Miracle-dz/UniFAR-FTUs huggingface dataset:
Salvia miltiorrhiza-Containing Chinese Herbal Medicine Combined With GnRH Agonist for Postoperative Treatment of Endometriosis: A Systematic Review and meta-Analysis

Background: Endometriosis is an estrogen-dependent gynecological inflammatory condition that may lead to infertility and recurrent pelvic pain. The purpose of this research was to determine the efficacy and safety of Salvia miltiorrhiza-containing Chinese herbal medicine (CHM) combined with gonadotropin-releasing hormone agonist (GnRH-a) for postoperative endometriosis management. Methods:Eight databases were systematically searched before October 2021, including PubMed, Embase, Cochrane Library, Scopus, Web of Sceince, CNKI, VIP, and Wanfang. Finally, all randomized controlled studies comparing Salvia miltiorrhiza-containing CHM paired with GnRH-a to GnRH-a alone for postoperative endometriosis management were included. Results: A total of 10 trials involving 836 patients were reported and analyzed. Compared with the control group, the Salvia miltiorrhiza-containing CHM combined with GnRH-a group showed significant superiority in decreasing endometriosis recurrence (risk ratio [RR] = 0.26; 95% confidence intervals [CI]: 0.16–0.41) and increasing the pregnancy rate ([RR] = 1.96; 95% CI: 1.58–2.44). Similarly, the effect of the Salvia miltiorrhiza-containing CHM combined with GnRH-a on CA-125 serum levels was positive (standardized mean difference [SMD] = -0.79; 95% CI: −1.11 to −0.47). Furthermore, this group showed a significant reduction in adverse effects. Conclusion: The results indicate that Salvia miltiorrhiza-containing CHM may be a viable choice for postoperative endometriosis therapy, with the potential to enhance pregnancy while decreasing recurrence and adverse effects.
Multidimensional Clinical Assessment in Frontotemporal Dementia and Its Spectrum in Latin America and the Caribbean: A Narrative Review and a Glance at Future Challenges

Frontotemporal dementia (FTD) is the third most common form of dementia across all age groups and is a leading cause of early-onset dementia. The Frontotemporal dementia (FTD) includes a spectrum of diseases that are classified according to their clinical presentation and patterns of neurodegeneration. There are two main types of FTD: behavioral FTD variant (bvFTD), characterized by a deterioration in social function, behavior, and personality; and primary progressive aphasias (PPA), characterized by a deficit in language skills. There are other types of FTD-related disorders that present motor impairment and/or parkinsonism, including FTD with motor neuron disease (FTD-MND), progressive supranuclear palsy (PSP), and corticobasal syndrome (CBS). The FTD and its associated disorders present great clinical heterogeneity. The diagnosis of FTD is based on the identification through clinical assessments of a specific clinical phenotype of impairments in different domains, complemented by an evaluation through instruments, i.e., tests and questionnaires, validated for the population under study, thus, achieving timely detection and treatment. While the prevalence of dementia in Latin America and the Caribbean (LAC) is increasing rapidly, there is still a lack of standardized instruments and consensus for FTD diagnosis. In this context, it is important to review the published tests and questionnaires adapted and/or validated in LAC for the assessment of cognition, behavior, functionality, and gait in FTD and its spectrum. Therefore, our paper has three main goals. First, to present a narrative review of the main tests and questionnaires published in LAC for the assessment of FTD and its spectrum in six dimensions: (i) Cognitive screening; (ii) Neuropsychological assessment divided by cognitive domain; (iii) Gait assessment; (iv) Behavioral and neuropsychiatric symptoms; (v) Functional assessment; and (vi) Global Rating Scale. Second, to propose a multidimensional clinical assessment of FTD in LAC identifying the main gaps. Lastly, it is proposed to create a LAC consortium that will discuss strategies to address the current challenges in the field.
Genome-Wide Methylation Profiling in the Thalamus of Scrapie Sheep

