The yarn dyeing factories discharge liquid waste laden with a variety of hazardous substances, including microplastic fibre (MPFs), which are found in aquatic ecosystems. During dyeing, MPF shedding factors were determined in this study. Shedding factors were determined at six polyester yarn dyeing factories to assess MPF release for (1) dark and (2) light shading. Three dyeing processes were considered including normal, carrier and high temperature. Sawdust-based activated carbon was utilized to decolourize dye wastewater. Flocculation and clarification were done without a flotation process to obtain low-density MPF. A hot needle test was applied to visual identification under an optical microscope and quantification was done by filtering, weighting and count of the yarn. A maximum of 0.00399 % weight loss (wt.) was found for dark shade in the high-temperature dyeing process and 0.00392 % (light) was found in carrier dyeing to dye a coarser yarn. In contrast, 0.4562 mg L-1 fibre particles (≤ 0.225 mm) shedding in normal dyeing, for a light shade, was observed to a fine yarn where a minimum of 0.00138 % wt. was found. Shorter fibre length, higher denier, and courser yarn were associated with the greatest MPF discharge at high-temperature dyeing for a dark shade. The usual effluent treatment plant (ETP) of the textile industry can remove only 75.52 % MPFs of wastewater. Shedding of MPF during dyeing is remarkably higher than the domestic wash cycle of garments. Wastewater of textiles containing MPFs would appear as a regular and extensive initial source of MP emissions, which can damage the ecological system.

This study was carried out to determine the best ring size for a certain yarn count. In a ring frame with three separate rings of varying sizes, 20s/1 kW yarn was created. The spinning method used the same ring speed, twist, ring traveller, and spacer but three distinct rings with diameters of 38 mm (Ring- A), 40 mm (Ring- B), and 42 mm (Ring- C). Then, under the same testing conditions, ten samples of each yarn were examined by Uster Evenness Tester (UT-6) and compared to determine the best one. The \"Ring- C″ production yield was 0.22% and 1.34% greater than the \"Ring- B″ and \"Ring- A″ yields, respectively. Yarn breakage for \"Ring- C″ was 47.78% and 200% greater than for \"Ring- B″ and \"Ring- A,\" respectively. Yarn unevenness for \"Ring- C″ was found to be 4.15% and 4.14% higher than \"Ring- B″ and \"Ring- A,\" imperfection of yarn produced by \"Ring- B″ was 18.54% and 3.47% lower than \"Ring- C″ and \"Ring- A,\" and tenacity of yarn produced by \"Ring- B″ was 3.35% and 0.64% higher than \"Ring- C″ and \"Ring- A.\" \"Ring- C″ yarn hairiness was 10.63% and 12.31% higher than \"Ring- B″ and \"Ring- A″ yarn hairiness, respectively. According to the study of the tested report, yarn generated from \"Ring- B″ delivered optimized results in terms of both quality and output. Ring \"Ring- B″ had a lower production loss than Ring \"A\" but a higher loss than Ring \"C.\" Also, the hairiness of yarn made from \"Ring- B″ was remarkably similar to yarn made from \"Ring- A.\"

Pollination is a vitally important function in nature and becomes an ecosystem service because it influences the food and nutritional security for people. However, the contribution of different functional traits of insects for pollen transport of plants is still poorly known. We explore the relationship between pollinator insect functional traits and the transport of pollen of sweet granadilla (Passiflora ligularis Juss) in eight crops. We sampled flower-visiting insects of this crop and recorded 10 functional traits (five by direct measurements and five from the literature) that were related to the amount of pollen carried by each insect. Bees (Apidae) were not only the most abundant insects but also the ones that loaded the highest amounts of pollen. Within these, the most abundant species was the exotic common honeybee (Apis mellifera (Linnaeus)) making up almost half of the specimens collected; however, this bee carried less pollen grains than other native bees. Bombus hortulanus (Smith) was one of the large-bodied native bees that carried more sweet granadilla pollen, despite not being an abundant species in the community. Body size was the most important trait determining the transport of sweet granadilla pollen, while the traits related to body hairs were not significant for the body\'s pollen load. None of the functional traits evaluated was influenced by taxonomy at species-level. Our results suggest that large body sizes in bees are the most important traits in granadilla pollen transport, regardless of other changes in composition and structure of pollinating insect assemblages in the crop.

