Miracle-dz/UniFAR-FTUs huggingface dataset:
Mitochondrial DNA D-Loop Polymorphisms among the Galla Goats Reveals Multiple Maternal Origins with Implication on the Functional Diversity of the HSP70 Gene

Despite much attention given to the history of goat evolution in Kenya, information on the origin, demographic history, dispersal route, and genetic diversity of Galla goats remains unclear. Here, we examined the genetic background, diversity, demographic history, and population genetic variation of Galla goats using mtDNA D-loop and HSP70 single-nucleotide polymorphism markers. The results revealed 90 segregating sites and 68 haplotypes in a 600-bp mtDNA D-loop sequence. The overall mean mitochondrial haplotype diversity was 0.993. The haplotype diversities ranged between 0.8939 ± 0.0777 and 1.0000 ± 0.0221 in all populations supporting high genetic diversity. Mitochondrial phylogenetic analysis revealed three Galla goat haplogroups (A, G, and D), supporting multiple maternal ancestries, of which haplogroup A was the most predominant. Analysis of molecular variance (AMOVA) showed considerable variation within populations at 94.39%, evidence of high genetic diversity. Bimodal mismatch distribution patterns were observed while most populations recorded negative results for Tajima and Fu's Fs neutrality tests supporting population expansion. Genetic variation among populations was also confirmed using HSP70 gene fragment sequences, where six polymorphic sites which defined 21 haplotypes were discovered. Analysis of molecular variance revealed a significant FST index value of 0.134 and a high FIS index value of 0.746, an indication of inbreeding. This information will pave the way for conservation strategies and informed breeding to improve Galla or other goat breeds for climate-smart agriculture.
GCFormer: Granger Causality based Attention Mechanism for Multivariate Time Series Anomaly Detection

Multivariate time series anomaly detection, crucial for ensuring the safety of real-world systems, primarily focuses on extracting characteristics from time series under normal condition, and identifying potential anomalies throughout the evaluation process. Recent studies have achieved fruitful progress through mining the spatio-temporal relationships from multivariate time series, however, these approaches mostly neglect the latency among series which could lead to higher false alarm. Granger causality presents a promising solution to extract these inherent time-lagged relationships. Nonetheless, the intricate and dynamic relationships among numerous time series in real-world systems surpass the ability of linear Granger causality. To address this, we extend the linear Granger causality and propose the Granger Causal Former (GCFormer), a novel approach that leverages attention mechanisms to learn the inherent causal spatio-temporal relationships between historical and current timestamps across multiple time series. Specifically, GCFormer develops a Spatio-Mask (SM) to select the top-k most relevant series and a Temporal-Mask (TM) to concentrate attention on more recent historical timestamps. Moreover, to mitigate overfitting and ensure a smooth training process, GCFormer introduces an adjust top-k method and a TM penalty term. We evaluated GCFormer on four real-world benchmark datasets, demonstrating its superior performance over state-of-the-art approaches. Further analysis and a case study highlight the model’s novelty and interpretability.
Evapotranspiration and Above Ground Biomass of Acer rubrum from Liners to 8 m Tall Trees

To meet minimum spring flows, water management districts in Florida sought to make both agriculture and urban landscapes water efficient, which includes tree farms. Acer rubrum L. (red maple) trees are endemic to Central Florida and native to the eastern portion of the United States. Urban and suburban expansion has increased use of A. rubrum in landscape plantings and their production in nurseries. In Florida A. rubrum is planted around stormwater retention areas, but also in urban landscapes. To provide a basis for irrigation allocations both during production and in landscapes, daily actual evapotranspiration (ETA) for three red maple trees were measured with weighing lysimeters, beginning with rooted cuttings and continuing until trees averaged 8 m in height. Empirical models were derived to calculate ETA based on crown horizontal projected area or trunk caliper, adjusted daily by changes in reference evapotranspiration (ETo). Water use efficiency, based on carbon sequestered in above ground wood mass, was calculated at the end of five growing seasons. Average ETA to produce these maples was 29,107 L over 4.75 years, with an average water use efficiency of 1 kg dry mass of wood per 709 L of water lost by transpiration.
Mitigating Multisource Biases in Graph Neural Networks via Real Counterfactual Samples

