Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFECT OF AWILD TYPE AND THREE DIFFERENT MUTANTS OF CHITINOLYTIC BACTERIA, Bacillus thuringiensis BY U.V. IRRADIATION ON VIABILITY OF Meloidogyne incognita EGGS UNDER LABORATORY CONDITIONS

The nematicidal activity of the wild type and three mutants namely (24, 10, 32 ) of the chitinolytic bacteria, Bacillus thuringiensis was evaluated against eggs of the root knot nematode, Meloidogyne incognita, using U.V. irradiation as a safer and environment friendly control, alternate of chemical nematicides under laboratory conditions at 25 ± 5 oC. Results, generally indicated that the mutant no. 24 is more effective on the nematode eggs followed by mutant no.10, then mutant no. 32 as compared to the wild type which achieved the lowest mortality. These values ranged from (90.8 to 96.6 % ), (79.8 to 89.9 %), ( 70.2 to 79.5 %), (46.3 to 68.8 %); respectively compared to 2.7% in untreated control after 96 he time. There were positive relation ship between the percentage of mortality and the chitinase production from mutants, the production evaluated 385 %, 350 % and 300 %; respectively. Keyword : Chitinolytic bacteria, Bacillus thuringiensis, mutantion, U.V. irradiation Meloidogyne incognita
Effectiveness and Safety of Ceftriaxone Compared to Standard of Care for Treatment of Bloodstream Infections Due to Methicillin-Susceptible Staphylococcus aureus: A Systematic Review and Meta-Analysis

(1) Background: Ceftriaxone is a potential alternative for the treatment of methicillin-susceptible Staphylococcus aureus (MSSA) bloodstream infections (BSIs) in acute care and outpatient parenteral antimicrobial therapy (OPAT) settings. We evaluated the effectiveness and safety of ceftriaxone for the treatment of MSSA BSIs. (2) Method: We searched PubMed, Embase, and Cochrane Library from their inception to October 30th 2021. Our outcomes included clinical cure, microbiological cure, 30- and 90-day mortality, 90-day hospital readmission, and adverse drug reactions (ADRs). We compared ceftriaxone against standard of care (SOC) therapy. We used the random-effects model for the meta-analysis, and our estimated effects were reported as odds ratios (ORs) with 95% confidence intervals (CI). (3) Results: Twelve retrospective cohort studies were included, comprising 1037 patients in the ceftriaxone arms and 2088 patients in the SOC arms. The clinical cure rate of the ceftriaxone regimen was not statistically different from SOC: OR 0.65 (95% CI: 0.29–1.45). Ceftriaxone was also not statistically different from SOC in microbiological cure: OR 1.48 (95% CI: 0.29–7.51); 30-day mortality: OR 0.79 (95% CI: 0.14–4.65); 90-day mortality: OR 0.82 (95% CI: 0.38–1.80); 90-day hospital readmission: OR 1.20 (95% CI: 0.92–1.56); and ADRs: OR 0.92 (95% CI: 0.39–2.18). (4) Conclusion: Ceftriaxone could provide an alternative for the treatment of MSSA BSIs in acute care and OPAT settings (except in patients whose BSIs were due to infective endocarditis).
Cross-sectional Observations on the Natural History of Mucolipidosis Type IV

