Screening, Brief Intervention, and Referral to Treatment for Alcohol Misuse in Primary Care

An annual average of 88,000 deaths in the United States from 2006 to 2010 has been attributed to alcohol misuse, defined as risky or heavy alcohol consumption. Heavy or risky alcohol use for all adults aged 65 and older and for women aged 18 and older is defined as consuming more than 7 drinks per week and/or 3 drinks per day. For men aged 18 to 65 years of age, heavy or risky alcohol use is defined as consuming more than 14 drinks a week and/or 4 drinks a day. Chronic alcohol misuse increases the incidence of heart problems, cognitive decline, hypertension, liver problems, and cancer. It is recommended to screen and treat patients for alcohol misuse in the primary care setting. The purpose of this project was to increase alcohol misuse screening and intervention for adults aged 18 years and older in an internal medicine practice. A screening and treatment protocol was established to align with the United States Preventive Services Task Force clinical recommendation guidelines. This protocol consisted of screening using the Alcohol Use Disorders Identification Test, brief intervention, and referral to treatment. During project implementation, 420 patients were screened and of those screened, 18 patients (4.3%) were positive for alcohol misuse. Of those that screened positive, 9 patients (50%) received brief intervention and verbal education in which 3 patients (33.3%) received educational handouts. Two patients were considered severe risk and both received brief intervention, refused the recommended referrals for psychiatric care, and considered follow-up treatments with the primary care physician. This project demonstrates that screening for alcohol misuse and providing brief intervention is feasible to implement in the primary care setting. Alcohol misuse awareness allows primary care providers to empower patients with the right tools to make informed decisions to their health

High cell density and latent membrane protein 1 expression induce cleavage of the mixed lineage leukemia gene at 11q23 in nasopharyngeal carcinoma cell line

<p>Abstract</p> <p>Background</p> <p>Nasopharyngeal carcinoma (NPC) is commonly found in Southern China and South East Asia. Epstein-Barr virus (EBV) infection is well associated with NPC and has been implicated in its pathogenesis. Moreover, various chromosome rearrangements were reported in NPC. However, the underlying mechanism of chromosome rearrangement remains unclear. Furthermore, the relationship between EBV and chromosome rearrangement with respect to the pathogenesis of NPC has not been established. We hypothesize that during virus- or stress-induced apoptosis, chromosomes are initially cleaved at the base of the chromatin loop domain structure. Upon DNA repair, cell may survive with rearranged chromosomes.</p> <p>Methods</p> <p>In this study, cells were seeded at various densities to induce apoptosis. Genomic DNA extracted was processed for Southern hybridization. In order to investigate the role of EBV, especially the latent membrane protein 1 (LMP1), <it>LMP1 </it>gene was overexpressed in NPC cells and chromosome breaks were analyzed by inverse polymerase chain (IPCR) reaction.</p> <p>Results</p> <p>Southern analysis revealed that high cell density resulted in cleavage of the mixed lineage leukemia (<it>MLL</it>) gene within the breakpoint cluster region (bcr). This high cell density-induced cleavage was significantly reduced by caspase inhibitor, Z-DEVD-FMK. Similarly, IPCR analysis showed that <it>LMP1 </it>expression enhanced cleavage of the <it>MLL </it>bcr. Breakpoint analysis revealed that these breaks occurred within the matrix attachment region/scaffold attachment region (MAR/SAR).</p> <p>Conclusions</p> <p>Since <it>MLL </it>locates at 11q23, a common deletion site in NPC, our results suggest a possibility of stress- or virus-induced apoptosis in the initiation of chromosome rearrangements at 11q23. The breakpoint analysis results also support the role of chromatin structure in defining the site of chromosome rearrangement.</p

