The response of communities to climate change is expected to vary among feeding guilds. To evaluate the response of guilds to environmental factors without considering the taxonomic specificities, it is useful to examine Aculeata bees and wasps, which consist of closely related taxa including different guilds, pollinators, predators, and parasitoids. In this study, we evaluated changes in species diversity (SD) and functional traits of each feeding guild along an elevational gradient in a boreal forest in northern Japan. We used yellow pan traps to collect Aculeata bees and wasps at 200–1600 m above sea level. We investigated six functional traits (trophic level, flight duration, body size, elevational range, nesting position, and soil dependency) and the horizontal distribution of the species. The SD of all Aculeata predators and parasitoids decreased with an increase in elevation; however, the SD of pollinators did not show any specific trend. Although the functional trait composition of all Aculeata species did not show any trend, that of each feeding guild responded to elevation in different ways. Pollinators increased in body size and showed a decrease in flight duration with increasing elevation, suggesting that tolerance and seasonal escape from physical stress at high elevations are important for shaping pollinator communities. Predators showed increased elevational range and above-ground nesting species with increasing elevation, suggesting that the ability to live in a wider range of environments and avoid unsuitable soil environments at high elevations might be important. Parasitoids changed their hosts and variable traits with increasing elevation, suggesting that brood parasitoids have difficulty in surviving at high elevation. The traits for each guild responded in different ways, even if they were dominated by the same environmental factors. Differences in the response of functional traits would produce different patterns of community formation in different guilds during further climate change.

Soil microorganisms are crucial contributors to the function of permafrost ecosystems, as well as the regulation of biogeochemical cycles. However, little is known about the distribution patterns and drivers of high-latitude permafrost microbial communities subject to climate change and human activities. In this study, the vertical distribution patterns of soil bacterial communities in the Greater Khingan Mountain permafrost region were systematically analyzed via Illumina Miseq high-throughput sequencing. Bacterial alpha diversity varied significantly at different soil depths, and the bacterial diversity and richness in the active layer were significantly higher than in the permafrost layer. The relative abundance of the dominant phyla Chloroflexi (17.92%–52.79%) and Actinobacteria (6.34%–34.52%) was significantly higher in the permafrost layer than in the active layer, whereas that of Acidobacteria (4.98%–38.82%) exhibited the opposite trend, and the abundance of Proteobacteria (2.49%–22.51%) generally decreased with depth. More importantly, the abundance of microorganisms linked to human infectious diseases was significantly higher in the permafrost layer according to Tax4Fun prediction analysis. Redundancy analysis (RDA) showed that NH4+-N, TOC and TP were major factors affecting the bacterial community composition. Collectively, our findings provide insights into the soil bacteria at different depths in high-latitude permafrost regions, as well as their vertical distribution patterns and major environmental drivers, which is key to grasping the response of cold region ecosystem processes to global climate changes.

As a result of international competition and a globalized environment, companies today face the remarkable challenge of how to achieve and maintain their competitive advantage in a highly competitive environment in which international competitors play a significant role. The goal of digital governance has always been to make people's lives easier and improve customer satisfaction. In response to the recovery in markets, organizations around the world are now playing a different role. There is no doubt that companies are making considerable efforts to boost the factors which make them more competitive, including adoption of the principles of digital governance and automation. The role of digital governance is currently more of a facilitation than a regulation. Leaders and executives understand the importance of digital strategy and governance for competitive advantage. This paper examines the challenges and competitiveness that can be gained by digital governance on the world's business strategy using numerous real-world examples and anecdotes. This paper examines the challenges and competitiveness that can be gained by digital governance on the world's business strategy using numerous real-world examples and anecdotes. It is true that digitization has been around for some time, but there is still a long way to go to fully realize its potential. The aim of this paper is to explain how concrete elements of digital governance can affect the competitiveness of companies. Thus, digital governance was a key element in the successful recovery of the markets after the Covid era, despite the fact that it's been around for so long. In order for a company to be successful in international markets and maintain its quality, its productivity performance compared to its competitor's digital governance plays a very important role.

