Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial Habitat Suitability Modeling of the Roti Snake-Necked Turtle (Chelodina Mccordi) Based on Landsat-8 Imagery and GIS

Landsat imagery can be used to establish spatial habitat suitability modeling for prediction habitat quality and evaluation potential habitat. However, the imagery has to limit information for specific habitat characteristics such as turtles' habitat. Chelodina mccordi is an endemic turtle from Roti Island but many previous studies have been done either biological or non-spatial studies. This study uses Landsat-8 OLI and TIRS as actual data and support with Landsat-5 TM as historical data, through a Geographic Information System (GIS). This study aims to create a spatial habitat suitability map model of C. mccordi and its mapping accuracy. The method that is used for modeling is overlaying indicative parameter maps use of logistic regression to presents the habitat suitability index (HSI). There are nine parameters used for modeling in this study, i.e. normalized difference water index (NDWI), land surface temperature (LST), slope, the topographic wetness index (TWI), distance from high canopy density, distance from settlement and agriculture, distance from freshwater, distance from the sea or salty, and distance from the street. We found three parameters that have strong contribution i.e. the NDWI, the LST, and the TWI. The result of spatial habitat suitability model of C. mccordi based on Landsat-8 OLI/TIRS and GIS analysis presents the value of mapping accuracy is about 75%.
Glioma Grading on Conventional MR Images: A Deep Learning Study With Transfer Learning

Background: Accurate glioma grading before surgery is of the utmost importance in treatment planning and prognosis prediction. But previous studies on magnetic resonance imaging (MRI) images were not effective enough. According to the remarkable performance of convolutional neural network (CNN) in medical domain, we hypothesized that a deep learning algorithm can achieve high accuracy in distinguishing the World Health Organization (WHO) low grade and high grade gliomas. Methods: One hundred and thirteen glioma patients were retrospectively included. Tumor images were segmented with a rectangular region of interest (ROI), which contained about 80% of the tumor. Then, 20% data were randomly selected and leaved out at patient-level as test dataset. AlexNet and GoogLeNet were both trained from scratch and fine-tuned from models that pre-trained on the large scale natural image database, ImageNet, to magnetic resonance images. The classification task was evaluated with five-fold cross-validation (CV) on patient-level split. Results: The performance measures, including validation accuracy, test accuracy and test area under curve (AUC), averaged from five-fold CV of GoogLeNet which trained from scratch were 0.867, 0.909, and 0.939, respectively. With transfer learning and fine-tuning, better performances were obtained for both AlexNet and GoogLeNet, especially for AlexNet. Meanwhile, GoogLeNet performed better than AlexNet no matter trained from scratch or learned from pre-trained model. Conclusion: In conclusion, we demonstrated that the application of CNN, especially trained with transfer learning and fine-tuning, to preoperative glioma grading improves the performance, compared with either the performance of traditional machine learning method based on hand-crafted features, or even the CNNs trained from scratch.
FOXC1 silencing inhibits the epithelial-to-mesenchymal transition of glioma cells: Involvement of β-catenin signaling

Glioma is a type of malignant brain tumor. Forkhead box C1 (FOXC1) is a conserved transcription factor that is involved in tumorigenesis; however, the function of FOXC1 in glioma remains unclear. The present study aimed to investigate the effects of FOXC1 silencing on the epithelial-to-mesenchymal transition (EMT) of glioma cells. FOXC1-specific small interfering RNAs were employed to downregulate the expression levels of FOXC1 in glioma cells. The proliferation, migration and invasion of glioma cells were assessed by MTT assay, wound healing assay and Transwell assay. Western blot analysis was performed to reveal the effects of FOXC1 on EMT-associated proteins and β-catenin signaling. The results revealed that, following FOXC1 silencing, the proliferation, migration and invasion of glioma cells were decreased. The expression levels of EMT-associated proteins were also affected. Further examination demonstrated that β-catenin signaling was involved in the effects of FOXC1 on glioma cells. Previous results suggested that overexpression of β-catenin reversed the effects of FOXC1 silencing on glioma cells. The present study demonstrated that FOXC1 may regulate the EMT of glioma cells, potentially via β-catenin signaling. Therefore, FOXC1 may be a potential therapeutic target for the treatment of glioma.
Parasitic infections in relation to practices and knowledge in a rural village in Northern Thailand with emphasis on fish-borne trematode infection

