Miracle-dz/UniFAR-FTUs huggingface dataset:
A Phase 2, Single-Arm, Open-Label Clinical Trial on Adjuvant Peptide-Based Vaccination in Dogs with Aggressive Hemangiosarcoma Undergoing Surgery and Chemotherapy

Simple Summary Canine hemangiosarcoma shares many clinical, histologic and molecular features with human angiosarcoma. Thus, the dog represents a powerful and alternative model of spontaneously occurring hemangiosarcoma for comparative studies and developmental therapeutic investigation. In both species, surgery and adjuvant dose-intense chemotherapy represent the gold standard treatment. Several attempts have been made to improve patient outcomes. Novel clinical protocols have been designed, and different pharmacological and surgical approaches have been tested. However, hemangiosarcoma treatment remains an unmet medical need. Immunotherapy has emerged as a promising strategy. An anticancer vaccine was administered to 28 dogs with biologically aggressive hemangiosarcoma in combination with the standard treatment. The endpoints of the study were efficacy and safety. In addition to the tolerability of the vaccine, the induction of immune responses was reported, ultimately leading to improved outcomes compared with historical controls receiving the standard of care treatment only. The current findings provide promising potential for future management in both species. Abstract To test the antitumor effect and safety of peptide-based anticancer vaccination in dogs with hemangiosarcoma undergoing the standard of care (SOC; surgery and doxorubicin), canine hemangiosarcoma cells were infected with Salmonella typhi Ty21a to release immunogenic endoplasmic reticulum stress-related peptides into the extracellular milieu via CX43 hemichannels opening. The infected tumor cell secretome constituted the vaccine. Following the SOC, dogs with biologically aggressive hemangiosarcoma were vaccinated a total of five times, once every 3 weeks, and were followed up with serial imaging. A retrospective population of dogs undergoing the SOC alone served as controls. The primary endpoints were the time to progression (TTP) and overall survival (OS), and the secondary endpoints were toxicity and immune responses. A total of 28 dogs were vaccinated along with the SOC, and 32 received only the SOC. A tumor-specific humoral response along with a vaccine-specific T-cell response was observed. Toxicity did not occur. The TTP and OS were significantly longer in vaccinated versus unvaccinated dogs (TTP: 195 vs. 160 days, respectively; p = 0.001; OS: 276 vs. 175 days, respectively; p = 0.002). One-year survival rates were 35.7% and 6.3% for vaccinated and unvaccinated dogs, respectively. In dogs with hemangiosarcoma undergoing the SOC, the addition of a peptide-based vaccine increased the TTP and OS, while maintaining a safe profile. Moreover, vaccinated dogs developed a tumor-specific response, supporting the feasibility of future phase three studies.
Scutellaria baicalensis Attenuated Neurological Impairment by Regulating Programmed Cell Death Pathway in Ischemic Stroke Mice

Stroke is a major global health problem that causes significant mortality and long-term disability. Post-stroke neurological impairment is a complication that is often underestimated with the risk of persistent neurological deficits. Although traditional Chinese medicines have a long history of being used for stroke, their scientific efficacy remains unclear. Scutellaria baicalensis, an herbal component known for its anti-inflammatory and antioxidant properties, has traditionally been used to treat brain disorders. This study investigated the therapeutic effects of the Scutellaria baicalensis extraction (SB) during the acute stage of ischemic stroke using photothrombotic (PTB)-induced and transient middle cerebral artery occlusion (tMCAO) model mice. We found that SB mitigated ischemic brain injury, as evidenced by a significant reduction in the modified neurological severity score in the acute stage of PTB and both the acute and chronic stages of tMCAO. Furthermore, we elucidated the regulatory role of SB in the necroptosis and pyroptosis pathways during the acute stage of stroke, underscoring its protective effects. Behavioral assessments demonstrated the effectiveness of SB in ameliorating motor dysfunction and cognitive impairment compared to the group receiving the vehicle. Our findings highlight the potential of SB as a promising therapeutic candidate for stroke. SB was found to help modulate the programmed cell death pathways, promote neuroprotection, and facilitate functional recovery.
Comparative analysis of liver transcriptome reveals adaptive responses to hypoxia environmental condition in Tibetan chicken

