Large Trajectory Models are Scalable Motion Predictors and Planners

Motion prediction and planning are vital tasks in autonomous driving, and recent efforts have shifted to machine learning-based approaches. The challenges include understanding diverse road topologies, reasoning traffic dynamics over a long time horizon, interpreting heterogeneous behaviors, and generating policies in a large continuous state space. Inspired by the success of large language models in addressing similar complexities through model scaling, we introduce a scalable trajectory model called State Transformer (STR). STR reformulates the motion prediction and motion planning problems by arranging observations, states, and actions into one unified sequence modeling task. With a simple model design, STR consistently outperforms baseline approaches in both problems. Remarkably, experimental results reveal that large trajectory models (LTMs), such as STR, adhere to the scaling laws by presenting outstanding adaptability and learning efficiency. Qualitative results further demonstrate that LTMs are capable of making plausible predictions in scenarios that diverge significantly from the training data distribution. LTMs also learn to make complex reasonings for long-term planning, without explicit loss designs or costly high-level annotations

Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring

Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the space-borne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model’s SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0–10 cm) from the farmland (2.5 × 104 m2) of Fukang City, Xinjiang Uygur AutonomousRegion, China. The random forest (RF) method and extreme learning machine (ELM) were used to estimate the SMC using six methods of pretreatments combined with four optimal spectral indices. The validation accuracy of the estimated method clearly increased compared with that of linear models. The combination of pretreatments and indices by our assessment effectively eliminated the interference and the noises. Comparing two machine learning algorithms showed that the RF models were superior to the ELM models, and the best model was PIR (R2val = 0.907, RMSEP = 1.477, and RPD = 3.396). The SMC map predicted via the best scheme was highly similar to the SMC map measured. We conclude that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy (R2val = 0.907) via UAV hyperspectral imagery on a regional scale. Ultimately, our program might improve management and conservation strategies for agroecosystem systems in arid regions

Identification and classification of the genomes of novel microviruses in poultry slaughterhouse

Microviridae is a family of phages with circular ssDNA genomes and they are widely found in various environments and organisms. In this study, virome techniques were employed to explore potential members of Microviridae in a poultry slaughterhouse, leading to the identification of 98 novel and complete microvirus genomes. Using a similarity clustering network classification approach, these viruses were found to belong to at least 6 new subfamilies within Microviridae and 3 higher-level taxonomic units. Genome size, GC content and genome structure of these new taxa showed evident regularities, validating the rationality of our classification method. Our method can divide microviruses into about 45 additional detailed clusters, which may serve as a new standard for classifying Microviridae members. Furthermore, by addressing the scarcity of host information for microviruses, the current study significantly broadened their host range and discovered over 20 possible new hosts, including important pathogenic bacteria such as Helicobacter pylori and Vibrio cholerae, as well as different taxa demonstrated different host specificities. The findings of this study effectively expand the diversity of the Microviridae family, providing new insights for their classification and identification. Additionally, it offers a novel perspective for monitoring and controlling pathogenic microorganisms in poultry slaughterhouse environments

DEVELOPMENT AND SYNTHETIC APPLICATIONS OF BIOCATALYTIC METHODS: NATURAL AND NON-NATURAL ENZYMATIC REACTIONS

Biocatalysis is a catalytic method that originates from nature and utilizes biological enzymesystems or specific enzymes. These enzymes are incredibly good at accelerating reaction rates with incomparable selectivity. Moreover, they can be precisely altered and tailored to particular reactions through directed evolution, which was awarded Nobel Prize in 2018. As a result, the biocatalytic process in organic chemistry has become even more powerful for synthesizing highvalue compounds. Consequently, it has been adopted into many pharmaceuticals' synthetic routes. Enzymes are excellent catalysts because they inherently bear excellent enantioselectivity and regioselectivity, high turnover numbers, and mild reaction conditions. However, enzymes are often recognized as particular catalysts for a narrow scope of substrates and reactivities, downgrading their general utilization in organic chemistry. In the first chapter, I summarized the general information about asymmetric catalysis, biocatalysis, and radical chemistry. In the second chapter, I outline my work utilizing commercially available enzymes and their natural functionality paired with a novel racemization mechanism to rapidly access libraries of enatio- and diastereo-enriched 1,2 amino alcohols. In the third chapter, I discussed my collaborative work with Dr. Yuxuan for developing evolved ‘ene’-reductases (EREDs) to control the highly reactive Nitrogen-Centered Radical (NCR) and catalyze an asymmetric radical intramolecular and intermolecular hydroamination reaction. In the last chapter, I complied three collaborative works with Dr. Haigen, Dr. Jose, and Claire on charge transfer complex-enabled photo-enzymatic enantioselective and regioselective C-C bond formation. Specially, Dr. Haigen and I utilized enzyme and charge transfer complex as a general platform for an enzymatic Csp3– Csp3 reductive cross electrophile coupling (XEC); Claire and I demonstrated a biocatalystcontrolled, charge transfer complex enabled method for the regioselective alkylation of electronrich and deficient heteroarenes; Dr. Jose and I took advantage of charge transfer complex and developed method to carry out radical cyclization using 620 nm red light

