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Abstract Although robot-based automation in chemistry laboratories can accelerate the material development process, surveillance-free environments may lead to dangerous accidents primarily due to machine control errors. Object detection techniques can play vital roles in addressing these safety issues; however, existing detection models still suffer from insufficient accuracy in environments involving complex and noisy scenes. With the aim of improving safety in a surveillance-free laboratory, we report a deep learning (DL)-based object detector, namely, DenseSSD. For the foremost and frequent problem of detecting positions of transparent chemical vessels, DenseSSD achieved a mean average precision (mAP) over 95% based on a complex dataset involving both empty and solution-filled vials, greatly exceeding those of conventional detectors; such high precision is critical to minimizing failure-induced accidents. Additionally, DenseSSD was observed to be generalizable to other laboratory environments, maintaining its high precisions under the variations of solution colors, camera view angles, background scenes, experiment hardware and type of chemical vessels. Such robustness of DenseSSD supports that it can universally be implemented in diverse laboratory settings. This study conclusively demonstrates the significant utility of DenseSSD in enhancing safety within automated material synthesis environments. Furthermore, the exceptional detection accuracy of DenseSSD opens up possibilities for its application in various other fields and scenarios where precise object detection is paramount.

Objective This study aimed to investigate the similarities and differences in risk factors for suicide among adult and adolescent women in South Korea and identify subtypes of suicidal ideation or suicide attempt in each group. Methods Multifaceted data were collected and analyzed by linking survey and social media data. Interpretable machine learning models were constructed to predict suicide risk and major risk factors were extracted by investigating their feature importance. Additionally, subtypes of suicidal adult and adolescent women were identified and explained using risk factors. Results The risk factors for adult women were primarily related to mental disorders, while those for adolescent women were primarily related to interpersonal experiences and needs. Two subtypes of suicidal adult women were one with high psychiatric symptoms and mental disorders of them and/or their families and the other with excessive social media use and high online victimization. Two subtypes of suicidal adolescent women were one with high psychiatric symptoms, high ACEs, and high social connectedness, and the other with frequent social media use, high online sexual victimization, and high social assurance. Conclusions These findings enable a stratified and targeted understanding of suicide in women and help develop customized suicide prevention plans in South Korea.

Summary: To achieve the successful separation of emulsions containing fine dispersed droplets and low volume fractions, a membrane with pore sizes comparable to or smaller than the droplet size is typically required. Although this approach is effective, its utilization is limited to the separation of emulsions with relatively large droplets. To overcome this limitation, a secondary membrane can be formed on the primary membrane to reduce pore size, but this can also be time-consuming and costly. Therefore, a facile and effective method is still required to be developed for separating emulsions with fine droplets. We introduce a pre-wetted mesh membrane with a pore size significantly larger than droplets, easily fabricated by wetting a hydrophilic stainless-steel mesh with water. Applying this membrane to emulsion separation via gravity-driven flow confirms a high efficiency greater than 98%, even with droplets approximately 10 times smaller than the pore size.

Abstract Suppressing the oxidation of active-Ir(III) in IrOx catalysts is highly desirable to realize an efficient and durable oxygen evolution reaction in water electrolysis. Although charge replenishment from supports can be effective in preventing the oxidation of IrOx catalysts, most supports have inherently limited charge transfer capability. Here, we demonstrate that an excess electron reservoir, which is a charged oxygen species, incorporated in antimony-doped tin oxide supports can effectively control the Ir oxidation states by boosting the charge donations to IrOx catalysts. Both computational and experimental analyses reveal that the promoted charge transfer driven by excess electron reservoir is the key parameter for stabilizing the active-Ir(III) in IrOx catalysts. When used in a polymer electrolyte membrane water electrolyzer, Ir catalyst on excess electron reservoir incorporated support exhibited 75 times higher mass activity than commercial nanoparticle-based catalysts and outstanding long-term stability for 250 h with a marginal degradation under a water-splitting current of 1 A cm−2. Moreover, Ir-specific power (74.8 kW g−1) indicates its remarkable potential for realizing gigawatt-scale H2 production for the first time.

