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Abstract Background Light is a critical factor in plant growth and development, particularly in controlled environments. Light-emitting diodes (LEDs) have become a reliable alternative to conventional high pressure sodium (HSP) lamps because they are more efficient and versatile in light sources. In contrast to well-known specialized LED light spectra for vegetables, the appropriate LED lights for crops such as cotton remain unknown. Results In this growth chamber study, we selected and compared four LED lights with varying percentages (26.44%–68.68%) of red light (R, 600–700 nm), combined with other lights, for their effects on growth, leaf anatomy, and photosynthesis of cotton seedlings, using HSP lamp as a control. The total photosynthetic photon flux density (PPFD) was (215 ± 2) μmol·m−2·s−1 for all LEDs and HSP lamp. The results showed significant differences in all tested parameters among lights, and the percentage of far red (FR, 701–780 nm) within the range of 3.03%–11.86% was positively correlated with plant growth (characterized by leaf number and area, plant height, stem diameter, and total biomass), palisade layer thickness, photosynthesis rate (P n), and stomatal conductance (G s). The ratio of R/FR (4.445–11.497) negatively influenced the growth of cotton seedlings, and blue light (B) suppressed stem elongation but increased palisade cell length, chlorophyll content, and P n. Conclusion The LED 2 was superior to other LED lights and HSP lamp. It had the highest ratio of FR within the total PPFD (11.86%) and the lowest ratio of R/FR (4.445). LED 2 may therefore be used to replace HPS lamp under controlled environments for the study of cotton at the seedling stage.

Abstract Background The global burden of invasive fungal infections (IFIs) has shown an upsurge in recent years due to the higher load of immunocompromised patients suffering from various diseases. The role of early and accurate diagnosis in the aggressive containment of the fungal infection at the initial stages becomes crucial thus, preventing the development of a life-threatening situation. With the changing demands of clinical mycology, the field of fungal diagnostics has evolved and come a long way from traditional methods of microscopy and culturing to more advanced non-culture-based tools. With the advent of more powerful approaches such as novel PCR assays, T2 Candida, microfluidic chip technology, next generation sequencing, new generation biosensors, nanotechnology-based tools, artificial intelligence-based models, the face of fungal diagnostics is constantly changing for the better. All these advances have been reviewed here giving the latest update to our readers in the most orderly flow. Main text A detailed literature survey was conducted by the team followed by data collection, pertinent data extraction, in-depth analysis, and composing the various sub-sections and the final review. The review is unique in its kind as it discusses the advances in molecular methods; advances in serology-based methods; advances in biosensor technology; and advances in machine learning-based models, all under one roof. To the best of our knowledge, there has been no review covering all of these fields (especially biosensor technology and machine learning using artificial intelligence) with relevance to invasive fungal infections. Conclusion The review will undoubtedly assist in updating the scientific community’s understanding of the most recent advancements that are on the horizon and that may be implemented as adjuncts to the traditional diagnostic algorithms.

Abstract Background Cotton production in China is challenged by high labor input including manual topping (MT). Recently, to replace MT in the Xinjiang cotton region of China, mepiquat chloride (MC) was applied once more than the traditional multiple-application; this was designated as chemical topping (CT), but it is unclear whether the amount of irrigation needs to be adjusted to accommodate CT. Results The main plots were assigned to three drip irrigation amounts [300 (I1), 480 (I2), and 660 (I3) mm], and the subplots were assigned to the CT treatments [450 (MC1), 750 (MC2), and 1 050 (MC3) mL·hm−2 25% MC] with MT as a control that was performed after early bloom. The optimum drip irrigation amount for CT was explored based on leaf photosynthesis, chlorophyll fluorescence, biomass accumulation, and yield. There were significant influences of drip irrigation, topping treatments and their interaction on chlorophyll fluorescence characteristics, gas exchange parameters and biomass accumulation characteristics as well as yield. The combination of I2 and MC2 (I2MC2) performed best. Compared with I2MT, the net photosynthetic rate (Pn), stomatal conductance (Gs), transpiration rate (Tr), and photochemical quenching coefficient (qP) of I2MC2 significantly increased by 4.0%∼7.2%, 6.8%∼17.1%, 5.2%∼17.6%, and 4.8%∼9.6%, respectively, from the peak flowering to boll opening stages. Moreover, I2MC2 showed fast reproductive organ biomass accumulation and the highest seed cotton yield; the latter was 6.6%∼12.8% higher than that of I2MT. Further analysis revealed that a 25% MC emulsion in water (MCEW) application resulted in yield improvement by increasing Pn, φPSII, and qP to promote biomass accumulation and transport to reproductive organs. Conclusion The results showed that the 480 mm drip irrigation combined with 750 mL·hm−2 MC increased the rate of dry matter accumulation in reproductive organs by increasing Pn, φPSII, and qP to improve photosynthetic performance, thus achieving higher yield.

Abstract Background Mepiquat chloride (MC), a plant growth regulator, enhances root growth by promoting lateral root formation in cotton. However, the underlying molecular mechanisms of this phenomenon is still unknown. Methods In this study, we used 10 cotton (Gossypium hirsutum Linn.) cultivars to perform a seed treatment with MC to investigate lateral root formation, and selected a MC sensitive cotton cultivar for dynamic monitor of root growth and transcriptome analysis during lateral root development upon MC seed treatment. Results The results showed that MC treated seeds promotes the lateral root formation in a dosage-depended manner and the effective promotion region is within 5 cm from the base of primary root. MC treated seeds induce endogenous auxin level by altering gene expression of both gibberellin (GA) biosynthesis and signaling and abscisic acid (ABA) signaling. Meanwhile, MC treated seeds differentially express genes involved in indole acetic acid (IAA) synthesis and transport. Furthermore, MC-induced IAA regulates the expression of genes related to cell cycle and division for lateral root development. Conclusions Our data suggest that MC orchestrates GA and ABA metabolism and signaling, which further regulates auxin biosynthesis, transport, and signaling to promote the cell division responsible for lateral root formation.

Abstract Background In order to efficiently determine genotypic differences in rooting patterns of crops, novel hardware and software are needed simultaneously to characterize dynamics of root development. Results We describe a prototype robotic monitoring platform—the RhizoChamber-Monitor for analyzing growth patterns of plant roots automatically. The RhizoChamber-Monitor comprises an automatic imaging system for acquiring sequential images of roots which grow on a cloth substrate in custom rhizoboxes, an automatic irrigation system and a flexible shading arrangement. A customized image processing software was developed to analyze the spatio-temporal dynamics of root growth from time-course images of multiple plants. This software can quantify overall growth of roots and extract detailed growth traits (e.g. dynamics of length and diameter) of primary roots and of individual lateral roots automatically. It can also identify local growth traits of lateral roots (pseudo-mean-length and pseudo-maximum-length) semi-automatically. Two cotton genotypes were used to test both the physical platform and the analysis software. Conclusions The combination of hardware and software is expected to facilitate quantification of root geometry and its spatio-temporal growth patterns, and therefore to provide opportunities for high-throughput root phenotyping in support of crop breeding to optimize root architecture.