Miracle-dz/UniFAR-FTUs huggingface dataset:
Xrn1 Exoribonuclease-An Intrinsic Marker of Yeast Population Growth.

BACKGROUND Xrn1 exoribonuclease is the major mRNA degradation enzyme in Saccharomyces cerevisiae. In exponentially growing cells, Xrn1 is localised in the yeast cells and directs the degradation of mRNA molecules. Xrn1 is gradually deposited and presumably inactivated in the processing bodies (P-bodies) as the yeast population ages. Xrn1 can also localise to the membrane compartment of the arginine permease Can1/eisosome compartment at the yeast plasma membrane. This localisation correlates with the metabolic (diauxic) shift from glucose fermentation to respiration, although the relevance of this Xrn1 localisation remains unknown. METHODS We monitored the growth rates and morphology of Xrn1-green fluorescent protein (GFP) cells compared to wild-type and Δxrn1 cells and observed the Xrn1-GFP localisation pattern in different media types for up to 72 hours using fluorescence microscopy. RESULTS We present the dynamic changes in the localisation of Xrn1 as a versatile tool for monitoring the growth of yeast populations at the single-cell level using fluorescence microscopy. CONCLUSIONS The dynamic changes in the localisation of Xrn1 can be a versatile tool for monitoring the growth of yeast populations at the single-cell level. Simultaneously, Xrn1 localisation outside of P-bodies in post-diauxic cells supports its storage and cytoprotective function, yet the role of P-bodies in cell metabolism has still not yet been entirely elucidated.
Changes in bacterial communities during rice cultivation remove phenolic constraints on peatland carbon preservation

Abstract Northern peatlands contain ~30% of terrestrial carbon (C) stores, but in recent decades, 14% to 20% of the stored C has been lost because of conversion of the peatland to cropland. Microorganisms are widely acknowledged as primary decomposers, but the keystone taxa within the bacterial community regulating C loss from cultivated peatlands remain largely unknown. In this study, we investigated the bacterial taxa driving peat C mineralization during rice cultivation. Cultivation significantly decreased concentrations of soil organic C, dissolved organic C (DOC), carbohydrates, and phenolics but increased C mineralization rate (CMR). Consistent with the classic theory that phenolic inhibition creates a “latch” that reduces peat C decomposition, phenolics were highly negatively correlated with CMR in cultivated peatlands, indicating that elimination of inhibitory phenolics can accelerate soil C mineralization. Bacterial communities were significantly different following peatland cultivation, and co-occurrence diagnosis analysis revealed substantial changes in network clusters of closely connected nodes (modules) and bacterial keystone taxa. Specifically, in cultivated peatlands, bacterial modules were significantly negatively correlated with phenolics, carbohydrates, and DOC. While keystone taxa Xanthomonadales, Arthrobacter, and Bacteroidetes_vadinHA17 can regulate bacterial modules and promote carbon mineralization. Those observations indicated that changes in bacterial modules can promote phenolic decomposition and eliminate phenolic inhibition of labile C decomposition, thus accelerating soil organic C loss during rice cultivation. Overall, the study provides deeper insights into microbe-driven peat C loss during rice cultivation and highlights the crucial role of keystone bacterial taxa in the removal of phenolic constraints on peat C preservation.
Drip irrigation for potatoes in rain fed agriculture - evalua- tion of drip tape/drip line positions and irrigation control strategies

