Miracle-dz/UniFAR-FTUs huggingface dataset:
Expression, Purification, and Characterization of Bovine Leukemia Virus-Like Particles Produced in Drosophila S2 Cells

Bovine leukemia virus (BLV) is an oncogenic deltaretrovirus that infects cattle worldwide. In Uruguay, it is estimated that more than 70% of dairy cattle are infected, causing serious economic losses due to decreased milk production, increased calving interval, and livestock losses due to lymphosarcoma. Several attempts to develop vaccine candidates that activate protective immune responses against BLV were performed, but up to date, there is no vaccine that ensures efficient protection and/or decreased viral transmission. The development and application of new vaccines that effectively control BLV infection represent a major challenge for countries with a high prevalence of infection. In this study, we generated two Drosophila melanogaster S2 stable cell lines capable of producing BLV virus-like particles (BLV-VLPs). One of them, BLV-VLP1, expressed both Gag and Env wild-type (Envwt) full-length proteins, whereas BLV-VLP2 contain Gag together with a mutant form of Env non-susceptible to proteolytic maturation by cellular furin type enzymes (EnvFm). We showed that Envwt is properly cleaved by cellular furin, whereas EnvFm is produced as a full-length gp72 precursor, which undergoes some partial cleavage. We observed that said mutation does not drastically affect its expression or its entry into the secretory pathway of S2 insect cells. In addition, it is expressed on the membrane and retains significant structural motifs when expressed in S2 insect cells. Morphology and size of purified BLV-VLPs were analyzed by transmission electron microscopy and dynamic light scattering, showing numerous non-aggregated and approximately spherical particles of variable diameter (70–200 nm) as previously reported for retroviral VLPs produced using different expression systems. Furthermore, we identified two N-glycosylation patterns rich in mannose in EnvFm protein displayed on VLP2. Our results suggest that the VLPs produced in Drosophila S2 cells could be a potential immunogen to be used in the development of BLV vaccines that might contribute, in conjunction with other control strategies, to reduce the transmission of the virus.
The Role of Dynamic Social Norms in Promoting the Internalization of Sportspersonship Behaviors and Values and Psychological Well-Being in Ice Hockey

Conducted among parents of young ice hockey players, this field experiment tested if making salient increasingly popular (i.e., dynamic) social norms that promote sportspersonship, learning, and having fun in sports, increases parents’ own self-determined endorsement of these behaviors and values, improves their psychological well-being, and impacts on their children’s on-ice behaviors. Hockey parents (N = 98) were randomly assigned to the experimental condition (i.e., presenting dynamic norms that increasingly favor sportspersonship, learning, and fun) vs. control condition (i.e., presenting neutral information). Parents’ motivations for encouraging their child to learn and to have fun in hockey were then assessed. Score sheets for the games that followed the study provided access to their children’s on-ice behaviors (i.e., penalties), as indicators of sportspersonship. Parents in the experimental condition reported higher self-determination for encouraging their child to learn and have fun in hockey compared to parents in the control condition. Furthermore, children of parents in the experimental condition had more assists. A mediation model revealed that the dynamic norms manipulation increased parents’ self-determined motivation for encouraging their child to learn and to have fun in hockey, which in turn, predicted higher psychological well-being (i.e., lower anxiety, more vitality). Together, these results provide support for the contention that highlighting increasingly popular social norms that promote sportspersonship, learning, and fun in sports, represents a promising strategy for creating positive social change in this life context.
Quantum Capacity of Partially Corrupted Quantum Network

Masahito Hayashi 2, 3, 4, ∗ and Seunghoan Song † Graduate School of Mathematics, Nagoya University, Nagoya, 464-8602, Japan Shenzhen Institute for Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China Center for Quantum Computing, Peng Cheng Laboratory, Shenzhen 518000, China Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, 117542, Singapore (Dated: November 11, 2019)
Multi-Center Randomized Phase II Clinical Trial on Remote Ischemic Conditioning in Acute Ischemic Stroke Within 9 Hours of Onset in Patients Ineligible to Recanalization Therapies (TRICS-9): Study Design and Protocol

