Miracle-dz/UniFAR-FTUs huggingface dataset:
“Pandemic Fatigue” in South America: A Multi-Center Report from Argentina, Bolivia, Paraguay, Peru, and Uruguay

The COVID-19 pandemic has had a heavy impact on daily life, leading to physical and psychosocial consequences. Nowadays, clinicians and health researchers are particularly interested in describing and facing the long-term effects of COVID-19, also known as “long-COVID syndrome”. Pandemic fatigue has been defined as a cluster of demotivation, tiredness, and psychological effects that emerge gradually over time after the infection or through the adoption of the recommended measures to combat it. In this study, we report the findings of a large survey conducted in South America involving 1448 participants (mean age: 33.9 ± 11.2 years old) from Argentina, Bolivia, Uruguay, Peru, and Paraguay. An online survey was launched through the common social media based on a specific assessment aimed to detect the prevalence of pandemic fatigue and associated factors. Socio-demographic characteristics, medical, and personal information were collected; the Pandemic Fatigue Scale (PFS) and the Coronavirus Anxiety Scale (CAS) were also administered. We found mid-levels of pandemic fatigue among respondents (21.7 ± 7.95 score at PFS) as well as significant anxiety related to the COVID-19 pandemic (1.56 ± 2.76 score at CAS). In addition, pandemic fatigue was significantly associated with the experience of the loss of a relative/friend due to COVID-19, anxiety related to the infection, and reliance on social media as a primary source of information on the pandemic. Vaccination significantly reduced the levels of fatigue among respondents. Our findings may add to the international debate regarding the long-term health consequences of the COVID-19 pandemic and strategies to manage them in the general population of South America.
Damping Scaling Factors for Elastic Response Spectra for Shallow Crustal Earthquakes in Active Tectonic Regions: “Average” Horizontal Component

Ground motion prediction equations (GMPEs) for elastic response spectra are typically developed at a 5% viscous damping ratio. In reality, however, structural and nonstructural systems can have other damping ratios. This paper develops a new model for a damping scaling factor (DSF) that can be used to adjust the 5% damped spectral ordinates predicted by a GMPE for damping ratios between 0.5% to 30%. The model is developed based on empirical data from worldwide shallow crustal earthquakes in active tectonic regions. Dependencies of the DSF on potential predictor variables, such as the damping ratio, spectral period, ground motion duration, moment magnitude, source-to-site distance, and site conditions, are examined. The strong influence of duration is captured by the inclusion of both magnitude and distance in the DSF model. Site conditions show weak influence on the DSF. The proposed damping scaling model provides functional forms for the median and logarithmic standard deviation of DSF, and is developed for both RotD50 and GMRotI50 horizontal components. A follow-up paper develops a DSF model for vertical ground motion.
Arbuscular Mycorrhizal Fungi Improve the Growth, Water Status, and Nutrient Uptake of Cinnamomum migao and the Soil Nutrient Stoichiometry under Drought Stress and Recovery

Drought greatly influences the growth and ecological stoichiometry of plants in arid and semi-arid regions such as karst areas, where Cinnamomum migao (C. migao) is an endemic tree species that is used as a bioenergy resource. Arbuscular mycorrhizal fungi (AMF) play a key role in nutrient uptake in the soil-plant continuum, increasing plant tolerance to drought. However, few studies have examined the contribution of AMF in improving the growth of C. migao seedlings and the soil nutrient stoichiometry under drought-stress conditions. A pot experiment was conducted under natural light in a plastic greenhouse to investigate the effects of individual inoculation and Co-inoculation of AMF [Funneliformis mosseae (F. mosseae) and Claroideoglomus etunicatum (C. etunicatum)] on the growth, water status, and nutrient uptake of C. migao as well as the soil nutrient stoichiometry under well-watered (WW) and drought-stress (DS) conditions. The results showed that compared with non-AMF control (CK), AM symbiosis significantly stimulated plant growth and had higher dry mass. Mycorrhizal plants had better water status than corresponding CK plants. AMF colonization notably increased the total nitrogen and phosphorus content of C. migao seedlings compared with CK. Mycorrhizal plants had higher leaf and stem total carbon concentrations than CK. The results indicated that AM symbiosis protects C. migao seedlings against drought stress by improving growth, water status, and nutrient uptake. In general, the C. migao seedlings that formed with C. etunicatum showed the most beneficial effect on plant growth, water status, and nutrient uptake among all treatments. In the future, we should study more about the biological characteristics of each AMF in the field study to understand more ecological responses of AMF under drought stress, which can better provide meaningful guidance for afforestation projects in karst regions.
Multiomics and machine-learning identify novel transcriptional and mutational signatures in amyotrophic lateral sclerosis

