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Purpose We delineate the clinical spectrum and describe the histology in arterial tortuosity syndrome (ATS), a rare connective tissue disorder characterized by tortuosity of the large and medium-sized arteries, caused by mutations in SLC2A10 . Methods We retrospectively characterized 40 novel ATS families (50 patients) and reviewed the 52 previously reported patients. We performed histology and electron microscopy (EM) on skin and vascular biopsies and evaluated TGF-β signaling with immunohistochemistry for pSMAD2 and CTGF. Results Stenoses, tortuosity, and aneurysm formation are widespread occurrences. Severe but rare vascular complications include early and aggressive aortic root aneurysms, neonatal intracranial bleeding, ischemic stroke, and gastric perforation. Thus far, no reports unequivocally document vascular dissections or ruptures. Of note, diaphragmatic hernia and infant respiratory distress syndrome (IRDS) are frequently observed. Skin and vascular biopsies show fragmented elastic fibers (EF) and increased collagen deposition. EM of skin EF shows a fragmented elastin core and a peripheral mantle of microfibrils of random directionality. Skin and end-stage diseased vascular tissue do not indicate increased TGF-β signaling. Conclusion Our findings warrant attention for IRDS and diaphragmatic hernia, close monitoring of the aortic root early in life, and extensive vascular imaging afterwards. EM on skin biopsies shows disease-specific abnormalities.

In the published version of this paper the author Neus Baena's name was incorrectly given as Neus Baena Diez. This has now been corrected in both the HTML and PDF versions of the paper.In the published version of this paper the author Neus Baena's name was incorrectly given as Neus Baena Diez. This has now been corrected in both the HTML and PDF versions of the paper.

Global patterns of regional (gamma) plant diversity are relatively well known, but whether these patterns hold for local communities, and the dependence on spatial grain, remain controversial. Using data on 170,272 georeferenced local plant assemblages, we created global maps of alpha diversity (local species richness) for vascular plants at three different spatial grains, for forests and non-forests. We show that alpha diversity is consistently high across grains in some regions (for example, Andean-Amazonian foothills), but regional ‘scaling anomalies’ (deviations from the positive correlation) exist elsewhere, particularly in Eurasian temperate forests with disproportionally higher fine-grained richness and many African tropical forests with disproportionally higher coarse-grained richness. The influence of different climatic, topographic and biogeographical variables on alpha diversity also varies across grains. Our multi-grain maps return a nuanced understanding of vascular plant biodiversity patterns that complements classic maps of biodiversity hotspots and will improve predictions of global change effects on biodiversity. Global patterns of regional plant diversity are relatively well known, but whether they hold for local communities is debated. This study created multi-grain global maps of alpha diversity for vascular plants to provide a nuanced understanding of plant diversity hotspots and improve predictions of global change effects on biodiversity.

Background Potato serves as a major non-cereal food crop and income source for small-scale growers in Punjab, Pakistan. Unfortunately, improper fertilization practices have led to low crop yields, worsened by challenging environmental conditions and poor groundwater quality in the Cholistan region. To address this, we conducted an experiment to assess the impact of two fertilizer application approaches on potato cv. Barna using plant growth-promoting bacteria (PGPB) coated biofertilizers. The first approach, termed conventional fertilizer application (CFA), involved four split applications of PGPB-coated fertilizers at a rate of 100:75 kg acre –1 (N and P). The second, modified fertilizer application (MFA), employed nine split applications at a rate of 80:40 kg acre –1 . Results The MFA approach significantly improved various plant attributes compared to the CFA. This included increased plant height (28%), stem number (45%), leaf count (46%), leaf area index (36%), leaf thickness (three-folds), chlorophyll content (53%), quantum yield of photosystem II (45%), photosynthetically active radiations (56%), electrochromic shift (5.6%), proton flux (24.6%), proton conductivity (71%), linear electron flow (72%), photosynthetic rate (35%), water use efficiency (76%), and substomatal CO 2 (two-folds), and lowered non-photochemical quenching (56%), non-regulatory energy dissipation (33%), transpiration rate (59%), and stomatal conductance (70%). Additionally, the MFA approach resulted in higher tuber production per plant (21%), average tuber weight (21.9%), tuber diameter (24.5%), total tuber yield (29.1%), marketable yield (22.7%), seed-grade yield (9%), specific gravity (9.6%), and soluble solids (7.1%). It also reduced undesirable factors like goli and downgrade yields by 57.6% and 98.8%, respectively. Furthermore, plants under the MFA approach exhibited enhanced nitrogen (27.8%) and phosphorus uptake (40.6%), with improved N (26.1%) and P uptake efficiency (43.7%) compared to the CFA approach. Conclusion The use of PGPB-coated N and P fertilizers with a higher number of splits at a lower rate significantly boosts potato production in the alkaline sandy soils of Cholistan.

