Miracle-dz/UniFAR-FTUs huggingface dataset:
The relationship between self-cohesion and smartphone addiction: the mediating role of rejection sensitivity

Background The increasing use of smartphones with attractive applications has yielded concerns over problematic overuse, also called smartphone addiction, thus creating a need to investigate the antecedents and pathways of this addiction. Objective The aim of this study was to investigate the relationships between the self-cohesion dimensions of presence and consistency and smartphone addiction, and the potentially mediating role of rejection sensitivity. Methods A total sample of 910 respondents (58% females, mean age = 34.9, SD = 13.6) participated in the study. They completed a questionnaire including demographic characteristics and queries about frequency and duration of smartphone use, as well as the Self-Cohesion Scale, the Short Version of Smartphone Addiction Scale, and the Rejection Sensitivity RS-Adult Questionnaire. Structural equation modeling was used to examine the relationships in question. Results The findings indicated that smartphone addiction was positively correlated with rejection sensitivity and negatively correlated with the self-cohesion dimensions of presence and consistency. Further, both presence and consistency were negatively associated with smartphone addiction and rejection sensitivity negatively mediated these relationships. Conclusion Self-cohesion and rejection sensitivity appear to be important predictors of smartphone addiction. Efforts to counteract smartphone addiction should, therefore, endeavor to increase smartphone users’ self-cohesion and reduce their rejection sensitivity.
The Impact of Social Media Text Tone on Stock Prices: Evidence from SeekingAlpha.Com

This paper investigates whether social media text tone impacts financial markets. I collect data from SeekingAlpha.com. I find that social media text tone positively correlates with stock return. User-level analyses indicate that the effect on return is more significant if a user is an influencer, which I define as a person who has many followers and has the power to influence many people through social media, and if the user is more senior, which I define as having extended access to social media platforms. Content analyses indicate that the length of articles negatively correlates with market returns. Additionally, investors overreact to information on social media, and a reversal pattern is observed in the long run. Overall, tone and market returns positively correlate in the short period, but in the long term, it has a reversal.
Computer-aided screening of aspiration risks in dysphagia with wearable technology: a Systematic Review and meta-analysis on test accuracy

Aspiration caused by dysphagia is a prevalent problem that causes serious health consequences and even death. Traditional diagnostic instruments could induce pain, discomfort, nausea, and radiation exposure. The emergence of wearable technology with computer-aided screening might facilitate continuous or frequent assessments to prompt early and effective management. The objectives of this review are to summarize these systems to identify aspiration risks in dysphagic individuals and inquire about their accuracy. Two authors independently searched electronic databases, including CINAHL, Embase, IEEE Xplore® Digital Library, PubMed, Scopus, and Web of Science (PROSPERO reference number: CRD42023408960). The risk of bias and applicability were assessed using QUADAS-2. Nine (n = 9) articles applied accelerometers and/or acoustic devices to identify aspiration risks in patients with neurodegenerative problems (e.g., dementia, Alzheimer’s disease), neurogenic problems (e.g., stroke, brain injury), in addition to some children with congenital abnormalities, using videofluoroscopic swallowing study (VFSS) or fiberoptic endoscopic evaluation of swallowing (FEES) as the reference standard. All studies employed a traditional machine learning approach with a feature extraction process. Support vector machine (SVM) was the most famous machine learning model used. A meta-analysis was conducted to evaluate the classification accuracy and identify risky swallows. Nevertheless, we decided not to conclude the meta-analysis findings (pooled diagnostic odds ratio: 21.5, 95% CI, 2.7–173.6) because studies had unique methodological characteristics and major differences in the set of parameters/thresholds, in addition to the substantial heterogeneity and variations, with sensitivity levels ranging from 21.7% to 90.0% between studies. Small sample sizes could be a critical problem in existing studies (median = 34.5, range 18–449), especially for machine learning models. Only two out of the nine studies had an optimized model with sensitivity over 90%. There is a need to enlarge the sample size for better generalizability and optimize signal processing, segmentation, feature extraction, classifiers, and their combinations to improve the assessment performance. Systematic Review Registration: (https://www.crd.york.ac.uk/prospero/), identifier (CRD42023408960).
Dissolved organic carbon dynamics and fluxes in Mississippi-Atchafalaya deltaic system impacted by an extreme flood event and hurricanes: a multi-satellite approach using Sentinel-2/3 and Landsat-8/9 da.ta