Abstract Surface Pourbaix diagrams are critical to understanding the stability of nanomaterials in electrochemical environments. Their construction based on density functional theory is, however, prohibitively expensive for real-scale systems, such as several nanometer-size nanoparticles (NPs). Herein, with the aim of accelerating the accurate prediction of adsorption energies, we developed a bond-type embedded crystal graph convolutional neural network (BE-CGCNN) model in which four bonding types were treated differently. Owing to the enhanced accuracy of the bond-type embedding approach, we demonstrate the construction of reliable Pourbaix diagrams for very large-size NPs involving up to 6525 atoms (approximately 4.8 nm in diameter), which enables the exploration of electrochemical stability over various NP sizes and shapes. BE-CGCNN-based Pourbaix diagrams well reproduce the experimental observations with increasing NP size. This work suggests a method for accelerated Pourbaix diagram construction for real-scale and arbitrarily shaped NPs, which would significantly open up an avenue for electrochemical stability studies.

Building design optimization is a highly complex problem, requiring long computational running processes because of the many options that exist when a building is being designed. This paper introduces an integrated approach through which to perform this optimization within an acceptable time frame. The approach includes the methods of variable selection, model simplification, and a sequential optimization process. Using singular value decomposition, a large number of design variables is reduced to a smaller subset that can be solved more quickly through the optimization algorithm. To expedite the variable selection process, a modeling approach that quickly simulates annual energy consumption was developed to replace full annual energy simulations. The developed methodology was applied to two residential buildings in the US, and the results are discussed herein. To assess the accuracy of the integrated optimization methodology, the optimized life cycle costs are compaa variables demonstrating the strongest contributions in the optimization study were identified. The proposed methodology significantly shortened the time requirements for the optimization processes of the two case studies by 74% and 84%; the optimized life cycle costs were within 0.05% and 0.06%, respectively, of the optimum point.

BackgroundAn increasing number of medical journals are using social media to promote themselves and communicate with their readers. However, little is known about how medical journals use Twitter and what their social media management strategies are. ObjectiveThis study aimed to understand how medical journals use Twitter from a global standpoint. We conducted a broad, in-depth analysis of all the available Twitter accounts of medical journals indexed by major indexing services, with a particular focus on their social networks and content. MethodsThe Twitter profiles and metadata of medical journals were analyzed along with the social networks on their Twitter accounts. ResultsThe results showed that overall, publishers used different strategies regarding Twitter adoption, Twitter use patterns, and their subsequent decisions. The following specific findings were noted: journals with Twitter accounts had a significantly higher number of publications and a greater impact than their counterparts; subscription journals had a slightly higher Twitter adoption rate (2%) than open access journals; journals with higher impact had more followers; and prestigious journals rarely followed other lesser-known journals on social media. In addition, an in-depth analysis of 2000 randomly selected tweets from 4 prestigious journals revealed that The Lancet had dedicated considerable effort to communicating with people about health information and fulfilling its social responsibility by organizing committees and activities to engage with a broad range of health-related issues; The New England Journal of Medicine and the Journal of the American Medical Association focused on promoting research articles and attempting to maximize the visibility of their research articles; and the British Medical Journal provided copious amounts of health information and discussed various health-related social problems to increase social awareness of the field of medicine. ConclusionsOur study used various perspectives to investigate how medical journals use Twitter and explored the Twitter management strategies of 4 of the most prestigious journals. Our study provides a detailed understanding of medical journals’ use of Twitter from various perspectives and can help publishers, journals, and researchers to better use Twitter for their respective purposes.

Abstract To accelerate the discovery of materials through computations and experiments, a well-established protocol closely bridging these methods is required. We introduce a high-throughput screening protocol for the discovery of bimetallic catalysts that replace palladium (Pd), where the similarities in the electronic density of states patterns were employed as a screening descriptor. Using first-principles calculations, we screened 4350 bimetallic alloy structures and proposed eight candidates expected to have catalytic performance comparable to that of Pd. Our experiments demonstrate that four bimetallic catalysts indeed exhibit catalytic properties comparable to those of Pd. Moreover, we discover a bimetallic (Ni-Pt) catalyst that has not yet been reported for H2O2 direct synthesis. In particular, Ni61Pt39 outperforms the prototypical Pd catalyst for the chemical reaction and exhibits a 9.5-fold enhancement in cost-normalized productivity. This protocol provides an opportunity for the catalyst discovery for the replacement or reduction in the use of the platinum-group metals.