A huge amount of waste was generated from the apparel industries. This study aims to develop the process of producing recycled yarn from apparel waste. The apparel leftover fabric was converted to fiber, and the fiber was mixed with virgin cotton in different ratios to produce sustainable 6/1 Ne rotor yarn. The produced yarn qualities viz. count strength product (CSP), elongation percentage, total quality index (TQI) and tenacity were decreased linearly, and opposite scenario observed for thick and thin places, neps, imperfection index (IPI) and hairiness (H) attributes with increasing the amount of waste addition with virgin cotton. The leftover fabric (LOF) can be utilized to develop a sustainable yarn and to zero waste management.

Understanding the causes of morphological variation of organisms along climatic gradients has been a central challenge in ecological research. We studied the variation of community weighted mean (CWM) and two functional diversity metrics (Rao-Q and functional richness) computed for five morphological traits of wild bees (Hymenoptera: Apoidea) related to thermal performance (namely body size, relative appendage length and hairiness), at community and interspecific levels, along an elevation gradient in a Mexical-type scrubland. At the community level we found a decreasing CWM of body size pattern with increasing elevation which is consistent with the species-energy theory (and contrary to Bergmann\'s rule). We also found an increase in the CWM of relative tibia length, which is contrary to Allen\'s rule. Additionally, we found an increase in the CWM of relative hair length towards high levels of elevation, which would be consistent with the hypothesis that hairiness plays an important role as thermal insulation. We found that functional diversity was larger at low elevations with respect to high elevation for body size and hair length, which could imply that highland communities were more sensitive towards environmental changes than lowland communities. Overall, at intraspecific level, most of species showed no pattern for any of the traits along the elevation gradient. Future research should provide further evidence on the possible behavioral or physiological mechanisms behind it.

Textile yarns are the fundamental building blocks in the fabric industry. The measurement of the diameter of the yarn textile and fibers is crucial in textile engineering as the diameter size and distribution can affect the yarn\'s properties, and image processing can provide automatic techniques for faster and more accurate determination of the diameters. In this paper, facile and new methods to measure the yarn\'s diameter and its individual fibers diameter based on image processing algorithms that can be applied to microscopic digital images. Image preprocessing such as binarization and morphological operations on the yarn image were used to measure the diameter automatically and accurately compared to the manual measuring using ImageJ software. In addition to the image preprocessing, the circular Hough transform was used to measure the diameter of the individual fibers in a yarn\'s cross-section and count the number of fibers. The algorithms were built and deployed in a MATLAB (R2020b, The MathWorks, Inc., Natick, Massachusetts, United States) environment. The proposed methods showed a reliable, fast, and accurate measurement compared to other different image measuring softwares, such as ImageJ.

Bees play a very important role in pollination, especially western honey bees, which contribute upwards of billions of dollars concerning crop pollination. Hairiness plays an important role in pollination success by transporting pollen, and pollen intake, but there is a lack of detailed studies on the morphological mechanisms. The hairiness trait is barely discussed in pollinator trait analysis because of the lack of systematic techniques used to measure hairiness. This paper reports a novel method that is used to measure the hair length of different body parts of a western honey bee through a stereomicroscope equipped with live measurement module software. Scanning electron microscope (SEM) was used to update the knowledge regarding the hair structure of a western honey bee. We explained different types of hairs, hair branches, and their distributions on different body parts, which are discussed in detail. A positive correlation was found between hair length and the number of branches on all body parts. Five types of branches were observed, and these branches vary with different body parts. Our study provides sufficient details about the hair morphology of the western honey bee and a new methodology for measuring hair length. This methodology will improve the knowledge about understanding the pollination efficiency of the western honey bee.

