bluuebunny/crossref_metadata_2025 huggingface dataset:
A recessive mutation in muscadine grapes causes berry color-loss without influencing anthocyanin pathway
Authors: Ismail Ahmed , Gajjar Pranavkumar , Park Minkyu , Mahboob Abdulla , Tsolova Violeta , Subramanian Jayasankar , Darwish Ahmed G. , El-Sharkawy Islam
Published: September 2022

Anthocyanins, a major class of flavonoids, are important pigments of grape berries. Despite the recent discovery of the genetic cause underlying the loss of color, the metabolomic and molecular responses are unknown. Anthocyanin quantification among diverse berry color muscadines suggests that all genotypes could produce adequate anthocyanin quantities, irrespective of berry color. Transcriptome profiling of contrasting color muscadine genotypes proposes a potential deficiency that occurs within the anthocyanin transport and/or degradation mechanisms and might cause unpigmented berries. Genome-wide association studies highlighted a region on chromosome-4, comprising several genes encoding glutathione S-transferases involved in anthocyanin transport. Sequence comparison among genotypes reveals the presence of two GST4b alleles that differ by substituting the conserved amino acid residue Pro171-to-Leu. Molecular dynamics simulations demonstrate that GST4b2–Leu171encodes an inactive protein due to modifications within the H-binding site. Population genotyping suggests the recessive inheritance of the unpigmented trait with a GST4b2/2 homozygous. A model defining colorless muscadines’ response to the mutation stimulus, avoiding the impact of trapped anthocyanins within the cytoplasm is established.
Non-Hermitian skin clusters from strong interactions
Authors: Shen Ruizhe , Lee Ching Hua
Published: September 2022

Strong, non-perturbative interactions often lead to new exciting physics, as epitomized by emergent anyons from the Fractional Quantum hall effect. Within the actively investigated domain of non-Hermitian physics, we provide a family of states known as non-Hermitian skin clusters. Taking distinct forms as Vertex, Topological, Interface, Extended and Localized skin clusters, they generically originate from asymmetric correlated hoppings on a lattice, in the strongly interacting limit with quenched single-body energetics. Distinct from non-Hermitian skin modes which accumulate at boundaries, our skin clusters are predominantly translation invariant particle clusters. As purely interacting phenomena, they fall outside the purview of generalized Brillouin zone analysis, although our effective lattice formulation provides alternative analytic and topological characterization. Non-Hermitian skin clusters originate from the fragmentation structure of the Hilbert space and may thus be of significant interest in modern many-body contexts such as the Eigenstate thermalization hypothesis (ETH) and quantum scars.
Short-term local predictions of COVID-19 in the United Kingdom using dynamic supervised machine learning algorithms
Authors: Wang Xin , Dong Yijia , Thompson William David , Nair Harish , Li You
Published: September 2022

Background

Short-term prediction of COVID-19 epidemics is crucial to decision making. We aimed to develop supervised machine-learning algorithms on multiple digital metrics including symptom search trends, population mobility, and vaccination coverage to predict local-level COVID-19 growth rates in the UK.

Methods

Using dynamic supervised machine-learning algorithms based on log-linear regression, we explored optimal models for 1-week, 2-week, and 3-week ahead prediction of COVID-19 growth rate at lower tier local authority level over time. Model performance was assessed by calculating mean squared error (MSE) of prospective prediction, and naïve model and fixed-predictors model were used as reference models. We assessed real-time model performance for eight five-weeks-apart checkpoints between 1st March and 14th November 2021. We developed an online application (COVIDPredLTLA) that visualised the real-time predictions for the present week, and the next one and two weeks.

Results

Here we show that the median MSEs of the optimal models for 1-week, 2-week, and 3-week ahead prediction are 0.12 (IQR: 0.08–0.22), 0.29 (0.19–0.38), and 0.37 (0.25–0.47), respectively. Compared with naïve models, the optimal models maintain increased accuracy (reducing MSE by a range of 21–35%), including May–June 2021 when the delta variant spread across the UK. Compared with the fixed-predictors model, the advantage of dynamic models is observed after several iterations of update.

Conclusions

With flexible data-driven predictors selection process, our dynamic modelling framework shows promises in predicting short-term changes in COVID-19 cases. The online application (COVIDPredLTLA) could assist decision-making for control measures and planning of healthcare capacity in future epidemic growths.
TIAToolbox as an end-to-end library for advanced tissue image analytics
Authors: Pocock Johnathan , Graham Simon , Vu Quoc Dang , Jahanifar Mostafa , Deshpande Srijay , Hadjigeorghiou Giorgos , Shephard Adam , Bashir Raja Muhammad Saad , Bilal Mohsin , Lu Wenqi , Epstein David , Minhas Fayyaz , Rajpoot Nasir M. , Raza Shan E Ahmed
Published: September 2022

Background

Computational pathology has seen rapid growth in recent years, driven by advanced deep-learning algorithms. Due to the sheer size and complexity of multi-gigapixel whole-slide images, to the best of our knowledge, there is no open-source software library providing a generic end-to-end API for pathology image analysis using best practices. Most researchers have designed custom pipelines from the bottom up, restricting the development of advanced algorithms to specialist users. To help overcome this bottleneck, we present TIAToolbox, a Python toolbox designed to make computational pathology accessible to computational, biomedical, and clinical researchers.