Abstract Within first-principles density functional theory (DFT) frameworks, it is challenging to predict the electronic structures of nanoparticles (NPs) accurately but fast. Herein, a machine-learning architecture is proposed to rapidly but reasonably predict electronic density of states (DOS) patterns of metallic NPs via a combination of principal component analysis (PCA) and the crystal graph convolutional neural network (CGCNN). With the PCA, a mathematically high-dimensional DOS image can be converted to a low-dimensional vector. The CGCNN plays a key role in reflecting the effects of local atomic structures on the DOS patterns of NPs with only a few of material features that are easily extracted from a periodic table. The PCA-CGCNN model is applicable for all pure and bimetallic NPs, in which a handful DOS training sets that are easily obtained with the typical DFT method are considered. The PCA-CGCNN model predicts the R2 value to be 0.85 or higher for Au pure NPs and 0.77 or higher for Au@Pt core@shell bimetallic NPs, respectively, in which the values are for the test sets. Although the PCA-CGCNN method showed a small loss of accuracy when compared with DFT calculations, the prediction time takes just ~ 160 s irrespective of the NP size in contrast to DFT method, for example, 13,000 times faster than the DFT method for Pt147. Our approach not only can be immediately applied to predict electronic structures of actual nanometer scaled NPs to be experimentally synthesized, but also be used to explore correlations between atomic structures and other spectrum image data of the materials (e.g., X-ray diffraction, X-ray photoelectron spectroscopy, and Raman spectroscopy).

AbstractThis study investigates China’s international research collaboration with the United States through a bibliometric analysis of coauthorship over time using historical research publication data. We investigate from three perspectives: overall, high-impact, and high-technology research collaborations using data from Web of Science (WoS), Nature Index, and Technology Alert List maintained by the U.S. Department of State. The results show that the United States is China’s largest research collaborator and that in all three aspects, China and the United States are each other’s primary collaborators much of the time. From China’s perspective, we have found weakening collaboration with the United States over the past 2 years. In terms of high-impact research collaboration, China has historically shared a higher percentage of its research with the United States than vice versa. In terms of high-technology research, the situation is reversed, with the United States sharing more. The percentage of the United States’ high-technology research shared with China has been continuously increasing over the past 10 years, while in China the percentage has been relatively stable.

Abstract The robust and automated determination of crystal symmetry is of utmost importance in material characterization and analysis. Recent studies have shown that deep learning (DL) methods can effectively reveal the correlations between X-ray or electron-beam diffraction patterns and crystal symmetry. Despite their promise, most of these studies have been limited to identifying relatively few classes into which a target material may be grouped. On the other hand, the DL-based identification of crystal symmetry suffers from a drastic drop in accuracy for problems involving classification into tens or hundreds of symmetry classes (e.g., up to 230 space groups), severely limiting its practical usage. Here, we demonstrate that a combined approach of shaping diffraction patterns and implementing them in a multistream DenseNet (MSDN) substantially improves the accuracy of classification. Even with an imbalanced dataset of 108,658 individual crystals sampled from 72 space groups, our model achieves 80.12 ± 0.09% space group classification accuracy, outperforming conventional benchmark models by 17–27 percentage points (%p). The enhancement can be largely attributed to the pattern shaping strategy, through which the subtle changes in patterns between symmetrically close crystal systems (e.g., monoclinic vs. orthorhombic or trigonal vs. hexagonal) are well differentiated. We additionally find that the MSDN architecture is advantageous for capturing patterns in a richer but less redundant manner relative to conventional convolutional neural networks. The proposed protocols in regard to both input descriptor processing and DL architecture enable accurate space group classification and thus improve the practical usage of the DL approach in crystal symmetry identification.

