bluuebunny/crossref_metadata_2025_split huggingface dataset:
Elite controllers long-term non progressors present improved survival and slower disease progression
Authors: Capa Laura , Ayala-Suárez Rubén , De La Torre Tarazona Humberto Erick , González-García Juan , del Romero Jorge , Alcamí José , Díez-Fuertes Francisco 
Published: September 2022

AbstractDifferent phenotypes exhibiting no evidences of disease progression have been described in ART-naïve HIV-1 positive individuals. Long-term non progressors (LTNP) and elite controllers (EC) are low frequent examples of immunological and virological control in HIV-1 positive subjects, respectively. The combination of both phenotypes is even less frequent and studied despite being considered as models of HIV-1 functional cure. A multicenter, prospective study in retrospect including clinical and epidemiological data collected from 313 LTNP of 21 Spanish hospitals was carried out. LTNPs maintaining CD4+ T cell counts over 500 cells/µl and viral loads (VL) under 10,000 copies/mL for at least 10 years in the absence of antiretroviral therapy were followed for a median of 20.8 years (IQR = 15.6–25.5). A 52.1% were considered EC (undetectable VL) and LTNP (EC-LTNP) and a total of 171 (54.8%) and 42 (13.5%) out of the 313 participants maintained LTNP status for at least 20 and 30 years, respectively. EC-LTNP showed lower CD4+ T cell count loss (9.9 vs 24.2 cells/µl/year), higher CD4/CD8 ratio (0.01 vs − 0.09 in ratio), and lesser VL increase (no increase vs 197.2 copies/mL/year) compared with LTNPs with detectable VL (vLTNP). Survival probabilities for all-cause mortality at 30 years from HIV + diagnosis were 0.90 for EC-LTNP and 0.70 for vLTNP (p = 2.0 × 10−3), and EC-LTNP phenotype was the only factor associated with better survival in multivariate analyses (HR = 0.28; 95% CI 0.10–0.79). The probability to preserve LTNP status at 30 years was 0.51 for EC-LTNP and 0.18 for vLTNP (p < 2.2 × 10−16). Risk factors associated to the loss of LTNP status was: higher age at diagnosis and the increase of VL, whereas the increase of CD4+ T cell counts and CD4/CD8 ratio, the initial EC-LTNP phenotype and HCV coinfection were protective factors. EC-LTNP phenotype was associated with improved survival and slower disease progression compared with other phenotypes of LTNP. EC-LTNP individuals represent one of the most favorable phenotypes of immune activation against HIV-1 found in nature and, therefore, are strong candidates to be considered a model of functional cure of HIV-1 infection.
Conductivity and size quantization effects in semiconductor $$\delta$$-layer systems
Authors: Mendez Juan P. , Mamaluy Denis 
Published: September 2022

AbstractWe present an open-system quantum-mechanical 3D real-space study of the conduction band structure and conductive properties of two semiconductor systems, interesting for their beyond-Moore and quantum computing applications: phosphorus $$\delta$$
δ
-layers and P $$\delta$$
δ
-layer tunnel junctions in silicon. In order to evaluate size quantization effects on the conductivity, we consider two principal cases: nanoscale finite-width structures, used in transistors, and infinitely-wide structures, electrical properties of which are typically known experimentally. For devices widths $$W<10$$

W
<
10

nm, quantization effects are strong and it is shown that the number of propagating modes determines not only the conductivity, but the distinctive spatial distribution of the current-carrying electron states. For $$W>10$$

W
>
10

nm, the quantization effects practically vanish and the conductivity tends to the infinitely-wide device values. For tunnel junctions, two distinct conductivity regimes are predicted due to the strong conduction band quantization.
Sex-based differences in conjunctival goblet cell responses to pro-inflammatory and pro-resolving mediators
Authors: Yang Menglu , Fjærvoll Haakon K. , Fjærvoll Ketil A. , Wang Nicholas H. , Utheim Tor P. , Serhan Charles N. , Dartt Darlene A. 
Published: September 2022

