How hands-on gardening impacts behaviors including healthy eating and physical activity during early childhood can be of critical importance for preventing the early onset of obesity. This study ...investigates how participating in hands-on gardening impacts preschoolers' (3-5 years old) physical activity (measured by accelerometers) in childcare centers in the semi-arid climate zone. The research was conducted in eight licensed childcare centers located in West Texas with 149 children (
= 149). Four childcare centers in the experimental group received hands-on garden interventions; the other four in the control group did not. In both experimental (intervention) and control (non-intervention) centers, children wore Actigraph GT3X+ accelerometers continuously for 5 days before and for 5 days after intervention (a total of 10 days). Results show that the duration of sedentary behavior of children in the experimental (intervention) group significantly decreased compared to children in the control (non-intervention) group. The finding suggests that the positive effects of childcare hands-on gardening on physical activity extend to semi-arid climate zones where gardening is challenging due to high temperatures and lack of annual rainfall. The research emphasizes the critical need to incorporate hands-on gardening in childcare centers as an obesity prevention strategy nationally in the US and beyond.
This scoping review examined the impact of nature-based outdoor learning environments on the formation of STEAM (science, technology, engineering, arts, and mathematics) concepts in preschoolers. ...Preschool age (3–5 years) is the time when physical interaction with surrounding built environments increases, and spontaneous learning from the environment intensifies—making it an ideal age range to promote nature-based informal learning. An outdoor learning environment can influence STEAM concept formations of preschoolers with an intentional design that offers STEAM learning affordances. Despite the rising interest in early STEAM education, there is still limited literature on how the outdoor environment may influence STEAM learning behaviors of preschoolers (3–5 years old). This scoping review intended to evaluate the existing knowledge regarding the physical factors contributing to STEAM learning affordances in an outdoor environment for children aged three to five. The review included studies from the last twenty years. This scoping review was conducted following the criteria outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). For this scoping review, 843 citations were discovered across four databases (JSTOR, Scopus, EBSCOhost, and Web of Science), ProQuest, and Google Scholar, and 31 articles were considered eligible for inclusion. The paper synthesized those 31 studies to identify the key STEAM learning behaviors of children and STEAM-activity-supportive settings that may positively influence preschoolers’ STEAM concept development.
DNA-Binding proteins (DBP) are associated with many cellular level functions which includes but not limited to body's defense mechanism and oxygen transportation. They bind DNAs and interact with ...them. In the past DBPs were identified using experimental lab based methods. However, in the recent years researchers are using supervised learning to identify DBPs solely from protein sequences. In this paper, we apply deep learning methods to identify DBPs. We have proposed two different deep learning based methods for identifying DBPs: DeepDBP-ANN and DeepDBP-CNN. DeepDBP-ANN uses a generated set of features trained on traditional neural network and DeepDBP-CNN uses a pre-learned embedding and Convolutional Neural Network. Both of our proposed methods were able to produce state-of-the-art results when tested on standard benchmark datasets. DeepDBP-ANN had a train accuracy of 99.02% and test accuracy of 82.80%.And DeepDBP-CNN though had train accuracy of 94.32%, it excelled at identifying test instances with 84.31% accuracy. All methods are available codes and methods are available for use at: https://github.com/antorkhan/DNABinding.
•Comparison between traditional Machine Learning and Deep Learning techniques.•Sequence based Feature Extraction.•Convolutional Neural Network based model.•Codes and data freely available.
Gene expression can serve as a powerful predictor for disease progression and other phenotypes. Consequently, microarrays, which capture gene expression genome-wide, have been used widely over the ...past two decades to derive biomarker signatures for tasks such as cancer grading, prognosticating the formation of metastases, survival, and others. Each of these signatures was selected and optimized for a very specific phenotype, tissue type, and experimental set-up. While all of these differences may naturally contribute to very heterogeneous and different biomarker signatures, all cancers share characteristics regardless of particular cell types or tissue as summarized in the hallmarks of cancer. These commonalities could give rise to biomarker signatures, which perform well across different phenotypes, cell and tissue types. Here, we explore this possibility by employing a network-based approach for pan-cancer biomarker discovery. We implement a random surfer model, which integrates interaction, expression, and phenotypic information to rank genes by their suitability for outcome prediction. To evaluate our approach, we assembled 105 high-quality microarray datasets sampled from around 13,000 patients and covering 13 cancer types. We applied our approach (NetRank) to each dataset and aggregated individual signatures into one compact signature of 50 genes. This signature stands out for two reasons. First, in contrast to other signatures of the 105 datasets, it is performant across nearly all cancer types and phenotypes. Second, It is interpretable, as the majority of genes are linked to the hallmarks of cancer in general and proliferation specifically. Many of the identified genes are cancer drivers with a known mutation burden linked to cancer. Overall, our work demonstrates the power of network-based approaches to compose robust, compact, and universal biomarker signatures for cancer outcome prediction.
The effects of uncertainties of laser exposure time, phase lag times, blood perfusion coefficient, scattering coefficient, and diffuse reflectance of light on the thermal damage of living biological ...tissue by laser irradiation are investigated using a sample-based stochastic model. The variabilities of input and output parameters are quantified using the coefficient of variance (COV) and interquartile range (IQR), respectively. The IQR analysis concluded that phase lag times for temperature gradient and heat flux, laser exposure time, and blood perfusion rate have more significant influences on the maximum temperature and maximum thermal damage of the living biological tissue induced by laser irradiation than the diffuse reflectance of light and scattering coefficient.
