Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Stepwise Recruitment Maneuvers on Cerebral Hemodynamics: Experimental Study in Neonatal Model

Background: Lung recruitment maneuvers (LRMs) have been demonstrated to be effective in avoiding atelectasis during general anesthesia in the pediatric population. Performing these maneuvers is safe at the systemic hemodynamic and respiratory levels. Aims: We aimed to evaluate the impact of a stepwise LRM and individualized positive end-expiratory pressure (PEEP) on cerebral hemodynamics in an experimental neonatal model. Methods: Eleven newborn pigs (less than 72 h old, 2.56 ± 0.18 kg in weight) were included in the study. The LRM was performed under pressure-controlled ventilation with a constant driving pressure (15 cmH2O) in a stepwise increasing PEEP model. The target peak inspiratory pressure (PIP) was 30 cmH2O and the PEEP was 15 cmH2O. The following hemodynamic variables were monitored using the PICCO® system: mean arterial pressure (MAP), central venous pressure (CVP), and cardiac output (CO). The cerebral hemodynamics variables monitored were intracranial pressure (ICP) (with an intraparenchymal Camino® catheter) and cerebral oxygen saturation (rSO2) (with the oximetry monitor INVOS 5100® system). The following respiratory parameters were monitored: oxygen saturation, fraction of inspired oxygen, partial pressure of oxygen, end-tidal carbon dioxide pressure, Pmean, PEEP, static compliance (Cstat), and dynamic compliance (Cdyn). Results: All LRMs were safely performed as scheduled without any interruptions. Systemic hemodynamic stability was maintained during the lung recruitment maneuver. No changes in ICP occurred. We observed an improvement in rSO2 after the maneuver (+5.8%). Conclusions: Stepwise LRMs are a safe tool to avoid atelectasis. We did not observe an impairment in cerebral hemodynamics but an improvement in cerebral oxygenation.
The Price of Success—The Long-Term Outcomes of Children with Craniopharyngioma—Two Institutions’ Experience

An analysis of patients below 21 years old treated due to craniopharyngioma in the years 1979–2022 was performed with the aim of evaluating the long-term outcome and treatment side-effects. The standard statistical tests were used, and 56 patients with a median age of 11 years were evaluated. Surgery was the primary treatment in 55 patients; however, in only 29 it was the only neurosurgical intervention. Eighteen children were treated with radiotherapy (RTH) in primary treatment. The most common neurosurgical side effects observed were visual and endocrine deficits and obesity, which were diagnosed in 27 (49%), 50 (91%), and 25 (52%) patients, respectively. Complications after RTH were diagnosed in 14 cases (32%). During the median follow-up of 8.4 years (range: 0.4–39.8 years), six patients died and the 5- and 10-year overall survival was 97% and 93%, respectively. Five-year progression-free survival for gross total resection, resection with adjuvant RTH, and non-radical resection alone was 83%, 68%, and 23%, respectively (p = 0.0006). Surgery combined with RTH provides comparable results to gross tumor resection in terms of oncologic outcome in craniopharyngioma patients. Adjuvant irradiation applied in primary or salvage treatment improves disease control. The rate of complications is high irrespective of improved surgical and radiotherapeutic management.
Low-Norm Graph Embedding

Learning distributed representations for nodes in graphs has become an important problem that underpins a wide spectrum of applications. Existing methods to this problem learn representations by optimizing a softmax objective while constraining the dimension of embedding vectors. We argue that the generalization performance of these methods are probably not due to the dimensionality constraint as commonly believed, but rather the small norm of embedding vectors. Both theoretical and empirical evidences are provided to support this argument: (a) we prove that the generalization error of these methods can be bounded regardless of embedding dimension by limiting the norm of vectors; (b) we show empirically that the generalization performance of existing embedding methods are likely due to the early stopping of stochastic gradient descent. Motivated by our analysis, we propose a new low-norm formulation of the graph embedding problem, which seeks to preserve graph structures while constraining the total squared $l_2$ norm of embedding vectors. With extensive experiments, we demonstrate that the empirical performance of the proposed method well backs our theoretical analysis. Furthermore, it notably outperforms state-of-the-art graph embedding methods in the tasks of link prediction and node classification.
The anti-PD-1 era of cervical cancer: achievement, opportunity, and challenge

