Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning

The recent surge of generative AI has been fueled by the generative power of diffusion probabilistic models and the scalable capabilities of large language models. Despite their potential, it remains elusive whether diffusion language models can solve general language tasks comparable to their autoregressive counterparts. This paper demonstrates that scaling diffusion models w.r.t. data, sizes, and tasks can effectively make them strong language learners. We build competent diffusion language models at scale by first acquiring knowledge from massive data via masked language modeling pretraining thanks to their intrinsic connections. We then reprogram pretrained masked language models into diffusion language models via diffusive adaptation, wherein task-specific finetuning and instruction finetuning are explored to unlock their versatility in solving general language tasks. Experiments show that scaling diffusion language models consistently improves performance across downstream language tasks. We further discover that instruction finetuning can elicit zero-shot and few-shot in-context learning abilities that help tackle many unseen tasks by following natural language instructions, and show promise in advanced and challenging abilities such as reasoning.Comment: added reference

Reclassification of the biocontrol agents Bacillus subtilis BY-2 and Tu-100 as Bacillus velezensis and insights into the genomic and specialised metabolite diversity of the species

The genomes of two historical Bacillus species strains isolated from the roots of oilseed rape and used routinely in PR China as biocontrol agents to suppress Sclerotinia disease were sequenced. Average nucleotide identity (ANI) and digital DNA–DNA hybridization analyses demonstrated that they were originally misclassified as Bacillus subtilis and now belong to the bacterial species Bacillus velezensis . A broader ANI analysis of available Bacillus genomes identified 292 B. velezensis genomes that were then subjected to core gene analysis and phylogenomics. Prediction and dereplication of specialized metabolite biosynthetic gene clusters (BGCs) defined the prevalence of multiple antimicrobial-associated BGCs and highlighted the natural product potential of B. velezensis . By defining the core and accessory antimicrobial biosynthetic capacity of the species, we offer an in-depth understanding of B. velezensis natural product capacity to facilitate the selection and testing of B. velezensis strains for use as biological control agents

Small Object Detection Based on Two-Stage Calculation Transformer

Despite the current small object detection task has achieved significant improvements, it still suffers from some problems. For example, it is a challenge to extract small object features because of little information in the scene of small objects, which may lose the original feature information of small object, resulting in poor detection results. To address this problem, this paper proposes a two-stage calculation Transformer (TCT) based small object detection network. Firstly, a two-stage calculation Transformer is embedded in the backbone feature extraction network for feature enhancement. Based on the traditional Transformer values computation, multiple 1D dilated convolutional layer branches with different feature fusions are utilized to implement global self-attention for the purpose of improving the feature representation and information interaction. Secondly, this paper proposes an effective residual connection module to improve the low-efficiency convolution and activation of the current CSPLayer, which helps to advance the information flow and learn more rich contextual details. Finally, this paper proposes a feature fusion and refinement module for fusing multi-scale features and improving the target feature representation capability. Quantitative and qualitative experiments on PASCAL VOC2007+2012 dataset, COCO2017 dataset and TinyPerson dataset show that the proposed algorithm has better ability of target feature extraction and higher detection accuracy for small target detection, compared with YOLOX

Integrated Analysis of Long Noncoding RNA and Coding RNA Expression in Esophageal Squamous Cell Carcinoma

Tumorigenesis is a complex dynamic biological process that includes multiple steps of genetic and epigenetic alterations, aberrant expression of noncoding RNA, and changes in the expression profiles of coding genes. We call the collection of those perturbations in genome space the “cancer initiatome.” Long noncoding RNAs (lncRNAs) are pervasively transcribed in the genome and they have key regulatory functions in chromatin remodeling and gene expression. Spatiotemporal variation in the expression of lncRNAs has been observed in development and disease states, including cancer. A few dysregulated lncRNAs have been studied in cancers, but the role of lncRNAs in the cancer initiatome remains largely unknown, especially in esophageal squamous cell carcinoma (ESCC). We conducted a genome-wide screen of the expression of lncRNAs and coding RNAs from ESCC and matched adjacent nonneoplastic normal tissues. We identified differentially expressed lncRNAs and coding RNAs in ESCC relative to their matched normal tissue counterparts and validated the result using polymerase chain reaction analysis. Furthermore, we identified differentially expressed lncRNAs that are co-located and co-expressed with differentially expressed coding RNAs in ESCC and the results point to a potential interaction between lncRNAs and neighboring coding genes that affect ether lipid metabolism, and the interaction may contribute to the development of ESCC. These data provide compelling evidence for a potential novel genomic biomarker of esophageal squamous cell cancer

