into our novel attention mechanism reveals that ArkDTA can identify potential regions for NCIs between candidate drug compounds and target proteins, as well as guiding internal operations of the model in a more interpretable and domain-aware manner.

Availability

ArkDTA is available at https://github.com/dmis-lab/ArkDTA

Contact

kangj@korea.ac.kr
Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function
Authors: Boadu Frimpong , Cao Hongyuan , Cheng Jianlin
Published: June 2023

Motivation

Millions of protein sequences have been generated by numerous genome and transcriptome sequencing projects. However, experimentally determining the function of the proteins is still a time consuming, low-throughput, and expensive process, leading to a large protein sequence-function gap. Therefore, it is important to develop computational methods to accurately predict protein function to fill the gap. Even though many methods have been developed to use protein sequences as input to predict function, much fewer methods leverage protein structures in protein function prediction because there was lack of accurate protein structures for most proteins until recently.

Results

We developed TransFun—a method using a transformer-based protein language model and 3D-equivariant graph neural networks to distill information from both protein sequences and structures to predict protein function. It extracts feature embeddings from protein sequences using a pre-trained protein language model (ESM) via transfer learning and combines them with 3D structures of proteins predicted by AlphaFold2 through equivariant graph neural networks. Benchmarked on the CAFA3 test dataset and a new test dataset, TransFun outperforms several state-of-the-art methods, indicating that the language model and 3D-equivariant graph neural networks are effective methods to leverage protein sequences and structures to improve protein function prediction. Combining TransFun predictions and sequence similarity-based predictions can further increase prediction accuracy.

Availability and implementation

The source code of TransFun is available at https://github.com/jianlin-cheng/TransFun.
SemiBin2: self-supervised contrastive learning leads to better MAGs for short- and long-read sequencing
Authors: Pan Shaojun , Zhao Xing-Ming , Coelho Luis Pedro
Published: June 2023

Motivation

Metagenomic binning methods to reconstruct metagenome-assembled genomes (MAGs) from environmental samples have been widely used in large-scale metagenomic studies. The recently proposed semi-supervised binning method, SemiBin, achieved state-of-the-art binning results in several environments. However, this required annotating contigs, a computationally costly and potentially biased process.

Results

We propose SemiBin2, which uses self-supervised learning to learn feature embeddings from the contigs. In simulated and real datasets, we show that self-supervised learning achieves better results than the semi-supervised learning used in SemiBin1 and that SemiBin2 outperforms other state-of-the-art binners. Compared to SemiBin1, SemiBin2 can reconstruct 8.3–21.5% more high-quality bins and requires only 25% of the running time and 11% of peak memory usage in real short-read sequencing samples. To extend SemiBin2 to long-read data, we also propose ensemble-based DBSCAN clustering algorithm, resulting in 13.1–26.3% more high-quality genomes than the second best binner for long-read data.

Availability and implementation

SemiBin2 is available as open source software at https://github.com/BigDataBiology/SemiBin/ and the analysis scripts used in the study can be found at https://github.com/BigDataBiology/SemiBin2_benchmark.
Scalable sequence database search using partitioned aggregated Bloom comb trees
Authors: Marchet Camille , Limasset Antoine
Published: June 2023

Motivation

The Sequence Read Archive public database has reached 45 petabytes of raw sequences and doubles its nucleotide content every 2 years. Although BLAST-like methods can routinely search for a sequence in a small collection of genomes, making searchable immense public resources accessible is beyond the reach of alignment-based strategies. In recent years, abundant literature tackled the task of finding a sequence in extensive sequence collections using k-mer-based strategies. At present, the most scalable methods are approximate membership query data structures that combine the ability to query small signatures or variants while being scalable to collections up to 10 000 eukaryotic samples. Results. Here, we present PAC, a novel approximate membership query data structure for querying collections of sequence datasets. PAC index construction works in a streaming fashion without any disk footprint besides the index itself. It shows a 3–6 fold improvement in construction time compared to other compressed methods for comparable index size. A PAC query can need single random access and be performed in constant time in favorable instances. Using limited computation resources, we built PAC for very large collections. They include 32 000 human RNA-seq samples in 5 days, the entire GenBank bacterial genome collection in a single day for an index size of 3.5 TB. The latter is, to our knowledge, the largest sequence collection ever indexed using an approximate membership query structure. We also showed that PAC’s ability to query 500 000 transcript sequences in less than an hour.