BackgroundAdvances in biomedical research using deep learning techniques have generated a large volume of related literature. However, there is a lack of scientometric studies that provide a bird’s-eye view of them. This absence has led to a partial and fragmented understanding of the field and its progress. ObjectiveThis study aimed to gain a quantitative and qualitative understanding of the scientific domain by analyzing diverse bibliographic entities that represent the research landscape from multiple perspectives and levels of granularity. MethodsWe searched and retrieved 978 deep learning studies in biomedicine from the PubMed database. A scientometric analysis was performed by analyzing the metadata, content of influential works, and cited references. ResultsIn the process, we identified the current leading fields, major research topics and techniques, knowledge diffusion, and research collaboration. There was a predominant focus on applying deep learning, especially convolutional neural networks, to radiology and medical imaging, whereas a few studies focused on protein or genome analysis. Radiology and medical imaging also appeared to be the most significant knowledge sources and an important field in knowledge diffusion, followed by computer science and electrical engineering. A coauthorship analysis revealed various collaborations among engineering-oriented and biomedicine-oriented clusters of disciplines. ConclusionsThis study investigated the landscape of deep learning research in biomedicine and confirmed its interdisciplinary nature. Although it has been successful, we believe that there is a need for diverse applications in certain areas to further boost the contributions of deep learning in addressing biomedical research problems. We expect the results of this study to help researchers and communities better align their present and future work.

Recent studies have provided strong evidence indicating that lone star tick bites are a cause of AGS (alpha-gal syndrome, also known as red meat allergy RMA) in humans. AGS is characterized by an increase in IgE antibody production against galactose-alpha-1,3-galactose (aGal), which is a common glycan found in mammalian tissue, except in Old World monkeys and humans. The main causative factor of AGS, the lone star tick (Amblyomma americanum), is broadly distributed throughout the east and midwest of the United States and is a vector of a wide range of human and animal pathogens. Our earlier glycomics study of the salivary glands of partially fed male and female ticks revealed relatively high levels of aGal epitopes. In this study, we found that partially fed males of A. americanum on bovine blood, which engage in multiple intrastadial feedings, carry a large amount of aGal in the salivary glands. In our current work, we aimed to test whether ticks mediate the transmission of the aGal sensitizer acquired from nonhuman blood to humans in the intrastadial host switch (referred to as the “transmission” hypothesis). To test this hypothesis, we used an alpha-galactosyltransferase knockout mutant mouse (aGT-KO) model system infested with ticks that were unfed or partially fed on bovine blood. Based on the levels of total IgE and specific IgG and IgE antibodies against aGal after tick feedings, aGT-KO mice significantly responded to tick feeding and injection of aGal (Galα1-3Galβ1-4GlcNAc) conjugated to human serum albumin or mouse serum albumin (aGal-HSA or aGal-MSA) by increasing total IgE and aGal-specific IgE levels compared to those in C57BL/6 control mice. All of the treatments of aGT-KO mice involving the feeding of partially fed and unfed ticks functioned as sensitizers that increased the levels of specific IgE against aGal, with large individual variations. The data in this study do not support the “transmission” component of AGS, although they confirmed that aGT-KO mice can be used as a model for RMA studies.

Objective Although there were few studies on how suicidal users behave on Twitter, they only investigated partial aspects such as tweeting frequency and tweet length. Therefore, we aim to understand the various information behavior of suicidal users in South Korea. Methods To achieve this goal, we annotated 20,000 tweets and identified 1097 tweets with the expression of suicidality (i.e. suicidal tweets) and 229 suicidal users (i.e. experimental group). Using the data, a user profile analysis, comparative analysis with control group, and tweets/hashtags analysis were performed. Results Our results show that many suicidal users used suicide-related keywords in their user IDs, usernames, descriptions, and pinned tweets. We also found that, compared to the control group, the experimental group show different patterns of information behavior. The experimental group did not frequently use Twitter and, on average, wrote longer texts than the control group. A clear seasonal pattern was also identified in the experimental group's tweeting behavior. Frequently used keywords/hashtags were extracted from tweets written by the experimental group for the purpose of understanding their concerns and detecting more suicidal tweets. Conclusions We believe that our study will help in the understanding of suicidal users’ information behavior on social media and lay the basis for more accurate actions for suicide prevention and early intervention on social media.