Cervical cancer is one of the three major female gynecological malignancies, becoming a major global health challenge. Although about 90% of early-stage patients can be cured by surgery, advanced-stage patients still need new treatment methods to improve their efficacy, especially for those with recurrence and metastasis tumors. Anti-PD-1 is currently the most widely used immune checkpoint inhibitor, which has revolutionized cancer therapy for different types of cancer. Pembrolizumab has been approved for second-line treatment of R/M CC but has a modest overall response rate of about 15%. Therefore, multiple types of anti-PD-1 have entered clinical trials successively and evaluated the efficacy in combination with chemotherapy, targeted therapy, and immunotherapy. At the same time, the dual specific antibody of PD-1/CTLA-4 was also used in clinical trials of cervical cancer, and the results showed better than anti-PD-1 monotherapy. In addition, anti-PD-1 has also been shown to sensitize radiotherapy. Therefore, understanding the current research progress of anti-PD-1 will better guide clinical application. This review summarizes ongoing clinical trials and published studies of anti-PD-1 monotherapy and combination therapy in the treatment of cervical cancer, as well as discusses the potential molecular biological mechanisms of combination, aiming to provide the basic evidence for support anti-PD-1 in the treatment of cervical cancer and new insights in combination immunotherapy.
Analyzing the Impact of Geopolitical Risk, and Renewable Energy Towards Sustainable Development in China

Geopolitical relationship among the economies plays a crucial role in the environmental sustainability. In lieu our research reveals that whether geopolitical risk asymmetrically affects the reduction of carbon emission? To investigate this issue, we scrutinize the impact of geopolitical risk using annual data spanning 1990-2018 for China. Furthermore, our study employs nonlinear and liner autoregressive distributed lag simulations at the same time to argue the impact of geopolitical risk on environmental sustainability. Our empirical outcomes evidenced the asymmetric effect of geopolitical risk on the environment by indicating a positive association among geopolitical risk and environmental sustainability. Moreover, increasing foreign direct investment upsurges CO2 emanation which proves the pollution haven proposition. The results expressed a significant positive link of energy ingesting and GDP influence on CO2 emanation while renewable energy consumption reduces CO2 emission. Therefore, utmost attention must be taken to balance the environment and geopolitical risk. It is necessary to take initiatives to reforms policies that protect the environment without affecting geopolitical risk. Additionally, government officials and policymakers should articulate strategies to encourage renewable energy consumption as the energy combination outpouring to enrich the excellence of the environment. The findings also advocate that policymakers to welcome the environment-friendly foreign direct investment as it is the perpetrator to upsurge environmental degradation in China.
Reversible Data Hiding Over Encrypted Images via Preprocessing-Free Matrix Secret Sharing

Cloud service is a natural choice to store and manage the exponentially produced images. Data privacy is one of the most concerned points in cloud-based image services. Reversible data hiding over encrypted images (RDH-EI) is an effective technique to securely store and manage confidential images in the cloud. However, existing RDH-EI schemes have obvious weaknesses such as reliable key management system dependence and single point of failure. To securely store and manage confidential images in the cloud, in this study, we propose a new reversible data hiding strategy via image secret sharing. We first design a secure <inline-formula> <tex-math notation="LaTeX">$(r,n)$ </tex-math></inline-formula>-threshold preprocessing-free matrix secret sharing (PFMSS) technique. It can directly share <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>-bit data by matrix multiplication without preprocessing. Using the PFMSS, we further design a secure <inline-formula> <tex-math notation="LaTeX">$(r,n)$ </tex-math></inline-formula>-threshold reversible data hiding scheme over encrypted images. The content owner divides a confidential image into <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> shares without accessing to a secret encryption key, and then sends the <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> shares to <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> cloud-based image servers from competing providers. For each share, some additional data, e.g., integrity and identification of the image, can be embedded into it and these data can also be losslessly extracted. An authorized receiver can recover the confidential image from <inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> shares. By designing, the content owner doesn’t need to access a secret key when encrypting the image and the scheme can withstand <inline-formula> <tex-math notation="LaTeX">$n-r$ </tex-math></inline-formula> points of failure. Simulation results show that our scheme can ensure image content confidentiality and has a much larger embedding capacity compared to state-of-the-art schemes.
Factors influencing agri-preneurial success among rural women entrepreneurs: An empirical analysis

