Miracle-dz/UniFAR-FTUs huggingface dataset:
Soil and Water Quality Indicators of Diversified Farming Systems in a Saline Region of the Mekong Delta, Vietnam

Saltwater intrusion, a consequence of climate change and decreased water levels, has been increasingly severe in the Mekong Delta region. Thanh Phu District, Ben Tre Province, Vietnam, is a coastal region where agricultural production and local livelihood have been impaired by saltwater intrusion, resulting in the adoption of multiple coping strategies, including rotations and intercropping. This study aims to measure and evaluate soil and water quality indicators of multiple farming systems in Thanh Phu district and contributes to developing suitable cropping patterns. Soil indicators were pH, electrical conductivity, and exchangeable Na+. Water quality characteristics include pH, salinity, dissolved N and P, alkalinity, H2S, and chemical oxygen demand (COD). The results indicated that water pH and salinity were at suitable levels to support the growth of prawn but were below the critical level required to grow black tiger shrimp and white-legged shrimp. Water alkalinity, dissolved N, P, and COD were not constraining for the growth of shrimps. However, a significant concentration of H2S may cause disadvantages for shrimp growth.
Copeptin as a Biomarker of Atherosclerosis in Type 1 Diabetic Patients

AIM: To evaluate copeptin as an early marker of atherosclerosis in adolescent type 1 diabetics. METHODS: Sixty-two type 1 diabetic patients and 50 healthy volunteers were enrolled in the study. Serum copeptin, glycosylated haemoglobin (HbA1c), lipid profile, oxidised low-density lipoprotein (OxLDL), urinary albumin/creatinine ratio, carotid intimal medial thickness (cIMT), aortic intimal medial thickness (aIMT) and resistivity index were assessed for all participants in the study. RESULTS: HbA1c, albumin/creatinine ratio, lipid profile, OxlDL, copeptin, cIMT and aIMT were significantly higher in diabetic patients. Copeptin was higher in patients with positive cIMT and aIMT. Copeptin correlated with cIMT and aIMT. Stepwise multiple regression analysis found that copeptin correlated with aIMT. ROC curve showed that copeptin had 100 % specificity with aIMT and cIMT and 95.2 and 60,7 sensitivity with aIMT and cIMT respectively. CONCLUSION: Copeptin can be used as a marker for early detection of atherosclerosis of type 1 diabetic patients.
The cross‐sectional study of hepatic lipase SNPs and plasma lipid levels

Abstract By the combination of meta‐analysis, the data of the 1,000 Genomes Project Phase 3, and the promoter sequence of hepatic lipase (LIPC), we performed the cross‐sectional study to explore the associations of four variants (rs1077835; rs1077834; rs1800588 [C‐514T], and rs2070895 [G‐250A]) in LIPC promoter with plasma lipid levels. Our results indicate that the first and the next three of the four SNPs are, respectively, reported to be associated with the decreased and increased HDL‐c level. Meta‐analysis of 87 studies with 101,988 participants indicates that HDL‐c level in rs1800588 (C‐514T) (pooled mean difference = 0.03, 95%CI (0.03, 0.04), p < . 001) and rs2070895 (G‐250A) (pooled mean difference = 0.07, 95%CI (0.05, 0.09), p < . 001) is higher in allele T or A carriers. Similarly, LDL‐c, TC, TG, and BMI levels are generally increased in T or A alleles carriers. We failed to conduct the meta‐analysis of rs1077835 and rs1077834 due to the limited previous reports. Data from the 1,000 Genomes indicate that the allele frequencies of the four SNPs in total or subpopulations are almost equal to each other. The paired value r 2 and D' of the four SNPs are larger than 0.8, which indicate the linkage disequilibrium of the four variants. The analysis of LIPC promoter indicate that C‐514T and G‐250A are, respectively, located in transcriptional factor binding sites of USF1and Pbx1b, which may partly explain the effect of the two SNPs on the decreased LIPC activity in the alleles carriers and the corresponding increased plasma lipids hydrolyzed by LIPC. These results may help us to better understand the different effects of the four SNPs on the plasma lipid levels among subpopulations and offer clues for future clinical treatment of dyslipidemia‐related diseases.
Acute Leptospirosis in Dog- A case report

