Miracle-dz/UniFAR-FTUs huggingface dataset:
PtrABR1 Increases Tolerance to Drought Stress by Enhancing Lateral Root Formation in Populus trichocarpa

Roots are the main organ for water uptake and the earliest part of a plant’s response to drought, making them of great importance to our understanding of the root system’s response to drought. However, little is known about the underlying molecular mechanisms that control root responses to drought stress. Here, we identified and functionally characterized the AP2/ERF family transcription factor (TF) PtrABR1 and the upstream target gene zinc-finger protein TF PtrYY1, which respond to drought stress by promoting the growth and development of lateral roots in Populus trichocarpa. A root-specific induction of PtrABR1 under drought stress was explored. The overexpression of PtrABR1 (PtrABR1-OE) promoted root growth and development, thereby increasing tolerance to drought stress. In addition, PtrYY1 is directly bound to the promoter of PtrABR1 under drought stress, and the overexpression of PtrYY1 (PtrYY1-OE) promoted lateral root growth and development and increased tolerance to drought stress. An RNA-seq analysis of PtrABR1-OE with wild-type (WT) poplar identified PtrGH3.6 and PtrPP2C44, which share the same pattern of expression changes as PtrABR1. A qRT-PCR and cis-element analysis further suggested that PtrGH3.6 and PtrPP2C44 may act as potential downstream targets of PtrABR1 genes in the root response pathway to drought stress. In conclusion, these results reveal a novel drought regulatory pathway in which PtrABR1 regulates the network through the upstream target gene PtrYY1 and the potential downstream target genes PtrGH3.6 and PtrPP2C44, thereby promoting root growth and development and improving tolerance to drought stress.
Enhancement of Radiation Therapy through Blockade of the Immune Checkpoint, V-domain Ig Suppressor of T Cell Activation (VISTA), in Melanoma and Adenocarcinoma Murine Models

Radiation therapy (RT) has recently demonstrated promise at stimulating an enhanced immune response. The recent success of immunotherapies, such as checkpoint inhibitors, CART cells, and other immune modulators, affords new opportunities for combination with radiation. The aim of this study is to evaluate whether and to what extent blockade of VISTA, an immune checkpoint, can potentiate the tumor control ability of radiation therapy. Our study is novel in that it is the first comparison of two VISTA-blocking methods (antibody inhibition and genetic knockout) in combination with RT. VISTA was blocked either through genetic knockout (KO) or an inhibitory antibody and combined with RT in two syngeneic murine flank tumor models (B16 and MC38). Selected mRNA, immune cell infiltration, and tumor growth delay were used to assess the biological effects. When combined with a single 15Gy radiation dose, VISTA blockade via genetic knockout in the B16 model and via anti-VISTA antibodies in the MC38 model significantly improved survival compared to RT alone by an average of 5.5 days and 6.3 days, respectively (p < 0.05). The gene expression data suggest that the mechanism behind the enhanced tumor control is primarily a result of increased apoptosis and immune-mediated cytotoxicity. VISTA blockade significantly enhances the anti-tumor effect of a single dose of 15Gy radiation through increased expression and stimulation of cell-mediated apoptosis pathways. These results suggest that VISTA is a biologically relevant immune promoter that has the potential to enhance the efficacy of a large single radiation dose in a synergic manner.
Upscaling Wetland Methane Emissions From the FLUXNET‐CH4 Eddy Covariance Network (UpCH4 v1.0): Model Development, Network Assessment, and Budget Comparison

Wetlands are responsible for 20%–31% of global methane (CH4) emissions and account for a large source of uncertainty in the global CH4 budget. Data‐driven upscaling of CH4 fluxes from eddy covariance measurements can provide new and independent bottom‐up estimates of wetland CH4 emissions. Here, we develop a six‐predictor random forest upscaling model (UpCH4), trained on 119 site‐years of eddy covariance CH4 flux data from 43 freshwater wetland sites in the FLUXNET‐CH4 Community Product. Network patterns in site‐level annual means and mean seasonal cycles of CH4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash‐Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH4 emissions of 146 ± 43 TgCH4 y−1 for 2001–2018 which agrees closely with current bottom‐up land surface models (102–181 TgCH4 y−1) and overlaps with top‐down atmospheric inversion models (155–200 TgCH4 y−1). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH4 fluxes has the potential to produce realistic extra‐tropical wetland CH4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid‐to‐arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ORNLDAAC/2253).
Advancing Fault Detection in HVAC Systems: Unifying Gramian Angular Field and 2D Deep Convolutional Neural Networks for Enhanced Performance

