Miracle-dz/UniFAR-FTUs huggingface dataset:
Survival Advantage of Both Human Hepatocyte Xenografts and Survival Advantage of Both Human Hepatocyte Xenografts and Genome-Edited Hepatocytes for Treatment of alpha-1 Antitrypsin Genome-Edited Hepatocytes for Treatment of alpha-1 Antitrypsin Deficiency Deficiency

Hepatocytes represent an important target for gene therapy and editing of single-gene disorders. In a -1 antitrypsin (AAT) de ﬁ ciency, one missense mutation results in impaired secretion of AAT. In most patients, lung damage occurs due to a lack of AAT-mediated protection of lung elastin from neutrophil elastase. In some patients, accumulation of misfolded PiZ mutant AAT protein triggers hepatocyte injury, leading to in ﬂ ammation and cirrhosis. We hypothesized that correcting the Z mutant defect in hepatocytes would confer a selective advantage for repopulation of hepatocytes within an intact liver. A human PiZ allele was crossed onto an im-mune-de ﬁ cient (NSG) strain to create a recipient strain (NSG-PiZ) for human hepatocyte xenotransplantation. Results indicate that NSG-PiZ recipients support heightened engraftment of normal human primary hepatocytes as compared with NSG recipients. This model can therefore be used to test hepatocyte cell therapies for AATD, but more broadly it serves as a simple, highly reproducible liver xenograft model. Finally, a promoterless adeno-associated virus (AAV) vector, expressing a wild-type AAT and a synthetic miRNA to silence the endogenous allele, was integrated into the albumin locus. This gene-editing approach leads to a selective advantage of edited hepatocytes, by silencing the mutant protein and aug-menting normal AAT production, and improvement of the liver pathology.
The Use of Synchronous Videoconference in Bipolar Patients

Background: A considerable literature has developed around demonstrating the clinical relevance of mentalizing as a construct. However, much of the emphasis has been on patients’ deficits rather than therapist’s abilities. Although it may be the case that therapist’s mentalization capacity can facilitate better outcomes in psychotherapy, there is a dearth of empirical evidence concerning the impact mentalization has on therapist competencies in psychotherapy and the implications of this in clinical practice dyads. Methods/Design: A pilot study will use an integrated design. A longitudinal case series alongside a qualitative grounded theory approach will be utilized to develop a context-specific, grounded micro theory model of therapeutic alliance rupture and resolution during online psychotherapy with patients with bipolar disorder. 10 dyads of therapists and patients will be assessed by pre-and post-session outcome ratings, as well as baseline and post-therapy performance ratings during the four-month intervention period. In addition, semi-structured grounded theory interviews will be conducted with participants to gain a deeper understanding of their experiences during the therapeutic process. Discussion: New research findings on videoconferencing-enabled clinical interventions have been needed since the COVID-19 pandemic began. According to an assessment of the available evidence, little is known about psychotherapy, and significant gaps remain. This paper describes a protocol of a pilot aimed to capture the explicit and implicit knowledge that emerge from therapists and patients during the therapeutic process in order to investigate the complex process of therapeutic interaction beyond “outcome effects”.
Analysis of Immune and Inflammation Characteristics of Atherosclerosis from Different Sample Sources