Background and Objectives Mucolipidosis type IV (MLIV) is an ultra-rare lysosomal disorder initially described as a static neurodevelopmental condition. However, patient caregivers frequently report progressive muscular hypertonicity and functional decline. We evaluated a cohort of patients with MLIV to determine whether neurologic disability correlates with age. Methods We performed a cross-sectional, observational study of 26 patients with MLIV in the United States and Israel ranging in age from 2 to 40 years. Medical history was obtained from caregivers, and patients underwent a full neurologic examination. The Brief Assessment of Motor Function (BAMF), Gross Motor Function Classification System, and modified Ashworth scales were applied. Caregivers identified developmental skills on the Oregon Project for Visually Impaired and Blind Children checklist that their child had lost the ability to perform. Results Three patients were clinically classified as mildly affected and the remaining 23 patients as typical, severely affected cases. Timing of first symptom onset ranged from 1.5 months to 8 years of age (median 7.25 months). Across typical patients, modified Ashworth scores demonstrated a positive age dependence illustrating worsening spasticity across the lifespan. Signs of extrapyramidal motor dysfunction were also qualitatively observed. In parallel, gross and fine motor function assessed with the BAMF and Gross Motor Function Classification System scales declined across age. All typical patients had restricted tongue mobility and lacked rotary jaw movement when chewing, but BAMF scores for deglutition declined only in the oldest patients. In contrast, scores for articulation were low in all patients and did not correlate with age. Finally, loss of developmental skills frequently occurred in early adolescence. Discussion This cross-sectional natural history study of MLIV demonstrates worse motor function in older patients. These data support a neurodegenerative component of MLIV that manifests as developmental regression in the second decade of life. Whether the emergence of functional decline results from the cumulative, nonlinear interactions of steadily progressive neurodegenerative processes or reflects an inflection from impaired CNS development to degeneration is uncertain. However, understanding the relationship between CNS pathology and clinical course of disease will be imperative to guiding future interventional trials and optimizing patient care.
Alteration of Oral and Perioral Soft Tissue in Mice following Incisor Tooth Extraction

Oral and perioral soft tissues cooperate with other oral and pharyngeal organs to facilitate mastication and swallowing. It is essential for these tissues to maintain their morphology for efficient function. Recently, it was reported that the morphology of oral and perioral soft tissue can be altered by aging or orthodontic treatment. However, it remains unclear whether tooth loss can alter these tissues’ morphology. This study examined whether tooth loss could alter lip morphology. First, an analysis of human anatomy suggested that tooth loss altered lip morphology. Next, a murine model of tooth loss was established by extracting an incisor; micro-computed tomography revealed that a new bone replaced the extraction socket. Body weight was significantly lower in the tooth loss (UH) group than in the non-extraction control (NH) group. The upper lip showed a greater degree of morphological variation in the UH group. Proteomic analysis and immunohistochemical staining of the upper lip illustrated that S100A8/9 expression was higher in the UH group, suggesting that tooth loss induced lip inflammation. Finally, soft-diet feeding improved lip deformity associated with tooth loss, but not inflammation. Therefore, soft-diet feeding is essential for preventing lip morphological changes after tooth loss.
STUDY ON STABILITY CONTROL OF LARGE-SECTION SUBSEA TUNNELS IN WEAK STRATA BY CRD CONSTRUCTION METHOD

Combined with the facts that the strata of the entrance at Xiang′an side of Xiamen Xiang′an subsea tunnel are weak and unstable,the excavation is difficult,and the preliminary lining deforms severely. The reason of the left tunnel deformation excavated by CRD method under the condition of surrounding rock of class V is analyzed. Based on the measuring data,the construction stability of the large-section subsea tunnel in weak strata is analyzed,including the characters of surrounding rock,the distance between each step of CRD working face, the time of each step of CRD first shoring finished,the time of the whole working face completed,the distance between the permanent inverted arch and the working face,and grouting behind preliminary support or not. Based on these,the measures controlling the deformation are put forward. Successful practice and techniques are summarized to provide experiences for similar engineering.
Step Attention: Sequential Pedestrian Trajectory Prediction

