A multifactorial fall risk assessment system for older people utilizing a low-cost, markerless Microsoft Kinect.

Falls among older people are a major health concern. This study aims to develop a multifactorial fall risk assessment system for older people using a low-cost, markerless Microsoft Kinect. A Kinect-based test battery was designed to comprehensively assess major fall risk factors. A follow-up experiment was conducted with 102 older participants to assess their fall risks. Participants were divided into high and low fall risk groups based on their prospective falls over a 6-month period. Results showed that the high fall risk group performed significantly worse on the Kinect-based test battery. The developed random forest classification model achieved an average classification accuracy of 84.7%. In addition, the individual's performance was computed as the percentile value of a normative database to visualise deficiencies and targets for intervention. These findings indicate that the developed system can not only screen out 'at risk' older individuals with good accuracy, but also identify potential fall risk factors for effective fall intervention.Practitioner summary: Falls are the leading cause of injuries in older people. We newly developed a multifactorial fall risk assessment system for older people utilising a low-cost, markerless Kinect. Results showed that the developed system can screen out 'at risk' individuals and identify potential risk factors for effective fall intervention.

TinyFallNet: A Lightweight Pre-Impact Fall Detection Model.

Falls represent a significant health concern for the elderly. While studies on deep learning-based preimpact fall detection have been conducted to mitigate fall-related injuries, additional efforts are needed for embedding in microcomputer units (MCUs). In this study, ConvLSTM, the state-of-the-art model, was benchmarked, and we attempted to lightweight it by leveraging features from image-classification models VGGNet and ResNet while maintaining performance for wearable airbags. The models were developed and evaluated using data from young subjects in the KFall public dataset based on an inertial measurement unit (IMU), leading to the proposal of TinyFallNet based on ResNet. Despite exhibiting higher accuracy (97.37% < 98.00%) than the benchmarked ConvLSTM, the proposed model requires lower memory (1.58 MB > 0.70 MB). Additionally, data on the elderly from the fall data of the FARSEEING dataset and activities of daily living (ADLs) data of the KFall dataset were analyzed for algorithm validation. This study demonstrated the applicability of image-classification models to preimpact fall detection using IMU and showed that additional tuning for lightweighting is possible due to the different data types. This research is expected to contribute to the lightweighting of deep learning models based on IMU and the development of applications based on IMU data.

Adaptability of wheat to future climate change: Effects of sowing date and sowing rate on wheat yield in three wheat production regions in the North China Plain.

Adjusting sowing dates and sowing rates is a key adaptation strategy for adapting to future climate change, and maintaining wheat production in the North China Plain (NCP). However, it is still unclear whether the current sowing date and sowing rate can adapt to future climate change, and how to adjust the sowing date and sowing rate to compensate for the adverse effects of climate change on wheat yields. This study predicts the adaptability of agricultural management practices like sowing dates and sowing rates, to future climate change in three wheat regions by referring to four global climate models (GCMs) and AquaCrop model. Population structure and yield were maximized for sowing dates from Oct.11-20 and sowing rates of 10-13 kg/667 m2 (or 13-16 kg/667 m2) in 2016-2021. Three wheat regions were expected to show a warming trend, while the total precipitation has large spatial fluctuations under both representative concentration pathways (rcp) scenarios in the 2022-2100. AquaCrop model could simulate yield with a good precision (RMSE≤1043.7 kg/ha). Compared to the average yield of the baseline period (2016-2021), in the 2022-2100, the average predicted wheat yields of three wheat regions simulated based on the current optimal sowing date and sowing rate decreased by 5.45 % âˆ¼ 11.05 % (9.35 % âˆ¼ 16.84 %) and 2.57 % âˆ¼ 10.95 % (6.97 % âˆ¼ 12.75 %) under the rcp4.5 (rcp8.5), respectively. Average wheat yield losses were effectively compensated when the combinations of Oct.15 and 14 kg/667 m2 for the dryland wheat, Oct.21 and 14 kg/667 m2 for the irrigated wheat, and Oct.21 and 13 kg/667 m2 for the high-yield-rainfed wheat were applied under both rcp scenarios, respectively, with predicted yield losses of -4.17 %, -3.50 %, and - 3.25 %. Thus, adjusting sowing dates and sowing rates are viable options to effectively address the adverse effects of future global climate change, thereby guaranteeing food security in the NCP.

