Miracle-dz/UniFAR-FTUs huggingface dataset:
Profiles of Wnt pathway gene expression during tooth morphogenesis

Mouse and human genetic studies indicate key roles of the Wnt10a ligand in odontogenesis. Previous studies have identified effectors and regulators of the Wnt signaling pathway actively expressed during key stages of tooth morphogenesis. However, limitations in multiplexing and spatial resolution hindered a more comprehensive analysis of these signaling molecules. Here, profiling of transcriptomes using fluorescent multiplex in situ hybridization and single-cell RNA-sequencing (scRNA-seq) provide robust insight into the synchronized expression patterns of Wnt10a, Dkk1, and Sost simultaneously during tooth development. First, we identified Wnt10a transcripts restricted to the epithelium at the stage of tooth bud morphogenesis, contrasting that of Sost and Dkk1 localization to the dental mesenchyme. By embryonic day 15.5 (E15.5), a marked shift of Wnt10a expression from dental epithelium to mesenchyme was noted, while Sost and Dkk1 expression remained enriched in the mesenchyme. By postnatal day 0 (P0), co-localization patterns of Wnt10a, Dkk1, and Sost were observed in both terminally differentiating and secreting odontoblasts of molars and incisors. Interestingly, Wnt10a exhibited robust expression in fully differentiated ameloblasts at the developing cusp tip of both molars and incisors, an observation not previously noted in prior studies. At P7 and 14, after the mineralization of dentin and enamel, Wnt10a expression was limited to odontoblasts. Meanwhile, Wnt modulators showed reduced or absent signals in molars. In contrast, strong signals persisted in ameloblasts (for Wnt10a) and odontoblasts (for Wnt10a, Sost, and Dkk1) towards the proximal end of incisors, near the cervical loop. Our scRNA-seq analysis used CellChat to further contextualize Wnt pathway-mediated communication between cells by examining ligand-receptor interactions among different clusters. The co-localization pattern of Wnt10a, Dkk1, and Sost in both terminally differentiating and secreting odontoblasts of molars and incisors potentially signifies the crucial ligand-modulator interaction along the gradient of cytodifferentiation starting from each cusp tip towards the apical region. These data provide cell type-specific insight into the role of Wnt ligands and mediators during epithelial-mesenchymal interactions in odontogenesis.
Users’ views on the use of a smartwatch app to collect daily symptom data in individuals with multiple long-term conditions (Multimorbidity): A qualitative study

Introduction Long-term conditions are a major burden on health systems. One way to facilitate more research and better clinical care among patients with long-term conditions is to collect accurate data on their daily symptoms (patient-generated health data) using wearable technologies. Whilst evidence is growing for the use of wearable technologies in single conditions, there is less evidence of the utility of frequent symptom tracking in those who have more than one condition. Aims To explore patient views of the acceptability of collecting daily patient-generated health data for three months using a smartwatch app. Methods Watch Your Steps was a longitudinal study which recruited 53 patients to track over 20 symptoms per day for a 90-day period using a study app on smartwatches. Semi-structured interviews were conducted with a sub-sample of 20 participants to explore their experience of engaging with the app. Results In a population of older people with multimorbidity, patients were willing and able to engage with a patient-generated health data app on a smartwatch. It was suggested that to maintain engagement over a longer period, more ‘real-time’ feedback from the app should be available. Participants did not seem to consider the management of more than one condition to be a factor in either engagement or use of the app, but the presence of severe or chronic pain was at times a barrier. Conclusion This study has provided preliminary evidence that multimorbidity was not a major barrier to engagement with patient-generated health data via a smartwatch symptom tracking app.
Anaerobic hexadecane degradation by a thermophilic Hadarchaeon from Guaymas Basin