Objective Tibetan chickens, which have unique adaptations to extreme high-altitude environments, exhibit phenotypic and physiological characteristics that are distinct from those of lowland chickens. However, the mechanisms underlying hypoxic adaptation in the liver of chickens remain unknown. Methods RNA-sequencing (RNA-Seq) technology was used to assess the differentially expressed genes (DEGs) involved in hypoxia adaptation in highland chickens (native Tibetan chicken [HT]) and lowland chickens (Langshan chicken [LS], Beijing You chicken [BJ], Qingyuan Partridge chicken [QY], and Chahua chicken [CH]). Results A total of 352 co-DEGs were specifically screened between HT and four native lowland chicken breeds. Gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses indicated that these co-DEGs were widely involved in lipid metabolism processes, such as the peroxisome proliferator-activated receptors (PPAR) signaling pathway, fatty acid degradation, fatty acid metabolism and fatty acid biosynthesis. To further determine the relationship from the 352 co-DEGs, protein-protein interaction network was carried out and identified eight genes (ACSL1, CPT1A, ACOX1, PPARC1A, SCD, ACSBG2, ACACA, and FASN) as the potential regulating genes that are responsible for the altitude difference between the HT and other four lowland chicken breeds. Conclusion This study provides novel insights into the molecular mechanisms regulating hypoxia adaptation via lipid metabolism in Tibetan chickens and other highland animals.
Mathematical modeling analysis and simulation of human papillomavirus infection and cervical cancer in Xinjiang, China

A dynamical model of human papillomavirus (HPV) infection and secondary cervical cancer transmission with a two‐stage recovery of HPV infection and precancerous lesions is constructed based on the transmission features relating HPV infection and secondary cervical cancer in Xinjiang, China. First, the dynamical behavior of the model is investigated, including the positivity and boundedness of solutions, the calculation regarding basic reproduction number, the existence and stability of equilibrium, and the uniform persistence of the model. The theoretical results show that the basic reproduction number completely decided the dynamical properties of the model. Second, the parameters and current basic reproduction number specific to the model are fitted and estimated considering the actual data of HPV infection and secondary cervical cancer cases in Xinjiang from January 2003 to December 2019. Furthermore, the sensitivity of main parameters to the basic reproduction number is discussed by the numerical simulations. Finally, some practical control strategies are proposed to control the further spread of HPV infection and cervical cancer in the region.
Immunomodulatory and Antioxidant Drugs in Glaucoma Treatment

Glaucoma, a group of diseases characterized by progressive retinal ganglion cell loss, cupping of the optic disc, and a typical pattern of visual field defects, is a leading cause of severe visual impairment and blindness worldwide. Elevated intraocular pressure (IOP) is the leading risk factor for glaucoma development. However, glaucoma can also develop at normal pressure levels. An increased susceptibility of retinal ganglion cells to IOP, systemic vascular dysregulation, endothelial dysfunction, and autoimmune imbalances have been suggested as playing a role in the pathophysiology of normal-tension glaucoma. Since inflammation and oxidative stress play a role in all forms of glaucoma, the goal of this review article is to present an overview of the inflammatory and pro-oxidant mechanisms in the pathophysiology of glaucoma and to discuss immunomodulatory and antioxidant treatment approaches.
Estimating the Physicochemical and Antioxidant Properties of Hardy Kiwi (Actinidia arguta) Treated with 1-Methylcyclopropene during Storage