Background Atherosclerosis is the predominant cause of cardiovascular diseases. Existing studies suggest that the development of atherosclerosis is closely related to inflammation and immunity, but whether there are differences and similarities between atherosclerosis occurring at different sites is still unknown. We elucidated the pathological characteristics of peripheral vascular diseases by using bioinformatic analyses on immune cells and inflammation-related gene expression in atherosclerotic arteries and plaques. Methods Eight data sets regarding atherosclerosis were downloaded from the Gene Expression Omnibus database. Human immune genes were obtained from the IMMPORT website. The samples were scored and divided into high- and low-immune groups. Then the samples were analysed using weighted gene co-expression network analysis, while the modules were analysed using functional enrichment. The protein–protein interaction network was constructed using the STRING and Cytoscape databases. The hub immune genes were screened, and the correlation between hub immune genes and immune cells was analysed. Results Immune cells and their functions were significantly different during atherosclerosis development. The infiltration proportion of immune cells was approximately similar in samples from different sources of patients with carotid atherosclerosis. However, the sensitivity of lower extremity atherosclerosis samples to immune cells is lower than that of carotid atherosclerosis samples.The samples from the plaque and artery were mainly infiltrated by macrophages, T cells and mast cells. After immune cells were assessed, resting NK cells, activated mast cells and M0 macrophages were found to be key immune cells in atherosclerosis and plaque formation. In addition, CCL4, TLR2, IL1B and PTPRC were considered to be immune marker genes in atherosclerosis development. Conclusion. Bioinformatic data analysis confirms the essential role of immune cells in cardiovascular diseases, and also indicates some differences of immune and inflammation characteristics of atherosclerosis between carotid and lower extremity arteries.
Psychometric Properties of Indonesian HEXACO-100: A Facet Level Analysis

The HEXACO personality model is a six-dimensional personality model that is partially independent from the Big Five personality model (Zettler et.al., 2020). Measurement using the HEXACO Personality Inventory-Revised, its sixth dimension, Honesty-Humility, can capture previously unexplored images of personality. This study aims to adapt the HEXACO Personality Inventory-Revised 100 items version (HEXACO-100) into the Indonesian language and examine its psychometric properties. A purposive sample of 605 participants responded to Indonesian HEXACO-100 (67,2% female; mean age: 25,1 years). The result showed that all six factors have good alpha reliability (all factors α > .7), and the exploratory factor analysis and correlation analysis between the dimensions support the validity of the six-dimensional personality model. It can be concluded that this study successfully adapted the HEXACO-100 personality inventory into the Indonesian language, strengthens the existence of a six-dimensional personality model in Indonesia, and underlines that the HEXACO personality inventory is a useful and reliable personality inventory for investigating personality structure in cross-cultural settings.
OutputContraction and Decreased Bone Marrow Associated with Impaired Ig Locus Cells as Compared with Children Is Increased ID2 Levels in Adult Precursor B

Precursor B cell production from bone marrow in mice and humans declines with age. Because the mechanisms behind are still unknown, we studied ﬁve precursor B cell subsets (ProB, PreBI, PreBII large, PreBII small, immature B) and their differentiation-stage characteristic gene expression proﬁles in healthy individual toddlers and middle-aged adults. Notably, the composition of the precursor B cell compartment did not change with age. The expression levels of several transcripts encoding V(D)J recombination factors were decreased in adults as compared with children: RAG1 expression was signiﬁcantly reduced in ProB cells, and DNA-PKcs, Ku80, and XRCC4 were decreased in PreBI cells. In contrast, TdTwas 3-fold upregulated in immature B cells of adults. Still, N-nucleotides, P-nucleotides, and deletions were similar for IGH and IGK junctions between children and adults. PreBII large cells in adults, but not in children, showed highly upregulated expression of the differentiation inhibitor, inhibitor of DNA binding 2 (ID2), in absence of changes in expression of the ID2-binding partner E2A. Further, we identiﬁed impaired Ig locus contraction in adult precursor B cells as a likely mechanism by which ID2-mediated blocking of E2A function results in reduced bone marrow B cell output in adults. The reduced B cell production was not compensated by increased proliferation in adult immature B cells, despite increased Ki67 expression. These ﬁndings demonstrate distinct regulatory mechanisms in B cell differentiation between adults and children with a central role for transcriptional regulation of ID2. The Journal of Immunology , 2013, 191: 000–000.
Quantification and mitigation of the instrument effects and uncertainties of the airborne limb imaging FTIR GLORIA