AbstractMany conjunctival inflammatory diseases differ between the sexes and altered conjunctival goblet cells (CGCs) response is often involved. Inflammation is initiated by the release of pro-inflammatory mediators and terminated by the biosynthesis of specialized pro-resolution mediators (SPMs). Herein, we determined the sex-based difference in the responses of CGCs to inflammatory stimuli or pro-resolving lipid SPMs and their interaction with sex hormones. GCs were cultured from pieces of human conjunctiva in RPMI media. CGCs were transferred 24 h before the start of experiments to phenol red-free and FBS-free media to minimize exogenous hormones. RT-PCR, immunofluorescence microscopy (IF), and Western Blot (WB) were performed to determine the presence of sex hormone receptors. Cellular response to pro-inflammatory stimuli or SPMs was studied by measuring the increase in intracellular [Ca2+] ([Ca2+]i) using fura 2/AM microscopy. Use of RT-PCR demonstrated estrogen receptor (ER) α in 4/5 males and 3/3 females; ERβ in 2/4 males and 2/3 females; and androgen receptors (AR) in 3/3 male and 3/3 female CGCs. Positive immunoreactivity by IF and protein expression by WB was detected using antibodies for the ERα and ERβ in 3/3 males and 3/3 females, while AR were only present in males. Significantly different Ca2+ responses between sexes were found with carbachol only at 10–3 M, but not with histamine or leukotriene (LT) B4 at any concentration used. Incubation with dihydrotestosterone (DHT), estrone (E1), or estradiol (E2) at 10–7 M for 30 min significantly inhibited the LTB4-stimulated [Ca2+]i increase in male and female CGCs. Incubation with DHT, E1, and E2 overnight significantly inhibited the LTB4 response in females, while DHT and E2 significantly inhibited the LTB4 response in males. The SPM lipoxin A4 (LXA4) (10–9–10−8 M), but not the resolvins D1 or D2, induced an [Ca2+]i increase that was significantly higher in males compared to females. We conclude that male and female CGCs showed differences in the expression of sex hormone receptors. Treatment with sex hormones altered pro-inflammatory mediator LTB4-induced response. Males compared to females have a higher response to the ω-6-fatty acid derived SPM LXA4, indicating males may terminate inflammation in conjunctival goblet cells faster than females.
Monoamine control of descending pain modulation after mild traumatic brain injury
Authors: Sahbaie Peyman , Irvine Karen-Amanda , Shi Xiao-you , Clark J. David 
Published: September 2022

AbstractTraumatic brain injury (TBI) is a significant public health concern, with the majority of injuries being mild. Many TBI victims experience chronic pain. Unfortunately, the mechanisms underlying pain after TBI are poorly understood. Here we examined the contribution of spinal monoamine signaling to dysfunctional descending pain modulation after TBI. For these studies we used a well-characterized concussive model of mild TBI. Measurements included mechanical allodynia, the efficacy of diffuse noxious inhibitory control (DNIC) endogenous pain control pathways and lumber norepinephrine and serotonin levels. We observed that DNIC is strongly reduced in both male and female mice after mild TBI for at least 12 weeks. In naïve mice, DNIC was mediated through α2 adrenoceptors, but sensitivity to α2 adrenoceptor agonists was reduced after TBI, and reboxetine failed to restore DNIC in these mice. The intrathecal injection of ondansetron showed that loss of DNIC was not due to excess serotonergic signaling through 5-HT3 receptors. On the other hand, the serotonin-norepinephrine reuptake inhibitor, duloxetine and the serotonin selective reuptake inhibitor escitalopram both effectively restored DNIC after TBI in both male and female mice. Therefore, enhancing serotonergic signaling as opposed to noradrenergic signaling alone may be an effective pain treatment strategy after TBI.
Exploration of urinary metabolite dynamicity for early detection of pregnancy in water buffaloes
Authors: Sarangi Archana , Ghosh Mayukh , Sangwan Suman , Kumar Rajesh , Balhara Sunesh , Phulia S. K. , Sharma R. K. , Sahu Subhasish , Kumar Sandeep , Mohanty A. K. , Balhara A. K. 
Published: September 2022

