Previous Surveys Research Articles

Prediction of drought in lake mead

Lake Mead is a reservoir on the Colorado River on the border of Nevada and Arizona. It is the largest reservoir in the United States. Due to the drought caused by climate change and the increasing water demand of the population served by Lake Mead, the capacity of this reservoir has been greatly reduced. The continuous water shortage has had a huge impact on the surrounding cities, and people are preparing to adopt wastewater recycling. To alleviate the shortage of water sources. this paper uses the water level data of the past years to establish a grey forecast Grey Model (1,1) model to predict the changes in the water level of Lake Mead in the next 30 years and compares them based on the prediction of short-term and long-term drought data. based on the previous survey of wastewater sources and collection methods, it is believed that the wastewater recovery system is mainly affected by four factors: financial support, collection pipe network coverage, treatment technology, and wastewater classification capabilities.

A conceptual analysis of public opinion regarding genome research in Japan

In the 20 years since the completion of the Human Genome Project, the gap between scientific development and public understanding of genome research has been widening. While genome research has been increasingly utilized for social and clinical purposes in a multifaceted manner, this has resulted in an increase in the potential risks associated with genomic data. In this context, our study aims to consider the nature of public perceptions of genome research, primarily by using as a case study the results of previous public surveys relevant to donations for social benefits in Japan. We explored certain types of awareness, attitude, and intention (A-A-I) in such surveys and discussed the resultant key findings through the cultural transmission framework. Reframing the public’s response toward genome research based on A-A-I analysis and behavioral science may contribute to developing more systematic communication approaches with the public. With a view to establishing such approaches, our perspective suggests some new insights to discuss the science–society gap in genome research internationally.

2314. Association Between Behavior and Risk of COVID-19 in a Cohort of Healthcare Personnel

Abstract Background Social behaviors are associated with COVID-19 infection. Most studies assessing exposure risk from daily activities were performed in the general population. Less is known about the impact of social behavior on the risk of COVID-19 among healthcare personnel (HCP), who have a higher risk of exposure at work and more awareness of prevention measures than the general population. Methods We conducted a prospective cohort study, with monthly surveys from Dec 2021 to June 2022 of HCP at an academic healthcare system. Each survey asked HCP if they had been tested for SARS-CoV-2, their results, and if they had engaged in any of 9 common social activities outside of work (see figure). A composite social activities exposure measure was defined as the count of “Yes” to the social activities at the previous survey, with a possible range from 0-9. Similarity of current overall social behavior compared to pre-pandemic was also reported on a scale of 0-10, with 10 reflecting the same level as pre-pandemic. We employed mixed effect logistic regression, weighted for dropout using covariate balancing propensity scores, to estimate the association between previously reported social behavior and activities and testing positive for SARS-CoV-2 after adjusting for covariates (see table). Results Recruitment emails were sent to 10,321 HCP, with 1,802 (17.5%) consenting to participate and 1,302 (72.3%) completing 2 or more surveys. 981 HCP reported being tested for SARS-CoV-2 with at least one positive test reported by 277 HCP (28.2%). Engaging in overall social behavior more similar to pre-pandemic levels was found to be statistically significantly associated with an increased likelihood of testing positive for SARS-CoV-2 (OR 1.32, 95% CI 1.11-1.56). However, while there was a trend to testing positive with an increased number of social activities reported at the previous survey, it was not significant (OR 1.06, 95% CI 0.95-1.18). Temporal Trends of Social Activities in Healthcare Workers Reported social activities of healthcare workers at an academic healthcare system from late November 2021 to May 2022. Association Between Social Activity Measures and Positive COVID-19 Test 1. Similarity of social behavior at the previous survey with pre-pandemic behavior reported on a scale of 0-10, with 10 reflecting the same level of activity as pre-pandemic. 2. Count of “Yes” to the social activities at the previous survey, with a possible range from 0-9. 3. Covariates adjusted: calendar month, age, gender, clinic role, working location, general health, comorbidities, household status, illnesses reported at the previous survey, time since last vaccine, and time since the previous survey. Conclusion This study, which included the Omicron surge, saw HCP participation in a range of social activities. Reporting levels of social activity similar to pre-pandemic levels was associated with an increased risk of subsequent COVID-19. This feedback is important for HCP consideration of exposures outside of work. Disclosures Jeanmarie Mayer, MD, GlaxoSmithKline: Stocks/Bonds Andrew T. Pavia, MD, GlaxoSmith Kline: Advisor/Consultant|Sanofi: Advisor/Consultant

A simulation-based method to inform serosurvey design for estimating the force of infection using existing blood samples.