Association is the basic unit of plant community classification. Exploring the distribution of plant associations can help improve the understanding of biodiversity conservation. Different associations depend on different habitats. Studying the association level is significant for ecological restoration, regional ecological protection, regulating the ecological balance, and maintaining biodiversity. However, previous studies have focused only on the suitable distribution areas of species and not on the distribution of plant associations. Larix gmelinii is a sensitive and abundant species spread in the southern margin of Eurasian boreal forests, and its distribution is closely related to permafrost. In this study, 420 original plots of L. gmelinii forests were investigated. We used Maxent model and ArcGIS software to project the potential geographical distribution of L. gmelinii associations in the future (by 2050 and 2070) according to the climate scenarios RCP 2.6, RCP 4.5, and RCP 8.5. The causes for the changes in spatial distribution were analyzed using multinomial logistic regression analysis. The results revealed that temperature is the most important factor affecting the distribution of L. gmelinii forests and most of its associations under different climate scenarios. Further, the suitable areas for each association type are shrinking by varying degrees, especially due to habitat loss at high altitudes in special terrains. For different L. gmelinii associations, management measures should also be different based on the different site conditions, composition structure, growth, development, and renewal succession trends. Furthermore, subsequent research should consider data on biological factors to obtain more accurate prediction results.

The rapid urbanization has caused changes in climate and environment and threatened the ecosystem with multiple risks. The ecological service capacity has shown a downward trend accordingly. It is significant to explore the spatio-temporal evolution of land use and ecological service value in mountainous counties at small scales, as it coordinates economic growth and ecological protection, and promotes sustainable and high-quality development. Based on the SD-PLUS model, taking Qianshan city as an example, the study simulated three scenarios of land use change: ecological protection, coordinated development, and economic priority, and studied the impacts of land use change on the value of ecosystem services. Results showed that: ① Under the three scenarios, the construction land in the study area increased significantly, the forest and water area have a decreasing trend, and the scale of gardens has partly increased. ② Construction land expands in clusters in the urban built-up areas and dots in mountainous areas; land use changes are primarily affected by roads, followed by areas where artificial facilities are relatively sparse, and DEM has the greatest impact on land use changes. ③ The overall ecosystem service value shows a downward trend, with the comprehensive coordination type dropping the least (8.79%). The value distribution changes little at space scale, and different regions demonstrate different degrees of changes. From the perspective of value type, the service values of climate regulation and water conservation are significantly reduced, while that of food production is relatively stable; and from the perspective of various lands with their ecological service values, cultivated land and forest remain stable. The study results can provide technical ideas for the coordinated economic development and ecological protection of mountainous cities, and boost the implementation of green development.

Background: Three-dimensional echocardiography (3DE) is an emerging method for volumetric cardiac measurements; however, few vendor-neutral analysis packages exist. Ventripoint Medical System Plus (VMS3.0+) proprietary software utilizes a validated MRI database of normal ventricular and atrial morphologies to calculate chamber volumes. This study aimed to compare left ventricular (LV) and atrial (LA) volumes obtained using VMS3.0+ to Tomtec echocardiography analysis software. Methods: Healthy controls (n=98) aged 0 to 18 years were prospectively recruited and 3D DICOM datasets focused on the LV and LA acquired. LV and LA volumes and ejection fractions were measured using TomTec Image Arena 3D LV analysis package and using VMS3.0+. Pearson correlation coefficients, Bland-Altman’s plots and intraclass coefficients (ICC) were calculated, along with analysis time. Results: There was a very good correlation between VMS and Tomtec LV systolic (r 2 = 0.88, ICC 0.89 [95% CI 0.81,0.94]), and diastolic (r 2 = 0.88, ICC 0.90 [95% CI 0.77,0.95]) volumes, and between VMS and Tomtec LA diastolic (r 2 =0.75, ICC 0.89 [95% CI 0.81,0.93]) and systolic (r 2 =0.88, ICC 0.91 [95% CI 0.78,0.96]) volumes on linear regression models. Natural log transformations eliminated heteroscedasticity, and power transformations provided best fit. The time (mins) to analyze volumes using VMS were less than using Tomtec (LV VMS 2.3±0.5, Tomtec 3.3±0.8, p<0.001; LA: VMS 1.9±0.4, Tomtec 3.4±1.0, p<0.001). Conclusions: There was very good correlation between knowledge-based (VMS3.0+) and 3D (Tomtec) algorithms when measuring 3D echocardiography derived LA and LV volumes in pediatric patients. VMS was slightly faster than Tomtec in analyzing volumetric measurements.

