Application of LightGBM hybrid model based on TPE algorithm optimization in sleep apnea detection.

Introduction: Sleep apnoea syndrome (SAS) is a serious sleep disorder and early detection of sleep apnoea not only reduces treatment costs but also saves lives. Conventional polysomnography (PSG) is widely regarded as the gold standard diagnostic tool for sleep apnoea. However, this method is expensive, time-consuming and inherently disruptive to sleep. Recent studies have pointed out that ECG analysis is a simple and effective diagnostic method for sleep apnea, which can effectively provide physicians with an aid to diagnosis and reduce patients' suffering. Methods: To this end, in this paper proposes a LightGBM hybrid model based on ECG signals for efficient detection of sleep apnea. Firstly, the improved Isolated Forest algorithm is introduced to remove abnormal data and solve the data sample imbalance problem. Secondly, the parameters of LightGBM algorithm are optimised by the improved TPE (Tree-structured Parzen Estimator) algorithm to determine the best parameter configuration of the model. Finally, the fusion model TPE_OptGBM is used to detect sleep apnoea. In the experimental phase, we validated the model based on the sleep apnoea ECG database provided by Phillips-University of Marburg, Germany. Results: The experimental results show that the model proposed in this paper achieves an accuracy of 95.08%, a precision of 94.80%, a recall of 97.51%, and an F1 value of 96.14%. Discussion: All of these evaluation indicators are better than the current mainstream models, which is expected to assist the doctor's diagnostic process and provide a better medical experience for patients.

ERTNet: an interpretable transformer-based framework for EEG emotion recognition.

Background: Emotion recognition using EEG signals enables clinicians to assess patients' emotional states with precision and immediacy. However, the complexity of EEG signal data poses challenges for traditional recognition methods. Deep learning techniques effectively capture the nuanced emotional cues within these signals by leveraging extensive data. Nonetheless, most deep learning techniques lack interpretability while maintaining accuracy. Methods: We developed an interpretable end-to-end EEG emotion recognition framework rooted in the hybrid CNN and transformer architecture. Specifically, temporal convolution isolates salient information from EEG signals while filtering out potential high-frequency noise. Spatial convolution discerns the topological connections between channels. Subsequently, the transformer module processes the feature maps to integrate high-level spatiotemporal features, enabling the identification of the prevailing emotional state. Results: Experiments' results demonstrated that our model excels in diverse emotion classification, achieving an accuracy of 74.23% ± 2.59% on the dimensional model (DEAP) and 67.17% ± 1.70% on the discrete model (SEED-V). These results surpass the performances of both CNN and LSTM-based counterparts. Through interpretive analysis, we ascertained that the beta and gamma bands in the EEG signals exert the most significant impact on emotion recognition performance. Notably, our model can independently tailor a Gaussian-like convolution kernel, effectively filtering high-frequency noise from the input EEG data. Discussion: Given its robust performance and interpretative capabilities, our proposed framework is a promising tool for EEG-driven emotion brain-computer interface.

Enhanced Spin Polarization from Biaxially Strained Colloidal Quantum Dots.

Electron and hole spin polarization is crucial for quantum dots to be used in spin lasers and quantum information processing. However, the degree of spin polarization in II-VI and III-V semiconductor quantum dots is low because of the degenerated valence band. Here, we increase the light and heavy hole degeneracy by introducing biaxial strain into CdSe-based quantum dots, enabling the degree of spin polarization to be increased from 20% to 50% under photoexcitation. The optical gain threshold measurement further reveals that the increase in polarization helps to reduce the gain threshold.

Preparation of ß-Cyclodextrin Functionalized Platform for Monitoring Changes in Potassium Content in Perspiration.

The monitoring of potassium ion (K+) levels in human sweat can provide valuable insights into electrolyte balance and muscle fatigue non-invasively. However, existing laboratory techniques for sweat testing are complex, while wearable sensors face limitations like drift, fouling and interference from ions such as Na+. This work develops printed electrodes using ß-cyclodextrin functionalized reduced graphene oxide (ß-CD-RGO) for selective K+ quantification in sweat. The ß-CD prevents the aggregation of RGO sheets while also providing selective binding sites for K+ capture. Electrodes were fabricated by screen printing the ß-CD-RGO ink onto conductive carbon substrates. Material characterization confirmed the successful functionalization of RGO with ß-CD. Cyclic voltammetry (CV) showed enhanced electrochemical behavior for ß-CD-RGO-printed electrodes compared with bare carbon and RGO. Sensor optimization resulted in a formulation with 30% ß-CD-RGO loading. The printed electrodes were drop-casted with an ion-selective polyvinyl chloride (PVC) membrane. A linear range from 10 µM to 100 mM was obtained along with a sensitivity of 54.7 mV/decade. The sensor showed good reproducibility over 10 cycles in 10 mM KCl. Minimal interference from 100 mM Na+ and other common sweat constituents validated the sensor's selectivity. On-body trials were performed by mounting the printed electrodes on human subjects during exercise. The K+ levels measured in sweat were found to correlate well with serum analysis, demonstrating the sensor's ability for non-invasive electrolyte monitoring. Overall, the facile synthesis of stable ß-CD-RGO inks enables the scalable fabrication of wearable sensors for sweat potassium detection.