An eight year old of female ND (Non-descriptive breed) dog with fever, vomiting, anorexic and depressed condition was monitored over 7 days till death. Icterus was common in the affected dog and increased liver enzyme activity and BUN and creatinine clearly showed the hepatic and renal dysfunction. Postmortem findings showed icteric, mottled liver and necrotic kidney. The antibody titer was 1:200 at the 5 day for the Leptospira microscopic agglutination test confirm the acute phase of infection.
Deep Learning for Visual SLAM in Transportation Robotics: A review

Visual SLAM (Simultaneously Localization and Mapping) is a solution to achieve localization and mapping of robots simultaneously. Significant achievements have been made during the past decades, geography-based methods are becoming more and more successful in dealing with static environments. However, they still cannot handle a challenging environment. With the great achievements of deep learning methods in the field of computer vision, there is a trend of applying deep learning methods to visual SLAM. In this paper, the latest research progress of deep learning applied to the field of visual SLAM is reviewed. The outstanding research results of deep learning visual odometry and deep learning loop closure detect are summarized. Finally, future development directions of visual SLAM based on deep learning is prospected.
Network Analysis Reveals a Distinct Axis of Macrophage Activation in Response to Conflicting Inflammatory Cues

Key Points A computational model of the signaling network for macrophage activation is developed. Conflicting cues induced a “mixed” activation state not seen with single cues. Knockdown simulations predict mechanisms underlying mutual inhibition or activation. Visual Abstract Macrophages are subject to a wide range of cytokine and pathogen signals in vivo, which contribute to differential activation and modulation of inflammation. Understanding the response to multiple, often-conflicting cues that macrophages experience requires a network perspective. In this study, we integrate data from literature curation and mRNA expression profiles obtained from wild type C57/BL6J mice macrophages to develop a large-scale computational model of the macrophage signaling network. In response to stimulation across all pairs of nine cytokine inputs, the model predicted activation along the classic M1–M2 polarization axis but also a second axis of macrophage activation that distinguishes unstimulated macrophages from a mixed phenotype induced by conflicting cues. Along this second axis, combinations of conflicting stimuli, IL-4 with LPS, IFN-γ, IFN-β, or TNF-α, produced mutual inhibition of several signaling pathways, e.g., NF-κB and STAT6, but also mutual activation of the PI3K signaling module. In response to combined IFN-γ and IL-4, the model predicted genes whose expression was mutually inhibited, e.g., iNOS or Nos2 and Arg1, or mutually enhanced, e.g., Il4rα and Socs1, validated by independent experimental data. Knockdown simulations further predicted network mechanisms underlying functional cross-talk, such as mutual STAT3/STAT6-mediated enhancement of Il4rα expression. In summary, the computational model predicts that network cross-talk mediates a broadened spectrum of macrophage activation in response to mixed pro- and anti-inflammatory cytokine cues, making it useful for modeling in vivo scenarios.
Research on the Application of Super Resolution Reconstruction Algorithm for Underwater Image

: Underwater imaging is widely used in ocean, river and lake exploration, but it is affected by properties of water and the optics. In order to solve the lower-resolution underwater image formed by the influence of water and light, the image super-resolution reconstruction technique is applied to the underwater image processing. This paper addresses the problem of generating super-resolution underwater images by convolutional neural network framework technology. We research the degradation model of underwater images, and analyze the lower-resolution factors of underwater images in different situations, and compare different traditional super-resolution image reconstruction algorithms. We further show that the algorithm of super-resolution using deep convolution networks (SRCNN) which applied to super-resolution underwater images achieves good results.
Concentration in Mortgage Markets: GSE Exposure and Risk-Taking in Uncertain Times