Efficiency and comfort in buildings rely on on well-functioning HVAC systems. However, system faults can compromise performance. Modern data-driven fault detection methods, considering diverse techniques, encounter challenges in understanding intricate interactions and adapting to dynamic conditions present in HVAC systems during occupancy periods. Implementing fault detection during active operation, which aligns with real-world scenarios and captures dynamic interactions and environmental changes, is considered highly valuable. To address this, utilizing the dynamic simulation system HVAC SIMulation PLUS (HVACSIM+), an HVAC fault model was developed using 194 sensor signals from each HVAC component within a single-story, four-room building. The advanced HVAC fault detection framework, leveraging simulated HVAC operational scenarios with the Gramian angular field (GAF) and two-dimensional convolutional neural networks (GAF-2DCNNs), offers a robust and proactive solution. By utilizing the GAF capacity to convert time-series sensor data into informative 2D images, integrated with 2DCNN for automated feature extraction, hidden temporal relationships within 1D signals are captured. After training on nine significant HVAC faults and normal conditions during occupancy, the effectiveness of the proposed GAF-2DCNN is evaluated through comparisons with support vector machine (SVM), random forest (RF), and hybrid RF-SVM, one-dimensional convolutional neural networks (1D-CNNs). The results demonstrates an impressive overall accuracy of 97%, accompanied by precision, recall, and F1 scores that surpass 90% for individual HVAC faults. Through the introduction of the unified approach that integrates HVACSIM+ simulated data and GAF-2DCNN, a notable enhancement in robustness and reliability for handling substantial HVAC faults is achieved.
Terbium and Europium Chlorocyananilate-Based 2D Coordination Polymers

Two-dimensional layered coordination polymers based on the hetero-substituted 3-chloro-6-cyano-2,5-dihydroxybenzoquinone ligands, hereafter ClCNAn2− anilate, and LnIII ions (Tb and Eu) are reported. Compounds 1 and 2, formulated as Ln2(ClCNAn)3(DMSO)6 (LnIII = Tb, 1; Eu, 2), and their related intermediates 1′ and 2′, formulated as Ln2(ClCNAn)3(H2O)x·yH2O (x + y likely = 12, Ln = Tb, 1′; and Eu, 2′), were prepared by a conventional one-pot reaction (the latter) and recrystallized from DMSO solvent (the former). Polyhydrated intermediates 1′ and 2′ show very similar XRPD patterns, while, despite their common stoichiometry, 1 and 2 are not isostructural. Compound 1 consists of a 2D coordination framework of 3,6 topology, where [Tb(DMSO)3]III moieties are bridged by three bis-chelating ClCNAn2− ligands, forming distorted hexagons. Ultrathin nanosheets of 1 were obtained by exfoliation via the liquid-assisted sonication method and characterized by atomic force microscopy, confirming the 2D nature of 1. The crystal structure of 2, still showing the presence of 2D sheets with a “hexagonal” mesh and a common (3,6) connectivity, is based onto flat, non-corrugated slabs. Indeed, at a larger scale, the different “rectangular tiles” show clear roofing in 1, which is totally absent in 2. The magnetic behavior of 1 very likely indicates depopulation of the highest crystal-field levels, as expected for TbIII compounds.
GREENHOUSE COST INDEX METHODOLOGY BASED ON THE DIVERSE REGIONS OF IRAQ

