Miracle-dz/UniFAR-FTUs huggingface dataset:
Responses of phytoplankton community to the input of different aerosols in the East China Sea

Atmospheric deposition can affect marine phytoplankton by supplying macronutrients and trace elements. We conducted mesocosm experiments by adding aerosols with different composition (dominated by mineral dust, biomass burning and high Cu, and secondary aerosol, respectively) to the surface seawater of the East China Sea. Chlorophyll a concentrations were found to be the highest and lowest after adding aerosols containing the highest Fe and dissolved inorganic nitrogen (DIN), respectively. The relative abundance of Haptophyceae increased significantly after adding mineral dust, whereas diatom, Dinophyceae and Cryptophyceae reached the maximum accompanied with the highest DIN. Our results suggest that Fe may be more important than DIN in promoting primary productivity in the sampled seawater. The input of mineral dust and anthropogenic aerosols may result in distinct changes of phytoplankton community structure.
Teaching by texting to promote positive health behaviours in pregnancy: a protocol for a randomised controlled trial of SmartMom

Introduction Prenatal education is associated with positive health behaviours, including optimal weight gain, attendance at prenatal care, acceptance of routine screening tests, smoking cessation, decreased alcohol consumption and breast feeding. Adoption of these behaviours has been associated with reduced rates of caesarean birth, preterm birth and low birth weight. Barriers to prenatal class attendance faced by parents in Canada include geography, socioeconomic status, age, education, and, among Indigenous peoples and other equity-deserving groups, stigma. To address the need for easily accessible and reliable information, we created ‘SmartMom’, Canada’s first prenatal education programme delivered by short message service text messaging. SmartMom provides evidence-based information timed to be relevant to each week of pregnancy. The overall goal of SmartMom is to motivate the adoption of positive prenatal health behaviours with the ultimate goal of improving health outcomes among women and their newborns. Methods and analysis We will conduct a two-arm single-blinded randomised controlled trial. Blinding of participants to trial intervention will not be possible as they will be aware of receiving the intervention, but data analysts will be blinded. Our primary research questions are to determine if women experiencing uncomplicated pregnancies randomly assigned to receive SmartMom messages versus messages addressing general topics related to pregnancy but without direction for behaviour change, have higher rates of: (1) weight gain within ranges recommended for prepregnancy body mass index and (2) adherence to Canadian guidelines regarding attendance at prenatal care appointments. Ethics and dissemination The study has been granted a Certificate of Approval, number H22-00603, by the University of British Columbia Research Ethics Board. To disseminate our findings, we are undertaking both integrated and end-of-grant knowledge translation, which will proactively involve potential end-users and stakeholders at every phase of our project. Trial registration number NCT05793944.
The Neutrophil Lymphocyte Ratio as a Predictor of Acute Exacerbations Among Patients with COPD in Uganda.