Development of a travel recommendation algorithm based on multi-modal and multi-vector data mining.

Given the rise of the tourism industry, there is an increasing urgency among tourists to access information about various tourist attractions. To address this challenge, innovative solutions have emerged, utilizing recommendation algorithms to offer customers personalized product recommendations. Nonetheless, existing recommendation algorithms predominantly rely on textual data, which is insufficient to harness the full potential of online tourism data. The most valuable tourism information is often found in the multi-modal data on social media, characterized by its voluminous and content-rich nature. Against this backdrop, our article posits a groundbreaking travel recommendation algorithm that leverages multi-modal data mining techniques. The proposed algorithm uses a travel recommendation platform, designed using multi-vector word sense segmentation and multi-modal data fusion, to improve the recommendation performance by introducing topic words. In our final experimental comparison, we verify the recommendation performance of the proposed algorithm on the real data set of TripAdvisor. Our proposed algorithm has the best degree of confusion with various topics. With a LOP of 20, the Precision and MAP values reach 0.0026 and 0.0089, respectively. It has the potential to better serve the tourism industry in terms of tourist destination recommendations. It can effectively mine the multi-modal data of the tourism industry to generate more excellent economic and social value.

Automatic Penaeus Monodon Larvae Counting via Equal Keypoint Regression with Smartphones.

Today, large-scale Penaeus monodon farms no longer incubate eggs but instead purchase larvae from large-scale hatcheries for rearing. The accurate counting of tens of thousands of larvae in these transactions is a challenging task due to the small size of the larvae and the highly congested scenes. To address this issue, we present the Penaeus Larvae Counting Strategy (PLCS), a simple and efficient method for counting Penaeus monodon larvae that only requires a smartphone to capture images without the need for any additional equipment. Our approach treats two different types of keypoints as equip keypoints based on keypoint regression to determine the number of shrimp larvae in the image. We constructed a high-resolution image dataset named Penaeus_1k using images captured by five smartphones. This dataset contains 1420 images of Penaeus monodon larvae and includes general annotations for three keypoints, making it suitable for density map counting, keypoint regression, and other methods. The effectiveness of the proposed method was evaluated on a real Penaeus monodon larvae dataset. The average accuracy of 720 images with seven different density groups in the test dataset was 93.79%, outperforming the classical density map algorithm and demonstrating the efficacy of the PLCS.

Improved HHT-microstate analysis of EEG in nicotine addicts.

Background: Substance addiction is a chronic disease which causes great harm to modern society and individuals. At present, many studies have applied EEG analysis methods to the substance addiction detection and treatment. As a tool to describe the spatio-temporal dynamic characteristics of large-scale electrophysiological data, EEG microstate analysis has been widely used, which is an effective method to study the relationship between EEG electrodynamics and cognition or disease. Methods: To study the difference of EEG microstate parameters of nicotine addicts at each frequency band, we combine an improved Hilbert Huang Transformation (HHT) decomposition with microstate analysis, which is applied to the EEG of nicotine addicts. Results: After using improved HHT-Microstate method, we notice that there is significant difference in EEG microstates of nicotine addicts between viewing smoke pictures group (smoke) and viewing neutral pictures group (neutral). Firstly, there is a significant difference in EEG microstates at full-frequency band between smoke and neutral group. Compared with the FIR-Microstate method, the similarity index of microstate topographic maps at alpha and beta bands had significant differences between smoke and neutral group. Secondly, we find significant class × group interactions for microstate parameters at delta, alpha and beta bands. Finally, the microstate parameters at delta, alpha and beta bands obtained by the improved HHT-microstate analysis method are selected as features for classification and detection under the Gaussian kernel support vector machine. The highest accuracy is 92% sensitivity is 94% and specificity is 91%, which can more effectively detect and identify addiction diseases than FIR-Microstate and FIR-Riemann methods. Conclusion: Thus, the improved HHT-Microstate analysis method can effectively identify substance addiction diseases and provide new ideas and insights for the brain research of nicotine addiction.

