Miracle-dz/UniFAR-FTUs huggingface dataset:
Nanostructured Lipid Carriers for Nose to Brain Delivery Targeting CNS: Diversified Role of Liquid Lipids for Synergistic Action

Neurological disorders such as Alzheimer’s disease, Parkinson’s disease, dementia, epilepsy, depression, migraine etc. are affecting more and more elderly people’s day by day. Conventional route of administration to treat these diseases has to face a major hindrance that is blood brain and blood-cerebrospinal fluid (CSF) barrier to achieve desired concentration of drug at the site of action for therapeutic effect. Hence, intranasal route of delivery is considered as promising and alternative route to achieve desired goals. In last four decades, brain targeting strategies are widely studied and considered having great potential by researchers; especially intranasal delivery owing to its benefits. Various nano formulations such as nanoemulsions, nanosuspensions, hydrogels, in situ gels, dendrimers and lipidic formulations are studied widely. Lipid nano formulations especially second generation nanostructured lipid carriers offer greater advantages in terms of stability, fabrication techniques, scalability, drug loading and drug targeting. Nanostructured lipid carrier (NLCs) constitute of two major components viz solid lipid and liquid lipid in a specific ratio. In this review, authors have discussed about the possible synergistic actions of oils/liquid lipids with synthetic drugs resulting into great therapeutic benefits.
Butea Monosperma Shows Anti-cancer and Anti-Oxidant Activity: In-vitro and In vivo Study

Breast cancer is one of the predominant cancers in Indian subcontinent and the global scenario is not very different. Treatment of this type of cancer is possible by chemotherapy, surgery, and radiotherapy, which relieve the patient; however, off target activity and severe toxicity of non-cancerous cells remained the major drawbacks of these chemotherapeutics. In order to overcome these issues, modern cancer therapeutics emphasizes the implication of natural bioactive compounds. In this current piece of work, we have studied the anti-cancer potentiality of Butea monosperma flower in in vitro breast cancer cells. Various extracts of this flower were found to be non-toxic to breast epithelial cells but toxic to breast cancer cells. However, the methanol extract of this flower was most effective amongst all other extracts. Data suggest that Butea monosperma flower induce DNA damage mediated apoptosis and inhibit cell proliferation, angiogenesis and metastasis. It also inhibits the angiogenesis and metastasis in xenograft mice model system. Moreover, the anti-cancer phenomenon was found to be induction of inflammatory cytokines mediated oxidative stress in breast cancer. Collectively, Butea monosperma flower shows excellent cancer therapeutics in order to induce DNA damage, inhibit angiogenesis and metastasis simultaneously.
Optimization of Spray Fluid for Herbicide Application for Drones in Irrigated Maize (Zea mays L.)

A field experiment was conducted in a randomized complete block design to screen the optimum spray fluid of herbicide application for drone based on visual toxicity and weed control efficiency in maize (Zea mays L.) during the summer season (March 2021) at eastern block farms of Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu. Three herbicide treatments namely Atrazine, Tembotrione and 2, 4-D with recommended dosages, 75% and 125% as pre-emergence herbicides applied on 3 days after, early post-emergence herbicides applied on 15 days after sowing and post-emergence applied on 25 days after sowing respectively. Totally thirty treatments with different spray fluids such as 500, 400, 300, 200, 100, 80, 60, 40, 30 L ha-1 and 20 L ha-1 were replicated three times. The study revealed that T5- Recommended dosage of pre emergence Atrazine – early post emergence Tembotrione – post emergence 2, 4-D (spray fluid 100 L of water ha-1),T6- Recommended dosage of pre emergence Atrazine – early post emergence Tembotrione–post emergence 2, 4-D (spray fluid 80 L of water ha-1), T7- Recommended dosage of pre emergence Atrazine – early post emergence Tembotrione – post emergence 2, 4-D (spray fluid 60 L of water ha-1) and T8- Recommended dosage of pre emergence Atrazine – early post emergence Tembotrione – post emergence 2, 4-D (spray fluid 40 L of water ha-1) produced the best results with respect to phytotoxicity and weed control efficiency. Based on the results it was concluded that the application of spray fluid 80 L ha-1 was optimum for herbicide application through drones with recommended dosage pre emergence Atrazine 1 kg a.i ha-1 on 3 days after sowing – early post emergence Tembotrione 120 g a.i ha-1 on 15-20 days after sowing - post emergence 2, 4-D 1 kg a.i ha-1 on 30 - 35 days after sowing.
The Effect of Optogenetically Activating Glia on Neuronal Function