Halyomorpha halys (Hemiptera: Pentatomidae), an important agricultural and nuisance pest, is highly invasive with peculiar hiding behavior in human-made structures for overwintering. To evaluate the contamination risk of overwintering H. halys in non-agricultural export goods, we conducted a two-year field survey in Republic of Korea to locate overwintering H. halys in two major ports of export, Ulsan and Pyeongtaek ports, and monitored both active and overwintering H. halys population levels with varying distances from the ports ranging from 1 km to 48 km. First, we deployed wooden shelters in the two ports to catch dispersing H. halys for overwintering and conducted visual inspections for human-made structures in the ports to locate overwintering H. halys. In addition, we sampled dead trees to find overwintering H. halys in wooded areas. Second, we monitored active H. halys populations using pheromone traps with varying distances from the ports. From the survey of overwintering populations, no H. halys was collected from wooden shelters deployed in the two ports. However, we found four adults overwintering in human-made structures in Pyeongtaek port in the first year of survey. One dead adult was also found from a dead tree located in a wooded area adjacent to Pyeongtaek port in the second year. For active populations, results of pheromone trapping indicated that H. halys populations were present during autumn dispersal period not only in agricultural areas, but also in wooded areas adjacent to the two ports. This study reports for the first time that overwintering H. halys were found from the inside the port of export in its native areas with a low density. The results were discussed for evaluating contamination risk of overwintering H. halys in export goods shipping from the Republic of Korea.

Insect ecdysis behavior, shedding off the old cuticle, is under the control of specific neuropeptides with the top command by the ecdysis triggering hormone (ETH). We characterized the ETH receptor (ETHR) of the malaria mosquito, Anopheles gambiae, by manual annotation of the NCBI gene (AGAP002881) and functional analysis, using a heterologous expression system in a mammalian cell line. The two splicing variants of ETHRs, ecdysis triggering hormone receptors (AgETHR-A and AgETHR-B), a conserved feature among insects, included of four (552 aa) and five exons (635 aa), respectively. The main feature of manual annotation of the receptor was a correction of N-terminal and exon-intron boundaries of an annotated gene (AGAP002881). Interestingly, the functional expression of the receptor in Chinese hamster ovary cells required modification of the transcription initiation site for mammalian Kozak consensus. In the calcium mobilization assay using the heterologous expression of each receptor, AgETHR-B showed a higher sensitivity to AgETH-1 (28 times) and AgETH-2 (320 times) than AgETHR-A. The AgETHRs showed specificity only to the ETH group of peptides but not to other groups carrying the C-termini motifs as PRXamide, such as pyrokinin1/DH and pyrokinin2/PBAN. Ecdysis triggering hormone receptors (AgETHR-B) responded to different ETH variants of other insect species more promiscuously than AgETHR-A.

Practical catalysts with a porous framework, such as zeolites, host catalytic reactions at active sites engrained in the pores and channels of the scaffold. The mechanism of interaction at these active sites, defining catalyst performance, remains elusive, in large part, due to the lack of surface characterization methods available for thick films or powders. Here, we present thin film analogs of practical catalysts that allow for the implementation of surface characterization tools, including advanced microscopy and operando spectroscopy methodologies. Specifically, we investigated bilayer silica, MFI nanosheets, and UiO-66 thin films using a multi-modal approach addressing film growth, characterization, and gas adsorption aimed at understanding catalytic activity, reactivity, and selectivity properties, as defined by molecular-level changes in the reaction mechanism.