Transport of riverine and wetland-derived dissolved organic carbon (DOC) spanning tidal wetlands, estuaries, and continental shelf waters functionally connects terrestrial and aquatic carbon reservoirs, yet the magnitude and ecological significance of this variable and its spatiotemporal linkage remains uncertain for coastal deltaic regions, such as Mississippi River Delta Plain, which includes Mississippi (MR) and Atchafalaya (AR) rivers and estuaries with vast expanses of wetlands and coastal forests. We examined DOC dynamics and fluxes in this large river-dominated wetland-estuarine system for the period between 2019 and 2021 that included an extreme river flood event in 2019, two major hurricanes (Barry in 2019 and Ida in 2021), and cold front passage using an improved adaptive quasi-analytical algorithm (QAA-AD) applied to multi-satellite sensors (Sentinel 3A/B OLCI, Landsat-8/OLI and Sentinel-2A/B MSI) with varying spectral and spatial (10/30/300 m) resolutions. The DOC estimates from multi-satellite sensors in combination with water fluxes were used to assess DOC fluxes from two large rivers (MR and AR) and small channels across the delta plain. Overall, this system delivered a total of 6.7 Tg C yr-1 (1 Tg = 1012g) into the estuarine zone and the northern Gulf of Mexico (nGoM) during 2019. High DOC fluxes from the AR (1.3 Tg C yr-1) and MR (4.5 Tg C yr-1) were associated with the extreme flood event in 2019. Hurricanes that occurred in the study period also contributed to the wetland and estuarine DOC fluxes into continental shelf waters; for example, the passage of Hurricane Barry in July 2019, delivered over a 3-day period ~1.33 ×109 g DOC from Barataria Basin into the nGoM. Sentinel 2-MSI land and water classification revealed that Hurricane Ida eroded a total of 1.34×108 m2 of marshes in middle Barataria Basin, converting those habitats into open water with 3.0 m inundation depth and high DOC concentrations (16.4 mg L-1), a potentially large DOC source to the coastal waters. Overall, storms and flood events are major sources of DOC flux that facilitate transport of upstream carbon as well as transformation of carbon in the wetlands, through the conversion of vegetated wetland to open water.
The effect of joint orientation on passive movement of the dog’s stifle

Introduction The cranial cruciate ligament (CCL) is one of numerous structures which determine the path of the tibia relative to the femur when passively flexing/extending the stifle of the dog. The effect of cutting the CCL on passive motion with the hind limb in different orientations, is unknown. The aim of this study was to describe passive movement of the tibia relative to the femur in dogs, with the hind limb in three different orientations, and with CCL intact and cut. Methods Ten cadaveric hind limbs were obtained from dogs weighing between 20 kg and 25 kg and prepared for testing in a custom-built joint testing machine. Each hind limb was tested in three different orientations with data collected, using an electromagnetic tracking system, during 2 cycles of flexion/extension with the CCL intact and cut. Each cycle was initiated with the stifle in full extension (0°) and data was collected at 0°, 20°, 30°, 40°, 45°, and 55° of stifle flexion/extension. Results Flexion of the stifle resulted in caudal translation and internal rotation of the tibia relative to the femur, with cranial translation and external rotation occurring during extension along the identical path. Cutting the cranial cruciate ligament did not result in significant differences in translation or rotation when the stifle was orientated to approximated the standing position of a dog. Discussion Isometric points at the origin and insertion of the CCL can potentially be identified in CCL deficient stifles using a technique based on passive motion of an intact stifle.
Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network

Although it may appear infeasible and impractical, building artificial intelligence (AI) using a bottom-up approach based on the understanding of neuroscience is straightforward. The lack of a generalized governing principle for biological neural networks (BNNs) forces us to address this problem by converting piecemeal information on the diverse features of neurons, synapses, and neural circuits into AI. In this review, we described recent attempts to build a biologically plausible neural network by following neuroscientifically similar strategies of neural network optimization or by implanting the outcome of the optimization, such as the properties of single computational units and the characteristics of the network architecture. In addition, we proposed a formalism of the relationship between the set of objectives that neural networks attempt to achieve, and neural network classes categorized by how closely their architectural features resemble those of BNN. This formalism is expected to define the potential roles of top-down and bottom-up approaches for building a biologically plausible neural network and offer a map helping the navigation of the gap between neuroscience and AI engineering.
Forecasting Arctic Sea Ice Concentration using Long Short-term Memory Networks