Aim: To assess the efficacy of remote ischemic conditioning (RIC) in patients with ischemic stroke within 9 h of onset, that are not candidates for recanalization therapies. Sample Size Estimates: A sample size of 80 patients (40 in each arm) should yield 80% power to detect a 20% difference in early neurological improvement at 72 h at p = 0.05, two sided. Methods and Design: TRICS-9 is a phase II, multicenter, controlled, block randomized, open-label, interventional clinical trial. Patients recruited in Italian academic hospitals will be randomized 1:1 to either RIC plus standard medical therapy or standard medical therapy alone. After randomization, RIC will be applied manually by four alternating cycles of inflation/deflation 5 min each, using a blood pressure cuff around the non-paretic arm. Study Outcomes: The primary efficacy outcome is early neurological improvement, defined as the percent change in the National Institute of Health Stroke Scale (NIHSS) at 72 h in each arm. Secondary outcomes include early neurologic improvement at 24 and 48 h, disability at 3 months, rate of symptomatic intracerebral hemorrhage, feasibility (proportion of patients completing RIC), tolerability after RIC and at 72 h, blood levels of HIF-1α, and HSP27 at 24 h and 72 h. Discussion/Conclusion: RIC in combination with recanalization therapies appears to add no clinical benefit to patients, but whether it is beneficial to those that are not candidates for recanalization therapies is still to be demonstrated. TRICS-9 has been developed to elucidate this issue. Clinical Trial Registration: ClinicalTrials.gov, identifier: NCT04400981.
Corn Grain Yield Prediction and Mapping from Unmanned Aerial System (UAS) Multispectral Imagery

Harvester-mounted yield monitor sensors are expensive and require calibration and data cleaning. Therefore, we evaluated six vegetation indices (VI) from unmanned aerial system (Quantix™ Mapper) imagery for corn (Zea mays L.) yield prediction. A field trial was conducted with N sidedress treatments applied at four growth stages (V4, V6, V8, or V10) compared against zero-N and N-rich controls. Normalized difference vegetation index (NDVI) and enhanced vegetation index 2 (EVI2), based on flights at R4, resulted in the most accurate yield estimations, as long as sidedressing was performed before V6. Yield estimations based on earlier flights were less accurate. Estimations were most accurate when imagery from both N-rich and zero-N control plots were included, but elimination of the zero-N data only slightly reduced the accuracy. Use of a ratio approach (VITrt/VIN-rich and YieldTrt/YieldN-rich) enables the extension of findings across fields and only slightly reduced the model performance. Finally, a smaller plot size (9 or 75 m2 compared to 150 m2) resulted in a slightly reduced model performance. We concluded that accurate yield estimates can be obtained using NDVI and EVI2, as long as there is an N-rich strip in the field, sidedressing is performed prior to V6, and sensing takes place at R3 or R4.
THE Impact of Disruption on the Relationship Between Exploitation, Exploration, and Organizational Adaptation

Firms should deploy exploration and exploitation to foster organizational adaptation. Previous research on exploration and exploitation lacked a focus on disruption implications in different contexts. This study aims to empirically test a moderation model including disruption events, exploration, exploitation, and organizational adaptation and enable a deeper understanding of organizational learning and innovation theory to yield competitive advantage and sustainability of innovative firms. Our results reveal that exploration is more effective during outside disruption events. The results do not support the concept that exploitation is more effective during inside disruptions. Disruptions also moderate the combined effect of exploration and exploitation. Although they are generally complementary in facilitating organizational adaptation, a singular focus on either exploration or exploitation is as effective as is combining exploration and exploitation during inside and outside disruption events. The results of an event study using seven Chinese international firms, including Alibaba, Meituan, Dianping, Baidu, Beibei, TP-link, and Maxio, provided 132 completed and usable questionnaires that supported our hypotheses. Our study contributes to a better understanding of disruption, exploration, exploitation, and related performance implications.
Privacy preserving hybrid recommender system based on deep learning