Microdialysis Determination of Cefquinome Pharmacokinetics in Murine Thigh From Healthy, Neutropenic, and Actinobacillus pleuropneumoniae-Infected Mice

This study was aimed at applying microdialysis to explore cefquinome pharmacokinetics in thigh and plasma of healthy, neutropenic, and Actinobacillus pleuropneumoniae-infected mice. The relative recoveries (RRs) were tested in vitro by dialysis and retrodialysis and in vivo by retrodialysis. ICR mice were randomly divided into four groups: H-40 (healthy mice receiving cefquinome at 40 mg/kg), H-160, N-40 (neutropenic mice), and I-40 mg/kg (thigh infected-mice with A. pleuropneumoniae). After cefquinome administration, plasma was collected by retro-orbital puncture and thigh dialysate was collected by using a microdialysis probe with Ringer’s solution at a perfusion rate of 1.5 μL/min. Plasma and thigh dialysate samples were assessed by HPLC–MS/MS and analyzed by a non-compartment model. The mean in vivo recoveries in the thigh were 39.35, 38.59, and 37.29% for healthy, neutropenic, and infected mice, respectively. The mean plasma protein-binding level was 16.40% and was independent of drug concentrations. For all groups, the mean values of the free AUCinf in plasma were higher than those in murine thigh, while the elimination T1/2β for plasma were lower than those for murine thigh. Cefquinome penetration (AUCthigh/AUCplasma) from the plasma to thigh was 0.76, 0.88, 0.47, and 0.98 for H-40, N-40, I-40, and H-160 mg/kg, respectively. These results indicated that infection significantly affected cefquinome pharmacokinetics in murine thigh. In conclusion, we successfully applied a microdialysis method to evaluate the pharmacokinetics of cefquinome in murine thigh of healthy, neutropenic, and A. pleuropneumonia-infected mice and the pharmacokinetics of cefquinome was obviously affected by infection in thigh

Pharmacokinetic/Pharmacodynamic Integration to Evaluate the Changes in Susceptibility of Actinobacillus pleuropneumoniae After Repeated Administration of Danofloxacin

To evaluate the relationship between pharmacokinetic/pharmacodynamic (PK/PD) parameters and changes in susceptibility and resistance frequency of Actinobacillus pleuropneumoniae CVCC 259, a piglet tissue cage (TC) infection model was established. After A. pleuropneumoniae populations maintained at 108 CFU/mL in TCs, piglets were treated with various doses of danofloxacin once daily for 5 consecutive days by intramuscular injection. Both the concentrations of danofloxacin and the population of vial cells were determined. Changes in susceptibility and resistance frequency were monitored. Polymerase chain reaction (PCR) amplification of quinolone resistance-determining regions (QRDRs) and DNA sequencing were performed to identify point mutations in gyrA, gyrB, parC, and parE genes. Furthermore, the susceptibility of mutants to danofloxacin and enrofloxacin was determined in the presence or absence of reserpine to assess whether the mutants were caused by efflux pumps. The MICs and resistant frequency of A. pleuropneumoniae both increased when danofloxacin concentrations fluctuated between MIC99 (0.05 μg/mL) and MPC (mutant prevention concentration, 0.4 μg/mL). As for PK/PD parameters, the resistant mutants were selected and enriched when AUC24h/MIC99 ranged from 34.68 to 148.65 h or AUC24h/MPC ranged from 4.33 to 18.58 h. Substitutions of Ser-83→Tyr or Ser-83→Phe in gyrA and Lys-53→Glu in parC were observed. The susceptibility of mutants obtained via danofloxacin treatment at 1.25 and 2.5 mg/kg were less affected by reserpine. These results demonstrate that maintaining the value of AUC24h/MPC above 18.58 h may produce a desirable antibacterial effect and protect against A. pleuropneumoniae resistance to danofloxacin