Recognition of Ginger Seed Growth Stages Using a Two-Stage Deep Learning Approach

Monitoring the growth of ginger seed relies on human experts due to the lack of salient features for effective recognition. In this study, a region-based convolutional neural network (R-CNN) hybrid detector-classifier model is developed to address the natural variations in ginger sprouts, enabling automatic recognition into three growth stages. Out of 1,746 images containing 2,277 sprout instances, the model predictions revealed significant confusion between growth stages, aligning with the human perception in data annotation, as indicated by Cohen’s Kappa scores. The developed hybrid detector-classifier model achieved an 85.50% mean average precision (mAP) at 0.5 intersections over union (IoU), tested with 402 images containing 561 sprout instances, with an inference time of 0.383 seconds per image. The results confirm the potential of the hybrid model as an alternative to current manual operations. This study serves as a practical case, for extensions to other applications within plant phenotyping communities

High-order dynamic Bayesian network learning with hidden common causes for causal gene regulatory network

Background: Inferring gene regulatory network (GRN) has been an important topic in Bioinformatics. Many computational methods infer the GRN from high-throughput expression data. Due to the presence of time delays in the regulatory relationships, High-Order Dynamic Bayesian Network (HO-DBN) is a good model of GRN. However, previous GRN inference methods assume causal sufficiency, i.e. no unobserved common cause. This assumption is convenient but unrealistic, because it is possible that relevant factors have not even been conceived of and therefore un-measured. Therefore an inference method that also handles hidden common cause(s) is highly desirable. Also, previous methods for discovering hidden common causes either do not handle multi-step time delays or restrict that the parents of hidden common causes are not observed genes. Results: We have developed a discrete HO-DBN learning algorithm that can infer also hidden common cause(s) from discrete time series expression data, with some assumptions on the conditional distribution, but is less restrictive than previous methods. We assume that each hidden variable has only observed variables as children and parents, with at least two children and possibly no parents. We also make the simplifying assumption that children of hidden variable(s) are not linked to each other. Moreover, our proposed algorithm can also utilize multiple short time series (not necessarily of the same length), as long time series are difficult to obtain. Conclusions: We have performed extensive experiments using synthetic data on GRNs of size up to 100, with up to 10 hidden nodes. Experiment results show that our proposed algorithm can recover the causal GRNs adequately given the incomplete data. Using the limited real expression data and small subnetworks of the YEASTRACT network, we have also demonstrated the potential of our algorithm on real data, though more time series expression data is needed

Recognition of Ginger Seed Growth Stages Using a Two-Stage Deep Learning Approach

Monitoring the growth of ginger seed relies on human experts due to the lack of salient features for effective recognition. In this study, a region-based convolutional neural network (R-CNN) hybrid detector-classifier model is developed to address the natural variations in ginger sprouts, enabling automatic recognition into three growth stages. Out of 1,746 images containing 2,277 sprout instances, the model predictions revealed significant confusion between growth stages, aligning with the human perception in data annotation, as indicated by Cohen’s Kappa scores. The developed hybrid detector-classifier model achieved an 85.50% mean average precision (mAP) at 0.5 intersections over union (IoU), tested with 402 images containing 561 sprout instances, with an inference time of 0.383 seconds per image. The results confirm the potential of the hybrid model as an alternative to current manual operations. This study serves as a practical case, for extensions to other applications within plant phenotyping communities

Fibroblast Growth Factor-10 Promotes Cardiomyocyte Differentiation from Embryonic and Induced Pluripotent Stem Cells