Availability and implementation

PAC’s open-source software is available at https://github.com/Malfoy/PAC.
PhaVIP: Phage VIrion Protein classification based on chaos game representation and Vision Transformer
Authors: Shang Jiayu , Peng Cheng , Tang Xubo , Sun Yanni
Published: June 2023

Motivation

As viruses that mainly infect bacteria, phages are key players across a wide range of ecosystems. Analyzing phage proteins is indispensable for understanding phages’ functions and roles in microbiomes. High-throughput sequencing enables us to obtain phages in different microbiomes with low cost. However, compared to the fast accumulation of newly identified phages, phage protein classification remains difficult. In particular, a fundamental need is to annotate virion proteins, the structural proteins, such as major tail, baseplate, etc. Although there are experimental methods for virion protein identification, they are too expensive or time-consuming, leaving a large number of proteins unclassified. Thus, there is a great demand to develop a computational method for fast and accurate phage virion protein (PVP) classification.

Results

In this work, we adapted the state-of-the-art image classification model, Vision Transformer, to conduct virion protein classification. By encoding protein sequences into unique images using chaos game representation, we can leverage Vision Transformer to learn both local and global features from sequence “images”. Our method, PhaVIP, has two main functions: classifying PVP and non-PVP sequences and annotating the types of PVP, such as capsid and tail. We tested PhaVIP on several datasets with increasing difficulty and benchmarked it against alternative tools. The experimental results show that PhaVIP has superior performance. After validating the performance of PhaVIP, we investigated two applications that can use the output of PhaVIP: phage taxonomy classification and phage host prediction. The results showed the benefit of using classified proteins over all proteins.

Availability and implementation

The web server of PhaVIP is available via: https://phage.ee.cityu.edu.hk/phavip. The source code of PhaVIP is available via: https://github.com/KennthShang/PhaVIP.
SVJedi-graph: improving the genotyping of close and overlapping structural variants with long reads using a variation graph
Authors: Romain Sandra , Lemaitre Claire
Published: June 2023

Motivation

Structural variation (SV) is a class of genetic diversity whose importance is increasingly revealed by genome resequencing, especially with long-read technologies. One crucial problem when analyzing and comparing SVs in several individuals is their accurate genotyping, that is determining whether a described SV is present or absent in one sequenced individual, and if present, in how many copies. There are only a few methods dedicated to SV genotyping with long-read data, and all either suffer of a bias toward the reference allele by not representing equally all alleles, or have difficulties genotyping close or overlapping SVs due to a linear representation of the alleles.

Results

We present SVJedi-graph, a novel method for SV genotyping that relies on a variation graph to represent in a single data structure all alleles of a set of SVs. The long reads are mapped on the variation graph and the resulting alignments that cover allele-specific edges in the graph are used to estimate the most likely genotype for each SV. Running SVJedi-graph on simulated sets of close and overlapping deletions showed that this graph model prevents the bias toward the reference alleles and allows maintaining high genotyping accuracy whatever the SV proximity, contrary to other state of the art genotypers. On the human gold standard HG002 dataset, SVJedi-graph obtained the best performances, genotyping 99.5% of the high confidence SV callset with an accuracy of 95% in less than 30 min.

Availability and implementation

SVJedi-graph is distributed under an AGPL license and available on GitHub at https://github.com/SandraLouise/SVJedi-graph and as a BioConda package.
Finding phylogeny-aware and biologically meaningful averages of metagenomic samples: L2UniFrac
Authors: Wei Wei , Millward Andrew , Koslicki David
Published: June 2023

Motivation

Metagenomic samples have high spatiotemporal variability. Hence, it is useful to summarize and characterize the microbial makeup of a given environment in a way that is biologically reasonable and interpretable. The UniFrac metric has been a robust and widely used metric for measuring the variability between metagenomic samples. We propose that the characterization of metagenomic environments can be improved by finding the average, a.k.a. the barycenter, among the samples with respect to the UniFrac distance. However, it is possible that such a UniFrac-average includes negative entries, making it no longer a valid representation of a metagenomic community.

