Miracle-dz/UniFAR-FTUs huggingface dataset:
Physiological and molecular mechanisms of Acacia melanoxylon stem in response to boron deficiency

Boron is an essential micronutrient for plant growth as it participates in cell wall integrity. The growth and development of Acacia melanoxylon stem can be adversely affected by a lack of boron. To explore the mechanism of boron deficiency in A. melanoxylon stem, the changes in morphological attributes, physiological, endogenous hormone levels, and the cell structure and component contents were examined. In addition, the molecular mechanism of shortened internodes resulting from boron deficiency was elucidated through transcriptome analysis. The results showed that boron deficiency resulted in decreased height, shortened internodes, and reduced root length and surface area, corresponding with decreased boron content in the roots, stems, and leaves of A. melanoxylon. In shortened internodes of stems, oxidative damage, and disordered hormone homeostasis were induced, the cell wall was thickened, hemicellulose and water-soluble pectin contents decreased, while the cellulose content increased under boron deficiency. Furthermore, plenty of genes associated with cell wall metabolism and structural components, including GAUTs, CESAs, IRXs, EXPs, TBLs, and XTHs were downregulated under boron deficiency. Alterations of gene expression in hormone signaling pathways comprising IAA, GA, CTK, ET, ABA, and JA were observed under boron deficiency. TFs, homologous to HD1s, NAC10, NAC73, MYB46s, MYB58, and ERF92s were found to interact with genes related to cell wall metabolism, and the structural components were identified. We established a regulatory mechanism network of boron deficiency-induced shortened internodes in A. melanoxylon based on the above results. This research provides a theoretical basis for understanding the response mechanism of woody plants to boron deficiency.
NOMA With Cache-Aided Maritime D2D Communication Networks

This work proposes a novel scheme of non-orthogonal multiple access (NOMA) combined with cache-enabled maritime device-to-device communication (D2D) networks, which aims to improve the performance of ship users located far from the shore and also reduce the data traffic load at the coastal base station. Taking into account the quality-of-service demands of near ship users along with the poor channel condition of far ship users, a joint beamforming design and NOMA power allocation issue is also considered. In addition, three kinds of decoding order at far ship users are also studied to comprehensively characterize the superiority of the proposed systems. Accordingly, the performance of users is then quantified in terms of outage probability and ergodic capacity, where the respective exact expressions are also evaluated in closed form. Qualitative numerical results corroborates the developed theoretical analysis and reveals that: (1) The adoption of the proposed decoding order scheme to improve the outage performance of far ship users is strongly dominated by the trade-off power setting between the coastal base station and the near ship use; and (2) The ergodic rate achieved by the proposed NOMA with cache-aided maritime full-duplex D2D communication network always outperforms those of benchmark schemes, particularly in saving at least 2.5 dB of transmitting power budget for far ship users and achieving double ergodic capacity for near ship users.
Practical Methods of Defective Input Feature Correction to Enable Machine Learning in Power Systems

In this research work, three practical correction methods are proposed to mitigate the impact of defective input features in power system data measurement for machine learning (ML) applications. A well-trained ML tool may become ineffective due to defective input features, which may originate from measurement issues, such as monitor malfunction, cyberattack, communication failure, or others. It is crucial to correct defective input features to enable ML tools with desirable performances. This letter first introduces the mechanism of three correction methods, i.e., statistical-value-based method, minimal-error-based method, and DNN-based adaptive method. Then, the methods are validated via a deep neural network (DNN) case for power system stability enhancement. Validation results demonstrate that the adaptive method achieves the best performance, enabling the well-trained ML tool with a similar accuracy level to the case of no data defects. Although actual measurements may have various data issues challenging ML applications in power systems, the proposed methods show promise for addressing these challenges.
On the processing of freeze-core samples with notes on theimpact of sample size

A method of invertebrates separation, developed for the processing of samples produced by freeze coring in riverbeds, is described. The procedure is demonstrated on the example of 20 samples taken at different depths of bottom in a gravel stream. The samples were elutriated using a device constructed at the Biological Station Lunz (Austria). A detailed description of the device is included. The elutriated material was obtained in two size fractions, which were divided into equal subsamples in a laboratory sample divider. These subsamples were examined. Sample volumes after each step of its processing are given. The resulting invertebrate densities showed no effect of the sample size, but reflected the natural situation, i.e. the strong influence of bottom depth stratification.
Antibiotic Use for Sepsis in Hospitalized Neonates in Botswana: Factors Associated with Guideline-Divergent Prescribing