To produce a stable amount of potatoes of high quality a well distributed water supply matched to the needs of the culture is essential. Although average precipitation in Germany resp. Bavaria delivers enough water to successfully grow most crops suitable to the locale climate, water is often the limiting factor especially for potatoes (and vegetables) on sandy soils and during periods with no or little precipitation. Drip irrigation has worldwide proven to be the most water and energy efficient irrigation method. Therefore it should be the preferred method for future irrigation applications. There is a broad knowledge about drip irrigation for regions with irrigation agriculture. But there is little knowledge about the application of drip irrigation as supplementary irrigation in rain fed agriculture. The aim of the project was to investigate and evaluate the effects of different drip tape / drip line position and irrigation control strategies for potatoes and to deduce extension recommendations from the results. To investigate and to evaluate the different drip tape / drip line positions and irrigation control strategies a randomized plot design with four repetitions was created. Due to the factors and their combination up to 12 irrigation circuits became necessary. All circular flows could be controlled via mobile data transfer and micro controllers. Three drip pipe / drip tape positions (on top of the ridge, between every ridge and between every second ridge) and four irrigation control strategies (start of irrigation at 60 % and 80% effective field capacity, irrigation rate of 4 mmd -1 , 6 mmd -1 ) have been investigated. Soil moisture was simultaneously determined using weather data and a soil moisture model and by using different sensors. Potato yields and quality were determined after harvest. The experiments have been carried out on 3 resp. 2 locations with different soil types (light and heavy soil). The gained results show that under German resp. Bavarian conditions drip irrigation significantly increases potato yield and quality especially on sandy soils. On heavy soils positive effects can only be reached in very dry years. On sandy soils only the drip tape /drip line position on top of the ridge is able to continuously produce increased yields without the risk of water leakage. A high frequency of small water applications is also required. On heavy soils also drip tap / drip line positions between every, or between every second ridge increased the potato yield, although yield effects could only be detected in years with very dry periods.
Comparative Analysis of Aerosol Vertical Characteristics over the North China Plain Based on Multi-Source Observation Data

In this paper, multi-source observation, such as aircraft, ground-based remote sensing, and satellite-retrieved data, has been utilized to compare and analyze the vertical characteristics of aerosol optical properties and the planetary boundary layer height (HPBL) over the North China Plain (NCP) region during May–June 2016. Aircraft observations show the vertical profiles of aerosol absorption coefficients (σabs), scattering coefficients (σsca), and extinction coefficients (σext) gradually decrease with altitude, with their maximum values near HPBL. The vertical profiles of σext depended most on the vertical distribution of measured σsca, indicating a significant contribution of scattering aerosols. In addition, the prominent characteristic of the inverse relationship between σext and moisture profile could serve as a reference for predicting air quality in the NCP region. The lower layer pollution during the field experiment was likely caused by the accumulation of fine-mode aerosols, characterized by the vertical distribution of the Ångström exponent and the Aerosol Robotic Network (AERONET) products. Typically, HPBL derived from aircraft and surface Micro Pulse Lidar (MPL) was approximate, while the predicted HPBL by meteorological data indicates an underestimation of ~192 m. Aerosol optical depth (AOD) calculated from aircraft and ground-based remote sensing (such as MPL and AERONET) experienced a strong correlation, and both of them exhibited a similar tendency. However, the AOD retrieved from satellites was significantly larger than that from aircraft and ground-based remote sensing. Overall, the inversion algorithm, cloud identification algorithm, representativeness of the space, and time of the observation may lead to an overestimation or underestimation of AOD under certain circumstances. This study may serve as a re-evaluation of AOD retrieved from multi-source observations and provide a reference to uncover the actual atmospheric environment in the NCP regions.
Gender Difference in REM Sleep Behavior Disorder in Japanese Population: Polysomnography and Sleep Questionnaire Study

