Miracle-dz/UniFAR-FTUs huggingface dataset:
“A Delicate balance”—Perceptions and Experiences of ICU Physicians and Nurses Regarding Controlled Donation After Circulatory Death. A Qualitative Study

Graphical Abstract Controlled donation after circulatory death (cDCD) is considered by many as a potential response to the scarcity of donor organs. However, healthcare professionals may feel uncomfortable as end-of-life care and organ donation overlap in cDCD, creating a potential barrier to its development. The aim of this qualitative study was to gain insight on the perceptions and experiences of intensive care units (ICU) physicians and nurses regarding cDCD. We used thematic analysis of in-depth semi-structured interviews and 6-month field observation in a large teaching hospital. 17 staff members (8 physicians and 9 nurses) participated in the study. Analysis showed a gap between ethical principles and routine clinical practice, with a delicate balance between end-of-life care and organ donation. This tension arises at three critical moments: during the decision-making process leading to the withdrawal of life-sustaining treatments (LST), during the period between the decision to withdraw LST and its actual implementation, and during the dying and death process. Our findings shed light on the strategies developed by healthcare professionals to solve these ethical tensions and to cope with the emotional ambiguities. cDCD implementation in routine practice requires a shared understanding of the tradeoff between end-of-life care and organ donation within ICU.
The cyclin dependent kinase inhibitor p21Cip1/Waf1 is a therapeutic target in high-risk neuroblastoma

Platinum-based chemotherapies such as cisplatin are used as first-line treatment for the paediatric tumour neuroblastoma. Although the majority of neuroblastoma tumours respond to therapy, there is a high fraction of high-risk neuroblastoma patients that eventually relapse with increased resistance. Here, we show that one key determinant of cisplatin sensitivity is phosphorylation of the cyclin-dependent kinase inhibitor p21Cip1/Waf1. A panel of eight neuroblastoma cell lines and a TH-MYCN mouse model were investigated for the expression of p21Cip1/Waf1 using RT-qPCR, Western blot, and immunofluorescence. This was followed by investigation of sensitivity towards cisplatin and the p21Cip1/Waf1 inhibitor UC2288. Whereas the cell lines and the mouse model showed low levels of un-phosphorylated p21Cip1/Waf1, the phosphorylated p21Cip1/Waf1 (Thr145) was highly expressed, which in the cell lines correlated to cisplatin resistance. Furthermore, the neuroblastoma cell lines showed high sensitivity to UC2288, and combination treatment with cisplatin resulted in considerably decreased cell viability and delay in regrowth in the two most resistant cell lines, SK-N-DZ and BE(2)-C. Thus, targeting p21Cip1/Waf1 can offer new treatment strategies and subsequently lead to the design of more efficient combination treatments for high-risk neuroblastoma.
Synaptic determinants of cholinergic interneurons hyperactivity during parkinsonism

Parkinson’s disease is a neurodegenerative ailment generated by the loss of dopamine in the basal ganglia, mainly in the striatum. The disease courses with increased striatal levels of acetylcholine, disrupting the balance among these modulatory transmitters. These modifications disturb the excitatory and inhibitory balance in the striatal circuitry, as reflected in the activity of projection striatal neurons. In addition, changes in the firing pattern of striatal tonically active interneurons during the disease, including cholinergic interneurons (CINs), are being searched. Dopamine-depleted striatal circuits exhibit pathological hyperactivity as compared to controls. One aim of this study was to show how striatal CINs contribute to this hyperactivity. A second aim was to show the contribution of extrinsic synaptic inputs to striatal CINs hyperactivity. Electrophysiological and calcium imaging recordings in Cre-mice allowed us to evaluate the activity of dozens of identified CINs with single-cell resolution in ex vivo brain slices. CINs show hyperactivity with bursts and silences in the dopamine-depleted striatum. We confirmed that the intrinsic differences between the activity of control and dopamine-depleted CINs are one source of their hyperactivity. We also show that a great part of this hyperactivity and firing pattern change is a product of extrinsic synaptic inputs, targeting CINs. Both glutamatergic and GABAergic inputs are essential to sustain hyperactivity. In addition, cholinergic transmission through nicotinic receptors also participates, suggesting that the joint activity of CINs drives the phenomenon; since striatal CINs express nicotinic receptors, not expressed in striatal projection neurons. Therefore, CINs hyperactivity is the result of changes in intrinsic properties and excitatory and inhibitory inputs, in addition to the modification of local circuitry due to cholinergic nicotinic transmission. We conclude that CINs are the main drivers of the pathological hyperactivity present in the striatum that is depleted of dopamine, and this is, in part, a result of extrinsic synaptic inputs. These results show that CINs may be a main therapeutic target to treat Parkinson’s disease by intervening in their synaptic inputs.
NANNOCHLOROPSIS OCULATA MICROALGA FOR FERTILIZATION: BIOGUIDED FRACTIONATION OF N-HEXANE EXTRACT BY STIMULATING GROWTH ACTIVITY FOR CUCURBITA MOSCHATA

