Miracle-dz/UniFAR-FTUs huggingface dataset:
Bacteria Living in Biofilms in Fluids: Could Chemical Antibiofilm Pretreatment of Culture Represent a Paradigm Shift in Diagnostics?

Biofilms are multicellular aggregates of bacteria immersed in an extracellular matrix that forms on various surfaces, including biological tissues and artificial surfaces. However, more and more reports point out the fact that even biological fluids and semifluid, such as synovial liquid, blood, urine, or mucus and feces, harbor “non-attached” biofilm aggregates of bacteria, which represent a significant phenomenon with critical clinical implications that remain to be fully investigated. In particular, biofilm aggregates in biological fluid samples have been shown to play a relevant role in bacterial count and in the overall accuracy of microbiological diagnosis. In line with these observations, the introduction in the clinical setting of fluid sample pretreatment with an antibiofilm chemical compound called dithiothreitol (DTT), which is able to dislodge microorganisms from their intercellular matrix without killing them, would effectively improve the microbiological yield and increase the sensitivity of cultural examination, compared to the current microbiological techniques. While other ongoing research continues to unveil the complexity of biofilm formation in biological fluids and its impact on infection pathogenesis and diagnosis, we here hypothesize that the routine use of a chemical antibiofilm pretreatment of fluid and semi-solid samples may lead to a paradigm shift in the microbiological approach to the diagnosis of biofilm-related infections and should be further investigated and eventually implemented in the clinical setting.
Convolutional neural network for estimation of harvest time of forage sorghum (sorghum bicolor) cultivar samurai-1

One of the economic alternatives to improve the quality of ruminant feed is combining grass as the main feed with high-protein forages such as sorghum. To get a quality sorghum harvest during the period, it must be right when it has good biomass content, nutrients, and digestibility. The problem is that measuring quality in the laboratory has additional costs and time, which is not short, causing delays. An approach with machine learning using a convolutional neural network can be a better solution. This research uses a convolutional neural network algorithm with the right architecture to estimate sorghum harvest time from imaging results of unmanned aerial vehicles. The stages of this research include data collection, pre-processing, modeling, and finally, the evaluation stage. This research compares the results of several convolutional neural network (CNN) algorithm architectural models: simple CNN, ResNet50 V2, visual geometry group-16 (VGG-16), MobileNet V2, and Inception V3. The result is determining the CNN algorithm architectural model that can estimate sorghum harvest time with maximum accuracy. The best result is the simple CNN architectural model with an accuracy of 0.95. This research shows that the classification model obtained from the CNN algorithm with a simple CNN architecture is the choice model for estimating sorghum harvest time.
Changes in Functional Groups and Crystal Structure of Coal Tar Pitch with Respect to Carbonization Temperature

In this study, changes in the microstructure of coal-tar pitch (CTP) during successive processes, including pyrolysis, polycondensation, and crystallization, were examined in connection with the resulting variations in structure factors, as measured by X-ray diffraction (XRD) analysis, and functional groups, as confirmed by Fourier transform infrared (FTIR) spectroscopy. To this end, four zones were defined based on variations in crystallinity, which were indicated by d002 and Lc. Each zone was further characterized by interpreting crystallinity development in relation to changes in functional groups and specimen height. At around 400 °C, polycondensation occurred as the C-Har and C-Hal peaks decreased in intensity. These peak reductions coincided with the formation of mesophase spheres, resulting in enhanced crystallinity. Subsequently, at around 500 °C, the peak intensity of C-H and COOH decreased, which was attributed to the release of large amounts of gases. This led to sharp volume changes and a temporary reduction in crystallinity. All these results suggest that changes in the functional groups of CTP at lower temperatures (600 °C or less) during the carbonization process are closely associated with variations in its crystallinity. The major findings of the present study provide valuable insights for designing highly effective processes in the manufacturing of synthetic graphite blocks using CTP as a binder material, including by selecting appropriate temperature ranges to minimize volume expansion and crystallinity degradation and determining the lowest possible carbonization temperature to ensure adequate binder strength.
Data and Model Uncertainty Aware Salient Object Detection