When home prices threaten to decline, lenders bearing more of a community’s mortgage risk have an incentive to combat this decline with new lending that boosts demand. We test whether this incentive drove the government-sponsored enterprises (GSEs) to guarantee riskier mortgages in early 2007, as the chance of substantial declines grew from small to signiﬁcant. To identify the eﬀect we relate new risky lending to regional variation in the GSEs’ exposure and the interaction of this variation with home-price elasticity. We focus on the GSEs’ discretion across potential purchases by reference to the credit-score threshold that triggers manual underwriting. We conclude that this incentive helps explain the GSEs’ expansion of risky lending shortly before the ﬁnancial crisis.
“I am not CF, I have CF”: Social Connection and Isolation During Young Adulthood With Cystic Fibrosis

Historically, individuals living with cystic fibrosis (CF) gathered strength and support attending summer camps, support groups, and community events. However, in 2003, medical guidelines recommended stringent separation of individuals with CF to reduce the risk of cross-infection and disease progression. This study seeks to examine the impact of these recommendations on the social experiences of young adults with CF. A qualitative, exploratory, grounded theory approach was employed to understand the experience of young adults living with CF, with specific emphasis on the impact of social connection and isolation on their lives. Semi-structured individual interviews were conducted with ten young adults aged 18 – 25 diagnosed with CF. An interview guide was used to elicit information about how individuals with CF understand, experience, and perceive contact and social isolation from others with CF, ways in which they experience their social worlds, and the impact isolation from others with CF has on their medical and psychosocial development during the young adult years. Interviews were digitally recorded, transcribed verbatim, and coded using grounded theory’s constant comparative method. Analysis identified the importance of social connection and isolation as a common theme, supported by young adults’ use of social media, familiarity with the infection prevention and control guidelines, relationships with their siblings, the value of their friendships, and supported autonomy. Participants struggled to define normal in their daily lives. Increased social work practitioners’ awareness of ways to support young adults with chronic illness to engage with the CF community may improve health and mental health outcomes. Degree Type Dissertation Degree Name Doctor of Social Work (DSW) First Advisor Allison Werner-Lin, PhD Second Advisor Maya Doyle, PhD Third Advisor Michael S. Schwartz, MD
Adaptation of Acidithiobacillus ferrooxidans, Acidithiobacillus thiooxidans and Leptospirillum ferrooxidans strains on sphalerite concentrate from mining waste

One of the main characteristics of the microorganisms used in the leaching process is their capacity to adapt to aggressive environments, characterized by a notable presence of heavy metals. In this study the adaptation of the strains Acidithiobacillus ferrooxidans, Acidithiobacillus thiooxidans and Leptospirillum ferrooxidans was evaluated on a sphalerite concentrate from mining waste. In the adaptation tests, the energy source (ferrous sulphate) was gradually replaced by percentages of mineral pulp, ending with subcultures without the addition of an external energy source. The results show that the strains A. ferrooxidans and A. thiooxidans are more resistant to high concentrations of sphalerite, compared to the strain of L. ferrooxidans, since, in the case of this strain, it was necessary to repeat some tests (8% of pulp), since a deficient development was evident. This was associated with factors such as the decrease of the Fe+2 energy source, the increase of the pulp density, the accumulation of toxic metals and secondary products of the dissolution of minerals and the increase of the pH.
Predicting future dynamics from short-term time series using an Anticipated Learning Machine

Abstract Predicting time series has significant practical applications over different disciplines. Here, we propose an Anticipated Learning Machine (ALM) to achieve precise future-state predictions based on short-term but high-dimensional data. From non-linear dynamical systems theory, we show that ALM can transform recent correlation/spatial information of high-dimensional variables into future dynamical/temporal information of any target variable, thereby overcoming the small-sample problem and achieving multistep-ahead predictions. Since the training samples generated from high-dimensional data also include information of the unknown future values of the target variable, it is called anticipated learning. Extensive experiments on real-world data demonstrate significantly superior performances of ALM over all of the existing 12 methods. In contrast to traditional statistics-based machine learning, ALM is based on non-linear dynamics, thus opening a new way for dynamics-based machine learning.
HYPOCALCEMIA INDUCED RHABDOMYOLYSIS AND ACUTE RENAL FAILURE AFTER THYROIDECTOMY: A CASE REPORT