Background The Neutrophil to Lymphocyte ratio (NLR) is an inexpensive biomarker that potentially predicts Acute Exacerbations of Chronic Obstructive Pulmonary Disease (AECOPD). We evaluated the association of baseline NLR and respiratory hospitalisation risk within one year among COPD patients in Uganda, Low- and Middle-Income Country. Methods 312 COPD patients were followed up for 1 year. Clinical characteristics and exacerbation rate were collected. Poisson regression with robust variance estimators were used to measure the association between NLR and hospital admissions due to COPD exacerbations. Receiver-operator characteristic (ROC) curves and the area under the curve were used to assess the ability of NLR to predict AECOPD. Results The median (Q 1, Q 3) age was 64.00 years (53.00, 71.00). Females comprised 50.96% (n=159) of the cohort, and 71.2% (n=222) of participants had moderate or severe COPD. 9.9% (n=31) of participants experienced a COPD exacerbation during the period of follow up. At baseline, the median (Q 1, Q 3) NLR ratio among participants who experienced an exacerbation was 1.46 (0.92, 2.33) compared to 1.03 (0.72,1.42) among those who did not experience one during the follow-up period (p=0.002). Using Youden and Liu's methods, the optimal NLR cut-off for predicting COPD exacerbation was 1.17. This cut-off resulted in a ROC curve area of 0.64 (95% CI: 0.56, 0.73). Conclusion The NLR could be used as a risk predictor for hospital admissions due to COPD exacerbations in low- and middle-income countries. A cut-off of 1.17 was an independent predictor of hospitalization due to acute exacerbations COPD within one year.
Cholesterol biosynthesis inhibition synergizes with AKT inhibitors in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is responsible for a disproportionate number of breast cancer deaths due to its molecular heterogeneity, high recurrence rate and lack of targeted therapies. Dysregulation of the phosphoinositide 3-kinase (PI3K)/AKT pathway occurs in approximately 50% of TNBC patients. We performed a genome-wide CRISPR/Cas9 screen with PI3Kα and AKT inhibitors to find targetable synthetic lethalities in TNBC. We identified cholesterol homeostasis as a collateral vulnerability with AKT inhibition. Disruption of cholesterol homeostasis with pitavastatin synergized with AKT inhibition to induce TNBC cytotoxicity in vitro, in mouse TNBC xenografts and in patient-derived, estrogen receptor (ER)-negative breast cancer organoids. Neither ER-positive breast cancer cell lines nor ER-positive organoids were sensitive to combined AKT inhibitor and pitavastatin. Mechanistically, TNBC cells showed impaired sterol regulatory element-binding protein 2 (SREBP-2) activation in response to single agent or combination treatment with AKT inhibitor and pitavastatin. This was rescued by inhibition of the cholesterol trafficking protein Niemann-Pick C1 (NPC1). NPC1 loss caused lysosomal cholesterol accumulation, decreased endoplasmic reticulum cholesterol levels and promoted SREBP-2 activation. Taken together, these data identify a TNBC-specific vulnerability to the combination of AKT inhibitors and pitavastatin mediated by dysregulated cholesterol trafficking. Our work motivates combining AKT inhibitors with pitavastatin as a therapeutic modality in TNBC.
Annotation-free prediction of microbial dioxygen utilization

Aerobes require dioxygen (O2) to grow; anaerobes do not. But nearly all microbes — aerobes, anaerobes, and facultative organisms alike — express enzymes whose substrates include O2, if only for detoxification. This presents a challenge when trying to assess which organisms are aerobic from genomic data alone. This challenge can be overcome by noting that O2 utilization has wide-ranging effects on microbes: aerobes typically have larger genomes, encode more O2-utilizing enzymes, and tend to use different amino acids in their proteins. Here we show that these effects permit high-quality prediction of O2 utilization from genome sequences, with several models displaying >70% balanced accuracy on a ternary classification task wherein blind guessing is only 33.3% accurate. Since genome annotation is compute-intensive and relies on many assumptions, we asked if annotation-free methods also perform well. We discovered that simple and efficient models based entirely on genome sequence content — e.g. triplets of amino acids — perform about as well as intensive annotation-based algorithms, enabling the rapid processing of global-scale sequence data to predict aerobic physiology. To demonstrate the utility of efficient physiological predictions we estimated the prevalence of aerobes and anaerobes along a well-studied O2 gradient in the Black Sea, finding strong quantitative correspondence between local chemistry (O2:sulfide concentration ratio) and the composition of microbial communities. We therefore suggest that statistical methods like ours can be used to estimate, or “sense,” pivotal features of the environment from DNA sequencing data. Importance We now have access to sequence data from a wide variety of natural environments. These data document a bewildering diversity of microbes, many known only from their genomes. Physiology — an organism’s capacity to engage metabolically with its environment — may provide a more useful lens than taxonomy for understanding microbial communities. As an example of this broader principle, we developed algorithms that accurately predict microbial dioxygen utilization directly from genome sequences without first annotating genes, e.g. by considering only the amino acids in protein sequences. Annotation-free algorithms enabled rapid characterization of natural samples, demonstrating a quantitative correspondence between sequences and local O2 levels. These results suggest that DNA sequencing can be repurposed as a multi-pronged chemical sensor, estimating concentrations of O2 and other key facets of complex natural settings.
Adaptive Differences in Circadian Clock Gene Expression Patterns and Photoperiodic Diapause Induction in Nasonia vitripennis