Rapid eye movement (REM) sleep behavior disorder (RBD) is generally common among males and is an established risk factor for neurodegenerative diseases. As the population ages, the prevalence of α-synucleinopathy (such as Parkinson’s disease) is increasing. Additionally, the number of female patients is increasing in Japan. Therefore, we aimed to clarify the clinical characteristics of RBD from the perspective of gender differences in the Japanese population. The proportion of male RBD patients (65.2%) was significantly higher than that of female patients (34.8%). Additionally, female patients (70.5 ± 8.2 years) were significantly older than male patients (67.9 ± 8.0 years, p < 0.05). The mean score on the RBD Screening Questionnaire was significantly higher in male patients than in female (8.6 ± 2.9 vs. 7.7 ± 3.1, p < 0.05), while the mean score on the Pittsburgh Sleep Quality Index was significantly higher in males (5.9 ± 3.8 vs. 7.2 ± 3.600, p < 0.001). The Self-rating Depression Scale in females was 41.7 ± 8.5, and they were more likely to show depressive tendencies than males. In polysomnography, higher rates of obstructive sleep apnea and longer duration of stage N1 sleep were detected in males. After adjusting for age, BMI, and polysomnographic parameters, we found that female RBD patients had significantly worse sleep quality and rates of depression compared to male patients.
Sifting out point matches for high buildings in multiview very-high-resolution urban remote sensing images under local translation transformation

Abstract. In multitemporal very-high-resolution urban remote sensing images, buildings, especially high-rise buildings, show difference in terms of morphology due to the different view angles. In the coregistered images, the pixels of the same building are not corresponding to each other, which causes false alarm in change detection. Our objective is to find out the matching points located on the roofs of high-rise buildings. When the difference of view angle between the coregistered images is fixed, we discover that there are spatial translation relationships, i.e., local translation transformation and fixed angle offset, between the point matches of high-rise building roofs. Therefore, using these relationships, a method that can sift out point matches of roofs to their correct positions is proposed. The experimental results show that most point matches located on the roof of the same building can be fast and correctly sifted out.
Contribution of elovl5a to Docosahexaenoic Acid (DHA) Synthesis at the Transcriptional Regulation Level in Common Carp, Cyprinus carpio

Simple Summary The expression of the elongase of the very-long-chain fatty acid 5 (elovl5) gene was investigated in this study using different molecular validation strategies. It has been found that the elovl5a gene is a causal gene responsible for DHA content differences in common carp. miR-26a-5P is a casual miRNA that negatively regulates DHA synthesis by targeting the elovl5a gene. Considering its down-regulation of the DHA synthesis pathways in common carp, knocking out, knocking down, or silencing the expression of elovl5a may increase DHA content in common carp. Overall, our study preliminarily explored the potential function of elovl5a in common carp. These results provide useful information for future functional research of the elovl5 gene and the interactions between its other gene family members in freshwater fish. Abstract Docosahexaenoic acid (DHA) is an essential nutrient for humans and plays a critical role in human development and health. Freshwater fish, such as the common carp (Cyprinus carpio), have a certain degree of DHA biosynthesis ability and could be a supplemental source of human DHA needs. The elongase of very-long-chain fatty acid 5 (Elovl5) is an important enzyme affecting polyunsaturated fatty acid (PUFA) biosynthesis. However, the function and regulatory mechanism of the elovl5 gene related to DHA synthesis in freshwater fish is not clear yet. Previous studies have found that there are two copies of the elovl5 gene, elovl5a and elovl5b, which have different functions. Our research group found significant DHA content differences among individuals in Yellow River carp (Cyprinus carpio var.), and four candidate genes were found to be related to DHA synthesis through screening. In this study, the expression level of elovl5a is decreased in the high-DHA group compared to the low-DHA group, which indicated the down-regulation of elovl5a in the DHA synthesis pathways of Yellow River carp. In addition, using a dual-luciferase reporter gene assay, we found that by targeting the 3’UTR region of elovl5a, miR-26a-5p could regulate DHA synthesis in common carp. After CRISPR/Cas9 disruption of elovl5a, the DHA content in the disrupted group was significantly higher than in the wildtype group; meanwhile, the expression level of elovl5a in the disrupted group was significantly reduced compared with the wildtype group. These results suggest that elovl5a may be down-regulating DHA synthesis in Yellow River carp. This study could provide useful information for future research on the genes and pathways that affect DHA synthesis.
Reasoning of abstract motion of a target object through task order with natural language — pre-knowledge of object-handling-task programming for a service robot