Abstract Hadarchaeota inhabit subsurface and hydrothermally heated environments, but previous to this study, they had not been cultured. Based on metagenome-assembled genomes, most Hadarchaeota are heterotrophs that grow on sugars and amino acids, or oxidize carbon monoxide or reduce nitrite to ammonium. A few other metagenome-assembled genomes encode alkyl-coenzyme M reductases (Acrs), β-oxidation, and Wood-Ljungdahl pathways, pointing toward multicarbon alkane metabolism. To identify the organisms involved in thermophilic oil degradation, we established anaerobic sulfate-reducing hexadecane-degrading cultures from hydrothermally heated sediments of the Guaymas Basin. Cultures at 70°C were enriched in one Hadarchaeon that we propose as Candidatus Cerberiarchaeum oleivorans. Genomic and chemical analyses indicate that Ca. C. oleivorans uses an Acr to activate hexadecane to hexadecyl-coenzyme M. A β-oxidation pathway and a tetrahydromethanopterin methyl branch Wood–Ljungdahl (mWL) pathway allow the complete oxidation of hexadecane to CO2. Our results suggest a syntrophic lifestyle with sulfate reducers, as Ca. C. oleivorans lacks a sulfate respiration pathway. Comparative genomics show that Acr, mWL, and β-oxidation are restricted to one family of Hadarchaeota, which we propose as Ca. Cerberiarchaeaceae. Phylogenetic analyses further indicate that the mWL pathway is basal to all Hadarchaeota. By contrast, the carbon monoxide dehydrogenase/acetyl-coenzyme A synthase complex in Ca. Cerberiarchaeaceae was horizontally acquired from Bathyarchaeia. The Acr and β-oxidation genes of Ca. Cerberiarchaeaceae are highly similar to those of other alkane-oxidizing archaea such as Ca. Methanoliparia and Ca. Helarchaeales. Our results support the use of Acrs in the degradation of petroleum alkanes and suggest a role of Hadarchaeota in oil-rich environments.
DUSFormer: Dual-Swin Transformer V2 Aggregate Network for Polyp Segmentation

The convolutional neural network method has certain limitations in medical image segmentation. As a result of the limited availability of polyp datasets, the model framework is vulnerable to instability and overfitting during training. Beyond that, ambiguous target boundaries can make segmentation more difficult. We propose a Dual-Swin Transformer V2 Aggregate Network called DUSFormer in order to address these issues, which can be used to more accurately capture spatial semantic features with different complexities. Specifically, the DUSFormer network consists of two encoders and decoders for progressive feature extraction and deep feature extraction. The decoder uses the Stepwise Feature Fusion (SFF) module to locally emphasize and fuse the feature maps at various levels. This architecture enables faster and more efficient dissemination of feature information at all levels, enabling faster integration of features with global dependencies and local details. In addition, an Adaptive Correction Module (ACM) is introduced to construct an aggregation relationship of edge information between layers of the encoder and the decoder, which can correct the predictive segmentation with irregular and blurred boundaries, and increase the precision of segmentation. The DUSFormer model has many advantages in terms quantitative and generalization ability on three polyp image segmentation datasets.
Deciphering the Unconventional Reduction of C=N Bonds by Old Yellow Enzymes Using QM/MM

The reduction of C=X (X = N, O) bonds is a cornerstone in both synthetic organic chemistry and biocatalysis. Conventional reduction mechanisms usually involve a hydride ion targeting the less electronegative carbon atom. In a departure from this paradigm, our investigation into Old Yellow Enzymes (OYEs) reveals a mechanism involving transfer of hydride to the formally more electronegative nitrogen atom within a C=N bond. Beyond their known ability to reduce electronically activated C=C double bonds, e.g., in α, β-unsaturated ketones, these enzymes have recently been shown to reduce α-oximo-β-ketoesters to the corresponding amines. It has been proposed that this transformation involves two successive reduction steps and proceeds via imine intermediates formed by the reductive dehydration of the oxime moieties. We employ advanced quantum mechanics/molecular mechanics (QM/MM) simulations, enriched by a two-tiered approach incorporating QM/MM (UB3LYP-6-31G*/OPLS2005) geometry optimization, QM/MM (B3LYP-6-31G*/amberff19sb) steered molecular dynamics simulations, and detailed natural-bond-orbital analyses to decipher the unconventional hydride transfer to nitrogen in both reduction steps and to delineate the role of active site residues as well as of substituents present in the substrates. Our computational results confirm the proposed mechanism and agree well with experimental mutagenesis and enzyme kinetics data. According to our model, the catalysis of OYE involves hydride transfer from the flavin cofactor to the nitrogen atom in oximoketoesters as well as iminoketoesters followed by protonation at the adjacent oxygen or carbon atoms by conserved tyrosine residues and active site water molecules. Two histidine residues play a key role in the polarization and activation of the C=N bond, and conformational changes of the substrate observed along the reaction coordinate underline the crucial importance of dynamic electron delocalization for efficient catalysis.
Humanness Features Could be Key Determinant in Driving Adoption Intention of Banking Services from Licensed Digital Banks: A Conceptual Paper