The study was purposively conducted among 97 women agri-preneurs of Vellore district of Tamil Nadu. Measuring precisely the success/failure of the rural women entrepreneurship is difficult and complex in the absence of a well- developed scale.Women agri-preneurs were grouped into four categories of success/failure by administering a measuring scale. As high as 47.43% of agri-preneurs were found to be in ‘somewhat success’ category, whereas 16.49% agri- preneurs were in ‘high success’ category and 9.27% of them were found to be in ‘high failure’ category and 26.81% of the agri-preneurs were found in ‘somewhat failure’ category. The Garrett ranking showed that successful women agri-preneurs perceived ‘self’, ‘family’, ‘skilled labour’, ‘credit transaction in time’ and ‘demand’, whereas unsuccessful agri-preneurs perceived ‘spouse’, ‘NGOs’, ‘friends’, Government incentives’ and ‘family’ as the five most important factors for success. Discriminant Function Analysis is a statistical tool used to discriminate between two classes of individuals on the basis of characters which are considered to be relevant. Stepwise procedure was used to identify the relevant variable and the significant ones were identified based on the ‘F’ test. The Fisher’s discriminant function equation established ‘achievement motivation’ ‘general perceived self-efficacy’, ‘collective efficacy’, ‘proactive attitude’ and ‘self-esteem’ as the top five most discriminating variables. Hence, trainings in achievement motivation development, team building, collective efficacy and developing personal managerial competencies like ‘proactive attitude’ need to be incorporated in women agri-preneurship development programmes for success in rural women agri-preneurships.
Intelligence Quotient (IQ) in school-aged preterm infants: A systematic review

Preterm birth (before 37 weeks of gestational age) is associated with certain risks to child development. The aim of this systematic review was to summarize available and updated empirical evidence on prematurity as a risk factor for cognitive development in school age. Thus, we attempted to identify similarities and differences with the full-term population and to point out possible risk or protective factors among the biological, psychosocial and family variables. The conceptualization and methodology of this review followed the PRISMA recommendations. The search was carried out in Web of Science, Scopus, PsycInfo, and Dialnet databases, in May 2022. The search was limited to journal articles, published between 2012 and 2022, in English and Spanish. Research articles selected were those focused on the intelligence quotient (IQ) of preterm children aged 6–12 years. The review included studies with cross-sectional or longitudinal cohorts, compared to a control group of children born at term or to standardized scales. The quality of evidence of the selected studies was verified with the Mixed Methods Appraisal Tool (MMAT). The initial search identified 1,040 articles. Forty articles met the inclusion criteria and were finally included in this review. These studies involved 5,396 preterm children from 37 different cohorts. Despite the diversity found among the results, in general, total IQ scores were within the normative mean for premature children; however, compared to their full-term peers, these scores were lower. The most studied variables in relation to IQ are perinatal (e.g., gestational age and birth weight) and family (e.g., socioeconomic level and education level of the mother). Recent studies corroborate that premature birth affects cognitive development in school age, and identify associated perinatal and family variables. Systematic review registration https://www.crd. york.ac. uk/PROSPERO/display_record. php? RecordID=337371; identifier: CRD42022337371.
Multi-Temporal Hyperspectral Classification of Grassland Using Transformer Network