In general, salient object detection (SOD) datasets have ambiguity due to annotation accuracy and human subjectivity in determining saliency. Since this data uncertainty causes inaccurate prediction, many techniques tackling data uncertainty have been proposed so far. Previous works estimated data uncertainty in terms of predictive inaccuracy and adjusted the learning contribution so that a given model can focus more on specific data. However, inaccurate predictions can occur due to not only data uncertainty but also model uncertainty in which the model does not fully explain the data. As a result, a region that is inaccurately predicted due to model uncertainty is considered a region with high data uncertainty, resulting in insufficient learning. To solve this problem, we propose a novel uncertainty-aware learning scheme where model uncertainty is decomposed from prediction uncertainty and it is minimized. Also, we propose a refinement method to further improve performance by correcting the prediction result using data uncertainty in the inference step. The proposed uncertainty-aware method excludes data uncertainty from learning step and inference step more effectively, making the model more accurately detect salient object(s). The experimental results prove that the proposed method achieves state-of-the-art performance on several SOD datasets and qualitatively detects salient objects more accurately than the prior arts. The code will be uploaded on Github.
Variation in the Lipid Profile of Pacific Oyster (Crassostrea gigas) Cultured in Khanh Hoa Coast, Vietnam, Based on Location and Harvest Period

The lipid profiles of the oyster Crassostrea gigas cultured in Khanh Hoa coast at three locations and harvested in different months throughout the year were compared. Seasonal and locational changes in total lipid (TL) content, phospholipid content, free fatty acid content, fatty acid (FA) composition, and thrombogenicity index were found. The total lipid content and n -3/ n -6 ratio in oysters from Ninh Hoa were higher compared to those from Cam Lam and Van Ninh. For oyster from all three farming areas, the total lipid content was the lowest during the two spawning seasons (May and September). Lipids from C. gigas were separated into neutral and polar lipid fractions using silica gel column chromatography. The amount of neutral lipids recovered from the oyster muscle was significantly higher than that of the polar lipids. The FA composition showed that C. gigas was abundant in saturated fatty acids (30.89–39.16 g/100 g TL), followed by polyunsaturated fatty acids (28.13–35.88 g/100 g TL), and monounsaturated fatty acids (19.32–23.75 g/100 g TL). The dominant polyunsaturated fatty acids of oysters from the three farming areas were eicosapentaenoic acid (9.09–13.77 g/100 g TL) and docosahexaenoic acid (6.71–16.47 g/100 g TL). Based on the present findings, it can be concluded that the Pacific oyster C. gigas cultured in Khanh Hoa, Vietnam is a promising source of highly nutritious exploitable lipids.
Comparative analysis of marketing research methods and their impact on product and service development

In the article, the authors conduct a comparative analysis of marketing research methods and examine their impact on the development of products and services. As a result of this analysis, the authors identified the main trends in the field of marketing research, which in the future can serve as the basis for recommendations for optimizing strategies aimed at increasing the competitiveness of products and services in the market. The study identifies key approaches to marketing research, such as market analysis, competitiveness, consumer behavior and others. The authors of the article highlight the advantages and disadvantages of each marketing research method, highlighting their applicability in various scenarios and industries. Particular attention is paid to the influence of marketing research results on product and service development strategies, as well as on management decision-making. The study provides readers with an in-depth analysis of how effective marketing research methods can contribute to successful innovation and increased competitiveness of the entire company. Ultimately, the scientific results obtained by the authors and presented in the article help business representatives, entrepreneurs and marketers make informed decisions based on the results of qualitative and quantitative marketing research, contributing to the long-term success of companies in the market.
An automated system for classifying types of cerebral hemorrhage based on image processing techniques

The brain is one of the most important vital organs in the human body. It is responsible for most of the body’s basic activities, such as breathing, heartbeat, thinking, remembering, speaking, and others. It also controls the central nervous system. Cerebral hemorrhage is considered one of the most dangerous diseases that a person may be exposed to during his life. Therefore, the correct and rapid diagnosis of the hemorrhage type is an important medical issue. The innovation in this work lies in extracting a huge number of effective features from computed tomography (CT) images of the brain using the Orange3 data mining technique, as the number of features extracted from each CT image reached (1,000). The proposed system then uses the extracted features in the classification process through logistic regression (LR), support vector machine (SVM), k-nearest neighbor algorithm (KNN), and convolutional neural networks (CNN), which classify cerebral hemorrhage into four main types: epidural hemorrhage, subdural hemorrhage, intraventricular hemorrhage, and intraparenchymal hemorrhage. A total of (1,156) CT images were tested to verify the validity of the proposed model, and the results showed that the accuracy reached the required success level with an average of (97.1%).
Observation of Beam Emittance Reduction due to Gas Sheet Injection for Beam Profile Measurement