Abstract Amyotrophic lateral sclerosis is a fatal and incurable neurodegenerative disease that mainly affects the neurons of the motor system. Despite the increasing understanding of its genetic components, their biological meanings are still poorly understood. Indeed, it is still not clear to which extent the pathological features associated with amyotrophic lateral sclerosis are commonly shared by the different genes causally linked to this disorder. To address this point, we combined multiomics analysis covering the transcriptional, epigenetic and mutational aspects of heterogenous human induced pluripotent stem cell-derived C9orf72-, TARDBP-, SOD1- and FUS-mutant motor neurons as well as datasets from patients’ biopsies. We identified a common signature, converging towards increased stress and synaptic abnormalities, which reflects a unifying transcriptional program in amyotrophic lateral sclerosis despite the specific profiles due to the underlying pathogenic gene. In addition, whole genome bisulphite sequencing linked the altered gene expression observed in mutant cells to their methylation profile, highlighting deep epigenetic alterations as part of the abnormal transcriptional signatures linked to amyotrophic lateral sclerosis. We then applied multi-layer deep machine-learning to integrate publicly available blood and spinal cord transcriptomes and found a statistically significant correlation between their top predictor gene sets, which were significantly enriched in toll-like receptor signalling. Notably, the overrepresentation of this biological term also correlated with the transcriptional signature identified in mutant human induced pluripotent stem cell-derived motor neurons, highlighting novel insights into amyotrophic lateral sclerosis marker genes in a tissue-independent manner. Finally, using whole genome sequencing in combination with deep learning, we generated the first mutational signature for amyotrophic lateral sclerosis and defined a specific genomic profile for this disease, which is significantly correlated to ageing signatures, hinting at age as a major player in amyotrophic lateral sclerosis. This work describes innovative methodological approaches for the identification of disease signatures through the combination of multiomics analysis and provides novel knowledge on the pathological convergencies defining amyotrophic lateral sclerosis.
pH-Responsive Water-Soluble Chitosan Amphiphilic Core–Shell Nanoparticles: Radiation-Assisted Green Synthesis and Drug-Controlled Release Studies

This work aims to apply water radiolysis-mediated green synthesis of amphiphilic core–shell water-soluble chitosan nanoparticles (WCS NPs) via free radical graft copolymerization in an aqueous solution using irradiation. Robust grafting poly(ethylene glycol) monomethacrylate (PEGMA) comb-like brushes were established onto WCS NPs modified with hydrophobic deoxycholic acid (DC) using two aqueous solution systems, i.e., pure water and water/ethanol. The degree of grafting (DG) of the robust grafted poly(PEGMA) segments was varied from 0 to ~250% by varying radiation-absorbed doses from 0 to 30 kGy. Using reactive WCS NPs as a water-soluble polymeric template, a high amount of DC conjugation and a high degree of poly(PEGMA) grafted segments brought about high moieties of hydrophobic DC and a high DG of the poly(PEGMA) hydrophilic functions; meanwhile, the water solubility and NP dispersion were also markedly improved. The DC-WCS-PG building block was excellently self-assembled into the core–shell nanoarchitecture. The DC-WCS-PG NPs efficiently encapsulated water-insoluble anticancer and antifungal drugs, i.e., paclitaxel (PTX) and berberine (BBR) (~360 mg/g). The DC-WCS-PG NPs met the role of controlled release with a pH-responsive function due to WCS compartments, and they showed a steady state for maintaining drugs for up to >10 days. The DC-WCS-PG NPs prolonged the inhibition capacity of BBR against the growth of S. ampelinum for 30 days. In vitro cytotoxicity results of the PTX-loaded DC-WCS-PG NPs with human breast cancer cells and human skin fibroblast cells proved the role of the DC-WCS-PG NPs as a promising nanoplatform for controlling drug release and reducing the side effects of the drugs on normal cells.
Ophiotholia (Echinodermata: Ophiuroidea): A little-known deep-sea genus present in polymetallic nodule fields with the description of a new species