A case of surgically-associated anti GQ1b antibody syndrome accompanied by saccadic ping pong gaze

Abstract Background Periodic alternating ping-pong gaze (PPG) is a rare disease with few reports. To our knowledge, there was no report on anti GQ1b antibody syndrome accompanied by PPG. This paper reported a case of anti GQ1b antibody syndrome with Bickerstaff’s Encephalitis (BBE) overlapping classic Guillain-Barre Syndrome (GBS) after aortic valve replacement, accompanied by an excessive PPG in the course of diagnosis and treatment, this was indeed rarely. Case presentation A 55-year-old male patient was admitted to our hospital with intermittent chest tightness for 3 months, and his condition has worsened in the past 10 days. Aortic valve replacement was performed because of the existence of the moderate and severe stenosis of aortic valve. Horizontal movement of the eyeball was involuntarily slow. The eyeball hovered and returned from one side to the other horizontally for 3–4 s per cycle. In combination with the patient’s typical clinical and laboratory tests, the final diagnosis was anti GQ1b antibody syndrome BBE combined with GBS, accompanied by saccadic ping pong gaze. Intravenous immunoglobulin (0.4 g/kg) was given for immunomodulation, methylprednisolone (1000 mg) therapy and symptomatic treatment were performed in the patient. Conclusions The patients were discharged from hospital on the thirtieth day because of economic reasons. After 6 months of follow up, the patients left behind a lack of fluency in speech and limb mobility, but the basic life can be taken care of by himself

Investigation of Local Weighting Filtering on Randomization Technique Estimates in a Data Assimilation System

Mainstream numerical weather prediction (NWP) centers usually estimate the standard deviations of background error by using a randomization technique to calibrate specific parameters of the background error covariance model in variational data assimilation (VAR) systems. However, the sampling size of the randomization technique is typically several orders of magnitude smaller than that of model state variables, and using finite-sized estimates as a proxy for the truth can lead to sampling noise, which may contaminate the estimation of the standard deviation. The sampling noise is firstly investigated in an atmospheric model to show that the sampling noise has a symmetrical structure oscillating around the truth on a small scale. To alleviate the sampling noise, a heterogeneous local weighting filtering is proposed based on distance-weighted correlation and similarity-weighted correlation. Local weighting filtering is easy to implement in the VAR operational systems and has a low computational cost in the post-processing of reducing the sampling noise. The validity and performance of local weighting filtering method are examined in a realistic model framework to show that the proposed filtering is able to eliminate most of the sampling noise dramatically, the details of the filtered results are more visible, and the accuracy of the filtered results is almost the same as that estimated from the larger sample. The signal-to-noise ratio of the optimal filtered field is improved by nearly 20%. A comparison with the widely used spectral filtering approach in the operational system is considered, showing that the proposed filtering method is more efficient to implement in the filtering procedure and exhibits very good performance in terms of preserving the local anisotropic features of the estimates. These attractive results show the potential efficiency of the local weighting filtering method for solving the noise issue in the randomization technique

Bladder entrance of microplastic likely induces toxic effects in carnivorous macrophyteUtricularia aureaLour

The global distribution of microplastic (particle size < 5 mm) is of growing concern, especially in aquatic environments where it may cause adverse effects on resident organisms. To date, however, few studies have focused on the impacts of microplastic on aquatic plants. Here, we conducted a microcosm study to investigate the toxic effects of microplastic on the carnivorous aquatic macrophyteUtricularia aureaLour. Based on microscopic images and Raman spectrum analysis, we found that most polyvinyl chloride (PVC) particles were smaller than the valve ofU. aureabladders, thus allowing entrance into the plant, but this was not so for polyethylene (PE) particles. Furthermore, PVC (50 mg L-1) had significantly negative effects on growth and physiological parameters such as macrophyte length, chlorophyll content, and fluorescence, whereas, at the same concentration, PE had no such effects. Further analysis revealed that after bladder removal, the macrophytes did not respond to PVC particle toxicity. Thus, intake of microplastics (i.e., PVC) through bladders is likely responsible for inducing toxic effects to the growth and physiological parameters ofU. aurea

The Characteristic of Fe as a &beta;-Ti Stabilizer in Ti Alloys

It is well known that adding elements, especially &beta;-Ti stabilizers, are holding a significant effect on titanium alloy strength due to the solution and precipitate strengthening mechanisms. In order to reveal the Fe strengthening mechanism in titanium, this study investigate the effect of Fe on the stability of &beta;-Ti and the phase transition between &alpha;, &beta; and &omega; phase with first-principle calculations. According to our study, Fe is a strong &beta;-Ti phase stabilizer could owe to the 3d orbital into eg and t2g states which results in strong hybridization between Fe-d orbital and Ti-d orbital. The phase transition from &omega; to &beta; or from &alpha; to &beta; becomes easier for Fe-doped Ti compared to pure titanium. Based on our results, it is found that one added Fe atom can lead the phase transition (&omega; &rarr; &beta;) of at least nine titanium atoms, which further proves that Fe has a strong stabilizing effect on &beta;-Ti phase. This result provides a solid guide for the future design of high-strength titanium with the addition of Fe