In a high intensity ion accelerator, a non-destructive beam monitor is required to realize a continuous measurement of the beam for improving the beam quality in operation for users. We have developed a non-destructive 2-D beam profile monitor detecting photons produced by interaction between a beam and a sheet-shaped gas. Though the developed monitor is a non-destructive type, the injected gas sheet should induce scattering of the beam particles which makes the beam emittance large. We have measured a beam current and a phase space distribution of the J-PARC 3 MeV, 60 mA H− beam with change of the gas sheet flux. It was found that the beam current reduction was in linear relation and the root mean square emittance was constant or decreased by up to 4.5% in y-y′ plane and did not change in x-x′ plane against a rise in the gas sheet flux. These result indicate that the developed monitor can be utilized as a non-destructive one depending on the gas sheet flux condition.
Pathways of Rice Diversification across Asia

The archaeology of rice has made important methodological advances over the past decade that have contributed new data on the domestication process, spread and ecology of cultivation. Growing evidence from spikelet bases indicates that non-shattering, domesticated forms evolved gradually in the Yangtze basin and that there were at least two distinct processes around the Middle Yangtze region pre-dating 6000 BC, and the in the Lower Yangtze region between 6000 and 4000 BC. Early rice cultivation in these areas was based on wet field ecologies, in contrast to rainfed rice that is indicated among the earliest systems in India. When rice first spread north it was not entirely suited to shorter temperate summer growth seasons, and we are able to infer from high levels of apparently green-harvested spikelets that genetic adaptations to temperate conditions evolved after 2000 BC. When rice first spread south, to mainland Southeast Asia, after 2500 BC, it was grown in rainfed, dry ecologies that were less labour- demanding and less-productive. More productive and intensive irrigated rice then redeveloped in Southeast Asia around 2000 years ago, supporting growing population densities and social complexity.
Everted U-Net for 3D scene reconstruction and segmentation

Abstract. The field of data science related to the processing of three-dimensional objects is becoming more and more relevant. After the successes in image processing, the apotheosis of which was the development of generative neural net-works, the intensification of efforts in the direction of three-dimensional data processing looks logical. Although there are now numerous systems for the reconstruction of three-dimensional objects and other processing, almost all of the existing solutions are aimed at working with a single object. The advances in image processing with neural networks have largely been made possible by huge datasets. There are also large datasets available for model training in this area. Freely avail-able datasets such as ShapeNet and ModelNet contain many thousands of different models, allowing for a high diversity of data. However, most of them provide single individual objects, which allows them to be used in tasks involving the processing of a single three-dimensional object, but when working with scenes containing many objects, there is often a problem of finding an appropriate dataset. This work is aimed at solving the problem of reconstruction and segmentation of three-dimensional scenes, as well as generation of datasets for the task of processing scenes from the real world.
Campylobacter jejuni Surface-Bound Protease HtrA, but Not the Secreted Protease nor Protease in Shed Membrane Vesicles, Disrupts Epithelial Cell-to-Cell Junctions

Fundamental functions of the intestinal epithelium include the digestion of food, absorption of nutrients, and its ability to act as the first barrier against intruding microbes. Campylobacter jejuni is a major zoonotic pathogen accounting for a substantial portion of bacterial foodborne illnesses. The germ colonizes the intestines of birds and is mainly transmitted to humans through the consumption of contaminated poultry meat. In the human gastrointestinal tract, the bacterium triggers campylobacteriosis that can progress to serious secondary disorders, including reactive arthritis, inflammatory bowel disease and Guillain–Barré syndrome. We recently discovered that C. jejuni serine protease HtrA disrupts intestinal epithelial barrier functions via cleavage of the tight and adherens junction components occludin, claudin-8 and E-cadherin. However, it is unknown whether epithelial damage is mediated by the secreted soluble enzyme, by HtrA contained in shed outer-membrane vesicles (OMVs) or by another mechanism that has yet to be identified. In the present study, we investigated whether soluble recombinant HtrA and/or purified OMVs induce junctional damage to polarized intestinal epithelial cells compared to live C. jejuni bacteria. By using electron and confocal immunofluorescence microscopy, we show that HtrA-expressing C. jejuni bacteria trigger efficient junctional cell damage, but not soluble purified HtrA or HtrA-containing OMVs, not even at high concentrations far exceeding physiological levels. Instead, we found that only bacteria with active protein biosynthesis effectively cleave junctional proteins, which is followed by paracellular transmigration of C. jejuni through the epithelial cell layer. These findings shed new light on the pathogenic activities of HtrA and virulence strategies of C. jejuni.
Attention gate and dilation U-shaped network (GDUNet): an efficient breast ultrasound image segmentation network with multiscale information extraction