Introduction The Clarion Clipperton Zone (CCZ) in the Northeast Pacific Ocean holds the largest deposits of polymetallic nodules at abyssal depths. These nodules are rock formations containing valuable metals and minerals targeted for mining. They further provide diverse habitat for a range of deep-sea species. Little is known so far on the taxonomy, natural history and biogeography of these deep-sea animals which is vital for accurate assessment of the risk of species extinctions from large-scale mining. One of the most abundant megafaunal groups in the CCZ is the Ophiuroidea (brittle stars), of which Ophiotholia is one of the more abundant genera found in the area. The genus Ophiotholia has a world-wide distribution and currently holds six species. Methods Material collected from seven scientific cruises to the CCZ was examined, morphologically, together with comparative material from all the known species. The small size and the damage caused during sampling often impeded their identification. The specimens were also genetically analyzed using a fragment of the mitochondrial COI gene. Scanning Electron Microscope images of the key microstructural characters were made using selected specimens from CCZ as well as from the comparative material. Result and discussion One morphotype was identified as the known species Ophiotholia supplicans Lyman, 1880, while the second is new to science and is described in this paper. The umbrella spines and the arrangement of their articulations on the lateral arm plate, were selected as the most relevant morphological characters in the taxonomy of the genus Ophiotholia and a revised identification key of all characters from all known Ophiotholia species is provided as a table in the supplement material. The identification and description of such a little-known genus improves the evaluation of the biodiversity not only in the CCZ but also for the deep sea.
Oral Microbiota in Children and Adolescents with Type 1 Diabetes Mellitus: Novel Insights into the Pathogenesis of Dental and Periodontal Disease

The oral microbiota can be influenced by multiple factors, but only a few studies have focused on the role of glycemic control in determining early alterations of oral microbiota and their association with pathogenesis of both periodontitis and caries. The aim of this study is to evaluate the interplay between bacteria composition, oral hygiene, and glycemic control in a cohort of children with T1D. A total of 89 T1D children were enrolled (62% males, mean age: 12.6 ± 2.2 years). Physical and clinical characteristics, glucometabolic parameters, insulin treatment, and oral hygiene habits data were collected. Microbiological analysis was performed from saliva samples. A high prevalence of cariogenic and periodontopathogens bacteria in our cohort was detected. In particular, in all subjects Actinomyces spp. , Aggregatibacter actinomycetemcomitans, Prevotella intermedia, and Lactobacillus spp. were isolated. S. mutans was found in about half of the analyzed sample (49.4%), in particular in patients with imbalance values of glycemic control. Moreover, a higher presence of both S. mutans and Veillonella spp. was detected in subjects with poorer glycemic control, in terms of HbA1c, %TIR and %TAR, even adjusting for age, sex, and hygiene habits as covariates. Virtuous oral hygiene habits, such as frequency of toothbrush changes and professional oral hygiene, negatively correlated with the simultaneous presence of Tannerella forsythia, Treponema denticola, and Porphyromonas gingivalis, red complex bacteria. Our study shows it is crucial to pay attention to glycemic control and regular oral hygiene to prevent the establishment of an oral microbiota predisposing to dental and periodontal pathology in subjects with T1D since childhood.
A new molecular subclassification and in silico predictions for diagnosis and prognosis of papillary thyroid cancer by alternative splicing profile