In this paper we propose a deep learning model, which we call step attention, for pedestrian trajectory prediction. The proposed model has a special architecture which consists of recurrent neural networks, convolutional neural networks, and an augmented attention mechanism. Rather than developing architectures to model factors that may affect the walking behavior, the proposed model learns trajectory patterns directly from input sequences. We evaluate the performance of the step attention model using TrajNet–a publicly available benchmark dataset collected from diverse real-world crowded scenarios. We compare the performance of step attention with three existing state-of-the-art algorithms, including social LSTM, social GAN, and occupancy LSTM on the TrajNet benchmark dataset. Our experiments show that the average displacement error (ADE) of step attention for a 4.8-seconds-long prediction horizon is about 0.53 m. The final displacement error (FDE) is 1.72 m. Both average and final displacement errors are favorable compared to the benchmark methods. We conduct a second set of experiments using data collected from a four-way intersection through roadside camera sensor platforms to study the effectiveness of the proposed model in uncrowded environments. On this dataset, the proposed model has an ADE of 0.74 m and a FDE of about 1.40 m for a 6-seconds-long prediction horizon. A complementary set of experiments is conducted to further investigate model performance in a real-world intersection. In these experiments, the model gains an ADE/FDE of 0.76/1.70 m. The proposed model also produces accurate prediction results on different scenarios composed of different walking patterns (e.g., straight and curvy) and different environments (e.g., sidewalk and street). The average displacement errors on all investigated datasets are within the length of a single step of an adult. The experiments also indicate that the displacement error grows almost linearly with the prediction horizon.
The “Learning Disabilities-to-Prison” Pipeline: Evidence From the Add Health National Longitudinal Study

Young people with learning disabilities, and in particular those of color, are significantly more at risk for having school difficulties, delinquency, and incarceration. The National Longitudinal Study of Adolescent to Adult Health (Add Health) data were used to investigate how learning disabilities, school experiences, gender, and race impacted delinquency and criminal activity and incarceration—looking at a learning disabilities-to-prison link. Structural equation modeling was used to analyze the relationship between measured variables and latent constructs, comparing those young people with learning disabilities and those without. It was found that these pathways are quite complicated to discern; reflecting the current knowledge of this “learning disabilities pipeline” hypothesis. For young people with learning disabilities compared to young people without learning disabilities, juvenile delinquency was more likely if the young person was male or Hispanic; criminal activities were more likely for Black children and for those delinquent or incarcerated as a juvenile; and females were less likely to be incarcerated as an adult, but school dropouts, Black children, and those incarcerated as a juvenile were more likely. Implications are set forth, as well as recommendations to stakeholders.
An Ensemble Learning Method for the Kernel-Based Nonlinear Multivariate Grey Model and its Application in Forecasting Greenhouse Gas Emissions

The global warming problem caused by greenhouse gas (GHG) emissions has aroused wide public concern. In order to give policy makers more power to set the specific target of GHG emission reduction, we propose an ensemble learning method with the least squares boosting (LSBoost) algorithm for the kernel-based nonlinear multivariate grey model (KGM) (1, N), and it is abbreviated as BKGM (1, N). The KGM (1, N) has the ability to handle nonlinear small-sample time series prediction. However, the prediction accuracy of KGM (1, N) is affected to an extent by selecting the proper regularization parameter and the kernel parameter. In boosting scheme, the KGM (1, N) is used as a base learner, and the use of early stopping method avoids overfitting the training dataset. The empirical analysis of forecasting GHG emissions in 27 European countries for the period 2015–2019 is carried out. Overall error analysis indicators demonstrate that the BKGM (1, N) provides remarkable prediction performance compared with original KGM (1, N), support vector regression (SVR), and robust linear regression (RLR) in estimating GHG emissions.
An analysis of classroom teachers’ awareness of inclusive education

ABSTRACT The targets of reaching the benefits expected from inclusive education and making necessary changes and arrangements can only be achieved by clearly determining the current situation regarding inclusive education. This study aimed to examine the competencies of classroom teachers in Turkey regarding inclusive education in the context of their perceptions, knowledge, and attitudes. A total of 128 classroom teachers of third-grade (20) and fourth-grade (108) participated in the study. We employed the ‘Classroom Teachers’ Awareness of Inclusive Education Scale’ as a data collection tool in this study carried out in accordance with the phenomenological method. The data were subjected to descriptive analysis. Apart from the generally positive awareness of classroom teachers towards inclusive education, it appears to be necessary that students’ social-emotional skills be supported so that inclusive education can be fully achieved.
EXTENDING WHITE-BOX CRYPTOGRAPHY BASED OBLIVIOUS TRANSFER PROTOCOL