In fruit storage, new methods are being sought to extend the distribution period while maintaining the highest quality parameters of the fruit, i.e., the physical and chemical characteristics of the fruit, but also the health-promoting properties. One method is to treat the fruit with 1-MCP, which effectively inhibits fruit ripening, since the main reasons for limiting the distribution of minikiwi fruit are rapid ripening and the loss of firmness. It is also highlighted that minikiwi is a source of antioxidants, which, as highly reactive compounds, are quickly degraded during storage. This study evaluates the effectiveness of using 1-MCP to reduce minikiwi softening and maintain the high antioxidant properties of the fruit. In the experiment, minikiwi fruits of the ‘Ananasnaya’ cultivar were used. After harvesting, the fruits were treated with 1-methylcyclopropene at a concentration of 0.65 µL/L. Fruits treated in this way were stored in ordinary cold storage (NA) and under low oxygen concentration (ULO) conditions for a period of 12 weeks. The fruits’ physicochemical properties, sugar and acid contents and antioxidant potential, which consisted of ascorbic acid, polyphenols, phenolic acids, flavonols and flavan-3-ols, were evaluated. The application of 1-MCP is effective in reducing the loss of firmness in the minikiwi during storage, but the blocking of minikiwi ripening by 1-MCP makes the fruit less sweet and more acidic. Fruits treated with 1-MCP had higher antioxidant activity and a higher content of biologically active compounds. The effect of 1-MCP is stronger for flavan-3-ols, but slightly weaker for phenolic acids. Fruits treated with 1-MCP have a higher antioxidant potential than untreated fruit after a long period of storage.
Regional Traffic Event Detection Using Data Crowdsourcing

Accurate detection and state analysis of traffic flows are essential for effectively reconstructing traffic flows and reducing the risk of severe injury and fatality. For this reason, several studies have proposed crowdsourcing to resolve traffic problems, in which drivers provide real-time traffic information using mobile devices to monitor traffic conditions. Using data collected via crowdsourcing for traffic event detection has advantages in terms of improved accuracy and reduced time and cost. In this paper, we propose a technique that employs crowdsourcing to collect traffic-related data for detecting events that influence traffic. The proposed technique uses various machine-learning methods to accurately identify events and location information. Therefore, it can resolve problems typically encountered with conventionally provided location information, such as broadly defined locations or inaccurate location information. The proposed technique has advantages in terms of reducing time and cost while increasing accuracy. Performance evaluations also demonstrated its validity and effectiveness.
DMH-CL: Dynamic Model Hardness Based Curriculum Learning for Complex Pose Estimation

When dealing with crowds, occlusions, and truncations in complex scenes, existing solutions for multi-person pose estimation remain challenging. This is because all examples are randomly organized and equally treated by previous methods during training, which ignores that examples vary significantly in their difficulty levels. Once trained, hard examples are underutilized due to the high proportion of simple training examples, resulting in poor model robustness for complex scenes. To tackle this, we propose a novel training strategy termed DMH-CL for complex pose estimation, brought from curriculum learning (CL) which mainly addresses easy examples in the early training stage and hard ones in the later stage. Different from typical CL methods, we define easy/hard examples via mining both the dataset-specific statistical difficulty and the multi-model evaluated difficulty. After that, we adopt an annealing arrangement strategy to construct learning courses from easy to hard. Furthermore, we introduce a model learning feedback indicator, i.e., Dynamic Model Hardness (DMH) to conduct course scheduling, and to explicitly explore hard poses and utilize the knowledge learned from easy poses to better handle complex scenes as well. Our DMH-CL is model-agnostic and can be easily applied to various pose estimators including single-stage models and two-stage models, and achieves significant improvements on two challenging benchmarks especially for complex scenes. Notably, it achieves substantial performance gains of 2.6% and 4.6% for hard poses compared to the strong single-stage model PETR on CrowdPose and COCO datasets, respectively. Source codes and models are publicly available online.
Analysis of Factors Affecting Company Performance (Case Study of a Honda Motorcycle Company in Medan City)