Licensed digital bank has become the hot topic in Asia and there are primed for growth. However, the path for licensed digital bank remains challenging as it is hard to gain customer affinity, trust and loyalty without human interaction. The establishment of licensed digital bank is significantly affected by the changes of technology and consumer preference in accepting banking innovation that operates fully digital without human interaction. This study integrates Diffusion of Innovation Theory and Anthropomorphic Theory into Stimulus-organism-response framework in investigating consumer perception on licensed digital bank that motivated by consumer innovativeness and humanness features of licensed digital bank. Since trust is crucial in getting consumer affinity in digital environment, the moderating effect of trust will be investigated. Quantitative research method will be used to facilitate the deductive approach for concepts and hypothesis testing. The population of study is scoped into Malaysian who are eligible to own bank account and use mobile banking application for banking activities. They represent diversity in gender and culture as well as people who are deemed likely to adopt licensed digital bank services with the aid of mobile internet. The findings of study will help stakeholders to develop their go-to-market strategy. This will help them to move this innovation from niche to mainstream strategically. Moreover, it provides a framework as a baseline for future research for a deeper knowledge and understanding related to financial technology innovations adoption in emerging countries.
An Effective Classification of DDoS Attacks in a Distributed Network by Adopting Hierarchical Machine Learning and Hyperparameters Optimization Techniques

Data privacy is essential in the financial sector to protect client’s sensitive information, prevent financial fraud, ensure regulatory compliance, and safeguard intellectual property. It has become a challenging task due to the increase in usage of the internet and digital transactions. In this scenario, DDoS attack is one of the major attacks that makes clients’ privacy questionable. It requires effective and robust attack detection and prevention techniques. Machine Learning (ML) is the most effective approach for employing cyber attack detection systems. It paves the way for a new era where human and scientific communities will benefit. This paper presents a hierarchical ML-based hyperparameter-optimization approach for classifying intrusions in a network. CICIDS 2017 standard dataset was considered for this work. Initially, data was preprocessed with the min-max scaling and SMOTE methods. The LASSO approach was used for feature selection, given as input to the hierarchical ML algorithms: XGboost, LGBM, CatBoost, Random Forest (RF), and Decision Tree (DT). All these algorithms are pretrained with hyperparameters to enhance the effectiveness of algorithms. Models performance was assessed in terms of recall, precision, accuracy, and F1-score metrics. Evaluated approaches have shown that the LGBM algorithm gives a proven performance in classifying DDoS attacks with 99.77% of classification accuracy.
Machine Learning Models of Polygenic Risk for Enhanced Prediction of Alzheimer Disease Endophenotypes