: Deep learning models are widely being used to provide relevant recommendations in hybrid recommender systems. These hybrid systems combine the advantages of both content based and collaborative filtering approaches. However, these learning systems hamper the user privacy and disclose sensitive information. This paper proposes a privacy preserving deep learning based hybrid recommender system. In hybrid deep neural network, user’s side information such as age, location, occupation, zip code along with user rating is embedded and provided as input. These embedding’s pose a severe threat to individual privacy. In order to eliminate this breach of privacy, we have proposed a private embedding scheme that protects user privacy while ensuring that the nonlinear latent factors are also learnt. In this paper, we address the privacy in hybrid system using differential privacy, a rigorous mathematical privacy mechanism in statistical and machine learning systems. In the reduced feature set, the proposed adaptive perturbation mechanism is used to achieve higher accuracy. The performance is evaluated using three datasets with root mean square error (RMSE), mean absolute error (MAE), mean squared error (MSE), R squared, precision and recall. These evaluation metrics are compared with varying values of privacy parameter ϵ . The experimental results show that the proposed solution provides high user privacy with reasonable accuracy than the existing system. As the engine is generic, it can be used on any recommendation framework.
Predicting Survival of Patients With Rectal Neuroendocrine Tumors Using Machine Learning: A SEER-Based Population Study

Background: The number of patients diagnosed with rectal neuroendocrine tumors (R-NETs) is increasing year by year. An integrated survival predictive model is required to predict the prognosis of R-NETs. The present study is aimed at exploring epidemiological characteristics of R-NETs based on a retrospective study from the Surveillance, Epidemiology, and End Results (SEER) database and predicting survival of R-NETs with machine learning. Methods: Data of patients with R-NETs were extracted from the SEER database (2000–2017), and data were also retrospectively collected from a single medical center in China. The main outcome measure was the 5-year survival status. Risk factors affecting survival were analyzed by Cox regression analysis, and six common machine learning algorithms were chosen to build the predictive models. Data from the SEER database were divided into a training set and an internal validation set according to the year 2010 as a time point. Data from China were chosen as an external validation set. The best machine learning predictive model was compared with the American Joint Committee on Cancer (AJCC) seventh staging system to evaluate its predictive performance in the internal validation dataset and external validation dataset. Results: A total of 10,580 patients from the SEER database and 68 patients from a single medical center were included in the analysis. Age, gender, race, histologic type, tumor size, tumor number, summary stage, and surgical treatment were risk factors affecting survival status. After the adjustment of parameters and algorithms comparison, the predictive model using the eXtreme Gradient Boosting (XGBoost) algorithm had the best predictive performance in the training set [area under the curve (AUC) = 0.87, 95%CI: 0.86–0.88]. In the internal validation, the predictive ability of XGBoost was better than that of the AJCC seventh staging system (AUC: 0.90 vs. 0.78). In the external validation, the XGBoost predictive model (AUC = 0.89) performed better than the AJCC seventh staging system (AUC = 0.83). Conclusions: The XGBoost algorithm had better predictive power than the AJCC seventh staging system, which had a potential value of the clinical application.
Short-Term Variability in Alaska Ice-Marginal Lake Area: Implications for Long-Term Studies