Methods

By creating modular and configurable components, we enable the implementation of computational pathology algorithms in a way that is easy to use, flexible and extensible. We consider common sub-tasks including reading whole slide image data, patch extraction, stain normalization and augmentation, model inference, and visualization. For each of these steps, we provide a user-friendly application programming interface for commonly used methods and models.

Results

We demonstrate the use of the interface to construct a full computational pathology deep-learning pipeline. We show, with the help of examples, how state-of-the-art deep-learning algorithms can be reimplemented in a streamlined manner using our library with minimal effort.

Conclusions

We provide a usable and adaptable library with efficient, cutting-edge, and unit-tested tools for data loading, pre-processing, model inference, post-processing, and visualization. This enables a range of users to easily build upon recent deep-learning developments in the computational pathology literature.
Novel ratiometric electrochemical sensing platform for uric acid based on electroactive cuprous oxide nanocubes combined with boron carbide
Authors: Cheng Dan , Liu Xiang , Li Peipei , Zhang Youyu , Liu Meiling , Yao Shouzhuo
Published: September 2022

Electrochemically active oxides play important roles in the fabrication of electrochemical sensing platforms, in which they can be utilized as electrochemical probes or catalysts in electrochemical reactions.
Metallacycle-cored fluorescent supramolecular polymer networks for the detection of acidic environments
Authors: Jia Haiqi , Zhang Jinjin , Li Yang , Xu Dongdong , Yin Shouchun
Published: September 2022

Despite the challenges, supramolecular polymers (SPs) have received significant attention.
Self-assembled mononuclear complexes: open metal sites and inverse dimension-dependent catalytic activity for the Knoevenagel condensation and CO2 cycloaddition
Authors: Xu Heng , Zhang Le-Xi , Xing Yue , Yin Yan-Yan , Tang Bo , Bie Li-Jian
Published: September 2022

Molecular complexes based on Cu or Zn were selected as heterogeneous catalysts for efficient and stable Knoevenagel condensation and CO2 cycloaddition. DFT demonstrated that the change in coordination mode accelerates the reactions.
Bioinspired total synthesis of boneratamides A–C
Authors: Ooka Kaito , Nakanishi Keisuke , Udagawa Yutaro , Ichikawa Yoshiyasu , Hosokawa Seijiro
Published: September 2022

A bioinspired Ugi reaction of (+)-axisonitrile-3 with acetone and l-glutamic acid brings about one-pot assembly of reaction components concomitant with γ-lactam ring formation to produce the marine natural product (+)-boneratamide A in 70% yield.
Multi-analyte sensing strategies towards wearable and intelligent devices
Authors: Huang Yu , Luo Cihui , Xia Fan , Song Yanlin , Jiang Lei , Li Fengyu
Published: September 2022

Continuous efforts to produce functional nanomaterials and flexible/stretchable devices have promoted cumbersome, laboratorial, detection processes toward wearable and portable intelligent sensing approaches.
Understanding the role of additive in the solvation structure and interfacial reactions on lithium metal anode
Authors: He Xin , Zhang Yujie , Wang Kangli , Shan Bin , Zhou Min , Wang Wei , Jiang Kai
Published: September 2022

It was found that LiDFBOP affected the solvated structure and the trend of molecular decomposition. It preferentially decomposes on the anode, and the local environment after decomposition has a significant influence on the subsequent reaction.
Multi-functional O2–H2 electrochemistry by an abundant mineral: a novel and sustainable alternative for noble metals in electrolyzers and metal–air batteries
Authors: Manappadan Zinoy , Selvaraj Kaliaperumal
Published: September 2022

Developing sustainable and efficient multifunctional electrocatalysts for clean energy-based technologies would hasten the commercialization of high-power devices such as metal–air batteries, electrolyzers, and fuel cells.
SAR‐IRGF: A novel improved rolling guidance filter for the synthetic aperture radar image
Authors: Li Jiamu , Wang Zijian , Yu Wenbo , Yu Zhongjun
Published: September 2022

Inspired by the framework of the rolling guidance filter (RGF), a novel improved RGF for synthetic aperture radar (SAR) images is proposed in this letter, named SAR‐IRGF. Based on the model of additive noise, the RGF has the complete control of detailed smoothing under a scale measure for optical images. However, the speckle is well known as a multiplicative noise, which invalidates the RGF for SAR images. In this letter, an appropriate edge guidance is utilized to optimize the RGF for SAR image denoising. Compared with the original version, better convergence is achieved. Experimental results on natural Ku‐band airborne SAR images show that the proposed SAR‐IRGF reduces more speckle while sharpening the edges effectively. Simultaneously, the SAR‐IRGF demonstrates a significant nearly 5.5‐fold improvement than RGF in the equivalent number of looks (ENL).
Protecting DFIG‐based multi‐machine power system under transient‐state by nonlinear adaptive backstepping controller‐based capacitive BFCL
Authors: Hossain Md. Arafat , Islam Md. Rashidul , Haque Md. Yah‐Ya Ul , Hasan Jakir , Roy Tushar Kanti , Sadi Mohammad Ashraf Hossain
Published: September 2022