Biological fertilizers have ability to supply the main nutrient elements from unavailable form to available ones during biological processes and provide favorable conditions for germination and initial growth. Microalgae are the cheap and best source of nitrogen, which does not cause soil and water pollution. Butternut squash ( Cucurbita moschata ) is a type of pumpkin from a group of horticultural annual plants with high agribusiness prospects, which, as many other crops is affected by the use of synthetic fertilizers . In this work, we tested the germination activities of fractions of extracts of the Microalgae Nannochloropsis oculata from the Moroccan coast (El Jadida). Nannochloropsis oculata is a unicellular small green alga belonging to the Eutigmatophyceae. We chose this microalga for our study, based on previous studies the proved its efficiency as a fertilizer. After maceration with solvents with increasing polarity n-hexane, ether, chloroform and water the extracts were tested for their stimulating activity on Cucurbita moschata seed germination. After these preliminary tests, we selected the n-hexane extract of this microalga for fractionation, where we collected eight fractions per gradient of Dichloromethane/Ethyl acetate/Methanol. The resulting fractions of the n-hexane extract were then tested for stimulating activity on Cucurbita moschata growth parameters such as length, weight and number of plants. The overall results show that the fractions from n-hexane extract of Nannochloropsis oculata has important fertilizing activity on the studied crop.
A COMPARATIVE STUDY ON IMAGE FILTERS FOR NOISE REDUCTION IN LUNG CT SCAN IMAGES

The main aim of image enhancement technique is to improve the quality of an image for early diagnosis. Enhanced medical images are desired by a surgeon to help diagnosis and interpretation because medical image qualities are often deteriorated by artifacts and noise. Our targets of medical image enhancement are mainly to solve problems of the high level noise of a medical image. Noise filtering techniques that maintain image contrast while decreasing image noise have the potential to optimize the quality of computed tomography (CT) images acquired at reduced radiation dose. In this paper we compare the various linear and non-linear filters on lung CT scan images for removing the noises and express the results in terms of evaluation metrics such as PSNR, MAE and etc.
Immunomodulatory Effect of Traditional Chinese Medicine Combined with Systemic Therapy on Patients with Liver Cancer: A Systemic Review and Network Meta-analysis