In low- and middle-income countries, where antimicrobial access may be erratic and neonatal sepsis pathogens are frequently multidrug-resistant, empiric antibiotic prescribing practices may diverge from the World Health Organization (WHO) guidelines. This study examined antibiotic prescribing for neonatal sepsis at a tertiary referral hospital neonatal unit in Gaborone, Botswana, using data from a prospective cohort of 467 neonates. We reviewed antibiotic prescriptions for the first episode of suspected sepsis, categorized as early-onset (EOS, days 0–3) or late-onset (LOS, >3 days). The WHO prescribing guidelines were used to determine whether antibiotics were “guideline-synchronous” or “guideline-divergent”. Logistic regression models examined independent associations between the time of neonatal sepsis onset and estimated gestational age (EGA) with guideline-divergent antibiotic use. The majority (325/470, 69%) were prescribed one or more antibiotics, and 31 (10%) received guideline-divergent antibiotics. Risk factors for guideline-divergent prescribing included neonates with LOS, compared to EOS (aOR [95% CI]: 4.89 (1.81, 12.57)). Prematurity was a risk factor for guideline-divergent prescribing. Every 1-week decrease in EGA resulted in 11% increased odds of guideline-divergent antibiotics (OR [95% CI]: 0.89 (0.81, 0.97)). Premature infants with LOS had higher odds of guideline-divergent prescribing. Studies are needed to define the causes of this differential rate of guideline-divergent prescribing to guide future interventions.
Eddy-feature phytoplankton bloom induced by a tropical cyclone in the South China Sea

This is the first report on an eddy-feature phytoplankton bloom induced by a tropical cyclone (the spiral structure of the bloom was coincident with that of a cold eddy) in the South China Sea (SCS). Applying satellite data, this report can furnish fresh evidence of the relationship between the bloom and the cold eddy. Tropical cyclone Linfa passed over the northern SCS from 16 to 21 June 2009. While it looped over for 2 days (from 17 to 19 June), a cold eddy, which lasted for 11 days in the looping area, was observed on 18 June. Subsequently, an eddy-feature phytoplankton bloom (central location: 18° N, 117.5° E) was detected on 22 June and it remained for 17 days. The character of both the cold eddy and the bloom was rotating counterclockwise, with two arms. High Ekman pumping velocity (>4.5 × 10−4 m s−1) was estimated during the passage of Linfa (from 17 to 19 June). The monthly climatology of the mixed layer depth was about 20 m in June, and the maximum Ekman layer depth was about 346 m after Linfa. The analysis indicated that Linfa may have induced the cold eddy, where it looped around. With upwelling and entrainment, the eddy potentially provided nutrients to the bloom in the surface water. The results suggested that the sea surface current changes that Linfa induced had caused the bloom in an eddy feature. Tropical cyclones appear frequently in the SCS, which may affect the activities of mesoscale eddies in this area.
Using conditional generative adversarial networks for the prediction of stresses in an adhesive composite

Fiber reinforced adhesives are an integral part of wind energy turbine blades and play an important role in evaluation of structural integrity of the blades. Digitization of manufacturing processes and demand for fast and efficient stress analysis argues for the application of deep learning. As a step forward in using deep learning to predict structural properties in composite, the work would explore the applicability of deep learning methods to 2D fiber reinforced composite. In this work, a composite adhesive used in wind energy turbine blades is used as an illustrative example. The composite adhesive when viewed using a CT scan machine shows a heterogeneous structure with two phases, a softer matrix phase and a much stiff fiber phase. For this purpose, several CT scan images of the fiber adhesive composite are used. The images on subsequent FEA analysis serves as the training data for the deep learning framework. Once trained, the neural network would be able to predict the stress distributions in the structure. We apply conditional adversarial networks as general purpose solutions to problems focused on stress prediction, which could be thought of as an image‐to‐image translation from the microstructure domain to the stress domain. These networks in addition to learning the mapping from input image to output image also learn a loss function to train for this mapping. This makes the method more generic because otherwise different loss formulations for different problems have to be formulated.
Relations between infant feeding practices and anthropometrical traits in Latvia