The extent to which dengue virus has been circulating globally and especially in Africa is largely unknown. Testing available blood samples from previous cross-sectional serological surveys offers a convenient strategy to investigate past dengue infections, as such serosurveys provide the ideal data to reconstruct the age-dependent immunity profile of the population and to estimate the average per-capita annual risk of infection: the force of infection (FOI), which is a fundamental measure of transmission intensity. In this study, we present a novel methodological approach to inform the size and age distribution of blood samples to test when samples are acquired from previous surveys. The method was used to inform SERODEN, a dengue seroprevalence survey which is currently being conducted in Ghana among other countries utilizing samples previously collected for a SARS-CoV-2 serosurvey. The method described in this paper can be employed to determine sample sizes and testing strategies for different diseases and transmission settings.

Metabolic health status and renal disorders: a cross-sectional study

Previous surveys suggests that body mass index (BMI) may be positively related to development of chronic kidney disease (CKD). However, this association might be altered by metabolic syndrome. Therefore, we aimed to evaluate the association of metabolic health status with CKD. The present cross-sectional study was carried out on 3322 representative sample of Iranian adults. Metabolic syndrome was identified based on the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) and BMI was assessed by anthropometric measurements. Estimated glomerular filtration rate (eGFR) was calculated by modification of diet in renal disease-Chronic Kidney Disease Epidemiology Collaboration (MDRD-EPI) formula. Subjects were categorized into four phenotypes: metabolically healthy normal weight (MHNW), metabolically healthy overweight and obesity (MHO), metabolically unhealthy normal weight (MUHNW), and metabolically unhealthy overweight and obesity (MUHO). Based on multivariate-adjusted models, the risk of CKD was significantly higher in MUHO compared with MHNW (OR: 1.48; p < 0.05). Although MUHNW and MUHO were associated with lower eGFR and albuminuria, the significant association was not observed in case of hematuria. Furthermore, subjects with kidney stones tended to be in MHO (OR: 1.42; p < 0.05) and MUHO phenotypes (OR: 1.64; p < 0.05), in comparison to the MHNW phenotype. The odds of kidney disorders were higher in adults with metabolic syndrome, regardless of BMI. However, this relationship might be strengthened by the concomitance of metabolic syndrome and obesity. To verify our findings, clarify the causality, and elucidate the underlying mechanisms, further research are warranted.

Attitudes towards minority and non-official languages in Spain

This paper presents a literature review that investigates the attitudes towards minority and non-official languages in Spain, such as Aranese, Asturian, Basque, Catalan, Valencian, and Galician, which play an important role in shaping regional identities and cultural expressions. The research methodology involved a comprehensive examination of previous studies, surveys, and academic papers, providing valuable insights into the perspectives of the inhabitants towards these languages in their respective regions. The findings of these studies reveal that language serves as a key element of identity and cultural belonging in the Aranese community of Valle de Arán. Moreover, factors such as educational linguistic models, regional spoken languages, and historical contexts influence attitudes toward Catalan and Valencian. The paper also explores the intricate relationship between the terms “Valencian” and “Catalan”, highlighting the complexities of linguistic identity. Overall, the research indicates a predominantly favorable outlook towards these minority languages. Despite certain challenges, the prevailing positive sentiment contributes to strengthening community cohesion, enhancing educational opportunities, and promoting cultural interactions.

