Cross Company Research Articles

A Cross Company Perspective on the Assessment of Therapeutic Protein Biotransformation.

Unlike with new chemical entities, the biotransformation of therapeutic proteins (TPs) has not been routinely investigated or included in regulatory filings. Nevertheless, there is an expanding pool of evidence suggesting that a more in-depth understanding of biotransformation could better aid the discovery and development of increasingly diverse modalities. For instance, such biotransformation analysis of TPs affords important information on molecular stability, which in turn may shed light on any potential impact on binding affinity, potency, pharmacokinetics, efficacy, safety, or bioanalysis. This perspective summarizes the current practices in studying biotransformation of TPs and related findings in the biopharmaceutical industry. Various TP case studies are discussed, and a fit-for-purpose approach is recommended when investigating their biotransformation. In addition, we provide a decision tree to guide the biotransformation characterization for selected modalities. By raising the awareness of this important topic, which remains relatively underexplored in the development of TPs (Bolleddula et al., 2022), we hope that current and developing practices can pave the way for establishing a consensus on the biotransformation assessment of TPs. SIGNIFICANCE STATEMENT: This article provides a comprehensive perspective of the current practices for exploring the biotransformation of therapeutic proteins across the drug development industry. We, the participants of the Innovation and Quality therapeutic protein absorption distribution metabolism excretion working group, recommend and summarize appropriate approaches for conducting biotransformation studies to support internal decision making based on the data generated in discovery and development.

A novel online supervised hyperparameter tuning procedure applied to cross-company software effort estimation

Software effort estimation is an online supervised learning problem, where new training projects may become available over time. In this scenario, the Cross-Company (CC) approach Dycom can drastically reduce the number of Within-Company (WC) projects needed for training, saving their collection cost. However, Dycom requires CC projects to be split into subsets. Both the number and composition of such subsets can affect Dycom’s predictive performance. Even though clustering methods could be used to automatically create CC subsets, there are no procedures for automatically tuning the number of clusters over time in online supervised scenarios. This paper proposes the first procedure for that. An investigation of Dycom using six clustering methods and three automated tuning procedures is performed, to check whether clustering with automated tuning can create well performing CC splits. A case study with the ISBSG Repository shows that the proposed tuning procedure in combination with a simple threshold-based clustering method is the most successful in enabling Dycom to drastically reduce (by a factor of 10) the number of required WC training projects, while maintaining (or even improving) predictive performance in comparison with a corresponding WC model. A detailed analysis is provided to understand the conditions under which this approach does or does not work well. Overall, the proposed online supervised tuning procedure was generally successful in enabling a very simple threshold-based clustering approach to obtain the most competitive Dycom results. This demonstrates the value of automatically tuning hyperparameters over time in a supervised way.

Cross-company defect prediction via semi-supervised clustering-based data filtering and MSTrA-based transfer learning

Cross-company defect prediction (CCDP) is a practical way that trains a prediction model by exploiting one or multiple projects of a source company and then applies the model to a target company. Unfortunately, larger irrelevant cross-company (CC) data usually make it difficult to build a prediction model with high performance. On the other hand, brute force leveraging of CC data poorly related to within-company data may decrease the prediction model performance. To address such issues, we aim to provide an effective solution for CCDP. First, we propose a novel semi-supervised clustering-based data filtering method (i.e., SSDBSCAN filter) to filter out irrelevant CC data. Second, based on the filtered CC data, we for the first time introduce multi-source TrAdaBoost algorithm, an effective transfer learning method, into CCDP to import knowledge not from one but from multiple sources to avoid negative transfer. Experiments on 15 public datasets indicate that: (1) our proposed SSDBSCAN filter achieves better overall performance than compared data filtering methods; (2) our proposed CCDP approach achieves the best overall performance among all tested CCDP approaches; and (3) our proposed CCDP approach performs significantly better than with-company defect prediction models.

