Miracle-dz/UniFAR-FTUs huggingface dataset:
Food Intake Activity Detection Using an Artificial Neural Network

The World Health Organization pointed out an increasing percentage of obese people in the past decades. It is the reason for many cases of cardiovascular diseases. For proper diagnosis, the food intake behaviour of the patients has to be monitored. We developed an approach to automatically analyze food intake using chewing and swallowing sounds. Two microphones of a sensor system in the outer ear canal and above the ear record these sounds. A central signal processing element is the food intake activity detection. It detects periods of eating and drinking activity and rejects sounds not related with food intake. An artificial neural network in feed-forward structure is used as main part of the food intake activity detection algorithm. It is trained on a small data set containing chewing and swallowing sounds, speech and environmental sounds. Input features were computed from time and frequency domain. The features were computed for the signals of the in-ear microphone and the reference microphone, respectively. A data set of food intake sounds and various environmental sounds of everyday life is used as test set for evaluation. We compared the results with those of a simple detection algorithm based on signal energy ratio. While the simple detection algorithm failed in many cases of environmental sounds, the artificial neural network classifier could strongly reduce the number of false positive detections. It provides a basic tool to analyze temporal patterns of human food intake behaviour. In cases of food intake monitoring, the algorithm can be used to trigger food classification and bite weight estimation at periods of real food intake.
Spatial-temporal habitat suitability for lemuru fish (Sardinella lemuru) using the Second-generation Global Imager (SGLI) and Maximum Entropy model in the Bali Strait, Indonesia

Lemuru fish (Sardinella lemuru), the most dominant fishery resource, has economic values for the fisherman fishing activities in the Bali Strait (between Jawa and Bali islands), Indonesia. Spatial and temporal prediction for the fishing location is essential information for effective fisheries management. The high spatial resolution of sea surface temperature (SST) and Chlorophyll-a (Chl-a) by the second-generation global imager (SGLI) on the global change observation mission (GCOM-C) satellite was employed for the input of the Maximum Entropy Model (MaxEnt) to predict the potential fishing area of lemuru fish in 2020. This study analyzed SST and Chl-a using the SGLI data and shows the variability of SST and Chl-a for lemuru fish-catching data. The MaxEnt model performance to predict the habitat suitability for lemuru fish in the Bali Strait has been shown in this study. As a result, the maximum average Chl-a estimated in August 2020 was around 1.62 mg m−3 and maximum SST in March 2020 around 28.12°C. The correlation between SST and Chl-a with total lemuru fish-catching were -0.209 and 0.375 for SST and Chl-a, respectively. The prediction of lemuru fishing areas using the MaxEnt model showed excellent model evaluations with a correlation value higher than 0.80.
Prediction and compensation of errors of inertial navigation systems

An original approach is proposed to the solution of prediction problems, the main peculiarity of which is in the creation of the relatively compact algorithms of long term prediction, based on the combination of the system state variables or the laws of their application, which remain constant at the prediction interval. The suggested prediction procedure is not based on the complication of the initial model of the system or process, but on the more accurate description of the variation laws of their parameters. The resulting concept is applied to the solution of the problem of extrapolation of heavy plane inertial navigation system (INS) errors after the external navigation sensors are switched off. <<ETX>>
Algorithm design for wind prediction in Berakit Bay, Bintan Island using Long Short-Term Memory (LSTM) method

Wind speed is a crucial parameter alongside coastal areas, especially Indonesia. Above average wind speed can cause harmful effects on human activities. This study uses wind speed data from Berakit Bay, Bintan Island is a potential location for coastal community settlement, fisheries, and tourist activities. The wind parameter then predicted using the Long Short-Term Memory or LSTM algorithm. This algorithm is able to study long-term dependencies by converting simple nervous system designs into specialized blocks containing cells. It is suitable to be applied to long-term wind predictions where the wind speed at this time is very influential with the wind speed in the future. In preparing the LSTM, the data preprocessing and the architecture used will determine the prediction results. In this study, four different architectures were made in order to determine the most optimal architecture. The results show that the LSTM architecture is able to obtain a relatively good RMSE value of 1.87 and an accuracy of 39.40% with the use of two LSTM layers, 256 units in the first layer and 128 in the second layer. The LSTM algorithm in predicting wind can also be applied to other areas in Indonesia.
Genetic structure and phylogenetic relationships of Phyllidiellapustulosa species from Seribu Islands, North Sulawesi, Halmahera, and West Papua

