Preharvest Maize Fungal Microbiome and Mycotoxin Contamination: Case of Zambia's Different Rainfall Patterns.

The preharvest maize mycobiome may be crucial in defining the health of the crop in terms of potential disease burden and mycotoxins. We investigated the preharvest maize mycobiome structure, including the influence of weather patterns, in terms of rainfall intensity, on its composition. In addition, we investigated correlation of genera Fusarium and Aspergillus with maize fumonisin-B1 and aflatoxin. Forty maize fields from selected districts in the wetter northern (N) and drier southern (S) agroecological zones of Zambia were sampled twice over two seasons (1 and 2). The defined weather variables over the two seasons were low rainfall with dry spell (S1), low rainfall (S2), and high rainfall (N1 and N2). High-throughput DNA amplicon sequencing of internal transcribed spacer 1 (ITS1) was used to determine the mycobiome structure and the composition in relation to rainfall patterns. We detected 61 genera, with Fusarium and previously unreported Sarocladium in Zambia to have the highest frequency of detection on the maize. There was a significant difference in fungal genera composition between S1 and S2 but no difference between N1 and N2. The weather pattern with dry spell, S1, had a strong proliferation of Meyerozyma and xerophiles Penicillium, Kodamaea, and Aspergillus. The four genera drove the difference in composition between S1 and S2 and the significantly higher fungal diversity in S1 compared to N2. Of the mycotoxin-important fungi, dry conditions (S1) were a key driver for proliferation of Aspergillus, while Fusarium proliferation occurred irrespective of weather patterns. The relative abundance of Aspergillus and Fusarium resonated with maize aflatoxin and fumonisin-B1 levels, respectively. IMPORTANCE Fungi contaminate various crops worldwide. Maize, an important human staple and livestock cereal, is susceptible to contamination with fungi in the field. Fungi are drivers of plant disease and can compromise yield. Some species of fungi are known to produce chemical compounds (mycotoxins), which are cancer-causing agents in humans and impair livestock productivity. It is important to understand the spectrum of fungi on maize and how weather conditions can impact their abundance. This is because the abundance of fungi in the field can have a bearing on the health of the crop as well as potential for mycotoxins contamination. By understanding the spectrum of the preharvest fungi, it becomes possible to know the key fungi adapted to the maize and subsequently the potential for crop disease as well as mycotoxins contamination. The influence of weather conditions on the spectrum of preharvest fungi on maize has not been fully explored.

State-of-the-Art Light-Driven Hydrogen Generation from Formic Acid and Utilization in Enzymatic Hydrogenations.

A concept of combining photocatalytically generated hydrogen with green enzymatic reductions is demonstrated. The developed photocatalytic formic acid (FA) dehydrogenation setup based on Pt(x)@TiO2 shows unprecedented hydrogen generation activity, which is two orders of magnitude higher than reported values of state-of-the-art systems. Mechanistic studies confirm that hydrogen generation proceeds via a photocatalytic pathway, which is entirely different from purely thermal reaction mechanisms previously reported. The viability of the presented approach is demonstrated by the synthesis of value-added compounds 3-phenylpropanal and (2R, 5S)-dihydrocarvone at ambient pressure and room temperature, which should be applicable for many other hydrogenation processes, e.g., for the preparation of flavours and fragrance compounds, as well as pharmaceuticals.

Spatial-Temporal Patterns of ß-Amyloid Accumulation: A Subtype and Stage Inference Model Analysis.

BACKGROUND AND OBJECTIVES: ß-amyloid (Aß) staging models assume a single spatial-temporal progression of amyloid accumulation. We assessed evidence for Aß accumulation subtypes by applying the data-driven Subtype and Stage Inference (SuStaIn) model to amyloid-PET data. METHODS: Amyloid-PET data of 3,010 participants were pooled from 6 cohorts (ALFA+, EMIF-AD, ABIDE, OASIS, and ADNI). Standardized uptake value ratios were calculated for 17 regions. We applied the SuStaIn algorithm to identify consistent subtypes in the pooled dataset based on the cross-validation information criterion and the most probable subtype/stage classification per scan. The effects of demographics and risk factors on subtype assignment were assessed using multinomial logistic regression. RESULTS: Participants were mostly cognitively unimpaired (n = 1890 [62.8%]), had a mean age of 68.72 (SD 9.1) years, 42.1% were APOE ε4 carriers, and 51.8% were female. A 1-subtype model recovered the traditional amyloid accumulation trajectory, but SuStaIn identified 3 optimal subtypes, referred to as frontal, parietal, and occipital based on the first regions to show abnormality. Of the 788 (26.2%) with strong subtype assignment (>50% probability), the majority was assigned to frontal (n = 415 [52.5%]), followed by parietal (n = 199 [25.3%]) and occipital subtypes (n = 175 [22.2%]). Significant differences across subtypes included distinct proportions of APOE ε4 carriers (frontal 61.8%, parietal 57.1%, occipital 49.4%), participants with dementia (frontal 19.7%, parietal 19.1%, occipital 31.0%), and lower age for the parietal subtype (frontal/occipital 72.1 years, parietal 69.3 years). Higher amyloid (Centiloid) and CSF p-tau burden was observed for the frontal subtype; parietal and occipital subtypes did not differ. At follow-up, most participants (81.1%) maintained baseline subtype assignment and 25.6% progressed to a later stage. DISCUSSION: Whereas a 1-trajectory model recovers the established pattern of amyloid accumulation, SuStaIn determined that 3 subtypes were optimal, showing distinct associations with Alzheimer disease risk factors. Further analyses to determine clinical utility are warranted.