Graph neural networks (GNNs) have demonstrated remarkable success in various real-world applications. However, they often inadvertently inherit and amplify existing societal bias. Most existing approaches for fair GNNs tackle this bias issue by assuming that discrimination solely arises from sensitive attributes such as race or gender, while disregarding the prevalent labeling bias that exists in real-world scenarios. Additionally, prior works attempting to address label bias through counterfactual fairness often fail to consider the veracity of counterfactual samples. This paper aims to bridge these gaps by investigating the identification of authentic counterfactual samples within complex graph structures and proposing strategies for mitigating labeling bias guided by causal analysis. Our proposed learning model, known as Real Fair Counterfactual GNNs (RFCGNN), also goes a step further by considering the learning disparity resulting from imbalanced data distribution across different demographic groups in the graph. Extensive experiments conducted on three real-world datasets and a synthetic dataset demonstrate the effectiveness and practicality of the proposed RFCGNN approach.
A Photomemristor With Temporal Dynamics for In-Sensor Reservoir Computing

In-sensor reservoir computing has recently gained considerable attention due to its highly efficient training process and advanced integration of sensing, storage, and processing functionalities. These advancements greatly enhance the machine vision capabilities by reducing data latency and energy overheads. However, the development of a highly efficient and low-cost in-sensor reservoir computing system remains a challenging task, primarily due to the lack of suitable materials and processes. In this letter, we present a simple ITO/NiOx/Au two-terminal photomemristor fabricated using the full physical vapor deposition (PVD) technique at room temperature without further treatment. This photomemristor leverages light-triggered dynamics to map input signals into a high-dimensional space and extract hidden information. As a proof of concept, we demonstrate an in-sensor reservoir computing system based on the photomemristor. Experimental results indicate that the system exhibits an impressive accuracy of 90.88% for image classification task and a low normalized root mean squared error (NRMSE) of 0.0082 for time-series prediction task. This work has complemented the wide spectrum of applications of NiOx in in-sensor neuromorphic computing.
Unplanned pregnancy and risk of peripartum depression: a prospective cohort study in Saudi pregnant women attending antenatal care clinic

Background: Few studies have been conducted on unintended pregnancies and peripartum depression in Saudi Arabia. This study aimed to evaluate the relationship between unplanned pregnancies and peripartum depression among pregnant women in Jeddah, Saudi Arabia. Methods: This prospective cohort study included pregnant women attending an antenatal care clinic in 2021. The London Measure of Unplanned Pregnancy was used to assess the prevalence of unplanned pregnancy, and the Edinburgh Postnatal Depression Scale (EPDS) was used to assess antenatal and postnatal depression. Results: A total of 236 participants were included, of which 25.8% had unplanned pregnancies, 36.0% had ambivalent pregnancies, and 38.1% had planned pregnancies. EPDS results revealed that 77.5% and 73.35% of the females were negative for antenatal and postnatal depression, respectively. A history of stressful events (P=0.001), husband (P=0.020), and family support (P=0.007) was significantly associated with antenatal EPDS score, whereas age (P=0.005), type of delivery (P=0.019), and family support (P=0.031) were significantly associated with the postnatal score. Conclusion: Unplanned pregnancies may affect the perinatal mental health of women. We demonstrated the importance of family or husbands’ support for women with perinatal depression. In addition, our research showed that pregnancy at an early age is a risk factor for postnatal depression. Therefore, these women should be closely monitored not only during their pregnancy but also during the first postpartum year.
Exploring Contagion Effects Between Commodity and Securities Markets During the Post-COVID-19 Pandemic and the Russia-Ukraine Conflict using a Dynamic Conditional Correlation Approach

