Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of water stress prior to harvest on yield and essential oil content of pot grown lemon balm

Lemon balm (Melissa officinalis L.) is a perennial herbaceous plant widely used in modern and traditional medicine. The aboveground part, particularly the leaves, has many benefits; in traditional medicine, it is used for the preparation of various teas and tea blends, while in modern medicine to obtain essential oil as it is considered a precious product. Nowadays, the needs for lemon balm are mostly met by growing it in the open field. The quality of its leaves as well as the content of its essential oil mostly depend on the way the plant is grown. In stress conditions, medicinal plants use to increase the content of their secondary metabolites. Therefore, this study aimed to determine whether water deficit has an impact on the content of essential oil in lemon balm grown as a pot culture. The experiment was conceived in such a way that at the beginning of the flowering phase, cultivated plants were given different treatments. Treatment A represented plants that were not subjected to water stress, while in treatments B and C, prior to harvesting the plants have been subjected to water stress for 7 and 14 days, respectively. By the end of the experiment, the morphological parameters per plant were measured and the contents of essential oil per treatment determined, all in triplicates. The obtained results indicate that water stress had a great impact on the yield of the aboveground biomass of lemon balm plants as well as on the content of essential oil in their leaves. The highest average yield of the aboveground biomass was obtained in treatment A (1204.0 g) while the highest average yield of the essential oil in treatment C (0.59 g). In short, an increase in water stress increased its positive effects on lemon balm grown as pot culture.
Do Digital Innovation and Risk Disclosure Control Performance? Evidence from Banking in ASEAN-6

The aim of this study to explain the influence of digital innovation and risk disclosure on bank performance in ASEAN countries. The research sample was 70 banks in ASEAN from Indonesia, Malaysia, Thailand, Philippines, Singapore, and Vietnam in the 2015-2018 period in which the annual reports used dual language or English. The study used regression panel analysis. Digital innovation and risk disclosure used content analysis. Indicators of digital innovation used digital branches and mobile banking. The results of the study found that the higher the digital innovation, the greater the bank profitability. However, only mobile banking has a significant effect. The next finding is the disclosure of potential risks in the annual report gave a negative signal to ASEAN banking, resulting in lower bank profitability. The uniqueness of the ASEAN setting is dominated by a very large, dynamic, and rapidly adapting population of productive age to digital technology.
Analysis of Factors Affecting Commitment and Ability of Families to Early Detection in Stunting

Background: Stunting is a public health problem in the world, stunting will have an impact on the growth and development of children. The first 1000 days of life are important to prevent stunting, this is a period when the child’s body system experiences physical growth, intelligence, and children’s abilities. Purpose:This study aims to analyze the factors that influence family commitment and ability to detect stunting. Methods: A cross-sectional study was conducted on 130 respondents in public health center, Surabaya, Indonesia. Data was collected through a questionnaire via Google form, anonymous online research questionnaire was collected through social media, such as Telegram, WhatsApp, Facebook which was conducted from June to September 2020. Data were analyzed using SPSS software version 21 with multivariate logistic regression. Results: The dominant factor affecting the commitment in early detection of stunting was community resources (p = 0.006; CI 95% = 0.888 4.272). Supporting (p = 0.000; CI 95% = 1.757-79.610), empowering (p = 0.000; CI 95% = 0.603 – 18.363) and enabling (p = 0.000; CI 95% = 0.395 – 4.869) were dominant factors that affected the ability to detect early stunting. Conclusion: The behavior of commitment and willingness in early detection of stunting in children is an important role that every parent must have with full support from the family. The main factor influencing family commitment is community resources and the main factor is the willingness of early detection to support, empower and enable families to provide care for their children.
Validation of Friedewald, Martin/Hopkins and Sampson Equations in the Low-Density Lipoprotein Cholesterol Estimation with Different Assays