BACKGROUND: Leaf hairiness (pubescence) is an important plant phenotype which regulates leaf transpiration, affects sunlight penetration, and provides increased resistance or susceptibility against certain insects. Cotton accounts for 80% of global natural fibre production, and in this crop leaf hairiness also affects fibre yield and value. Currently, this key phenotype is measured visually which is slow, laborious and operator-biased. Here, we propose a simple, high-throughput and low-cost imaging method combined with a deep-learning model, HairNet, to classify leaf images with great accuracy.
RESULTS: A dataset of [Formula: see text] 13,600 leaf images from 27 genotypes of Cotton was generated. Images were collected from leaves at two different positions in the canopy (leaf 3 & leaf 4), from genotypes grown in two consecutive years and in two growth environments (glasshouse & field). This dataset was used to build a 4-part deep learning model called HairNet. On the whole dataset, HairNet achieved accuracies of 89% per image and 95% per leaf. The impact of leaf selection, year and environment on HairNet accuracy was then investigated using subsets of the whole dataset. It was found that as long as examples of the year and environment tested were present in the training population, HairNet achieved very high accuracy per image (86-96%) and per leaf (90-99%). Leaf selection had no effect on HairNet accuracy, making it a robust model.
CONCLUSIONS: HairNet classifies images of cotton leaves according to their hairiness with very high accuracy. The simple imaging methodology presented in this study and the high accuracy on a single image per leaf achieved by HairNet demonstrates that it is implementable at scale. We propose that HairNet replaces the current visual scoring of this trait. The HairNet code and dataset can be used as a baseline to measure this trait in other species or to score other microscopic but important phenotypes.

Trichomes are unicellular or multicellular epidermal structures that play a defensive role against environmental stresses. Although unicellular trichomes have been extensively studied as a mechanistic model, the genes involved in multicellular trichome formation are not well understood. In this study, we first classified the trichome morphology structures in Capsicum species using 280 diverse peppers. We cloned a key gene (Hairiness) on chromosome 10, which mainly controlled the formation of multicellular non-glandular trichomes (types II, III, and V). Hairiness encodes a Cys2-His2 zinc-finger protein, and virus-induced gene silencing of the gene resulted in a hairless phenotype. Differential expression of Hairiness between the hairiness and hairless lines was due to variations in promoter sequences. Transgenic experiments verified the hypothesis that the promoter of Hairiness in the hairless line had extremely low activity causing a hairless phenotype. Hair controlled the formation of type I glandular trichomes in tomatoes, which was due to nucleotide differences. Taken together, our findings suggest that the regulation of multicellular trichome formation might have similar pathways, but the gene could perform slightly different functions in crops.

Wiedemann-Steiner syndrome (WSS) is a rare syndromic condition in which intellectual disability (ID) is associated with hypertrichosis cubiti, short stature, and characteristic facies. Following the identification of the causative gene (KMT2A) in 2012, only 31 cases of WSS have been described precisely in the literature. We report on 33 French individuals with a KMT2A mutation confirmed by targeted gene sequencing, high-throughput sequencing or exome sequencing. Patients\' molecular and clinical features were recorded and compared with the literature data. On the molecular level, we found 29 novel mutations. We observed autosomal dominant transmission of WSS in 3 families and mosaicism in one family. Clinically, we observed a broad phenotypic spectrum with regard to ID (mild to severe), the facies (typical or not of WSS) and associated malformations (bone, cerebral, renal, cardiac and ophthalmological anomalies). Hypertrichosis cubiti that was supposed to be pathognomonic in the literature was found only in 61% of our cases. This is the largest series of WSS cases yet described to date. A majority of patients exhibited suggestive features, but others were less characteristic, only identified by molecular diagnosis. The prevalence of WSS was higher than expected in patients with ID, suggesting than KMT2A is a major gene in ID.