This paper investigates the higher-order moment risk of commodity and securities markets (agricultural, industrial metals, and precious metals) before and during the COVID-19 pandemic and following the outbreak of the Russia-Ukraine conflict, by using the dynamic conditional correlation (DCC) based on the multivariate GARCH model. Further, this paper depicts the dynamic overall and pairwise correlations between assets. The empirical results demonstrate that all return series exhibit high volatility persistence at values above 0.90. Gold exhibited a negative correlation with the Dow Jones Industrial Average (DJI) before COVID-19, suggesting it as a safe-haven asset. However, this changed after the outbreak, and there was a shift in correlation during the Ukrainian-Russian war. Relying solely on gold for portfolio risk reduction may not be effective. The correlation patterns between DJI and the assets such as gold, corn, silver, Palladium, and Bitcoin fluctuated during and after the pandemic and invasion. Correlations between the US 10-year bond yield (TNX) and DJI, as well as TNX and Palladium, decreased from mid-2020 to late 2022. Moreover, wheat-corn and wheat-oil correlations dropped, but increased after the invasion. Furthermore, oil exhibited varying correlations with copper, corn, silver, and soy. Palladium showed higher correlations with gold and silver post-COVID-19.
The Impact of Digital Economy Industry Development on Manufacturing Innovation Path Driven by Big Data

To provide strong support for the digital development for the path innovation of China's manufacturing industry, this study examines the expansion of the digital economy driven by Big Data based on the essential elements of production in the age of the digital economy. With a focus on the significance of the development of the digital economy to the upgrading of the economic growth path, this study divides the entire country into three regions—the eastern, central, and western—and chooses the pertinent statistical data for 30 provinces, municipalities, and autonomous territories from 2014 to 2020. The digital economy's contribution to stimulating innovation and upgrading the manufacturing system is further understood through empirical research. The research claims that the digital economy in the eastern region has grown from a level of development of 1.725--2.682. The central area increased from 1.002 to 1.711. From 0.707 to 1.335, the westward movement increased. Digital industrialization increased in the eastern region from 0.398 to 0.671. The central area has increased from 0.096 to 0.221, and the westward movement has risen from 0.092 to 1.188. In general, there are differences in China's manufacturing industry's level of high-quality development. The expansion of China's industrial sector, which is already at a high level, might be significantly advanced by the growth of the digital economy. This study explores how the digital economy has affected the modernization of the manufacturing structure. It may be helpful in promoting innovation in the manufacturing sector in the Big Data era.
Concomitant inhibition of PI3K/mTOR signaling pathways boosts antiproliferative effects of lanreotide in bronchopulmonary neuroendocrine tumor cells

Introduction: Somatostatin analogues (SSAs) are commonly used in the treatment of hormone hypersecretion in neuroendocrine tumors (NETs), however the extent to which they inhibit proliferation is much discussed. Objective: We studied the antiproliferative effects of novel SSA lanreotide in bronchopulmonary NETs (BP-NETs). We focused on assessing whether pretreating cells with inhibitors for phosphatidylinositol 3-kinase (PI3K) and mammalian target for rapamycin (mTOR) could enhance the antiproliferative effects of lanreotide. Methods: BP-NET cell lines NCI-H720 and NCI-H727 were treated with PI3K inhibitor BYL719 (alpelisib), mTOR inhibitor everolimus and SSA lanreotide to determine the effect on NET differentiation markers, cell survival, proliferation and alterations in cancer-associated pathways. NT-3 cells, previously reported to express somatostatin receptors (SSTRs) natively, were used as control for SSTR expression. Results: SSTR2 was upregulated in NCI-H720 and NT-3 cells upon treatment with BYL719. Additionally, combination treatment consisting of BYL719 and everolimus plus lanreotide tested in NCI-H720 and NCI-H727 led to diminished cell proliferation in a dose-dependent manner. Production of proteins activating cell death mechanisms was also induced. Notably, a multiplexed gene expression analysis performed on NCI-H720 revealed that BYL719 plus lanreotide had a stronger effect on the downregulation of mitogens than lanreotide alone. Discussion/Conclusion: We report a widespread analysis of changes in BP-NET cell lines at the genetic/protein expression level in response to combination of lanreotide with pretreatment consisting of BYL719 and everolimus. Interestingly, SSTR expression reinduction could be exploited in therapeutic and diagnostic applications. The overall results of this study support the evaluation of combination-based therapies using lanreotide in preclinical studies to further increase its antiproliferative effect and ultimately facilitate its use in high-grade tumors.
Integrating First-Principles-Based Non-Fourier Thermal Analysis Into Nanoscale Device Simulation