Greenhouses have become widespread structures that create an ideal microclimate for growing crops worldwide. A greenhouse is a structure that allows people to regulate climatic conditions, such as, temperature and humidity. There are many different designs of greenhouses, but generally, these buildings include large areas of transparent material to capture the light and heat of the sun. They also offer protection from unfavorable weather conditions and pests, providing a popular solution for crop production worldwide, including Iraq, which uses alternative energy sources for climate control. Using machine learning models has helped design different greenhouse types; however, their ability to predict costs and designs based on features is yet to exist. Therefore, to address these issues, this study aimed to develop cost-effective and user-friendly greenhouse systems through two different approaches: Firstly, the use of random forests (RFs) model with the highest precision (0.99) formulated the cost of the greenhouse for new input data to calculate a greenhouse cost estimate based on the system's performance as a benchmark while selecting the greenhouse's features through training and testing, and secondly, the use of the farmer's desired price as a basis for developing a greenhouse design. This scientific approach will enable the farming community to manage the costs of various aspects, such as, building materials, energy sources, climate control devices, water and fertilizer delivery, growing substrates, internal logistics, and labor. The presented research will provide farmers with a practical basis that also considers the constraints, i.e., the economy, climate, law, market, and resource availability. It will empower the farmers to make the right decisions regarding greenhouse systems with their specific requirements and circumstances.
Feature Extraction Techniques for Deep Learning based Speech Classification

The process of classifying audio data into several classes or categories is referred to as audio classification. The purpose of speaker recognition, one particular use of audio classification, is to recognize a person based on the characteristics of their speech. The phrase "voice recognition" refers to both speaker and speech recognition tasks. Speaker verification systems have grown significantly in popularity recently for a variety of uses, such as security measures and individualized help. Computers that have been taught to recognize individual voices can swiftly translate speech or confirm a speaker’s identification as part of a security procedure by identifying the speaker. Four decades of research have gone into speaker recognition, which is based on the acoustic characteristics of speech that differ from person to person. Some systems use auditory input from those seeking entry, just like fingerprint sensors match input fingerprint markings with a database or photographic attendance systems map inputs to a database. Personal assistants, like Google Home, for example, are made to limit access to those who have been given permission. Even under difficult circumstances, these systems must correctly identify or recognize the speaker. This research proposes a strong deep learning-based speaker recognition solution for audio categorization. We suggest self-augmenting the data utilizing four key noise aberration strategies to improve the system’s performance. Additionally, we conduct a comparison study to examine the efficacy of several audio feature extractors. The objective is to create a speaker identification system that is extremely accurate and can be applied in practical situations.
Wideband Single-Sideband Time Modulator Based on Practical 2-bit Switched-Type Phase Shifter

A single-sideband time modulator (SSBTM) with a wide bandwidth of one decade ( $R$ = 10) is presented in this article. The proposed SSBTM is based on a periodically controlled wideband 2-bit switched-type phase shifter (STPS). With periodic phase modulation and ON–OFF amplitude control, it can realize an equivalent 6-bit amplitude–phase control at the generated first harmonic component. Compared to the conventional STPS, the proposed SSBTM has a concise structure, which requires only two phase shift stages. The bandwidth of the 2-bit STPS in the SSBTM is broadened by cascading multiple all-pass networks (APNs). Additionally, the tradeoff between bandwidth and efficiency for the amplitude–phase control using the SSBTM is derived theoretically. Wideband 90° and 180° phase shifters (PSs) in the SSBTM are designed by cascading four-stage APNs, which work from 300 MHz to 3 GHz. An equivalent 6-bit amplitude–phase control is measured with the root-mean-square (rms) amplitude and phase errors of 0.3 dB and 1.1°, respectively. The corresponding 8-element time-modulated antenna arrays (TMAAs) working at 750 MHz, 1.2 GHz, 2 GHz, and 2.6 GHz are fabricated, and the receiving power patterns are tested from −40° to 40° with a 10° step, demonstrating the effectiveness of the proposed wideband SSBTM for harmonic beam scanning.
Monitoring Sensors for Urban Air Quality: The Case of the Municipality of Lisbon