The invention of doubly‐fed induction generator (DFIG) brings the wind energy one step ahead as renewable power generation. But the performance of the grid‐connected DFIGs are greatly affected by grid disturbances as their stator windings are interfaced to the grid directly. Different fault current limiters are capable of improving fault ride through capability during short circuit faults. Nonlinear controller based fault current limiters (FCLs) are superior to deal with the nonlinearity of the power systems. This paper proposes a nonlinear adaptive backstepping controller (ABSC) based capacitive bridge‐type FCL (CBFCL) to enhance the fault ride through capability of a DFIG‐based wind farm connected to a multi‐machine power system. At first, a complete modelling of the CBFCL is derived to understand its behaviour during the normal and fault period more accurately. Then, the ABSC is designed based on that dynamic model, along with the backstepping controller (BSC) and sliding mode controller (SMC) for comparison purpose. Finally, the performance of the ABSC to control the CBFCL has been analysed and verified by comparing with that of the BSC and the sliding mode controller. All the graphical and mathematical analyses favour the ABSC based CBFCL under symmetrical and asymmetrical fault (both temporary and permanent) scenarios.
LW‐CovidNet: Automatic covid‐19 lung infection detection from chest X‐ray images
Authors: Ahmed Noor , Tan Xin , Ma Lizhuang
Published: September 2022

Coronavirus Disease 2019 (Covid‐19) overtook the worldwide in early 2020, placing the world's health in threat. Automated lung infection detection using Chest X‐ray images has a ton of potential for enhancing the traditional covid‐19 treatment strategy. However, there are several challenges to detect infected regions from Chest X‐ray images, including significant variance in infected features similar spatial characteristics, multi‐scale variations in texture shapes and sizes of infected regions. Moreover, high parameters with transfer learning are also a constraints to deploy deep convolutional neural network(CNN) models in real time environment. A novel covid‐19 lightweight CNN(LW‐CovidNet) method is proposed to automatically detect covid‐19 infected regions from Chest X‐ray images to address these challenges. In our proposed hybrid method of integrating Standard and Depth‐wise Separable convolutions are used to aggregate the high level features and also compensate the information loss by increasing the Receptive Field of the model. The detection boundaries of disease regions representations are then enhanced via an Edge‐Attention method by applying heatmaps for accurate detection of disease regions. Extensive experiments indicate that the proposed LW‐CovidNet surpasses most cutting‐edge detection methods and also contributes to the advancement of state‐of‐the‐art performance. It is envisaged that with reliable accuracy, this method can be introduced for clinical practices in the future.
On the input impedance of probe‐fed electromagnetic bandgap antennas based on lattice modes
Authors: Ceccuzzi Silvio , Baccarelli Paolo , Ponti Cristina , Tognolatti Ludovica , Schettini Giuseppe
Published: September 2022

The impedance of a coaxial probe, feeding an Electromagnetic Bandgap (EBG) structure conceived for radiation shaping, is studied from the theoretical and numerical viewpoints. The EBG medium is a square arrangement of dielectric cylinders placed in a parallel‐plate waveguide, where a suitable lattice mode is excited. A semi‐analytical model is developed and used to derive an approximate, closed‐form expression of the probe resistance. The model is based on a modal expansion in Floquet harmonics, on which a current distribution is projected according to the Lorentz reciprocity theorem to derive the amplitude of lattice modes propagating right above the bandgap along lattice axes. The dependence of probe impedance on lattice parameters is then investigated with the numerical simulations of a finite‐element method, which is also used to validate the developed model. A broad set of parametric analyses is presented, showing that the reactive part weakly depends on probe position, cylinder radius and permittivity, while the heights of probe and parallel‐plate waveguide play a major role in determining the resonance condition. As to the probe resistance, it decreases with cylinder radius and permittivity and decreases with probe and waveguide heights. The derived analytical formula correctly reproduces such functional dependences and its calculation is immediate, revealing its usefulness in antenna design. Matching issues are heuristically and experimentally approached by examples, demonstrating that the proposed work can be effectively employed to improve the electrical performance of EBG antennas with an embedded source.
A versatile control of solar DVR for enhanced utilization and power quality improvement in a series‐connected wind‐solar farm
Authors: Kathiresan Aravind Chellachi , Jeyaraj Pandia Rajan , Albert Brindhu Kumari , Thanikanti Sudhakar Babu , Nallapaneni Manoj Kumar , Alhelou Hassan Haes
Published: September 2022