AbstractEarly and precise pregnancy diagnosis can reduce the calving interval by minimizing postpartum period. The present study explored the differential urinary metabolites between pregnant and non-pregnant Murrah buffaloes (Bubalus bubalis) during early gestation to identify potential pregnancy detection biomarkers. Urine samples were collected on day 0, 10, 18, 35 and 42 of gestation from the pregnant (n = 6) and on day 0, 10 and 18 post-insemination from the non-pregnant (n = 6) animals. 1H-NMR-based untargeted metabolomics followed by multivariate analysis initially identified twenty-four differentially expressed metabolites, among them 3-Hydroxykynurenine, Anthranilate, Tyrosine and 5-Hydroxytryptophan depicted consistent trends and matched the selection criteria of potential biomarkers. Predictive ability of these individual biomarkers through ROC curve analyses yielded AUC values of 0.6–0.8. Subsequently, a logistic regression model was constructed using the most suitable metabolite combination to improve diagnostic accuracy. The combination of Anthranilate, 3-Hydroxykynurenine, and Tyrosine yielded the best AUC value of 0.804. Aromatic amino acid biosynthesis, Tryptophan metabolism, Phenylalanine and Tyrosine metabolism were identified as potential pathway modulations during early gestation. The identified biomarkers were either precursors or products of these metabolic pathways, thus justifying their relevance. The study facilitates precise non-invassive urinary metabolite-based pen-side early pregnancy diagnostics in buffaloes, eminently before 21 days post-insemination.
Explainable artificial intelligence (XAI) detects wildfire occurrence in the Mediterranean countries of Southern Europe
Authors: Cilli Roberto , Elia Mario , D’Este Marina , Giannico Vincenzo , Amoroso Nicola , Lombardi Angela , Pantaleo Ester , Monaco Alfonso , Sanesi Giovanni , Tangaro Sabina , Bellotti Roberto , Lafortezza Raffaele 
Published: September 2022

AbstractThe impacts and threats posed by wildfires are dramatically increasing due to climate change. In recent years, the wildfire community has attempted to estimate wildfire occurrence with machine learning models. However, to fully exploit the potential of these models, it is of paramount importance to make their predictions interpretable and intelligible. This study is a first attempt to provide an eXplainable artificial intelligence (XAI) framework for estimating wildfire occurrence using a Random Forest model with Shapley values for interpretation. Our findings accurately detected regions with a high presence of wildfires (area under the curve 81.3%) and outlined the drivers empowering occurrence, such as the Fire Weather Index and Normalized Difference Vegetation Index. Furthermore, our analysis suggests the presence of anomalous hotspots. In contexts where human and natural spheres constantly intermingle and interact, the XAI framework, suitably integrated into decision support systems, could support forest managers to prevent and mitigate future wildfire disasters and develop strategies for effective fire management, response, recovery, and resilience.
Influence of green technology, green energy consumption, energy efficiency, trade, economic development and FDI on climate change in South Asia
Authors: Tariq Gulzara , Sun Huaping , Ali Imad , Pasha Amjad Ali , Khan Muhammad Sohail , Rahman Mustafa Mutiur , Mohamed Abdullah , Shah Qasim 
Published: September 2022

AbstractClimate change policy has several potential risks. The purpose of this study is to investigate the impact of green technology development, green energy consumption, energy efficiency, foreign direct investment, economic growth, and trade (imports and exports) on greenhouse gas (GHG) emissions in South Asia from 1981 to 2018. We employed Breusch Pagan LM, bias-corrected scaled LM, and Pesaran CD as part of a series of techniques that can assist in resolving the problem of cross-sectional dependence. First and second generation unit root tests are used to assess the stationarity of the series, Pedroni and Kao tests are used to test co-integration. The long-term associations are examined using fully modified ordinary least square (FMOLS) and panel dynamic ordinary least square (DOLS) for robustness. The results revealed that trade, growth rate, and exports significantly increase GHG emissions. This accepted the leakage phenomenon. The results also demonstrated that green technology development, green energy consumption, energy efficiency, and imports all have a significant negative correlation with GHG emissions. Imports, advanced technical processes, a transition from non-green energy to green energy consumption, and energy efficiency are thus critical components in executing climate change legislation. These findings highlight the profound importance of green technology development and green energy for ecologically sustainable development in the South Asian countries and act as a crucial resource for other nations throughout the world when it comes to ecological security. This research recommends the consumption of environmentally friendly and energy-efficient technologies in order to mitigate climate change and the government's implementation of the most recent policies to neutralize GHG emissions in order to achieve sustainable development.
Crowding can impact both low and high contrast visual acuity measurements
Authors: Pluháček František , Siderov John , Macháčová Ivana 
Published: September 2022