Due to global warming, Arctic sea ice is now declining, and this loss is a self-accelerating process that speeds up sea ice melting and the severity of climate change. Accurate and timely sea ice information is critically important for better monitoring of global climate. Publicly available multi-source, multi-scale, and high-dimensional sea ice data from satellites is a game changer that allows researchers to better understand the Arctic through more sophisticated methods. This study proposes two Long short-term memory (LSTM) networks for sea ice concentration (SIC) forecasting in the arctic area over 1-, 3-, 6-, and 9-month forecast horizons. The first network forecasts the SIC of each grid in a single output with the grid coordinate must be supplied as an additional input, while the second network forecasts the SIC of all grids at once in a single output. The models with and without atmospheric and oceanic variables as external predictors were trained by using 43 years of data and tuned by using random search strategies. The model performance was evaluated and compared based on the root mean square errors and weighted absolute percentage errors to determine the impact of using climate variables in the prediction and arrive at the best-performing forecast model.
NLP And IR Applications For Financial Reporting And Non-Financial Disclosure. Framework Implementation And Roadmap For Feasible Integration With The Accounting Process

Corporations produce financial and non-financial reports containing structured and unstructured data. In general, all organisations report information of some kind. Natural Language Processing (NLP) and Information Retrieval (IR) were fields developed from approximately the 1950s and have presented important applications, especially in the last three decades. Nevertheless, applications in accounting and finance have not developed accordingly, and a comprehensive framework is missing in the existing literature. This paper examines how NLP and IR can facilitate reporting and disclosure, both Financial and Non-Financial. The paper provides a brief literature review on NLP/IR applications in accounting and finance. It better informs and expands on the discussion of NLP/IR applications in academic research, professional organisations (i.e., IFRS), and industry. It explores some innovative applications of NLP/IR in unstructured data and its use in reporting and disclosure and FinTech applications. The main contribution is the definition of a complete framework that consistently analyses the possible NLP/IR applications in the accounting processes. We find that there can be many more applications of NLP/IR in accounting and finance and suggest future directions for research.
Learning the Structure of Commands by Detecting Random Tokens Using Markov Model

Learning the syntax and structure of command-line commands is of utmost importance in the field of cyber security to identify valid and malicious sets of commands. It is hard to learn the syntax and structure of every command because of various reasons, such as the continuous evolution of commands, precise syntax requirement, huge volume of available commands, and no room for errors, etc. In this research work, we studied two approaches to learning the structure of the commands by detecting the random tokens in them, such as temp files, temp directories, numerical values, etc. In the first approach, we write hard-coded regular expressions to identify random tokens in a command whereas in the second approach we trained a second-order Markov model which detects the random tokens based on their probabilities. To validate the efficiency of these approaches, we clustered the commands using their word embeddings and sentence embeddings. For clustering, we explored KMeans, and DBScan with word embeddings and sentence clustering based on sentence embeddings. We evaluated the performance of clustering algorithms against three metrics, the Silhouette Coefficient, the Calinski-Harabasz Index, and the Davies-Bouldin Index. The results show that regular expression and the Markov model achieve the same scores for KMeans and DBScan based on word embeddings against three metrics, whereas when clustered using sentence embeddings, the Markov model performs better than regular expression. These results validate our idea of using the Markov model instead of regular expressions, to get similar scores or even better performance with less resource utilization, such as human effort, time to write regular expressions, and maintaining & storage of those regular expressions.
In vitro models to study natural killer cell dynamics in the tumor microenvironment

Immunotherapy is revolutionizing cancer therapy. The rapid development of new immunotherapeutic strategies to treat solid tumors is posing new challenges for preclinical research, demanding novel in vitro methods to test treatments. Such methods should meet specific requirements, such as enabling the evaluation of immune cell responses like cytotoxicity or cytokine release, and infiltration into the tumor microenvironment using cancer models representative of the original disease. They should allow high-throughput and high-content analysis, to evaluate the efficacy of treatments and understand immune-evasion processes to facilitate development of new therapeutic targets. Ideally, they should be suitable for personalized immunotherapy testing, providing information for patient stratification. Consequently, the application of in vitro 3-dimensional (3D) cell culture models, such as tumor spheroids and organoids, is rapidly expanding in the immunotherapeutic field, coupled with the development of novel imaging-based techniques and -omic analysis. In this paper, we review the recent advances in the development of in vitro 3D platforms applied to natural killer (NK) cell-based cancer immunotherapy studies, highlighting the benefits and limitations of the current methods, and discuss new concepts and future directions of the field.
Targeted delivery of silibinin via magnetic niosomal nanoparticles: potential application in treatment of colon cancer cells