Background Low-density lipoprotein cholesterol (LDL-C) is an important biomarker for determining cardiovascular risk and regulating lipid lowering therapy. Therefore, the accurate estimation of LDL-C concentration is essential in cardiovascular disease diagnosis and prognosis. Sampson recently proposed a new formula for the estimation of LDL-C. However, little is known regarding the validation of this formula. Objectives This study aimed to validate this new formula with other well-known formulas in Turkish population, composed of adults. Methods A total of 88,943 participants above 18 years old at Sivas Cumhuriyet University Hospital (Sivas, Turkey) were included to this study. LDL-C was directly measured by homogeneous assays, i.e., Roche, Beckman and Siemens and estimated by Friedewalds, Martin/Hopkins, extended Martin/Hopkins and Sampsons formulas. The accuracies between the estimations obtained by the formulas and the direct measurements were evaluated both in general and separately for the LDL-C, TG and non-HDL-C sublevels. Linear regression analysis was applied and residual error plots were generated between each estimation and direct measurement method. Coefficient of determination (R2) and mean absolute deviations were also calculated. Results The results showed that the extended Martin/Hopkins approach was the overall most accurate approach for LDL-C estimation. The results also showed that the highest concordances were obtained between the direct assays with the extended Martin/Hopkins formula calculated with the median statistics obtained from our own population. On the other hand, it was observed that the results of the methods may differ in different assays. Although the best performance was obtained by the Sampson formula in patients with "low LDL-C level and hypertriglyceridemia", the extended Martin/Hopkins approach, calculated from the median statistics of our population, gave the most accurate results in patients with "low LDL-C level or hypertriglyceridemia". Conclusions In conclusion, in patients with a low LDL-C level or hypertriglyceridemia, the extended Martin/Hopkins method; in patients with a low LDL-C level and hypertriglyceridemia, the Sampson method may be preferred. The validity of the Martin Hopkins and Sampsons formulas has to be further investigated in different populations.
Serum Neutralizing Activity of mRNA-1273 Against the SARS-CoV-2 B.1.1.529 (Omicron) Variant: A Preliminary Report

The emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) B.1. 1.529 (Omicron) variant has led to growing concerns of increased transmissibility and escape of both natural and vaccine-induced immunity. In this analysis, sera from adult participants in a phase 2 clinical study (NCT04405076) were tested for neutralizing activity against B.1. 1.529 after a 2-dose (100 g) mRNA-1273 primary vaccination series and after a 50-g mRNA-1273 booster dose. Results from this preliminary analysis show that 1 month after completing the primary series, mRNA-1273-elicited serum neutralization of B.1. 1.529 was below the lower limit of quantification; however, neutralization was observed at 2 weeks after the mRNA-1273 booster dose, although at a reduced level relative to wild-type SARS-CoV-2 (D614G) and lower than that observed against D614G at 1 month after the primary series.
Evaluation of the systemic and mucosal immune response induced by COVID-19 and the BNT162b2 mRNA vaccine for SARS-CoV-2

Background The currently used SARS-CoV-2 mRNA vaccines have proven to induce a strong and protective immune response. However, functional relevance of vaccine-generated antibodies and their temporal progression are still poorly understood. Thus, the central aim of this study is to gain a better understanding of systemic and mucosal humoral immune response after mRNA vaccination with BNT162b2. Methods We compared antibody production against the S1 subunit and the RBD of the SARS-CoV-2 spike protein in sera of BNT162b2 vaccinees, heterologous ChAdOx1-S/BNT162b2 vaccinees and COVID-19 patients. We monitored the neutralizing humoral response against SARS-CoV-2 wildtype strain and three VOCs over a period of up to eight months after second and after a subsequent third vaccination. Results In comparison to COVID-19 patients, vaccinees showed higher or similar amounts of S1- and RBD-binding antibodies but similar or lower virus neutralizing titers. Antibodies peaked two weeks after the second dose, followed by a decrease three and eight months later. Neutralizing antibodies (nAbs) poorly correlated with S1-IgG levels but strongly with RBD-IgGAM titers. After second vaccination we observed a reduced vaccine-induced neutralizing capacity against VOCs, especially against the Omicron variant. Compared to the nAb levels after the second vaccination, the neutralizing capacity against wildtype strain and VOCs was significantly enhanced after third vaccination. In saliva samples, relevant levels of RBD antibodies were detected in convalescent samples but not in vaccinees. Conclusions Our data demonstrate that BNT162b2 vaccinated individuals generate relevant nAbs titers, which begin to decrease within three months after immunization and show lower neutralizing potential against VOCs as compared to the wildtype strain. Large proportion of vaccine-induced S1-IgG might be non-neutralizing whereas RBD-IgGAM appears to be a good surrogate marker to estimate nAb levels. A third vaccination increases the nAb response. Furthermore, the systemic vaccine does not seem to elicit readily detectable mucosal immunity.
Knowledge of Postgraduate Research Scholars of Anand Agricultural University about Global Warming