Improving Cross-Company Defect Prediction with Data Filtering

Defect prediction aims to estimate software reliability via learning from historical defect data. Cross-company defect prediction (CCDP) is a practical way that trains a prediction model by exploiting one or multiple projects of a source company and then applies the model to the target company. Unfortunately, larger irrelevant cross-company (CC) data usually makes it difficult to build a CCDP model with high performance. To address such issues, this paper proposes a data filtering method based on agglomerative clustering (DFAC) for CCDP. First, DFAC combines within-company (WC) instances and CC instances and uses agglomerative clustering algorithm to group these instances. Second, DFAC selects subclusters which consist of at least one WC instance, and collects the CC instances in the selected subclusters into a new CC data. Compared with existing data filter methods, the experiment results from 15 public PROMISE datasets show that DFAC increases the pd value, reduces the pf value and achieves higher [Formula: see text]-measure value.

Determinants of Labour Efficiency in Nigeria : A Cross Company Study of Manufacturing Firms

Determinants of Labour Efficiency in Nigeria : A Cross Company Study of Manufacturing Firms

The use of cross-company fault data for the software fault prediction problem

We investigated how to use cross-company (CC) data in software fault prediction and in predicting the fault labels of software modules when there are not enough fault data. This paper involves case studies of NASA projects that can be accessed from the PROMISE repository. Case studies show that CC data help build high-performance fault predictors in the absence of fault labels and remarkable results are achieved. We suggest that companies use CC data if they do not have any historical fault data when they decide to build their fault prediction models.

A Reality Check: The Need for a Deeper Understanding of Opioid Abuse Treatment Options: A Policy Statement From the American College of Clinical Pharmacology.

Most clinical pharmacologists agree that prescription drug abuse is no longer just a trending topic; it has reached epidemic levels in the United States. Although the United States makes up less than 5% of the global population, 80% of the opioids and 99% of the hydrocodone used in the world are used by people in the United States.1 Members of the American College of Clinical Pharmacology (ACCP) are intimately involved with the development of novel drugs, many for the treatment of chronic pain. The ACCP is uniquely positioned to serve both its membership and the public by providing scientific knowledge to optimize research, development, and the utilization of medication for the benefit of all, which is the vision of the organization. This position paper asserts that increased physician, pharmacist, and scientific researcher education is the best way to create a better understanding of opioid abuse and dependence as well as its treatment. Understanding, acceptance, and the promotion of supervised treatment options such as combined psychotherapy with Suboxone treatment paradigms provide rapid and significant lifesaving options as compared to programs relying on complete abstinence. Additionally, the FDA's recent guidances2, 3 supporting the development of abuse deterrent formulations of opioids and the thorough evaluation of the potential of a new chemical entity (NCE) to be abused has been proposed to prevent the widespread tampering that currently occurs through both patient abuse and general diversion of the supply.

Negative samples reduction in cross-company software defects prediction

ContextSoftware defect prediction has been widely studied based on various machine-learning algorithms. Previous studies usually focus on within-company defects prediction (WCDP), but lack of training data in the early stages of software testing limits the efficiency of WCDP in practice. Thus, recent research has largely examined the cross-company defects prediction (CCDP) as an alternative solution. ObjectiveHowever, the gap of different distributions between cross-company (CC) data and within-company (WC) data usually makes it difficult to build a high-quality CCDP model. In this paper, a novel algorithm named Double Transfer Boosting (DTB) is introduced to narrow this gap and improve the performance of CCDP by reducing negative samples in CC data. MethodThe proposed DTB model integrates two levels of data transfer: first, the data gravitation method reshapes the whole distribution of CC data to fit WC data. Second, the transfer boosting method employs a small ratio of labeled WC data to eliminate negative instances in CC data. ResultsThe empirical evaluation was conducted based on 15 publicly available datasets. CCDP experiment results indicated that the proposed model achieved better overall performance than compared CCDP models. DTB was also compared to WCDP in two different situations. Statistical analysis suggested that DTB performed significantly better than WCDP models trained by limited samples and produced comparable results to WCDP with sufficient training data. ConclusionsDTB reforms the distribution of CC data from different levels to improve the performance of CCDP, and experimental results and analysis demonstrate that it could be an effective model for early software defects detection.