Glia, or glial cells, are considered a vital component of the nervous system, serving as an electrical insulator and a protective barrier from the interstitial (extracellular) media. Certain glial cells (i.e., astrocytes, microglia, and oligodendrocytes) within the CNS have been shown to directly affect neural functions, but these properties are challenging to study due to the difficulty involved with selectively-activating specific glia. To overcome this hurdle, we selectively expressed light-sensitive ion channels (i.e., channel rhodopsin, ChR2-XXL) in glia of larvae and adult Drosophila melanogaster. Upon activation of ChR2, both adults and larvae showed a rapid contracture of body wall muscles with the animal remaining in contracture even after the light was turned off. During ChR2-XXL activation, electrophysiological recordings of evoked excitatory junction potentials within body wall muscles of the larvae confirmed a train of motor nerve activity. Additionally, when segmental nerves were transected from the CNS and exposed to light, there were no noted differences in quantal or evoked responses. This suggests that there is not enough expression of ChR2-XXL to influence the segmental axons to detect in our paradigm. Activation of the glia within the CNS is sufficient to excite the motor neurons.
An Evaluation of Natural Environment Interventions for Informal Cancer Caregivers in the Community

Home-based informal caregiving by friends and family members of patients with cancer is be-coming increasingly common globally with rates continuing to rise. Such caregiving is often emo-tionally and cognitively demanding, resulting in mental exhaustion and high perceived burden. Support for caregivers may be fostered by engagement with the natural environment. Interaction with nature is associated with mental health benefits such as stress reduction and improved well-being. The purpose of this paper was to evaluate the state of the science regarding the use of nat-ural environment interventions to support caregivers of cancer patients in the community. A comprehensive scoping review using the Arksey and O’Malley framework and the Preferred Re-porting Items for Systematic Reviews and Meta-analyses assessed natural environment therapies and mental health outcomes among cancer caregivers. Databases searched included CINAHL, PubMed, Scopus, Cochrane, and Alt HealthWatch. Findings recovered a total of five studies over a 10-year period that met criteria, demonstrating a lack of empirical evidence addressing this po-tential resource to support caregivers. Often, study appraisal was not on nature exposure, but ra-ther other aspects of the projects such as program evaluation, exercise, or complementary thera-pies. Both qualitative and quantitative designs were used but sample sizes were small. Caregivers experienced beneficial results across the various studies and future work could enhance these findings.
Fractal-Based Retrieval and Potential Driving Factors of Lake Ice Fractures of Chagan Lake, Northeast China Using Landsat Remote Sensing Images

A thorough understanding of the freshwater ice process received considerable critical attention due to increasing winter recreations and ice engineering. The development of the lake ice process of Chagan Lake was monitored using MODIS and Landsat images over eight consecutive snow seasons from October 2013 to April 2021. We derived the lake ice phenology from an eight-day time series of lake water skin temperature (LWST) provided by MODIS, including freeze-up date, break-up date, and ice cover duration. We discovered a large-scale fracture extending from northwest to southeast that repeatedly appeared on Landsat images since 1986. A novel fractal-based auto-extraction is proposed to extract the length and angle of these fractures. We also carried out a field campaign and an ice ridge was found at the southernmost part of what we observed from the images. Moreover, we explained the fracturing development by thermal changes, wind in lake, and underlying flow. Results show that the lake ice fracture is nearly perpendicular to the dominant wind direction in the cold season, which indicates the crucial role of wind on lake ice fracturing.
Traffic state prediction based on stochastic repetitive hill climbing method