Rhabdomyolysis is a potentially life-threatening syndrome resulting from the breakdown of skeletal muscle fibers with leakage of muscle contents into the circulation. We report a case of a 24-year-old woman with hypocalcemia which occured after a thyroidectomy operation. This hypocalcemia was severe enough to cause convulsion, rhabdomyolysis and acute renal failure. The reason of severe hypocalcemia in our case may be multifactorial; hypoparathyroidism, rhabdomyolysis and acute renal failure. The development of hypocalcemia, convulsion attacks, rhabdomyolysis and acute renal failure occurring in the patient after iatrogenic hypoparathyroidism has rendered the case interesting due to the rarity of such developments.
The Formyl Peptide Receptor 2 Regulates Microglial Phenotype Through Immunometabolism: Implications for Alzheimer’s Disease

Microglia are key players in the pathology of Alzheimer’s disease (AD), driving chronic inflammation, oxidative stress, and the altered metabolism seen in the brains of patients. With clinical trials continuing to fail, new approaches towards drug development are critical. Strategies to reduce microglial activation may therefore be a viable therapeutic approach to tackling AD. Formyl peptide receptor 2 (Fpr2), which drives peripheral inflammatory resolution, is expressed in microglia. However, its functional role in neuroinflammation is unclear. This thesis provides evidence to support the peripheral findings of Fpr2 stimulation, wherein it may also hold promise for exploitation as a therapeutic for neurodegenerative disorders, including AD. We also highlight novel findings surrounding the modulation of both oxidative stress and microglial metabolism associated with Fpr2 activation. Under inflammatory conditions, we report that selective agonists for Fpr2 modulate the microglial inflammatory response, actively shifting from a pro-inflammatory to a pro-resolving phenotype, emphasised by the reduction of pro-inflammatory cytokines and concomitant increases in both pro-resolving cytokines and phagocytosis. Metabolic shifting away from glycolysis was also observed for pro-resolving microglia. Moreover, we describe for the first time that Fpr2 completely reverses reactive oxygen species (ROS) production from the mitochondria and NADPH oxidase enzymes following an inflammatory stimulus. We also highlight that the toxic oligomeric amyloid (oAβ) facilitates microglial ROS production and subsequent metabolic changes without triggering an inflammatory response. oAβ facilitated NADPH oxidase activation, which in turn resulted in the activation of glucose 6-phosphate dehydrogenase (G6PD), the rate limiting step for the pentose phosphate pathway. This metabolic pathway is responsible for producing NADPH, which in turn NADPH oxidases exploit for further ROS production. These changes resulted in noticeable reductions in both microglial glycolysis and oxidative phosphorylation. We present data underlining that Fpr2/3 stimulation reverses oAβ-induced ROS production, with a resultant reduction in G6PD activity and the return of homeostatic glycolysis. These oAβ-induced microglial changes triggered the apoptosis of SH-SY5Y cells in co-culture with BV-2 microglia. However, supporting our interest in Fpr2/3 for therapeutic approaches to neurodegenerative diseases, post-treatment with a select agonist for the receptor successfully prevented apoptosis of these neuronal like SH-SY5Y cells. This original data unveils novel functions of Fpr2/3 in the central nervous system (CNS), supplementing the well-established pro-resolving functions the receptor facilitates within the periphery. The combination of pro-resolving, anti-oxidative, immunometabolic and anti-apoptotic functions of Fpr2/3 support the exploitation of this receptor for therapeutic research into multiple different CNS disorders, including AD.
Social Return on Investment Analysis of the Health Precinct Community Hub for Chronic Conditions