In this era of globalization, every business activity develops for the better which results in increasingly fierce competition in the business world for the company itself. A good company at least has good performance and can also provide solutions to problems that may be encountered in the future. Managers must be able to translate the vision and mission into concrete steps so that they can be implemented. In business, it is very important for this company to continue to maintain maximum company performance. The importance of company performance because company performance can be seen from the company's ability to achieve its goals by utilizing resources efficiently and effectively. By measuring the performance of the company, then of course you can find out how far the level of achievement of the company's goals goes. To improve company performance it is very important to know how internal control is running, who is involved in the budgeting process and whether there is transparency to improve company performance and achieve its vision and mission. The population in this study were all employees who worked at 41 Honda motorcycle companies in Medan City. Sampling using purposive sampling method and the test tool used is multiple linear regression analysis. The results of this study prove that internal control, the budgeting process has a positive and significant effect on company performance. Transparency negative and significant effect on company performance. Organizational characteristics are able to moderate the effect of internal control on company performance. Organizational characteristics are unable to moderate the effect of the budgeting process and transparency on company performance.
Effect of initial-state geometric configurations on the nuclear liquid-gas phase transition

Within the framework of an extended quantum molecular dynamics model, we simulated $^{40}$Ca + $^{16}$O collisions at beam energies ranging from 60 to 150 MeV/nucleon for $^{16}$O with different $\alpha$-cluster configurations. Results imply that different $\alpha$-cluster configurations lead to different yields of deuteron, triton, $^3$He and $^4$He, but not for proton and neutron. We discuss the effect of geometric fluctuations which are presented by double ratios of light nuclei, namely $\mathcal{O}_\text{p-d-t}$ and $\mathcal{O}_\text{p-d-He}$. It is found that magnitude hierarchy of geometric fluctuations is chain, kite, square and tetrahedron structure of $^{16}$O. $\mathcal{O}_\text{p-d-t}$ has maximum value around 80 -- 100 MeV/nucleon which could be related to liquid-gas phase transition, that is consistent with results from the charge distribution of the heaviest fragments in the collisions.
Social Sustainability-Oriented Multi-Period Medical Supply Adjustment in Response to Disasters

Before the occurrence of disasters, medical supplies should have been prepared at warehouses to support the infected cases. As the possible mismatch between initial medical supply preparedness and actual disaster situation, it would trigger off serious social outcomes. Thus, taking medical supply adjustment operations among the warehouses is an imperative task to ensure the sustainable development of society. However, a single-period logistics action with fully uncertain information usually leads to sub-optimal schemes. In this sense, multi-period planning for medical resource adjustment is required with the concern of social sustainability, where the uncertain information is promptly adjusted following real-time cases in different periods. With the socially sustainable goals, a multi-objective stochastic optimization model is developed to facilitate the multi-period sustainable relief supply adjustment problem, which can be divided into two subproblems. The first sub-problem contains two conflicting objective functions, where a specific $\varepsilon $ -constraint method is developed. After that, a linearization approach is applied due to the nonlinearity of the proposed model. Finally, Yushu Earthquake is considered to validate the effectiveness of the proposed model. It is found that the multi-period planning for the relief supply adjustment problem outperforms compared with a single-period one in terms of social sustainability.
Os Centros de Interpretación en Galicia: un recurso para o desenvolvemento turístico sostible?

Interpretation centres are resources to communicate and give value to the heritage, aimed at tourists who come to the place in their leisure time. There are many voices which qualify them for waste and problem. On the contrary, it may be an opportunity for social promotion and the development of sustainable tourism. In this article, we present a characterization of the existing centres in Galicia and its potential as the first step in a strategic planning and coordination of the sector.
Food Allergens: When Friends Become Foes—Caveats and Opportunities for Oral Immunotherapy Based on Deactivation Methods

Food allergies represent a serious health concern and, since the 1990s, they have risen gradually in high-income countries. Unfortunately, the problem is complex because genetic, epigenetic, and environmental factors may be collectively involved. Prevention and diagnoses have not yet evolved into efficacious therapies. Identification and control of allergens present in edible substances hold promise for multi-purpose biomedical approaches, including oral immunotherapy. This review highlights recent studies and methods to modify the otherwise innocuous native proteins in most subjects, and how oral treatments targeting immune responses could help cancel out the potential risks in hypersensitive individuals, especially children. We have focused on some physical methods that can easily be conducted, along with chemo-enzymatic modifications of allergens by means of peptides and phytochemicals in particular. The latter, accessible from naturally-occurring substances, provide an added value to hypoallergenic matrices employing vegetal wastes, a point where food chemistry meets sustainable goals as well.
CephXNet: A Deep Convolutional Squeeze-and-Excitation Model for Landmark Prediction on Lateral Cephalograms