A reasonable structure of traffic state network is a prerequisite for traffic state prediction. In order to overcome the shortcomings of the hill climbing method, a traffic state prediction method based on the random repetitive hill climbing method is proposed. A multi-network structure is obtained by iteratively running the hill-climbing method on the randomly generated directed acyclic graph; the node and directed edge selection criteria in the optimal Bayesian network structure are determined by defining the confidence degree of directed edges and calculating the confidence threshold; using the optimal Bayesian network structure, four traffic states, such as smooth, smooth, congested and blocked, are predicted and evaluated comprehensively. The analysis results show that the overall accuracy of the method for traffic state prediction exceeds 85\% when only two variables such as time of day and holiday are selected, which can provide effective methods and data support for highway operation state monitoring and early warning and decision analysis.
Machine Learning Evaluation of Biliary Atresia Patients to Predict Long-Term Outcome after the Kasai Procedure

Kasai portoenterostomy (KP) represents the first-line treatment for biliary atresia (BA). The purpose was to compare the accuracy of quantitative parameters extracted from laboratory tests, US imaging, and MR imaging studies using machine learning (ML) algorithms to predict the long-term medical outcome in native liver survivor BA patients after KP. Twenty-four patients were evaluated according to clinical and laboratory data at initial evaluation (median follow-up = 9.7 years) after KP as having ideal (n = 15) or non-ideal (n = 9) medical outcomes. Patients were re-evaluated after an additional 4 years and classified in group 1 (n = 12) as stable and group 2 (n = 12) as non-stable in the disease course. Laboratory and quantitative imaging parameters were merged to test ML algorithms. Total and direct bilirubin (TB and DB), as laboratory parameters, and US stiffness, as an imaging parameter, were the only statistically significant parameters between the groups. The best algorithm in terms of accuracy, sensitivity, specificity, and AUCROC was naive Bayes algorithm, selecting only laboratory parameters (TB and DB). This preliminary ML analysis confirms the fundamental role of TB and DB values in predicting the long-term medical outcome for BA patients after KP, even though their values may be within the normal range. Physicians should be alert when TB and DB values change slightly.
Convergence of the Weighted Nonlocal Laplacian on Random Point Cloud

We analyze the convergence of the weighted nonlocal Laplacian (WNLL) on the high dimensional randomly distributed point cloud. Our analysis reveals the importance of the scaling weight, µ ∼ | P | / | S | with | P | and | S | being the number of entire and labeled data, respectively, in WNLL. The established result gives a theoretical foundation of the WNLL for high dimensional data interpolation
NIDM: Novel Intrusion Detection Model Using CNN-LSTM for Big Data Environment

Abstract: Machine learning-based (IDS) have become a critical component of safeguarding our economic and national security because of the massive quantities of data produced each day and the growing interconnection of the world's Internet infrastructures. The existing machine Learning Model technique may have difficulty comprehending the ever-increasingly complex distribution of data invasion patterns. With a small number of data points, a single deep learning algorithm may be ineffective at capturing different patterns for intrusive attacks. We presented CNN-LSTM Novel Intrusion Detection Model for Big Data to improve the efficiency of IDS-based CNN-LSTM even further (NIDM). NIDM uses behavioural traits and content functions to understand the characteristics when compared to earlier single learning model tactics, this strategy can improve the rate of intrusive attack detection. Keywords: IDS, Machine Learning, LSTM, CNN.
Evaluating the Disaster Risk of the COVID-19 Pandemic Using an Ecological Niche Model