Global warming has an impact on our planet, agriculture, human and animal lives. The future problems causedby global warming are raising seas, growing deserts and more frequent droughts all look set to affect the developing world more than rich countries. In an effort to combat against global warming issues related to agricultural development, agricultural research scholars as a future generation of agricultural development need to possess general awareness and knowledge about global warming issuesand problems. Focused and timely efforts to apply knowledge by the agricultural research scholars can enable them to stand successfully against the global warming problems to play a leadershiprole in overall development of agriculture. The present study was undertaken on a random sample of 100 postgraduate research scholars studying in agriculture faculty of B. A. College of Agriculture, Anand Agricultural University located at Anand of Gujarat state. The selected dependent and independent variables were measured with the help of suitable scale and procedures. The level of knowledge of the postgraduate scholars was observed significantly higher among those who had higher academic performance, Involvement in extra curricular activity, information collection habit, more than one occupation of family, elevated scientific orientation and positive attitudes toward IPM.
FVC-NET: An Automated Diagnosis of Pulmonary Fibrosis Progression Prediction Using Honeycombing and Deep Learning

Pulmonary fibrosis is a severe chronic lung disease that causes irreversible scarring in the tissues of the lungs, which results in the loss of lung capacity. The Forced Vital Capacity (FVC) of the patient is an interesting measure to investigate this disease to have the prognosis of the disease. This paper proposes a deep learning-based FVC-Net architecture to predict the progression of the disease from the patient's computed tomography (CT) scan and the patient's metadata. The input to the model combines the image score generated based on the degree of honeycombing for a patient identified based on segmented lung images and the metadata. This input is then fed to a 3-layer net to obtain the final output. The performance of the proposed FVC-Net model is compared with various contemporary state-of-the-art deep learning-based models, which are available on a cohort from the pulmonary fibrosis progression dataset. The model showcased significant improvement in the performance over other models for modified Laplace Log-Likelihood (−6.64). Finally, the paper concludes with some prospects to be explored in the proposed study.
A phenotypic and molecular investigation of biofilm formation in clinical samples of Pseudomonas aeruginosa

Pseudomonas aeruginosa is identified as a versatile opportunistic microorganism with metabolic diversity contributing to a wide range of health burdens, especially in immunocompromised patients. This bacterium is the cause of 10 to 20% of nosocomial infections. In this study, we evaluated the phenotypic characterizations of biofilm formation in P. aeruginosa clinical isolates using micro-titer plate assay. Indeed, we estimated the prevalence of QS (rhlI, rhlR, rhlAB, lasB, lasI, lasR, aprA) and virulence genes (pslA and cupA) by PCR. The results showed that among 69% of the isolates forming biofilm, 9% were strong biofilm producers, whereas 13% and 47% of isolates produced moderate and low amounts of biofilm, respectively. All isolates possessed cupA and seven QS genes (rhlI, rhlR, rhlAB, lasB, lasI, lasR, aprA), while 92% of the isolates possessed the pslA gene. Identification of these genes and their association with biofilm formation can be advantageous in adopting therapeutic methods.
LncRNA GAS5 Interacts with MicroRNA-10b to Inhibit Cell Proliferation and Migration and Induces Apoptosis in Colorectal Cancer

