Production Lot Research Articles

Detection of equine parvovirus-hepatitis and efficacy of governmental regulation for equine biologics purity.

In 2018, a new virus, named equine parvovirus-hepatitis (EqPV-H), was discovered in a biologic product that had been administered to horses that subsequently developed clinical signs of equine serum hepatitis (Theiler disease). Further correlation of the virus with the disease sparked federal requirements that all equine biologics be free of EqPV-H. The initial quantitative real-time PCR (qPCR) test for EqPV-H has proved to be sensitive to co-extracted PCR inhibitors in template nucleic acids, causing false-negative results. We investigated the use of digital PCR (dPCR) as a more robust test. Examination of 227 equine biologic product lots available for purchase both before and after regulatory implementation using both detection methods indicated that dPCR is a more reliable platform. Nevertheless, use of the qPCR method for product screening had reduced the fraction of serials with EqPV-H detected from 39.6% prior to regulation to 6.8% after regulatory implementation. Adoption of dPCR testing is an opportunity to further decrease the prevalence of EqPV-H in equine biologics.

Validation of the Peel Plate Staphylococcus Aureus (SA) Test for Enumeration of S. aureus in Selected Foods and Non-cultured Dairy Products: AOAC Performance Tested MethodSM 082401.

The microbial testing platform, Peel Plate™, was developed with Baird Parker ingredients for Staphylococcus aureus (SA) specificity. After rehydrating with 1 mL of the diluted food sample, Peel Plate SA is incubated for 24-48 h at 35-37 °C and observed for purple colonies. A validation study was performed to evaluate Peel Plate SA for enumeration of S. aureus in selected foods and non-cultured dairy products for Performance Tested MethodsSM (PTM) certification. Peel Plate SA was evaluated in inclusivity/exclusivity and matrix studies, product consistency, stability, and robustness testing. Non-fat dried milk, frozen raw cod fish, corned beef, frozen raw vegetables, potato salad, vegetable soup, frozen unshelled raw shrimp and baby food (apple, cinnamon, and granola) were evaluated in the matrix studies comparing Peel Plate SA to U.S. and international reference methods. Three levels of contamination along with non-inoculated controls were tested for each matrix. Peel Plate SA detected 49 of 50 S. aureus isolates tested. Of the 38 exclusivity species tested, none were detected. In the matrix studies, the Peel Plate SA results were determined to be equivalent to the reference method results by paired statistical analysis. In an accelerated stability study, the shelf-life of Peel Plate SA was shown to be stable for 12 months. Equivalence was demonstrated between different production lots. Small changes to critical test parameters did not significantly affect the Peel Plate SA results. Peel Plate SA is a ready-to-use method for enumeration of S. aureus in a variety of foods and non-cultured dairy products. As with reference methods, colonies Peel Plate SA colonies should be isolated and confirmed with differentiation tests such as catalase and strong coagulase positive. The data were reviewed by the PTM Program and certification was granted for the Peel Plate SA method PTM 082401.

Post-irradiation darkening model for EBT-3 films characterized using a single lot calibration approach

Objective.In this study, we present a model to correct the progressive post-irradiation darkening of EBT3 films. The model allows for a clinical use of EBT3 using application and calibration films scanned with different post-irradiation times.Approach.The model is a post-irradiation time- and dose-dependent power-law function, projecting the scanned transmittance of application films to the transmittance matching the same post-irradiation time of calibration films. The model was characterized for two EBT3 production lots within the dose range 0.1-12.8 Gy. A first characterization was performed utilizing calibration films scanned repeatedly for 54 d post-irradiation (lot 1), while a fast re-characterization of a second lot used three post-irradiation times (lot 2). For a long-term validation of the model, 16 film strips were irradiated at 2 Gy on different time points starting from the day of film calibration up to 43 d afterwards (lot 1). For the multiple-dose validation of the model, 8 strips were irradiated with dose levels ranging 0-12 Gy deposited 25 d after the calibration (lot 2). As a proof of principle, the model was applied to four clinical patient-specific quality assurance film measurements with prescribed dose/fraction ranging 2.66 Gy-8 Gy.Main results. The post-irradiation transmittance decreased for higher doses up to -2.5% at 12.8 Gy, and 54 d post-irradiation. With a lot-specific model correction, the mean dose accuracy of validation strips that ranged from initial -3.4% (triple-channel) and -9.90% (blue-channel) reduced to within 3.0% (all colour channels) for doses above 1 Gy. The median dose difference with the planned dose improved from -3.5% to -1.1%, and the 3%/2 mm local gamma ranged from (48.5-92.5)% to (81.2-99.2)%.Significance.A film darkening model corrects the transmittance of EBT3 films and increases the flexibility of existing dosimetry protocols. The correction ensures dose accuracies within 3%.