Objective: As immune combination therapy in the treatment of liver cancer made significant achievements, and the modulating effect of traditional Chinese medicine (TCM) on immunity gradually appeared. The main purpose of this study was to study the effect of different TCM combined with systemic therapy (ST) on immune regulation in patients with liver cancer, as well as the efficacy and safety of combined therapy, and to find the best combined application scheme by ranking. Methods: Nine electronic databases were searched from January 1, 2010, to November 12, 2021, to search for RCTs of TCM combined ST in the field of liver cancer for literature screening, quality evaluation and data extraction. STATA 15.0 and RevMan 5.3 software were used to conduct network meta-analysis to analyze and explore the significance of TCM combined ST in immune regulation, efficacy and safety in clinical application. The probability value of the surface under the cumulative ranking curve was used to rank the processing studied. Results: A total of 25 studies involving 2,152 participants were included in the network meta-analysis, including six traditional Chinese medicine injections and seven proprietary Chinese medicines. The results showed that Dahuang Zhechong Wan and Kangai injection combined with ST were the best choices for immune regulation. Moreover, the Huaier granule was the best choice to reduce vascular endothelial growth factors. Conclusion: For patients with liver cancer, TCM combined with ST was better than that of ST alone and can significantly improve the immune function of patients as well as the efficacy and safety of treatment. However, given the limited sample size and methodological quality of the trials that we included in our study, more centralized and randomized controlled trials with a large sample size are required to verify our findings.
Formal Premise Selection With Language Models

Premise selection, the problem of selecting a useful premise to prove a new theorem, is an essential part of theorem proving. Existing language models cannot access knowl-edge beyond a small context window, and therefore are unsatisfactory at retrieving useful premises (i.e., premise selection) from large databases for theorem proving. In this work, we provide a solution to this problem, by combining a premise selection model with a language model. We first select a handful (e.g., 8) of premises from a large theorem database consisting of 100 K premises, and present them in the context along with proof states. The language model then utilizes these premises to construct a proof step. We show that this retrieval-augmented prover achieves significant improvements in proof rates compared to the language model alone.
EXPLORING THE IMPACT OF WORKLOAD ON EMPLOYEE INNOVATIVE BEHAVIOR IN NIGERIAN MANUFACTURING SECTOR: THE ROLE OF PSYCHOLOGICAL CAPITAL

This study develops and tests a conceptual model of the impact of workload on employee's innovative behavior in manufacturing companies in Nigeria and the role of psychological capital. Specifically, the study hypothesizes that workload negatively affects employee's innovative behavior. Psychological capital positively affects employee's innovative behavior and has a moderating role in the relationship between workload and employee's innovative behavior. Using a sample size of 315 which was borne out of questionnaires administered online to employees in Nestle Nigeria Plc, Unilever Nigeria Plc, Nigerian Breweries Company, and Nigeria Bottling Company, the correlation, multiple regression, and hierarchical moderated multiple regression were used to analyze the relations. Results show that workload produces a negative and significant impact on employee's innovative behavior whereas psychological capital produced a positive and significant impact on employee innovative behavior. Results also indicate that the relationship between workload and employee innovative behavior was positively and significantly moderated by psychological capital. The study contributes to the JD-R theory where the outcome presents workload as job demand which could be mitigated by psychological capital which is presented as job resources with the view of promoting employee innovative behavior. Management should ensure workload is fairly managed to empower employees psychologically to ensure innovativeness.
Chaos, Fractals, and Noise: Stochastic Aspects of Dynamics

This is a treatment of a variety of mathematical systems generating densities, ranging from one-dimensional discrete time transformations through continuous time systems described by integro-partial differential equations. Examples have been drawn from a variety of the sciences to illustrate the utility of the techniques presented. This material was organized and written to be accessible to scientists with knowledge of advanced calculus and differential equations. The past few years have witnessed an explosive growth in interest in physical, biological and economic systems that could be profitably studied using densities. Due to the general inaccessibility of the mathematical literature to the non-mathematician, there has been little diffusion of the concepts and techniques from ergodic theory into the study of these "chaotic" systems. This book intends to bridge that gap.
A rotation free formulation for static and free vibration analysis of thin beams using gradient smoothing technique

In this paper, a gradient smoothed formulation is proposed to deal with a fourthorder differential equation of Bernoulli-Euler beam problems for static and dynamic analysis. Through the smoothing operation, the C 1 continuity requirement for fourth-order boundary value and initial value problems can be easily relaxed, and C 0 interpolating function can be employed to solve C 1 problems. In present thin beam problems, linear shape functions are employed to approximate the displacement field, and smoothing domains are further formed for computing the smoothed curvature and bending moment field. Numerical examples indicate that very accurate results can be yielded when a reasonable number of nodes are used.
Automatic registration of multi-temporal remote sensing images based on nature-inspired techniques