ELECTRONIC AGREEMENTS FROM THE LENS OF THE LEGAL PERSPECTIVE “LAW AS A TOOL OF SOCIAL ENGINEERING” PROPOSED BY ROSCOE POUND

The purpose of this research is to provide a juridical analysis of electronic agreements from the legal perspective of “Law as A Tool Of Social Engineering” coined by Roscoe Pound. This article emphasizes on a normative juridical research with a statutory approach, empirical approach, and case study approach to comprehend electronic agreements and the law that regulates it as norms implemented in society. The type of data analyzed in this article are primarily secondary data from books, previous studies, journals related to electronic agreements, and Indonesian laws concerning electronic agreements. Previous survey found that more than 660 thousand electronic agreements were signed in early 2018, which is 159 percent higher than the previous period, indicating that the use of electronic agreements continue to increase and expand to various sectors in Indonesia. Therefore, the Indonesian law system must have an explanatory approach regarding this phenomena of technological advancement, specifically about electronic agreements. Several regulations researched and analyzed in this research disclose the importance of law related to electronic agreements, one of which is providing legal protection for the interests of the parties in electronic agreements. Supported by the legal perspective of “Law as A Tool Of Social Engineering” coined by Roscoe Pound, the existence of laws or regulations, in this case specifically regarding electronic agreements, functions as a law that can maintain the stability and balance of conflicting interests in society. Furthermore, the legal perspective of “Law as A Tool Of Social Engineering” sees the laws related to electronic agreements direct human attitudes to react to changes in society, in this case related to the technological advances that offers electronic agreements, so that people understand that agreements signed electronically have the same validity and binding force as conventional agreements signed physically. Keywords: electronic agreement; law as a tool of social engineering; Roscoe Pound.

New insights into ancestry and health of Polynesians and New Zealand Māori

We are now moving towards the new era of personalised medicine. In prospect, a DNA-tailored healthcare system will utilise genomic information from patients and populations for the prevention and treatment of diseases. This near-future medicine recognises that there is already evidence of genetic disparity between people of different ethnicity. Our own previous genetic surveys of human genomes have demonstrated abundant genetic variation in New Zealanders. These now include extensive new work on immune system genes. We have produced a comprehensive reference set of genotypes for clinically relevant antigens in transplantation (human leukocyte antigen, major histocompatibility complex class I chain-related gene A and killer cell immunoglobulin-like receptor) and transfusion (blood group and human platelet antigens) medicine in Māori and Polynesians. This report sets these data in context and highlights allelic variations and their implications for ancestry and health. It is hoped that this information may help to resolve some of the present inequalities in Māori and Pacific health status.

A near-infrared variability survey of young planetary-mass objects

ABSTRACT We present a photometric variability survey of young planetary-mass objects using the New Technology Telescope in the JS and KS bands. Surface gravity plays an important role in the atmospheric structure of brown dwarfs, as young low-gravity L dwarfs have a higher variability rate than field L dwarfs. In this study, we extend variability studies to young T-type planetary-mass objects and investigate the effects of surface gravity on the variability of L and T dwarfs across a large sample. We conduct continuous monitoring for 18 objects with spectral types from L5 to T8 and detect four new variables and two variable candidates. Combining with previous variability surveys of field and young L and T objects, we find that young objects tend to be more variable than field objects within peak-to-peak variability amplitude ranges of 0.5–10 per cent and period ranges of 1.5–20 h. For the first time, we constrain the variability rate of young T dwarfs to be $56_{-18}^{+20}$ per cent compared to $25_{-7}^{+8}$ per cent for field T dwarfs. Both field and young samples have higher variability rates at the L/T transition than outside the L/T transition. The differences in the variability rates between field and young samples are about 1σ and therefore larger sample sizes are needed to confirm and refine the results. Besides the L/T transition, young L dwarfs with strong variability tend to assemble in a narrow spectral type range of L6–L7.5. This work supports the critical role of surface gravity on the atmospheric structure from L to T spectral types.

