AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models

There are growing interests in adapting large-scale language models using parameter-efficient fine-tuning methods. However, accelerating the model itself and achieving better inference efficiency through model compression has not been thoroughly explored yet. Model compression could provide the benefits of reducing memory footprints, enabling low-precision computations, and ultimately achieving cost-effective inference. To combine parameter-efficient adaptation and model compression, we propose AlphaTuning consisting of post-training quantization of the pre-trained language model and fine-tuning only some parts of quantized parameters for a target task. Specifically, AlphaTuning works by employing binary-coding quantization, which factorizes the full-precision parameters into binary parameters and a separate set of scaling factors. During the adaptation phase, the binary values are frozen for all tasks, while the scaling factors are fine-tuned for the downstream task. We demonstrate that AlphaTuning, when applied to GPT-2 and OPT, performs competitively with full fine-tuning on a variety of downstream tasks while achieving >10x compression ratio under 4-bit quantization and >1,000x reduction in the number of trainable parameters.Comment: Findings of EMNLP 202

Prognostic role of computed tomography analysis using deep learning algorithm in patients with chronic hepatitis B viral infection

Background/Aims The prediction of clinical outcomes in patients with chronic hepatitis B (CHB) is paramount for effective management. This study aimed to evaluate the prognostic value of computed tomography (CT) analysis using deep learning algorithms in patients with CHB. Methods This retrospective study included 2,169 patients with CHB without hepatic decompensation who underwent contrast-enhanced abdominal CT for hepatocellular carcinoma (HCC) surveillance between January 2005 and June 2016. Liver and spleen volumes and body composition measurements including subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and skeletal muscle indices were acquired from CT images using deep learning-based fully automated organ segmentation algorithms. We assessed the significant predictors of HCC, hepatic decompensation, diabetes mellitus (DM), and overall survival (OS) using Cox proportional hazard analyses. Results During a median follow-up period of 103.0 months, HCC (n=134, 6.2%), hepatic decompensation (n=103, 4.7%), DM (n=432, 19.9%), and death (n=120, 5.5%) occurred. According to the multivariate analysis, standardized spleen volume significantly predicted HCC development (hazard ratio [HR]=1.01, P=0.025), along with age, sex, albumin and platelet count. Standardized spleen volume (HR=1.01, P<0.001) and VAT index (HR=0.98, P=0.004) were significantly associated with hepatic decompensation along with age and albumin. Furthermore, VAT index (HR=1.01, P=0.001) and standardized spleen volume (HR=1.01, P=0.001) were significant predictors for DM, along with sex, age, and albumin. SAT index (HR=0.99, P=0.004) was significantly associated with OS, along with age, albumin, and MELD. Conclusions Deep learning-based automatically measured spleen volume, VAT, and SAT indices may provide various prognostic information in patients with CHB

Post-neoadjuvant treatment pancreatic cancer resectability and outcome prediction using CT, 18F-FDG PET/MRI and CA 19–9

Background CT prediction of resectability and prognosis following neoadjuvant treatment (NAT) in patients with pancreatic ductal adenocarcinoma (PDAC) remains challenging. This study aims to determine whether addition of 18F-fluorodeoxyglucose (FDG) postiron emission tomography (PET)/MRI and carbohydrate antigen (CA) 19–9 to contrast-enhanced CT (CECT) can improve accuracy of predicting resectability compared to CECT alone and predict prognosis in PDAC patients after NAT. Methods In this retrospective study, 120 PDAC patients (65 women; mean age, 66.7 years [standard deviation, 8.4]) underwent CECT, PET/MRI, and CA 19–9 examinations after NAT between January 2013 and June 2021. Three board-certified radiologists independently rated the overall resectability on a 5-point scale (score 5, definitely resectable) in three sessions (session 1, CECT; 2, CECT plus PET/MRI─no FDG avidity and no diffusion restriction at tumor-vessel contact indicated modification of CECT scores to ≥ 3; 3, CECT plus PET plus CA 19–9─no FDG avidity at tumor-vessel contact and normalized CA 19–9 indicated modification of CECT scores to ≥ 3). Jackknife free-response receiver operating characteristic method and generalized estimating equations were used to compare pooled area under the curve (AUC), sensitivity, and specificity of three sessions. Predictors for recurrence-free survival (RFS) were assessed using Cox regression analyses. Results Each session showed different pooled AUC (session 1 vs. 2 vs. 3, 0.853 vs. 0.873 vs. 0.874, p = 0.026), sensitivity (66.2% [137/207] vs. 86.0% [178/207] vs. 84.5% [175/207], p < 0.001) and specificity (67.3% [103/153] vs. 58.8% [90/153] vs. 60.1% [92/153], p = 0.048). According to pairwise comparison, specificity of CECT plus PET/MRI was lower than that of CECT alone (adjusted p = 0.042), while there was no significant difference in specificity between CECT alone and CECT plus PET plus CA 19–9 (adjusted p = 0.081). Twenty-eight of 69 patients (40.6%) with R0 resection experienced tumor recurrence (mean follow-up, 18.0 months). FDG avidity at tumor-vessel contact on post-NAT PET (HR = 4.37, p = 0.033) and pathologically confirmed vascular invasion (HR = 5.36, p = 0.004) predicted RFS. Conclusion Combination of CECT, PET and CA 19–9 increased area under the curve and sensitivity for determining resectability, compared to CECT alone, without compromising the specificity. Furthermore, 18F-FDG avidity at tumor-vessel contact on post-NAT PET predicted RFS