Background. Breast milk is the only true natural product for the feeding of infants. World Health Organization suggests breastfeeding exclusively during the first six months of life. The new standards are based on the breastfed child as the norm for growth and development. Thus, the aim of our research was to find the influence of the feeding mode on anthropometrical parameters of the newborns and infants in Latvia. Material and methods. The longitudinal study method was used in the Riga Maternity Hospital from December 2004 until January 2007. 503 healthy, term newborns were measured (head and chest circumference, height, weight) during visit. Ponderal index and body mass index were calculated. Detailed information on feeding patterns was collected from parents. Data were processed by the means of SPSS/PCT software. Results. BMI for 12-month-old boys correlated with PI of newborn boys statistically significantly. For girls, the correlation was not found. A statistically significant difference was observed between breastfed and formula-fed boy-babies in weight, BMI and head circumference at one month. At 3 months, there was no difference seen between anthropometric measurements of breastfed or formula-fed newborns. At 6 months, the difference appeared for girls in height and for boys in BMI depending on the way of feeding. At 9 months, height differed between breastfed boys and formula-fed boys. At 12 months there was no difference observed between the genders. Conclusions. Evidence shows that breastfed and formula-fed infants grow differently, although generally there is no statistically significant influence of the feeding mode on the anthropometry in the first year of life.
Hyperforin modulates MAPK/CCL11 signaling to reduce the inflammatory response of nasal mucosal epithelial cells caused by allergic rhinitis by targeting BCL6

Hyperforin is a type of bicyclic tetraketone with four isoprenoid chains extracted from Hypericum perforatum L. that has multiple biological activities such as anti-diabetes, antitumor and anti-inflammation. However, the role and potential mechanism of hyperforin in allergic rhinitis (AR) remains to be clarified. In the present study, cell viability was analyzed using Cell Counting Kit-8 assay, while inflammation was detected using ELISA and reverse transcription-quantitative PCR. Epithelial cell barrier damage was measured using western blotting and immunofluorescence staining. The expression levels of B-cell lymphoma 6 (BCL6) and the p38 MAPK/C-C motif chemokine 11 (CCL11) pathway were detected using western blotting. In addition, the association between hyperforin and BCL6 was analyzed by SWISS TargetPrediction, DisGeNET, Gene Ontology and Pathway databases. Molecular docking was performed using AutoDockTools 1.5. 6 and Discovery Studio 4.5 software. The data demonstrated that there were 16 interlinking target genes of hyperforin with AR, in which BCL6 was the most relevant one with hyperforin in AR. The binding between hyperforin and BCL6 was verified, and molecular docking was modeled. The results revealed that hyperforin inhibited IL-13-induced nasal epithelial inflammatory cytokine release and repressed the damage to the cellular barrier from IL-13 stimulation. In addition, hyperforin activated BCL6 expression and significantly suppressed the expression of p38 MAPK/CCL11. Silencing of BCL6 reversed the effects of hyperforin on IL-13-induced inflammation and barrier damage. In summary, the present results revealed that hyperforin suppressed IL-13-induced nasal epithelial cell inflammation and barrier damage by targeting BCL6/p38 MAPK/CCL11, which may provide promising therapeutic targets for AR.
Strengthening Resilient Built Environments through Human Social Capital: A Path to Post-COVID-19 Recovery

There are strong indications that the built environment has had a great influence on the course of the COVID-19 pandemic and the post-disaster recovery. The COVID-19 pandemic has adversely affected both human and global development, while efforts to combat this menace call for an integrated human social capital index. This research seeks to enhance understanding of how the built environment can be enhanced through resilience against the backdrop of the COVID-19 pandemic. This study aims to investigate the impact of a resilient built environment on increasing resilience in the aftermath of the COVID-19 pandemic in Nigeria. The quantitative studies test the impact of four built environment resilience indices (built environment capital, disaster management indices, awareness of the COVID-19 pandemic, and built environment adaptive strategies) on human social capital and COVID-19 pandemic indices. This study reveals the role of human social capital in achieving a resilient built environment in the wake of the COVID-19 pandemic in Nigeria. Built environment capital, disaster management indices, and awareness of COVID-19 also indirectly affect the COVID-19 pandemic indices through human social capital. This study’s implications are useful for post-COVID-19 recovery, which is important for future planning of the built environment in Nigeria.
A Pyramid Deep Feature Extraction Model for the Automatic Classification of Upper Extremity Fractures