Host galaxies of ultra-strong Mg ii absorbers at z ∼ 0.7

ABSTRACT We report spectroscopic identification of the host galaxies of 18 ultra-strong Mg ii systems (USMg ii) at 0.6 ≤ z ≤ 0.8. We created the largest sample by merging these with 20 host galaxies from our previous survey within 0.4 ≤ z ≤ 0.6. Using this sample, we confirm that the measured impact parameters ($\rm 6.3\leqslant D[kpc] \leqslant 120$ with a median of 19 kpc) are much larger than expected, and the USMg ii host galaxies do not follow the canonical $\rm {\it W}_{2796}-{\it D}$ anticorrelation. We show that the presence and significance of this anticorrelation may depend on the sample selection. The $\rm {\it W}_{2796}-{\it D}$ anticorrelation seen for the general Mg ii absorbers show a mild evolution at low $\rm W_{2796}$ end over the redshift range 0.4 ≤ z ≤ 1.5 with an increase of the impact parameters. Compared to the host galaxies of normal Mg ii absorbers, USMg ii host galaxies are brighter and more massive for a given impact parameter. While the USMg ii systems preferentially pick star-forming galaxies, they exhibit slightly lower ongoing star-forming rates compared to main sequence galaxies with the same stellar mass, suggesting a transition from star-forming to quiescent states. For a limiting magnitude of mr &amp;lt; 23.6, at least 29 per cent of the USMg ii host galaxies are isolated, and the width of the Mg ii absorption in these cases may originate from gas flows (infall/outflow) in isolated haloes of massive star forming but not starbursting galaxies. We associate more than one galaxy with the absorber in $\ge 21~{{\ \rm per\ cent}}$ cases, where interactions may cause wide velocity spread.

Improved cluster analysis of Werner solutions for geologic depth estimation using unsupervised machine learning

Werner deconvolution is a widely used method for computer-aided solution estimations using magnetic field data. Because of the inherent unpredictability of potential field data, the techniques frequently yield unpredictable results and may not always be able to predict the full extent of the geological structures depicted. The unsupervised clustering algorithm was then used to improve the detection of geological structures generated from magnetic field data. A synthetic magnetic model was created by combining two models that resembled dikes. To add a little more complexity to the model, sporadic noise was applied to the synthetic model. The synthetic model was subjected to Werner deconvolution to produce solutions. The developed solutions resulting from the synthetic data were applied to an unsupervised machine-learning algorithm. The algorithm was able to detect two clusters from the dataset and the centroids generated from the number of clusters formed indicated the depth of the two models, which was 5 m and 8 m respectively. The algorithm was then subjected to a real case dataset from the Banke complex, which is part of the Nigerian Younger Granite region. Three clusters were formed, having centroids of 536 m, 635 m, and 530 m. These depths were validated with previous surveys carried out in the area. This algorithm proved successful in determining the exact position in terms of the depth of the geologic bodies. This proposed approach is fully data-driven and is successful even when there is noise.

Three-dimensional printer guided new morphological classification of left atrial appendage -rock, paper and scissors-

Abstract Background Cardiogenic embolism by left atrial thrombi is one of the most important atrial fibrillation (AF)-related clinical problems. In a previous survey, 91% of nonrheumatic AF-related left atrial thrombi was originated in the left atrial appendage (LAA). The WATCHMAN device was developed as a permanent implantable device to seal off the LAA to prevent cardiogenic embolism. It has been used in Japan since September 2019 only for patients who are at the high risk of bleeding events. The WATCHMAN family is the only left atrial appendage closure (LAAC) device in JAPAN. It is known that the LAA diameter of Asians is larger than that of non-Asians. Furthermore, the LAA of the Japanese are more complicated, compared to the Western. Whereas the four classifications from previous reports are widely used in the world, the classification differs with different physicians in the real-world practice. Purpose We aimed to identify the relationship between morphological features of LAA and clinical events such as ischemic stroke in Japanese patients. Methods The LAAC database of our institution was respectively analyzed. All LAAC cases were performed between February 2020 and February 2022. The three-dimensional printing of LAA was generated from cardiovascular computed tomography imaging data. The shapes of the LAA were classified into 3 morphological types: (1) The LAA with bi lobe was defined as "Scissors" type; (2) The one with complicated erasing branches was "Paper" type; (3) The simple one was "Rock" type. We retrospectively examined the relationship between the risk of ischemic stroke and the morphological features of the LAA. Results The cohort consisted of 63 consecutive patients (age, 73±8 years; male gender, 73%; BMI, 24±4 kg/m²; the size of WATCHMAN device, 29±42 mm). Of those, 14 (22%), 21 (33%) and 28 (44%) patients were divided into the Scissors, Paper and Rock types, respectively. All groups were identically matched for comorbidities. Table shows the patient characteristics and the results of univariate and multivariate analysis. There were 35 (56%) patients who had a history of ischemic stroke (4 vs. 9 vs. 22; Rock vs. Scissors vs. Paper types, p-value=0.01). In the multivariate analysis, the Paper type is the potential risk factor of ischemic stroke (p-value&amp;lt;0.01, respectively). Conclusions The morphological features of LAA by three-dimensional printing may be related to cardioembolic stroke. Patients with Paper type LAA were significantly more likely to have had a history of ischemic stroke. These results could have a relevant impact on the antithrombotic management of patients who are at the high risk of embolic and bleeding events and, moreover, LAAC could be the best option for these patients.Table 1Table 2