Secure computation platforms are becoming one of the most demanded cryptographic tools utilized in diverse applications, where the performance is critical. This point makes important the optimization of every component of secure computation systems. Oblivious Transfer (OT) is a fundamental cryptographic primitive heavily used in such protocols. Most of the OT protocols used today are based on public-key cryptography, hence their efficiency suffers heavily from the number of modular exponentiation operations done. OT extensions were introduced to reduce the number of basic OT protocol execution rounds requiring public-key cryptography operations. Recently a white-box cryptography based OT protocol (WBOT) was introduced that avoids using expensive public-key operations. In this article extension protocols for WBOT are presented, that further improve the novel approach by dramatically decreasing the protocol invocation count required.
Identification of the Potential Key Genes and Pathways Involved in Lens Changes of High Myopia

Aim High myopia (HM) is a global problem; however, the molecular pathogenesis of HM underlying lens remains largely unknown. The aims of the present study were to identify the potential key genes and pathways involved in lens changes of HM. Methods Gene set enrichment analysis was carried out to identify the HM-specific pathway gene sets. The differentially expressed genes (DEGs) in lens epithelia of HM eyes compared to emmetropic control were screened using limma R package. A DEG-based protein–protein interaction network was constructed and used to identify hub genes and gene cluster analysis. The functional enrichment analysis was performed to reveal the potential biological functions for each gene cluster. Results Multiple metabolism-related pathways were significantly enriched in lens epithelia of HM. The expression patterns of DEGs could accurately distinguish HM and emmetropic and CD34, CD40, EGF, IL1A, CD40LG, and CXCL12 maybe the potential key genes involved in HM. Three gene clusters were identified and involved in distinct pathways. MAPK signaling pathway and calcium signaling pathway were considered the key pathways involved in lens changes of HM, due to two gene clusters both involve in these two pathways. Conclusion We identified potential key genes in pathological lens growth of HM eyes and proposed that the imbalances of MAPK signaling pathway and calcium signaling pathway may be the two crucial steps of pathological lens growth in HM.
Alternative Promoters of GRIK2 (GluR6) Gene in Human Carcinoma Cell Lines Are Regulated by Differential Methylation of CpG Dinucleotides

The ionotropic glutamate receptor 6 (GluR6 or GRIK2) gene is transcribed by two cell-type-specific promoters in neuronal and non-neuronal cells, which results in five different transcript variants. The purpose of this study was to explore cell-type-specific silencing of these promoters by epigenetic mechanisms. The neuronal and non-neuronal promoter sequences were cloned upstream of the luciferase gene in the pGL3 luciferase reporter vector. Promoter susceptibility to methylation was confirmed by 5-azacytidine and trichostatin treatment, and the status of CpG dinucleotides was determined by bisulfite sequencing of the promoter was determined by bisulfite sequences. GluR6A transcript variant was expressed in the brain, and GluR6B was most abundant in tumor cell lines. The neuronal promoter was methylated in non-neuronal cell lines. The treatment with 5-azacytidine and trichostatin upregulated transcription of the GluR6 gene, and methylation of the GluR6 promoter sequence in the luciferase reporter system led to downregulation of the luciferase gene transcription. Bisulfite sequencing revealed methylation of 3 and 41 CpG sites in non-neuronal and neuronal promoters, respectively. The differential activation/silencing of GluR6 promoters suggests that the transcript variants of GluR6 are involved in tissue-specific biological processes and their aberrant regulation in tumor cells may contribute to distinct properties of tumor cells.
Prediction and Analysis of Artwork Price Based on Deep Neural Network