Since 2019, the novel coronavirus has spread rapidly worldwide, greatly affecting social stability and human health. Pandemic prevention has become China’s primary task in responding to the transmission of COVID-19. Risk mapping and the proposal and implementation of epidemic prevention measures emphasize many research efforts. In this study, we collected location information for confirmed COVID-19 cases in Beijing, Shenyang, Dalian, and Shijiazhuang from 5 October 2020 to 5 January 2021, and selected 15 environmental variables to construct a model that comprehensively considered the parameters affecting the outbreak and spread of COVID-19 epidemics. Annual average temperature, catering, medical facilities, and other variables were processed using ArcGIS 10.3 and classified into three groups, including natural environmental variables, positive socio-environmental variables, and benign socio-environmental variables. We modeled the epidemic risk distribution for each area using the MaxEnt model based on the case occurrence data and environmental variables in four regions, and evaluated the key environmental variables influencing the epidemic distribution. The results showed that medium-risk zones were mainly distributed in Changping and Shunyi in Beijing, while Huanggu District in Shenyang and the southern part of Jinzhou District and the eastern part of Ganjingzi District in Dalian also represented areas at moderate risk of epidemics. For Shijiazhuang, Xinle, Gaocheng and other places were key COVID-19 epidemic spread areas. The jackknife assessment results revealed that positive socio-environmental variables are the most important factors affecting the outbreak and spread of COVID-19. The average contribution rate of the seafood market was 21.12%, and this contribution reached as high as 61.3% in Shenyang. The comprehensive analysis showed that improved seafood market management, strengthened crowd control and information recording, industry-catered specifications, and well-trained employees have become urgently needed prevention strategies in different regions. The comprehensive analysis indicated that the niche model could be used to classify the epidemic risk and propose prevention and control strategies when combined with the assessment results of the jackknife test, thus providing a theoretical basis and information support for suppressing the spread of COVID-19 epidemics.
SBX­HY at RuShiftEval 2021: Доверяй, но проверяй

Research in computational lexical semantic change, due to the inherent nature of language change, has been no­ toriously difficult to evaluate. This led to the creation of many new exciting models that cannot be easily compared. In this system paper, we describe our submissions at RuShiftEval 2021 – one of the few recently shared tasks that enable researchers, through a standard evaluation set and control conditions, to systematically compare models and gain insights from previous work. We show that despite top results in similar tasks on other languages, Temporal Referencing does not seem to perform as well on Russian.
Climate Security and Policy Options in Japan

Climate security has been discussed in both academia and policy documents in the West. A key point that surfaces from these discussions is that the cooperation of non-military organizations is essential for effective responses to climate change-related threats. This overlaps considerably with debates on security in Japan, where the use of force is constitutionally restricted. Therefore, it is possible to localize the concept of climate security to the genealogy of Japan’s security policy that, in the 1980s and 1990s, sought a non-traditional security strategy that did not rely solely on military power in the name of “comprehensive security,” “environmental security,” and “human security. ” In Japan, the perspective of climate security is rare. However, the introduction of a unique climate security concept into security policy enables the maintenance of national security and environmental conservation. Additionally, struggling with climate change alongside neighboring countries contributes to mutual confidence building and stability in international relations in Northeast Asia. To achieve this objective, we first show that climate security includes many kinds of security concerns by surveying previous studies and comparing Western countries’ climate security policies. Second, we follow the evolution of Japan’s security policy from 1980 to 2021. Finally, we review Japanese climate security policies and propose policy options.
CP-DSL: Supporting Configuration and Parametrization of Ocean Models with UVic (2.9) and MITgcm (67w)

Abstract. Ocean models are long-living software systems facing challenges with increasing complexity, architecture erosion, and managing legacy code. These challenges increase maintenance costs in development and use, which reduces the time and resources available for research. Software engineering addresses these challenges by separation of concerns and modularization. One particular approach is to separate concerns by tailor-made notations, i.e. Domain-Specific Languages (DSLs). Using DSLs, the model developer can focus on one concern at a time without the need to consider other concerns of a software system simultaneously. In ocean and climate models, DSL tooling, like PSyclone and Dusk/Dawn, is used for instance to separate scientific and technical code. CP-DSL complements this approach with a focus on configuration and parametrization, which play an important role in ocean models, especially in parameter optimization and scenario-based simulations. CP-DSL is designed to be model agnostic and provides a unified interface to different ocean models. Furthermore, the DSL can be integrated into tools and processes used by domain experts. In this paper we report on the DSL design, implementation, and the evaluation with scientists and research software engineers. The implementation of CP-DSL is available as open source software and a replication package for configuration and parameterization of UVic and MITgcm is provided.
Spatiotemporal Distribution of Total Suspended Matter Concentration in Changdang Lake Based on In Situ Hyperspectral Data and Sentinel-2 Images