In this paper, an attempt is made to reason an abstract motion of a target object when the object is manipulated through a task order. The reasoning algorithm is used as a function in the system that navigates layman's robot programming. The task order consists of two words: Task and Target (e.g., 'switch on' and 'light'). There are three kinds of motions: Linear, Circular and Point-To-Point motion. The system chooses a suitable motion. In the reasoning, it is important to be able to reason from various input words using a limited knowledge base. Therefore, a knowledge base is proposed that consists of a thesaurus and minimum knowledge. The knowledge defines only words that directly stand for the motions (e.g., 'turn' means Circular motion). The knowledge is propagated through hypernyms and hyponyms in the thesaurus. A motion is reasoned using the propagated knowledge in Task and Target. Moreover, learning, which results from on-site updating of the knowledge from the user, achieves reinforcement/customization of the knowledge base. The system successfully reasoned motions from various task orders. Moreover, for the robot programming of a door-opening task, a robot with the reasoning system reasoned a motion of the door and realized the task.
AI-Based Detection of Oral Squamous Cell Carcinoma with Raman Histology

Simple Summary Stimulated Raman Histology (SRH) is a technique that uses laser light to create detailed images of tissues without the need for traditional staining. This study aimed to use deep learning to classify oral squamous cell carcinoma (OSCC) and different non-malignant tissue types using SRH images. The performances of the classifications between SRH images and the original images obtained from stimulated Raman scattering (SRS) were compared. A deep learning model was trained on 64 images and tested on 16, showing that it could effectively identify tissue types during surgery, potentially speeding up decision making in oral cancer surgery. Abstract Stimulated Raman Histology (SRH) employs the stimulated Raman scattering (SRS) of photons at biomolecules in tissue samples to generate histological images. Subsequent pathological analysis allows for an intraoperative evaluation without the need for sectioning and staining. The objective of this study was to investigate a deep learning-based classification of oral squamous cell carcinoma (OSCC) and the sub-classification of non-malignant tissue types, as well as to compare the performances of the classifier between SRS and SRH images. Raman shifts were measured at wavenumbers k1 = 2845 cm−1 and k2 = 2930 cm−1. SRS images were transformed into SRH images resembling traditional H&E-stained frozen sections. The annotation of 6 tissue types was performed on images obtained from 80 tissue samples from eight OSCC patients. A VGG19-based convolutional neural network was then trained on 64 SRS images (and corresponding SRH images) and tested on 16. A balanced accuracy of 0.90 (0.87 for SRH images) and F1-scores of 0.91 (0.91 for SRH) for stroma, 0.98 (0.96 for SRH) for adipose tissue, 0.90 (0.87 for SRH) for squamous epithelium, 0.92 (0.76 for SRH) for muscle, 0.87 (0.90 for SRH) for glandular tissue, and 0.88 (0.87 for SRH) for tumor were achieved. The results of this study demonstrate the suitability of deep learning for the intraoperative identification of tissue types directly on SRS and SRH images.
Integrated physical and mental management for China’s elderly: insights from Long-gang District, Shenzhen

China is in a period of rapid population aging. The total population of the elderly aged 60 and above in mainland China was 264 million in 2020, and is the country with the largest elderly population in the world, which is home to 1/5 of the world’s older people. The urgency of actively coping with the aging population has never been greater, and China has raised it to the height of national strategy. To this end, China has issued several plans and projects on aging work. Many of them include multiple overlapping components. The management of physical illness and mental illness in the elderly is over-differentiated and segmented. However, it is common for older adults with complex health problems. The body and mind are inherently integrated and interact with each other, and should not be separated. There is an urgent need for integrated healthcare services for the physical and mental health of the elderly population. The national basic public health services play an important role in early detection and awareness of health problems for the elderly in community health services. This paper introduces the elderly health management services, one of the national basic public health projects, and the psychological care project for the elderly in Shenzhen, Guangdong Province, China. Taking Long-gang District’s exploration of the joint management of physical and mental health of the elderly as an example, this review discusses the difficulties of the elderly health work, and the feasibility of integrating the elderly mental health and physical health in medical care. We outlook to build an integrated platform for physical and mental health of the elderly in China. Focus on the needs of older population, strengthen community health services, build a integrative team, fully publicize and improve health literacy of the elderly, link up and down and work together, improve coordination between providers of medical care and social services. It is of great significance to construct a strong public health system for the elderly and promote the realization of the grand goal of Healthy China.
Artificial Intelligence for Precision Oncology of Triple-Negative Breast Cancer: Learning from Melanoma