In recent years, grassland monitoring has shifted from traditional field surveys to remote-sensing-based methods, but the desired level of accuracy has not yet been obtained. Multi-temporal hyperspectral data contain valuable information about species and growth season differences, making it a promising tool for grassland classification. Transformer networks can directly extract long-sequence features, which is superior to other commonly used analysis methods. This study aims to explore the transformer network’s potential in the field of multi-temporal hyperspectral data by fine-tuning it and introducing it into high-powered grassland detection tasks. Subsequently, the multi-temporal hyperspectral classification of grassland samples using the transformer network (MHCgT) is proposed. To begin, a total of 16,800 multi-temporal hyperspectral data were collected from grassland samples at different growth stages over several years using a hyperspectral imager in the wavelength range of 400–1000 nm. Second, the MHCgT network was established, with a hierarchical architecture, which generates a multi-resolution representation that is beneficial for grass hyperspectral time series’ classification. The MHCgT employs a multi-head self-attention mechanism to extract features, avoiding information loss. Finally, an ablation study of MHCgT and comparative experiments with state-of-the-art methods were conducted. The results showed that the proposed framework achieved a high accuracy rate of 98.51% in identifying grassland multi-temporal hyperspectral which outperformed CNN, LSTM-RNN, SVM, RF, and DT by 6.42–26.23%. Moreover, the average classification accuracy of each species was above 95%, and the August mature period was easier to identify than the June growth stage. Overall, the proposed MHCgT framework shows great potential for precisely identifying multi-temporal hyperspectral species and has significant applications in sustainable grassland management and species diversity assessment.
Digital hate speech and othering: The construction of hate speech from Malaysian perspectives

Abstract Hate speech is a phenomenon that affects communication and undermines multicultural society by disrupting intercultural engagement. The internet’s euphoria allows media around the world to witness the troubling rise of vitriolic language. Moving beyond the Euro-America of hate speech phenomenon, cross-cultural nuanced by ethnography has evolved in Malaysia. Therefore, the challenge of protecting against discrimination, dehumanisation, and incitement to violence while preserving individual freedom of expression seems to be contextual when defining hate speech. A series of three different online focus groups which consist of a total of 22 participants were conducted to gather insights from civil society and experts to build concrete definitions and understand the construction of hate speech from a Malaysian perspective. Hence, it tries to explore the extent how social media act as a platform that exacerbates the othering discourse in the online sphere. This study contributes to the academic discussions about the public sphere, and social media in the creation of meaning, discourses, and ensuing cultural and social change, as well as understanding the trajectories across digital spheres by utilising online focus groups for discussion.
Rosiglitazone has a null association with the risk of prostate cancer in type 2 diabetes patients

Background This study investigated the risk of prostate cancer in ever users and never users of rosiglitazone in diabetes patients in Taiwan. Methods The nationwide database of the National Health Insurance was used to enroll male patients who had a new diagnosis of type 2 diabetes mellitus at an age ≥ 25 years from 1999 to 2005. A total of 11,495 ever users and 11,495 never users of rosiglitazone matched on propensity score were selected and they were followed up for the incidence of prostate cancer from January 1, 2006 until December 31, 2011. Cox proportional hazard model incorporated with the inverse probability of treatment weighting using the propensity score was used to estimate hazard ratios. Results At the end of follow-up, incident cases of prostate cancer were found in 84 never users and 90 ever users of rosiglitazone. The calculated incidence was 173.20 per 100,000 person-years in never users and was 187.59 per 100,000 person-years in ever users. The overall hazard ratio (95% confidence intervals) for ever versus never users was 1.089 (0.808-1.466). The hazard ratios were 0.999 (0.643-1.552) for the first tertile (< 672 mg), 1.147 (0.770-1.709) for the second tertile (672-3584 mg) and 1.116 (0.735-1.695) for the third tertile (> 3584 mg) of cumulative dose. Sensitivity analyses consistently showed a null association between rosiglitazone and prostate cancer risk. Conclusion Rosiglitazone has a null effect on the risk of prostate cancer.
When light gets pushy : radiation pressure effects in interferometric gravitational wave detectors