Thermal analysis is an essential component of semiconductor device simulation for device design and thermal management. The prevalent approach of device thermal analysis uses Fourier-law-based heat diffusion equation (HDE). However, Fourier’s law is known to fail when the characteristic length is smaller than the phonon mean free path (MFP), resulting in a significant underestimation of local temperature rise. In this study, we implement non-Fourier thermal analysis on nanoscale devices using the first-principles-based nongray Boltzmann transport equation (BTE). A 3-D structure of nanoscale silicon-based FinFET is adopted as a case study. Non-Fourier effects are considered in both thermal generation and transport processes. In the thermal generation process, we use first-principles methods to investigate the selective electron–phonon energy transfer process and obtain the mode-level phonon generation rates. In the thermal transport process, we solve the nongray phonon BTE to determine the temperature distribution of the devices, in which the material-dependent phonon properties are calculated by first-principles methods. Through comparisons with HDE and previous models, we demonstrate the considerable impact of non-Fourier effects on temperature rise and electrical performance, highlighting the significance of incorporating non-Fourier thermal analysis into nanoscale device simulations. Our method also shows good agreement with experimental temperature measurements, which can be readily extended to a variety of devices and operating conditions.
Organic residues: origins, analysis and scope – an overview for the archaeological ceramicist

The capacity of ceramic sherds to retain and preserve organic residues is now widely recognised and residue analysis is increasingly specified as a component of ceramic assemblage studies. This article aims to introduce the range of organic residues that are typically encountered in association with archaeological ceramics, highlighting factors that can affect their preservation and outlining the most usual approaches to analysis as well as considering the types of information that can be reasonably inferred from such data.
A State of Art of Cardiovascular Diseases Using Machine Learning Algorithms

One of the primary causes of death on the globe is heart disease. It can be accurately and early predicted, which can help to avoid negative effects. The prediction of cardiac disease has made substantial use of machine learning techniques in the last decade. The main objective of this paper is to give an overview of current advancements in the field of heart disease prediction using machine learning approaches over the previous 17 years. The paper addresses the various machine learning algorithms that were employed in 40 research investigations for the prediction of heart disease and their performance evaluation measures. The paper concludes by discussing the challenges and future directions using machine learning techniques with the corresponding recommendations.
Application of hydrothermal alteration mineral mapping using airborne hyperspectral remote sensing: data taken in the Baixianishan region of Gansu Province as an example

Hyperspectral remote sensing, featured by integrated images and spectra, is now a frontier of the remote sensing. Using meticulous spectra, hyperspectral remote sensing technology can depict spectral features of objects in detail and are capable of identifying objects rather than simply discriminating them. This study took the Baixianishan region in Gansu Province as an example, and CASI/SASI airborne hyperspectral data were utilized to extract and map alteration minerals by MTMF mapping method. Six hydrothermal alteration minerals were mapped, which contained limonite, sericite and epidote. In addition, we analyzed the types, combinations and distribution of the alteration minerals and divided three stages of hydrothermal activity. It is considered that the favorable ore-forming elements for gold deposits are middle Hercynian porphyraceous granite, fracture and veined distribution of sericite and limonite. The application of CASI/SASI airborne hyperspectral remote sensing data in the Baixianishan area has achieved ideal results, indicative of their wide application potential in the geological research.
Validity and reliability of the Portuguese version of the modi�ed Migraine Disability Assessment