In the postgenomics era, comparative genomics have advanced the understanding of evolutionary processes of neuropeptidergic signaling systems. The evolutionary origin of many neuropeptidergic signaling systems can be traced date back to early metazoan evolution based on the conserved sequences. Insect parathyroid hormone receptor (iPTHR) was previously described as an ortholog of vertebrate PTHR that has a well-known function in controlling bone remodeling. However, there was no sequence homologous to PTH sequence in insect genomes, leaving the iPTHR as an orphan receptor. Here, we identified the authentic ligand insect PTH (iPTH) for the iPTHR. The taxonomic distribution of iPTHR, which is lacking in Diptera and Lepidoptera, provided a lead for identifying the authentic ligand. We found that a previously described orphan ligand known as PXXXamide (where X is any amino acid) described in the cuttlefish Sepia officinalis has a similar taxonomic distribution pattern as iPTHR. Tests of this peptide, iPTH, in functional reporter assays confirmed the interaction of the ligand-receptor pair. Study of a model beetle, Tribolium castaneum, was used to investigate the function of the iPTH signaling system by RNA interference followed by RNA sequencing and phenotyping. The results suggested that the iPTH system is likely involved in the regulation of cuticle formation that culminates with a phenotype of defects in wing exoskeleton maturation at the time of adult eclosion. Moreover, RNAi of iPTHRs also led to significant reductions in egg numbers and hatching rates after parental RNAi.

Delivery of Cas9 ribonucleoprotein complexes can be done via microinjection into eggs, though this is a technically challenging procedure. Here the authors demonstrate ReMOT Control, delivery of RNPs to the mosquito germline by tagging with peptide derived from yolk protein 1.

Although the thermal mass of floors in buildings has been demonstrated to help shift cooling load, there is still a lack of information about how floor covering can influence the floor’s load shifting capability and buildings’ demand flexibility. To fill this gap, we estimated demand flexibility based on the daily peak cooling load reduction for different floor configurations and regions, using EnergyPlus simulations. As a demand response strategy, we used precooling and global temperature adjustment. The result demonstrated an adverse impact of floor covering on the building’s demand flexibility. Specifically, under the same demand response strategy, the daily peak cooling load reductions were up to 20–34% for a concrete floor whereas they were only 17–29% for a carpet-covered concrete floor. This is because floor covering hinders convective coupling between the concrete floor surface and the zone air and reduces radiative heat transfer between the concrete floor surface and the surrounding environment. In hot climates such as Phoenix, floor covering almost negated the concrete floor’s load shifting capability and yielded low demand flexibility as a wood floor, representing low thermal mass. Sensitivity analyses showed that floor covering’s effects can be more profound with a larger carpet-covered area, a greater temperature adjustment depth, or a higher radiant heat gain. With this effect ignored for a given building, its demand flexibility would be overestimated, which could prevent grid operators from obtaining sufficient demand flexibility to maintain a grid. Our findings also imply that for more efficient grid-interactive buildings, a traditional standard for floor design could be modified with increasing renewable penetration.

A solution-processable electrode is a key component in flexible electronics. Despite recent studies on silver nanocrystal (Ag NC)-based electrodes with high conductivity, the absence of a strategy to control the work function has limited the development of low-cost and high-performance electronic devices. In this report, we introduce a chemical route to manipulate the work function of solution-processed Ag NC-based electrodes. The structural, chemical, optical, and electronic properties, as well as the work functions, of the Ag NC thin films treated with three types of halide ligands (Cl−, Br−, and I−) were investigated. Ultraviolet photoelectron spectroscopy analysis shows that the work functions are shifted to 4.76, 4.43, and 4.04 eV when the ligands are changed to Cl−, Br−, and I−, respectively. The trend in the shift induced by the halide ligands matches the results of atomistic density functional theory calculations and scales with the strength of the dipoles formed by the electron transfer at the NC/ligand interfaces. To demonstrate the impact of our strategy in device applications, we fabricated all-NC-based thin-film transistors and complementary metal-oxide-semiconductor inverters. This study provides a fundamental understanding of the surface states of nanomaterials and also offers technological benefits for the construction of low-cost, high-performance electronic devices.

Ticks transmit a large number of pathogens that cause human diseases. Here, the authors sequence the genome of the tick Ixodes scapularisand uncover expansion of genes associated with parasitic processes unique to ticks and tick-host interactions.