Modelling for design and evaluation of industrial exoskeletons: A systematic review.

Industrial exoskeletons are developed to relieve workers' physical demands in the workplace and to alleviate ergonomic issues associated with work-related musculoskeletal disorders. As a safe and economical alternative to empirical/experimental methods, modelling is considered as a powerful tool for design and evaluation of industrial exoskeletons. This systematic review aims to provide a comprehensive understanding of the current literature on the design and evaluation of industrial exoskeletons through modelling. A systematic study was conducted by general keyword searches of five electronic databases over the last two decades (2003-2022). Out of the 701 records initially retrieved, 33 eligible articles were included and analyzed in the final review, presenting a variety of model inputs, model development, and model outputs used in the modelling. This systematic review study revealed that existing modelling methods can evaluate the biomechanical and physiological effects of industrial exoskeletons and provide some design parameters. However, the modelling method is currently unable to cover some of the main evaluation metrics supported by experimental assessments, such as task performance, user experience/discomfort, change in metabolic costs etc. Standard guidelines for model construction and implementation, as well as validation of human-exoskeleton interactions, remain to be established.

ZmGI2 regulates flowering time through multiple flower development pathways in maize.

GIGANTEA (GI) encodes a component of the circadian clock core oscillator and has been identified as a regulatory pathway of the circadian rhythm and photoperiodic flowering in model plants. However, the regulatory pathway of GI affecting flowering time is unknown in maize. Here, we identified that the zmgi2 mutant flowered earlier than the wild type under long day (LD) conditions, whereas the difference in flowering time was not apparent under short day (SD) conditions. The 24 h optimal expression of the gene in the stem apex meristems (SAM) appeared at 9 h after dawn under LD conditions and at 11 h after dawn under SD conditions. DAP-Seq and RNA-Seq further revealed that ZmGI2 delays flowering by directly binding to the upstream regions of ZmVOZs, ZmZCN8 and ZmFPF1 to repress the expression of these genes and by directly binding to the upstream regions of ZmARR11, ZmDOF and ZmUBC11 to promote the expression of these genes. The genetic and biochemical evidence suggests a model for the potential role of ZmGI2 in regulating the flowering time-dependent photoperiodic pathway. This study provides novel insights into the function of ZmGIs in maize and further demonstrates their potential importance for floral transition. These results contribute to a comprehensive understanding of the molecular mechanisms and regulatory networks of GI transcription factors in regulating flowering time in maize.

Data Augmentation to Address Various Rotation Errors of Wearable Sensors for Robust Pre-impact Fall Detection.

OBJECTIVE: This paper proposes a novel application of data augmentation to address various rotation errors of wearable sensors for robust pre-impact fall detection. In such systems, sensor rotation errors are inevitable because of loose attachment and body movement during long deployment. METHODS: Two augmented models with uniform and normal strategies were compared with a non-augmented model on the original dataset (no rotation error) and a validation dataset (with rotation error). The validation dataset was constructed with three types of rotation errors, namely, pitch, roll, and compound roll and pitch (CRP) at three levels of range (low: 15°, medium: 30°, and high: 45°). RESULTS: Five-fold cross validation showed the two augmented models maintained accuracy (>98.5%) as high as the non-augmented model on the original dataset but showed considerable improvements of 6.11% and 6.50% on the validation dataset, respectively. CRP error negatively affected the model accuracy the most, followed by pitch and then roll errors. In addition, the normal model had advantages over the uniform model in the low-to-medium range of error, which is expected to be the typical error range in practical applications. As for lead time, similarly, the augmented models achieved performance similar to the non-augmented model on the original dataset but showed significant improvements on the validation dataset. CONCLUSION AND SIGNIFICANCE: Data augmentation had notable capacities to address sensor rotation errors for practical applications and augmented models especially the normal model showed good potential to be embedded in a wearable system for robust pre-impact fall detection and injury prevention.

ROS-Triggered Self-Assembled Nanoparticles Based on a Chemo-Sonodynamic Combinational Therapy Strategy for the Noninvasive Elimination of Hypoxic Tumors.