BACKGROUND: The fibroblast growth factor (FGF) family is essential to normal heart development. Yet, its contribution to cardiomyocyte differentiation from stem cells has not been systemically studied. In this study, we examined the mechanisms and characters of cardiomyocyte differentiation from FGF family protein treated embryonic stem (ES) cells and induced pluripotent stem (iPS) cells. METHODOLOGY/PRINCIPAL FINDINGS: We used mouse ES cells stably transfected with a cardiac-specific α-myosin heavy chain (αMHC) promoter-driven enhanced green fluorescent protein (EGFP) and mouse iPS cells to investigate cardiomyocyte differentiation. During cardiomyocyte differentiation from mouse ES cells, FGF-3, -8, -10, -11, -13 and -15 showed an expression pattern similar to the mesodermal marker Brachyury and the cardiovascular progenitor marker Flk-1. Among them, FGF-10 induced cardiomyocyte differentiation in a time- and concentration-dependent manner. FGF-10 neutralizing antibody, small molecule FGF receptor antagonist PD173074 and FGF-10 and FGF receptor-2 short hairpin RNAs inhibited cardiomyocyte differentiation. FGF-10 also increased mouse iPS cell differentiation into cardiomyocyte lineage, and this effect was abolished by FGF-10 neutralizing antibody or PD173074. Following Gene Ontology analysis, microarray data indicated that genes involved in cardiac development were upregulated after FGF-10 treatment. In vivo, intramyocardial co-administration of FGF-10 and ES cells demonstrated that FGF-10 also promoted cardiomyocyte differentiation. CONCLUSION/SIGNIFICANCE: FGF-10 induced cardiomyocyte differentiation from ES cells and iPS cells, which may have potential for translation into clinical applications

Evaluation of hazardous airborne carbonyls in five urban roadside dwellings: A comprehensive indoor air assessment in Sri Lanka

Indoor hazardous airborne carbonyls were quantified in five natural-ventilated roadside dwellings in Colombo, Sri Lanka. The total concentrations of all targeted carbonyls ranged from 13.6 to 18.6 mu g/m(3). Formaldehyde (C1) was the most abundant carbonyl, followed by acetaldehyde (C2) and acetone (C3K). The concentrations of C1 and C2 ranged from 3.3 to 8.5 mu g/m(3) and 2.3 to 4.4 mu g/m(3), respectively, which accounted for 23 to 42% and 18 to 26% respectively, to the total quantified carbonyls. The highest carbonyls levels were obtained in the dwelling located in an urban district with a mixture of industrial, commercial and residential areas. Much lower concentrations of carbonyls were measured in a light local traffic value was counted. Moderate correlations between individual combustion markers from vehicular emissions suggest the strong impacts from traffics to the indoor airs. The concentrations of C1 and C2 were compared with international indoor guidelines established by different authorities. A health assessment was conducted by estimation of inhalation cancer risk, implementing the inhalation unit risk values provided by Integrated Risk Information System (IRIS), associated with C1 and C2, which were 6.2 x 10(-5) and 7.7 x 10(-6), respectively. Even though the risks did not reach the action level (1 x 10(-4)), their health impact should not be overlooked. This kick-off indoor monitoring study provides valuable scientific data to the environmental science community since only limit data is available in Sri Lanka

Is 'oil pulling' a 'snake oil'? : a clinical trial

The traditional Ayurveda practice of ‘oil pulling’ has become a recent phenomenon and concerns about its efficacy have been raised. Objectives: (1) to determine awareness about the practice of ‘oil pulling’ among a group of young adults, and to determine variations in awareness with respect to socio-demographic factors, oral health behaviours (oral hygiene and dental attendance) and use of natural health products; (2) to determine the effectiveness of ‘oil pulling’ and conventional oral hygiene practice compared to the use of conventional oral hygiene practice alone in terms of oral hygiene and (3) to determine the effectiveness of ‘oil pulling’ and conventional oral hygiene practice compared to the use of conventional oral hygiene practice alone in terms of gingival health. Methods: Group members recruited seventy-four young adults to participate in a clinical trial over a two-month period comparing the effectiveness of (a) ‘oil pulling’ and conventional oral hygiene methods (toothbrush and toothpaste) versus (b) conventional oral hygiene methods alone. Oral hygiene was assessed using the Plaque Index - PI (Silness and Löe, 1964) and the proportion of sites with visible plaque (PVP). Gingival health was assessed using the Gingival Index – GI (Silness and Löe,1963) and the proportion of sites with gingival bleeding (PGB). Participants were block randomized in groups of four to a cross over clinical trial and assessments were conducted at one-month and two-months. Results: Approximately a quarter (28.4%, 21) of participants was aware of the practice of ‘oil pulling’. Awareness of the practice was associated with reported use of natural dental/oral health products (p0.05). There were observed significant differences in gingival health among both the test and control groups from baseline to one-month (p0.05). No significant differences were observed in oral health parameters from one-month to two-month among neither the test nor control groups (p>0.05). Conclusion: Awareness of the practice of ‘oil pulling’ is relatively common and is associated with use of natural dental/oral health products. Findings from the clinical trial failed to support the adjunct use of ‘oil pulling’ in addition to conventional oral hygiene practices.published_or_final_versio