Cong Fang#, Hua-Yao Li#, Long Li, Hu-Yin Su, Jiang Tang, Xiang Bai* and Huan Liu*C. FangSchool of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, ChinaH.-Y. Li, L. Li, H.-Y. Su, J. Tang, H. Liu School of Optical and Electronic Information, Optics Valley Laboratory, Huazhong University of Science and Technology, Wuhan 430074, ChinaX. BaiSchool of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaE-mail: xbai@hust.edu.cn; huan@hust.edu.cn#These authors contributed equally: C. Fang, H.-Y. LiKeywords : electronic nose, all-feature extraction, deep learning, odor recognition, sensor arrayAbstract: An electronic nose (e-nose) mimics the mammalian olfactory system in identifying odors and expands human olfaction boundaries by tracing toxins and explosives. However, existing feature-based odor recognition algorithms rely on domain-specific expertise, which may limit the performance due to information loss during the feature extraction process. Inspired by human olfaction, we propose a smart electronic nose enabled by an all-feature olfactory algorithm (AFOA), whereby all features in a gas sensing cycle of semiconductor gas sensors, including the response, equilibrium, and recovery processes are utilized. Specifically, our method combines one-dimensional convolutional and recurrent neural networks with channel and temporal attention modules to fully utilize complementary global and dynamic information. We further demonstrate that a novel data augmentation method can transform the raw data into a suitable representation for feature extraction. Results show that the e-nose simply comprising of six semiconductor gas sensors achieves superior performances to state-of-the-art methods on the Chinese liquor data. Ablation studies reveal the contribution of each sensor in odor recognition. Therefore, a deep learning-enabled codesign of sensor arrays and recognition algorithms can reduce the heavy demand for a huge amount of highly specialized gas sensors and provide interpretable insights into odor recognition dynamics in an iterative way.1. IntroductionHumans can create their perception of the world through sight, hearing, touch, olfaction and taste. Olfaction is important for the survival of living species and allows living species to be able to identify suitable food, detect dangerous chemical substances, etc. The olfactory system of human is based on a chemical reaction that is more complicated than the physical stimulus in the vision and auditory systems. Odors are complex and always contain various types of gas molecules. For mammalian olfaction, each olfactory receptor cell possesses only one type of odorant receptors, and each receptor can detect a limited number of odorant substances. Our olfactory receptor cells (ORCs) are therefore highly specialized for a few odors. Namely, each odor molecule activates very few odorant receptors, leading to a combinatorial code and forming an “odor pattern”[1]. Based on a large number of receptors and complex neural networks, we can discriminate more than one trillion olfactory stimuli[2].Inspired by the nature of the mammalian olfactory system, a “mode nose” was first introduced for gas identification in 1982[3]. It contains a semiconductor gas sensor array that mimics the function of mammalian ORCs with a pattern recognition algorithm to simulate the operations of the nervous system. The non-specific semiconductor gas sensor detects a certain gas from a change in electrical resistance caused by the reaction between gas molecules and preabsorbed oxygen, thereby having cross sensitivity to a wide variety of odors[4]. In 1987, Kaneyasu et al . from Hitachi in Japan named it an “electronic nose”[5] (e-nose), and e-noses were introduced into many fields in the 1990s[6-8]. The e-nose has shown great potentials for expanding the human sense, especially in recognizing gases with no flavor or low concentrations, which may find wide applications in environmental monitoring, food quality assessment, medical diagnosis, etc.[9-12]. Unfortunately, to date, an e-nose mimics the mammalian olfactory system at a gross level. This is because semiconductor gas sensors are far behind ORCs in specificity, diversity and scale[13]. Therefore, a powerful pattern recognition algorithm to handle complex gas-solid interactions under limited hardware conditions is needed.Generally, traditional feature-based methods are all multistage, including feature extraction, dimensionality reduction, and classification[14-16]. In feature extraction, some bioinspired or manually designed features[17-19] are extracted from the response curves based on a basic understanding of the gas sensing mechanism, which mainly contains equilibrium statuses such as resistance values, response/recovery times, and the maximum derivation of the response times. In dimensionality reduction, variants of principal components analysis (PCA) are often used[20-21]. Finally, existing feature-based methods use unsupervised learning[22-23] and backpropagation artificial neural networks (BP-ANNs) for classification[21, 24-26]. These feature-based methods mainly contain equilibrium statuses while neglecting response and recovery features, which may lead to local optima and information loss, before feeding them to the classifier. Therefore, the features extracted from the whole gas sensing curves, including the response, equilibrium, and recovery processes, can play an important role in odor recognition. Humans have only one-third as many types of olfactory receptors as mice but have superior processing power due to stronger brain connections[27], and some studies have even found that odors can affect cognition[28-29]. Enabled by the power of deep-learning, the focus of this study is on the accurate recognition of various odorant mixtures using only a small number of sensor units combined with an all-feature extraction algorithm in a complex circumstance (uncontrolled temperature and humidity). We hypothesize that all features in a gas sensing cycle of the sensor array can produce more distinguishing and robust features, thus reducing the heavy demand for the quantity and diversity of sensors. Hence, we need a more effective algorithm for application-specific sensing scenarios.Recently, deep learning-based methods have exhibited surprising progress in computer vision, natural language processing, medical imaging, etc.[30]. Unlike feature-based methods that heavily rely on intuition or domain-specific experience, deep learning-based methods attempt to learn high-level semantic features from mass data and jointly optimize feature extractors and classifiers to significantly decrease the burden on users. Introducing deep learning-based methods to e-nose technology can improve performance by learning nonintuitive features with deep learning. In addition, the learned features can also help us understand the principle of gas sensing and odor discrimination. Recently, some researchers have treated multichannel response curves as an image and used two-dimensional convolutional neural networks (CNNs) to extract local features and fully connected (FC) layers for classification in an end-to-end manner[31-32]. Although these methods achieve performance improvements over feature-based methods, they ignore the long-term dependencies in time-series signals of the sensor array and bring nonnegligible computational and memory overhead. Wang et al .[33] proposed a quantitative detection method of mixed gases based on long short-term memory (LSTM). This method heavily relies on domain-specific expertise, as the preprocessed response data to be analyzed are manually designed, which may lead to information loss before they are fed into LSTM. However, deep learning applied to raw data can help to better mine cross selectivity among only a few sensors.To tackle the above issues, we fabricate an e-nose that consists of six different metal-oxide semiconductor (MOS) gas sensors, including SnO2 QDs, SnO2 nanowires, SnO2 nanoparticles (NPs) synthesized by flame spray pyrolysis (FSP), In2O3 QDs, NiO NPs and WO3 QDs. MOS favors the e-nose due to its high response rate, low cost, easy fabrication, and long-term stability. In particular, QDs are critical low-dimensional semiconductor materials[34-35], whose dimensions in three axes are not larger than twice the Exciton Bohr radius. To reduce the heavy demand for the number and diversity of sensors, we use tailored data augmentation to handle all features in a gas sensing cycle and therefore transform the raw curves into different shapes to simplify distinguishing and robust feature mining. Specifically, our method combines one-dimensional CNNs and recurrent neural networks (RNNs) with channel and temporal attention modules to fully utilize complementary global and dynamic information in an end-to-end manner. We also demonstrate the generalization power of this data augmentation process, which can significantly improve the performance of feature-based methods. It is worth noting that the people who performed the measurements were not well trained, i.e., experimental errors were introduced into the data, which is similar to practical application scenarios. By consisting of only six non-specific semiconductor gas sensors, the AFOA-enabled e-nose can discriminate these Chinese liquors with high accuracy. It can also be concluded that QDs are superior to other sensors.2. Methods