Phyllidiella pustulosa are brightly coloured gastropod molluscs frequently found in coral reefs of the tropical Indo-Pacific. Phyllidiella pustulosa is widely distributed in Indonesia, such as Seribu Island, North Sulawesi, West Papua, and Halmahera. Based on the genetic characteristics of an individual’s DNA sequence, differences between species can be identified. In this paper, we would like to provide the molecular analysis and phylogenetic relationship among nudibranchs from Indonesian waters. Identification was made by measuring the genetic distance between species. The phylogenetic tree reconstruction was made using the Kimura 2-parameter model with 1000 times bootstrap with neighbor-joining and maximum likelihood method. There is 46 DNA Sequence obtained from 4 different regions (Seribu Island, Halmahera, North Sulawesi, and West Papua). The genetic distance of West Papua and Halmahera has the smallest value among other populations, which is between 0.0051-1.4629, compared to the population in Halmahera. The phylogenetic tree also shows populations from West Papua and Halmahera are on the same lineage, indicating that the population in West Papua and Halmahera had the closest relation. The study suggested that North Sulawesi, Halmahera and West Papua have genetic mixing of the same region, which is distinctive from Seribu Island.
Suitability analysis of mangrove conversation areas in Mundu District, Cirebon Regency using Geographic Information Systems (GIS)

This study aims to analyze the suitability of mangrove conservation areas in the Mundu District, Cirebon Regency. In this study, we used Sentinel-2A satellite data obtained from ESA Copernicus’s web to analyze the normalized difference vegetation index (NDVI) of mangrove that will be used as a mangrove health indicator and several oceanographic characteristics to assess the suitability of mangrove conservation areas based on weighing-scores systems. All data were then analyzed using image processing combined with geographic information systems (GIS) tools available in the open-access QGIS software. Results show that the health of the mangrove based on vegetation index was in the range of 0.3–0.6. Mangroves in Mundu Region have consisted of two species, Avicennia marina, and Rhizophora apiculata. The substrate in this area is dominated by muddy sand with a pH value of 8 and an average salinity of 32 PSU. The tidal type in Mundu region has mixed semi-diurnal characteristics with an average tidal height of 1.22 m. Based on those parameters and NDVI analysis, the suitability rank for mangrove conservation areas can be classified as suitable with a total score in the ranges of 101-200.
Carbon stock estimation and mapping of mangrove forest using ALOS-2 PALSAR-2 in Benoa Bay Bali, Indonesia

Mangrove forests in tropics coastlines area play an essential role in carbon fixation and carbon storage. Mangrove forests in coastal areas are very effective and efficient in reducing the concentration of carbon dioxide (CO2) in the atmosphere because mangroves can absorb CO2 through photosynthesis by diffusion through stomata and then store carbon in the form of biomass. With the lack of efforts to manage mangrove forests, it needs to be developed so that forest functions can be utilized sustainably. This paper describes examining the use of remote sensing data, particularly dual-polarization ALOS-2 PALSAR-2 data, with the primary objective to estimate the carbon stock of mangrove forests in Benoa Bay, Bali. The carbon stock was estimated by analyzing HV Polarization, Above Ground Biomass (AGB), and ground biomass (BGB). The total carbon stock was obtained by multiplying the total biomass with the organic carbon value of 0.47. The potential carbon stock in the mangrove Benoa Bay area is 209,027.28 ton C to absorb carbon dioxide (CO2) of 767,130.11 ton CO2 Sequestration same with 3.87 x 10 bottles in 2015 and 204.422,59 ton C to absorb carbon dioxide (CO2) of 750.230,93 ton CO2 Sequestration same with 3.79 x 10 bottles in 2020.
Research on Time Series Anomaly Detection: Based on Deep Learning Methods