Abstract The present study integrates several aspects of a parasitological survey in a rural community village combining community knowledge of parasites, their potential transmission routes and health risk factors. A rural community located in Northern Thailand was surveyed for intestinal parasites, and an overall prevalence of 45.2% for helminths and 4.8% for protozoan infections was identified. Socio-demographic characteristics, customs and perceptions were compiled using individual questionnaires and interviews for participants surveyed for parasitic screening. The results allowed us to determine the knowledge and perception of local people concerning helminthic infection and transmission. Despite the fact that the participants in this community were aware of parasitic transmission routes, their widespread custom of eating raw fish and meat render the reduction of helminthiasis difficult. A detailed study on the infection of fish-borne parasitic trematodes, the most prevalent helminth, allowed us to determine that the distance from a given household to the river is a determinant of infection intensity. Health education activities organised in the local community resulted in a change in perception of risks associated with parasite transmission.
DNA barcodes corroborating identification of mosquito species and multiplex real-time PCR differentiating Culex pipiens complex and Culex torrentium in Iran

Identifying mosquito species is a fundamental step in risk assessment and implementation of preventative strategies. Moreover, Culex pipiens is the most widespread mosquito vector in several regions of Iran and is the main vector for transmission of West Nile virus (WNV). Mosquitoes were collected at 14 sites in northern regions of Iran in 2015 and 2016. A subset of mosquito specimens was selected for identification confirmation using a DNA-barcoding technique. Construction of a phylogenetic tree showed clustering of mosquito sequences into three main genera: Aedes, Anopheles and Culex with individuals of a single species clustered closely together, regardless of where and when they were collected. Cx. pipiens complex and Cx. torrentium were identified and differentiated using multiplex real-time PCR targeting the gene locus for acetylcholinesterase 2 (ace2) to discriminate between Cx. pipiens pipiens and Cx. torrentium. The CQ11 microsatellite locus was used for discrimination between Cpp. biotypes. The predominant mosquito species in investigated regions were Cx. pipiens pipiens biotype pipiens, but we also detected Culex pipiens pipiens biotype molestus and hybrids of the two pipiens biotypes, as well as Cx. torrentium. The results of this study represent the first certain evidence of the presence of Cx. pipiens pipiens biotype molestus and hybrids between pipiens and molestus forms, and Cx. torrentium in Iran through a molecular identification approach. This report of a potentially important bridge vector for WNV might have key influence in the risk projections for WNV in Iran.
Nucleoside supplementation modulates mitochondrial DNA copy number in the dguok −/− zebrafish

Abstract Deoxyguanosine kinase (dGK) is an essential rate‐limiting component of the mitochondrial purine nucleotide salvage pathway, encoded by the nuclear gene encoding deoxyguanosine kinase (DGUOK). Mutations in DGUOK lead to mitochondrial DNA (mtDNA) depletion typically in the liver and brain, causing a hepatocerebral phenotype. Previous work has shown that in cultured DGUOK patient cells it is possible to rescue mtDNA depletion by increasing substrate amounts for dGK. In this study we developed a mutant dguok zebrafish (Danio rerio) line using CRISPR/Cas9 mediated mutagenesis; dguok−/− fish have significantly reduced mtDNA levels compared with wild‐type (wt) fish. When supplemented with only one purine nucleoside (dGuo), mtDNA copy number in both mutant and wt juvenile animals was significantly reduced, contrasting with previous cell culture studies, possibly because of nucleotide pool imbalance. However, in adult dguok−/− fish we detected a significant increase in liver mtDNA copy number when supplemented with both purine nucleosides. This study further supports the idea that nucleoside supplementation has a potential therapeutic benefit in mtDNA depletion syndromes by substrate enhancement of the purine nucleoside salvage pathway and might improve the liver pathology in patients.
Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos

Learning to predict scene depth from RGB inputs is a challenging task both for indoor and outdoor robot navigation. In this work we address unsupervised learning of scene depth and robot ego-motion where supervision is provided by monocular videos, as cameras are the cheapest, least restrictive and most ubiquitous sensor for robotics. Previous work in unsupervised image-to-depth learning has established strong baselines in the domain. We propose a novel approach which produces higher quality results, is able to model moving objects and is shown to transfer across data domains, e.g. from outdoors to indoor scenes. The main idea is to introduce geometric structure in the learning process, by modeling the scene and the individual objects; camera ego-motion and object motions are learned from monocular videos as input. Furthermore an online refinement method is introduced to adapt learning on the fly to unknown domains. The proposed approach outperforms all state-of-the-art approaches, including those that handle motion e.g. through learned flow. Our results are comparable in quality to the ones which used stereo as supervision and significantly improve depth prediction on scenes and datasets which contain a lot of object motion. The approach is of practical relevance, as it allows transfer across environments, by transferring models trained on data collected for robot navigation in urban scenes to indoor navigation settings. The code associated with this paper can be found at https://sites.google. com/view/struct2depth.
Reference values of bioelectrical impedance analysis for detecting breast cancer-related lymphedema