The musculoskeletal system plays a crucial role in our daily lives, and the accurate diagnosis of musculoskeletal issues is essential for providing effective healthcare. However, the classification of musculoskeletal system radiographs is a complex task, requiring both accuracy and efficiency. This study addresses this challenge by introducing and evaluating a pyramid deep feature extraction model for the automatic classification of musculoskeletal system radiographs. The primary goal of this research is to develop a reliable and efficient solution to classify different upper extremity regions in musculoskeletal radiographs. To achieve this goal, we conducted an end-to-end training process using a pre-trained EfficientNet B0 convolutional neural network (CNN) model. This model was trained on a dataset of radiographic images that were divided into patches of various sizes, including 224 × 224, 112 × 112, 56 × 56, and 28 × 28. From the trained CNN model, we extracted a total of 85,000 features. These features were subsequently subjected to selection using the neighborhood component analysis (NCA) feature selection algorithm and then classified using a support vector machine (SVM). The results of our experiments are highly promising. The proposed model successfully classified various upper extremity regions with high accuracy rates: 92.04% for the elbow region, 91.19% for the finger region, 92.11% for the forearm region, 91.34% for the hand region, 91.35% for the humerus region, 89.49% for the shoulder region, and 92.63% for the wrist region. These results demonstrate the effectiveness of our deep feature extraction model as a potential auxiliary tool in the automatic analysis of musculoskeletal system radiographs. By automating the classification of musculoskeletal radiographs, our model has the potential to significantly accelerate clinical diagnostic processes and provide more precise results. This advancement in medical imaging technology can ultimately lead to better healthcare services for patients. However, future studies are crucial to further refine and test the model for practical clinical applications, ensuring that it integrates seamlessly into medical diagnosis and treatment processes, thus improving the overall quality of healthcare services.
Paul Bishop: recalling an academic life

ABSTRACT The rationale for this theme section of the journal on Paul Bishop, eminent physical geographer, earth scientist and researcher of human-environment relations, is explained. Paul Bishop (1949–2022), a long-time Professor of Geography at the University of Glasgow, was – indeed, still very much is – a major figure at the cutting-edge of research, scholarship, education and applications in the fields indicated. His academic work has been global in its focus – spanning four continents (Africa, Asia, Australia, and Europe), including a substantial body of inquiries centred in Scotland – and has encompassed the time-spans of planetary history (and the geotectonics of long-term landscape change), human history (and the dynamics of environmental influence on human settlement, resource use and cultural practice) and human life-spans (including the histories and heritages of locality). Paul died, too soon, early in 2022. An event to commemorate his academic life and work was held in September 2022, and several contributions to that event have now been written through in substantially revised form for the present theme section. The current piece introduces this theme section, providing a sketch of Paul’s biography, including notes on his time in Glasgow, and cross-referencing with the articles that follow. Appended is also a near-comprehensive bibliography of Paul’s published outputs.
A Phenological Comparison of NDVI Products within Contiguous United States

This study computed the Normalized Difference Vegetation Index (NDVI) products derived from NOAA AVHRR, MODIS, and SPOT VGT sensors. NDVI products from different instruments vary in spatial resolution, temporal coverage and spectral range. As a result, multisensor NDVI products are rarely used in a single phenological study. In order to evaluate the difference and similarity of NDVI records from the three sensors, I used the EPA Eco-region framework to determine the average annual Start of Season (SOS) and End of Season (EOS) of Contiguous United States, and analyzed dates among datasets. In addition, I created 1127 sample points within the study area, and compared the relationship between SOS/EOS based on land cover. The objectives of this thesis are to: 1) compare multi-sensor NDVI data using phenological models, 2) define a strategy to merge multi-sensor NDVI products to a single phenological product without direct NDVI conversion. The spatial and statistical analysis revealed that the Land Surface Phenology (LSP) measurements retrieved from NDVI time series from different sensors follow linear and positive relationships where compared by either ecoregion or sample point. The historical record of AVHRR combined with the modern MODIS and SPOT data provides a critical and reliable perspective on phenological patterns in Contiguous United States area. The success of this study will help LSP by providing understanding of how different instruments can be combined to generate multi-sensor NDVI data for phenology.
Assessing of Wire Bonding on Gold Plated Pads on Epoxy Mold Compound Wafer

Epoxy mold is one of the materials being used in Fan-out Wafer Level Packaging (FOWLP). It is challenging to wire bond on epoxy mold compound wafer which the mold materials property is less stiff at high temperature if compare to silicon wafer. In this study, it has been using the reference bonding parameters from blanket Aluminium silicon chip to bond on gold pad on epoxy mold compound wafer. The reference bonding parameters could not be repeated on the epoxy mold compound wafer especially for copper wire. Nonstick on pad were seen. The bonding parameters required to stick the copper wire on the bond pad is far away different from the reference parameter. The wire pull readings were collected from the epoxy mold compound wafer after the wires were successfully bonded continuously. Wire pull readings of average 5.427gf was achieved for Au wire and average of 5.99gf for palladium-coated copper core wire. After 150DegC baking for 96hours, the wire pull average is 5.24gf and ball shear average is 21.65gf. Average wire pull of 6.32g and average ball shear of 29.69gf was achieved after 175DegC baking for 96 hours. In generally, all wire pulls >3gf.