Advanced LIGO made the first direct observation of a gravitational wave in 2015. This signal, the largest measured so far, had a peak strain amplitude of 1 x 10-21 and frequency range 35-250 Hz. LIGO's sensitivity was achieved after decades of development. Suspended optics made from low-loss materials are employed in an optical configuration with multiple coupled cavities to increase the circulating power and shape the frequency response of the detector. This design results in radiation pressure that substantially alters the detector's behaviour. One challenging consequence is parametric instabilities, an unstable coupling between the optical field and mechanical resonances in the mirrors. My detailed investigations of parametric instabilities in both LIGO and ET, a planned next-generation detector, show that the severity of the instabilities depends on the complete optical configuration. With this model, an optimal operating point for LIGO can be determined and ET design choices can be weighed against potential instabilities. An 'optomechanical filter' has been proposed that uses radiation pressure to enhance the detector bandwidth. I test the analytical models against numerical simulations and outline an experimental research programme that will implement a trial filter.
ESTIMATE OF REFERENCE EVAPOTRANSPIRATION THROUGH CONTINUOUS PROBABILITY MODELLING

This study aimed at testing the fit of continuous probability distributions to a daily reference evapotranspiration dataset (ET0) at a 75% probability level for designing of irrigation systems. Reference evapotranspiration was estimated by the Penman-Monteith method (FAO-56-PM) for eight locations, within the state of Espirito Santo (Brazil), where there are automatic gauge stations. The assessed probability distributions were beta, gamma, generalized extreme value (GEV), generalized logistic (GLO), generalized normal (GN), Gumbel (G), normal (N), Pearson type 3 (P3), Weibull (W), two- and three-parameter lognormal (LN2 and LN3). The fitting of the probability distributions to the ET0 daily dataset was checked by the Kolmogorov-Smirnov's test. Among the studied distributions, GN was the only one to fit the ET0 data for all studied months and locations. We should also infer that continuous probability models have a good fit to the studied ET0 dataset, enabling its estimation at 75% probability through a Generalized Normal distribution (GN). Therefore, it can be used for the sizing of irrigation systems according to a given degree of risk.
Psychometric Properties for Persian Version of the Ottawa Self-injury Inventory-functions Among Adolescents

Objectives Nonsuicidal self-injury (NSSI) is an issue primarily of concern in adolescents and young adults. The Ottawa self-Injury inventory (OSI) is a self-report measure that offers a comprehensive assessment of NSSI, including the measurement of its functions and addictive features. Thus, this study evaluated the psychometric properties of the Ottawa self-Injury inventory-functions (OSI-F) for assessing NSSI for gifted adolescents. Methods In this correlational study, 350 gifted adolescents who were selected using the convenience sampling method, answered the OSI-F, the short version of the self-harm screening inventory for adolescents, the depression, anxiety and stress scale, and the psychological capital questionnaire. Results The results of confirmatory factor analysis in line with the results of other studies supported the factor structure consisting of four factors. The model showed significant correlations between factors (rs=0.55-0.75, p<0.001). The results of the confirmatory factor analysis also confirmed the factor structure of the addictive characteristics of self-injurious behaviors. All the items had significant path estimates (0.42 to 0.83). Cronbach’s alpha for factors of internal emotional regulation, social influence, external emotional regulation, and sensation seeking in the scale of self-injurious behavior functions was equal to 0.86, 0.87, 0.64, and 0.72, respectively, and for the scale of addictive characteristics was 0.87. Convergent validity of scales of functions and addictive characteristics of self-injurious behavior was obtained through the relationship with scores in scales of anxiety, depression, stress, and deliberate self-injurious behaviors, as well as psychological capital factors. Conclusion Results showed preliminary psychometric support for the OSI as a valid and reliable assessment tool to be used in both research and clinical contexts.