Day length (photoperiod) and temperature oscillate daily and seasonally and are important cues for season-dependent behavior. Larval diapause of the parasitoid Nasonia vitripennis is maternally induced following a certain number of days (switch point) of a given critical photoperiod (CPP). Both the switch point and the CPP follow a latitudinal cline in European N. vitripennis populations. We previously showed that allelic frequencies of the clock gene period correlate with this diapause induction cline. Here we report that circadian expression of four clock genes—period (per), cryptochrome-2 (cry-2), clock (clk), and cycle (cyc)—oscillates as a function of photoperiod and latitude of origin in wasps from populations from the extremes of the cline. Expression amplitudes are lower in northern wasps, indicating a weaker, more plastic clock. Northern wasps also have a later onset of activity and longer free-running rhythms under constant conditions. RNA interference of per caused speeding up of the circadian clock, changed the expression of other clock genes, and delayed diapause in both southern and northern wasps. These results point toward adaptive latitudinal clock gene expression differences and to a key role of per in the timing of photoperiodic diapause induction of N. vitripennis.
Design of a Robust Normal Distribution Sampler for Ring-Learning-With-Errors Cryptographic Scheme

Due to the various characteristics from the pseudo random number generator or many kinds of deterministic devices such as arithmetic processing units, new principles and test schemes should be proposed for assessment of true random number generator. In this contribution, a novel viewpoint on designing a Normal distribution sampler applicable for implementing a homomorphic encryption system based on Ring-LWE crypto scheme is proposed. We suggest a Gaussian normal distribution sampler described with HDL to create uniformly distributed pseudo random numbers which will be used for generating non-symmetric key matrices and error matrices using an open-source AES cryptographic module. The implemented sampler can be conducted with high-speed clock frequency with its succinct critical delay paths as well.
Inference of subgenomes resulting from polyploid events using synteny based dynamic linking and maximum neighbourhood

Polyploidy is common in flowering plants, resulting in extra sets of chromosomes, known as subgenomes. These events are widespread in plant evolution, making the assignment of synteny blocks to subgenomes challenging due to gene fractionation and gene order rearrangements. Current methods for subgenome identification are labor-intensive and require expertise, lacking automation. To address this challenge, we introduce the SyntenyLink algorithm, which automates subgenome reconstruction from synteny blocks. This algorithm considers differences in substitution and fractionation patterns in synteny blocks and maintains the continuity of gene order. SyntenyLink starts by identifying synteny blocks using BLASTP and DAGchainer, then Automatically partitions them into subgenomes by traversing the maximum weighted path on the "super-synteny graph". We validated the SyntenyLink algorithm using verified subgenomes of Brassica rapa, Brassica oleracea, Brassica nigra, Brassica napus, and Sinapis alba. The results demonstrate its effectiveness, especially for subgenome 1, with accuracy ranging from 82% to 88%. Subgenomes 2 and 3 showed slightly lower accuracy (60%-85%) due to their similar fractionation patterns. Furthermore, we applied SyntenyLink to separate the six subgenomes in Brassica juncea and Brassica carinata, illustrating its versatility. In summary, the SyntenyLink algorithm offers a powerful and automated approach for reconstructing subgenomes in complex polyploid genomes. This advancement has significant implications for studying the evolutionary history of flowering plants and other polyploid organisms.
Attention Based Approach for Molecular Property Prediction