Sorting Real Numbers in Constant Time Using n^2/log^cn Processors

We study the sorting of real numbers into a linked list on the PRAM (Parallel Random-Access Machine) model. We show that n real numbers can be sorted into a linked list in constant time using n2 processors. Previously n numbers can be sorted into a linked list using n2 processors in O(loglogn) time. We also study the time processor trade-off for sorting real numbers into a linked list on the PRAM (Parallel Random Access Machine) model. We show that n real numbers can be sorted into a linked list with n2/t processors in O(logt) time. Previously n real numbers can be sorted into a linked list using n3 processors in constant time and n2 processors in O(loglogn). And then we show that input array of n real numbers  can be sorted into linked list in constant time using n2/logcn  processors for any positive constant c. We believe that further reduction on the number of processors for sorting real numbers in constant time will be very difficult if not impossible

Predicting Compressive Strength and Hydration Products of Calcium Aluminate Cement using Data-Driven Approach

Calcium aluminate cement (CAC) has been explored as a sustainable alternative to Portland cement, the most widely used type of cement. However, the hydration reaction and mechanical properties of CAC can be influenced by various factors such as water content, Li2CO3 content, and age. Due to the complex interactions between the precursors in CAC, traditional analytical models have struggled to predict CAC binders\u27 compressive strength and porosity accurately. To overcome this limitation, this study utilizes machine learning (ML) to predict the properties of CAC. The study begins by using thermodynamic simulations to determine the phase assemblages of CAC at different ages. The XGBoost model is then used to predict the compressive strength, porosity, and hydration products of CAC based on the mixture design and age. The XGBoost model is also used to evaluate the influence of input parameters on the compressive strength and porosity of CAC. Based on the results of this analysis, a closed-form analytical model is developed to predict the compressive strength and porosity of CAC accurately. Overall, the study demonstrates that ML can be effectively used to predict the properties of CAC binders, providing a valuable tool for researchers and practitioners in the field of cement science

Identification of Regulatory Requirements Relevant to Business Processes: A Comparative Study on Generative AI, Embedding-based Ranking, Crowd and Expert-driven Methods

Organizations face the challenge of ensuring compliance with an increasing amount of requirements from various regulatory documents. Which requirements are relevant depends on aspects such as the geographic location of the organization, its domain, size, and business processes. Considering these contextual factors, as a first step, relevant documents (e.g., laws, regulations, directives, policies) are identified, followed by a more detailed analysis of which parts of the identified documents are relevant for which step of a given business process. Nowadays the identification of regulatory requirements relevant to business processes is mostly done manually by domain and legal experts, posing a tremendous effort on them, especially for a large number of regulatory documents which might frequently change. Hence, this work examines how legal and domain experts can be assisted in the assessment of relevant requirements. For this, we compare an embedding-based NLP ranking method, a generative AI method using GPT-4, and a crowdsourced method with the purely manual method of creating relevancy labels by experts. The proposed methods are evaluated based on two case studies: an Australian insurance case created with domain experts and a global banking use case, adapted from SAP Signavio's workflow example of an international guideline. A gold standard is created for both BPMN2.0 processes and matched to real-world textual requirements from multiple regulatory documents. The evaluation and discussion provide insights into strengths and weaknesses of each method regarding applicability, automation, transparency, and reproducibility and provide guidelines on which method combinations will maximize benefits for given characteristics such as process usage, impact, and dynamics of an application scenario

Multi-Behavior Recommendation with Cascading Graph Convolution Networks

Multi-behavior recommendation, which exploits auxiliary behaviors (e.g., click and cart) to help predict users' potential interactions on the target behavior (e.g., buy), is regarded as an effective way to alleviate the data sparsity or cold-start issues in recommendation. Multi-behaviors are often taken in certain orders in real-world applications (e.g., click>cart>buy). In a behavior chain, a latter behavior usually exhibits a stronger signal of user preference than the former one does. Most existing multi-behavior models fail to capture such dependencies in a behavior chain for embedding learning. In this work, we propose a novel multi-behavior recommendation model with cascading graph convolution networks (named MB-CGCN). In MB-CGCN, the embeddings learned from one behavior are used as the input features for the next behavior's embedding learning after a feature transformation operation. In this way, our model explicitly utilizes the behavior dependencies in embedding learning. Experiments on two benchmark datasets demonstrate the effectiveness of our model on exploiting multi-behavior data. It outperforms the best baseline by 33.7% and 35.9% on average over the two datasets in terms of Recall@10 and NDCG@10, respectively.Comment: Accepted by WWW 202

