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Abstract Investigating the genetic factors influencing human birth weight may lead to biological insights into fetal growth and long-term health. We report analyses of rare variants that impact birth weight when carried by either fetus or mother, using whole exome sequencing data in up to 234,675 participants. Rare protein-truncating and deleterious missense variants are collapsed to perform gene burden tests. We identify 9 genes; 5 with fetal-only effects on birth weight, 1 with maternal-only effects, 3 with both, and observe directionally concordant associations in an independent sample. Four of the genes were previously implicated by GWAS of birth weight. IGF1R and PAPPA2 (fetal and maternal-acting) have known roles in insulin-like growth factor bioavailability and signalling. PPARG, INHBE and ACVR1C (fetal-acting) are involved in adipose tissue regulation, and the latter two also show associations with favourable adiposity patterns in adults. We highlight the dual role of PPARG (fetal-acting) in adipocyte differentiation and placental angiogenesis. NOS3 (fetal and maternal-acting), NRK (fetal), and ADAMTS8 (maternal-acting) have been implicated in placental function and hypertension. To conclude, our analysis of rare coding variants identifies regulators of fetal adipose tissue and fetoplacental angiogenesis as determinants of birth weight, and further evidence for the role of insulin-like growth factors.

Abstract Regulatory T cells (Tregs) are a type of lymphocyte that is key to maintaining immunological self-tolerance, with great potential for therapeutic applications. A long-standing challenge in the study of Tregs is that the only way they can be unambiguously identified is by using invasive intracellular markers. Practically, the purification of live Tregs is often compromised by other cell types since only surrogate surface markers can be used. We present here a non-invasive method based on Raman spectroscopy that can detect live unaltered Tregs by coupling optical detection with machine learning implemented with regularized logistic regression. We demonstrate the validity of this approach first on murine cells expressing a surface Foxp3 reporter, and then on peripheral blood human T cells. By including methods to account for sample purity, we could generate reliable models that can identify Tregs with an accuracy higher than 80%, which is already comparable with typical sorting purities achievable with standard methods that use proxy surface markers. We could also demonstrate that it is possible to reliably detect Tregs in fully independent donors that are not part of the model training, a key milestone for practical applications.

Low back pain (lumbar pain) is one of the most common causes of disability in the population. In cases where conservative treatment of discogenic or musculoskeletal low back pain does not have a positive effect, interventional methods of diagnosis and treatment are used, which are developing actively on the background of rapid technological progress. Being on the border of interdisciplinary interaction, interventional methods can significantly shorten the duration of conservative therapy and, in many cases, avoid surgical decompressive interventions, which are associated with a considerable risk of complications. The methods, efficacy and safety of epidural administration of glucocorticoids for discogenic radiculopathy and lumbar stenosis as well as the methods of radiofrequency denervation for musculoskeletal pain due to the facet joints and the sacroiliac joint involvement are discussed. The possibilities of drug therapy for musculoskeletal pain of the lumbar spine, and combination of non-steroidal anti-inflammatory drugs and a vitamin B complex, are discussed.

Abstract Background Despite the clear benefits of physical activity in healthy ageing, engagement in regular physical activity among community-dwelling older adults remains low, with common barriers including exertional discomfort, concerns with falling, and access difficulties. The recent rise of the use of technology and the internet among older adults presents an opportunity to engage with older people online to promote increased physical activity. This study aims to determine the feasibility and acceptability of training volunteers to deliver online group exercises for older adults attending community social clubs. Methods This was a pre-post mixed-methods study. Older adults aged ≥ 65 years attending community social clubs who provided written consent and were not actively participating in exercise classes took part in the feasibility study. Older adults, volunteers, and staff were interviewed to determine the acceptability of the intervention. The intervention was a once weekly volunteer-led online group seated strength exercises using resistance bands. The duration of the intervention was 6 months. The primary outcome measures were the feasibility of the intervention (determined by the number of volunteers recruited, trained, and retained, participant recruitment and intervention adherence) and its acceptability to key stakeholders. Secondary outcome measures included physical activity levels (Community Health Model Activities Programme for Seniors (CHAMPS) questionnaire), modified Barthel Index, Health-related quality of life (EQ-5D-5L), frailty (PRISMA-7) and sarcopenia (SARC-F), at baseline and 6 months. Results Nineteen volunteers were recruited, 15 (78.9%) completed training and 9 (47.3%) were retained after 1 year (mean age 68 years). Thirty older adults (mean age 77 years, 27 female) participated, attending 54% (IQR 37–67) of exercise sessions. Participants had no significant changes in secondary outcome measures, with a trend towards improvement in physical activity levels (physical activity in minutes per week at baseline was 1770 min, and 1909 min at six months, p = 0.13). Twenty volunteers, older adults, and staff were interviewed and found the intervention acceptable. The seated exercises were perceived as safe, manageable, and enjoyable. Conclusions Trained volunteers can safely deliver online group exercise for community-dwelling older adults which was acceptable to older adults, volunteers, and club staff. Trials registration NCT04672200.