Background Methods Studied population consisted of adult migraine patients attending a headache outpatient clinic. Reliability was assessed by internal consistency and reproducibility in a 3-week test-retest. Content validity was evaluated by two expert panels. Construct validity was tested by comparing mMIDAS-P index in socioeconomic and clinical patient groups and scale unidimensionality was evidenced by factor analysis. Criterion validity was tested using EQ-5D-5L and HADS. demonstrated reproductive good
A non-canonical Aux/IAA gene MsIAA32 regulates peltate glandular trichome development in spearmint

Phytohormone auxin controls various aspects of plant growth and development. The typical auxin signalling involves the degradation of canonical Aux/IAA proteins upon auxin perception releasing the auxin response factors (ARF) to activate auxin-regulated gene expression. Extensive research has been pursued in deciphering the role of canonical Aux/IAAs, however, the function of non-canonical Aux/IAA genes remains elusive. Here we identified a non-canonical Aux/IAA gene, MsIAA32 from spearmint (Mentha spicata), which lacks the TIR1-binding domain and shows its involvement in the development of peltate glandular trichomes (PGT), which are the sites for production and storage of commercially important essential oils. Using yeast two-hybrid studies, two canonical Aux/IAAs, MsIAA3, MsIAA4 and an ARF, MsARF3 were identified as the preferred binding partners of MsIAA32. Expression of a R2R3-MYB gene MsMYB36 and a cyclin gene MsCycB2-4 was altered in MsIAA32 suppressed plants indicating that these genes are possible downstream targets of MsIAA32 mediated signalling. Ectopic expression of MsIAA32 in Arabidopsis affected non-glandular trichome formation along with other auxin related developmental traits. Our findings establish the role of non-canonical Aux/IAA mediated auxin signalling in PGT development and reveal species-specific functionalization of Aux/IAAs.
Treatment of Tendon Injuries in the Servicemember Population across the Spectrum of Pathology: From Exosomes to Bioinductive Scaffolds

Tendon injuries in military servicemembers are one of the most commonly treated nonbattle musculoskeletal injuries (NBMSKIs). Commonly the result of demanding physical training, repetitive loading, and frequent exposures to austere conditions, tendon injuries represent a conspicuous threat to operational readiness. Tendon healing involves a complex sequence between stages of inflammation, proliferation, and remodeling cycles, but the regenerated tissue can be biomechanically inferior to the native tendon. Chemical and mechanical signaling pathways aid tendon healing by employing growth factors, cytokines, and inflammatory responses. Exosome-based therapy, particularly using adipose-derived stem cells (ASCs), offers a prominent cell-free treatment, promoting tendon repair and altering mRNA expression. However, each of these approaches is not without limitations. Future advances in tendon tissue engineering involving magnetic stimulation and gene therapy offer non-invasive, targeted approaches for improved tissue engineering. Ongoing research aims to translate these therapies into effective clinical solutions capable of maximizing operational readiness and warfighter lethality.
Progress, Challenges and Opportunities of NMR and XL-MS for Cellular Structural Biology

The validity of protein structures and interactions, whether determined under ideal laboratory conditions or predicted by AI tools such as Alphafold2, to precisely reflect those found in living cells remains to be examined. Moreover, understanding the changes in protein structures and interactions in response to stimuli within living cells, under both normal and disease conditions, is key to grasping proteins’ functionality and cellular processes. Nevertheless, achieving high-resolution identification of these protein structures and interactions within living cells presents a technical challenge. In this Perspective, we summarize the recent advancements in in-cell nuclear magnetic resonance (NMR) and in vivo cross-linking mass spectrometry (XL-MS) for studying protein structures and interactions within a cellular context. Additionally, we discuss the challenges, opportunities, and potential benefits of integrating in-cell NMR and in vivo XL-MS in future research to offer an exhaustive approach to studying proteins in their natural habitat.