AbstractThe adverse impact of adjacent contours on letter visual acuity is known as crowding but there is conflicting evidence that foveal crowding may be reduced or disappears under low contrast conditions. Potential differences in foveal crowding with contrast on clinical measurements of visual acuity, including test–retest repeatability, were assessed. Visual acuity was measured at the fovea on adult participants with normal vision under three different contrast levels (− 90, − 10 and − 5%). Three rows of 5 letters, each row differing in size by 0.05 logarithm of the minimum angle of resolution (logMAR) from largest to smallest were displayed at the center of a monitor. Crowding was varied by varying the separation between horizontally adjacent letters from 100% optotype size to 50%, 20% and 10% optotype size. Inter-row spacing was proportional to optotype size. Observers read the letters on the middle row only. Measurements continued by reducing the size of the letters until 3 or more errors were made and were repeated on two separate days. Visual acuity worsened as both letter contrast decreased and inter-optotype separation reduced (expressed as a percentage of letter width). When expressed in minutes of arc of separation the impact of crowding was the same across all contrasts. Crowding occurs for both high and low contrast charts and should be considered when assessing low contrast visual acuity. Test–retest repeatability showed little or no dependence on either contrast or inter-optotype separation.
Patient-derived tumor organoids as a platform of precision treatment for malignant brain tumors
Authors: Chen Chun-Chung , Li Hong-Wei , Wang Yuan-Liang , Lee Chuan-Chun , Shen Yi-Chun , Hsieh Ching-Yun , Lin Hung-Lin , Chen Xian-Xiu , Cho Der-Yang , Hsieh Ching-Liang , Guo Jeng-Hung , Wei Sung-Tai , Wang John , Wang Shao-Chun 
Published: September 2022

AbstractMalignant brain tumors consist of malignancies originated primarily within the brain and the metastatic lesions disseminated from other organs. In spite of intensive studies, malignant brain tumors remain to be a medical challenge. Patient-derived organoid (PDO) can recapitulate the biological features of the primary tumor it was derived from and has emerged as a promising drug-screening model for precision therapy. Here we show a proof-of-concept based on early clinical study entailing the organoids derived from the surgically resected tumors of 26 patients with advanced malignant brain tumors enrolled during December 2020 to October 2021. The tumors included nine glioma patients, one malignant meningioma, one primary lymphoma patient, and 15 brain metastases. The primary tumor sites of the metastases included five from the lungs, three from the breasts, two from the ovaries, two from the colon, one from the testis, one of melanoma origin, and one of chondrosarcoma. Out of the 26 tissues, 13 (50%) organoids were successfully generated with a culture time of about 2 weeks. Among these patients, three were further pursued to have the organoids derived from their tumor tissues tested for the sensitivity to different therapeutic drugs in parallel to their clinical care. Our results showed that the therapeutic effects observed by the organoid models were consistent to the responses of these patients to their treatments. Our study suggests that PDO can recapitulate patient responses in the clinic with high potential of implementation in personalized medicine of malignant brain tumors.
The combined impact of low temperatures and shifting phosphorus availability on the competitive ability of cyanobacteria
Authors: Briddon Charlotte L. , Szekeres Edina , Hegedüs Adriana , Nicoară Maria , Chiriac Cecilia , Stockenreiter Maria , Drugă Bogdan 
Published: September 2022

AbstractIn freshwater systems, cyanobacteria are strong competitors under enhanced temperature and eutrophic conditions. Understanding their adaptive and evolutionary potential to multiple environmental states allows us to accurately predict their response to future conditions. To better understand if the combined impacts of temperature and nutrient limitation could suppress the cyanobacterial blooms, a single strain of Microcystis aeruginosa was inoculated into natural phytoplankton communities with different nutrient conditions: oligotrophic, eutrophic and eutrophic with the addition of bentophos. We found that the use of the bentophos treatment causes significant differences in prokaryotic and eukaryotic communities. This resulted in reduced biodiversity among the eukaryotes and a decline in cyanobacterial abundance suggesting phosphorus limitation had a strong impact on the community structure. The low temperature during the experiment lead to the disappearance of M. aeruginosa in all treatments and gave other phytoplankton groups a competitive advantage leading to the dominance of the eukaryotic families that have diverse morphologies and nutritional modes. These results show cyanobacteria have a reduced competitive advantage under certain temperature and nutrient limiting conditions and therefore, controlling phosphorus concentrations could be a possible mitigation strategy for managing harmful cyanobacterial blooms in a future warmer climate.
Frontiers of the Lower Palaeolithic expansion in Europe: Tunel Wielki Cave (Poland)
Authors: Kot Małgorzata , Berto Claudio , Krajcarz Maciej T. , Moskal-del Hoyo Magdalena , Gryczewska Natalia , Szymanek Marcin , Marciszak Adrian , Stefaniak Krzysztof , Zarzecka-Szubińska Katarzyna , Lipecki Grzegorz , Wertz Krzysztof , Madeyska Teresa 
Published: September 2022