Diagnostic criteria of perfluorobutane-enhanced ultrasonography for diagnosing hepatocellular carcinoma in high-risk individuals: how is late washout determined?

© 2022 Korean Society of Ultrasound in Medicine (KSUM).Purpose: The aim of this study was to investigate the optimal washout criteria of perfluorobutane-enhanced ultrasonography (PFB-US) for the diagnosis of hepatocellular carcinoma (HCC) in high-risk individuals. Methods: Participants at risk of HCC with treatment-naïve solid hepatic observations (≥1 cm) who underwent PFB-US from March 2019 to September 2020 were prospectively recruited. Arterial phase hyperenhancement (APHE), washout time, and washout degree were evaluated. The diagnosis of HCC was made by non-rim APHE with late and mild washout. The per-lesion diagnostic performance for diagnosing HCC using different cutoffs for late washout (50, 55, 60, 65, and 70 seconds postcontrast) and the different time windows for determining washout (until 2, 3, 4, 5, 6, 7, 8, 9, and 10 minutes postcontrast) were compared using the McNemar test. Results: In total, 101 participants with 113 observations (mean size, 33.5±2.8 mm; HCCs [n=82], non-HCC malignancies [n=16], benign [n=15]) were evaluated. Non-rim APHE was observed in 86.6% (71/82) of HCCs. As the cutoff time for late washout increased, the specificity increased to 100% (95% confidence interval [CI], 88.8% to 100%) at the 60-second cutoff with 62.2% sensitivity (95% CI, 50.8% to 72.7%). When the time window for determining washout became wider, the sensitivity and accuracy increased until 6 minutes, with 100% specificity at all times. Conclusion: Determining washout within 6 minutes after contrast injection with a 60-second cutoff for late washout showed the highest sensitivity without losing specificity for diagnosing HCC using PFB-US in individuals at high risk.N

Assessment of spatial tumor heterogeneity using CT growth patterns estimated by tumor tracking on 3D CT volumetry of multiple pulmonary metastatic nodules.

PurposeOur purpose was to assess the differences in growth rates of multiple pulmonary metastatic nodules using three-dimensional (3D) computed tomography (CT) volumetry and propose a concept of CT spatial tumor heterogeneity.Materials and methodsWe manually measured the largest diameter of metastatic pulmonary nodules on chest CT scans, and calculated the 3D maximum diameter and the volume using a semi-automated 3D CT volumetry of each nodule. The tumor response was assessed according to the revised RECIST 1.1. We defined a nodule as an outlier based on 1.5 times growth during follow-up. The CT spatial tumor heterogeneity was statistically analyzed by the "minimum combination t-test method" devised in our study.ResultsOn manual measurement, the tumor response category was stable disease (SD) in all 10 patients. Of them, total 155 metastatic nodules (4-52 nodules per patient) were segmented using the 3D CT volumetry. In the 3D maximum diameter, 9 patients had SD except for one patient with partial response in the two selected nodules; for the volume, all 10 patients were SD. For the 3D maximum diameter, six patients had at least one outlier; whereas five patients had the outlier on the volume measurement. Six patients were proven to have overall CT spatial tumor heterogeneity.ConclusionsThe spatial tumor heterogeneity determined in a CT parametric approach could be statistically assessed. In patients with CT spatial heterogeneity, tumors with different growth rates may be neglected when the nodules are assessed according to the current guideline