Introduction: Papillary thyroid cancer (PTC) is the most common endocrine malignancy. However, different PTC variants reveal high heterogeneity at histological, cytological, molecular and clinicopathological levels, which complicates the precise diagnosis and management of PTC. Alternative splicing (AS) has been reported to be potential cancer biomarkers and therapeutic targets. Method: Here, we aim to find a more sophisticated molecular subclassification and characterization for PTC by integrating AS profiling. Based on six differentially expressed alternative splicing (DEAS) events, a new molecular subclassification was proposed to reclassify PTC into three new groups named as Cluster0, Cluster1 and Cluster2 respectively. Results: An in silico prediction was performed for accurate recognition of new groups with the average accuracy of 91.2%. Moreover, series of analyses were implemented to explore the differences of clinicopathology, molecular and immune characteristics across them. It suggests that there are remarkable differences among them, but Cluster2 was characterized by poor prognosis, higher immune heterogeneity and more sensitive to anti-PD1 therapy. The splicing correlation networks proved the complicated regulation relationships between AS events and splicing factors (SFs). An independent prognostic indicator for PTC overall survival (OS) was established. Finally, three compounds (orantinib, tyrphostin-AG-1295 and AG-370) were discovered to be the potential therapeutic agents. Discussion: Overall, the six DEAS events are not only potential biomarkers for precise diagnosis of PTC, but also the probable prognostic predictors. This research would be expected to highlight the effect of AS events on PTC characterization and also provide new insights into refining precise subclassification and improving medical therapy for PTC patients.
Frequencies and Characteristics of Global Oceanic Precipitation from Shipboard Present-Weather Reports

Abstract Ship reports of present weather obtained from the Comprehensive Ocean-Atmosphere Data Set are analyzed for the period 1958–91 in order to elucidate regional and seasonal variations in the climatological frequency, phase. intensity, and character of oceanic precipitation. Specific findings of note include the following: 1) The frequency of thunderstorm reports, relative to all precipitation reports, is a strong function of location, with thunderstorm activity being favored within 1000–3000 km of major tropical and subtropical landmasses, while being quite rare at other locations, even within the intertropical convergence zone. 2) The latitudinal frequency of precipitation over the southern oceans increases steadily toward the Antarctic continent and shows relatively little seasonal variation. The frequency of convective activity, however, shows considerable seasonal variability, with sharp winter maxima occurring near 381 latitude in both hemispheres. 3) Drizzle is the preferred form of precipitat.
Rate of Convergence for Sums and Maxima and Doubly Ideal Metrics

It is well known that the minimal $L_p $-metrics are ideal with respect to summation and maxima of order $r = \min (p,1)$. This implies that one can get rate of convergence results in stable limit theorems with $0 1$ has an essential negative answer. In spite of this the minimal $L_p $-metrics behave like ideal metrics of order $r > 1$ with respect to maxima and sums. This allows to improve results on the stability of queueing models respect to departures from the ideal model.
Measuring affect and complex working memory in natural and urban environments

Introduction Research suggests that spending time in natural environments is associated with cognitive and affective benefits, while increased use of technology and time spent in urban environments are associated with depletion of cognitive resources and an increasing prevalence of mental illness. Attention Restoration Theory suggests that exposure to natural environments can restore depleted attentional resources and thereby improve cognitive functioning and mood. Specifically, recent meta-analyses have revealed that the most improved cognitive abilities after nature exposure include selective attention, working memory, and cognitive flexibility. Methods While existing studies examined these cognitive abilities, few have examined the Operation Span (OSPAN), a complex measure of working memory capacity. Therefore, the current study (N = 100) compared performance on the OSPAN and self-reported mood using the Positive and Negative Affect Schedule before and after a 30-min walk in a natural or urban environment. Results Results from the study showed that both groups exhibited an increase in positive affect and a decrease in negative affect, suggesting that going outside for a walk can boost mood regardless of environment type. Inconsistent with past work, there were no significant changes in OSPAN scores before and after the walk for either environment type. Discussion Future studies should analyze how the length of time spent in the environment, certain characteristics of the environment, and individual differences in connectedness to nature may impact attention restoration to gain insight on nature’s ability to improve our affect and cognition.
Landmark Recognition Model for Smart Tourism using Lightweight Deep Learning and Linear Discriminant Analysis