Results

To overcome this intrinsic issue, we propose a special version of the UniFrac metric, termed L2UniFrac, which inherits the phylogenetic nature of the traditional UniFrac and with respect to which one can easily compute the average, producing biologically meaningful environment-specific “representative samples.” We demonstrate the usefulness of such representative samples as well as the extended usage of L2UniFrac in efficient clustering of metagenomic samples, and provide mathematical characterizations and proofs to the desired properties of L2UniFrac.

Availability and implementation

A prototype implementation is provided at https://github.com/KoslickiLab/L2-UniFrac.git. All figures, data, and analysis can be reproduced at https://github.com/KoslickiLab/L2-UniFrac-Paper
GAN-based data augmentation for transcriptomics: survey and comparative assessment
Authors: Lacan Alice , Sebag Michèle , Hanczar Blaise
Published: June 2023

Motivation

Transcriptomics data are becoming more accessible due to high-throughput and less costly sequencing methods. However, data scarcity prevents exploiting deep learning models’ full predictive power for phenotypes prediction. Artificially enhancing the training sets, namely data augmentation, is suggested as a regularization strategy. Data augmentation corresponds to label-invariant transformations of the training set (e.g. geometric transformations on images and syntax parsing on text data). Such transformations are, unfortunately, unknown in the transcriptomic field. Therefore, deep generative models such as generative adversarial networks (GANs) have been proposed to generate additional samples. In this article, we analyze GAN-based data augmentation strategies with respect to performance indicators and the classification of cancer phenotypes.

Results

This work highlights a significant boost in binary and multiclass classification performances due to augmentation strategies. Without augmentation, training a classifier on only 50 RNA-seq samples yields an accuracy of, respectively, 94% and 70% for binary and tissue classification. In comparison, we achieved 98% and 94% of accuracy when adding 1000 augmented samples. Richer architectures and more expensive training of the GAN return better augmentation performances and generated data quality overall. Further analysis of the generated data shows that several performance indicators are needed to assess its quality correctly.

Availability and implementation

All data used for this research are publicly available and comes from The Cancer Genome Atlas. Reproducible code is available on the GitLab repository: https://forge.ibisc.univ-evry.fr/alacan/GANs-for-transcriptomics
Coriolis: enabling metagenomic classification on lightweight mobile devices
Authors: Mikalsen Andrew J , Zola Jaroslaw
Published: June 2023

Motivation

The introduction of portable DNA sequencers such as the Oxford Nanopore Technologies MinION has enabled real-time and in the field DNA sequencing. However, in the field sequencing is actionable only when coupled with in the field DNA classification. This poses new challenges for metagenomic software since mobile deployments are typically in remote locations with limited network connectivity and without access to capable computing devices.

Results

We propose new strategies to enable in the field metagenomic classification on mobile devices. We first introduce a programming model for expressing metagenomic classifiers that decomposes the classification process into well-defined and manageable abstractions. The model simplifies resource management in mobile setups and enables rapid prototyping of classification algorithms. Next, we introduce the compact string B-tree, a practical data structure for indexing text in external storage, and we demonstrate its viability as a strategy to deploy massive DNA databases on memory-constrained devices. Finally, we combine both solutions into Coriolis, a metagenomic classifier designed specifically to operate on lightweight mobile devices. Through experiments with actual MinION metagenomic reads and a portable supercomputer-on-a-chip, we show that compared with the state-of-the-art solutions Coriolis offers higher throughput and lower resource consumption without sacrificing quality of classification.

Availability and implementation

Source code and test data are available from http://score-group.org/?id=smarten.
Robust reconstruction of single-cell RNA-seq data with iterative gene weight updates
Authors: Sheng Yueqi , Barak Boaz , Nitzan Mor
Published: June 2023

Motivation

Single-cell RNA-sequencing technologies have greatly enhanced our understanding of heterogeneous cell populations and underlying regulatory processes. However, structural (spatial or temporal) relations between cells are lost during cell dissociation. These relations are crucial for identifying associated biological processes. Many existing tissue-reconstruction algorithms use prior information about subsets of genes that are informative with respect to the structure or process to be reconstructed. When such information is not available, and in the general case when the input genes code for multiple processes, including being susceptible to noise, biological reconstruction is often computationally challenging.