Effectiveness of conditional cash transfers, subsidized child care and life skills training on adolescent mothers’ schooling, sexual and reproductive health, and mental health outcomes in Burkina Faso and Malawi: the PROMOTE Project pilot randomized controlled trial protocol

IntroductionGirls’ and women’s health as well as social and economic wellbeing are often negatively impacted by early childbearing. In many parts of Africa, adolescent girls who get pregnant often drop out of school, resulting in widening gender inequalities in schooling and economic participation. Few interventions have focused on education and economic empowerment of adolescent mothers in the region. We aim to conduct a pilot randomized controlled trial in Blantyre (Malawi) and Ouagadougou (Burkina Faso) to examine the acceptability and feasibility of three interventions in improving educational and health outcomes among adolescent mothers and to estimate the effect and cost-effectiveness of the three interventions in facilitating (re)entry into school or vocational training. We will also test the effect of the interventions on their sexual and reproductive health (SRH) and mental health.InterventionsThe three interventions we will assess are: a cash transfer conditioned on (re)enrolment into school or vocational training, subsidized childcare, and life skills training offered through adolescent mothers’ clubs. The life skills training will cover nurturing childcare, SRH, mental health, and financial literacy. Community health workers will facilitate the clubs. Each intervention will be implemented for 12 months.MethodsWe will conduct a baseline survey among adolescent mothers aged 10–19 years (N = 270, per site) enrolled following a household listing in select enumeration areas in each site. Adolescent mothers will be interviewed using a structured survey adapted from a previous survey on the lived experiences of pregnant and parenting adolescents in the two sites. Following the baseline survey, adolescent mothers will be individually randomly assigned to one of three study arms: arm one (adolescent mothers’ clubs only); arm two (adolescent mothers’ clubs + subsidized childcare), and arm three (adolescent mothers’ clubs + subsidized childcare + cash transfer). At endline, we will re-administer the structured survey and assess the average treatment effect across the three groups following intent-to-treat (ITT) analysis, comparing school or vocational training attendance during the intervention period. We will also compare baseline and endline measures of SRH and mental health outcomes. Between the baseline and endline survey, we will conduct a process evaluation to examine the acceptability and feasibility of the interventions and to track the implementation of the interventions.DiscussionOur research will generate evidence that provides insights on interventions that can enable adolescent mothers to continue their education, as well as improve their SRH and mental health. We aim to maximize the translation of the evidence into policy and action through sustained engagement from inception with key stakeholders and decision makers and strategic communication of research findings.Trial registration number AEARCTR-0009115, May 15, 2022.

Assessment of musculoskeletal pain and surgical ergonomic parameters among members of the American Society of Pediatric Otolaryngology

ObjectivesTo assess rates of self-reported work-related pain and knowledge of ergonomic principles among fellowship trained pediatric otolaryngologists within the American Society of Pediatric Otolaryngology (ASPO). We hypothesized that pediatric otolaryngologists experience rates of musculoskeletal pain and injury similar to what has been reported among adult otolaryngologists and other surgical subspecialties. MethodsAn IRB-approved survey was distributed to members of ASPO through email listserv. Collected variables included surgeon demographic information, practice settings, surgical volume, procedure types, work-related musculoskeletal pain-related metrics, modifying factors, and knowledge/attitudes on surgical ergonomics. ResultsA total of 685 ASPO members were approached via email, of which 435 opened the survey email and 118 attending pediatric otolaryngologists completed the survey (response rate 27%). In all, 78% of respondents reported current or prior pain and/or injury attributed to performing surgery, 20% higher than that reported in the previous ergonomics survey of ASPO members in 2012. The most affected areas were neck/cervical spine (63%), shoulders/arms (44%), lower back/lumbar spine (36%), and hands/wrist (31%). Half of the respondents were diagnosed with musculoskeletal condition(s) attributed to performing surgery. Two-thirds required treatment (62% pharmacologic only, 9% pharmacologic and surgical intervention) for their work-related pain. Leveraging intermittent pauses during surgical procedures to adjust body position was the most reported method of addressing pain in the operating room. Only 21% report ever having received ergonomic training during their training or career. ConclusionMusculoskeletal pain associated with performing pediatric otolaryngology procedures is highly prevalent and has not attenuated despite increased awareness of surgical ergonomics. Results from this study underscore the need to develop standardized surgical ergonomics curricula for pediatric otolaryngologists and trainees.