Air pollution is a global issue that impacts environmental inequalities, and air quality sensors can have a decisive role in city policymaking for future cities. Science and society are already aware that during the most challenging times of COVID-19, the levels of air pollution in cities decreased, especially during lockdowns, when road traffic was reduced. Several pollution parameters can be used to analyse cities’ environmental challenges, and it is more pressing than ever to have city climate decisions supported by sensor data. We have applied a data science approach to understand the evolution of the levels of carbon monoxide, nitrogen dioxide, particulate matter 2.5, and particulate matter 10 between August 2021 and July 2022. The analysis of the air quality levels, captured for the first time via 80 monitoring stations distributed throughout the municipality of Lisbon, has allowed us to realize that nitrogen dioxide and particulate matter 10 exceed the levels that are recommended by the World Health Organization, thereby increasing the health risk for those who live and work in Lisbon. Supported by these findings, we propose a central role for air quality sensors for policymaking in future cities, taking as a case study the municipality of Lisbon, Portugal, which is among the European cities that recently proposed be climate-neutral and smart city by 2030.
Economic Renewal and Demographic Change

An evaluation of local labour market performance in the Nordic countries. Regional imbalances caused by and effecting demographic change are unevenly spread across the Nordic countries. A common emergent problem here is that of a future labour shortage, something which has already been experienced in certain sectors and in particular regions, and something that is forecast on a much broader scale in future. Moreover, the common characteristics of an ageing labour force, such as increasing rates of sickness leave, have important side effects, from a social and economic perspective, on the communities they blight. The differences in fertility rates between in particular Sweden, Finland and Denmark on the one hand, and Iceland and Norway on the other are complexly interrelated to differing economic performance levels and to national institutional structures. The sustainability, in economic and social terms, of several labour markets is thus increasingly challenged as population decline continues.
Hyperoxia Increases Kidney Injury During Renal Ischemia and Reperfusion in Mice

BACKGROUND: Renal ischemia and reperfusion (IR) contribute to perioperative acute kidney injury, and oxygen is a key regulator of this process. We hypothesized that oxygen administration during surgery and renal IR would impact postoperative kidney function and injury in mice. METHODS: Mice were anesthetized, intubated, and mechanically ventilated with a fraction of inspired oxygen (Fio2) 0.10 (hypoxia), 0.21 (normoxia), 0.60 (moderate hyperoxia), or 1.00 (severe hyperoxia) during 67 minutes of renal IR or sham IR surgery. Additional mice were treated before IR or sham IR surgery with 50 mg/kg tempol, a superoxide scavenger. At 24 hours, mice were sacrificed, and blood and kidney collected. We assessed and compared kidney function and injury across groups by measuring blood urea nitrogen (BUN, primary end point), renal histological injury, renal expression of neutrophil gelatinase–associated lipocalin (NGAL), and renal heme oxygenase 1 (Ho-1), peroxisome proliferator–activated receptor gamma coactivator 1-α (Pgc1-α), and glutathione peroxidase 4 (Gpx-4) transcripts, to explore potential mechanisms of any effect of oxygen. RESULTS: Hyperoxia and hypoxia during renal IR surgery decreased renal function and increased kidney injury compared to normoxia. Baseline median (interquartile range) BUN was 22.2 mg/dL (18.4–26.0), and 24 hours after IR surgery, BUN was 17.5 mg/dL (95% confidence interval [CI], 1.3–38.4; P = .034) higher in moderate hyperoxia–treated animals, 51.8 mg/dL (95% CI, 24.9–74.8; P < .001) higher in severe hyperoxia–treated animals, and 64.9 mg/dL (95% CI, 41.2–80.3; P < .001) higher in hypoxia-treated animals compared to animals treated with normoxia (P < .001, overall effect of hyperoxia). Hyperoxia-induced injury, but not hypoxia-induced injury, was attenuated by pretreatment with tempol. Histological injury scores, renal NGAL staining, and renal transcription of Ho-1 and suppression of Pgc1-α followed the same pattern as BUN, in relation to the effects of oxygen treatment. CONCLUSIONS: In this controlled preclinical study of oxygen treatment during renal IR surgery, hyperoxia and hypoxia impaired renal function, increased renal injury, and impacted expression of genes that affect mitochondrial biogenesis and antioxidant response. These results might have implications for patients during surgery when high concentrations of oxygen are frequently administered, especially in cases involving renal IR.
Management of Type 2 Diabetes Mellitus and Kidney Failure in People with HIV-Infection in Africa: Current Status and a Call to Action