Results

We propose an algorithm that iteratively identifies manifold-informative genes using existing reconstruction algorithms for single-cell RNA-seq data as subroutine. We show that our algorithm improves the quality of tissue reconstruction for diverse synthetic and real scRNA-seq data, including data from the mammalian intestinal epithelium and liver lobules.

Availability and implementation

The code and data for benchmarking are available at github.com/syq2012/iterative_weight_update_for_reconstruction.
Privacy preserving identification of population stratification for collaborative genomic research
Authors: Dervishi Leonard , Li Wenbiao , Halimi Anisa , Jiang Xiaoqian , Vaidya Jaideep , Ayday Erman
Published: June 2023

The rapid improvements in genomic sequencing technology have led to the proliferation of locally collected genomic datasets. Given the sensitivity of genomic data, it is crucial to conduct collaborative studies while preserving the privacy of the individuals. However, before starting any collaborative research effort, the quality of the data needs to be assessed. One of the essential steps of the quality control process is population stratification: identifying the presence of genetic difference in individuals due to subpopulations. One of the common methods used to group genomes of individuals based on ancestry is principal component analysis (PCA). In this article, we propose a privacy-preserving framework which utilizes PCA to assign individuals to populations across multiple collaborators as part of the population stratification step. In our proposed client-server-based scheme, we initially let the server train a global PCA model on a publicly available genomic dataset which contains individuals from multiple populations. The global PCA model is later used to reduce the dimensionality of the local data by each collaborator (client). After adding noise to achieve local differential privacy (LDP), the collaborators send metadata (in the form of their local PCA outputs) about their research datasets to the server, which then aligns the local PCA results to identify the genetic differences among collaborators’ datasets. Our results on real genomic data show that the proposed framework can perform population stratification analysis with high accuracy while preserving the privacy of the research participants.
KG-Hub—building and exchanging biological knowledge graphs
Authors: Caufield J Harry , Putman Tim , Schaper Kevin , Unni Deepak R , Hegde Harshad , Callahan Tiffany J , Cappelletti Luca , Moxon Sierra A T , Ravanmehr Vida , Carbon Seth , Chan Lauren E , Cortes Katherina , Shefchek Kent A , Elsarboukh Glass , Balhoff Jim , Fontana Tommaso , Matentzoglu Nicolas , Bruskiewich Richard M , Thessen Anne E , Harris Nomi L , Munoz-Torres Monica C , Haendel Melissa A , Robinson Peter N , Joachimiak Marcin P , Mungall Christopher J , Reese Justin T
Published: June 2023

Motivation

Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking.

Results

Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract–transform–load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial–environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification.

Availability and implementation

https://kghub.org.
O047 Registry outcomes on gasless insufflation-less laparoscopic surgery in rural North-East India
Authors: Aruparayil N , Gnanaraj J , Mishra A , Bains L , Cundill B , Gordon K , Shinkins B , Brown J , Jayne D
Published: June 2023

Introduction

Gasless insufflation-less laparoscopic surgery (GILLS) was introduced in rural northeast India as part of the NIHR Global Health Research Group - Surgical Technologies (GHRG-ST) project. The GILLS Registry aims to provide a reliable mechanism for monitoring the safe implementation of the GILLS technique in everyday clinical practice by the newly trained rural surgeons deemed competent in independent practice.

Methods

The GILLS Registry captured all gasless laparoscopic abdominal procedures for patients consenting to participate in the Registry and performed by trained rural surgeons. They attended the NIHR GHRG-ST TARGET training programme in March 2019 and proctorship in May 2019. Patients were recruited prospectively. Complications were reported using ISOS classification. The data were captured on REDCap from October 2019 to July 2020.

Results

Four rural surgeons from three regions of North-East India participated. Of 286 recorded procedures, 123 had general surgical and gynaecological GILLS procedures. Mean age was 34.51 (SD 11.17), 89% were female. Cholecystectomy (44%), tubal ligation (24%) and appendicectomy (16%) were the commonest procedures. More patients received regional (53%) over general anaesthesia (47%). Overall, 11% GILLS cases were converted to open and 9% to conventional laparoscopy. The intra and postoperative complication rates were both 5%. One patient died due to postoperative pancreatitis. Mean length of stay 3.02 (2.12) days.