Multiplex sequencing of paired-end ditags (MS-PET): a strategy for the ultra-high-throughput analysis of transcriptomes and genomes

The paired-end ditagging (PET) technique has been shown to be efficient and accurate for large-scale transcriptome and genome analysis. However, as with other DNA tag-based sequencing strategies, it is constrained by the current efficiency of Sanger technology. A recently developed multiplex sequencing method (454-sequencing™) using picolitre-scale reactions has achieved a remarkable advance in efficiency, but suffers from short-read lengths, and a lack of paired-end information. To further enhance the efficiency of PET analysis and at the same time overcome the drawbacks of the new sequencing method, we coupled multiplex sequencing with paired-end ditagging (MS-PET) using modified PET procedures to simultaneously sequence 200 000 to 300 000 dimerized PET (diPET) templates, with an output of nearly half-a-million PET sequences in a single 4 h machine run. We demonstrate the utility and robustness of MS-PET by analyzing the transcriptome of human breast carcinoma cells, and by mapping p53 binding sites in the genome of human colorectal carcinoma cells. This combined sequencing strategy achieved an approximate 100-fold efficiency increase over the current standard for PET analysis, and furthermore enables the short-read-length multiplex sequencing procedure to acquire paired-end information from large DNA fragments

Ad hoc influenza vaccination during years of significant antigenic drift in a tropical city with 2 seasonal peaks

We evaluated the acceptability of an additional ad hoc influenza vaccination among the health care professionals following seasons with significant antigenic drift. Self-administered, anonymous surveys were performed by hard copy questionnaires in public hospitals, and by an on-line platform available to all healthcare professionals, from April 1st to May 31st, 2015. A total of 1290 healthcare professionals completed the questionnaires, including doctors, nurses, and allied health professionals working in both the public and private systems. Only 31.8% of participating respondents expressed an intention to receive the additional vaccine, despite that the majority of them agreed or strongly agreed that it would bring benefit to the community (88.9%), save lives (86.7%), reduce medical expenses (76.3%), satisfy public expectation (82.8%), and increase awareness of vaccination (86.1%). However, a significant proportion expressed concern that the vaccine could disturb the normal immunization schedule (45.5%); felt uncertain what to do in the next vaccination round (66.0%); perceived that the summer peak might not occur (48.2%); and believed that the summer peak might not be of the same virus (83.5%). Furthermore, 27.8% of all respondents expected that the additional vaccination could weaken the efficacy of previous vaccinations; 51.3% was concerned about side effects; and 61.3% estimated that there would be a low uptake rate. If the supply of vaccine was limited, higher priority groups were considered to include the elderly aged ≥65 years with chronic medical conditions (89.2%), the elderly living in residential care homes (87.4%), and long-stay residents of institutions for the disabled (80.7%). The strongest factors associated with accepting the additional vaccine included immunization with influenza vaccines in the past 3 years, higher perceived risk of contracting influenza, and higher perceived severity of the disease impact. The acceptability to an additional ad hoc influenza vaccination was low among healthcare professionals. This could have a negative impact on such additional vaccination campaigns since healthcare professionals are a key driver for vaccine acceptance. The discordance in perceived risk and acceptance of vaccination regarding self versus public deserves further evaluation