Objective The purpose of this study was to study the effects of the GAS5/microRNA-10b (miR-10b) axis on proliferation, migration, and apoptosis of colorectal cancer (CRC). Methods The expression levels of GAS5 and miR-10b in CRC tissues and cells were detected by quantitative real-time polymerase chain reaction (qRT-PCR). Wound healing experiment was used to detect the effects of GAS5 and miR-10b on the migration of CRC cells. The luciferase reporter gene experiment was used to verify miRNA targets. Immunohistochemical assay was used to detect the expression of proteins related to metastasis and apoptosis in tumor tissues. Results The expression of GAS5 was downregulated in CRC tissues and cell lines. The overexpression of GAS5 can inhibit cell proliferation and progression, induce apoptosis in vitro, and inhibit the growth of CRC tumor in vivo. In contrast, the expression of miR-10b, a downstream target of GAS5, was increased in CRC tissues and cells. Suppression of the miR-10b gene can inhibit proliferation and metastasis and cause apoptosis of CRC cells. In addition, luciferase reports show that GAS5 inhibits the progression of CRC cells by binding to miR-10b. Rescue experiments showed that overexpressed miR-10b could reverse GAS5-mediated antitumor effect on CRC cells in vivo and in vitro. Conclusions LncRNA GAS5 interacts with miR-10b to inhibit cell proliferation and migration and induces apoptosis in colorectal cancer. GAS5 and miR-10b could become potential therapeutic targets for CRC.
Changes of Routine Hematological Parameters in COVID-19 Patients: Correlation with Imaging Findings, RT-PCR and Outcome

Background & Objective: Coronavirus disease 2019 (COVID-19) is progressively spreading, and many researchers have focused on the prognostic value of laboratory analyses. This study reviewed routine blood parameters, upper respiratory viral load, and chest imaging in recovered and expired COVID-19 patients and evaluated possible correlations. Methods: In this retrograde study, 138 COVID-19 cases were enrolled. Chest tomography scores of patients, routine hematologic and biochemical parameters, and respiratory viral loads were measured. Furthermore, their correlation with severity of disease and the outcome was investigated during a week of admission. Results: The mean age of participants was 58.6±16; 36.2% of whom were diagnosed as critical, 8.7% expired, and 46% showed less than 50% lung opacity. The expiring rate was only correlated to the severity of illness and viral load. During admission, hemoglobin concentration was decreased in critical patients (from 11.49±0.27 to 10.59±0.36, P=0.042) and also among CT-scan scoring groups (P=0.000), while neutrophils (P=0.04), WBC (P=0.03), and platelets (P=0.000) count were increased. In patients with more than 50% lung opacity, leukocyte counts were decreased, but neutrophil and platelets counts showed raise (all P<0.05), while other hematologic parameters did not change. CRP and LDH demonstrated no increase based on the severity of the illness, RT-PCR viral loads and/or outcome. However, both CRP and LDH were increased in patients with more than 50% lobal opacity (CRP: 69.3±9.9 to 1021.1±7.5 and LDH:589.5±93.2 to 1128.6±15.81, P<0.05). Conclusion: We found that hemoglobin, white blood cells, neutrophil, lymphocytes, and platelets count together with chest tomography score might be beneficial for expedition the diagnosis, assessmen the severity of the disease, and outcome in the hospitalized cases, while CRP and LDH might be considered as the consequence of lung involvement.
Limitations to Electrical Probing of Spontaneous Polarization in Ferroelectric-Dielectric Heterostructures

An accurate estimate of the ferroelectric polarization in ferroelectric-dielectric stacks is important from a materials science perspective, and it is also crucial for the development of ferroelectric based electron devices. This paper revisits the theory and application of the PUND technique in Metal-Ferroelectric-Dielectric-Metal (MFDM) structures by using analytical derivations and numerical simulations. In an MFDM structure the results of the PUND technique may largely differ from the polarization actually switched in the stack, which in turn is different from the remnant polarization of the underlying ferroelectric. The main hindrances that prevent PUND measurements from providing a good estimate of the polarization switching in MFDM stacks are thus discussed. The inspection of the involved physical quantities, not always accessible in experiments, provides a useful insight about the main sources of the errors in the PUND technique, and clarifies the delicate interplay between the depolarization field and the charge injection and trapping in MFDM stacks with a thin dielectric layer.
PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence Embeddings