In this article, we present nature-inspired techniques for automatic image registration of multi-temporal satellite images. Multi-temporal satellite image registration is becoming increasingly important to aid in flood damage assessment. We consider two images in the registration process: one before-flood image and another during-flood image. The objective is to maximise the similarity metric (of these two images) using information theoretic measures such as mutual information (MI). The maximum MI would imply that the images are better registered. The function of these metrics for transformation parameters is generally non-convex and irregular and, therefore, makes it difficult to use standard optimisation methods for the global solution. In this study, nature-inspired techniques – genetic algorithm (GA), particle swarm optimisation (PSO) and firefly algorithm (FA) are used to search for the maximum MI. The multi-temporal images – Linear Imaging Self-Scanning Sensor III (LISS III) image (before flood) and Synthetic Aperture Radar (SAR) image (during flood) and Moderate Resolution Imaging Spectroradiometer (MODIS) images (before and during flood) are used to demonstrate the performance of the proposed image registration approach. From the results obtained, we compare performance evaluations and conclude that nature-inspired techniques are accurate and reliable in solving satellite image registration.
Finite sample results for lasso and stepwise partialling out Poisson

High-dimensional models that include many covariates which might potentially affect an outcome are increasingly common. One approach to high-dimensional models makes a sparsity assumption which requires that the number of covariates that must be included in the model is small relative to the sample size. As discussed below, this sparse approach to high-dimensional models uses covariate selection and moment conditions that are robust to the mistakes made by the covariate-selection technique to produce reliable inference for some of the model parameters. We call this sparse approach the partialing-out (PO) approach, because it extends the classic partialing-out technique for obtaining some regression coefficients after removing the impact of other covariates. The PO approach was derived in Belloni, Chen, Chernozhukov, and Hansen (2012), Belloni, Chernozhukov, and Hansen (2014), and Belloni, Chernozhukov, and Wei (2016). This paper makes four contributions to the rapidly evolving literture on PO estimators for the parameters of interest. First, it presents simulation evidence that there are DGPs for
EMMM: A Unified Meta-Model for Tracking Machine Learning Experiments

Traditional software engineering tools for managing assets—specifically, version control systems—are inadequate to manage the variety of asset types used in machine-learning model development experiments. Two possible paths to improve the management of machine learning assets include 1) Adopting dedicated machine-learning experiment management tools, which are gaining popularity for supporting concerns such as versioning, traceability, auditability, collaboration, and reproducibility; 2) Developing new and improved version control tools with support for domain-specific operations tailored to machine learning assets. As a contribution to improving asset management on both paths, this work presents Experiment Management Meta-Model (EMMM), a meta-model that unifies the conceptual structures and relationships extracted from systematically selected machine-learning experiment management tools. We explain the meta-model’s concepts and relationships and evaluate it using real experiment data. The proposed meta-model is based on the Eclipse Modeling Framework (EMF) with its meta-modeling language, Ecore, to encode model structures. Our meta-model can be used as a concrete blueprint for practitioners and researchers to improve existing tools and develop new tools with native support for machine-learning-specific assets and operations.
BENCHMARKING MACHINE LEARNING MODELS FOR COLLISION RISK PREDICTION IN LOW EARTH ORBIT

The number of trackable resident space objects (RSOs) is increasing due to new launches and novel space surveillance sensors. Therefore, it is desired to automate the collision risk prediction and mitigation (CREAM) to keep pace with the number of conjunction data messages (CDMs). Recently, the feasibility of leveraging machine learning models has been investigated to reduce the false detections in the literature, and ESA collision risk prediction challenge hosted on Kelvins platform provided the publicly available dataset. The proposed work benchmarks the machine learning models and frameworks that have been studied for collision risk prediction to evaluate their suitability for real world deployment. Therefore, machine learning models are benchmarked against the naive solution, which considers the risk value at cut-off day as risk at close approach time, using JSpOC CDMs issued for satellites that ESA maintains. This work shows that Bayesian neural networks, Siamese-embedding, Boosting decision trees models, and Linear Discriminant Analysis (LDA) are promising machine learning approaches for collision risk prediction.
TETIS: A Catchment Hydrological Distributed Conceptual Model