Background and Objectives Alzheimer disease (AD) has a polygenic architecture, for which genome-wide association studies (GWAS) have helped elucidate sequence variants (SVs) influencing susceptibility. Polygenic risk score (PRS) approaches show promise for generating summary measures of inherited risk for clinical AD based on the effects of APOE and other GWAS hits. However, existing PRS approaches, based on traditional regression models, explain only modest variation in AD dementia risk and AD-related endophenotypes. We hypothesized that machine learning (ML) models of polygenic risk (ML-PRS) could outperform standard regression-based PRS methods and therefore have the potential for greater clinical utility. Methods We analyzed combined data from the Mayo Clinic Study of Aging (n = 1,791) and the Alzheimer's Disease Neuroimaging Initiative (n = 864). An AD PRS was computed for each participant using the top common SVs obtained from a large AD dementia GWAS. In parallel, ML models were trained using those SV genotypes, with amyloid PET burden as the primary outcome. Secondary outcomes included amyloid PET positivity and clinical diagnosis (cognitively unimpaired vs impaired). We compared performance between ML-PRS and standard PRS across 100 training sessions with different data splits. In each session, data were split into 80% training and 20% testing, and then five-fold cross-validation was used within the training set to ensure the best model was produced for testing. We also applied permutation importance techniques to assess which genetic factors contributed most to outcome prediction. Results ML-PRS models outperformed the AD PRS (r2 = 0.28 vs r2 = 0.24 in test set) in explaining variation in amyloid PET burden. Among ML approaches, methods accounting for nonlinear genetic influences were superior to linear methods. ML-PRS models were also more accurate when predicting amyloid PET positivity (area under the curve [AUC] = 0.80 vs AUC = 0.63) and the presence of cognitive impairment (AUC = 0.75 vs AUC = 0.54) compared with the standard PRS. Discussion We found that ML-PRS approaches improved upon standard PRS for prediction of AD endophenotypes, partly related to improved accounting for nonlinear effects of genetic susceptibility alleles. Further adaptations of the ML-PRS framework could help to close the gap of remaining unexplained heritability for AD and therefore facilitate more accurate presymptomatic and early-stage risk stratification for clinical decision-making.
A Rainbow Framework for Coded Caching and Its Applications

The centralized coded caching focuses on reducing the network burden in peak times in a wireless network system. In this paper, motivated by the study of the only rainbow 3-term arithmetic progressions set, we propose a combinatorial framework for constructing coded caching schemes. This framework builds bridges between coded caching schemes and lots of combinatorial objects due to the freedom of the choices of families and binary operations. We prove that any scheme based on a placement delivery array (PDA) can be represented by a rainbow scheme under this framework and lots of other known schemes can also be included in this framework. Moreover, we also present a new coded caching scheme with linear subpacketization and near constant rate using the only rainbow 3-term arithmetic progressions set. Finally, we modify the framework to be applicable to the coded caching problem in Device-to-Device (D2D) networks and the distributed computing problem.
Efficient Crosswell EM Tomography for Monitoring Geological Sequestration of CO2

ABSTRACT Carbon dioxide (CO2) sequestration in oil reservoirs can be one of the most effective strategies for long-term removal of greenhouse gas from atmosphere. This paper presents an advantage of the localized nonlinear approximation of integral equation solutions for inverting crosswell electromagnetic data, which are observed as a part of pilot project of CO2 flooding at the Lost Hills oil field in central California, U.S.A. To monitor the migration of CO2, we have used two-dimensional (2D) cylindrically symmetric and 2.5D tomographic inversion methods. These two schemes produce similar images if the borehole separation is large enough compared with the skin depth. However, since the borehole separation is much less than five skin depths in this CO2 injection experiment, the 2.5D model seems to be more reliable than the 2D model. In fact, the 2.5D image derived from pre-injection data is more successfully compared with induction logs observed in the two wells than the 2D image. From the time-lapse crosswell imaging, we can confirm that the replacement of brine with CO2 makes a decrease of conductivity.
Competing Bandits in Non-Stationary Matching Markets

Understanding complex dynamics of two-sided online matching markets, where the demand-side agents compete to match with the supply-side (arms), has recently received substantial interest. To that end, in this paper, we introduce the framework of decentralized two-sided matching market under non stationary (dynamic) environments. We adhere to the serial dictatorship setting, where the demand-side agents have unknown and different preferences over the supply-side (arms), but the arms have fixed and known preference over the agents. We propose and analyze an asynchronous and decentralized learning algorithm, namely Non-Stationary Competing Bandits (NSCB), where the agents play (restrictive) successive elimination type learning algorithms to learn their preference over the arms. The complexity in understanding such a system stems from the fact that the competing bandits choose their actions in an asynchronous fashion, and the lower ranked agents only get to learn from a set of arms, not dominated by the higher ranked agents, which leads to forced exploration. With carefully defined complexity parameters, we characterize this forced exploration and obtain sub-linear (logarithmic) regret of NSCB. Furthermore, we validate our theoretical findings via experiments.
Time vs. frequency domain ground motion modification: Effects on site response analyses and seismic displacements