Ticks are important ectoparasites and vectors of numerous human and animal pathogens. Ticks secrete saliva that contains various bioactive materials to evade the host defense system, and often facilitates the pathogen transmission. In addition, the Lone star tick saliva is thought to be the sensitizer in red meat allergy that is characterized by an allergic reaction to glycan moieties carrying terminal galactose-alpha-1,3-galactose (aGal). To assess N-glycome of Amblyomma americanum, we examined the N-glycan structures in male and female salivary glands at three different feeding stages and in carcasses of partially fed lone star ticks. We also surveyed the genes involved in the N-glycosylation in the tick species. The aGal epitopes and cross-reactive carbohydrate determinants (CCD) increases over time after the onset of blood feeding in both male and female A. americanum. These CCDs include xylosylation of the core mannose, 1,3-mono and 1,3- and 1,6-difucosylations of the basal GlcNac and mono- or diantennary aGal. Combinations of both xylosylation and aGal and fucosylation and aGal were also found on the N-glycan structures. While the enzymes required for the early steps of the N-glycosylation pathway are quite conserved, the enzymes involved in the later stages of N-glycan maturation in the Golgi apparatus are highly diverged from those of insects. Most of all, we propose that the aGal serves as a molecular mimicry of bioactive proteins during tick feedings on mammalian hosts, while it contributes as a sensitizer of allergy in atypical host human.

Tick salivary glands play critical roles in maintaining water balance for survival, as they eliminate excess water and ions during blood feeding on hosts. In the long duration of fasting in the off-host period, ticks secrete hygroscopic saliva into the mouth cavity to uptake atmospheric water vapor. Type I acini of tick salivary glands are speculated to be involved in secretion of hygroscopic saliva based on ultrastructure studies. However, we recently proposed that type I acini play a role in resorption of water/ions from the primary saliva produced by other salivary acini (i.e., types II and III) during the tick blood feeding phase. In this study, we tested the function of type I acini in unfed female Ixodes scapularis. The route of ingested water was tracked after forced feeding of water with fluorescent dye rhodamine123. We found that type-I acini of the salivary glands, but not type II and III, are responsible for water uptake. In addition, the ingestion of water through the midgut was also observed. Injection or feeding of ouabain, a Na/K-ATPase inhibitor, suppressed water absorption in type I acini. When I. scapularis was offered a droplet of water, ticks rarely imbibed water directly (5%), while some approached the water droplet to use the high humidity formed in the vicinity of the droplet (23%). We conclude that during both on- and off-host stages, type I acini in salivary glands of female Ixodes scapularis absorb water and ions.

In this paper, we propose a new haptic-assisted virtual cane system operated by a simple finger pointing gesture. The system is developed by two stages: development of visual information delivery assistant (VIDA) with a stereo camera and adding a tactile feedback interface with dual actuators for guidance and distance feedbacks. In the first stage, user’s pointing finger is automatically detected using color and disparity data from stereo images and then a 3D pointing direction of the finger is estimated with its geometric and textural features. Finally, any object within the estimated pointing trajectory in 3D space is detected and the distance is then estimated in real time. For the second stage, identifiable tactile signals are designed through a series of identification experiments, and an identifiable tactile feedback interface is developed and integrated into the VIDA system. Our approach differs in that navigation guidance is provided by a simple finger pointing gesture and tactile distance feedbacks are perfectly identifiable to the blind.

The high-temperature thermoelectric properties of p-type Tl-filled skutterudites TlxFe1Co3Sb12 (x = 0, 0.2, 0.4, 0.6, and 0.8) were examined. While samples with x ≤ 0.4 were single-phase Tl-filled skutterudite, samples with x = 0.6 and 0.8 were composed of two phases: TlxFe1Co3Sb12 (x ≈ 0.4) as the matrix phase and a Tl-Fe-Sb ternary alloy. The thermal conductivity (κ) was reduced effectively by Tl addition, but the secondary phase increased κ slightly. The maximum value of the dimensionless figure of merit ZT (=S2T/ρ/κ, where T is the absolute temperature) was 0.36 at 723 K for Tl0.2Fe1Co3Sb12.