The concentration of total suspended matter (TSM) is an important parameter for evaluating lake water quality. We determined in situ hyperspectral data and TSM concentration data for Changdang Lake, China, to establish a TSM concentration inversion model. The model was applied using 60 Sentinel-2 images acquired from 2016 to 2021 to determine the temporal and spatial distribution of TSM concentration. Remote sensing images were also utilized to monitor the effect of ecological dredging in Changdang Lake. The following results were obtained: (1) Compared with four existing models, the TSM concentration inversion model established in this study exhibited higher accuracy and was suitable for Changdang Lake. (2) TSM concentrations obtained for the period 2016–2021 were higher in spring and summer, and lower in autumn and winter. (3) The dredging process influenced a small area of the surrounding water body, resulting in higher TSM concentrations. However, a subsequent reduction in TSM concentrations indicated that the ecological dredging project might improve the water quality of Changdang Lake to a considerable extent. Therefore, it was inferred that the use of Sentinel-2 images was effective for the long-term monitoring of water quality changes in small and medium-sized lakes.
Scoliosis in Children: Impact of Goal-Directed Therapies on Intraoperative and Postoperative Outcomes

Background: Scoliosis is among interventions with high postoperative complication rates due to the characteristics of the surgery, where blood loss, transfusion and fluid requirements can be increased. A monocentric retrospective observational study was undertaken earlier to determine predictors of intraoperative and postoperative outcomes in surgical patients. In this initial cohort, there were patients who underwent scoliosis surgery, and a secondary analysis to describe outcomes in these patients was realized and presented here. Objective: To describe intraoperative and postoperative outcomes in patients under 18 years old in scoliosis surgery included in the initial study and to propose improvement and implementation measures. Methods: A secondary analysis of patients undergoing scoliosis surgery from 1 January 2014 to 17 May 2017 was undertaken in our institution—Necker Enfants Malades univer-sity hospital. The study was approved by the Ethics Committee. Results: There were 116 patients with a mean age of 147.5 ± 40.2 months. Twenty-eight patients (24.1%) presented intraoperative and/or postoperative complications. The most common intraoperative complication was hemorrhagic shock in 3 patients (2.6%). The most common postoperative organ failure was neurologic in seven patients (6%), 24.1%, integrating goal-directed therapies in this surgical setting could improve postoperative outcomes.
Environmental indicator disclosure of international contractors

ABSTRACT Companies have acknowledged their corporate social responsibility (CSR) in recent years. However, environmental performance indicators are generally insufficiently disclosed. This study has investigated the disclosure of environmental indicators in the CSR reports of international contractors and proposed the sequence and method for indicator disclosure. Measurement scales were used to analyze 34 environmental indicators of the Global Reporting Initiative (GRI) G4 guidelines in terms of content requirements and characteristics. CSR reports of eight selected contractors were examined to calculate percentages of indicator disclosure. Finally, the indicators were ranked in three levels to present a meaningful disclosure sequence for construction companies. The average disclosure rate of the 34 indicators of the eight contractors was 29%. A total of 3, 11, and 20 indicators were high, medium, and low disclosure, respectively. The disclosure percentages for materials, water, effluents and wastes, emissions, and energy were 63%, 42%, 37%, 36%, and 33%, respectively. A company can disclose high-disclosure quantitative indicators with priority, such as materials and water, and supplement them with qualitative ones. Materials are a crucial input of the construction process. Therefore, data regarding materials should be collected early to ensure that data are available for other indicators.