Scrapie is a neurodegenerative disorder belonging to the group of transmissible spongiform encephalopathy (TSE). Scrapie occurs in sheep and goats, which are considered good natural animal models of these TSE. Changes in DNA methylation occur in the central nervous system (CNS) of patients suffering from prion-like neurodegenerative diseases, such as Alzheimer's disease. Nevertheless, potential DNA methylation alterations have not yet been investigated in the CNS of any prion disease model or naturally infected cases, neither in humans nor in animals. Genome-wide DNA methylation patterns were studied in the thalamus obtained from sheep naturally infected with scrapie at a clinical stage (n = 4) and from controls (n = 4) by performing a whole-genome bisulfite sequencing (WGBS) analysis. Ewes carried the scrapie-susceptible ARQ/ARQ PRNP genotype and were sacrificed at a similar age (4–6 years). Although the average genomic methylation levels were similar between the control and the scrapie animals, we identified 8,907 significant differentially methylated regions (DMRs) and 39 promoters (DMPs). Gene Ontology analysis revealed that hypomethylated DMRs were enriched in genes involved in transmembrane transport and cell adhesion, whereas hypermethylated DMRs were related to intracellular signal transduction genes. Moreover, genes highly expressed in specific types of CNS cells and those previously described to be differentially expressed in scrapie brains contained DMRs. Finally, a quantitative PCR (qPCR) validation indicated differences in the expression of five genes (PCDH19, SNCG, WDR45B, PEX1, and CABIN1) that matched the methylation changes observed in the genomic study. Altogether, these results suggest a potential regulatory role of DNA methylation in prion neuropathology.
Investigation of the Utility of Features in a Clinical De-identification Model: A Demonstration Using EHR Pathology Reports for Advanced NSCLC Patients

Background Electronic health record (EHR) systems contain a large volume of texts, including visit notes, discharge summaries, and various reports. To protect the confidentiality of patients, these records often need to be fully de-identified before circulating for secondary use. Machine learning (ML) based named entity recognition (NER) model has emerged as a popular technique of automatic de-identification. Objective The performance of a machine learning model highly depends on the selection of appropriate features. The objective of this study was to investigate the usability of multiple features in building a conditional random field (CRF) based clinical de-identification NER model. Methods Using open-source natural language processing (NLP) toolkits, we annotated protected health information (PHI) in 1,500 pathology reports and built supervised NER models using multiple features and their combinations. We further investigated the dependency of a model's performance on the size of training data. Results Among the 10 feature extractors explored in this study, n-gram, prefix–suffix, word embedding, and word shape performed the best. A model using combination of these four feature sets yielded precision, recall, and F1-score for each PHI as follows: NAME (0.80; 0.79; 0.80), LOCATION (0.85; 0.83; 0.84), DATE (0.86; 0.79; 0.82), HOSPITAL (0.96; 0.93; 0.95), ID (0.99; 0.82; 0.90), and INITIALS (0.97; 0.49; 0.65). We also found that the model's performance becomes saturated when the training data size is beyond 200. Conclusion Manual de-identification of large-scale data is an impractical procedure since it is time-consuming and subject to human errors. Analysis of the NER model's performance in this study sheds light on a semi-automatic clinical de-identification pipeline for enterprise-wide data warehousing.
Is Adolescent Risk Behavior Associated With Cross-Household Family Complexity? An Analysis of Post-separation Families in 42 Countries

We examine whether complex cross-household structures of post-separation families are associated with higher risk-taking behavior in adolescence (substance use, bullying, early sexual onset) and whether the proportion, and thus statistical normality, of complex family types in a certain country is a potential moderator of this association. Drawing on representative data from 42 countries and regions from the Health Behavior in School-aged Children (HBSC) study in 2001, 2006, and 2010 (N = 506,977), we provide detailed analyses on adolescent risk behavior even for very rare family types, thereby accounting for the complex cross-household structure present in many post-separation families. We combine logistic and count regression models to analyze risk incidence and intensity. Controlling for relevant child and family characteristics, our results reveal a gradient along which adolescent risk-taking increases with family complexity: The incidence and intensity of risk-taking among adolescents is lowest in two-biological-parent and highest in two-household families with stepparents in both households. The association decreases with a higher proportion of the respective family type in a country. However, the differences between family types, other than the two-biological parent family, are not as pronounced as expected.
Um Novo Algoritmo Ciente de Crosstalk para Alocação de Núcleo e Espectro em Redes Ópticas Elásticas Multi-Núcleos