Major disruptions of the winter season, high-latitude stratospheric polar vortices can result in stratospheric anomalies that persist for months. These sudden stratospheric warming events are recognized as an important potential source of forecast skill for surface climate on subseasonal to seasonal timescales. Realizing this skill in operational subseasonal forecast models remains a challenge, as models must capture both the evolution of the stratospheric polar vortices in addition to their coupling to the troposphere. The processes involved in this coupling remain a topic of open research. We present here the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project. SNAPSI is a new model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortex disturbances for surface predictability in subseasonal to seasonal forecast models. Based on a set of controlled, subseasonal ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. First, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. Second, to attribute specific extreme events to stratospheric variability. Third, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models. Fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. Although not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. The output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of subseasonal forecasts.

Disasters are not easy to predict because they occur suddenly, and the scale of disasters is increasing compared to the past. Since a new type of disaster field always appears, when a disaster occurs, responders who are put into the site recognize the same risk as secondary damage and are put into the field. In this regard, the robot performs missions such as search and rescue in the initial response process at the disaster field. It is a technology with high potential to reduce damage to people and property. Most of the robots are equipped with site accessibility and monitoring through cameras and remote control, but considering the specificity of the disaster field, it is not easy to fully monitor the site with a camera sensor as the possibility of poor visibility is very high. LiDAR uses a laser to recognize the distance to a nearby object in a relatively wide range and acquires three-dimensional information, so its accuracy and precision are higher than other sensors.In this study, one multi-sensor module was manufactured by combining LiDAR and IMU sensor with a computing board for real-time monitoring so that it could be used in the field of robots. In addition, we studied how to stably mount this multi-sensor module to a robot so that it can maintain optimal accuracy at disaster field, and it is intended to be utilized as a prior research for field operation of robots equipped with sensors in the future.

As the risk of disaster scenes increases, the number of cases of acquiring disaster scenes information using unmanned robots is increasing. Because unmanned robots can be remotely controlled, sensors such as LiDAR and optical cameras are installed so that investigators can safely observe disaster scenes and acquire information. In particular, the information that can be acquired is different depending on the sensor characteristics, and a sensor module suitable for the purpose is being developed. Accordingly, the National Disaster Management research Institute(NDMI) also developed a investigation robot capable of acquiring information on disaster sites independently. Based on 3D point cloud data, we developed a multi-sensor module and SLAM algorithm customized to the investigation robot to collect quantitative information on the damage situation. To test the performance of the independently developed multi-sensor module, SLAM mapping was performed in a disaster building reproduced like a disaster scene, and various SLAM algorithms and distance comparison were performed. As a result, PackSLAM developed in this study showed the lowest error. In the future, to increase applicability at disaster sites, more precise experiments will be conducted by conducted by establishing a rough terrain environment.

Disasters that occur in recent years are linked to various factors and occur in a chain, and the scale is also increasing. In order to solve the more complex and serious disaster problem, it is necessary to expand the affect scope of the disaster and to acquire and manage information by integrating and operating various sensors. With the introduction of UAVs for disaster work, various studies such as investigation of the affected area, rescue of survivors, and establishment of emergency communication networks are being conducted. This is useful for strengthening the field of disaster management because it is very effective in terms of time and human resources. In addition, it is possible to acquire high-resolution images, and by using the 3D point cloud data and ortho-mosaic imagery generation, it is possible to produce a disaster scene close to the real world. Therefore, if UAV information can be linked to disaster types and characteristics prior to supporting disaster damage investigation, it is possible to establish a response method according to the disaster damage situation and help decision-making. In order to examine the applicability of UAV technology for actual disaster sites, this study conducted data collection and analysis of damaged areas targeting sites damaged by heavy rain and chemical accidents. In the heavy rain damage site, flooding damage occurred in the ginseng cultivation patches due to river overflow caused by the torrential rain, and the damaged area and lots were investigated. The site of the chemical accident was damaged by hydrochloric acid gas leaking into the atmosphere, and the surrounding forest, crops, and residential facilities were identified and the affected area was calculated. As a result of investigating the damage from heavy rain and chemical accidents using UAV, it was possible to quickly collect data on a wide disaster site. In addition, it was easy to calculate the damaged area based on ortho-imagery, and the limitations of spatial analysis were reduced, so that it could be used more specifically and efficiently to the disaster site.

This study aims to testify the applicability of UAV photogrammetry and artificial intelligence (AI) for the management of natural disaster. Recently artificial intelligence is considered as an emerging tool for recognizing disaster events from aerial imagery of drones. In this paper, we present firstly the approach related to use of AI techniques for disaster detecting and identification. Secondly, we suggest small easy-to-use UAV-based investigation procedure for natural disaster damaged area in the phase of disaster recovery in Korea. Finally, we evaluate the mapping accuracy and work efficiency of drone mapping for disaster investigation application through comparing with traditional investigation work process which was dependent on labor-intensive field survey. The resolution ortho-image map of within less 5cm of GSD generated by aerial photos acquired from UAVs at the altitude of 100m–250m enabled us to check damage information such as facilities destroy or the trace of soil erosion around the river flooded and reservoir collapsed area. The photogrammetry-based drone mapping technology for the disaster damage investigation is expected to be an alternative approach to support or replace the labor-intensive disaster site survey that needs to investigate the disaster site quickly and timely.