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Dostopno za:
BFBNIB, DOBA, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Pectin is a principal component of all plants' primary cell walls which can be hydrolyzed by pectinase enzymes. The present research was aimed at the isolation, screening and identification of ...pectinase producing bacteria from samples collected from jute degrading soil and water, rotten leaves, sawdust, and kitchen wastes. Out of 14 isolates, nine showed positive pectinolytic activity. Identification of Bacillus spp was done on the basis of morphological, cultural and biochemical characterization and Bacillus marisflavi(PSD2) by 16S rRNA sequencing. This is the first study to report Bacillus marisflavi as a significant pectinase producing bacteria from the world.Among the pectinase producing strains, Bacillus marisflavi(PSD2) showed the best pectinase activity (zone size 69 mm) and the pectinolytic activity observed was 0.61 IU/ml.The present study suggests Bacillus marisflavi as a potential source for pectinase enzyme and could be used in industrial production upon further investigation.
Bangladesh J. Bot. 52(1): 159-163, 2023 (March)
Background: BCMAxCD3 bispecific antibodies (BsAb) have changed the treatment landscape in relapsed refractory multiple myeloma (RRMM). However, despite high response rates, the majority of patients ...continue to relapse. Infectious complications, mainly viral, represent another challenge with BsAb, seen in up to 90% of patients with half of them requiring hospitalization. Both issues, loss of response as well as infectious complications, can be linked to impaired T-cell function potentially emerging during BsAb therapy. Most BsAbs have been developed as weekly treatments and one hypothesis is that dose dense treatment could overstimulate T cells finally leading to exhaustion and dysfunctionality. Thus, we compared T cell profiles and their functionality from patients with weekly dosing to patients who switched to a monthly schedule due to toxicity or patient request. Methods: We longitudinally biobanked peripheral blood samples of 19 patients of whom 7 switched from a weekly (q1w) to a monthly (q4w) BsAb treatment schedule. Peripheral blood mononuclear cells (PBMCs) were analysed using 16-color flow cytometry, including T-cell subset phenotyping and the exhaustion marker PD1, TIM3, SLAMF6 and KLRG1. Furthermore, CITEseq, scTCRseq, and Seahorse metabolic studies were performed on selected patients. Results: The enrolled patients were heavily pretreated with a median number of 6 prior therapy lines. All patients responded to BCMA BsAb with VGPR or better and were on treatment for >3 month. Only one patient from the q4w cohort relapsed during the study period. High-risk cytogenetics, lines of previous therapy, and penta-refractory disease status were equally distributed between patients on weekly and monthly schedule. Infectious complications occurred in 10/19 (q1w) (52.6%) and 1/7 patients (q4w) (14.2%). First, we addressed the question if a monthly treatment schedule confers less T-cell exhaustion by comparing PD-1 checkpoint expression and T-cell subsets between q1w and q4w administration. Indeed, flow cytometry showed a significant decrease in the CD4+PD1+ (36.9 vs. 21.1% p<0.05, unpaired t-test) and in the CD8+PD1+ (29.44 vs. 16.54% p<0.05) T cells. We also observed a decrease in the frequency of the effector memory subsets (CD45RA-, CD62L-, TEM) of CD4+ (66.19 vs. 49.63% p<0.05) and CD8+ (53.7 vs. 40.32% p=0.09), further supporting a concept in which a treatment free interval leads to recovery of the T-cell system. We also had access to paired samples of five patients who switched from q1w (1 st sample) to q4w dosing (2 nd sample). Here, flow cytometry showed a decrease of exhausted CD4+PD1+ T cells (33.68 vs. 21.76% p=0.046, paired t-test) and CD8+PD1+ T cells (29.2% vs. 15.1% p=0.117), confirming our cross-sectional analysis. Likewise, expression of SLAMF6, a regulator of T cell exhaustion, also decreased from weekly to monthly schedule (SLAMF6/CD4+:20.7 vs. 15.2% p=0.06; and CD8+:17.8 vs. 11.3% p=0.1), but differences were not statistically significant probably due to sample size. Our analysis on T-cell metabolism did not reveal significant differences between weekly and monthly dosing schedules. After strict quality control, three of the paired samples were further analysed using CITE-seq and scTCRseq. In line with our flow results, patients showed a significant decrease in CD8+ pre-exhausted T-cell subset (CD8+TCF-1+CD27+) (3.55 vs. 1.54% p=0.02) and CD8+ terminal differentiated effector memory T-cells (CD8+ TEMRA) (4.66 vs. 3.57% p=0.04) from weekly to monthly schedules, again suggesting recovery of the T-cells after a longer treatment-free interval. We also observed a trend towards decrease in the CD4+ effector memory subset (CD4+TEM, 6.96 vs. 4.89% p=0.07). Interestingly, the number of hyper-expanded T cell clonotypes increased from weekly to monthly dosing in paired samples. This was somewhat unexpected but expansion could be interpreted as reactivation of a few previously exhausted T cells. Conclusions: The optimal dosing schedule of bispecific antibodies has yet to be determined. Our results support a more extended dose schedule to allow T-cell-recovery between BsAb administrations. Whether such a strategy will result in higher antitumor efficacy or reduced susceptibility to infections, has to be evaluated in future studies.
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