Learning sentence embeddings in an unsupervised manner is fundamental in natural language processing. Recent common practice is to couple pre-trained language models with unsupervised contrastive learning, whose success relies on augmenting a sentence with a semantically-close positive instance to construct contrastive pairs. Nonetheless, existing approaches usually depend on a mono-augmenting strategy, which causes learning shortcuts towards the augmenting biases and thus corrupts the quality of sentence embeddings. A straightforward solution is resorting to more diverse positives from a multi-augmenting strategy, while an open question remains about how to unsupervisedly learn from the diverse positives but with uneven augmenting qualities in the text field. As one answer, we propose a novel Peer-Contrastive Learning (PCL) with diverse augmentations. PCL constructs diverse contrastive positives and negatives at the group level for unsupervised sentence embeddings. PCL performs peer-positive contrast as well as peer-network cooperation, which offers an inherent anti-bias ability and an effective way to learn from diverse augmentations. Experiments on STS benchmarks verify the effectiveness of PCL against its competitors in unsupervised sentence embeddings.
Toward Training at ImageNet Scale with Differential Privacy

Differential privacy (DP) is the de facto standard for training machine learning (ML) models, including neural networks, while ensuring the privacy of individual examples in the training set. Despite a rich literature on how to train ML models with differential privacy, it remains extremely challenging to train real-life, large neural networks with both reasonable accuracy and privacy. We set out to investigate how to do this, using ImageNet image classification as a poster example of an ML task that is very challenging to resolve accurately with DP right now. This paper shares initial lessons from our effort, in the hope that it will inspire and inform other researchers to explore DP training at scale. We show approaches that help make DP training faster, as well as model types and settings of the training process that tend to work better in the DP setting. Combined, the methods we discuss let us train a Resnet-18 with DP to $47.9\%$ accuracy and privacy parameters $\epsilon = 10, \delta = 10^{-6}$. This is a significant improvement over"naive"DP training of ImageNet models, but a far cry from the $75\%$ accuracy that can be obtained by the same network without privacy. The model we use was pretrained on the Places365 data set as a starting point. We share our code at https://github.com/google-research/dp-imagenet, calling for others to build upon this new baseline to further improve DP at scale.
On the Anonymity of Peer-To-Peer Network Anonymity Schemes Used by Cryptocurrencies

Cryptocurrency systems can be subject to deanonimization attacks by exploiting the network-level communication on their peer-to-peer network. Adversaries who control a set of colluding node(s) within the peer-to-peer network can observe transactions being exchanged and infer the parties involved. Thus, various network anonymity schemes have been proposed to mitigate this problem, with some solutions providing theoretical anonymity guarantees. In this work, we model such peer-to-peer network anonymity solutions and evaluate their anonymity guarantees. To do so, we propose a novel framework that uses Bayesian inference to obtain the probability distributions linking transactions to their possible originators. We characterize transaction anonymity with those distributions, using entropy as metric of adversarial uncertainty on the originator's identity. In particular, we model Dandelion, Dandelion++ and Lightning Network. We study different configurations and demonstrate that none of them offers acceptable anonymity to their users. For instance, our analysis reveals that in the widely deployed Lightning Network, with 1% strategically chosen colluding nodes the adversary can uniquely determine the originator for about 50% of the total transactions in the network. In Dandelion, an adversary that controls 15% of the nodes has on average uncertainty among only 8 possible originators. Moreover, we observe that due to the way Dandelion and Dandelion++ are designed, increasing the network size does not correspond to an increase in the anonymity set of potential originators. Alarmingly, our longitudinal analysis of Lightning Network reveals rather an inverse trend -- with the growth of the network the overall anonymity decreases.