Background We aimed to evaluate whether genotype-guided warfarin dosing is superior to conventional clinical dosing for the outcomes of interest in Chinese patients. Methods - All patients with nonvalvular AF were randomly divided into two groups, genetic group and control group. We included genotypes for CYP2C9 and VKORC1 in the gene group，then doctors and pharmacists used the warfarin dosing algorithm and clinical information to determine patients’ initial dose, while in control group doses were determined by experince. The international normalized ratio (INR) measurement and standard protocols were used for further dose adjustment in both groups. The primary outcome measure was the percentage of time in the therapeutic range (%TTR) of the INR during follow up after initiation of warfarin treatment. Results The average TTR was (68.36 ± 20.57) % vs (48.52 ± 21.56) %, P<0.001) in the gene group compared with the control group. At the end of follow-up, the genetic group had a significant lower risk of cumulative incidences of ischemic stroke events in the adjusted model [relative risk (RR) 0.38 (95% CI 0.18 to 0.80), log-rank test P =0.008] than control group. There was no significant difference in the risk ratios (RR) for cumulative incidence of total bleeding events, minor bleeding events, gastrointestinal bleeding and intracerebral bleeding events between the two groups(P>0.05). Conclusion Genotype-guided dosing could improve the average TTR, improve the safety of treatment, achieve a higher level of TTR in the early anticoagulation period and reduce the risk of ischemic stroke