This paper presents a solar dynamic voltage restorer for hybrid series‐connected solar‐wind farms to mitigate the power quality problems. The operating region of the proposed hybrid system is derived and studied through graphical analysis. Upon examination of the system's operation under various grid conditions, the feasibility of solar PV power injected into the grid is verified. It is further proved that the series injection of voltage is also capable of mitigating the effects of voltage sag/swell, and unbalance, which have adverse effects on wind‐connected induction generators. Further, the effectiveness of Solar DVR to mitigate the fault ride‐through capability of the wind farm is analyzed. The non‐requirement for an energy storage device such as a battery is also validated. A control system for the series PV inverter is proposed and the computer simulations are performed to confirm the efficacy and ride‐through capability of the system.
Connecting Solar Orbiter remote-sensing observations and Parker Solar Probe in situ measurements with a numerical MHD reconstruction of the Parker spiral
Authors: Biondo Ruggero , Bemporad Alessandro , Pagano Paolo , Telloni Daniele , Reale Fabio , Romoli Marco , Andretta Vincenzo , Antonucci Ester , Da Deppo Vania , De Leo Yara , Fineschi Silvano , Heinzel Petr , Moses Daniel , Naletto Giampiero , Nicolini Gianalfredo , Spadaro Daniele , Stangalini Marco , Teriaca Luca , Landini Federico , Sasso Clementina , Susino Roberto , Jerse Giovanna , Uslenghi Michela , Pancrazzi Maurizio
Published: September 2022

As a key feature, NASA’s Parker Solar Probe (PSP) and ESA-NASA’s Solar Orbiter (SO) missions cooperate to trace solar wind and transients from their sources on the Sun to the inner interplanetary space. The goal of this work is to accurately reconstruct the interplanetary Parker spiral and the connection between coronal features observed remotely by the Metis coronagraph on-board SO and those detected in situ by PSP at the time of the first PSP-SO quadrature of January 2021. We use the Reverse in situ and MHD Approach (RIMAP), a hybrid analytical-numerical method performing data-driven reconstructions of the Parker spiral. RIMAP solves the MHD equations on the equatorial plane with the PLUTO code, using the measurements collected by PSP between 0.1 and 0.2 AU as boundary conditions. Our reconstruction connects density and wind speed measurements provided by Metis (3–6 solar radii) to those acquired by PSP (21.5 solar radii) along a single streamline. The capability of our MHD model to connect the inner corona observed by Metis and the super Alfvénic wind measured by PSP, not only confirms the research pathways provided by multi-spacecraft observations, but also the validity and accuracy of RIMAP reconstructions as a possible test bench to verify models of transient phenomena propagating across the heliosphere, such as coronal mass ejections, solar energetic particles and solar wind switchbacks.
The emergence of multimorbidity as a matter of concern: a critical review
Authors: van Blarikom Esca , Fudge Nina , Swinglehurst Deborah
Published: September 2022

Multimorbidity is considered one of the greatest emerging challenges for contemporary health care systems. However, the meaning of the term ‘multimorbidity’ is not straightforward. Despite many attempts to clarify the definition and its measurement, the concept remains elusive. Still, academic interest in the study of multimorbidity has grown exponentially in the past ten years. In this paper, we trace the emergence of multimorbidity as a ‘matter of concern’ within health care research, exploring what has been called ‘the multimorbidity epidemic’ in the context of changing disease categories. We analyse how multimorbidity as a concept lays bare some major unresolved challenges within contemporary care services and summons up traditional primary care ideals of holistic, person-centred care. However, we argue that the current focus on the measurement and the identification of disease clusters falls short in contributing to better care for people who live with multiple long-term conditions now. Instead, we propose a novel understanding of ‘multimorbidity’ as an experience that manifests through people’s navigations of care infrastructures. To study this experience of multimorbidity, we discuss the potential of social science approaches that focus on ‘living well’ with illness.
The importance of institutional quality: Reviewing the relevance of Indonesia’s Omnibus Law on national competitiveness
Authors: Jazuli Muhamad Rosyid , Idris Maimanah Mohammed , Yaguma Penlope
Published: September 2022

Institutional quality is significantly relevant in determining national competitiveness when reflected in economic growth and successful development. Quality institutions manifest in robust ‘rules of the game’ reflected by sound governance and policymaking in at least four streams: social, economic, administrative, and political. Policy reforms intended to improve national competitiveness should aim to strengthen the streams simultaneously, whereas partial reforms could instead lead to poorer outcomes. Through the lens of institutional quality analysis, this paper critically reviews the relevance of Indonesia’s Job Creation Law, colloquially known as the Omnibus Law, to improve the country’s national competitiveness as conceptually intended. Declared as an overarching regulatory framework, the Law postulates Indonesia as prospecting for increased foreign investments. However, the Law is a partial policy reform as it overlooks the pivotal aspects necessary to improve institutional quality in Indonesia, such as inter-community relations, intellectual property regime certainty, quality vocational education, and meritocratic political recruitment. Thus, regardless of the opportunities, the Law may bring about, it may weaken national competitiveness instead. The Law is currently ruled conditionally unconstitutional by Indonesia’s Constitutional Court. If it is to be defended by the government, however, further multi-sectoral collaboration is necessary for the future implementation of the Law to enhance Indonesia’s institutional quality. In a more global context, this review indicates how developing nations should be mindful of various non-economic aspects (e.g., cultural and educational levels of the population) when undertaking policy reforms especially to enhance their national competitiveness. There may be future downsides to these implications and as it is too early to critically assess them, there is an opportunity through time and future research to do so.
Key comparison BIPM.RI(I)-K1 of the air-kerma standards of the NMIJ, Japan and the BIPM in 60Co gamma radiation
Authors: Kessler C , Burns D , Kato M , Kurosawa T
Published: September 2022