Simple Summary New technologies, particularly artificial intelligence (AI) and machine learning, enable the utilization of extensive data for personalized medicine. Future challenge involve acquiring limited biological data and extracting information for predictive models. This perspective focuses on triple-negative breast cancer (TNBC), a disease lacking specific treatment, where ongoing investigations explore therapies like immunotherapy. Drawing from the successful use of immunotherapy in melanoma, this perspective explores the identified strengths and weaknesses to inform more successful strategies. In this context, AI and predictive tools are invaluable. Advancements in melanoma immunotherapy serve as a foundation for developing effective immunotherapies for TNBC. Common principles, including immune system activation, checkpoint inhibitors, and personalized treatment, offer prospects for improving the outcomes in aggressive breast cancer cases, presenting new hope for challenging-to-treat patients, avoiding overtreatment, and reducing costs. Abstract Thanks to new technologies using artificial intelligence (AI) and machine learning, it is possible to use large amounts of data to try to extract information that can be used for personalized medicine. The great challenge of the future is, on the one hand, to acquire masses of biological data that nowadays are still limited and, on the other hand, to develop innovative strategies to extract information that can then be used for the development of predictive models. From this perspective, we discuss these aspects in the context of triple-negative breast cancer, a tumor where a specific treatment is still lacking and new therapies, such as immunotherapy, are under investigation. Since immunotherapy is already in use for other tumors such as melanoma, we discuss the strengths and weaknesses identified in the use of immunotherapy with melanoma to try to find more successful strategies. It is precisely in this context that AI and predictive tools can be extremely valuable. Therefore, the discoveries and advancements in immunotherapy for melanoma provide a foundation for developing effective immunotherapies for triple-negative breast cancer. Shared principles, such as immune system activation, checkpoint inhibitors, and personalized treatment, can be applied to TNBC to improve patient outcomes and offer new hope for those with aggressive, hard-to-treat breast cancer.
Analysis of Arabic Language Learning Difficulties Among Students in the Qawaid Al-Lughah Al-Arabiyyah Materials

Mastery of qawaid al-lughah al-arabiyyah material is fundamental for students in the Arabic language education study program as it lays the groundwork for mastering the four essential language skills: istima', kalam, qira'ah, and kitabah. Despite its significance, students encounter difficulties in comprehending this material. This qualitative descriptive study conducted at IAIN Bone involves eight students and two lecturers from the Arabic education study program, employing in-depth interviews, observations, and documentation for data collection. The thematic analysis reveals three main findings: 1) Learning difficulties are categorized into nahwu and sharaf, encompassing challenges in grasping new terms, creating examples, applying rules, analyzing sentence elements, and understanding wazan and shigat. 2) Factors contributing to these difficulties include inadequate face-to-face learning schedules, infrequent lesson repetition, limited self-practice, excessive reliance on online learning, a less supportive environment, and insufficient learning facilities. 3) To overcome these challenges, students take individual initiatives, including self-motivation, completing learning activities, forming communication and study groups, actively participating in the learning process, and enrolling in training/courses. Addressing these challenges can enhance Arabic language education, enabling students to achieve proficiency in qawaid al-lughah al-arabiyyah and the four language skills.Keywords: Arabic learning, Learning difficulties, Nahwu, Sharaf, Qawaid al-lughah al-arabiyyah