Many techniques have been introduced to improve both brain-computer interface (BCI) steps: feature extraction and classification. One of the emerging trends in this field is the implementation of deep learning algorithms. There is a limited number of studies that investigated the application of deep learning techniques in electroencephalography (EEG) feature extraction and classification. This work is intended to apply deep learning for both stages: feature extraction and classification. This paper proposes a modified convolutional neural network (CNN) feature extractorclassifier algorithm to recognize four different EEG motor imagery (MI). In addition, a four-class linear discriminant analysis (LDR) classifier model was built and compared to the proposed CNN model. The paper showed very good results with 92.8% accuracy for one EEG four-class MI set and 85.7% for another set. The results showed that the proposed CNN model outperforms multi-class linear discriminant analysis with an accuracy increase of 28.6% and 17.9% for both MI sets, respectively. Moreover, it has been shown that majority voting for five repetitions introduced an accuracy advantage of 15% and 17.2% for both EEG sets, compared with single trials. This confirms that increasing the number of trials for the same MI gesture improves the recognition accuracy

Laser ablation is one of the promising methods that can be used to synthesize nanomaterials and improve the surface plasmon resonance (SPR) of gold-silver alloy nanomaterials. Parameters (laser wavelength, exposure time, laser pulse duration, repetition rate, and energy density of laser) and medium of ablation ambient parameters (mixing ratio, liquid, and ambient gas) could affect the formation and SPR of gold-silver alloy nanomaterials. The parameters that can affect the synthesis of Au–Ag alloy nanomaterials were energy density, SPR, laser wavelength, and pulse repetition rate. The method of using ambient gas instead of liquid was known as pulsed laser ablation deposition (PLAD). It is the simplest way to change the SPR by adjusting the pressure of the gas in the atmosphere. Finally, this review provided useful knowledge for newcomers and scholars and good references to conduct future laboratory works.

Predisposition to acute illness from COVID-19 is suggested to correlate with cigarette smoking as it augments the risk of developing cardiovascular and respiratory illnesses, including infections. However, the effects of smoking on COVID-19 symptoms are not well described and controversial. In this study, we aim to explore the associations between smoking and COVID-19 symptoms. A cross-sectional study using the Ministry of Public Health (MoPH), Qatar database was administered to a Qatari population with confirmed COVID-19 disease who filled in pre-defined phone-call questionnaire between 27th February 2020 and 31st December 2020. We analyzed 11,701 non-vaccinated COVID-19 individuals (2952 smokers and 8749 non-smokers) with confirmed RT-PCR test results. The association of smoking and the presence of symptoms as well as patient characteristics was calculated using Pearson's Chi-square and Fisher's exact tests, adjusting for potential covariates. Compared with the non-smokers, symptomatic COVID-19 infection is significantly higher in smokers. In addition, we found fever as the most common symptom developed in COVID-19 patients followed by cough, headache, muscle ache, and sore throat. As compared to other symptoms, association of smoking with chills and abdominal pain was less evident (P < 0.05 and P < 0.001, respectively). However, both groups showed similar rates of developing cough. In conclusion, smoking is associated with COVID-19 symptoms frequency in non-vaccinated patients; nevertheless, further investigations are necessary to understand the mechanism of this association which could generate new targets for the management of COVID-19 in smoker patients.