Main text

A new key comparison of the standards for air kerma of the National Metrology Institute of Japan (NMIJ), Japan, and the Bureau International des Poids et Mesures (BIPM) was carried out in the 60Co radiation beam of the BIPM in February 2022. The comparison result, based on the calibration coefficients for two transfer chambers and expressed as a ratio of the NMIJ and the BIPM standards for air kerma, is 1.0013 with a combined standard uncertainty of 2.2 parts in 103. The result agrees within the uncertainties with the direct comparison carried out in 2012. The results are analysed and presented in terms of degrees of equivalence, suitable for entry in the BIPM key comparison database.

To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/.

The final report has been peer-reviewed and approved for publication by the CCRI, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).
Linking research of biomedical datasets
Authors: Zhao Xiu-Ju George , Cao Hui
Published: September 2022

Biomedical data preprocessing and efficient computing can be as important as the statistical methods used to fit the data; data processing needs to consider application scenarios, data acquisition and individual rights and interests. We review common principles, knowledge and methods of integrated research according to the whole-pipeline processing mechanism diverse, coherent, sharing, auditable and ecological. First, neuromorphic and native algorithms integrate diverse datasets, providing linear scalability and high visualization. Second, the choice mechanism of different preprocessing, analysis and transaction methods from raw to neuromorphic was summarized on the node and coordinator platforms. Third, combination of node, network, cloud, edge, swarm and graph builds an ecosystem of cohort integrated research and clinical diagnosis and treatment. Looking forward, it is vital to simultaneously combine deep computing, mass data storage and massively parallel communication.
A review and performance evaluation of clustering frameworks for single-cell Hi-C data
Authors: Zhen Caiwei , Wang Yuxian , Geng Jiaquan , Han Lu , Li Jingyi , Peng Jinghao , Wang Tao , Hao Jianye , Shang Xuequn , Wei Zhongyu , Zhu Peican , Peng Jiajie
Published: September 2022

The three-dimensional genome structure plays a key role in cellular function and gene regulation. Single-cell Hi-C (high-resolution chromosome conformation capture) technology can capture genome structure information at the cell level, which provides the opportunity to study how genome structure varies among different cell types. Recently, a few methods are well designed for single-cell Hi-C clustering. In this manuscript, we perform an in-depth benchmark study of available single-cell Hi-C data clustering methods to implement an evaluation system for multiple clustering frameworks based on both human and mouse datasets. We compare eight methods in terms of visualization and clustering performance. Performance is evaluated using four benchmark metrics including adjusted rand index, normalized mutual information, homogeneity and Fowlkes–Mallows index. Furthermore, we also evaluate the eight methods for the task of separating cells at different stages of the cell cycle based on single-cell Hi-C data.
How does the structure of data impact cell–cell similarity? Evaluating how structural properties influence the performance of proximity metrics in single cell RNA-seq data
Authors: Watson Ebony Rose , Mora Ariane , Taherian Fard Atefeh , Mar Jessica Cara
Published: September 2022

Accurately identifying cell-populations is paramount to the quality of downstream analyses and overall interpretations of single-cell RNA-seq (scRNA-seq) datasets but remains a challenge. The quality of single-cell clustering depends on the proximity metric used to generate cell-to-cell distances. Accordingly, proximity metrics have been benchmarked for scRNA-seq clustering, typically with results averaged across datasets to identify a highest performing metric. However, the ‘best-performing’ metric varies between studies, with the performance differing significantly between datasets. This suggests that the unique structural properties of an scRNA-seq dataset, specific to the biological system under study, have a substantial impact on proximity metric performance. Previous benchmarking studies have omitted to factor the structural properties into their evaluations. To address this gap, we developed a framework for the in-depth evaluation of the performance of 17 proximity metrics with respect to core structural properties of scRNA-seq data, including sparsity, dimensionality, cell-population distribution and rarity. We find that clustering performance can be improved substantially by the selection of an appropriate proximity metric and neighbourhood size for the structural properties of a dataset, in addition to performing suitable pre-processing and dimensionality reduction. Furthermore, popular metrics such as Euclidean and Manhattan distance performed poorly in comparison to several lessor applied metrics, suggesting that the default metric for many scRNA-seq methods should be re-evaluated. Our findings highlight the critical nature of tailoring scRNA-seq analyses pipelines to the dataset under study and provide practical guidance for researchers looking to optimize cell-similarity search for the structural properties of their own data.
A review of biomedical datasets relating to drug discovery: a knowledge graph perspective
Authors: Bonner Stephen , Barrett Ian P , Ye Cheng , Swiers Rowan , Engkvist Ola , Bender Andreas , Hoyt Charles Tapley , Hamilton William L
Published: September 2022

Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Of these, those that use Knowledge Graphs (KG) have promise in many tasks, including drug repurposing, drug toxicity prediction and target gene–disease prioritization. In a drug discovery KG, crucial elements including genes, diseases and drugs are represented as entities, while relationships between them indicate an interaction. However, to construct high-quality KGs, suitable data are required. In this review, we detail publicly available sources suitable for use in constructing drug discovery focused KGs. We aim to help guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources. The datasets are selected via strict criteria, categorized according to the primary type of information contained within and are considered based upon what information could be extracted to build a KG. We then present a comparative analysis of existing public drug discovery KGs and an evaluation of selected motivating case studies from the literature. Additionally, we raise numerous and unique challenges and issues associated with the domain and its datasets, while also highlighting key future research directions. We hope this review will motivate KGs use in solving key and emerging questions in the drug discovery domain.
DGHNE: network enhancement-based method in identifying disease-causing genes through a heterogeneous biomedical network
Authors: He Binsheng , Wang Kun , Xiang Ju , Bing Pingping , Tang Min , Tian Geng , Guo Cheng , Xu Miao , Yang Jialiang
Published: September 2022

The identification of disease-causing genes is critical for mechanistic understanding of disease etiology and clinical manipulation in disease prevention and treatment. Yet the existing approaches in tackling this question are inadequate in accuracy and efficiency, demanding computational methods with higher identification power. Here, we proposed a new method called DGHNE to identify disease-causing genes through a heterogeneous biomedical network empowered by network enhancement. First, a disease–disease association network was constructed by the cosine similarity scores between phenotype annotation vectors of diseases, and a new heterogeneous biomedical network was constructed by using disease–gene associations to connect the disease–disease network and gene–gene network. Then, the heterogeneous biomedical network was further enhanced by using network embedding based on the Gaussian random projection. Finally, network propagation was used to identify candidate genes in the enhanced network. We applied DGHNE together with five other methods into the most updated disease–gene association database termed DisGeNet. Compared with all other methods, DGHNE displayed the highest area under the receiver operating characteristic curve and the precision-recall curve, as well as the highest precision and recall, in both the global 5-fold cross-validation and predicting new disease–gene associations. We further performed DGHNE in identifying the candidate causal genes of Parkinson’s disease and diabetes mellitus, and the genes connecting hyperglycemia and diabetes mellitus. In all cases, the predicted causing genes were enriched in disease-associated gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways, and the gene–disease associations were highly evidenced by independent experimental studies.
Immune responses in the injured olfactory and gustatory systems: a role in olfactory receptor neuron and taste bud regeneration?
Authors: Lakshmanan Hari G , Miller Elayna , White-Canale AnnElizabeth , McCluskey Lynnette P
Published: September 2022

Sensory cells that specialize in transducing olfactory and gustatory stimuli are renewed throughout life and can regenerate after injury unlike their counterparts in the mammalian retina and auditory epithelium. This uncommon capacity for regeneration offers an opportunity to understand mechanisms that promote the recovery of sensory function after taste and smell loss. Immune responses appear to influence degeneration and later regeneration of olfactory sensory neurons and taste receptor cells. Here we review surgical, chemical, and inflammatory injury models and evidence that immune responses promote or deter chemosensory cell regeneration. Macrophage and neutrophil responses to chemosensory receptor injury have been the most widely studied without consensus on their net effects on regeneration. We discuss possible technical and biological reasons for the discrepancy, such as the difference between peripheral and central structures, and suggest directions for progress in understanding immune regulation of chemosensory regeneration. Our mechanistic understanding of immune-chemosensory cell interactions must be expanded before therapies can be developed for recovering the sensation of taste and smell after head injury from traumatic nerve damage and infection. Chemosensory loss leads to decreased quality of life, depression, nutritional challenges, and exposure to environmental dangers highlighting the need for further studies in this area.
Bronchial Washing Using a Thin Versus a Thick Bronchoscope to Diagnose Pulmonary Tuberculosis: A Randomized Trial
Authors: Eom Jung Seop , Park Seyeon , Jang Hyojin , Kim Saerom , Yoo Wan Ho , Kim Soo Han , Mok Jeongha
Published: September 2022

Background

This study was performed to evaluate the efficacy of using a thin bronchoscope for the diagnosis of pulmonary tuberculosis (PTB).

Methods

Between March 2019 and November 2021, we prospectively enrolled participants with suspected PTB whose sputum acid-fast bacilli (AFB) smear and tuberculosis (TB) polymerase chain reaction (PCR) tests were negative or who could not produce self-expectorated sputum. Participants were randomized to a control group (bronchial washing [BW] using a 5.9-mm conventional bronchoscope guided by chest computed tomography) or an investigational group (BW using a 4.0-mm thin bronchoscope under virtual bronchoscopic navigation guidance). The primary outcome was detection of TB in BW fluid, defined as a positive result in the Xpert MTB/RIF assay. The secondary outcomes included AFB smear and Mycobacterium tuberculosis culture positivity, time to treatment initiation, and bronchoscopy-related complications.

