Plant growth promotion under phosphate deficiency and improved phosphate acquisition by new fungal strain, Penicillium olsonii TLL1.

Microbiomes in soil ecosystems play a significant role in solubilizing insoluble inorganic and organic phosphate sources with low availability and mobility in the soil. They transfer the phosphate ion to plants, thereby promoting plant growth. In this study, we isolated an unidentified fungal strain, POT1 (Penicillium olsonii TLL1) from indoor dust samples, and confirmed its ability to promote root growth, especially under phosphate deficiency, as well as solubilizing activity for insoluble phosphates such as AlPO4, FePO4·4H2O, Ca3(PO4)2, and hydroxyapatite. Indeed, in vermiculite containing low and insoluble phosphate, the shoot fresh weight of Arabidopsis and leafy vegetables increased by 2-fold and 3-fold, respectively, with POT1 inoculation. We also conducted tests on crops in Singapore's local soil, which contains highly insoluble phosphate. We confirmed that with POT1, Bok Choy showed a 2-fold increase in shoot fresh weight, and Rice displayed a 2-fold increase in grain yield. Furthermore, we demonstrated that plant growth promotion and phosphate solubilizing activity of POT1 were more effective than those of four different Penicillium strains such as Penicillium bilaiae, Penicillium chrysogenum, Penicillium janthinellum, and Penicillium simplicissimum under phosphate-limiting conditions. Our findings uncover a new fungal strain, provide a better understanding of symbiotic plant-fungal interactions, and suggest the potential use of POT1 as a biofertilizer to improve phosphate uptake and use efficiency in phosphate-limiting conditions.

Could the 2017 ILAE and the four-dimensional epilepsy classifications be merged to a new "Integrated Epilepsy Classification"?

Over the last few decades the ILAE classifications for seizures and epilepsies (ILAE-EC) have been updated repeatedly to reflect the substantial progress that has been made in diagnosis and understanding of the etiology of epilepsies and seizures and to correct some of the shortcomings of the terminology used by the original taxonomy from the 1980s. However, these proposals have not been universally accepted or used in routine clinical practice. During the same period, a separate classification known as the "Four-dimensional epilepsy classification" (4D-EC) was developed which includes a seizure classification based exclusively on ictal symptomatology, which has been tested and adapted over the years. The extensive arguments for and against these two classification systems made in the past have mainly focused on the shortcomings of each system, presuming that they are incompatible. As a further more detailed discussion of the differences seemed relatively unproductive, we here review and assess the concordance between these two approaches that has evolved over time, to consider whether a classification incorporating the best aspects of the two approaches is feasible. To facilitate further discussion in this direction we outline a concrete proposal showing how such a compromise could be accomplished, the "Integrated Epilepsy Classification". This consists of five categories derived to different degrees from both of the classification systems: 1) a "Headline" summarizing localization and etiology for the less specialized users, 2) "Seizure type(s)", 3) "Epilepsy type" (focal, generalized or unknown allowing to add the epilepsy syndrome if available), 4) "Etiology", and 5) "Comorbidities & patient preferences".

Using Human Movement Data to Identify Potential Areas of Zika Transmission: Case Study of the Largest Zika Cluster in Singapore.

Singapore experienced its first Zika virus (ZIKV) cluster in August 2016. To understand the implication of human movement on disease spread, a retrospective study was conducted using aggregated and anonymized mobile phone data to examine movement from the cluster to identify areas of possible transmission. An origin⁻destination model was developed based on the movement of three groups of individuals: (i) construction workers, (ii) residents and (iii) visitors out of the cluster locality to other parts of the island. The odds ratio of ZIKV cases in a hexagon visited by an individual from the cluster, independent of the group of individuals, is 3.20 (95% CI: 2.65⁻3.87, p-value < 0.05), reflecting a higher count of ZIKV cases when there is a movement into a hexagon from the cluster locality. A comparison of independent ROC curves tested the statistical significance of the difference between the areas under the curves of the three groups of individuals. Visitors (difference in AUC = 0.119) and residents (difference in AUC = 0.124) have a significantly larger difference in area under the curve compared to the construction workers (p-value < 0.05). This study supports the proof of concept of using mobile phone data to approximate population movement, thus identifying areas at risk of disease transmission.