In this study, hydrological modeling at the watershed level is used to assess the impacts of climate and land use changes on the catchment area of the Khanpur Dam, which is an important water source for Rawalpindi and Islamabad. The hydrological impact of past and anticipated precipitation in the Khanpur Dam watershed was forecast by using a HEC-HMS model. After calibration, the framework was employed to analyze the effects of changes in land cover and climate on the hydrological regime. The model used information from three climatic gauge stations (Murree, Islamabad Zero Point, and Khanpur Dam) to split the Khanpur Dam catchment area into five sub-basins that encompass the entire watershed region, each with distinctive characteristics. The model was evaluated and checked for 2016–2018 and 2019–2020, and it produced an excellent match with the actual and anticipated flows. After statistical downscaling with the CMhyd model, the most effective performing GCM (MPI-ESM1-2-HR) among the four GCMs was chosen and used to forecast projections of temperature and precipitation within two shared socioeconomic pathways (SSP2 and SSP5). The predictions and anticipated changes in land cover were incorporated into the calibrated HEC-HMS model to evaluate the potential impact of climate change and land cover change at the Khanpur Dam. The starting point era (1990–2015) and the projected period (2016–2100), which encompassed the basis in the present century, were analyzed annually. The results indicated a spike in precipitation for the two SSPs, which was predicted to boost inflows all year. Until the end of the twenty-first century, SSP2 predicted a 21 percent rise in precipitation in the Khanpur Dam catchment area, while SSP5 predicted a 28% rise in precipitation. Increased flows were found to be projected in the future. It was found that the calibrated model could also be used effectively for upcoming studies on hydrological effects on inflows of the Khanpur Dam basin.

Enhancing polymer properties by incorporating organic–inorganic nanoparticles results in improved optical characteristics of the resulting foil nanocomposite. The casting technique was utilized to fabricate foil nanocomposites using three types of neat polymers: PS, PMMA, and PVA, along with three groups of nanoparticles: (Al + ZnO), (Al + Ag), and (Ag + ZnO). Surface characterization of the resulting foil nanocomposite was carried out using AFM, FTIR, and UV–Vis spectroscopy. The RMS surface roughness of PS/(Ag + ZnO) nanocomposite was the lowest at 17.6 nm, while PMMA/(Al + Ag) and PVA/(Al + ZnO) nanocomposites had the lowest roughness at 10.4 and 24.2 nm, respectively. However, the polymer nanocomposites’ average thickness was around 110–120 μm. The nanocomposites’ linear optical parameters, such as optical conductivity, real and imaginary parts of dielectric constants, and extinction coefficient, were investigated and evaluated. All foil nanocomposites exhibited high UV absorption but low Vis absorption, especially when mixed with Ag and Al nanoparticles in the 200–400 nm range. The predicted refractive index values for the resulting nanocomposites exceed 1.5, highlighting the significant impact of incorporating ZnO, Al, and Ag nanoparticles into the polymer matrices. The optical parameters were the highest when incorporating (Ag + ZnO) nanoparticles into the as-mentioned polymer matrices and lowest when incorporating (Al + Ag) nanoparticles into the same polymers. The use of foil nanocomposites paves the way for the future production of high-efficiency optoelectronic devices.

Polymers/Al + Ag, Al + ZnO, and Ag + ZnO nanocomposites will receive much attention in their optical characterization due to their wide range of applications in optical devices. So, it is imperative to enhance their optical capabilities. This work uses the casting method to create flexible foil from nanocomposites made of three different polymer types: Al + Ag, Al + ZnO, and Ag + ZnO nanoparticles. Al + Ag, Ag + ZnO, and Al + ZnO nanoparticles are fillers, whereas polymers such as poly (methyl methacrylate), polystyrene, and polyvinyl alcohol are used as the polymer matrix. The exceptional purity of the materials as prepared is confirmed by the examination of chemical mapping (EDS) spectroscopy. High absorption in the UV region has been seen in UV–visible absorption spectra, which is inversely related to the presence of Al + Ag, Al + ZnO, and Ag + ZnO nanocomposites. A linear absorption coefficient (α) has been used to show that absorption edges exist. The energy band gaps of all nanocomposites are found to be redshifted upon determination of the energy gap. The energy gap values for all samples decreased when Al + Ag, Al + ZnO, and Ag + ZnO nanoparticles were mixed with polymers. However, there is a noticeable reduction in the energy band gap in all nanocomposite samples. The created foil nanocomposites offer great potential for producing extremely efficient optoelectronic devices.