As redes ópticas elásticas com multiplexação por divisão de espacial (SDM-EONs) são apontadas como tecnologia para prover comunicação no núcleo da Internet. Este artigo propõe o Algoritmo Ciente de Crosstalk para Alocação de Núcleo e Espectro (ACiNE) em SDM-EONs. O ACiNE escolhe faixas espectrais com menor valor de crosstalk e de forma balanceada, obtendo melhor eﬁciência quando comparado a outros trabalhos da literatura. Neste contexto, o ACiNE obteve ganhos de pelo menos 45,62% em termos de probabilidade de bloqueio de circuito, 37,95% em termos de razão de dados bloqueados e 7,19% de eﬁciência energética. Além disso, o ACiNE possui menor tempo de execução quando comparado a outro algoritmo ciente de crosstalk.
Usando Redes Neurais para Reconstruir Traços de Sessões de Usuários de Sistemas de Larga Escala

A monitoração de presença online de entidades em sistemas distribuídos é fundamental para compreender o comportamento de tais sistemas e simular a dinâmica dos mesmos, entre outros. Em muitos sistemas, a presença online de entidades pode ser monitorada via amostragem – em intervalos regulares – das entidades atualmente online. Exemplos incluem usuários online em aplicações distribuídas, ou estações ativas na internet. A monitoração pode ser falha, e algumas entidades podem não aparecer como online em uma ou mais listas, comprometendo assim a acurácia dos dados coletados. Investigações anteriores aplicaram métodos estatísticos para identiﬁcar a ocorrência de tais falhas, e usaram limiares para corrigi-las. No presente artigo, propõe-se investigar a potencialidade de métodos de aprendizado de máquina para regenerar dados de monitoração coletados via amostragem. Em particular, avaliamos a potencialidade de se corrigir dados usando técnicas de aprendizado profundo, e mostramos que a acurácia, precisão e recall podem ser substancialmente melhorada em comparação com os métodos estatísticos existentes.
New “balanced Scorecard” (BSC)

Summary. The article describes the BSC-modification which is more sensitive and more precise strategic control instrument as compare to contem-porary BSC. This modification is based on enterprise’s functions and recourses multilevel decomposition. Main indicators content was formulized. Key words: market; control system; BSC; crisis; risk; thread. Initial orientation. Nowadays neither progressive states nor protection branch theory haven’t had justifiable ways to resolve the Enterprises’ problems ring “Non-competitive products – Enterpris-es’ low-income – Small budget revenue – Insufficient budget fund-ing for education – Poor training – Non-competitive products”. The-se challenges are forcing control theory and practice to find eligible approaches to resolve some of these problems or to find the “Ariad-ne’s thread” in order to gradually unravel the entire ring. Unfortu-nately contemporary theory and practice use particular criterions owing to what economic, information, internal, fair, external safety, physical protection and others take place and growth. Enterprises perform fundamental function in every state economic system and produce the significant part of gross domestic product. Because of it the effectiveness enterprise control is the subject of meticulous at-tention in our devising. We are interested in two kinds of enterprise interactions: “market” and “influence” (Fig.1). Influence is unidirectional pur-poseful resources interaction, where one participant’s resources car-ry out an un-authorized impact on the other participant’s resources; herewith Earthquake and other “acts of God” are examples of un-authorized, but purposeless impact processes.
Gender Diversity and Community Smells: A Double-Replication Study on Brazilian Software Teams