Cephalometric landmark identification is a crucial and significant procedure that is generally used for orthodontic treatment planning and diagnosis. Computer-aided fully automated solutions can assist orthodontists and orthognathic surgeons’ to precisely identify the landmarks from cephalograms more efficiently. Most of the existing research studies deployed Convolutional Neural Network models, transfer learning methods, and pre-trained architectures to predict the XY coordinates of landmarks from cephalometric radiographs. Deep learning architectures have achieved good results in accurately predicting landmarks compared to machine learning methodologies. In this paper, a custom CNN model integrated with Squeeze-and-Excitation (SEB) attention block named CephXNet architecture is proposed to automatically classify and predicts the XY coordinates of 19 landmarks from lateral cephalograms. The SEB block, which can adaptively refine information from various feature channels of X-ray images models the interdependence between the channels to enhance the discriminative features and suppress noise. The Squeeze-and-Excitation block incorporated with multiple Convolution and Max pooling layers enhances independent channel feature learning and thereby improves the representational power of the proposed CephXNet architecture. The proposed framework obtained an accuracy of 97.72% for 19 landmark classifications and has achieved 88.06% and 78.72% of Successful Detection Rate (SDR) in clinically accepted 2mm precision range for test1 and test2 datasets provided in the 2015 International Symposium on Biomedical Imaging (ISBI) grand challenge for dental X-ray analysis conducted by IEEE. Furthermore, the proposed CephXNet is also tested with private clinical dataset comprised of 100 cephalograms collected from Solanki Dental Care Clinic in Sharjah, United Arab Emirates. The proposed CephXNet model obtained a classification accuracy of 90.08% and an average SDR of 73.94% in the 2mm precision range. The experimental outcomes show proposed CephXNet model is efficient and has great potential to deploy in clinical practice.
Rank Based Anomaly Detection Algorithms

Anomaly or outlier detection problems are of considerable importance, arising frequently in diverse real-world applications such as finance and cyber-security. Several algorithms have been formulated for such problems, usually based on formulating a problem-dependent heuristic or distance metric. This dissertation proposes anomaly detection algorithms that exploit the notion of “rank," expressing relative outlierness of different points in the relevant space, and exploiting asymmetry in nearest neighbor relations between points: a data point is “more anomalous" if it is not the nearest neighbor of its nearest neighbors. Although rank is computed using distance, it is a more robust and higher level abstraction that is particularly helpful in problems characterized by significant variations of data point density, when distance alone is inadequate. We begin by proposing a rank-based outlier detection algorithm, and then discuss how this may be extended by also considering clustering-based approaches. We show that the use of rank significantly improves anomaly detection performance in a broad range of problems. We then consider the problem of identifying the most anomalous among a set of time series, e.g., the stock price of a company that exhibits significantly different behavior than its peer group of other companies. In such problems, different characteristics of time series are captured by different metrics, and we show that the best performance is obtained by combining several such metrics, along with the use of rank-based algorithms for anomaly detection. In practical scenarios, it is of interest to identify when a time series begins to diverge from the behavior of its peer group. We address this problem as well, using an online version of the anomaly detection algorithm developed earlier. Finally, we address the task of detecting the occurrence of anomalous sub-sequences within a single time series. This is accomplished by refining the multiple-distance combination approach, which succeeds when other algorithms (based on a single distance measure) fail. The algorithms developed in this dissertation can be applied in a large variety of application areas, and can assist in solving many practical problems. RANK BASED ANOMALY DETECTION ALGORITHMS