Background In recent years, computer-aided diagnosis (CAD) systems have played an important role in breast cancer screening and diagnosis. The image segmentation task is the key step in a CAD system for the rapid identification of lesions. Therefore, an efficient breast image segmentation network is necessary for improving the diagnostic accuracy in breast cancer screening. However, due to the characteristics of blurred boundaries, low contrast, and speckle noise in breast ultrasound images, breast lesion segmentation is challenging. In addition, many of the proposed breast tumor segmentation networks are too complex to be applied in practice. Methods We developed the attention gate and dilation U-shaped network (GDUNet), a lightweight, breast lesion segmentation model. This model improves the inverted bottleneck, integrating it with tokenized multilayer perceptron (MLP) to construct the encoder. Additionally, we introduce the lightweight attention gate (AG) within the skip connection, which effectively filters noise in low-level semantic information across spatial and channel dimensions, thus attenuating irrelevant features. To further improve performance, we innovated the AG dilation (AGDT) block and embedded it between the encoder and decoder in order to capture critical multiscale contextual information. Results We conducted experiments on two breast cancer datasets. The experiment’s results show that compared to UNet, GDUNet could reduce the number of parameters by 10 times and the computational complexity by 58 times while providing a double of the inference speed. Moreover, the GDUNet achieved a better segmentation performance than did the state-of-the-art medical image segmentation architecture. Conclusions Our proposed GDUNet method can achieve advanced segmentation performance on different breast ultrasound image datasets with high efficiency.
Research progress and applications of transcritical carbon dioxide heat pumps: A review

Heat pump technology is an energy-saving technology that can efficiently utilize low-grade energy. It has broad application prospects in building heating, industrial waste heat utilization, new energy and other fields. However, the refrigerants used in traditional heat pump systems have serious negative impacts on the environment, and there is an urgent need to find a safe, environmentally friendly, and efficient alternative refrigerant. As a natural refrigerant, CO2 has good physical and chemical properties and is very suitable as a working fluid in transcritical cycles, showing great advantages in the field of heat pump technology. At present, research on CO2 heat pumps has made certain progress, but there are few reviews of the research status and development trends of CO2 heat pumps in different applications. Therefore, this article systematically summarizes the latest research results of transcritical CO2 heat pumps in different application fields, pointing out the difficulties such as high pressure and low operating efficiency in system design and operation. It also summarizes the latest optimization research on system components, cycle structure, mixed refrigerants and control strategies. The results show that each optimization method can significantly improve system performance, among which mixed refrigerant is the simplest optimization method. Finally, the outlook for CO2 heat pump technology is put forward. With policy support and technological advancement, more comprehensive, energy-saving, and intelligent CO2 heat pump technology will continue to develop and innovate.
Irreversible Electroporation (IRE) for Prostate Cancer (PCa) Treatment: The State of the Art

We evaluated the most recent research from 2000 to 2023 in order to deeply investigate the applications of PCa IRE, first exploring its usage with primary intent and then salvage intent. Finally, we discuss the differences with other focal PCa treatments. In the case of primary-intent IRE, the in-field recurrence is quite low (ranges from 0% to 33%). Urinary continence after the treatment remains high (>86%). Due to several different patients in the studies, the preserved potency varied quite a lot (59–100%). Regarding complications, the highest occurrence rates are for those of Grades I and II (20–77% and 0–29%, respectively). Grade III complications represent less than 7%. Regarding the specific oncological outcomes, both PCa-specific survival and overall survival are 100%. Metastasis-free survival is 99.6%. In a long-term study, the Kaplan–Meier FFS rates reported are 91% at 3 years, 84% at 5 years, and 69% at 8 years. In the single study with salvage-intent IRE, the in-field recurrence was 7%. Urinary continence was still high (93%), but preserved potency was significantly lower than primary-intent IRE patients (23%). In addition, Grade III complications were slightly higher (10.8%). In conclusion, in males with localized low–intermediate-risk prostate cancer, IRE had an excellent safety profile and might have positive results for sexual and urinary function.
Improving Organizational Commitment among Healthcare Employees in Angola: The Role of Psychological Capital and Perceived Transformational Leadership

While previous studies conducted in sub-Saharan African countries have focused on verifying standards of clinical care and assessing challenges faced by healthcare professionals, the present study fills a gap in the literature in that it explores the factors that may drive the organizational commitment of healthcare professionals in Angola. This study aimed to analyze the relationship between psychological capital and organizational commitment through perceived transformational leadership. Therefore, using the quantitative methodology, a self-report questionnaire was applied to 342 healthcare professionals (174 male, 168 female) from different public and private hospitals located in three large cities in Angola. The results confirmed that psychological capital is positively related to affective commitment and that perceived transformational leadership is a mediating variable of this relationship. Therefore, this study highlights the role of psychological capital and perceived transformational leadership in improving affective commitment in challenging environments.