Time series anomaly detection has always been an important research direction. The early time series anomaly detection methods are mainly statistical methods and machine learning methods. With the powerful functions of deep neural network being continuously mined by researchers, the effect of deep neural network in anomaly detection task has been significantly better than the traditional methods. In view of the continuous development and application of deep neural networks such as transformer and graph neural network (GNN) in time series anomaly detection in recent years, the body of research lacks a comparative evaluation of deep learning methods in recent years. This paper studies various deep neural networks suitable for time series, which are divided into three categories according to anomaly detection methods. The evaluation is conducted on public datasets. By analyzing the evaluation criteria, this paper discusses the performance of each model, as well as the problems and development direction in the field of time series anomaly detection in the future. This study found that in the time series anomaly detection task, transformer is suitable for dealing with long-time series prediction, and studying the graph structure of time series may be the best way to deal with time series anomaly detection in the future
Single object tracking algorithm in occlusion scene based on improved ECO

To alleviate the occlusion problem in a single object tracking scene, this paper proposes an ECO-MHDU object tracking algorithm with a more powerful anti-occlusion performance based on the ECO tracker. The algorithm first uses the pre-trained MobileNetV3 lightweight backbone network on the ImageNet dataset to replace the ResNet network in the ECO to increase the speed of the algorithm to obtain the shallow and deep feature information of the image, while effectively using the attention mechanism in the MobileNetV3 network to strengthen the algorithm’s ability to extract target features; secondly, use the DropBlock operation on the acquired feature map to generate a random continuous mask on the feature map channel to improve the algorithm’s learning of the global robust spatial structure information; finally, a confidence update strategy is introduced into the GMM sample generation space. To improve the quality of training samples, unreliable tracking states such as confidence detection and occlusion are designed to avoid updating the sample space with damaging information. Compared with the ECO algorithm, the ECO-MHDU algorithm proposed in this paper has a success rate of 68.0% on the occlusion attributes of the OTB100 dataset, which is 2.3% higher than the ECO algorithm, and the ECO-MHDU algorithm also showed the best performance on the entire dataset sequence, with a success rate of 69.3%.
Research on Similar Animal Classification Based on CNN Algorithm

Animal image classification with CNN (convolutional neural network) is commonly investigated in aera of image recogniation and classification, but major studies focus on species pictures classification with obvious distinctions. For example, CNN is usually employed to distinghish images between dogs and cats. This article puts the effort on similar animal images classification by applying simple 2D CNN via python. It focus on the binary classification for snub-nosed monkeys and normal monkeys. This distinguishment is hard to be done manually in a short time. For constructing complete convolutional neural network, some preparations are done in advance, such as the database construction and preprocess. The database is constructed by python crawler (downloading from google images), with 800 and 200 images for each class respectively as train data and test data. The pre-work includes image resizing, decoding and standardization. After that, the model is trained and then tested for verifying the model reliability. The training accuracy is 96.67% without any abnormality. On the basis of successful training, the test accuracy almost coincides with train accuracy in each 50 generations and plots in a graph. It indicates similar trends and results for them in the whole process. Because of this, CNN model in the study can help people identify rare animals in time and then people can effectively protect them. Therefore, CNN will be helpful in field of animal conservation, especially for rare species.
Distinct within-host bacterial populations ensure function, colonization and transmission in leaf symbiosis