Evaluating the Genetic Background Effect on Dissecting the Genetic Basis of Kernel Traits in Reciprocal Maize Introgression Lines.

Maize yield is mostly determined by its grain size. Although numerous quantitative trait loci (QTL) have been identified for kernel-related traits, the application of these QTL in breeding programs has been strongly hindered because the populations used for QTL mapping are often different from breeding populations. However, the effect of genetic background on the efficiency of QTL and the accuracy of trait genomic prediction has not been fully studied. Here, we used a set of reciprocal introgression lines (ILs) derived from 417F × 517F to evaluate how genetic background affects the detection of QTLassociated with kernel shape traits. A total of 51 QTL for kernel size were identified by chromosome segment lines (CSL) and genome-wide association studies (GWAS) methods. These were subsequently clustered into 13 common QTL based on their physical position, including 7 genetic-background-independent and 6 genetic-background-dependent QTL, respectively. Additionally, different digenic epistatic marker pairs were identified in the 417F and 517F ILs. Therefore, our results demonstrated that genetic background strongly affected not only the kernel size QTL mapping via CSL and GWAS but also the genomic prediction accuracy and epistatic detection, thereby enhancing our understanding of how genetic background affects the genetic dissection of grain size-related traits.

High risks adverse events associated with trastuzumab emtansine and trastuzumab deruxtecan for the treatment of HER2-positive/mutated malignancies: a pharmacovigilance study based on the FAERS database.

BACKGROUND: T-DM1 and T-DXd are two promising antibody-drug conjugates for treating advanced HER2-positive breast cancer and HER2-mutated lung cancer. Understanding the differences in the adverse events (AEs) profile of both drugs may help clinicians make an appropriate treatment decision. RESEARCH DESIGN AND METHODS: All data obtained from the FDA Adverse Event Reporting System (FAERS) database from Q1 2004 to Q3 2022 underwent disproportionality analysis and Bayesian analysis to detect and assess the AE signals of T-DM1 and T-DXd for comparison. RESULTS: A total of 2,113 and 1,269 AE reports associated with T-DM1 and T-Dxd, respectively, were retrieved from FAERS database, in which, respondents were mostly elderly women. Their statistical differences (p < 0.001), poses high incidence of thrombocytopenia, including cardiotoxicity (p < 0.05) for T-DM1, while myelosuppression, interstitial lung disease (ILD), and pneumonitis for T-DXd. Splenomegaly, nodular regenerative hyperplasia, hepatic cirrhosis, portal hypertension, neuropathy peripheral, and spider nevus, are particular to T-DM1. Similarly, febrile neutropenia, pneumocystis jirovecii pneumonia, neutrophil count decreased, and KL-6 increased, are unique to T-DXd. CONCLUSIONS: T-DXd is more likely to induce ILD/pneumonia and myelosuppression than T-DM1, whereas T-DM1 has higher risk of hepatotoxicity, cardiotoxicity, and thrombocytopenia than T-DXd. T-DM1-related hepatotoxicity may need redefinition. Clinicians may need to balance the benefits and risks of antibody-drug conjugates treatment for certain patients.

Self-Assembled and Wavelength-Tunable Quantum Dot Whispering-Gallery-Mode Lasers for Backlight Displays.

Thanks to the narrow line width and high brightness, colloidal quantum dot (CQD) lasers show promising applications in next-generation displays. However, CQD laser-based displays have yet to be demonstrated because of two challenges in integrating red, green, and blue (RGB) lasers: absorption from red CQDs deteriorates the optical gain of blue and green CQDs, and imbalanced white spectra lack blue lasing due to the high lasing threshold of blue CQDs. Herein, we introduce a facile surfactant-free self-assembly method to assemble RGB CQDs into high-quality whispering-gallery-mode (WGM) RGB lasers with close lasing thresholds among them. Moreover, these RGB lasers can lase nearly independently even when they are closely integrated, and they can construct an ultrawide color space whose color gamut is 105% of that of the BT.2020 standard. These combined strategies allow us to demonstrate the first full-color liquid crystal displays using CQD lasers as the backlight source.

Ocular adverse events associated with anti-VEGF therapy: A pharmacovigilance study of the FDA adverse event reporting system (FAERS).