Results

In total, 85 participants were included in the final analysis (43 in the control group and 42 in the investigational group). Twenty-three and 29, respectively, were finally diagnosed with PTB. The TB detection rate in BW fluid was higher in the investigational group (72.4% vs 43.5%, P = .035). Mycobacterium tuberculosis culture positivity was also higher in the investigational group (79.3% vs 52.2%, P = .038). No participants required premature bronchoscopy termination because of complications. Of the participants with PTB, the time to treatment initiation was shorter in the investigational group (median, 2.0 days vs 4.0 days, P = .001).

Conclusions

BW using a thin bronchoscope increases the TB detection rate in patients with PTB compared to conventional bronchoscopy.

Clinical Trials Registration.ȃNCT03802812.
Donor obesity and weight gain after transplantation: two still overlooked threats to long-term graft survival
Authors: Kanbay Mehmet , Copur Sidar , Ucku Duygu , Zoccali Carmine
Published: September 2022

The effect of donor obesity on kidney transplantation success has long been an overlooked clinical research area. Even though there is no strict guideline in most countries prohibiting donation from obese individuals, most candidates with a body mass index >35–40 kg/m2 are rejected due to concerns regarding long-term renal functional deterioration in the donor. The effects of excessive fat mass on renal function and allograft survival have been analysed by several longitudinal and follow-up studies. These studies have documented the deleterious effect on long-term graft outcomes of excessive body mass in living kidney donors and de novo obesity or pre-existing obesity worsening after transplantation on kidney outcomes. However, there is a paucity of clinical trials aimed at countering overweight and obesity in living and deceased kidney donors and in transplant patients. In this review we will briefly discuss the mechanism whereby fat excess induces adverse kidney outcomes and describe the effects on graft function and survival in living obese donors.
Double-Lead Content Search And Producer Location Prediction Scheme For Producer Mobility In Named Data Networking
Authors: Rui Lanlan , Dai Shiyue , Gao Zhipeng , Qiu Xuesong , Chen Xingyu
Published: September 2022

In recent years, Named Data Network (NDN) has become a popular network architecture because of high resource utilization, strong security and high transmission efficiency. Meanwhile, mobile multimedia communication has become the mainstream with the popularization and application of smart terminals. Most of the research on NDN mobility is focused on consumer mobility without taking producer mobility into account. To solve the delay and high cost carried by producer moving, we propose a Double-Lead content search algorithm based on neighbor and proxy and a location prediction algorithm based on traffic features. We use a neural network model to predict a new location of producers and calculate route before switching, which can save the rerouting latency in advance when predicting accurately. In a few cases of inaccurate predictions, we select different search methods according to the distance of the producer’s movement, to complete the Double-Lead search between the producer and the consumer. Experimental results show that DLPNDN can reduce the delay and traffic overhead well in NDN when the producer moves.
Combining counterfactual outcomes and ARIMA models for policy evaluation
Authors: Menchetti Fiammetta , Cipollini Fabrizio , Mealli Fabrizia
Published: September 2022

The Rubin Causal Model (RCM) is a framework that allows to define the causal effect of an intervention as a contrast of potential outcomes. In recent years, several methods have been developed under the RCM to estimate causal effects in time series settings. None of these makes use of autoregressive integrated moving average (ARIMA) models, which are instead very common in the econometrics literature. In this paper, we propose a novel approach, named Causal-ARIMA (C-ARIMA), to define and estimate the causal effect of an intervention in observational time series settings under the RCM. We first formalise the assumptions enabling the definition, the estimation and the attribution of the effect to the intervention. We then check the validity of the proposed method with a simulation study. In the empirical application, we use C-ARIMA to assess the causal effect of a permanent price reduction on supermarket sales. The CausalArima R package provides an implementation of the proposed approach.
Civicness Drain*
Authors: Michaeli Moti , Casari Marco , Ichino Andrea , De Paola Maria , Marandola Ginevra , Scoppa Vincenzo
Published: September 2022

Migration may cause, not only a brain drain, but also a ‘civicness’ drain, leading to a poverty trap. Using migration choices of southern Italian high-school students classified as civic if not cheating in a modified die-roll experiment, we uncover a key role of local civicness (average civicness in the class): a civicness drain is observed only at high and low local civicness. We rationalise this pattern with a model in which civic and uncivic types balance hope versus fear of migration outcomes, taking into account economic gains, risk preferences and their beliefs about being considered civic in the place of destination.
Seasonal variation in temperature sensitivity of bacterial growth in a temperate soil and lake
Authors: Kritzberg Emma , Bååth Erland
Published: September 2022