Optimal product pricing and lot sizing decisions for an omni-channel supply chain

In an omni-channel supply chain, companies offer customers multiple channels to interact with their brand. This allows for greater outreach and sales, resulting in increased customer loyalty and trust. Furthermore, there has been little discussion on the simultaneous consideration of strategic and tactical decisions under return policies in omni-channel supply chains. To maximize total profit, this research proposes a two-echelon supply chain, comprising a manufacturer and multiple retailers, including traditional, electronic and omni-channels. The non-deterministic demand function takes into account the sales price, refund price, delivery time and order quantities. A heuristic algorithm based on analysis of Karush–Kuhn–Tucker optimality conditions used to solve a nonlinear programming model. This research presents an analysis of the factors influencing the decision variables and profit of the entire supply chain and offers retailers a unique way to evaluate their performance and make operational decisions regarding price, delivery time and order quantities.

Optimasi Ukuran Lot Persediaan dalam Model Integrasi Single Vendor dan Single Buyer dengan Mempertimbangkan Kekurangan Buyer

<p><strong>The supply chain is the main factor influencing the production process in achieving goals. This research develops a model to determine the optimal production lot size in the supply chain system between vendors and buyers by considering the inventory shortage at the buyer. Minimizing the combined inventory cost is the goal of optimizing the production lot size by developing an integration model to determine the optimal lot size. The proposed model uses a periodic delivery method from the vendor to the buyer and allows for inventory shortages at the buyer. An iterative algorithm is applied to find the optimal lot size and minimum integrated inventory cost. The results showed that the initial model with n=1 resulted in a total cost of 2252 and an optimal production size (Q*) of 457. The proposed model reduces the total cost to 1605 with an optimal production size (Q*) of 737. Sensitivity analysis showed that an increase in the frequency of deliveries to vendors and vendor set-up costs contributed to a rise in total cost, while an increase in vendor storage costs led to a decrease in production lot size. This research can improve the efficiency of production planning and inventory management in the supply chain.</strong></p><p><strong><em>Keywords</em></strong> - <em>Inventory, Shortages, Single Buyer</em><em>, Single Vendor.</em></p>

Alterations of meat quality, lipid composition and flavor in breast meat of laying hens with fatty liver hemorrhagic syndrome

Fatty liver hemorrhagic syndrome (FLHS) has a high incidence rate in laying hens, and lots of FLHS-affected meat products enter the market every year. At the same time, the meat of laying hens is an important component of the human diet. However, the impact of FLHS on meat of laying hens is unknown, which could have a negative impact on consumers. To explore the effects of FLHS on chicken breast meat, a total of 36 twenty-five-wk-old Jingfen laying hens were used in the experiment. The hens were randomly divided into Control group and Model group, with 6 replicates per group and 3 hens per replicate. All chickens were raised in double-story step cages with individual pens. After a 3-wk acclimation period, the formal experiment began at 28 wk of age and lasted for 8 wk. Blood, liver, and breast meat samples were collected for the study of FLHS effects on breast meat. The impact of FLHS on meat quality was assessed by measuring indicators such as water-holding capacity and tenderness of the breast meat. Absolute quantitative lipidomics was employed to reveal the impact of FLHS on the lipid composition of chicken breast meat, and then validated by using RT-qPCR. Moreover, the volatilomics was utilized to detect changes in volatile organic compounds (VOCs) in chicken breast meat and to elucidate the resulting flavor changes. This research results showed that the meat quality of chicken breast meat decreased under FLHS, mainly manifested as reduced water holding capacity and decreased tenderness. The lipid content in the breast meat of FLHS-affected hens was significantly increased (P < 0.05). Among the affected lipids, 38 triglycerides exhibited notable elevation, possibly linked to heightened gene expression, such as lysophosphatidylcholine acyltransferase 3. The breast meat of laying hens with FLHS demonstrated an increased presence of VOCs, with 20 differential VOCs identified. Notably, 14 VOCs, particularly in 2-Undecenal, trans-Geranylacetone and ethyl nonanoate, exhibited substantial increases. These 3 VOCs had been identified as playing an important role in the formation of flavor in the breast meat of FLHS-affected laying hens. Correlation analysis suggested that the increase in these 3 VOCs might be related to the increase in lipid molecules such as phosphatidylethanolamine (38;3e) and acyl carnitine (10:3). In summary, FLHS reduced the breast meat quality of laying hens, altered its lipid profiles, and enhanced its flavor. These findings underscore the profound impact of FLHS on lipid and VOC profiles in chicken breast meat, offering valuable insights for chicken meat quality affected by FLHS.