Lakes in direct contact with glaciers (ice-marginal lakes) are found across alpine and polar landscapes. Many studies characterize ice-marginal lake behavior over multi-decadal timescales using either episodic ~annual images or multi-year mosaics. However, ice-marginal lakes are dynamic features that experience short-term (i.e., day to year) variations in area and volume superimposed on longer-term trends. Through aliasing, this short-term variability could result in erroneous long-term estimates of lake change. We develop and implement an automated workflow in Google Earth Engine to quantify monthly behavior of ice-marginal lakes between 2013 and 2019 across south-central Alaska using Landsat 8 imagery. We employ a supervised Mahalanobis minimum-distance land cover classifier incorporating three datasets found to maximize classifier performance: shortwave infrared imagery, the normalized difference vegetation index (NDVI), and spatially filtered panchromatic reflectance. We observe physically-meaningful ice-marginal lake area variance on sub-annual timescales, with the median area fluctuation of an ice-marginal lake found to be 10.8% of its average area. The median signal (slow lake growth) to noise (physically-meaningful short-term area variability) ratio is 1.5:1, indicating that short-term variability is responsible for ~33% of observed area change in the median ice-marginal lake. The magnitude of short-term area variability is similar for ice-marginal and nonglacial lakes, suggesting that the cause of observed variations is not of glacial origin. These data provide a new context for interpreting behaviors observed in multi-decadal studies and encourage attention to sub-annual behavior of ice-marginal lakes even in long-term studies.
“He Who Relies on His Brother's Property Dies Poor”: The Complex Narratives of Livestock Care in Northern Tanzania

Background: Endemic zoonoses have important impacts for livestock-dependent households in East Africa. In these communities, people's health and livelihoods are severely affected by livestock disease losses. Understanding how livestock keepers undertake remedial actions for livestock illness has the potential for widespread benefits such as improving health interventions. Yet, studies about livestock and human health behaviours in the global south tend to focus on individual health choices. In reality, health behaviours are complex, and not solely about individualised health experiences. Rather, they are mediated by a range of “upstream” factors (such as unequal provision of services), which are beyond the control of the individual. Methods: This paper presents qualitative research conducted from 2014 to 2019 for a study focused on the Social, Economic, and Environmental Drivers of Zoonoses in Tanzania (SEEDZ). Qualitative data were collected via focus group discussions, community meetings, informal interviews, formal in-depth interviews, observations and surveys that addressed issues of health, disease, zoonotic disease risks, and routes for treatment across 21 villages. Thematic analysis was carried out on in-depth interviews and focus group discussions. Conceptual analyses and observations were made through application of social science theories of health. Findings: Livestock keepers undertake a range of health seeking strategies loosely categorised around self and formal treatment. Two key themes emerged that are central to why people make the decisions they do: access to resources and trust in health care providers. These two issues affect individual sense of agency which impacts their ability to act to improve livestock health outcomes. We suggest that individual choice and agency in veterinary health seeking decisions are only beneficial if health systems can offer adequate care and health equity is addressed. Significance: This study demonstrates the value of in-depth qualitative research which reveals the nuance and complexity of people's decisions around livestock health. Most importantly, it explains why “better” knowledge does not always translate into “better” practise. The paper suggests that acknowledging and addressing these aspects of veterinary health seeking will lead to more effective provision.
Postural Balance in Individuals With Knee Osteoarthritis During Stand-to-Sit Task

Objective: Stand-to-sit task is an important daily function, but there is a lack of research evidence on whether knee osteoarthritis (knee OA) affects the postural balance during the task. This study aimed to compare individuals with knee OA and asymptomatic controls in postural balance and identify kinematic and lower extremity muscle activity characteristics in individuals with knee OA during the stand-to-sit task. Methods: In total, 30 individuals with knee OA and 30 age-matched asymptomatic controls performed the 30-s Chair Stand Test (30sCST) at self-selected speeds. Motion analysis data and surface electromyography (sEMG) were collected while participants performed the 30sCST. To quantify postural balance, the displacement of the center of mass (CoM) and the peak instantaneous velocity of the CoM were calculated. The kinematic data included forward lean angles of the trunk and pelvic, range of motion (RoM) of the hip, knee, and ankle joints in the sagittal plane. The averaged activation levels of gluteus maximus, vastus lateralis, vastus medialis, rectus femoris, biceps femoris (BF), tibialis anterior (TA), and medial head of gastrocnemius muscles were indicated by the normalized root mean square amplitudes. Results: Compared with the asymptomatic control group, the knee OA group prolonged the duration of the stand-to-sit task, demonstrated significantly larger CoM displacement and peak instantaneous CoM velocity in the anterior-posterior direction, reduced ankle dorsiflexion RoM, greater anterior pelvic tilt RoM, and lower quadriceps femoris and muscles activation level coupled with higher BF muscle activation level during the stand-to-sit task. Conclusion: This study indicates that individuals with knee OA adopt greater pelvic forward lean RoM and higher BF muscle activation level during the stand-to-sit task. However, these individuals exist greater CoM excursion in the anterior-posterior direction and take more time to complete the task. This daily functional activity should be added to the rehabilitation goals for individuals with knee OA. The knee OA group performs reduced ankle dorsiflexion RoM, quadriceps femoris, and TA activation deficit. In the future, the rehabilitation programs targeting these impairments could be beneficial for restoring the functional transfer in individuals with knee OA.
Phytoplankton Genera Structure Revealed from the Multispectral Vertical Diffuse Attenuation Coefficient