Abstract Secondary lymphedema is a chronic debilitating lifelong complication and early diagnosis is crucial. The Inbody 720, which is widely used, has no universal index of diagnostic criteria for test results. We aim to determine the normal range, cutoff values, and mean + standard deviation values of extracellular fluid (ECF) and the single frequency bioimpedance (SFBIA) ratios for the diagnosis of lymphedema and suggest the usefulness of these values for detecting lymphedema. Seventy patients with unilateral breast cancer-related lymphedema and 643 healthy subjects were enrolled. All patients with breast cancer underwent surgeries with dissection of lymph nodes. We analyzed the ECF volume, SFBIA at 1- and 5-kHz frequencies using Inbody 720. There were significant differences between patients with BCRL and healthy controls. The optimal cutoff values for ECF ratios were 1.010 for both the dominant and non-dominant arms. At 1 kHz, the cutoff values of SFBIA were 1.050 and 1.046, and at 5 kHz, those were 1.070 and 1.030 for the dominant and non-dominant affected arms, respectively. The mean + 2SD values for ECF ratio were 1.018 and 1.020 and at 1 kHz, the mean + 2SD values of SFBIA were 1.144 and 1.0135 and at 5 kHz, the cutoff values of SFBIA were 1.141 and 1.124 for the dominant and non-dominant affected arms, respectively. The mean + 3SD values for ECF ratio were 1.026 and 1.030 and at 1 kHz, the mean + 3SD values of SFBIA were 1.206 and 1.203 and at 5 kHz, those were 1.201 and 1.187 for the arms, respectively. The cutoff, mean + 2SD, and mean + 3SD values were applied to 70 patients with unilateral BCRL. When the cutoff values were applied, a higher proportion of BCRL patients were included. When these figures were applied to the patient group, the cutoff values included a higher proportion of patients with lymphedema. Further studies are needed to investigate whether bioimpedance analysis can accurately predict the development of lymphedema.
Efficacy and safety of rituximab in childhood-onset, difficult-to-treat nephrotic syndrome

Background: The anti-CD20 monoclonal antibody rituximab (RTX) has been proposed as a rescue therapy for difficult-to-treat nephrotic syndrome (NS). We conducted a clinical trial to evaluate the efficacy and safety of RTX in children with difficult-to-treat NS dependent on or resistant to steroids and calcineurin inhibitors (CNIs). Methods: A multicenter open-label trial was performed at 8 major pediatric nephrology centers in Korea. The investigation consisted of a randomized controlled trial for steroid- and CNI-dependent NS (DDNS; randomization into the RTX group and the control group, at a ratio of 2:1) and a single-arm study of steroid and CNI-resistant NS (DRNS). DDNS patients in the RTX group and DRNS patients received a single dose of intravenous RTX (375 mg/m2 of body surface area) for B-cell depletion. A second RTX dose was administered at week 2 if the first dose failed to achieve depletion of CD19(+) cells. The primary endpoint was rate of maintaining remission at 6 months after treatment for DDNS and rate of remission achievement for DRNS. Results: Sixty-one children with DDNS were enrolled while in remission and randomized to the control group (21 patients) or the RTX group (40 patients). At 6 months after treatment, the remission rates were 74.3% in the RTX group and 31.3% in the control group (P = .003). The mean duration of remission maintenance was significantly higher in the RTX group than in the control group (9.0 vs 2.9 months, P = .004). Of the 23 patients with DRNS enrolled in the single-arm study and treated with RTX, 9 (39.1%) achieved partial or complete remission within 6 months. Depletion of B cells occurred in all patients with RTX therapy. Thirty patients (50.8% of 59 patients analyzed) experienced mild and transient infusion reaction during RTX administration, and most adverse events were mild. Conclusions: RTX administration was safe and effective in patients with difficult-to-treat NS. One or 2 doses of RTX may be sufficient to deplete B cells and achieve better control of pediatric NS.
PARP inhibitor re-sensitizes Adriamycin resistant leukemia cells through DNA damage and apoptosis

Resistance to Adriamycin (ADR) is an increasing problem in the treatment of leukemia and the development of novel therapeutic strategies is becoming increasingly important. Olaparib is a poly (adenosine diphosphate-ribose) polymerase (PARP) 1 inhibitor, which has promising antitumor activity in patients with metastatic breast cancer and germline BRCA mutations. Previously published studies have indicated that Olaparib is able to overcome drug resistance in cancer; however, its underlying mechanism of action is yet to be elucidated. The aim of the present study was to explore the mechanism underlying re-sensitization. Annexin V-propidium iodide staining indicated that the percentage of apoptotic ADR resistant cells was markedly increased and the cell cycle was blocked at the G2/M-phase following treatment with ADR combined with Olaparib, when compared with the control group. The alkaline comet assay demonstrated that ADR combined with Olaparib significantly upregulated the induction of the DNA damage response in ADR-resistant cells. Western blot analysis revealed that the protein expression of γ-H2A histone family member X, cleaved PARP, caspase 3 and cleaved caspase 3 was markedly enhanced, while the cell cycle-associated protein cyclin B1 was downregulated in K562/ADR cells following treatment with a combination of ADR and Olaparib. Similar synergistic cytotoxicity was observed in blood mononuclear cells, which were isolated from patients with chemotherapy-resistant leukemia. As Olaparib is available for clinical use, the results of the present study provide a rationale for the development of Olaparib combinational therapies for cases of ADR resistant leukemia.
Exploiting Deep Neural Networks for Tweet-based Emoji Prediction