Over recent years there have been concomitant advances in the development of stratosphere-resolving numerical models, our understanding of stratosphere–troposphere interaction, and the extension of long-range forecasts to explicitly include the stratosphere. These advances are now allowing for new and improved capability in long-range prediction. We present an overview of this development and show how the inclusion of the stratosphere in forecast systems aids monthly, seasonal, and annual-to-decadal climate predictions and multidecadal projections. We end with an outlook towards the future and identify areas of improvement that could further benefit these rapidly evolving predictions.

Liew et al. describe an efficient monoclonal antibody (mAb) production method and as a proof-of-concept, develop mAbs against multiple epitopes on hANKRD1 in a single hybridoma production cycle. With rapid screening and validation techniques, this pipeline shows promise of general applicability for antibody panel generation.

Two reader antennas, which are designed using the L-probe-fed suspended patch, are designed for the UHF band. To generate circular polarization, the L-shaped probe is modified by including an extra bend to provide one more degree of tuning freedom to the reflection coefficient without affecting the axial-ratio performance much. The proposed single-element reader antenna is able to achieve a CP bandwidth of 5.5 % (890 MHz - 940 MHz) and a maximum antenna gain of 9.7 dBi. When designed into a 2x2 array, the proposed reader antenna is able to produce CP bandwidth of 5.3 % (910 MHz - 960 MHz) with an antenna gain of 14.7 dBi. The read ranges of the proposed antenna and its array are 6.3 m and 10.9 m, respectively.

This paper presents a dielectric resonator antenna (DRA) reflectarray unit element with multiple loading circular slots underneath in concentric form. The radii of the three slots are varied simultaneously to function as phase-shifting elements. For the case of three under-loading slots, it is very interesting to find out that the slot and DRA resonances can be pulled together to provide a very broad phase range of 916°. Study shows that the gradient and phase range of the S curve can be easily tuned by manipulating the dimensions of the under-loading slots. Waveguide method has been used to establish the simulation and measurement models. The reflection properties of the DRA unit elements loaded with different ring-shaped slots are compared, along with a complete parametric analysis. The proposed reflectarray unit element is very compact as its phase shifter can be entirely hidden beneath the DRA.

E-shaped patch resonator is proposed for designing a novel broadband linearly polarized reflectarray for the first time. The element is made up of a shorted E-shaped patch with a polystyrene foam placed beneath it, and no dielectric substrate is needed in the reflectarray design. The unit element is simulated using Floquet method and it is found that a reflection phase range of ~360° is easily obtainable by varying the arm length of the shorted E-shaped patch. A full 11 × 11 reflectarray has been designed to achieve an antenna gain of ~23.7dBi and a -1dB gain bandwidth of 8.1%. The cross-polarization is found to be ~18dBi smaller than its co-polarization in the boresight direction. The proposed reflectarray is simple to design as it requires the use of only a single layer.

Background Neutrophil gelatinase‐associated lipocalin (NGAL) or lipocalin 2 may promote atherosclerosis and plaque instability leading to increased risk of cardiac events. We investigated the relationships between plasma NGAL, cardiovascular disease biomarkers, and long‐term cardiac events. Methods and Results The study population consisted of 1131 ambulant older white women (mean age 75 years) without clinical coronary heart disease (CHD) and measures of plasma NGAL in the Perth Longitudinal Study of Ageing Women with 14.5‐year CHD and heart failure hospitalizations or death (events) captured using linked records. Over 14.5 years, 256 women had CHD events, while 118 had heart failure events. Per SD increase in log‐transformed NGAL there was a 35% to 37% increase in relative hazards for CHD and heart failure events in unadjusted analyses, which remained significant after adjustment for conventional risk factors for CHD events (hazard ratio 1.29, 95% CI 1.13–1.48, P0.05). Women in the highest 2 quartiles of NGAL had higher relative hazards for CHD events compared with women in the lowest quartile hazard ratio 1.61, 95% CI 1.08–2.39, P=0.019 and hazard ratio 1.97, 95% CI 1.33–3.93, P=0.001, respectively. These associations were independent of high‐sensitivity cardiac troponin I, homocysteine, and estimated renal function. NGAL correctly reclassified 1 in 4 women who sustained a CHD event up in risk and 1 in 10 women without CHD events down in risk. Conclusions NGAL was associated with increased risk of long‐term CHD events, independent of conventional risk factors and biomarkers. These findings provide mechanistic insight into the role of NGAL with cardiac events.