Deep Learning for Software Defect Prediction: An LSTM-based Approach

Software defect prediction is an important aspect of software development, as it helps developers and organizations to identify and resolve bugs in the software before they become major issues. In this paper, we explore the use of machine learning algorithms for software defect prediction. We discuss the different types of machine learning algorithms that have been used for software defect prediction and their advantages and disadvantages. We also provide a comprehensive review of recent studies that have used machine learning algorithms for software defect prediction. The paper concludes with a discussion of the challenges and opportunities in using machine learning algorithms for software defect prediction and the future directions of research in this field. This paper surveys the existing literature on software defect prediction, focusing specifically on deep learning techniques. Compared to existing surveys on the topic, this paper offers a more in-depth analysis of the strengths and weaknesses of deep learning approaches for software defect prediction. It explores the use of LSTMs for this task, which have not been extensively studied in previous surveys. Additionally, this paper provides a comprehensive review of recent research in the field, highlighting the most promising deep learning models and techniques for software defect prediction. The results of this survey demonstrate that LSTM-based deep learning models can outperform traditional machine learning approaches and achieve state-of-the-art results in software defect prediction. Furthermore, this paper provides insights into the challenges and limitations of deep learning approaches for software defect prediction, highlighting areas for future research and improvement. Overall, this paper offers a valuable resource for researchers and practitioners interested in using deep learning techniques for software defect prediction..

Deep Learning for Software Defect Prediction: An LSTM-based Approach

Software defect prediction is an important aspect of software development, as it helps developers and organizations to identify and resolve bugs in the software before they become major issues. In this paper, we explore the use of machine learning algorithms for software defect prediction. We discuss the different types of machine learning algorithms that have been used for software defect prediction and their advantages and disadvantages. We also provide a comprehensive review of recent studies that have used machine learning algorithms for software defect prediction. The paper concludes with a discussion of the challenges and opportunities in using machine learning algorithms for software defect prediction and the future directions of research in this field. This paper surveys the existing literature on software defect prediction, focusing specifically on deep learning techniques. Compared to existing surveys on the topic, this paper offers a more in-depth analysis of the strengths and weaknesses of deep learning approaches for software defect prediction. It explores the use of LSTMs for this task, which have not been extensively studied in previous surveys. Additionally, this paper provides a comprehensive review of recent research in the field, highlighting the most promising deep learning models and techniques for software defect prediction. The results of this survey demonstrate that LSTM-based deep learning models can outperform traditional machine learning approaches and achieve state-of-the-art results in software defect prediction. Furthermore, this paper provides insights into the challenges and limitations of deep learning approaches for software defect prediction, highlighting areas for future research and improvement. Overall, this paper offers a valuable resource for researchers and practitioners interested in using deep learning techniques for software defect prediction..

Recent trends of in-vehicle time sensitive networking technologies, applications and challenges

With the vigorous development of automobile industry, in-vehicle network is also constantly upgraded to meet data transmission requirements of emerging applications. The main transmission requirements are low latency and certainty especially for autonomous driving. Time sensitive networking (TSN) based on Ethernet gives a possible solution to these requirements. Previous surveys usually investigated TSN from a general perspective, which referred to TSN of various application fields. In this paper, we focus on the application of TSN to the in-vehicle networks. For in-vehicle networks, we discuss all related TSN standards specified by IEEE 802.1 work group up to now. We further overview and analyze recent literature on various aspects of TSN for automotive applications, including synchronization, resource reservation, scheduling, certainty, software and hardware. Application scenarios of TSN for in-vehicle networks are analyzed one by one. Since TSN of in-vehicle network is still at a very initial stage, this paper also gives insights on open issues, future research directions and possible solutions.