TETIS is a distributed and conceptually based hydrological model, for the simulation of ﬂood events and continuous simulation. In the model the natural basins are dis-cretizated in grid cells according to drainage network. The conceptualization permits all parameters do not loose its physical meaning. At each cell the main soil prop-erties need to be estimated previously using topographical, environmental, land use, geological and soil maps. This initial approach allows capturing the spatial variability of the main soil characteristics. A geomorphological characterization at grid cell is required as well for runoff propagation through the channels river network. The in-terpolation procedure to obtain spatial variability from point data has been performed using inverse square distance technique, but varying the number of stations to be used and including the possibility of a correlation between height and rainfall and tempera-ture. Runoff production is modelled using ﬁve linked tanks at each cell with different outﬂow relationships at each tank, which represent main hydrological processes as snowmelt, ET, direct runoff, interﬂow and base ﬂow, respectively. The routing along the channel network has been proposed using basin geomorphologic characteristics coupled to the kinematic wave procedure. The SCE-UA methodology has been imple-mented into TETIS model to optimize the initial state variables and parameters. TETIS has been tested in several scenarios including semiarid and humid regions, basin size varying from 10 km 2 to 21500 km 2 , different rain gauge density network values, cell size from 100 m x 100 m to 1000 m x 1000 m and
An exploration of corruption and human trafficking: The south african case

Abstract Human trafficking and corruption are closely related criminal activities. However, correlations between these two phenomena and the actual impact of corruption on human trafficking are common. Neglected in the development and implementation of anti-trafficking strategies and policies. This lack of attention can seriously undermine the initiative to combat human trafficking and prevent the customization of responses as needed. The paper outlined patterns of corruption in human trafficking. Qualitative approach was used and forty interviews were carried out among officials deployed in the criminal justice cluster, as well as with the victims. The key findings indicated that corruption is the major contributor and catalyst to human trafficking, findings further indicated that some officials from stakeholders are involved in corruption involving human trafficking. Recommendations suggest that stakeholders have thus acted as catalyst for increased secondary exploitation of victims. Stakeholder engagement in anti-trafficking policy implementation and service responses are insufficient.
Development of High Performance Molecular Dynamics with Application to Multimillion-Atom Biomass Simulations

An understanding of the recalcitrance of plant biomass is important for efficient economic production of biofuel. Lignins are hydrophobic, branched polymers and form a residual barrier to effective hydrolysis of lignocellulosic biomass. Understanding lignin’s structure, dynamics and its interaction and binding to cellulose will help with finding more efficient ways to reduce its contribution to the recalcitrance. Molecular dynamics (MD) using the GROMACS software is employed to study these properties in atomic detail. Studying complex, realistic models of pretreated plant cell walls, requires simulations significantly larger than was possible before. The most challenging part of such large simulations is the computation of the electrostatic interaction. As a solution, the reaction-field (RF) method has been shown to give accurate results for lignocellulose systems, as well as good computational efficiency on leadership class supercomputers. The particle-mesh Ewald method has been improved by implementing 2D decomposition and thread level parallelization for molecules not accurately modeled by RF. Other scaling limiting computational components, such as the load balancing and memory requirements, were identified and addressed to allow such large scale simulations for the first time. This work was done with the help of modern software engineering principles, including codereview, continuous integration, and integrated development environments. These methods were adapted to the special requirements for scientific codes. Multiple simulations of lignocellulose were performed. The simulation presented primarily, explains the temperature-dependent structure and dynamics of individual softwood