A Generalized Design for Affinity Chromatography Columns

In affinity chromatography, an adsorbent with a high selectivity for a target solute is used to isolate the target molecule from other impurities. With sufficient selectivity, the target molecule can be isolated in a highly purified and concentrated state. Common applications of affinity chromatography include Protein A chromatography for antibody purification and Immobilized Metal Affinity Chromatography (IMAC) for protein purification. The well-known design method based on constant-pattern mass transfer zone analysis does not apply to small feed batches, which are insufficient to form constant-pattern frontal waves. Other literature design methods rely on simulation or experimental trials that can be time-consuming and costly. In addition, it can be difficult to optimize the process to achieve the desired purity, yield, and throughput. In this study, a convenient graphical design method based on intrinsic adsorption parameters (void fractions, adsorption isotherm, and solute diffusivity), mass transfer parameters, and dimensionless groups is developed for affinity chromatography systems with Langmuir isotherms. Only a small number of experiments are needed to obtain these parameters. The method is tested with literature data for Protein A chromatography for antibody purification, and close agreement is obtained. Graphs can be used to examine the effects of material properties, capture yield, and throughput on column utilization. In addition, it can easily adjust to meet various design requirements and can take into account variations in the intrinsic parameters. Various sorbents can be evaluated for cost effectiveness based on the intrinsic parameters, making this method applicable to a broad range of affinity chromatography systems

DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text

Large language models (LLMs) have notably enhanced the fluency and diversity of machine-generated text. However, this progress also presents a significant challenge in detecting the origin of a given text, and current research on detection methods lags behind the rapid evolution of LLMs. Conventional training-based methods have limitations in flexibility, particularly when adapting to new domains, and they often lack explanatory power. To address this gap, we propose a novel training-free detection strategy called Divergent N-Gram Analysis (DNA-GPT). Given a text, we first truncate it in the middle and then use only the preceding portion as input to the LLMs to regenerate the new remaining parts. By analyzing the differences between the original and new remaining parts through N-gram analysis in black-box or probability divergence in white-box, we can clearly illustrate significant discrepancies between machine-generated and human-written text. We conducted extensive experiments on the most advanced LLMs from OpenAI, including text-davinci-003, GPT-3.5-turbo, and GPT-4, as well as open-source models such as GPT-NeoX-20B and LLaMa-13B. Results show that our zero-shot approach exhibits state-of-the-art performance in distinguishing between human and GPT-generated text on four English and one German dataset, outperforming OpenAI's own classifier, which is trained on millions of text. Additionally, our methods provide reasonable explanations and evidence to support our claim, which is a unique feature of explainable detection. Our method is also robust under the revised text attack and can additionally solve model sourcing. Codes are available at https://github.com/Xianjun-Yang/DNA-GPT

Dynamic Prompting: A Unified Framework for Prompt Tuning

It has been demonstrated that the art of prompt tuning is highly effective in efficiently extracting knowledge from pretrained foundation models, encompassing pretrained language models (PLMs), vision pretrained models, and vision-language (V-L) models. However, the efficacy of employing fixed soft prompts with a predetermined position for concatenation with inputs for all instances, irrespective of their inherent disparities, remains uncertain. Variables such as the position, length, and representations of prompts across diverse instances and tasks can substantially influence the performance of prompt tuning. In this context, we provide a theoretical analysis, which reveals that optimizing the position of the prompt to encompass the input can capture additional semantic information that traditional prefix or postfix prompt tuning methods fail to capture. Building upon our analysis, we present a unified dynamic prompt (DP) tuning strategy that dynamically determines different factors of prompts based on specific tasks and instances. To accomplish this, we employ a lightweight learning network with Gumble-Softmax, allowing us to learn instance-dependent guidance. Experimental results underscore the significant performance improvement achieved by dynamic prompt tuning across a wide range of tasks, including NLP tasks, vision recognition tasks, and vision-language tasks. Furthermore, we establish the universal applicability of our approach under full-data, few-shot, and multitask scenarios. Codes are available at https://github.com/Xianjun-Yang/DPT.Comment: updat

Analisis Anteseden Orientasi Pasar Dan Pengaruhnya Terhadap Pembelajaran Organisasi UMKM Di Eks Karesidenan Surakarta

The weakness of leaders in Small and Medium Business (UKM) in Indonesia dealing with market oriented are the low motivation of entrepreneurs, the low of leader commitment to apply market orientedmethod in his organization, and lack of training for themselves (Suliyanto, 2011). This research aimed to develop the culture of market oriented throughout the learning of organization that be done by examining the effect of customers and competitorsoriented to the learning of organization. Respondents of this research are 300 owners or managers of UKM in the Greater of Surakarta. The technique of sampling in this research is purposive sample who has two criteria; an Indonesian and at least has two employees. The technique of analysis was done by Structural Equation Model (SEM). The result of this research shows antecedent variable; entrepreneurs\u27 orientation, under-pressured of managers, training programs, and reward system has effect to customer orientation in Small and Medium Business (UKM)