Abstract. The Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA) is an infrared imaging FTS (Fourier transform spectrometer) with a 2-D infrared detector that is operated on two high-flying research aircraft. It has flown on eight campaigns and measured along more than 300 000 km of flight track. This paper details our instrument calibration and characterization efforts, which, in particular, almost exclusively leverage in-flight data. First, we present the framework of our new calibration scheme, which uses information from all three available calibration sources (two blackbodies and upward-pointing “deep space” measurements). Part of this scheme is a new algorithm for correcting the erratically changing nonlinearity of a subset of detector pixels and the identification of the remaining bad pixels. Using this new calibration, we derive a 1σ bound of 1 % on the instrument gain error and a bound of 30 nW cm−2 sr−1 cm on the instrument offset error. We show how we can examine the noise and spectral accuracy for all measured atmospheric spectra and derive a spectral accuracy of 5 ppm on average. All these errors are compliant with the initial instrument requirements. We also discuss, for the first time, the pointing system of the GLORIA instrument. Combining laboratory calibration efforts with the measurement of astronomical bodies during the flight, we can achieve a pointing accuracy of 0.032∘, which corresponds to one detector pixel. The paper concludes with a brief study of how these newly characterized instrument parameters affect temperature and ozone retrievals. We find that the pointing uncertainty and, to a lesser extent, the instrument gain uncertainty are the main contributors to the error in the result.
Quantification of groundwater storage variation in Himalayan & Peninsular River basins correlating with land deformation effects observed at different Indian cities

Groundwater is a significant resource that supports almost one-fifth population globally, but has been is diminishing at an alarming rate in recent years. To delve into this objective more thoroughly, we calculated interannual (2002–2020) GWS (per grid) distribution using GRACE & GRACE-FO (CSR-M, JPL-M and SH) Level 3 RL06 datasets in seven Indian river basins and found comparatively higher negative trends (−20.10 ± 1.81 to −8.60 ± 1.52 mm/yr) in Basin 1–4 than in Basin 5–7 (−7.11 ± 0.64 to −0.76 ± 0.47 mm/yr). After comparing the Groundwater Storage (GWS) results with the CHIRPS (Climate Hazards Group Infrared Precipitation with Stations) derived SPI (Standardized Precipitation Index) drought index, we found that GWS exhausts analogously in the same period (2005–2020) when SPI values show improvement (∼1.89–2), indicating towards wet condition. Subsequently, the GWSA time series is decomposed using the STL (Seasonal Trend Decomposition) (LOESS Regression) approach to monitor long-term groundwater fluctuation. The long term GWS rate (mm/yr) derived from three GRACE & GRACE-FO solutions vary from −20.3 ± 5.52 to −13.19 ± 3.28 and the GWS mass rate (km3/yr) lie in range of −15.17 ± 4.18 to −1.67 ± 0.49 for basins 1–3. Simultaneously, in basin 4–7 the GWS rate observed is −8.56 ± 8.03 to −0.58 ± 7.04 mm/yr, and the GWS mass rate differs by −1.71 ± 0.64$ to −0.26 ± 3.19 km3/yr. The deseasonalized GWS estimation (2002–2020) states that Himalayan River basins 1,2,3 exhibit high GWS mass loss (−260 to −35.12 km3), with Basin 2 being the highest (−260 km3). Whereas the Peninsular River basin 4,6,7 gives moderate mass loss value from −26.72 to −23.58 km3. And in River basin 5, the GWS mass loss observed is the lowest, with a value of −8 km3.
Influence of doping additive on thermophysical and rheological properties of halogen-free polymer composition for cable insulation and sheaths