Defatted microalgae Haematococcus pluvialis: A sustainable source for gold recovery

The defatted microalgae Haematococcus pluvialis (HP), obtained from various production lots, were subjected to two different treatments: drying without chemical treatment (DH1, DH2, and DH3) and treatment with concentrated sulfuric acid (TH1, TH2, and TH3). These treated materials were employed as efficient and eco-friendly adsorbents to investigate the adsorption behavior of Au(III) in acidic chloride media. The treated adsorbents demonstrated exceptional performance, achieving 100 % adsorption of Au(III) across a broad range of hydrochloric acid (HCl) concentrations from 0.01 to 5.00 M, whereas the dry adsorbents exhibited up to 90 % adsorption only at low HCl concentrations. Both dry and treated adsorbents exhibited superior selectivity for Au(III) in the presence of a mixture of precious metal ions. The adsorption isotherms of Au(III) followed the Langmuir type of adsorption mechanism, and the maximum adsorption capacities for Au(III) were estimated as 1.57, 0.88, and 1.52 mol kg–1 for DH1, DH2, and DH3, and 6.17, 4.80, and 5.37 mol kg–1 for TH1, TH2, and TH3, respectively. The adsorbed Au(III) ions were reduced to the elemental state, as confirmed by X-ray diffraction patterns, scanning electron microscopy/energy-dispersive X-ray analyses, and digital micrographs of the adsorbents after Au(III) adsorption. Successful desorption of the adsorbed Au(III) was achieved using acidic thiourea solution.

Optimum production lot size for a perishable product under exponential demand with partial backordering and rework

The management of inventory significantly impacts an organization's profitability. Numerous researchers have developed the production model tailored for decaying items to boost sales and minimize inventory, thereby increasing turnover ratios. This study focuses on the EPQ model for reworkable items under exponential combined with inventory dynamics. The primary objective is to optimize the production plan to manage maintenance costs effectively, ensuring optimal Leveraging human and technological resources. The research determines the optimal cycle length, backordered quantities, and production quantities to minimize overall costs. Specifically, penalties are incurred for excessive production times during rework and non-rework phases. This model finds applications in industries such as printed circuit board assembly in semiconductor manufacturing, metal parts, and plastic molding. The study concludes with numerical examples comparing total costs under the proposed model, supplemented by sensitivity analyses that explore the impacts of parameter variations on decision-making.

Sustainable machining of Inconel 718 using minimum quantity lubrication: Artificial intelligence-based process modelling

Governments and industries are developing aggressive policies to reduce carbon emissions and shift from fossil fuels to renewable energy. On the other hand, industries struggle to reduce energy consumption and depend on production lot sizes to control energy requirements. In this regard, energy-efficient processing through CNC machine tools can potentially influence energy demand and requires energy-aware power consumption strategies for machining processes. For manufacturing a single product, predicting energy demand can be decisive in determining parametric control and other factors. Previously analytical models have been largely used to model machining requirements and energy demand. However, these models largely depend on parameterization and do not facilitate the integration of external sub-systems. Therefore, in this paper, an artificial intelligence-based power reduction strategy is developed and implemented on single material (Inconel 718), four control parameters (cutting speed, feed rate, depth of cut and flow rate) and two sub-systems (minimum quantity lubrication (MQL) and nanofluids-based minimum quantity lubrication (NF-MQL)). The paper employs four machine learning algorithms,’ K-Nearest Neighbor’, ‘Gaussian Regression’, ‘Decision Tree’, and ‘Logistic Regression’, to evaluate their functionality in predicting power consumption (Pc) of CNC machining systems using a real experimental data set. As per evaluation based on five performance metrics (R-square, MSE, RMSE, MAE, and MedAE), ‘Decision Tree’ has achieved the most accurate power consumption predictions. The comparative results highlight ‘Decision Tree’ as the most better predictor with the optimal max_depth of 2 showing Pc MQL R2 of 0.915 and Pc NF-MQL R2 of 0.931.