Investigation of Properties Alternation during Super-Critical CO2 Injection in Shale

The low recovery of oil from tight liquid-rich formations is still a major challenge for a tight reservoir. Thus, supercritical CO2 flooding was proposed as an immense potential recovery method for production improvement. While up to date, there have been few studies to account for the formation properties\u27 variation during the CO2 Enhanced Oil Recovery (EOR) process, especially investigation at the micro-scale. This work conducted a series of measurements to evaluate the rock mechanical change, mineral alteration and the pore structure properties\u27 variation through the supercritical CO2 (Sc-CO2) injection process. Corresponding to the time variation (0 days, 10 days, 20 days, 30 days and 40 days), the rock mechanical properties were analyzed properly through the nano-indentation test, and the mineralogical alterations were quantified through X-ray diffraction (XRD). In addition, pore structures of the samples were measured through the low-temperature N2 adsorption tests. The results showed that, after Sc-CO2 injection, Young\u27s modulus of the samples decreases. The nitrogen adsorption results demonstrated that, after the CO2 injection, the mesopore volume of the sample would change as well as the specific Brunauer-Emmett-Teller (BET) surface area which could be aroused from the chemical reactions between the CO2 and some authigenic minerals. XRD analysis results also indicated that mesopore were altered due to the chemical reaction between the injected Sc-CO2 and the minerals

A Deep Learning Approach to Design and Discover Sustainable Cementitious Binders: Strategies to Learn from Small Databases and Develop Closed-Form Analytical Models

To reduce the energy-intensity and carbon footprint of Portland cement (PC), the prevailing practice embraced by concrete technologists is to partially replace the PC in concrete with supplementary cementitious materials [SCMs: geological materials (e.g., limestone); industrial by-products (e.g., fly ash); and processed materials (e.g., calcined clay)]. Chemistry and content of the SCM profoundly affect PC hydration kinetics; which, in turn, dictates the evolutions of microstructure and properties of the [PC + SCM] binder. Owing to the substantial diversity in SCMs\u27 compositions–plus the massive combinatorial spaces, and the highly nonlinear and mutually-interacting processes that arise from SCM-PC interactions–state-of-the-art computational models are unable to produce a priori predictions of hydration kinetics or properties of [PC + SCM] binders. In the past 2 decades, the combination of Big data and machine learning (ML)—commonly referred to as the fourth paradigm of science–has emerged as a promising approach to learn composition-property correlations in materials (e.g., concrete), and capitalize on such learnings to produce a priori predictions of properties of materials with new compositions. Notwithstanding these merits, widespread use of ML models is hindered because they: 1) Require Big data to learn composition-property correlations, and, in general, large databases for concrete are not publicly available; and 2) Function as black-boxes, thus providing little-to-no insights into the materials laws like theory-based analytical models do. This study presents a deep learning (DL) model capable of producing a priori, high-fidelity predictions of composition- and time-dependent hydration kinetics and phase assemblage development in [PC + SCM] pastes. The DL is coupled with: 1) A fast Fourier transformation algorithm that reduces the dimensionality of training datasets (e.g., kinetic datasets), thus allowing the model to learn intrinsic composition-property correlations from a small database; and 2) A thermodynamic model that constrains the model, thus ensuring that predictions do not violate fundamental materials laws. The training and outcomes of the DL are ultimately leveraged to develop a simple, easy-to-use, closed-form analytical model capable of predicting hydration kinetics and phase assemblage development in [PC + SCM] pastes, using their initial composition and mixture design as inputs