Background: The purpose of this study is to identify and characterize ocular adverse events (AEs) that are significantly associated with anti-VEGF drugs for treatment of neovascular age-related macular degeneration and compare the differences between each drug, and provide clinical reference. Methods: Ocular AEs submitted to the US Food and Drug Administration were analyzed to map the safety profile of anti-VEGF drugs. The Pharmacovigilance tools used for the quantitative detection of signals were reporting odds ratio and bayesian confidence propagation neural network. Results: A total of 10,608,503 AE reports were retrieved from FAERS, with 20,836 for ranibizumab, 19,107 for aflibercept, and 2,442 for brolucizumab between the reporting period of Q1, 2004 and Q3, 2021. We found and analyzed the different AEs with the strongest signal in each drug-ranibizumab-macular ischaemia (ROR = 205.27, IC-2SD = 3.70), retinal pigment epithelial tear (ROR = 836.54, IC-2SD = 7.19); aflibercept-intraocular pressure increased (ROR = 31.09, IC-2SD = 4.61), endophthalmitis (ROR = 178.27, IC-2SD = 6.70); brolucizumab-retinal vasculitis (ROR = 2930.41, IC-2SD = 7.47) and/or retinal artery occlusion (ROR = 391.11, IC-2SD = 6.10), dry eye (ROR = 12.48, IC-2SD = 2.88). Conclusion: The presence of AEs should bring clinical attention. The use of anti-VEGF drugs should be based on the patient's underlying or present medical condition to reduce any adverse event associated with the treatment.

Plant growth-promoting effect and genomic analysis of the P. putida LWPZF isolated from C. japonicum rhizosphere.

Plant growth-promoting rhizobacteria are a type of beneficial bacteria which inhabit in the rhizosphere and possess the abilities to promote plant growth. Pseudomonas putida LWPZF is a plant growth-promoting bacterium isolated from the rhizosphere soil of Cercidiphyllum japonicum. Inoculation treatment with LWPZF could significantly promote the growth of C. japonicum seedlings. P. putida LWPZF has a variety of plant growth-promoting properties, including the ability to solubilize phosphate, synthesize ACC deaminase and IAA. The P. putida LWPZF genome contained a circular chromosome (6,259,530 bp) and a circular plasmid (160,969 bp) with G+C contents of 61.75% and 58.25%, respectively. There were 5632 and 169 predicted protein-coding sequences (CDSs) on the chromosome and the plasmid respectively. Genome sequence analysis revealed lots of genes associated with biosynthesis of IAA, pyoverdine, ACC deaminase, trehalose, volatiles acetoin and 2,3-butanediol, 4-hydroxybenzoate, as well as gluconic acid contributing phosphate solubilization. Additionally, we identified many heavy metal resistance genes, including arsenate, copper, chromate, cobalt-zinc-cadmium, and mercury. These results suggest that P. putida LWPZF shows strong potential in the fields of biofertilizer, biocontrol and heavy metal contamination soil remediation. The data presented in this study will allow us to better understand the mechanisms of plant growth promotion, biocontrol, and anti-heavy metal of P. putida LWPZF.

ZmBSK1 positively regulates BR-induced H2O2 production via NADPH oxidase and functions in oxidative stress tolerance in maize.

Brassinosteroid (BR) has been indicated to induce the production of hydrogen peroxide (H2O2) in plants in response to various environmental stimuli. However, it remains largely unknown how BR induces H2O2 production. In this study, we found that BR treatment significantly raised the kinase activity of maize (Zea mays L.) brassinosteroid-signaling kinase 1 (ZmBSK1) using the immunoprecipitation kinase assay. ZmBSK1 could modulate the gene expressions and activities of nicotinamide adenine dinucleotide phosphate (NADPH) oxidases (EC 1.6.3.1) to modulate BR-induced H2O2 production. BR could enhance the interaction between ZmBSK1 and maize calcium/calmodulin-dependent protein kinase (ZmCCaMK), a previously identified substrate of ZmBSK1. The BR-induced phosphorylation and kinase activity of ZmCCaMK are dependent on ZmBSK1. Moreover, we showed that ZmBSK1 regulated the NADPH oxidase gene expression and activity via directly phosphorylating ZmCCaMK. Genetic analysis suggested that ZmBSK1-ZmCCaMK module strengthened plant tolerance to oxidative stress induced by exogenous application of H2O2 through improving the activities of antioxidant defense enzyme and alleviating the malondialdehyde (MDA) accumulation and electrolyte leakage rate. In conclusion, these findings provide the new insights of ZmBSK1 functioning in BR-induced H2O2 production and the theoretical supports for breeding stress-tolerant crops.