The hypopermeability and hypoxia in the tumor milieu are important factors that limit multiple treatments. Herein, the reactive oxygen species (ROS)-triggered self-assembled nanoparticles (RP-NPs) was constructed. The natural small molecule Rhein (Rh) was encapsulated into RP-NPs as a sonosensitizer highly accumulated at the tumor site. Then highly tissue-permeable ultrasound (US) irradiation induced apoptosis of tumor cells through the excitation of Rh and acoustic cavitation, which prompted the rapid production of large amounts of ROS in the hypoxic tumor microenvironment. In addition, the thioketal bond structures in the innovatively designed prodrug LA-GEM were triggered and broken by ROS to achieve rapid targeted release of the gemcitabine (GEM). Sonodynamic therapy (SDT) increased the tissue permeability of solid tumors and actively disrupted redox homeostasis via mitochondrial pathways to kill hypoxic tumor cells, and the triggered response mechanism to GEM synergistically amplified the effect of chemotherapy. The chemo-sonodynamic combinational treatment approach is highly effective and noninvasive, with promising applications for hypoxic tumor elimination, such as in cervical cancer (CCa) patients who want to maintain their reproductive function.

Acute Effects of Carbon Fiber Insole on Three Aspects of Sports Performance, Lower Extremity Muscle Activity, and Subjective Comfort.

Carbon fiber insole (CFI), which is lightweight and stiff to reduce energy loss and help wearers perform better in sports, has recently been introduced. However, reports are scarce on the effects of CFI on sports performance, muscle activation, and wearing comfort. This study investigated the acute effects of CFI on sports performance, lower extremity muscle activity, and subjective comfort. Thirty young healthy males with shoe sizes between 260 and 270 mm performed various sports tasks (power generation, agility, and speed) and treadmill runs with wearable sensors under two experimental insole conditions (benchmark insole as a baseline, CFI). The results showed that, compared to the benchmark insole, CFI significantly improved sports performance in terms of power generation (~1.5%) and agility (~1%). However, it activated more of the Tibialis Anterior (~0.7%) and Gastrocnemius Medialis (~0.8%) muscles, and was perceived to be stiffer and less comfortable. These findings suggested that CFI could improve sports performance, but could cause more lower extremity muscle activation and subjective discomfort.

Trunk range of motion: A wearable sensor-based test protocol and indicator of fall risk in older people.

Conventionally, trunk range of motion (TROM) requires manual measurement by an external health professional with a general-purpose goniometer. This study aims to propose a convenient test protocol to assess TROM based on a single wearable sensor and to further investigate the relationship between TROM and fall risk of older people. We first explored the optimal sensor position by comparing TROMs from four representative locations (T1, T12, L5 and sternum) and optical motion capture system (golden reference). A follow-up experiment was conducted to evaluate the relationship between TROM and fall risk. The results showed that T12 achieved the minimum root mean square error (3.8 ± 2.2°) against the golden reference and the non-faller group had significantly higher TROMs than the faller group. These findings suggest that the newly proposed protocol is convenient yet valid and TROM can be a promising indicator of fall risk in older people.

Effects of elevated atmospheric [CO2] on grain starch characteristics in different specialized wheat.

The increasing atmospheric [CO2] poses great challenges to wheat production. Currently, the response of starch characteristics in different specialized wheat cultivars to elevated [CO2], as well as the underlying physiological and molecular mechanisms remains unclear. Therefore, an experiment was conducted with open-top chambers to study the effects of ambient [CO2] [a(CO2)] and elevated [CO2] [e(CO2)] on photosynthetic performance, yield and starch characteristics of bread wheat (Zhengmai 369, ZM369) and biscuit wheat (Yangmai 15, YM15) from 2020 to 2022. The results demonstrated a significant improvement in photosynthetic performance, yield, amylose and amylopectin content, volume ratio of large granules under e[CO2]. Moreover, e[CO2] upregulated the gene expression and enzyme activities of GBSS (Granule-bound starch synthase) and SSS (Soluble starch synthase), increased starch pasting viscosity, gelatinization enthalpy and crystallinity. Compared to YM15, ZM369 exhibited a higher upregulation of GBSSI, greater increase in amylose content and volume ratio of large granules, as well as higher gelatinization enthalpy and crystallinity. However, ZM369 showed a lower increase in amylopectin content and a lower upregulation of SSSI and SSSII. Correlation analysis revealed amylose and amylopectin content had a positive correlation with GBSS and SSS, respectively, a significant positively correlation among the amylose and amylopectin content, starch granule volume, and pasting properties. In conclusion, these changes may enhance the utilization value of biscuit wheat but exhibit an opposite effect on bread wheat. The results provide a basis for selecting suitable wheat cultivars and ensuring food security under future climate change conditions.