Social debts in software teams are gaining increasing attention from the research community due to their potential adverse effects on software quality. For instance, community smells are indicators of sub-optimal organizational structures and may well lead to the emergence of social debt. Previous studies analyzed which factors influence the emergence/mitigation of such smells. In particular, studies by Catolino et al. showed how factors related to team composition, particularly gender diversity, correlated to the mitigation of community smells. However, a confirmation survey on 60 practitioners suggested that these results were not aligned with the experts' perceptions. In a separate survey, Catolino et al. collected the most common team refactoring strategies for those community smells. In this work we replicate two studies by those authors, focusing on the Brazilian software teams; culture-specific expectations on the behavior of people of different genders might have affected the perception of the importance of gender diversity and refactoring strategies when mitigating community smells. We translated the survey instrument used by Catolino et al. to Brazilian Portuguese and recruited 184 Brazilian developers. Re-sults did not show significant differences from the original study; indeed, participants perceived gender diversity as less valuable to mitigate community smells than such factors like experience or team size. Additionally, we performed a qualitative analysis of an open question within the questionnaire for the refactoring strategies. Brazilian developers agree with the original studies for most smells, mainly promoting restructuring communities, creating a communication plan and mentoring. We believe these results provide further evidence on the problem and its implications when managing software teams, avoiding technical debt and maintenance issues due to team communication and coordination problems.
MOLECULAR MECHANISMS FOR PHOSPHOINOSITIDE REGULATION OF CONE PHOTORECEPTOR CNG CHANNEL GATING

By Gucan Dai, Ph.D. Washington State University May, 2014 Chair: Michael D. Varnum Photoreceptor cyclic nucleotide-gated (CNG) channels are critical for converting light inputs into electrical signals that are ultimately processed as visual information. However, it is not well understood how exactly photoreceptor CNG channels are regulated. Since the first study showing the direct activation of inward rectifier K channels by phosphoinositides in 1998 (Huang et al., 1998), emerging evidences have revealed that phosphoinositides (PIPn), which are low-abundance membrane-bound phospholipids, have tremendous effects on membrane ion channels. Previous studies on rod, cone and olfactory CNG channels have shown that the activities of CNG channels are inhibited by phosphoinositides [phosphatidylinositol 4,5-biphosphate (PIP2) and phosphatidylinositol 3,4,5-triphosphate (PIP3)], exhibiting a decrease in apparent cGMP affinity. Using electrophysiology combined with biochemistry and molecular manipulations, we have investigated the structural elements that are critical for PIP2 and PIP3 regulation of cone CNG channels. We have discovered two phosphoinositideregulation sites within the amino (N) -terminal and the carboxyl (C) -terminal regions of CNGA3 subunits, respectively; CNGB3 subunits do not contain PIPn-sensitive elements. Both of these two phosphoinositide-regulation modules are necessary for PIPn-
Squeal and Thermal Analysis of Automobile Disc Brake Rotors

The brakes of an automobile are among the most critical components regarding the safety features, and disc brakes are the most common type used in passenger vehicles. In this research, the squeal phenomena of a swirl-vent brake rotor and the thermal analysis of two straight-vent brake rotors, made of cast-iron and aluminum-alloy, are investigated. For the squeal analysis, finite element models are created and analyzed using a prestressed modal analysis with complex eigen-solutions. For the thermal analysis, heat transfer coefficients on the surfaces of a rotor as functions of time are first estimated by CFD simulation, and then imported to a thermal analysis program as the boundary condition. Finally, the temperature distribution of the rotor can be calculated by finite element analysis. The simulation results show that vortices will arise in the vented passages of straight-vent rotors, which means less heat carried away and lower heat transfer coefficients. The swirl-vent brake design is clearly better for thermal ventilation. Furthermore, under the same condition, aluminum-alloy rotors exhibit more uniform temperature distributions with smaller temperature gradients than cast-iron rotors do.
A Systematic Review of Machine Learning Classification Approach for Suicide Ideation Detection