LLUSD Articulator - Volume 26, Number 2

Contents: 4 | Dean\u27s message7 | Bracing for accreditation14 | Developing IPE17 | Shirley Lee\u27s long commute22 | Commencement 201532 | Science: Accessing Zirconia crowns40 | How to treat a lion42 | News: Noah\u27s cleft cookies LLUUSD Ironman eBook pedagogy Healing Hands Lobbying for kids57 | Elmer Kelln: LLUSD pioneer60 | Fond Farewellshttps://scholarsrepository.llu.edu/articulator/1009/thumbnail.jp

EFECTOS DE LA SIEMBRA Y EL TRASPLANTE A RECIPIENTE C 3NICO EN EL CRECIMIENTO DE PITHECELLOBIUM DULCE Y PLATYMISCIUM DIADELPHUM EN VIVERO

Atendiendo al potencial para el arbolado urbano de \ue1reas tropicales secas con Pithecellobium dulce (Roxb.) Benth y Platymiscium diadelphum S. F. Blake y la importancia del recipiente de propagaci\uf3n en el crecimiento y conformaci\uf3n de las ra\uedces de plantas le\uf1osas, se estudi\uf3 la siembra y trasplante de ambas especies en dos tipos de recipientes c\uf3nicos (dise\uf1ados para la producci\uf3n de forestales) en fase inicial de vivero. Los tratamientos consistieron en (T1): semillas de ambas especies, propagadas mediante siembra en tubetes c\uf3nicos; (T2): semillas de las dos especies sembradas en tubetes para ra\uedz pivotante; y (T3): plantas propagadas como en T1 y trasplantadas al recipiente usado en T2. Las evaluaciones se iniciaron a los 30 d\uedas despu\ue9s de iniciado el ensayo y se prolongaron durante 90 d\uedas. Se determinaron las siguientes variables: altura de la planta, di\ue1metro del tallo, n\ufamero de hojas, \ue1rea foliar, longitud de ra\uedces, biomasa seca de la parte a\ue9rea y ra\uedces. Los datos registrados permitieron estimar la relaci\uf3n de biomasa seca a\ue9rea/biomasa seca ra\uedces, \uedndice de esbeltez e \uedndice de calidad de Dickson. Estas variables biom\ue9tricas e \uedndices morfol\uf3gicos se utilizaron para caracterizar cuantitativa y cualitativamente el crecimiento de ambas especies los cuales indicaron que las plantas cumplen con las caracter\uedsticas de calidad y potencial para la sobrevivencia al trasplante durante el per\uedodo de evaluaci\uf3n. El tubete para ra\uedz pivotante (especialmente el T3) favoreci\uf3 la mejor distribuci\uf3n, disposici\uf3n y conformaci\uf3n del cepell\uf3n. Palabras clave adicionales: Siembra, trasplante, tubete. ABSTRACT Considering the potential for the urban tree planting of dry tropical areas of Pithecellobium dulce (Roxb.) Benth and Platymiscium diadelphum S.F. Blake and the importance of the container on the initial growth and conformation of the roots in woody plants, sowing and transplantation were studied in both species in two types of conical containers (designed to produce forestry plants). The treatments consisted of (T1): seeds propagated by seeding in conical tubes; (T2): seeds sown in tubes for pivoting roots; and (T3): seeds sown as in T1 and later transplanted to the same container used in T2. The evaluations were started 30 days after the start of the trial and extended for 90 days, determining the following variables: height of the plant, stem diameter, number of leaves, leaf area, relative chlorophyll index, length of roots, dry mass of aerial part and roots. The data allowed to calculate the ratio of dry mass of aerial part/roots, slenderness index, and Dickson index. These biometric variables and morphological indices were used to quantitatively and qualitatively characterize the growth of both species, which indicated that the plants meet the quality characteristics and potential for transplant survival during the evaluation period. The T3 favored the best distribution, arrangement and conformation of the root ball. Additional key words: Sowing, transplant, propagation tube. <br

Voxel2Hemodynamics: An End-to-end Deep Learning Method for Predicting Coronary Artery Hemodynamics

Local hemodynamic forces play an important role in determining the functional significance of coronary arterial stenosis and understanding the mechanism of coronary disease progression. Computational fluid dynamics (CFD) have been widely performed to simulate hemodynamics non-invasively from coronary computed tomography angiography (CCTA) images. However, accurate computational analysis is still limited by the complex construction of patient-specific modeling and time-consuming computation. In this work, we proposed an end-to-end deep learning framework, which could predict the coronary artery hemodynamics from CCTA images. The model was trained on the hemodynamic data obtained from 3D simulations of synthetic and real datasets. Extensive experiments demonstrated that the predicted hemdynamic distributions by our method agreed well with the CFD-derived results. Quantitatively, the proposed method has the capability of predicting the fractional flow reserve with an average error of 0.5\% and 2.5\% for the synthetic dataset and real dataset, respectively. Particularly, our method achieved much better accuracy for the real dataset compared to PointNet++ with the point cloud input. This study demonstrates the feasibility and great potential of our end-to-end deep learning method as a fast and accurate approach for hemodynamic analysis.Comment: 8page