Solar Magnetic Polarity Effect on Neutron Monitor Count Rates: Comparing Latitude Surveys and Antarctic Stations

The Galactic cosmic-ray spectrum manifests pronounced variations over the 11 yr sunspot cycle and more subtle variations over the 22 yr solar magnetic cycle. An important tool to study these variations is repeated latitude surveys with neutron monitors (NMs) on board icebreakers in conjunction with land-based references. We revisit 13 annual latitude surveys from 1994 to 2007 using reference data from the Mawson NM instead of McMurdo NM (which closed in 2017). We then consider two more latitude surveys (2018 and 2019) with a monitor similar to the 3NM64 in the previous surveys but without lead rings around the central tube, a so-called “semi-leaded neutron monitor.” The new surveys extend the linear relationship among data taken at different cutoff rigidity ranges. They also confirm the “crossover” measured near solar minima during epochs of opposite solar magnetic polarity and the absence of a crossover for epochs having the same solar magnetic polarity.

2021 Epidemiological Survey of Dermatomycoses in Japan

This is a report of the results of the epidemiological survey on dermatomycoses conducted in 2021. A total of 9,442 patients with dermatomycosis were reported for one year. They include 8,151 (86.3%) with dermatophytosis, 796 (8.4%) with candidiasis, 484 (5.1%) with Malassezia infection, and 11 (0.1%) with deep cutaneous mycosis. In order, the most common types of dermatophytoses were tinea pedis (4,195 cases, 2,341 males and 1,854 females), tinea unguium (2,711 cases, 1,509 males and 1,202 females), tinea corporis (674 cases, 445 males and 229 females), tinea cruris (399 cases, 305 males and 94 females), tinea manus (125 cases, 78 males and 47 females), and tinea capitis (47 cases, 25 males and 22 females). The number of cases of tinea pedis and tinea unguium increased during the summer. A higher percentage of patients were aged 80 or older than in previous surveys. These findings may reflect the increasing percentage of elderly patients seen and the superannuation of the population. As in previous surveys, Trichophyton rubrum and Trichophyton interdigitale were the two most frequently isolated species of fungi causing dermatophytoses. Microsporum canis and Trichophyton tonsurans were the two species most often causing tinea capitis.Regarding cutaneous candidiasis, while candidal intertrigo was the most common in previous surveys, diaper candidiasis in the elderly was the most common in this survey. A background check revealed that this was because a facility included a semi-prophylactic approach to address diaper candidiasis occurring within the ward.Malassezia infections by Malassezia folliculitis clearly increased with each survey. The tendency of certain facilities with many reports of Malassezia folliculitis suggests that it is greatly affected by the presence of physicians familiar with the disease.

Host-specificity of biocontrol agents in the field – The significance of sampling sympatric relatives of target weeds (Acacia holosericea versus A. auriculiformis)

Acacia auriculiformis is an invasive weed that causes significant environmental damage across Florida, USA. Previous native range surveys in Australia identified a number of herbivores that are commonly associated with this plant, including several species of leaf-tying moths (Macrobathra spp.) and a chrysomelid beetle (Calomela intemerata). These herbivores are considered strong candidates as biological control agents, but the range of host species they use has not been determined. We therefore conducted a transect survey of herbivorous insects on A. holosericea, a closely-related sympatric relative of A. auriculiformis. These species share areas of sympatry and the latter was sampled simultaneously (in sympatry and allopatry). DNA barcoding approach identified an array of herbivorous insects feeding on A. holosericea, but few were found on both the Acacia species sampled. A literature survey suggested this pattern of host specificity may be consistent across Australian acacias, to the extent that each Acacia species may largely host its own suite of herbivores. These results do not negate the need for host testing procedures in biological control, but they do increase our confidence substantially that several promising potential biological control agents of A. auriculiformis (Macrobathra moths and Calomela beetles) have a restricted host species range. Our study illustrates how exploring the herbivore diversity of plant species closely related to the target weed can provide crucial information for biological control researchers.