Introduction. The demand for halogen-free fire-resistant compositions for the manufacture of fire-retardant wires and cables is constantly growing. Problem. Therefore, the creation and further processing of these materials is an urgent problem. Goal. The aim of the article is to study the effect of the doping additive on the thermophysical and rheological properties of halogen-free compositions for power cables with voltage 1 kV with the determination of both the temperatures of phase and structural transformations of polymer compositions. Methodology. Experiments investigating the phase transformations were carried out with the help device of thermogravimetric analysis and differential scanning calorimetry TGA/DSC 1/1100 SF of METTLER TOLEDO company. Rheological studies of polymeric materials were conducted by using the method of capillary viscosimetry in the device IIRT–AM. Results. The influence of the doping additive on the formation of the supramolecular structure of the filled polymer compositions for cable products was determined, that resulted in the temperature increase of the decomposition beginning by 11 °С and the end of decomposition by 7 °С. Originality. The effect of a doping additive on reducing the effective melt viscosity of a polymer composition from 6·104 to 1·104 Pa·s with increasing shear rate has been shown for the first time. The shear rate of the polymer composition containing the doping additive increases from 0.5 to 20 s–1 with increasing shear stress. Practical value. The research results provide an opportunity to reasonably approach the development of effective technological processes for the manufacture of the insulation and sheaths of power cables from halogen-free polymer compositions.
Reiki sessions in professionals of a public university: randomized clinical trial

Ambiente ABSTRACT Aim: The aim of this study was to evaluate the effectiveness of reiki therapy in professionals of a public university through the perception of subjective well-being. Method: This is a single-blind, randomized clinical trial with an experimental group and a control group. Twenty-eight professionals participated in the study. The experimental group received three Reiki sessions and the control group received three false sessions. Results: the ANOVA test showed significance in reducing the “negative affect” dimension in the experimental group (p=0.023). Conclusion: Reiki reduces “negative affect” and can therefore reduce unpleasant sensations in workers and serve as an effective tool for care.
Test of ocean tide loading models on the basis of strain data measured at the Vyhne Tidal Station, Slovakia

Tidal strain time series recorded at the Vyhne Tidal Station are used to test thirteen different ocean tide loading models. Ten models have been calculated using SPOTL: EOT11a, HAMTIDE11a, OSU.TPXO72atlas, OSU.TPXO72, TPX070, DTU10, CSR4.0, FES2004, FES95.2. 1, SCHW1 and three other models were chosen from the Free Ocean Tide Loading Provider (FOTLP) created by Scherneck and Bos: FES2012, FES2014b, GOT00.2. Hourly sampled strain data, corrected for temperature, were subjected to correction for ocean tide loading. The test of models was focused on the diurnal and semi-diurnal tidal harmonic constituents O1, P1, K1 and M2. A negligible difference between the individual global ocean tide loading models was only found mainly due to using different Earth models and Green functions. The amplitude factors for O1, P1, K1 and M2 derived from the measurements are 1.019, 1.226, 0.842 and 1.131, respectively. The average amplitude factors for these tidal components were obtained after ocean load correction using SPOTL routines: 1.121, 1.332, 0.916, 1.283, and in the case of the three models using FOTLP: 1.046, 1.486, 1.067, 1.317. The corrected amplitude factor became closer to the theoretical value only for K1.
Teaching Group Decision Making Skills to Emergency Managers via Digital Games

This chapter discusses how a focus on establishing the appropriate learning outcomes of an educational programme, and creatively incorporating these learning outcomes within the design of a game, can lead to the development of a useful educational game. Specifically, it demonstrates the process involved in generating game design criteria from a multi-disciplinary literature review. The design of a game that has been developed as part of a project to train emergency managers in group decision making and communications skills is presented, along with some initial evaluations of that game design. It appears that the game presented can function as a valid practical element of a programme for the training of group decision making and communication skills with emergency management personnel. Teaching Group Decision Making Skills to Emergency Managers via Digital Games
An Attribute Extraction for Automated Malware Attack Classification and Detection Using Soft Computing Techniques

Malware has grown in popularity as a method of conducting cyber assaults in former decades as a result of numerous new deception methods employed by malware. To preserve networks, information, and intelligence, malware must be detected as soon as feasible. This article compares various attribute extraction techniques with distinct machine learning algorithms for static malware classification and detection. The findings indicated that merging PCA attribute extraction and SVM classifier results in the highest correct rate with the fewest possible attributes, and this paper discusses sophisticated malware, their detection techniques, and how and where to defend systems and data from malware attacks. Overall, 96% the proposed method determines the malware more accurately than the existing methods.
Postharvest performance interpretation and storage temperature optimization in some newly introduced melon hybrids