Hereditary symbioses have the potential to drive transgenerational effects, yet the mechanisms responsible for transmission of heritable plant symbionts are still poorly understood. The leaf symbiosis between Dioscorea sansibarensis and the bacterium Orrella dioscoreae offers an appealing model system to study how heritable bacteria are transmitted to the next generation. Here, we demonstrate that inoculation of apical buds with a bacterial suspension is sufficient to colonize newly-formed leaves and propagules, and to ensure transmission to the next plant generation. Flagellar motility is not required for movement inside the plant, but is important for the colonization of new hosts. Further, stringent tissue-specific regulation of putative symbiotic functions highlight the presence of two distinct subpopulations of bacteria in the leaf gland and at the shoot meristem. We propose that bacteria in the leaf gland dedicate resources to symbiotic functions, while dividing bacteria in the shoot tip ensure successful colonization of meristematic tissue, glands and propagules. Compartmentalization of intra-host populations, together with tissue-specific regulation may serve as a robust mechanism for the maintenance of mutualism in leaf symbiosis. Importance Several plant species form associations with bacteria in their leaves, called leaf symbiosis. These associations are highly specific, but the mechanisms responsible for symbiont transmission are poorly understood. Using the association between the yam species Dioscorea sansibarensis and Orrella dioscoreae as a model leaf symbiosis, we provide experimental evidence that bacteria are transmitted vertically and distributed to specific leaf structures via association with shoot meristems. Flagellar motility is required for initial infection, but does not contribute to spread within host tissue. We also provide evidence that bacterial subpopulations at the meristem or in the symbiotic leaf gland differentially express key symbiotic genes. We argue that this separation of functional symbiont populations, coupled to tight control over bacterial infection and transmission, explain the evolutionary robustness of leaf symbiosis. These findings may provide insights into how plants may recruit and maintain beneficial symbionts at the leaf surface.
Seroprevalence of brucellosis and molecular characterization of Brucella spp. from slaughtered cattle in Rwanda

Bovine brucellosis is endemic in Rwanda, although, there is paucity of documented evidence about the disease in slaughtered cattle. A cross-sectional study was conducted in slaughtered cattle (n=300) to determine the seroprevalence of anti-Brucella antibodies using the Rose Bengal Test (RBT), and indirect enzyme-linked immunosorbent assay (i-ELISA). Corresponding tissues were cultured onto a modified Centro de Investigación y Tecnología Agroalimentaria (CITA) selective medium and analysed for Brucella spp. using the 16S-23S ribosomal interspacer region (ITS), AMOS, and Bruce-ladder PCR assays. The RBT seroprevalence was 20.7% (62/300), and 2.9% (8/300) with i-ELISA and 2.9% (8/300) using both tests in parallel. Brucella specific 16S-23S ribosomal DNA interspace region (ITS) PCR detected Brucella DNA in 5.6% (17/300; Brucella culture prevalence). AMOS-PCR assay identified mixed B. abortus and B. melitensis (n=3), B. abortus (n=3) and B. melitensis (n=5) while Bruce-ladder PCR also identified B. abortus (n=5) and B. melitensis (n=6). The gold standard culture method combined with PCR confirmation identified 5.6% Brucella cultures which is higher than the more sensitive seroprevalence of 2.9%. This emphasizes the need to validate the serological tests in Rwanda. The mixed infection caused by B. abortus and B. melitensis in slaughtered cattle indicates cross-infection and poses a risk of exposure potential to abattoir workers. It is essential to urgently strengthen the national bovine brucellosis control program through vaccination as well as test- and-slaughter.
A phenotypic rescue approach identifies lineage regionalization defects in a mouse model of DiGeorge syndrome