A novel fuzzy finite-horizon economic lot and delivery scheduling model with sequence-dependent setups

This paper addresses the economic lot and delivery scheduling problem (ELDSP) within three-echelon supply chains, focusing on the complexities of demand uncertainty, limited shelf-life of products, and sequence-dependency of setups. We develop a novel mixed-integer non-linear programming (MINLP) model for a supply chain comprising one supplier, multiple manufacturers with flexible flow shop (FFS) production systems, and multiple retailers, all operating over a finite planning horizon. The common cycle (CC) strategy is adopted as the synchronization policy. Our model employs fuzzy set theory, particularly the “Me measure,” to effectively handle the retailers’ demand uncertainty. Our findings indicate that total supply chain costs escalate with an increase in demand, final components’ holding costs, and sequence-dependent setup costs, but decrease with increasing production rates. Furthermore, while total costs are significantly sensitive to changes in demand, they are relatively insensitive to fluctuations in sequence-dependent setup times. The models developed offer valuable managerial insights for optimizing costs in synchronized multi-stage supply chains, aiding managers in making informed decisions about production lot sizes and delivery schedules under both deterministic and fuzzy demand scenarios. Additionally, the proposed models bridge key research gaps and provide robust decision-making tools for cost optimization, enhancing supply chain synchronization in practical settings.

Development and validation of quantitative procedure of clotrimazole and eugenol in gel product using high-performance liquid chromatography

Vulvovaginitis is a prevalent gynaecological ailment worldwide, often attributed to bacteria, Candida fungi, or Trichomonas. The synergistic action of antifungal agents and essential oils can enhance the efficacy against pathogenic microorganisms. In this study, a simultaneous quantitative method for determining clotrimazole and eugenol in a laboratory-compounded gel product was developed using high-performance liquid chromatography with a photodiode array detector (HPLC-PDA). The HPLC-PDA system utilised in this study was the Shimadzu LC-20AD, with a HiQ Sil RP C18 HS column (250 x 4.6 mm, 5 μm), and a mobile phase consisting of methanol-water in an 80:20 (v/v) ratio, employing isocratic elution at a flow rate of 1.0 ml/min. The injection volume was set at 20 μl, and detection was performed at a wavelength of 229.0 nm. The analytical procedure was validated in accordance with ASEAN guidelines for the validation of analytical procedures, successfully meeting the criteria for specificity, accuracy, system suitability, repeatability, intermediate precision, and linearity within the concentration range of 2.25 to 36.00 ppm for eugenol and 12.00 to 200.00 ppm for clotrimazole. Subsequently, this validated procedure was applied to quantify the clotrimazole and eugenol content in bulk materials and three different lots of gel products. The results for clotrimazole and eugenol content in both the raw materials and the gel product lots fell within an acceptable range, with deviations of less than ±10% compared to the labelled content.

32‐3: Heterogeneous Resource Constrained Reinforcement Learning Photolithography Scheduler with Heterogeneous Graph Attention Network

In mass manufacturing of OLED displays, photolithography process is a critical bottleneck in production capacity. As a result, effective scheduling of the photolithography process is crucial to the overall throughput, productivity, and efficiency of the production. The processing of OLED panels depends upon heterogeneous resources. Aside from that, the complexity, stochastic nature, and highly dynamic characteristic of the processpresent considerable challenges. To address these, we propose a new framework employing a heterogeneous graph neural networkbased Rainbow algorithm. This framework optimizes the schedule used in photolithography process. Considering the constraints on machines, masks, and the stochastic nature of arrival processes, we optimize for maximizing productivity while minimizing the associated costs. We model the interactions among product lot steps, machines, and masks as a heterogeneous graph. This graph encodes the information of lot steps, machines, and masks into distinctive nodes. We then implement a Graph Attention Networkbased architecture for deep representation learning. This transforms state information into node embeddings for each step of each product lot, facilitating decision‐making. Reinforcement learning agents leverage these embeddings to prioritize products via a parameterized Q‐function. Our extensive experiments demonstrate the superior performance of our approach across multiple evaluation metrics in both static and dynamic environment benchmarks, with a higher winning rate and scheduling reward return when compared to existing reinforcement learning methods.