HEIH Promotes Malignant Progression of Gastric Cancer by Regulating STAT3-Mediated Autophagy and Glycolysis.

To study the clinical value of HEIH hyperexpression in gastric cancer and the molecular mechanism of promoting malignant proliferation of gastric cancer cells, qRT-PCR was used to detect the expression of HEIH in gastric cancer and nontumor gastric tissues. HEIH interference sequence was constructed to downregulate HEIH expression in MGC-803 and BGC-823 cell lines. CCK8, clonogenesis, and Transwell assay were used to detect the effects of HEIH on proliferation and invasion of tumor cells. The protein levels of STAT3, p-STAT3, P62, and LC3 were detected by Western blotting. The results showed that HEIH was highly expressed in gastric cancer (P < 0.01). Interference of HEIH expression in MGC-803 and BGC-823 cells reduced the proliferation and invasion of gastric cancer cells, and the results were statistically significant (P < 0.05). HEIH acts as a miRNA sponge for miR-4500. HEIH promotes gastric cancer development by inhibiting miR-4500. STAT3 is a downstream target of miR-4500. HEIH inhibits autophagy and promotes glycolysis. In conclusion, HEIH is highly expressed in gastric cancers. HEIH promotes malignant proliferation and development of gastric cancer cells. HEIH may be a new candidate site for pathological diagnosis and molecular drug therapy for future clinical treatment of gastric cancer.

Pseudo-haptics and self-haptics for freehand mid-air text entry in VR.

Despite the lack of haptic feedback, two-finger typing remains as a common freehand mid-air text entry method in VR. To compensate for this limitation, we investigated the effect of pseudo-haptics and thumb-to-finger pinch self-haptics. Three types of virtual keyboards were developed for comparison: a normal virtual keyboard without any haptic features, a pseudo-haptic keyboard providing a haptic illusion by adding pseudo-haptic features to the normal virtual keyboard, and a pinch keyboard providing passive self-haptic feedback when the index finger touches the thumb to type bubble-shaped keys. A follow-up user study was conducted on the three keyboards to measure typing performance, perceived workload, and user experience. The results showed that the pseudo-haptic and pinch keyboards were comparable to the normal one in typing performance and workload, but beneficial in user experience and preference. The findings suggest that pseudo-haptics and self-haptics could achieve experiential benefits in VR text entry without obtrusive extra devices.

Effectiveness and Usability of a Novel Kinect-Based Tailored Interactive Fall Intervention System for Fall Prevention in Older People: A Preliminary Study.

Falls are prevalent among older people and can lead to serious health problems. We newly developed a novel Kinect-based tailored interactive fall intervention system, which seamlessly integrates multifactorial fall risk assessment and tailored intervention programs to prevent falls in older people. This preliminary study aimed to examine the effectiveness and usability of this developed system for fall prevention in older people. Thirty community-dwelling older women participated in this experiment; they were allocated to an intervention group (IG) or a control group (CG) for a quasi-randomized trial (15 people each). Participants in IG followed an 8-week tailored intervention (40 min/session × 2 sessions/week × 8 weeks) using the Kinect-based interactive fall intervention system, while participants in CG maintained their habitual activities. Various outcome measures were evaluated at baseline (Week 0), interim (Week 4), and post-intervention (Week 8). Experimental results showed that IG led to significant improvements in TUG-Timed Up and Go (p = 0.010), BBS-Berg Balance Scale (p = 0.011), and Montreal Cognitive Assessment-MoCA (p = 0.022) between baseline and post-intervention. In comparison to the baseline, TUG and BBS were even significantly improved at interim (p = 0.004 and 0.047, respectively). There were no significant changes in static balance-related performance outcomes and the Short Falls Efficacy Scale-SFES after the intervention. Whereas in CG, most performance measures did not show significant changes during the 8-week period, TUG completion time became significantly longer at post-intervention in comparison to interim (p = 0.028) and fear of falling was also significantly higher at post-intervention than baseline (p = 0.021). These findings suggest that the Kinect-based 8-week tailored interactive fall interventions effectively improved older people's physical and cognitive abilities. Regarding the usability of the developed system, the average System Usability Scale (SUS) score was 83.5 out of 100, indicating excellent system usability. The overall mean Computer Literacy Scale (CLS) score was 2.5 out of 26, showing that older participants in this study had very limited experience with computers. No significant correlation between SUS and CLS scores demonstrated that newly developed Kinect-based tailored interactive fall intervention system was easy to use for older people, regardless of their computer experience. This novel system should help health professionals and older people proactively manage the risk of falls.