The composition of phytoplankton and the concentration of pigments in their cells make their absorption and specific absorption coefficients key parameters for bio-optical modeling. This study investigated whether the multispectral vertical diffuse attenuation coefficient of downward irradiance (Kd) gradients could be a good framework for accessing phytoplankton genera. In situ measurements of remote sensing reflectance (Rrs), obtained in an Amazon Floodplain Lake (Lago Grande do Curuai), were used to invert Kd, focusing on Sentinel-3/Ocean and Land Color Instrument (OLCI) sensor bands. After that, an analysis based on the organization of three-way tables (STATICO) was applied to evaluate the relationships between phytoplankton genera and Kd at different OLCI bands. Our results indicate that phytoplankton genera are organized according to their ability to use light intensity and different spectral ranges of visible light (400 to 700 nm). As the light availability changes seasonally, the structure of phytoplankton changes as well. Some genera, such as Microcystis, are adapted to low light intensity at 550–650 nm, therefore high values of Kd in this range would indicate the dominance of Microcysts. Other genera, such as Aulacoseira, are highly adapted to harvesting blue-green light with higher intensity and probably grow in lakes with lower concentrations of colored dissolved organic matter that highly absorbs blue light (405–498). These findings are an important step to describing phytoplankton communities using orbital data in tropical freshwater floodplains. Furthermore, this approach can be used with biodiversity indexes to access phytoplankton diversity in these environments.
The standard, reverse and total Quasi-Steady-State Approximations revised: Timescale, small parameters, normal forms and singularities in enzyme kinetics

We revisit the scaling analysis and commonly accepted conditions for the validity of the standard, reverse and total quasi-steady-state approximations (sQSSA, rQSSA and tQSSA) through the lens of dimensional Tikhonov--Fenichel small parameters and their respective critical manifolds. By combining Tikhonov--Fenichel small parameter with scaling analysis, we derive improved upper bounds on the error for the sQSSA, rQSSA and tQSSA. We find that the condition for the validity of the rQSSA is far less restrictive than was previously assumed, and we derive a new small parameter that determines the validity of this approximation. Moreover, we show for the first time that the critical manifold of the rQSSA contains a singular point where normal hyperbolicity is lost. Associated with this singularity is a transcritical bifurcation, and the corresponding normal form of this bifurcation is recovered through scaling analysis. It is commonly accepted that the rQSSA is only valid in regions of parameter space where the influence of the singular point can be ignored. However, by using the method of slowly-varying Lyapunov functions, we provide asymptotic estimates for the validity of the rQSSA in parameter regions where the effect of the singular point cannot be disregarded. In doing so, we extend the established domain of validity for the rQSSA. Previous analysis has suggested that the rQSSA is only valid when initial enzyme concentrations greatly exceed initial substrate concentrations. However, our results indicate that this approximation can be valid when initial enzyme and substrate concentrations are of equal magnitude. Consequently, this opens the possibility of utilizing the rQSSA to model enzyme catalyzed reactions and estimate kinetic parameters in enzymatic assays at much lower enzyme to substrate ratios than was previously thought.