Introduction: In recent years, various nanoparticles (NPs) have been discovered and synthesized for the targeted therapy of cancer cells. Targeted delivery increases the local concentration of therapeutics and minimizes side effects. Therefore, NPs-mediated targeted drug delivery systems have become a promising approach for the treatment of various cancers. As a result, in the current study, we aimed to design silibinin-loaded magnetic niosomes nanoparticles (MNNPs) and investigate their cytotoxicity property in colorectal cancer cell treatment. Methods: MNPs ferrofluids were prepared and encapsulated into niosomes (NIOs) by the thin film hydration method. Afterward, the morphology, size, and chemical structure of the synthesized MNNPs were evaluated using the TEM, DLS, and FT-IR techniques, respectively. Results and Discussion: The distribution number of MNNPs was obtained at about 50 nm and 70 nm with a surface charge of −19.0 mV by TEM and DLS analysis, respectively. Silibinin loading efficiency in NIOs was about 90%, and the drug release pattern showed a controlled release with a maximum amount of about 49% and 70%, within 4 h in pH = 7.4 and pH = 5.8, respectively. To investigate the cytotoxicity effect, HT-29 cells were treated with the various concentration of the drugs for 24 and 48 h and evaluated by the MTT as well as flow cytometry assays. Obtained results demonstrated promoted cell cytotoxicity of silibinin-loaded MNNPs (5-fold decrease in cell viability) compared to pure silibinin (3-fold decrease in cell viability) while had no significant cytotoxic effect on HEK-293 (normal cell line) cells, and the cellular uptake level of MNNPs by the HT-29 cell line was enhanced compared to the control group. In conclusion, silibinin-loaded MNNPs complex can be considered as an efficient treatment approach for colorectal cancer cells.
Editorial: Women in science: pharmacological treatment of pain

COPYRIGHT © 2023 Araya and Chichorro. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Editorial: Community series in insights into new strategies to combat biofilms, volume II

COPYRIGHT © 2023 Tiwari, Buroni, Silva and Panda. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: Community series in insights into new strategies to combat biofilms, volume II
Prime Time for HLA Desensitization: Imlifidase in the Spotlight

An increasing number of highly human leukocyte antigen (HLA) sensitized patients are currently on kidney transplant waiting lists worldwide. The leading causes of sensitization are previously failed transplants, previous pregnancies, or blood transfusions. Because the HLAs to which patients have been previously exposed—or to which they have made an anti-HLA antibody—are listed with organ allocation bodies as “unacceptable,” sensitized patients have a significantly reduced chance of finding an HLA compatible donor. Thus, they can wait for a very long time on chronic dialysis therapy, which has deleterious consequences in terms of mortality, quality of life, and healthcare costs. In the last decade, different strategies have been developed and implemented in many countries, aimed at increasing the likelihood of finding HLA compatible organs for these patients, whilst maintaining a balanced equity access to kidney transplantation for all waitlisted patients. These include sliding-scale prioritization score programs, the acceptable mismatch program, and the expansion of kidney-pair exchange programs [1]. While all these strategies have been variously successful for many highly sensitized patients, a group of (very) highly sensitized individuals (>99.9% cPRA) have failed to benefit, and this expanding group still have an extremely low chance of finding a HLA compatible organ. There are two approaches to improve the chances of these patients; “de-listing” which involves ignoring selected low-level HLA antibodies in the allocation process, and “desensitization” to reduce higher levels of HLA antibodies down to permissive levels that can then be ignored. These approaches offer no survival disadvantage [2] to waiting for a well-matched organ but are associated with higher rates of early and late aggressive rejection and a shortened graft half-life. Unfortunately, many patients have no delisting options, and current desensitization strategies have failed to demonstrate a consistent success, especially for those without a living donor. Therefore, there is an important unmet need for new drug development enabling access to transplantation to these (very) highly sensitized kidney transplant candidates [3]. Notably, a new drug Imlifidase, a recombinant cysteine protease with an unprecedented capacity to rapidly cleave all four IgG subclasses, both soluble and on the B-cell surface [4], has the potential to offer hope for these patients. By transiently depleting all circulating IgG, including HLA antibodies for several days, Imlifidase can uniquely create a window of opportunity for highly sensitized patients with high pathogenic anti-HLA antibody levels to undergo kidney transplantation [5]. Two phase I/II clinical trials [6] have demonstrated the capacity of Imlifidase to effectively convert a positive crossmatch to a negative one, leading to optimal 3year graft and patient survival rates. Even though *Correspondence: Oriol Bestard oriol.bestard@vallhebron.cat