AbstractPeopling of Central Europe by Middle Pleistocene hominids is highly debatable, mainly due to the relatively harsh climatic and environmental conditions that require cultural and anatomical adjustments. At least several archaeological sites certify human occupation in the region dated back to MIS 13-11, but they represent open-air settlements. Based on the new fieldwork conducted in Tunel Wielki Cave, we can date the human occupation traces in the cave to MIS 14-12. Bipolar-on-anvil knapping technique prevails in the lithic assemblage, made exclusively in flint. The obtained results have given ground for studying the frontiers of human oikumene and the required cultural adaptive abilities.
Potential progression biomarkers of diabetic kidney disease determined using comprehensive machine learning analysis of non-targeted metabolomics
Authors: Hirakawa Yosuke , Yoshioka Kentaro , Kojima Kensuke , Yamashita Yasuho , Shibahara Takuma , Wada Takehiko , Nangaku Masaomi , Inagi Reiko 
Published: September 2022

AbstractDiabetic kidney disease is the main cause of end-stage renal disease worldwide. The prediction of the clinical course of patients with diabetic kidney disease remains difficult, despite the identification of potential biomarkers; therefore, novel biomarkers are needed to predict the progression of the disease. We conducted non-targeted metabolomics using plasma and urine of patients with diabetic kidney disease whose estimated glomerular filtration rate was between 30 and 60 mL/min/1.73 m2. We analyzed how the estimated glomerular filtration rate changed over time (up to 30 months) to detect rapid decliners of kidney function. Conventional logistic analysis suggested that only one metabolite, urinary 1-methylpyridin-1-ium (NMP), was a promising biomarker. We then applied a deep learning method to identify potential biomarkers and physiological parameters to predict the progression of diabetic kidney disease in an explainable manner. We narrowed down 3388 variables to 50 using the deep learning method and conducted two regression models, piecewise linear and handcrafted linear regression, both of which examined the utility of biomarker combinations. Our analysis, based on the deep learning method, identified systolic blood pressure and urinary albumin-to-creatinine ratio, six identified metabolites, and three unidentified metabolites including urinary NMP, as potential biomarkers. This research suggests that the machine learning method can detect potential biomarkers that could otherwise escape identification using the conventional statistical method.
A conditional GAN-based approach for enhancing transfer learning performance in few-shot HCR tasks
Authors: Elaraby Nagwa , Barakat Sherif , Rezk Amira 
Published: September 2022

AbstractSupervised learning with the restriction of a few existing training samples is called Few-Shot Learning. FSL is a subarea that puts deep learning performance in a gap, as building robust deep networks requires big training data. Using transfer learning in FSL tasks is an acceptable way to avoid the challenge of building new deep models from scratch. Transfer learning methodology considers borrowing the architecture and parameters of a previously trained model on a large-scale dataset and fine-tuning it for low-data target tasks. But practically, fine-tuning pretrained models in target FSL tasks suffers from overfitting. The few existing samples are not enough to correctly adjust the pretrained model’s parameters to provide the best fit for the target task. In this study, we consider mitigating the overfitting problem when applying transfer learning in few-shot Handwritten Character Recognition (HCR) tasks. A data augmentation approach based on Conditional Generative Adversarial Networks is introduced. CGAN is a generative model that can create artificial instances that appear more real and indistinguishable from the original samples. CGAN helps generate extra samples that hold the possible variations of human handwriting instead of applying traditional image transformations. These transformations are low-level, data-independent operations, and only produce augmented samples with limited diversity. The introduced approach was evaluated in fine-tuning the three pretrained models: AlexNet, VGG-16, and GoogleNet. The results show that the samples generated by CGAN can enhance transfer learning performance in few-shot HCR tasks. This is by achieving model fine-tuning with fewer epochs and by increasing the model’s $$F1-score$$

F
1
-
s
c
o
r
e

and decreasing the Generalization Error $$(E_{test})$$

(

E

test

)

.