An integrated optimization model for procurement and production lot sizing and scheduling problems

Abstract Lot sizing is a prevalent issue within manufacturing companies, where determining the optimal procurement and production lot sizes is crucial for maximizing profits. This problem has become more complex, given that numerous suppliers can provide the same raw materials with different prices and quantity discount schemes. A company should also determine optimal carriers to deliver materials to the company’s warehouse. In a manufacturing process, the company should determine the optimal production lot size and its schedules. In this paper, a model was developed to solve simultaneously procurement and production lot sizing, as well as production scheduling problems. The model encompasses multiple suppliers offering quantity discounts, aiming to maximize company profit by accounting for various costs, including procurement, production, inventory, and quality costs. A case study is taken from a company producing noodles and its related derivative products to illustrate the application of the model. Based on the optimization results, the company obtained a total profit of IDR. 14,656,550,000 or $950,921.30 (the exchange rate of $1 at IDR. 15,413). The sensitivity analysis results show that the objective function is sensitive to changes in the purchase cost, sale revenue, and discount rate parameters. The decision variables for accepted product demand, product quantity, and the starting and completion time of product family are only sensitive to changes in certain parameters. Meanwhile, the decision variables for product inventory, product backlog, raw material inventory, and purchased raw material quantity are sensitive to the changes in all the analyzed parameters.

The coupling transformation process of biodegradation and pyrolysis for Dananhu low-rank coal

The study presented a novel approach for coupling biodegradation and pyrolysis to transform low-rank coal. In the first stage, four kinds of bacteria, Planomicrobium huatugouensis (P. huatugouensis), Sphingomonas polyaromaticivorans (S. polyaromaticivorans), Pseudomonas putida (P. putida), and Bacillus subtilis subsp. subtilis (B. subtilis subsp. subtilis), were used to transform the Dananhu low-rank coal. After the first stage, the coal that has not been biodegraded (residual coal) undergoes the second stage of pyrolytic transformation, which is carried out by a thermogravimetric analyzer combined with Fourier-transform infrared spectrometry (TG-FTIR). The results showed that the maximum coupling transformation rate of biodegradation and pyrolysis could reach 73.95%, which was 21.88% higher than the thermal transformation rate of Dananhu low-rank coal and 27.37% higher than the optimal microbial transformation rate. This indicated that coupling transformation could significantly improve the utilization rate of Dananhu low-rank coal. In the stage of biodegradation transformation, bacteria destroyed the carboxyl groups, ether bonds, and other oxygen-containing functional groups in Dananhu low-rank coal, converting the large molecules of coal into liquid molecules, meanwhile, the compact coal structure became loose, and the residual coal was also pyrolyzed easily than Dananhu low-rank coal. During the pyrolysis process, a large number of fatty chains, carboxyl groups, oxy groups, and hydroxy groups were decomposed to produce lots of gas-phase products. The pyrolysis products showed an increase in the release of CO and CH4, and an increase in the proportion of aliphatic hydrocarbons in the volatiles. The high efficiency of coupling transformation is due to the combination of the advantages of the two technologies, which is achieving the green and energy-efficient utilization of low-rank coal.

Manajemen Supply pada Produk HNI-HPAI Halal Mart di Kabupaten Sambas Perspektif Ekonomi Islam

Islam is a religion that has a universal system that regulates good business and is in accordance with religion. Many companies are switching to an Islamic business system, called sharia economics. Currently, people really like the Islamic business system. Sharia-based companies include PT HPAI (Herbal Penawar al-Wahida Indonesia), which later became HNI-HPAI (Herba Penawar Al-Wahida Indonesia-Halal Network International). This research aims to determine the supporting and inhibiting factors for implementing supply management at the herbal HPAI halal mart in Sambas Regency. In this research, qualitative research was used. The data collection technique is carried out by means of interviews, then analyzed through data reduction, classifying the data, presenting the data and finally drawing conclusions. The data sources in this research consist of 2, namely primary data and secondary data. One of the supporting factors for carrying out herbal medicine supply management in Sambas Regency can involve operational efficiency, product quality and good marketing strategies. Meanwhile, inhibiting factors may include raw material supply instability, regulatory changes, or logistical constraints. The external supporting factor is that many people know the quality of HPAI products, so they are easy to sell because the prices are affordable. The internal inhibiting factor is not having its own way or rules to handle orders for lots of herbal products by consumers and delays in delivery of goods. These delays are due to an overload of goods to be sent by the expedition.