Construction and Interpretation of Prediction Model of Teicoplanin Trough Concentration via Machine Learning.

Objective: To establish an optimal model to predict the teicoplanin trough concentrations by machine learning, and explain the feature importance in the prediction model using the SHapley Additive exPlanation (SHAP) method. Methods: A retrospective study was performed on 279 therapeutic drug monitoring (TDM) measurements obtained from 192 patients who were treated with teicoplanin intravenously at the First Affiliated Hospital of Army Medical University from November 2017 to July 2021. This study included 27 variables, and the teicoplanin trough concentrations were considered as the target variable. The whole dataset was divided into a training group and testing group at the ratio of 8:2, and predictive performance was compared among six different algorithms. Algorithms with higher model performance (top 3) were selected to establish the ensemble prediction model and SHAP was employed to interpret the model. Results: Three algorithms (SVR, GBRT, and RF) with high R 2 scores (0.676, 0.670, and 0.656, respectively) were selected to construct the ensemble model at the ratio of 6:3:1. The model with R 2 = 0.720, MAE = 3.628, MSE = 22.571, absolute accuracy of 83.93%, and relative accuracy of 60.71% was obtained, which performed better in model fitting and had better prediction accuracy than any single algorithm. The feature importance and direction of each variable were visually demonstrated by SHAP values, in which teicoplanin administration and renal function were the most important factors. Conclusion: We firstly adopted a machine learning approach to predict the teicoplanin trough concentration, and interpreted the prediction model by the SHAP method, which is of great significance and value for the clinical medication guidance.

Enhanced Resting-State Functional Connectivity With Decreased Amplitude of Low-Frequency Fluctuations of the Salience Network in Mindfulness Novices.

Mindfulness and accordant interventions are often used as complementary treatments to psychological or psychosomatic problems. This has also been gradually integrated into daily lives for the promotion of psychological well-being in non-clinical populations. The experience of mindful acceptance in a non-judgmental way brought about the state, which was less interfered by a negative effect. Mindfulness practice often begins with focused attention (FA) meditation restricted to an inner experience. We postulate that the brain areas related to an interoceptive function would demonstrate an intrinsic functional change after mindfulness training for the mindful novices along with paying more attention to internal processes. To further explore the influence of mindfulness on the organization of the brain regions, both functional connectivity (FC) in the voxel and the region of interest (ROI) level were calculated. In the current study, 32 healthy volunteers, without any meditation experiences, were enrolled and randomly assigned to a mindfulness-based stress reduction group (MBSR) or control group (CON). Participants in the MBSR group completed 8 weeks of mindfulness-based stress reduction (MBSR) and rated their mindfulness skills before and after MBSR. All subjects were evaluated via resting-state functional MRI (rs-fMRI) in both baselines and after 8 weeks. They also completed a self-report measure of their state and trait anxiety as well as a positive and negative affect. Pre- and post-MBSR assessments revealed a decreased amplitude of low-frequency fluctuations (ALFF) in the right anterior cingulate gyrus (ACC.R), left anterior and posterior insula (aIC.L, pIC.L), as well as left superior medial frontal gyrus (SFGmed.L) in MBSR practitioners. Strengthened FC between right anterior cingulate cortex (ACC.R) and aIC.R was observed. The mean ALFF values of those regions were inversely and positively linked to newly acquired mindful abilities. Along with a decreased negative affect score, our results suggest that the brain regions related to attention and interoceptive function were involved at the beginning of mindfulness. This study provides new clues in elucidating the time of evaluating the brain mechanisms of mindfulness novices.

Enhanced emission directivity from asymmetrically strained colloidal quantum dots.

Current state-of-the-art quantum dot light-emitting diodes have reached close to unity internal quantum efficiency. Further improvement in external quantum efficiency requires more efficient photon out-coupling. Improving the directivity of the photon emission is considered to be the most feasible approach. Here, we report improved emission directivity from colloidal quantum dot films. By growing an asymmetric compressive shell, we are able to lift their band-edge state degeneracy, which leads to an overwhelming population of exciton with in-plane dipole moment, as desired for high-efficiency photon out-coupling. The in-plane dipole proportion determined by back-focal plane imaging method is 88%, remarkably higher than 70% obtained from conventional hydrostatically strained colloidal quantum dots. Enhanced emission directivity obtained here opens a path to increasing the external quantum efficiencies notably.