Proteins are the fundamental units of all living cells and play a vital role in various cellular functions. Moreover, proteins are the building blocks of all diseases, making them imperative to the drug discovery process. The ability to identify proteins' characteristics and properties enables scientists with in-depth knowledge of the mechanisms of a disease, that can alter protein behavior. One important aspect in studying proteins is their structure flexibility since the changes of amino acid residue could change the size, shape, and its property. Moreover, proteins usually have a large number of residues and atoms, the approaches considering protein structures are computationally expensive. In this work, we address these problems by combining machine learning approaches that are able to process high dimensional data and feature analyzing techniques to extract underlying relationships within the data. Specifically, we propose a novel metric to capture the locality of the protein structures and a modified transformer architecture to take advantage of both 3D and string representations of proteins. The results show that our model has a comparable performance with state-of-the-art models on the selected tasks from MoleculeNet.
Real-time Monitoring of Women's Safety Through Bluetooth Low Energy and iBeacons

This paper presents real-time monitoring of women's safety, which uses Bluetooth Low Energy (BLE) and iBeacons to provide real-time tracking. Data on the iBeacons' Received Signal Strength Indicator (RSSI) is gathered from BLE-enabled calibration devices. Users' real RSSIs are estimated using the refined least squares estimation method. The predicted RSSI data are used with the Android Application Package (APK) to transmit the precise GPS coordinates of the mobile device to the server. When combined with the APK, the proposed tracking method provides accurate data regarding device position and iBeacon distance. In addition, Google Maps can be incorporated with the system for even more precise real-time monitoring. This system can follow a destination in real time, identify its location, and work with various desktop and mobile platforms. While initially designed to improve women's safety, it can be easily adapted to monitor people's location in multiple settings. Accurate tracking and distance estimates are achieved by integrating wearable BLE-enabled devices, mobile phones, server connectivity, and exact RSSI measurement. Google Maps' incorporation into real-time surveillance enhances women's and other people's safety and security.
LDE-UNet: A Novel Model for Rapid COVID-19 Diagnosis via CT Image Segmentation

The COVID-19 pandemic has had a profound impact on human society. It has highlighted the need for faster diagnostic methods. Research has shown that combining semantic segmentation with traditional medical approaches can significantly accelerate the process. To address this, leveraging COVID-19 CT images, our team designs a revolutionary semantic segmentation model called Level of Detail Enhancement U-Net (LDE-UNet), which shows the lesion area on CT images. By introducing the LDE block, the model has the unique advantage of overcoming the loss of data details during the downsampling process by emphasizing and transmitting details at the same level. Our SOTA model outperforms the second-best model by at least 0.7% in the most critical indicator precision. Compared with other models, LDE-UNet’s strong reliability determines its ability to be used in the medical field to accelerate the localization and division of lesion areas on CT images by professional doctors, thus completing patient diagnosis faster. In addition, we also propose a standardized method for processing medical images.
Identification of Important SNPs using Bayesian Deep Learning on Whole-Genome Arabidopsis thaliana Data

With the advent of Genome-Wide Association Studies (GWAS) and sequencing techniques, researchers can now examine the genetic basis of various traits and diseases. GWAS present their challenges, primarily originating from the high dimensionality of the Single Nucleotide Polymorphism (SNP) data. Deep Learning (DL) has been manifested as a promising method for such high-dimensional data. This article proposes a DL-based workflow, BayesDL, to identify potentially important SNPs from the Arabidopsis thaliana data. BayesDL incorporates Bayesian methodologies to DL by using filtered SNPs obtained from Chi-square and ANOVA tests. We use coefficient of variation in BayesDL to guide the identification of important SNPs. The SNPs obtained from BayesDL are subsequently validated, genes are pinpointed, and a comparison is drawn with the results from GWAS software GAPIT and TASSEL. Our results show that BayesDL not only mitigates over-fitting but also performs better than the traditional Convolutional Neural Network (CNN). Our findings suggest that integrating BayesDL and GWAS offers the potential to uncover more genes associated with the phenotypes of interest.
Research on Energy Management Strategy and Multi-energy Integrated Control of Hybrid Electric Car Considering Regenerative Braking