Local governments and Health Boards are seeking to develop integrated services to promote well-being. Social participation and physical activity are key in promoting well-being for older people. The Health Precinct is a community hub in North Wales that people with chronic conditions are referred to through social prescribing. To improve community-based assets there is a need to understand and evidence the social value they generate. Data collection took place October 2017–September 2019. Social Return on Investment (SROI) analysis was used to evaluate the Health Precinct. Stakeholders included participants aged 55+, participants’ families, staff, the National Health Service and local government. Participants’ health and well-being data were collected upon referral and four months later using the EQ-5D-5L, Campaign to End Loneliness Scale and the Rosenberg Self-Esteem Scale. Family members completed questionnaires at four months. Baseline data were collected for 159 participants. Follow-up data were available for 66 participants and 38 family members. The value of inputs was £55,389 (attendance fees, staffing, equipment, overheads), and the value of resulting benefits was £281,010; leading to a base case SROI ratio of £5.07 of social value generated for every £1 invested. Sensitivity analysis yielded estimates of between 2.60:1 and 5.16:1.
Causal Impact

In Chapter 9, the author discusses the causal impact framework, an approach to epidemiologic methods that can be used to move from internally valid estimates to externally valid estimates to valid estimates of the effects of population interventions. Such work is essential if epidemiologists want the results of their experimental or observational studies to directly inform public health policy decisions. The bulk of this chapter outlines and describes an approach to epidemiologic methods relevant to implementation science. The author summarizes the entire book by briefly addressing the lessons of the previous chapters for the so-called hierarchy of evidence (hierarchy of study designs).
A review of the role of school-related factors in the promotion of student social and emotional wellbeing at post-primary level

In 2017, the National Council for Curriculum and Assessment (NCCA) in the Republic of Ireland introduced a universal social and emotional learning (SEL) intervention in the form of the NCCA wellbeing guidelines. These guidelines are aimed at facilitating the promotion of student wellbeing during the first three years of post-primary education. This paper will provide an overview of the NCCA wellbeing guidelines, while also establishing the educational context within which these guidelines have become necessary. While the introduction of these guidelines is arguably timely, little is known of the attitudes and perspectives that are held by the principal stakeholders, namely the educators, regarding these guidelines and the promotion of student wellbeing in general. This constitutes a significant gap in knowledge as the success (or otherwise) of the new wellbeing guidelines in Ireland may be in some way contingent upon teachers’ attitudes toward both the guidelines themselves, and their ability to deliver them. It will be proposed that research, which is currently being conducted, is necessary to further understand educators’ perspectives in this area in order to secure ‘whole-school’ implementation of the wellbeing guidelines and curricula.
ISPRS Journal of Photogrammetry and Remote Sensing

Visual inspection has been a common practice to determine the number of plants in orchards, which is a labor-intensive and time-consuming task. Deep learning algorithms have demonstrated great potential for counting plants on unmanned aerial vehicle (UAV)-borne sensor imagery. This paper presents a convolutional neural network (CNN) approach to address the challenge of estimating the number of citrus trees in highly dense orchards from UAV multispectral images. The method estimates a dense map with the confidence that a plant occurs in each pixel. A flight was conducted over an orchard of Valencia-orange trees planted in linear fashion, using a multispectral camera with four bands in green, red, red-edge and near-infrared. The approach was assessed considering the individual bands and their combinations. A total of 37,353 trees were adopted in point feature to evaluate the method. A variation of σ (0.5; 1.0 and 1.5) was used to generate different ground truth confidence maps. Different stages (T) were also used to refine the confidence map predicted. To evaluate the robustness of our method, we compared it with two state-of-the-art object detection CNN methods (Faster R-CNN and RetinaNet). The results show better performance with the combination of green, red and near-infrared bands, achieving a Mean Absolute Error (MAE), Mean Square Error (MSE), R 2 and Normalized Root-Mean-Squared Error (NRMSE) of 2.28, 9.82, 0.96 and 0.05, respectively. This band combination, when adopting σ = 1 and a stage (T = 8), resulted in an R 2 , MAE, Precision, Recall and F1 of 0.97, 2.05, 0.95, 0.96 and 0.95, respectively. Our method outperforms significantly object detection methods for counting and geolocation. It was concluded that our CNN approach developed to estimate the number and geolocation of citrus trees in high-density orchards is satisfactory and is an effective strategy to replace the traditional visual inspection method to determine the number of plants in orchards trees.