—Scene recognition algorithm is crucial for landmark recognition model development. Landmark recognition model is one of the main modules in the intelligent tour guide system architecture for the use of smart tourism industry. However, recognizing the tourist landmarks in the public places are challenging due to the common structure and the complexity of scene objects such as building, monuments and parks. Hence, this study proposes a super lightweight and robust landmark recognition model by using the combination of Convolutional Neural Network (CNN) and Linear Discriminant Analysis (LDA) approaches. The landmark recognition model was evaluated by using several pretrained CNN architectures for feature extraction. Then, several feature selections and machine learning algorithms were also evaluated to produce a super lightweight and robust landmark recognition model. The evaluations were performed on UMS landmark dataset and Scene-15 dataset. The results from the experiments have found that the Efficient Net (EFFNET) with CNN classifier are the best feature extraction and classifier. EFFNET-CNN achieved 100% and 94.26% classification accuracy on UMS-Scene and Scene-15 dataset respectively. Moreover, the feature dimensions created by EFFNet are more compact compared to the other features and even have significantly reduced for more than 90% by using Linear Discriminant Analysis (LDA) without jeopardizing classification performance but yet improved its performance.
Spiradenocarcinoma: SEER Study of Epidemiology, Survival, and Treatment Options

(1) Background: Spiradenocarcinoma is an extremely rare malignant adnexal tumor and there are only few studies on survival outcomes. Our aim was to perform an analysis of the demographic and pathological characteristics, treatment patterns, and survival outcomes of patients affected by spiradenocarcinoma. (2) Methods: The Surveillance, Epidemiology, and End Results program database of the National Cancer Institute was searched for all cases of spiradenocarcinoma diagnosed between 2000 and 2019. This database is considered representative of the US population. Demographic, pathological, and treatment variables were retrieved. Overall and disease-specific survival were computed according to the different variables. (3) Results: 90 cases of spiradenocarcinoma (47 females, 43 males) were identified. Mean age at diagnosis was 62.8 years. Regional and distant disease at diagnosis were rare, occurring in 2.2% and 3.3% of cases, respectively. Surgery alone was the most frequent treatment (87.8%), followed by a combination of surgery and radiotherapy (3.3%) and radiation therapy only (1.1%). Five-year overall survival was 76.2% and five-year disease-specific survival was 95.7%. (4) Conclusions: Spiradenocarcinoma equally affects males and females. Regional and distant invasion rates are low. Disease-specific mortality is low and is probably overestimated in the literature. Surgical excision remains the main form of treatment.
Determination of optimal stent length: a survey of urologic surgeons

Introduction Ureteral double-J stent length is an important factor affecting stent-related symptoms. Multiple techniques exist to determine ideal stent length for a given patient, however, little is known about what techniques urologists rely on. Our objective was to identify how urologists determine optimal stent length. Material and methods An online survey was e-mailed in 2019 to all members of the Endourology Society. The survey sought to assess what methods are commonly used to determine choice of stent length, along with frequency of stent placement post ureteroscopy, duration of stenting, availability of different stent lengths and the use of stent tether. Results 301 urologists (15.1%) responded to our survey. Following ureteroscopy, 84.5% of respondents would stent at least 50% of the time. Following uncomplicated ureteroscopy, most respondents (52.0%) would leave a stent for 2–7 days. Patient height was most commonly ranked first as the method of choice in determining stent length (47.0%), followed by estimation based on experience only (20.6%) and intra-operative direct measurement of ureteric length (19.1%). Most respondents utilized multiple methods in determination of optimal stent length. Most respondents (66.5%) were interested in a simple intra-operative technique utilizing a special ureteral catheter that would help choose the most appropriate stent length. Conclusions Post-ureteroscopy stent insertion is common and patient height is the most common method of choice used in determining optimal stent length. Most respondents were interested in using a simple, novel ureteral catheter device that would allow them to more accurately select optimal stent length.