For many years now, emojis have been used in social networks and chat services in order to enrich written text with auxiliary graphical content, achieving a higher degree of empathy. In particular, given the wide use of this medium, emojis are now available in such a number and variety that every basic concept and mood is covered by at least one. For this reason, the connection between the emoji and its semantical meaning grows stronger. In this paper, we will be describing the work performed in order to develop a Machine Learning based tool that, given a tweet, predicts the most likely emoji associated with the text. The task resembles the one presented by Barbieri et al., [23], and is placed within the context of the International Workshop on Semantic Evaluation (SemEval) 2018. We designed a baseline with standard Support Vector Machines and another baseline based on fastText, which was provided as part of the Workshop. In addition, we implemented several models based on Neural Networks such as Bidirectional Long Short-Term Memory Recurrent Neural Networks and Convolutional Neural Networks. We found that the latter is the most effective since it outperformed all our models and ranks in the 6th position out of 47 total participants. Our work aims to illustrate the potential of simpler models, which, thanks to the fine-tuning of hyper-parameters, could achieve accuracy comparable to the more complex models of the challenge.
Factors Affecting the Adoption of Software as a Service in South African Small Medium Enterprises

Software as a Service (SaaS) seems to be the most attractive solution among cloud services, but there is a low adoption rate among South African Small Medium Enterprises (SMEs). The aim of this study was to investigate the significant factors hindering the adoption of SaaS among South African SMEs. The study adopted Diffusion of Innovation Theory (DOI) as a theoretical basis. Data was collected through questionnaires and analyzed using regression analysis. The study findings indicate that some SMEs have not used SaaS because they have never heard of it. But may consider SaaS solutions if it reduces the organization's IT expenditure. For those considering using SaaS, complexity and cost of SaaS were the most significant factors determining their potential for the adoption.
IoT deployment for city scale air quality monitoring with Low-Power Wide Area Networks

Air Quality (AQ) is a very topical issue for many cities and has a direct impact on the health of its citizens. We propose to investigate the air quality of a large UK city using low-cost Particulate Matter (PM) micro-sensors, and compare them with government operated air quality stations. In this pilot deployment we design and build six AQ IoT devices, each with four different low-cost PM sensors and deploy them at two locations within the city. These devices are equipped with LoRaWAN wireless network transceivers to test city scale Low-Power WideArea Network network coverage. We conclude that some low-cost PM sensors are viable for monitoring AQ and demonstrate that our device design can be used via LoRaWAN to facilitate more granular city coverage without limitations of network access. Based on these findings we intend to deploy a larger LoRaWAN enabled Air Quality sensor network deployment across the city.
Experimental disturbances reveal group-level costs of social instability

In group-living species, social stability is an important trait associated with the evolution of complex behaviours such as cooperation. While the drivers of stability in small groups are relatively well studied, little is known about the potential impacts of unstable states on animal societies. Temporary changes in group composition, such as a social group splitting and recombining (i.e. a disturbance event), can result in individuals having to re-establish their social relationships, thus taking time away from other tasks such as foraging or vigilance. Here, we experimentally split socially stable groups of captive zebra finches (Taeniopygia guttata), and quantified the effects of repeated disturbance events on (1) group foraging efficiency, and (2) co-feeding associations when subgroups were recombined. We found that the efficiency of groups to deplete a rich, but ephemeral, resource patch decreased after just a single short disturbance event. Automated tracking of individuals showed that repeated disturbances reduced efficiency by causing social relationships to become more differentiated and weaker, resulting in fewer individuals simultaneously accessing the patch. Our experiment highlights how short-term disturbances can severely disrupt social structure and group functionality, revealing potential costs associated with group instability that can have consequences for the evolution of animal societies.
A critical analysis of informed use of context-aware technologies

There is a move towards a future in which consumers of technology are untethered from the devices and technology recedes to the subconscious. One way of achieving this vision is with context-aware technologies, which smartphones exemplify. Key figures in the creation of modern technologies suggest that consumers are fully informed of the implications of the use of these technologies. Typically, privacy policy documents are used both to inform, and gain consent from users of these technologies, on how their personal data will be used. This paper examines opinions of African-based users of smartphones. There is also an examination of the privacy policy statement of a popular app, using Critical Discourse Analysis. The analysis reveals concerns of consumers regarding absence of choice, a lack of knowledge and information privacy erosion are not unfounded.