The state estimation and sensor placement for a continuous pulp digester with delayed measurements are investigated. The underlying model of interest is heat transfer in a pulp digester modeled by two coupled hyperbolic partial differential equations and an ordinary differential equation. Output measurements are considered with delay due to the possible low sampling rate. The Cayley-Tustin transformation is utilized to realize model time discretization in a late lumping manner which does not account for any type of spatial approximation or model reduction. The discrete Kalman filter is applied to estimate the system states using the delayed measurements. The selection of sensor location is addressed along with estimator design accounting for the delayed measurements and investigated by minimizing the variance of estimation error. The performance of the state estimator is evaluated, and the sensor placement is analyzed through simulation studies, which provide guidance for sensor location selection in industrial applications.

Background: The nucleoprotein (N protein) of respiratory syncytial virus (RSV) is a candidate antigen for new RSV vaccine development. The aim of the present study was to investigate the association between maternal antibody titers against the RSV N protein at birth and the newborns’ risk of developing very-severe lower respiratory tract infection (VS-LRTI). Methods: In this single-center prospective cohort study, 578 infants born during the RSV epidemic season in France were included. Among these, 36 were hospitalized for RSV VS-LRTI. A generalized linear model was used to test the occurrence of a VS-LRTI in function of sex, mode of delivery, parity of the mother, type of pregnancy, date of birth in relation to the peak of the epidemic, and antibody titer against N protein. Results: All cord blood samples had detectable antibodies against N protein. The mean titers were significantly lower in newborns with risk factors for RSV severe LRTI (preterm infants, birth before the peak epidemic, multiparous mother). There was no association between antibody titer against the N protein and a protection against VS-LRTI. Conclusions The present study found that transfer of maternal antibodies against the RSV N protein may not provide a significant immune protection early in infancy. Clinical Trials Registration. NCT04144816.

Our current understanding of ecological and evolutionary processes underlying island biodiversity is heavily shaped by empirical data from plants and birds, although arthropods comprise the overwhelming majority of known animal species. This is due to inherent problems with obtaining high-quality arthropod data. Novel high throughput sequencing approaches are now emerging as powerful tools to overcome such limitations, and thus comprehensively address existing shortfalls in arthropod biodiversity data. Here we explore how, as a community, we might most effectively exploit these tools for comprehensive and comparable inventory and monitoring of insular arthropod biodiversity. We first review the strengths, limitations and potential synergies among existing approaches of high throughput barcode sequencing. We consider how this can be complemented with deep learning approaches applied to image analysis to study arthropod biodiversity. We then explore how these approaches can be implemented within the framework of an island Genomic Observatories Network (iGON) for the advancement of fundamental and applied understanding of island biodiversity. To this end, we identify seven island biology themes at the interface of ecology, evolution and conservation biology, within which collective and harmonised efforts in HTS arthropod inventory could yield significant advances in island biodiversity research.

At GTC 2022 Nvidia announced a new product family that aims to cover from small enterprise workloads through exascale HPC and trillion-parameter AI models. This column highlights the most interesting features of their new Hopper GPU and Grace CPU computer chips and the Hopper product family. We also discuss some of the history behind Nvidia technologies and their most useful features for computational scientists such as the Hopper DPX dynamic programming  instruction set, increased number of SMs, and FP 8 tensor core availability. Also included are descriptions of the new Hopper Clustered SMs architecture and updated NVSwitch technologies that integrates their new ARM-based Grace CPU. 

Following surgery for thyroid carcinoma, tracheal necrosis is extremely rare with few reports in literature. We report a patient who underwent a thyroid lobectomy for potential papillary carcinoma, followed by a completion thyroidectomy due to follicular variant papillary carcinoma pathology. Management was successful after debridement and tracheostomy, followed by decannulation.