Conclusion

GILLS procedure demonstrates favourable outcomes for patients in rural settings. This prospective Registry indicates that GILLS can be safely introduced in low resource settings and findings support wider adoption of GILLS technology in India.

Take-home message

Gas Insufflation-Less Laparoscopic Surgery (Gasless laparoscopy) can be safely introduced in low resource settings and the registry supports its wider adoption and implementation in India.
O053 An audit of the diagnostic accuracy of ultrasound-guided fine-needle aspiration of axillary lymph nodes in breast cancer, and its pathological significance to axillary nodal burden
Authors: Jalil OA
Published: June 2023

Introduction

Axillary lymph nodes have long been recognized as a route for breast cancer to spread systemically, thus the assessment of the axilla with ultrasound should be undertaken if suspicious findings are identified in the breast or axilla. Needle biopsy: (Fine needle aspiration (FNA) or core biopsy (CB) -under imaging guidance- is indicated if enlarged axillary nodes or a mass lesion (U2–5) is identified. Multiple meta-analyses revealed similar specificities between the two methods, however, CB appeared to be more sensitive than FNA when assessing the axilla. Our aim is to detect our breast unit's (ELHT) diagnostic accuracy of US-FNAC of axillary lymph nodes in breast cancer.

Methods

This was a retrospective audit that looked into all axillary FNAs done in the year 2019. 192 FNAs were evaluated: 23 of which were repeats and only the first FNA was accounted for, 5 were found to be breast masses and were excluded and 4 patients lost to follow up and did not have a final pathology and thus were excluded too. In total, 162 FNAs were examined.

Results

Multiple accuracy measures were calculated: The absolute sensitivity was found to be 69.69% while the specificity was 86.54% with an absolute diagnostic accuracy of 69.75%. In addition, the false-negative rate was 18.18% and the overall inadequate reports were found to be 12.3%.

Conclusion

In our breast unit, among this cohort of patients, who had FNAC-axilla, we have reported an overall lower than average sensitivity and specificity rate.

Take-home message

FNAC for axilla in our trust reported lower than average sensitivity and specificity.
Risk of chronic kidney disease in patients with acute kidney injury following a major surgery: a US claims database analysis
Authors: Ohlmeier Christoph , Schuchhardt Johannes , Bauer Chris , Brinker Meike , Kong Sheldon X , Scott Charlie , Vaitsiakhovich Tatsiana
Published: June 2023

Background

Acute kidney injury (AKI) is a common complication after major surgery. This study assessed the risk of developing or worsening of chronic kidney disease (CKD) and other clinical outcomes in patients experiencing AKI after major surgery.

Methods

This retrospective observational study used Optum's de-identified Clinformatics Data Mart Database to investigate cardiorenal outcomes in adult patients at the first AKI event following major surgery. The primary outcome was CKD stage ≥3; secondary outcomes included myocardial infarction (MI), stroke, heart failure, all-cause hospitalization, end-stage kidney disease, need for dialysis or kidney transplant and composite measures. Follow-up was up to 3 years. Additionally, the effect of intercurrent events on the risk of clinical outcomes was assessed.

Results

Of the included patients (N = 31 252), most were male (61.9%) and White (68.9%), with a median age of 72 years (interquartile range 64–79). The event rates were 25.5 events/100 patient-years (PY) for CKD stage ≥3, 3.1 events/100 PY for end-stage kidney disease, 3.0 events/100 PY for dialysis and 0.1 events/100 PY for kidney transplants. Additionally, there were 6.9 events/100 PY for MI, 8.7 events/100 PY for stroke and 49.8 events/100 PY for all-cause hospitalization during follow-up. Patients with AKI relapses as intercurrent events were more likely to develop CKD stage ≥3 than those with just one AKI event after major surgery.

Conclusion

This analysis demonstrated that patients experiencing AKI following major surgery are at high risk of developing severe CKD or worsening of pre-existing CKD and other cardiorenal clinical outcomes such as MI and stroke.