Differential and prognostic MRI features of gallbladder neuroendocrine tumors and adenocarcinomas

Objectives To identify MRI features that are helpful for the differentiation of gallbladder neuroendocrine tumors (GB-NETs) from gallbladder adenocarcinomas (GB-ADCs) and to evaluate their prognostic values. Methods Between January 2008 and December 2018, we retrospectively enrolled patients who underwent MRI for GB malignancy. Two radiologists independently assessed the MRI findings and reached a consensus. Significant MRI features, which distinguish GB-NETs from GB-ADCs, were identified. Cox regression analyses were performed to find MRI features that were prognostic for overall survival. Results There were 63 patients with GB-NETs (n = 21) and GB-ADCs (n = 42). Compared with GB-ADCs, GB-NETs more frequently demonstrated the following MRI features: well-defined margins, intact overlying mucosa, and thick rim contrast enhancement and/or diffusion restriction (ps &lt; 0.001). Liver metastases were more common and demonstrated thick rim contrast enhancement and diffusion restriction in GB-NETs (ps &lt; 0.001). Lymph node (LN) metastasis showed thick rim diffusion restriction more often in GB-NETs than in GB-ADCs (p = 0.009). On quantitative analysis, the sizes of the GB mass and metastatic LNs in GB-NETs were larger than those in GB-ADCs (p = 0.002 and p = 0.010, respectively). The ratio of apparent diffusion coefficient values between the lesion and the spleen was lower in the GB mass, liver metastases, and LN metastases of GB-NETs than those of GB-ADCs (p &lt; 0.001, p = 0.017, and p &lt; 0.001, respectively). Survival analysis revealed that a large metastatic LN (hazard ratio 1.737; 95% confidence interval, 1.112-2.712) was the only poor prognostic factor (p = 0.015). Conclusion Several MRI features aided in differentiating between GB-NETs and GB-ADCs. A large metastatic LN was associated with poor survival.N

Facile method for enrofloxacin detection in milk using a personal glucose meter

We developed a simple method for enrofloxacin detection in water and milk using a personal glucose meter (PGM). After mixing a test sample with Escherichia coli (E. coli) in lysogeny broth (LB) and glucose, the amount of glucose consumed (change in concentration) by bacterial metabolism was measured using the PGM. The antibacterial activity of enrofloxacin obstructs bacterial metabolism and so reduces glucose consumption in proportion to the enrofloxacin concentration. The limit of detection for enrofloxacin in both matrices was 5 ng/mL and the assay time was 2 h. Further, the change in glucose concentration could be determined qualitatively by the naked eye using glucose test strips for high-throughput screening. (C) 2017 Elsevier B.V. All rights reserved.111sciescopu

Energy, safety, and absorption efficiency evaluation of a pilot-scale H2S abatement process using MDEA solution in a coke-oven gas

This study models an H2S removal unit for steel-manufacturing coke-oven gas and suggests optimal operating conditions and safety analysis for absorption and regeneration columns. Sensitivity analysis was performed on H2S removal to identify the minimum methyldiethanolamine (MDEA) solution flow rate using 99% H2S removal as a constraint and minimal heat duty. The optimized L/G ratio and regeneration column pressure were investigated accordance with change in H2S emission limit or desulfurization efficiency (99-99.9%), to reflect the different usage of COG. For the absorption column, the MDEA solution and heating duty were expected to be reduced in optimal conditions by 35% and 23% respectively when absorbent to gas mass ratio was decreased from 2.0 to 1.3, respectively. The most effective operating pressure for the distillation column was 2.5 bar (Basis pressure: 1.2 bar) based on a trade-off between reboiler energy and the H2S remaining in the recycle stream. According to optimization result with variation in efficiency, the higher optimized L/G ratio, higher reboiler duty, higher intermediate heat exchanger duty, and lower optimized regenerator pressure were required to maintain higher efficiency with different COG utilization. To ensure safe operating conditions, chemical reactivity tests were conducted for mixture of chemicals. Additionally, Gas dispersion analysis predicted the concentration and distance of gas spread to ensure safety from gas leak accident