Length of anticoagulation for thrombotic events related to COVID-19 is unknown. We present a patient with COVID-19 complicated by a thrombotic anterior STEMI and multiple left ventricular (LV) thrombi that resolved after 8 weeks of anticoagulation. We suggest a shorter length of anticoagulation with COVID-19 related LV thrombus.

Discussions of the factors regulating nutrient recycling by consumers have focused on predictions from Ecological Stoichiometry (ES) and the Metabolic Theory of Ecology (MTE). ES posits that imbalances between the composition of an animal’s body tissues and its diet should determine its nutrient excretion rates, whereas the MTE predicts that excretion should directly reflect metabolic activity arising from body size and temperature. Each framework has been supported by data, but they are rarely tested together. In this study, we measured excretion rates of nitrogen (NH4), phosphorus (SRP) and N:P excretion ratio, body N:P stoichiometry, body size, and temperature for 12 species of fish from an Atlantic rainforest stream in Brazil. We fitted 8 competing models reflecting different combinations of ES (body N:P, armor classification, diet group) and MTE (body size, temperature) variables. For both N and P excretion, as well as excreted N:P ratio, only body size was included in the best model, and interspecific differences in size-scaling were greater for N than for P. Fitted size scaling coefficients were lower than the MTE prediction of 0.75 for both N (0.59, 95% CI = 0.45, 0.73) and P (0.56, 95% CI = 0.40, 0.77). There was only weak evidence that body armor in 3 of 12 species led to more retention of P, and there was no discernable effect of diet group, body N:P, or water temperature. We conclude that differences in nutrient excretion among species within a shared environment primarily reflect contrasts in metabolic rates arising from body size, rather than disparities between consumer and resource stoichiometry. Our findings align with those from other ecosystems and synthesis across aquatic taxa, expanding support for the MTE as the primary framework for predicting nutrient excretion rates. Key words: ecological stoichiometry, metabolic ecology, animals, nitrogen, phosphorus, freshwater.

Introduction Course outlines are instrumental to a student's success in post-secondary education. We argue that there are fundamental issues with the current state of course outlines at McMaster University. As a result, students often miss important information. To address these issues, we created scannable visual course outlines using UI/UX design principles that aimed to reduce the time students spend finding specific information and to increase comprehension for each specific component of the course outline.Methods Through a single-blind interventional randomised control trial, we explored differences between the standard and visual course outline in terms of speed and comprehension among McMaster students. We also captured key perceptions of each course outline as described by the students through a thematic analysis. Results Participants in the intervention group (n = 84) found that the visual course outline was quicker to locate all components, easier to understand, and quicker to understand than the control group (n = 91) that received the standard course outline (all p < 0.05). The intervention group found the visual course outline more appealing and expressed they were more likely to refer back to it during a semester (all p < 0.05). The intervention group also had more positive sentiments toward the visual course outline than the control group had toward the standard.Conclusions Visual course outlines help increase knowledge synthesis, understanding, and perceptions of the course content. We recommend that other faculties at McMaster University and other universities adopt visual course outlines to further support student learning.

Peripheral odontogenic fibroma (POdF) is a rare, benign, and ectomesenchymal tumor. We report a case of a 15-year-old girl who developed POdF in the mandible. The lesion was resected, including the periosteum. Histopathological findings revealed a small mass and cord-like epithelium. No sign of post-operative recurrence appeared after 16 months.

Ectodermal dysplasia (ED) is defined as a rare hereditary disorder involving two or more of the ectodermal structures, which include the skin, hair, nails, teeth, and sweat glands. The two most common forms of the disease are hypohidrotic/anhidrotic ED and hidrotic ED.