Matheuristic for the lot-sizing and scheduling problem in integrated pulp and paper production

Vertical pulp and paper production is challenging from a process point of view. Managers must deal with floating bottlenecks, intermediate storage levels, and by-product production to control the whole process while reducing unexpected downtimes. Thus, this paper aims to address the integrated lot sizing and scheduling problem considering continuous digester production, multiple paper machines, and a chemical recovery line to treat by-products. The aim is to minimize the total production cost to meet customer demands, considering all productive resources and encouraging steam production (which can be used in power generation). Production planning should define the sizes of production lots, the sequence of paper types produced in each machine, and the digester working speed throughout the planning horizon. Furthermore, it should indicate the rate of by-product treatment at each stage of the recovery line and ensure the minimum and maximum storage limits. Due to the difficulty of exactly solving the mixed integer programming model representing this problem for real-world instances, mainly with planning horizons of over two weeks, constructive and improvement heuristics are proposed in this work. Different heuristic combinations are tested on hundreds of instances generated from data collected from the industry. Comparisons are made with a commercial Mixed-Integer and Linear Programming solver and a hybrid metaheuristic. The results show that combining the greedy constructive heuristic with the new variation of a fix-and-optimize improvement method delivers the best performance in both solution quality and computational time and effectively solves realistic size problems in practice. The proposed method achieved 69.41% of the best solutions for the generated set and 55.40% and 64.00% for the literature set for 1 and 2 machines, respectively, compared with the best solution method from the literature and a commercial solver.

Manufacture of Low-Na White Soft Brined Cheese: Effect of NaCl Substitution with a Combination of Na-K Salts on Proximate Composition, Mineral Content, Microstructure, and Sensory Acceptance.

All over the world, especially in Western societies, table salt intake that is inordinately higher than the acceptable level has been observed. An excess of Na in the human diet, mostly from processed foods, is becoming the "number one killer", leading to increased blood pressure. Therefore, the food industry is faced with a need to reduce Na in human nutrition in an effort to raise public health protection to a higher level. In this study, a commercially available combination of Na/K salts (COMB) at different concentrations was used as a NaCl substitute in the production of a modified, healthier, Na-reduced cheese. Samples of the modified low-Na white soft-brined cheese (WSBC) were produced by adding four different concentrations of COMB to production lots PL-1 to PL-4, and the control (CON) samples were prepared by salting with the usual, non-reduced concentration of NaCl. The effects of NaCl replacement on the physical-chemical parameters, major- and micro-elements, and microstructural and sensory properties of the WSBC were investigated. The obtained results indicated that there was no significant influence on the ash content, pH, and aw. The Na and K levels differed among treatments (p < 0.001). The lowest Na level in this study was recorded in PL-4 (only COMB was added) and was 334.80 ± 24.60 mg/100 g. According to the Na content, WSBC PL4 can be labeled with the nutrient claim "reduced amount of Na". A significant difference (p < 0.05) was noticed in overall acceptance between the CON and PL-4, with no statistically significant difference found amongst other WSBC production lots. The replacement of NaCl resulted in a slightly greater firmness of the WSBC. The results confirm the possibility of producing low-Na WSBC when optimal amounts of a suitable mineral salt are used as a substitute for NaCl, thus reducing the risk of high Na intake in the human body through the consumption of evaluated cheese.