The use of deep learning methods to solve problems in the field of artwork prices has attracted widespread attention, especially the superiority of long short-term memory network (LSTM) in dealing with time series problems. However, the potential for deep learning in the prediction of artwork price has not been fully explored. This paper proposes a deep prediction network structure that considers the correlation between time series data and the combination of two-way LSTM as well as one-way LSTM networks to predict the price of artworks. This paper proposes a deep-level two-way and one-way LSTM to predict the price of artworks in the art market. Taking into account the potential reverse dependence of the time series, the bidirectional LSTM layer is used to obtain bidirectional time correlation from historical data. This research uses a matrix to represent the artwork price data and fully considers the spatial correlation characteristics of the artwork price. Simultaneously, this paper uses the two-way LSTM network to correlate the potential contextual information of the historical data of the artwork price stream and fully perform feature learning. This study applies the two-way LSTM network layer to the building blocks of the deep architecture to measure the inverse dependence of the price fluctuation data. The comparison with other prediction models shows that the LSTM neural network fused with one-way and two-way proposed in this paper is superior to other neural networks for predicting price of artworks in terms of prediction accuracy.
A Collaboration Algorithm for Computer Generated Forces Based on Multi-agent Systems

To increase the robustness,scalability and adaptivness of the collaboration mission distribution mechanism,an integrated contract net protocol for computer generated forces(CGF) collaboration is presented,in which the interaction trust degree,witness trust degree and threshold are introduced into the classical contract net protocol of multi-agent systems.To assure the correctness,completeness and reliability of the integrated contract net protocol,colored Petri nets are employed for modelling,to analyze its liveness,reachability and boundness.Finally,the integrated contract net protocol is applied to the ballistic missile attack-defense confrontation CGF system based on MAS.The simulation results show that the integrated contract net protocol reduces the communication cost effectively,and increases the ratio of accomplished tasks.
Bundling regions for promoting Sustainable Development Goals

The needs and capacities to achieve the 17 Sustainable Development Goals (SDGs) differ across regions and nations, but little research has been done to investigate their similarities and differences. Here, we proposed using SDG bundles (i.e. groups of regions with similar performances on all individual SDGs) to classify regions when assessing SDG progress and applied the method at the provincial level in China from 2000 to 2015. Five SDG bundles with distinct characteristics were identified. The dominant bundles changed from ‘poor performance for all SDGs’ in 2000 to ‘high scores for environmental and some social SDGs and intermediate scores for others’ and ‘low scores for environmental SDGs but high scores for others’ in 2015, indicating the overall improvement of China’s sustainable development level. However, no bundle had relatively high scores in all SDGs, implying that China has much work left to do. Changes in the SDG bundles across space and time were related to regional socioeconomic development, climate, and geographic conditions. This study sheds light on identifying regions’ strengths and weaknesses in achieving all SDGs, which can inform targeted sustainability actions for regions within certain SDG bundles and promote collaborations among regions with different bundles.
Forecast of Chemical Export Trade Based on PSO-BP Neural Network Model

With the gradual deepening of China’s reform and opening up, the degree of foreign development has been deepened, and its dependence on foreign trade has increased. The “export-oriented” economic development has achieved results. Export trade is introducing advanced technology and equipment, expanding employment opportunities, and increasing government revenue. The export trade is affected by various domestic and international factors and is a complex nonlinear system. Although the traditional linear prediction method has the advantages of intuitiveness, simplicity, and strong interpretability, it is difficult to deal with the prediction problem of dynamic and complex nonlinear systems. The neural network is a nonlinear dynamic system, with strong nonlinear mapping ability, strong robustness, and fault tolerance. It has unique advanced advantages for solving nonlinear problems and is very suitable for solving nonlinear problems.
Explanatory Models of Burnout Diagnosis Based on Personality Factors and Depression in Managing Nurses

Nurse managers are affected by burnout due to the high degree of interaction between managers with their registered nurses. Explanatory models based on psychological, and personality related variables purvey an estimation to level changes in the three dimensions of the burnout syndrome. A categorical-response logistic ordinal regression model, supported on a quantitative, crosscutting, multicentre, descriptive study with 86 nursing managers in the Andalusian Health Service in Granada, Spain is performed for each dimension. The three models included different variables related to personality, as well as depression as the only explanatory variable included in all the models. The risk factor neuroticism was significant at population level and related to emotional exhaustion, whilst responsibility was significant in the model estimated to personal accomplishment dimension. Finally, depression was significant for the three dimensions of Burnout. This analysis provides useful information to help the diagnosis and evolution of this syndrome in this collective.