Graphene is an optical material of unusual characteristics because of its linearly dispersive conduction and valence bands and the strong interband transitions. It allows broadband light-matter interactions with ultrafast responses and can be readily pasted to surfaces of functional structures for photonic and optoelectronic applications. Recently, graphene-based optical modulators have been demonstrated with electrical tuning of the Fermi level of graphene. Their operation bandwidth, however, was limited to about 1 GHz by the response of the driving electrical circuit. Clearly, this can be improved by an all-optical approach. Here, we show that a graphene-clad microfiber all-optical modulator can achieve a modulation depth of 38% and a response time of ∼ 2.2 ps, limited only by the intrinsic carrier relaxation time of graphene. This modulator is compatible with current high-speed fiber-optic communication networks and may open the door to meet future demand of ultrafast optical signal processing.

As an emerging star of 2D nanomaterials, 2D transition metal carbides and nitrides, named MXenes, present a large potential in various research areas owing to their intrinsic multilayer structure and intriguing physico‐chemical properties. However, the fabrication and application of functional MXene‐based devices still remain challenging as they are prone to oxidative degradation under ambient environment. Within this review, the preparation methods of MXenes focusing on the recent investigations on their thermal structure–stability relationships in inert, oxidizing, and aqueous environments are systematically introduced. Moreover, the key factors that affect the oxidation of MXenes, such as, atmosphere, temperature, composition, microstructure, and aqueous environment, are reviewed. Based on different scenarios, strategies for avoiding or delaying the oxidation of MXenes are proposed to encourage the utilization of MXenes in complicated environments, especially at high temperature. Furthermore, the chemistry of MXene‐derived oxides is analyzed, which can offer perspectives on the further design and fabrication of novel 2D composites with the unique structures of MXenes being preserved.

Storm identification, tracking, and forecasting make up an essential part of weather radar and severe weather surveillance operations. Existing nowcasting algorithms using radar data can be generally classified into two categories: centroid and cross-correlation tracking. Thunderstorm Identification, Tracking, and Nowcasting (TITAN) is a widely used centroid-type nowcasting algorithm based on this paradigm. The TITAN algorithm can effectively identify, track, and forecast individual convective storm cells, but TITAN tends to provide incorrect identification, tracking, and forecasting in cases where there are dense cells whose shape changes rapidly or where clusters of storm cells occur frequently. Aiming to improve the performance of TITAN in such scenarios, an enhanced TITAN (ETITAN) algorithm is presented. The ETITAN algorithm provides enhancements to the original TITAN algorithm in three aspects. First, in order to handle the false merger problem when two storm cells are adjacent, and to isolate individual storm cells from a cluster of storms, ETITAN uses a multithreshold identification method based on mathematical morphology. Second, in the tracking phase, ETITAN proposes a dynamic constraint-based combinatorial optimization method to track storms. Finally, ETITAN uses the motion vector field calculated by the cross-correlation method to forecast the position of the individual isolated storm cells. Thus, ETITAN combines aspects of the two general classes of nowcasting algorithms, that is, cross-correlation and centroid-type methods, to improve nowcasting performance. Results of experiments presented in this paper show the performance improvements of the ETITAN algorithm.

Replicating circular RNAs are independent plant pathogens known as viroids, or act to modulate the pathogenesis of plant and animal viruses as their satellite RNAs. The rate of discovery of these subviral pathogens was low over the past 40 years because the classical approaches are technical demanding and time-consuming. We previously described an approach for homology-independent discovery of replicating circular RNAs by analysing the total small RNA populations from samples of diseased tissues with a computational program known as progressive filtering of overlapping small RNAs (PFOR). However, PFOR written in PERL language is extremely slow and is unable to discover those subviral pathogens that do not trigger in vivo accumulation of extensively overlapping small RNAs. Moreover, PFOR is yet to identify a new viroid capable of initiating independent infection. Here we report the development of PFOR2 that adopted parallel programming in the C++ language and was 3 to 8 times faster than PFOR. A new computational program was further developed and incorporated into PFOR2 to allow the identification of circular RNAs by deep sequencing of long RNAs instead of small RNAs. PFOR2 analysis of the small RNA libraries from grapevine and apple plants led to the discovery of Grapevine latent viroid (GLVd) and Apple hammerhead viroid-like RNA (AHVd-like RNA), respectively. GLVd was proposed as a new species in the genus Apscaviroid, because it contained the typical structural elements found in this group of viroids and initiated independent infection in grapevine seedlings. AHVd-like RNA encoded a biologically active hammerhead ribozyme in both polarities, and was not specifically associated with any of the viruses found in apple plants. We propose that these computational algorithms have the potential to discover novel circular RNAs in plants, invertebrates and vertebrates regardless of whether they replicate and/or induce the in vivo accumulation of small RNAs.

This study aims to characterize the seasonal variation of the shallow‐to‐deep convection transition and understand how environmental conditions impact the behavior of this transition using data collected from the Observations and Modeling of the Green Ocean Amazon (GOAmazon) field campaign in the Central Amazon (Manaus). The diurnal cycle of the rain/cloud fraction shows that the wet season has more extensive shallow convection before the transition to deep convection with larger fractional coverage and rainfall; deep convection in the transition season is more intense and has higher vertical extension and a stronger updraft. Surface meteorology, atmospheric moisture, instability, and wind shear are contrasted for the shallow/congestus convection (SC) cases and the locally formed shallow‐to‐deep convection transition (LD) cases. The comparisons suggest that occurrence of the LD is generally promoted under the conditions of high atmospheric moisture and instability but has a weaker dependence on wind shear. The relative importance of these environmental controls also varies in different seasons: The dry and transition seasons require a deeper moist layer from the boundary layer to midtroposphere than the wet season; convective available potential energy (CAPE) is higher during the transition season, but it is a less important factor for shallow‐to‐deep convection transition than in other seasons; LD only has significantly larger wind shears than SC during the dry season.