The environmental pollution situation that we are currently experiencing is becoming more and more serious as the environmental pressure that we are under continues to increase. As a result of this, the goal of developing new vehicles that have a low displacement has emerged as a very important endeavour in the automotive industry. At the moment, the most popular method utilised by hybrid electric vehicles to recuperate the energy lost during braking is called regenerative braking. It is possible to realise regenerative braking in electric vehicles, hybrid electric vehicles, but not in traditional vehicles. This is one of the most significant differences between these three types of vehicles and traditional vehicles. This paper reveals the performance of braking safety performance and braking energy recovery rate under the regenerative braking control strategy of hybrid electric car. It does so by means of theoretical analysis and simulation research. As a result, it provides reference basis for further improving the performance of regenerative braking system and multi-energy integrated control of hybrid electric car.
Prior Information Guided Coarse-to-fine Dual-branch Encoding Network for Fovea Localization and Optic Disc/Cup Segmentation

Fundus images are commonly used to document the presence and severity of various retinal degenerative diseases, where the fovea, optic disc (OD), and optic cup (OC) serve as important anatomical landmarks. Locating and segmenting these landmarks are crucial for clinical diagnosis and treatment. Many existing methods treat the recognition of the fovea, OD, and OC as separate tasks without incorporating any clinical prior knowledge related to various anatomical structures. In this paper, we propose a prior information guided coarse-to-fine dual-branch encoding network, which enables fovea localization and OD/OC segmentation. In coarse stage, we employ a dual-branch network consisting of convolutional neural network (CNN) and Transformer to encode local and global features, and then utilize multi-scale feature fusion techniques to merge the extracted semantic features, aiming to enhance the localization accuracy. In addition, we effectively use the distance information from each pixel to the landmark of interest, and output the results of distance map and heat map regression as prior information to further guide the network to learn the positional relationship between fovea and OD. In fine stage, we refine the region of interest (ROI) of the OD, balance the distribution of the OD and OC using polar coordinate transformation (PCT), extract critical boundary features using the boundary attention module (BAM), and improve the generalization performance of our method through model ensemble strategy. Extensive experimental results demonstrate that our proposed method outperforms existing state-of-the-art (SOTA) methods on the publicly available GAMMA and REFUGE datasets.
Actuation and Flight Control of High-DOF Dynamic Morphing Wing Flight by Shifting Structure Response

Bat's dynamically morphing wings are highly versatile with many active and passive modes which allows them to display highly dexterous flight maneuvers. We take inspiration from bat wings and attempt to mimic their high degrees of freedom and flexibility in our small bat robot with dynamically morphing wings called the Aerobat. This small robot uses linkages, or computational structure, to animate the robot's flapping gait. In this work, we present the theoretical framework of using small low-energy actuators, called the primers, to adjust highly sensitive linkages length for changing the robot's flapping gait and use it to control the robot's orientation. This method is applied in a dynamic simulation to show its feasibility.
Advances in Machine Learning-driven Quality Control within ERP Systems: A Comprehensive Survey

Enterprise Resource Planning (ERP) systems have become essential instruments in contemporary corporate operations, offering significant benefits in process optimization and efficient data management. The assurance of quality and dependability in these intricate systems is of utmost significance given that errors or inconsistencies might yield extensive ramifications. This study highlights the importance of quality control within ERP systems, emphasizing the crucial role of accuracy and integrity in facilitating efficient decision-making processes and optimizing operational efficiency. This analysis explores the increasing importance of upholding ERP quality as firms become increasingly dependent on these systems to traverse intricate business environments. In addition, this study explores the capacity of machine learning to enhance quality control procedures. Machine learning techniques, owing to their ability to examine large datasets and identify subtle patterns, provide a transformative shift away from the traditional rule-based approaches. Machine learning enhances the capacity to anticipate problems, reduce interruptions, and maintain consistent operational performance by employing real-time anomaly detection, predictive modeling, and adaptation to shifting ERP settings. This study explores the complex confluence of technology and business imperatives, highlighting the transformative impact of the collaboration between machine learning and quality control on the evolution of ERP systems.