TBX1 is a key regulator of pharyngeal apparatus (PhAp) development. Vitamin B12 treatment partially rescues aortic arch patterning defects of Tbx1+/- embryos. Here we show that it also improves cardiac outflow tract septation and branchiomeric muscle anomalies of Tbx1 hypomorphic mutants. At molecular level, the in vivo vB12 treatment let us to identify genes that were dysregulated by Tbx1 haploinsufficiency and rescued by treatment. We found that SLUG, encoded by the rescued gene Snai2, identified a population of mesodermal cells that was partially overlapping with but distinct from ISL1+ and TBX1+ populations. In addition, SLUG+ cells were mislocalized and had a greater tendency to aggregate in Tbx1+/- and Tbx1-/- embryos and vB12 treatment restore cellular distribution. Adjacent neural crest-derived mesenchymal cells, which do not express TBX1, were also affected, showing enhanced segregation from cardiopharyngeal mesodermal cells. We propose that TBX1 regulates cell distribution in core mesoderm and the arrangement of multiple lineages within the PhAp.
Traditional martial arts and shooting training have different effects on auditory fine structure processing ability——Evidence from behavioral tests and fMRI

Explore the influence of traditional martial arts and shooting training on the ability of auditory temporal fine structure (TFS) processing. Twenty-five college students participated in the experiment, including 8 traditional martial arts practitioners, 8 high-level shooting athletes, and 9 control groups without any regular exercise habits. The BIC (break in interaural correlation) delay threshold and TFS1 test were used to evaluate the temporary storage capacity and sensitivity of TFS, respectively, and a fMRI test was performed after the test. The results found that the traditional martial arts group had stronger TFS sensitivity, while the shooting group had stronger TFS retention ability, and the performance of the behavioral test of the shooting group is related to the fALFF value of the brain area of interest. Traditional martial arts and shooting training have improved the ability of auditory information processing from different angles, diversified exercise habits will lead to the development of diversity in brain structure and function.
Decrowding Expansion Pathology: Unmasking Previously Invisible Nanostructures and Cells in Intact Human Brain Pathology Specimens

Proteins are densely packed in cells and tissues, where they form complex nanostructures. Expansion microscopy (ExM) variants have been used to separate proteins from each other in preserved biospecimens, improving antibody access to epitopes. Here we present an ExM variant, decrowding expansion pathology (dExPath), which can expand proteins away from each other in human brain pathology specimens, including formalin-fixed paraffin-embedded (FFPE) clinical specimens. Immunostaining of dExPath-expanded specimens reveals, with nanoscale precision, previously unobserved cellular structures, as well as more continuous patterns of staining. This enhanced molecular staining results in observation of previously invisible disease marker-positive cell populations in human glioma specimens, with potential implications for tumor aggressiveness. dExPath results in improved fluorescence signals even as it eliminates lipofuscin-associated autofluorescence. Thus, this form of expansion-mediated protein decrowding may, through improved epitope access for antibodies, render immunohistochemistry more powerful in clinical science and diagnosis.
MacroH2A impedes metastatic growth by enforcing a discrete dormancy program in disseminated cancer cells

MacroH2A variants have been associated with tumor suppression through inhibition of proliferation and metastasis, as well as their role in cellular senescence. However, their role in regulating the dormant state of disseminated cancer cells (DCCs) remains unclear. Here we reveal that solitary dormant DCCs display increased levels of macroH2A variants in head and neck squamous cell carcinoma PDX models and patient samples compared to proliferating primary or metastatic lesions. We further demonstrate that microenvironmental and stress adaptive signals such as TGFβ2 and p38α/β, which induce DCC dormancy, upregulate macroH2A expression. Functionally, we find that overexpression of macroH2A variants is sufficient to induce tumor cells into dormancy and notably, inducible expression of the macroH2A2 variant suppresses the growth of DCCs into overt metastasis. However, this dormant state does not require well-characterized dormancy factors such as DEC2 and NR2F1, suggesting alternate pathways. Our transcriptomic analyses reveal that macroH2A2 overexpression inhibits E2F, RAS and MYC signaling programs, while upregulating inflammatory cytokines commonly secreted by senescent cells. Taken together, our results demonstrate that macroH2A2 enforces a stable dormant phenotype in DCCs by activating a select subset of dormancy and senescence genes that limit metastasis initiation.