[Effects of irrigation at flowering stage on soil nutrient and root distribution in wheat field]. / å°éº¦å¼€èŠ±æœŸçŒæ°´å¯¹åœŸå£¤å »åˆ†åŠæ ¹ç³»åˆ†å¸ƒçš„å½±å“.

To provide theoretical support the full use of water and fertilizer resources for wheat, we explored the effects of irrigation on wheat yield, plant and soil nutrient distribution during flowering period and its relationship with root characteristics. We set up two treatments by using the 2 m deep soil column cultivation method with irrigation during flowering (T1) and no irrigation during flowering (T2), with the drought-resistant and high-yield cultivar Luomai 28 (LM28) and the high photosynthetic efficiency cultivar Bainong 207 (BN207) as materials. We measured contents of nitrogen, phosphorus and potassium in plants and soils, as well as the characteristics of soil roots. The results showed that ammonium, available phosphorus, and available potassium were mainly distributed in 0-80 cm soil layer, and that nitrate was mainly distributed in soil layer below 80 cm during wheat harvest. Irrigation at anthesis stage promoted wheat to absorb ammonium, available phosphorus and available potassium from the upper layer of soil and nitrate nitrogen from the lower layer but did not aggravate the deep leaching of nitrate. The root of wheat mainly concentrated in 0-60 cm soil layer and decreased with increasing soil depth. Dry matter accumulation, total nitrogen and total phosphorus were mainly distributed in wheat grains at maturity, while total potassium was mainly distributed in stems. Irrigation at anthesis stage significantly increased the 100-grain weight of wheat, and consequently the yield. Root morphology was negatively correlated with soil nitrate in 0-40 cm soil layer, positively correlated with soil ammonium in 80-100 cm soil layer and soil available phosphorus in 0-100 cm soil layer. Irrigation at anthesis stage promoted the full absorption of soil nutrients by roots at late filling stage, delayed the senescence of flag leaves after flowering, prolonged the functional period of transporting nutrients from vegetative organs to reproductive organs, leading nutrients in vegetative organs more fully transported to grains, increasing grain weight and yield.

A Large-Scale Open Motion Dataset (KFall) and Benchmark Algorithms for Detecting Pre-impact Fall of the Elderly Using Wearable Inertial Sensors.

Research on pre-impact fall detection with wearable inertial sensors (detecting fall accidents prior to body-ground impacts) has grown rapidly in the past decade due to its great potential for developing an on-demand fall-related injury prevention system. However, most researchers use their own datasets to develop fall detection algorithms and rarely make these datasets publicly available, which poses a challenge to fairly evaluate the performance of different algorithms on a common basis. Even though some open datasets have been established recently, most of them are impractical for pre-impact fall detection due to the lack of temporal labels for fall time and limited types of motions. In order to overcome these limitations, in this study, we proposed and publicly provided a large-scale motion dataset called "KFall," which was developed from 32 Korean participants while wearing an inertial sensor on the low back and performing 21 types of activities of daily living and 15 types of simulated falls. In addition, ready-to-use temporal labels of the fall time based on synchronized motion videos were published along with the dataset. Those enhancements make KFall the first public dataset suitable for pre-impact fall detection, not just for post-fall detection. Importantly, we have also developed three different types of latest algorithms (threshold based, support-vector machine, and deep learning), using the KFall dataset for pre-impact fall detection so that researchers and practitioners can flexibly choose the corresponding algorithm. Deep learning algorithm achieved both high overall accuracy and balanced sensitivity (99.32%) and specificity (99.01%) for pre-impact fall detection. Support vector machine also demonstrated a good performance with a sensitivity of 99.77% and specificity of 94.87%. However, the threshold-based algorithm showed relatively poor results, especially the specificity (83.43%) was much lower than the sensitivity (95.50%). The performance of these algorithms could be regarded as a benchmark for further development of better algorithms with this new dataset. This large-scale motion dataset and benchmark algorithms could provide researchers and practitioners with valuable data and references to develop new technologies and strategies for pre-impact fall detection and proactive injury prevention for the elderly.