Characterizing Spoilage of Coconut-based Creamers: A Multifaceted Approach to Identify Problematic Bacteria and Their Potential Sources in a New Product Category

Beverage innovation is a growing trend with a reliance on comanufacturing relationships to launch products quickly. A recent comanufacturing relationship is the utilization of dairy processing facilities to process plant-based beverages using high-temperature short-time (HTST) pasteurization. While the shelflife of HTST bovine milk is well established at 21 days, retailers are expecting new refrigerated beverages to achieve a 60-day shelflife. Little is known about the microbial stability of these new beverages, particularly those with complex formulations. Our objective was to identify bacterial taxa leading to the spoilage of four coconut-based creamers and their potential sources (raw ingredients or packaging). We used a multifaceted approach including plate counting and 16S rRNA metabarcoding to monitor microbial growth in products throughout shelflife (60 d, 4 °C), and cold enrichment (7 °C, 11 d) of ingredients and packaging. Nearly all product units (25/26) had elevated microbial loads (>4.3 log CFU/mL) prior to the 60-d target, with early spoilage detected at 21 d. Key spoilage taxa included Pseudomonas, Streptococcus, Aerococcus, Paenibacillus, Sphingomonas, and Oceanobacillus. Pseudomonas were responsible for “early” product spoilage (21–32 d), whereas Oceanobacillus were important in products with very “late” spoilage (60–62 d). All key spoilage taxa were identified in cold enrichments of multiple units of waxboard cartons. Paenibacillus was the dominant bacterium in 47% (10/21) of product units. In addition to carton samples, Paenibacillus was also identified in one raw ingredient (mushroom extract). Metabarcoding identified Listeria sensu stricto as a dominant taxon in three individual product units from three distinct production lots. Listeria was also found in 31% (5/16) of cold enrichments of individual cartons. Taxa responsible for spoilage of plant-based beverages were identified as well as demonstrating packaging as an important contamination source.

A novel high-titer, bifunctional lentiviral vector for autologous hematopoietic stem cell gene therapy of sickle cell disease

A major limitation of gene therapy for sickle cell disease (SCD) is the availability and access to a potentially curative one-time treatment, due to high treatment costs. We have developed a high-titer bifunctional lentiviral vector (LVV) in a vector backbone that has reduced size, high vector yields, and efficient gene transfer to human CD34+ hematopoietic stem and progenitor cells (HSPC). This LVV contains locus control region cores expressing an anti-sickling βAS3-globin gene and two microRNA adapted short hairpin RNA simultaneously targeting BCL11A and ZNF410 transcripts to maximally induce fetal hemoglobin (HbF) expression. This LVV induces high levels of anti-sickling hemoglobins (HbAAS3 + HbF), while concurrently reducing sickle hemoglobin (HbS). The reduction in HbS and increased anti-sickling hemoglobin impedes deoxygenated HbS polymerization and red blood cell sickling at low vector copy per cell in transduced SCD patient CD34+ cells differentiated into erythrocytes. The dual alterations in red cell hemoglobins ameliorated the SCD phenotype in the SCD Berkeley mouse model in vivo. With high titer and enhanced transduction of HSPC at low multiplicity of infection, this LVV will increase the number of patient doses of vector from production lots to reduce costs and help improve accessibility to gene therapy for SCD.

Robust Control Chart Application in Semiconductor Manufacturing Process

Statistical Process Control (SPC) charts are frequently used in the semiconductor manufacturing environment to monitor process quality and detect special-cause variations, hence, to take corrective actions when necessary. The important aspects of control charts to consider on production floors are identifying the primary objective of implementing control charts, the type of data to monitor and the most appropriate control limits to establish. When the quality data is a type of attribute data like the proportion of defectives from a production lot, a p-chart approach is most suitable. In p-chart applications, although the assumption of normally distributed process data is not mandatory, the widespread practice is to assume the normal distribution of process data when establishing the control limits. Yet again, the reality of industrial settings is that process data are most likely influenced by outliers, resulting in highly skewed distributions. This paper addresses these issues by proposing robust SPC charting techniques to detect special-cause variations in the semiconductor manufacturing processes. Here, we present a case study of a semiconductor company in Malaysia, Dominant Opto Technologies Sdn. Bhd. to propose three robust statistical approaches for monitoring the proportion of defectives in production lots. We apply M-estimates, median, and interquartile range to calculate the upper control limits (UCL) and found that robust estimators are more effective in detecting early process deterioration and capturing the out-of-control (OOC) conditions better than traditional control charts. By proposing robust methods, this study enlightens the practical aspects of process quality improvement for real-life manufacturing setups. Because a high OOC rate may impact manufacturing productivity, we recommend the decision-makers choose the types of control charts based on the implications of each robust approach toward quality and productivity. The significance of this study includes providing insights into setting up the appropriate attribute control charts for detecting defective proportions for professionals and SPC researchers working in these areas.