Problem Statement: Does the blue light exposure cause any temporary/permanent damage to ocular structures? Background: Blue light (BL) is located at the end of the visible spectrum from 380 to 500 nm, according to ANSI, ISO, WHO. Most of the blue light we've always experienced in life comes from the sun. The incandescent bulb and Light-emitting Diode (LED) are potential sources of indoor BL. Digital devices and displays such as laptops and smart TVs also contribute to BL exposure. BL blocking lenses/filters are being dispensed claiming that it reduces eye fatigue, improves sleep, and prevent age-related macular degeneration (AMD) Evidences: 1. The effect of blue light on sleep-studies found that retinal cells communicate with the pineal gland in the brain to produce melatonin. Extended screen time was found to cause sleep deficiency and disrupted circadian rhythms. [Chang AM et al. 2015; Ostrin LA et al., 2017] Strong Evidence 2. The effect of blue light on AMD-Studies are done in animals using high energy BL. Studies have mainly focused on human retinal cells under lab conditions and these retinal cells were developed at the laboratory level. They were all done using high energy LEDs of 3-5 microwatts but the electronic devices we use emit <1 microwatt energy. [Ratnayake K et al, 2018; Liu X et al., 2019] Moderate Evidence 3. Systematic review focused on answering effects on improving visual performance, alleviating the symptoms of visual fatigue, improving sleep quality and found a low-very low evidence which means the effect of blue blocking lenses on the above parameters is highly unclear and needs more studies to be done systematically.

Pneumothorax as a sequalae of vaping is a relatively recent complication being described in the literature. Smoking has classically been associated with increased risk of pneumothorax, and emerging evidence is showing that electronic cigarettes (e-cigarettes) likely carry some of the same risks. Since electronic cigarettes increased in popularity, especially among the adolescent population, there has been reported increased incidence of lung injury, including pneumothorax. We present a case of a 15-year-old female with a history of e-cigarette use admitted for recurrent pneumothorax with failure to re-expand requiring surgical intervention.

The horned gall aphid Schlechtendalia chinensis, is an economically important insect that induces galls valuable for medicinal and chemical industries. S. chinensis manipulates its host plant to form well-organized horned galls during feeding. So far, more than twenty aphid genomes have been reported; however, all of those are derived from free-living aphids. Here we generated a high-quality genome assembly of S. chinensis, representing the first genome sequence of a galling aphid. The final genome assembly was 280.43 Mb, with 97% of the assembled sequences anchored into thirteen chromosomes. S. chinensis presents the smallest aphid genome size among available aphid genomes to date. The contig and scaffold N50 values were 3.39 Mb and 20.58 Mb, respectively. The assembly included 96.4% of conserved arthropod and 97.8% of conserved Hemiptera single-copy orthologous genes based on BUSCO analysis. A total of 13,437 protein-coding genes were predicted. Phylogenomic analysis showed that S. chinensis formed a single clade between the Eriosoma lanigerum clade and the Aphidini+Macrosiphini aphid clades. In addition, salivary proteins were found to be differentially expressed when S. chinensis underwent host alternation, indicating their potential roles in gall formation and plant defense suppression. A total of 36 cytochrome P450 genes were identified in S. chinensis, considerably fewer compared to other aphids, probably due to its small host plant range. The high-quality S. chinensis genome assembly and annotation provide an essential genetic background for future studies to reveal the mechanism of gall formation and to explore the interaction between aphids and their host plants.

Metabarcoding has improved the way we understand plants within our environment, from their ecology and conservation to invasive species management. The notion of identifying plant taxa within environmental samples relies on the ability to match unknown sequences to known reference libraries. Without comprehensive reference databases, species can go undetected or be incorrectly assigned, leading to false positive and negative detections. To improve our ability to generate reference sequence databases we developed a targeted capture approach using the OZBaits_CP V1.0 set, designed to capture chloroplast gene regions across the entirety of flowering plant diversity. We focused on generating a reference database for coastal temperate plant species given the lack of reference sequences for these taxa. Our approach was successful across all specimens with a target gene recovery rate of 92% which was achieved in a single assay (i.e., samples were pooled), thus making this approach much faster and more efficient than standard barcoding. Further testing of this database highlighted 80% of all samples could be discriminated to family level across all gene regions with some genes achieving greater resolution than others – which was also dependant on the taxon of interest. Thus, we demonstrate the importance of generating reference sequences across multiple chloroplast gene regions as no single loci is sufficient to discriminate across all plant groups. The targeted capture approach outlined in this study provides a way forward to achieve this.

Re letter to British Journal of Clinical Pharmacology

In this paper, we propose an inertial algorithm for solving split equality of monotone inclusion and $f$-fixed point of Bregman relatively $f$-nonexpansive mapping problems in reflexive real Banach spaces. Using the Bregman distance function, we prove a strong convergence theorem for the algorithm produced by the method in real reflexive Banach spaces. As an application, we provide several applications of our method. Furthermore, we give a numerical example to demonstrate the behavior of the convergence of the algorithm.