The effect of slider design and length on user performance and preference of smartphone versions of the visual analogue scale.

This study aims to investigate the effect of slider design and length on user performance and preference of smartphone versions of Visual Analogue Scale (VAS). Twenty-eight participants performed a task to set random target values with 8 smartphone versions of VAS: 2 slider designs (traditional design, modern design) × 4 slider lengths (4.3 cm, 5.8 cm, 10 cm landscape, 10 cm portrait). Experimental results showed that both slider design and length significantly affected the accuracy, task completion time and subject preference. Compared with the traditional slider design, the modern slider design showed significantly smaller bias in setting values, shorter task completion time, and higher subject preference. The slider length significantly affected all measures, and 5.8 cm was recommended due to small bias, short task completion time, dominant preference and excellent ability to closely fit the width of smartphone display with the portrait mode. These findings could provide mobile VAS and slider designers with useful references.

Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content.

BACKGROUND: The leaf water content estimation model is established by hyperspectral technology, which is crucial and provides technical reference for precision irrigation. METHODS: In this study, two consecutive years of field experiments (different irrigation times and seven wheat varieties) in 2018-2020 were performed to obtain the canopy spectra reflectance and leaf water content (LWC) data. The characteristic bands related to LWC were extracted from correlation coefficient method (CA) and x-Loading weight method (x-Lw). Five modeling methods, spectral index and four other methods (Partial Least-Squares Regression (PLSR), Random Forest Regression (RFR), Extreme Random Trees (ERT), and K-Nearest Neighbor (KNN)) based characteristic bands, were employed to construct LWC estimation models. RESULTS: The results showed that the canopy spectral reflectance increased with the increase of irrigation times, especially in the near-infrared band (750-1350 nm). The prediction accuracy of the newly developed differential spectral index DVI (R1185, R1307) was higher than that of the existing spectral index, with R2 of 0.85 and R2 of 0.78 for the calibration and validation, respectively. Due to a large amount of hyperspectral data, the correlation coefficient method (CA) and x-Loading weight (x-Lw) were used to select the water characteristic bands (100 and 28 characteristic bands, respectively) from the full spectrum. We found that the accuracy of the model based on the characteristic bands was not significantly lower than that of the full spectrum-based models. Among these models, the ERT- x-Lw model performed the best (R2 and RMSE of 0.88 and 1.46; 0.84 and 1.62 for the calibration and validation, respectively). In addition, the accuracy of the LWC estimation model constructed by ERT-x-Lw was higher than that of DVI (R1185, R1307). CONCLUSION: The two models based on ERT-x-Lw and DVI (R1185, R1307) can effectively predict wheat leaf water content. The results provide a technical reference and a basis for crop water monitoring and diagnosis under similar production conditions.

Transcriptomic, proteomic, and physiological studies reveal key players in wheat nitrogen use efficiency under both high and low nitrogen supply.

The effective use of available nitrogen (N) to improve crop grain yields provides an important strategy to reduce environmental N pollution and promote sustainable agriculture. However, little is known about the common genetic basis of N use efficiency (NUE) at varying N availability. Two wheat (Triticum aestivum L.) cultivars were grown in the field with high, moderate, and low N supply. Cultivar Zhoumai 27 outperformed Aikang 58 independent of the N supply and showed improved growth, canopy leaf area index, flag leaf surface area, grain number, and yield, and enhanced NUE due to both higher N uptake and utilization efficiency. Further, transcriptome and proteome analyses were performed using flag leaves that provide assimilates for grain growth. The results showed that many genes or proteins that are up- or down-regulated under all N regimes are associated with N and carbon metabolism and transport. This was reinforced by cultivar differences in photosynthesis, assimilate phloem transport, and grain protein/starch yield. Overall, our study establishes that improving NUE at both high and low N supply requires distinct adjustments in leaf metabolism and assimilate partitioning. Identified key genes/proteins may individually or concurrently regulate NUE and are promising targets for maximizing crop NUE irrespective of the N supply.