Spectral matching is widely used in practice, despite being a source of controversy. Due to the recognized importance of selected ground motions in the engineering results, it is important to understand the effects of the ground motion matching technique on engineering analyses. A case of twelve ground motions selected and modified using the time domain and the frequency domain matching techniques is presented, and the effects of the modification technique on ground motion characteristics are studied. Seismic geotechnical analyses are also performed for a “soft” site and a “stiff” site. The acceleration response spectrum at the surface is found to be similar for both sets of modified ground motions, with the mean spectrum of the frequency domain matched ground motions being higher for certain frequencies. Maximum shear stress profiles are found to be similar. Newmark-type displacements are generally higher for frequency domain matched ground motions particularly for low yield coefficients, but analyses using time domain matched ground motions may yield greater displacement estimates at higher yield coefficients.
Seed priming: An overview of techniques, mechanisms, and applications

Seed priming is a pre-sowing seed treatment method that has gained significant attention in recent years due to its potential to improve seed germination and early seedling growth. This review article provides a comprehensive overview of seed priming techniques, their underlying mechanisms, and diverse applications in crop production during the recent years. The article introduces the concept of seed priming and its importance in optimizing seed performance, discussing various seed priming techniques including osmotic priming, hydro-priming, hormonal priming, and microbial priming. Each technique is described in detail, outlining the specific procedures and conditions involved, and their advantages and limitations are discussed. The underlying mechanisms of seed priming are explored, elucidating the biochemical, physiological, and molecular changes that occur during seed priming, and the interactions between priming substances and seeds. The article also provides an in-depth analysis of the applications of seed priming, discussing its positive effects on seed germination, seedling vigour, stress tolerance, and its potential for sustainable agriculture. This review article serves as a valuable resource for researchers, scientists and faculties working in the field of seed technological research and progressive farmers who can adopt this technology for enhancing crop yield. This article provides insights into the techniques, mechanisms, and applications of seed priming for optimizing seed performance and enhancing crop productivity in diverse agricultural systems.
A High-Isolation High-Gain MIMO Millimeter-Wave Antenna Array

In this study, a high-isolation and high-gain multiple-input multiple-output (MIMO) antenna array operating at 77 GHz is proposed. It consists of two columns of transmitting (TX) sawtooth linear antenna arrays and two rows of receiving (RX) sawtooth linear antenna arrays with a rectangle ring layout. Each TX and RX linear antenna array contains 12 and 24 antenna units, respectively. The TX and RX antenna units adopt the same topology of a metal cavity fed by a substrate-integrated waveguide (SIW) slot. We introduce several $3\times $ 3 metal pin arrays to increase the gain and isolation of the TX and RX antennas. The metal pin arrays surround the four edges of the metal cavity on the top surface, and adjacent metal cavities share two metal pin arrays. Simulated results reveal that the antenna units exhibit a broad bandwidth of 75.3–81 GHz with S 11 less than −15 dB. The boresight gains of the TX and RX antenna units surpass 12 dBi, and the isolation is around 42 dB at 75–81 GHz. To verify the antenna’s performance, four WR 12 waveguide ports are used to excite two adjacent TX and two adjacent RX antenna units. The measured voltage standing wave ratios (VSWRs) of the TX and RX antenna units are less than 2 at 75–81 GHz, and the measured isolation between adjacent antenna units is more than 37 dB. Moreover, the measured boresight gains and radiation efficiencies of the TX and RX antenna units are 10–11 dBi and 60%–70%, respectively. This high-gain and high-isolation MIMO antenna array can be used for millimeter-wave (MMW) automotive radar.