EEG microstate in obstructive sleep apnea patients.

Obstructive sleep apnea (OSA) is a common sleep respiratory disease. Previous studies have found that the wakefulness electroencephalogram (EEG) of OSA patients has changed, such as increased EEG power. However, whether the microstates reflecting the transient state of the brain is abnormal is unclear during obstructive hypopnea (OH). We investigated the microstates of sleep EEG in 100 OSA patients. Then correlation analysis was carried out between microstate parameters and EEG markers of sleep disturbance, such as power spectrum, sample entropy and detrended fluctuation analysis (DFA). OSA_OH patients showed that the microstate C increased presence and the microstate D decreased presence compared to OSA_withoutOH patients and controls. The fifth microstate E appeared during N1-OH, but the probability of other microstates transferring to microstate E was small. According to the correlation analysis, OSA_OH patients in N1-OH showed that the microstate D was positively correlated with delta power, and negatively correlated with beta and alpha power; the transition probability of the microstate B → C and E → C was positively correlated with alpha power. In other sleep stages, the microstate parameters were not correlated with power, sample entropy and FDA. We might interpret that the abnormal transition of brain active areas of OSA patients in N1-OH stage leads to abnormal microstates, which might be related to the change of alpha activity in the cortex.

Psychometric properties of the Caregiver Strain Questionnaire among Chinese parents of children with ADHD or ASD.

BACKGROUND: There is an urgent need in clinical practice to measure the stress of parenting. The Caregiver Strain Questionnaire (CGSQ) was found to be useful to measure parenting stress, but it has not been validated among the Chinese population. AIMS: To assess the reliability and construct validity of the Chinese version of CGSQ among Chinese parents. METHODS: From 2016 to 2017, 266 parents (patient group) with a child having DSM-5-defined attention deficit hyperactivity disorder (ADHD) (n=107) or autism spectrum disorder (ASD) (n=159) and 268 parents of healthy children (control group) were recruited to the present study in Kunming, Yunnan province. All the parents were asked to fill out the Chinese version of CGSQ. We conducted exploratory factor analysis and confirmatory factor analysis (CFA) to verify construct validity of CGSQ in both patient and control groups. Cronbach's α coefficient as an index of internal consistency was assessed for each subscale. Fourteen days later, 23 subjects filled out the scale again. Intra-class correlation coefficient was calculated to evaluate the test-retest reliability. RESULTS: (1) Cronbach's alpha of the global scale was 0.901 for the control group and 0.952 for the patient group. The test-retest reliability for the whole scale was 0.890; (2) CFA indicated that the three-factor model had better fitting indices compared with the two-factor model in both groups. Besides, the fitting indices in the patient group were more favourable than those of the control group, with χ2/df=1.564, Goodness-of-Fit Index=0.841, Comparative Fit Index=0.954, and root mean square error of approximation=0.065 for the patient group at three-factor model; (3) The caregiver strain of ASD parents was statistically higher than that of ADHD parents, and caregiver strain of ADHD parents was higher than that of control group. CONCLUSION: These findings provide initial evidence to support the construct validity and reliability of CGSQ as a parenting stress measurement tool for Chinese parents, especially for parents of children with ADHD or ASD.

BIOMASS YIELD 1 regulates sorghum biomass and grain yield via the shikimate pathway.

Biomass and grain yield are key agronomic traits in sorghum (Sorghum bicolor); however, the molecular mechanisms that regulate these traits are not well understood. Here, we characterized the biomass yield 1 (by1) mutant, which displays a dramatically altered phenotype that includes reduced plant height, narrow stems, erect and narrow leaves, and abnormal floral organs. Histological analysis suggested that these phenotypic defects are mainly caused by inhibited cell elongation and abnormal floral organ development. Map-based cloning revealed that BY1 encodes a 3-deoxy-D-arabino-heptulosonate-7-phosphate synthase (DAHPS) that catalyses the first step of the shikimate pathway. BY1 was localized in chloroplasts and was ubiquitously distributed in the organs examined, particularly in the roots, stems, leaves, and panicles, which was consistent with its role in biomass production and grain yield. Transcriptome analysis and metabolic profiling revealed that BY1 was involved in primary metabolism and that it affected the biosynthesis of various secondary metabolites, especially flavonoids. Taken together, these findings demonstrate that BY1 affects biomass and grain yield in sorghum by regulating primary and secondary metabolism via the shikimate pathway. Moreover, our results provide important insights into the relationship between plant development and metabolism.