Temperature is a key factor in melon cold storage. Thus, optimizing storage temperature is an important goal in postharvest research. In this experiment, postharvest attributes of four inbred lines and five derivative hybrids were investigated under three storage temperatures (1, 4, and 12°C). Melon fruit were evaluated for their main characteristics directly after harvest and postharvest changes were monitored through cold storage period. Cluster analysis results showed that most of the hybrids clustered with their maternal parents illustrating the significant role of cytoplasmic inheritance for the studied traits. Similarly, principal component analysis clustered the studied types into three clusters according to their average postharvest behaviour. The best postharvest performance belonged to inodorus and cantalupensis netted melon with their intercrossing breeds. While the dudaim inbred line and its hybrid scored the highest postharvest changes. Response surface analysis showed that 1.8°C was the optimum storage temperature for inodorus and cantalupensis clusters, while 5.1°C was the best storage temperature of dudaim cluster. The results of the current study are similar to previous reports for optimum storage temperature in similar melon types.
Detection of Lung Cancer Stages on Computed Tomography Image Using Laplacian Filter and Marker Controlled Watershed Segmentation Technique

Lung cancer is a form of malignant tumor distinguished by aggressive multiplication of abnormal cells in lung tissues. If we can assure the detection of lung cancer in the early stage, then we have a chance to increase the survival rate by five years as effective treatment is still available at this stage. Many researchers in the field of image processing sector have built various systems to detect cancer by using image processing techniques. Internationally TNM (Tumor, Nodule, Metastases respectively) method is followed by a physician and radiologist to describe the stage of lung cancer. Our proposed system uses image processing techniques to detect and classify the tumor according to the TNM staging method. First, a series of image processing techniques are performed in a Computed tomography (CT) image. Then, features are extracted to identify the region of interest (ROI). In our proposed system, the classification approach is different from the reviewed existing systems, and the detection rate is comparatively high.
Tomato leaf disease diagnosis based on improved convolutional neural network

Tomatoes are one of the world's most important and tasty crops. This is a high-value crop, and the nation grows a lot of them. Every year, pests and diseases cause more metric tonnes of tomato crop loss in India. Tomato leaf disease is a severe issue that costs farmers a lot of money and threatens the entire agricultural business. As a result, it is critical to precisely diagnose and characterise these diseases. A variety of diseases impede tomato production. Early diagnosis of these diseases will reduce the disease's impact on tomato plants and increase crop yield. Various novel ways of recognising and categorising certain ailments have been widely used. The purpose of this research is to help farmers identify diseases in their early stages. The improved Convolutional Neural Network (CNN) is used to appropriately define and categorise tomato diseases. Kaggle is used to undertake the dataset study, which includes 18160 images of tomato leaves affected with nine distinct diseases and a healthy leaf. The following is a breakdown of the complete procedure: The collected images are first pre-processed. Second, the improved CNN model is used for image feature extraction and classification.
MediCause: Causal Relation Modelling and Extraction from Medical Publications

Causal relations are one of the most important types of information that can be extracted from medical publications. Therefore, the automated extraction of such relations from medical text, and the development of ontologies for representing them, are active fields of research. Causal relation extraction is typically decomposed into causal sentence detection, entity recognition, and relation extraction. This study addresses the entity recognition sub-task, which remains largely unsolved since existing ontological models do not capture the various entities involved in causal relations, and datasets annotated with such entities are missing. Therefore, here we propose MediCause, an ontological model for entities involved in causal relations, and a novel dataset using it to annotate 1,202 causal sentences from existing datasets. We evaluate MediCause by training various BERT models that can recognize and label the entities in unseen texts, and we find that a BioBERT-large model fine-tuned with our dataset is the best model at this task (macro-averaged F1-score of 0.844). We also use MediCause to annotate entities in causal sentences from unseen, recent publications, and have experts evaluate them with encouraging results.