Abstract There is an increasing global burden of diabetes mellitus (DM) and chronic kidney disease (CKD), coupled with a high burden of people with HIV (PWH). Due to an increased lifespan on ART, PWH are now at risk of developing non-communicable diseases, including DM. Africa has the greatest burden of HIV infection and will experience the greatest increase in prevalence of DM over the next two decades. In addition, there is a rising number of people with CKD and progression to kidney failure. Therefore, there is an urgent need for the early identification and management of all 3 diseases to prevent disease progression and complications. This is particularly important in Africa for people with CKD where there is restricted or no access to dialysis and/or transplantation. This review focuses on the epidemiology and pathophysiology of the interaction between HIV infection and DM and the impact that these diseases have on the development and progression of CKD. Finally, it also aims to review the data on the management, which stems from the growing burden of all three diseases.
Highway drainage as a component of metal input into the catchment

Highway runoff, as a nonpoint source, may exert significant pollutant load on the catchment. Finding ways to mitigate nonpoint sources of pollutants is a matter of great concern for improving water quality. It was cited by the Environment Agency in 2005 that more than 80% of English rivers were at risk of failing to achieve Water Framework Directive (WFD) objectives through diffuse pollution (Faram, 2007; p.14). The presence and behaviour of metals were analysed and compared through seasonal sampling from one of the most trafficked roads in the Midlands, M1. These were compared with other sources: local streams and sewage works. The concentration of metals in stormwater from the M1, three neighbouring rural brooks and three local sewage works, all in the same catchment, were sampled. Three metals (Fe, Cu and Zn) were used as an indicator because of their predominance and potential harmful effect on biodiversity. The data was analysed to establish any links between the total and dissolved metals and standard water quality parameters. The thesis also examines the performance of a standard highway treatment system of interceptor and lagoon for removing metals. The results indicate that evaporation and de-icer salts had the strongest effect on metal concentrations and their solubility in the runoff from the M1. As additional factors, rainfall intensity and antecedent dry weather period (ADWP) had the most important influence on metal concentrations. Fe was always at the highest concentrations for all weather conditions (total and dissolved) and all sampling locations. The results also showed that Fe was affiliated with the particulate matter; however, it was also suggested that it was solubilised by anaerobic conditions. Zntot during wet weather exceeded the environmental quality standards (EQS) both on the M1 and in Woodbrook. The sewage work effluent did not exceed the EQS at any time of sampling. It was also found that Zn solubility was increased by the presence of de-icer salts which released it from the sediment by a process of ion-exchange, as suggested by the literature. Cu concentrations had the most erratic values and varied widely but were in the lowest concentrations compared to other metals. During wet weather Cudis from the M1 runoff exceeded the EQS. It was also concluded that the analytical and sample preservation methods chosen could have an effect on the concentrations of copper. Filtration with recycled glass and pea-gravel was able to remove particles down to 5 μm, but at the typical flow rate (5 m/hr), and solids loading the filters would need regular washing. The adsorption studies showed that metals are more effectively removed by alkaline conditions than acid conditions which release metals into the environment.
Washable and Flexible Screen-Printed Ag/AgCl Electrode on Textiles for ECG Monitoring

Electrocardiogram (ECG) electrodes are important sensors for detecting heart disease whose performance determines the validity and accuracy of the collected original ECG signals. Due to the large drawbacks (e.g., allergy, shelf life) of traditional commercial gel electrodes, textile electrodes receive widespread attention for their excellent comfortability and breathability. This work demonstrated a dry electrode for ECG monitoring fabricated by screen printing silver/silver chloride (Ag/AgCl) conductive ink on ordinary polyester fabric. The results show that the screen-printed textile electrodes have good and stable electrical and electrochemical properties and excellent ECG signal acquisition performance. Furthermore, the resistance of the screen-printed textile electrode is maintained within 0.5 Ω/cm after 5000 bending cycles or 20 washing and drying cycles, exhibiting excellent flexibility and durability. This research provides favorable support for the design and preparation of flexible and wearable electrophysiological sensing platforms.