S Suicide is a global health issue that is responsible for several deaths among young children and adults. Suicide can easily be handled if it is detected early. To address this issue several approaches have been used, which are clinical method and machine learning with feature engineering or deep learning for automatic detection of suicide ideation based on an individual social media messages. This paper aims to survey different methods used for suicide ideation detection. Some of the domains of machine learning applications used for suicide ideation detection were also reviewed based on their data sources like questionnaires, electronic health records suicide notes, and online user contents. Several specific tasks and datasets are introduced and summarized to facilitate further research. Finally, this paper summarizes the limitations of current work and provide an outlook for further research directions.
The Trade Effects of Pandemics

Early evidence suggests that COVID-19 caused a sharp decrease in international trade and a widening of current account imbalances. This paper shows that (qualitatively) similar responses have characterized the effects of previous pandemics. Using data from a sample of 170 countries, we find that a pandemic shock is typically followed by a sizable decrease in output and trade volumes, but an uneven current account response: balances improve in developed (or surplus) economies but deteriorate in developing (or deficit) ones. We also explore potential mechanisms for this asymmetry, and our evidence is pointing to national saving and the business cycle phase as the main reasons behind the divergent current account dynamic responses.
Domain Generalization using Causal Matching

We reproduced the results of the paper "Domain Generalization Using Causal Matching. " Traditional supervised learning assumes that the classes/labels seen in testing must have appeared during the training phase. However, this assumption is often violated in real-world applications. For instance, in e-commerce, new categories of products are released every day. A model that cannot detect new/unseen classes is hard to function in such open environments as they are not generalizable.
Assessment of the radiation dose associated with X-ray examinations using the DoseCal and CALDose_X software packages

Background : The aim of this study was to estimate the entrance skin dose (ESD) in adult patients undergoing skull and pelvis X - ray examinations in Najran, Saudi Arabia using the DoseCal (Radiological Protection Center of Saint Georges’ Hospital, London, England) and CALDose_X (Department of Nuclear Energy, Federal University of Pernambuco, Recife, Brazil) software packages. Additional aims included conducting comparisons between these two software packages and against international reference dose levels and estimating the radiogenic risk during X - ray examinations. Materials and Methods : A dataset of 410 patients seen at Najran University Hospital was examined to assess the radiation dose using the DoseCal and CALDose_X software packages. Results : The values of the entrance dose, organ doses and effective dose obtained by the DoseCal and CALDose_X software were reported in this study. The ESD values estimated from the X - ray units ranged from 0.32 to 2.65 mGy for skull anteroposterior/posteroanterior (AP/PA), 0.62 to 2.13 mGy for skull lateral (LAT), and 1.23 to 3.15 mGy for pelvis AP projection. According to the DoseCal and CALDose_X, the dose absorbed by the pelvis and skull varied by a factor ranging between 1.2 and 2.4. Conclusion : All entrance calculated for the skull and pelvis were found to be within the corresponding dose reference levels recommended by the international agency, board and commission highlighted in this study.
Evolutional Normal Maps: 3D Face Representations for 2D-3D Face Recognition, Face Modelling and Data Augmentation

We address the problem of 3D face recognition based on either 3D sensor data, or on a 3D face reconstructed from a 2D face image. We focus on 3D shape representation in terms of a mesh of surface normal vectors. The first contribution of this work is an evaluation of eight different 3D face representations and their multiple combinations. An important contribution of the study is the proposed implementation, which allows these representations to be computed directly from 3D meshes, instead of point clouds. This enhances their computational efficiency. Motivated by the results of the comparative evaluation, we propose a 3D face shape descriptor, named Evolutional Normal Maps, that assimilates and optimises a subset of six of these approaches. The proposed shape descriptor can be modified and tuned to suit different tasks. It is used as input for a deep convolutional network for 3D face recognition. An extensive experimental evaluation using the Bosphorus 3D Face, CASIA 3D Face and JNU-3D Face datasets shows that, compared to the state of the art methods, the proposed approach is better in terms of both computational cost and recognition accuracy.