[Effects of row spacing and sowing rate on vertical distribution of photosynthetically active radiation, biomass, and grain yield in winter wheat canopy.] / è¡Œè·å’Œæ’­ç§é‡å¯¹å†¬å°éº¦å† å±‚å ‰åˆæœ‰æ•ˆè¾å°„åž‚ç›´åˆ†å¸ƒã€ç”Ÿç‰©é‡å’Œç±½ç²’äº§é‡çš„å½±å“.

To clarify the effects of row spacing and sowing rate on the vertical distribution of canopy PAR, biomass, and grain yield in winter wheat, a field experiment was conducted without increa-sing water and fertilizer input. There were two row spacing modes, R1 (equal spacing, 20 cm+20 cm) and R2(wide and narrow row spacing, 12 cm+12 cm+12 cm+24 cm), and three sowing rates, D1 (low, 120 kg·hm-2), D2 (medium, 157.5 kg·hm-2), D3 (high, 195 kg·hm-2). The canopy photosynthetically active radiation (PAR) interception and utilization rate in different heights, population photosynthetic capacity, biomass, and grain yield were measured during the main growth stages of winter wheat. The results showed that both total PAR interception and upper layer PAR interception of winter wheat canopy under R1 treatment were significantly higher than those in R2 treatment, but those of the middle layer and lower layer were higher in R2 than in R1, and with significant difference in the middle layer. From flowering to maturity, the photosynthetic potential (LAD), population photosynthetic rate (CAP), PAR conversion rate, and utilization rate in R2 were all significantly higher than those in R1 under the same sowing rate, with the highest value under R2D2 treatment. With the increasing sowing rate, the population biomass (BA) and leaf biomass (BL) at different layers increased, but the individual biomass (BP) showed an opposite trend. Under the same sowing rate, BA, BL and BP in R2 were higher than that in R1 after the flowering stage. Among them, BA and BP had significant difference in row spacing treatments at the maturity stage, with significant difference between the two row spacing treatments being observed in BL of the middle and lower layers under D2 and D3 sowing rates. The spike number, grain number per spike, 1000-kernel weight, and grain yield of winter wheat among different treatments were the highest in R2D3, R2D1, R2D1, and R2D2, respectively. The 1000-kernel weight, grain number per spike and grain yield in R2 treatment were significantly higher than R1. In summary, the PAR interception in the middle and lower layers of winter wheat canopy was improved by changing row spacing, with positive consequence on the photosynthetic capacity of individual plant and population, PAR utilization and transformation efficiency, which finally increased biomass and grain yield. Therefore, optimizing the field structure and shaping the ideal population photosynthetic structure should pay more attention during the high-yield cultivation of winter wheat. Making full use of light resources per unit land area and excavating the photosynthetic production potential of crops were also critical to achieve high yield and efficiency. In this experiment, the population photosynthetic capacity, photosynthetic effective radiation utilization rate, and yield were the highest under the treatment of R2D2.

Localization, Gene Expression, and Functions of Glutamine Synthetase Isozymes in Wheat Grain (Triticum aestivum L.).

Glutamine synthetase (GS) plays a major role in plant nitrogen metabolism, but the roles of individual GS isoforms in grains are unknown. Here, the localization and expression of individual TaGS isozymes in wheat grain were probed with TaGS isoenzyme-specific antibodies, and the nitrogen metabolism of grain during the grain filling stage were investigated. Immunofluorescence revealed that TaGS1;1, TaGS1;3, and TaGS2 were expressed in different regions of the embryo. In grain transporting tissues, TaGS1;2 was localized in vascular bundle; TaGS1;2 and TaGS1;1 were in chalaza and placentochalaza; TaGS1;1 and TaGS1;3 were in endosperm transfer cells; and TaGS1;3 and TaGS2 were in aleurone layer. GS exhibited maximum activity and expression at 8 days after flowering (DAF) with peak glutamine content in grains; from then, NH 4 + increased largely from NO 3 - reduction, glutamate dehydrogenase (GDH) aminating activity increased continuously, and the activities of GS and glutamate synthase (GOGAT) decreased, while only TaGS1;3 kept a stable expression in different TaGS isozymes. Hence, GS-GOGAT cycle and GDH play different roles in NH 4 + assimilation of grain in different stages of grain development; TaGS1;3, located in aleurone layer and endosperm transfer cells, plays a key role in Gln into endosperm for gluten synthesis. At 30 DAF, grain amino acids are mainly transported from maternal phloem.