Faster bacterial biomass turnover is expected in water compared to soil, which would result in more rapid community adaption to changing environmental conditions, including temperature. Bacterial community adaptation for growth is therefore predicted to have larger seasonal amplitudes in lakes than in soil. To test this prediction, we compared the seasonal variation in temperature adaptation of bacterial community growth in a soil and lake in Southern Sweden (Tin situ 0–20°C, mean 10°C) during 1.5 years, based on monthly samplings including two winters and summers. An indicator of community adaptation, minimum temperature for growth (Tmin), was calculated from bacterial growth measurements (Leu incorporation) using the Ratkowsky model. The seasonal variation in Tmin (sinusoidal function, R2 = 0.71) was most pronounced for the lake bacterial community, with an amplitude for Tmin of 3.0°C (−4.5 to −10.5°C) compared to 0.6°C (−7 to −8°C) for the soil. Thus, Tmin in water increased by 0.32°C/degree change of Tin situ. Similar differences were also found when comparing four lakes and soils in the winter and summer (amplitudes 2.9°C and 0.9°C for lakes and soils, respectively). Thus, seasonal variation in temperature adaptation has to be taken into account in lakes, while for soils a constant Tmin can be used.
Effectiveness of building-level sewage surveillance during both community-spread and sporadic-infection phases of SARS-CoV-2 in a university campus population
Authors: Johnson William , Reeves Katelyn , Liebig Jennifer , Feula Antonio , Butler Claire , Alkire Michaela , Singh Samiha , Litton Shelby , O'Conor Kerry , Jones Keaton , Ortega Nikolas , Shimek Trace , Witteman Julia ,   Sampling Team Collaborators, Coe Elle , Heuer Heidi , Jones Jeffrey , Key Sara , Lilienfeld Jacob , Maggi Juniper , Nelson Lauren , Pulley Kevin , Wilkerson Paul , Vigil Bailey , Zak Gordon , Maxwell Kiersten , Karr Madeline , Freeman Nicholas , Saldana Emily , Salveson Lewis , Tomlinson Kate , Vargas-barriga Jorge , Bjorkman Kristen K , Mansfeldt Cresten
Published: September 2022

Pathogen surveillance within wastewater rapidly progressed during the SARS-CoV-2 pandemic and informed public health management. In addition to the successful monitoring of entire sewer catchment basins at the treatment facility scale, subcatchment or building-level monitoring enabled targeted support of resource deployment. However, optimizing the temporal and spatial resolution of these monitoring programs remains complex due to population dynamics and within-sewer physical, chemical, and biological processes. To address these limitations, this study explores the advancement of the building-scale network that monitored the on-campus residential population at the University of Colorado Boulder between August 2020 and May 2021 through a daily SARS-CoV-2 surveillance campaign. During the study period, SARS-CoV-2 infection prevalence transitioned from robust community spread in Fall 2020 to sporadic infections in Spring 2021. Temporally, these distinct phases enabled investigating the effectiveness of resource commitment by exploring subsets of the original daily sampling data. Spatially, select sampling sites were installed along the flow path of the pipe network, enabling the exploration of the conservation of viral concentrations within the wastewater. Infection prevalence and resource commitment for informed action displayed an inverted relationship: higher temporal and spatial resolution surveillance is more imperative during sporadic infection phases than during high prevalence periods. This relationship was reinforced when norovirus (two minor clusters) and influenza (primarily absent) were additionally surveilled at a weekly frequency. Overall, resource commitment should scale to meet the objectives of the monitoring campaign—providing a general prevalence estimate requires fewer resources than an early-warning and targeted-action monitoring framework.
Maximum SNP FST Outperforms Full-Window Statistics for Detecting Soft Sweeps in Local Adaptation
Authors: da Silva Ribeiro Tiago , Galván José A , Pool John E
Published: September 2022

Local adaptation can lead to elevated genetic differentiation at the targeted genetic variant and nearby sites. Selective sweeps come in different forms, and depending on the initial and final frequencies of a favored variant, very different patterns of genetic variation may be produced. If local selection favors an existing variant that had already recombined onto multiple genetic backgrounds, then the width of elevated genetic differentiation (high FST) may be too narrow to detect using a typical windowed genome scan, even if the targeted variant becomes highly differentiated. We, therefore, used a simulation approach to investigate the power of SNP-level FST (specifically, the maximum SNP FST value within a window, or FST_MaxSNP) to detect diverse scenarios of local adaptation, and compared it against whole-window FST and the Comparative Haplotype Identity statistic. We found that FST_MaxSNP had superior power to detect complete or mostly complete soft sweeps, but lesser power than full-window statistics to detect partial hard sweeps. Nonetheless, the power of FST_MaxSNP depended highly on sample size, and confident outliers depend on robust precautions and quality control. To investigate the relative enrichment of FST_MaxSNP outliers from real data, we applied the two FST statistics to a panel of Drosophila melanogaster populations. We found that FST_MaxSNP had a genome-wide enrichment of outliers compared with demographic expectations, and though it yielded a lesser enrichment than window FST, it detected mostly unique outlier genes and functional categories. Our results suggest that FST_MaxSNP is highly complementary to typical window-based approaches for detecting local adaptation, and merits inclusion in future genome scans and methodologies.