Translational models of lung disease.

The 2nd Cross Company Respiratory Symposium (CCRS), held in Horsham, U.K. in 2012, brought together representatives from across the pharmaceutical industry with expert academics, in the common interest of improving the design and translational predictiveness of in vivo models of respiratory disease. Organized by the respiratory representatives of the European Federation of Pharmaceutical Industries and Federations (EFPIA) group of companies involved in the EU-funded project (U-BIOPRED), the aim of the symposium was to identify state-of-the-art improvements in the utility and design of models of respiratory disease, with a view to improving their translational potential and reducing wasteful animal usage. The respiratory research and development community is responding to the challenge of improving translation in several ways: greater collaboration and open sharing of data, careful selection of the species, complexity and chronicity of the models, improved practices in preclinical research, continued refinement in models of respiratory diseases and their sub-types, greater understanding of the biology underlying human respiratory diseases and their sub-types, and finally greater use of human (and especially disease-relevant) cells, tissues and explants. The present review highlights these initiatives, combining lessons from the symposium and papers published in Clinical Science arising from the symposium, with critiques of the models currently used in the settings of asthma, idiopathic pulmonary fibrosis and COPD. The ultimate hope is that this will contribute to a more rational, efficient and sustainable development of a range of new treatments for respiratory diseases that continue to cause substantial morbidity and mortality across the world.

Cross Company and within Company Fault Prediction using Object Oriented Metrics

paper investigates fault predictions in the cross-project context focusing on the object oriented metrics for the organizations that do not track fault related data. In this study, empirical analysis is carried out to validate object-oriented Chidamber and Kemerer (CK) design metrics for cross project fault prediction. The machine learning techniques used for evaluation are J48, NB, SVM, RF, K-NN and DT. The results indicate CK metrics can be used as initial guideline for the projects where no previous fault data is available. Overall, the results of cross company is comparable to the within company data learning. Our analysis is in favour of reusability in object oriented technology and it has been empirically shown that object oriented metric data can be used for cross company fault prediction in initial stage when previous fault data of the project is not available. Keywordsprediction, cross company, Software metric, open source software.

Governance, monitoring and foreign investment in Chinese companies

This paper examines corporate governance and foreign equity home bias in Chinese companies. Free float measures are employed to account for bias introduced by insider control. It is found that foreign ownership relative to free float is negatively impacted by legal persons (large domestic cross company) holdings and positively related to large foreign institutional holdings, with the implication that the latter provide a monitoring function that reduces agency problems. Foreign ownership relative to free float is negatively related to firm size, possibly due to quasi government being the primary influence over insider control.

On the relative value of cross-company and within-company data for defect prediction

We propose a practical defect prediction approach for companies that do not track defect related data. Specifically, we investigate the applicability of cross-company (CC) data for building localized defect predictors using static code features. Firstly, we analyze the conditions, where CC data can be used as is. These conditions turn out to be quite few. Then we apply principles of analogy-based learning (i.e. nearest neighbor (NN) filtering) to CC data, in order to fine tune these models for localization. We compare the performance of these models with that of defect predictors learned from within-company (WC) data. As expected, we observe that defect predictors learned from WC data outperform the ones learned from CC data. However, our analyses also yield defect predictors learned from NN-filtered CC data, with performance close to, but still not better than, WC data. Therefore, we perform a final analysis for determining the minimum number of local defect reports in order to learn WC defect predictors. We demonstrate in this paper that the minimum number of data samples required to build effective defect predictors can be quite small and can be collected quickly within a few months. Hence, for companies with no local defect data, we recommend a two-phase approach that allows them to employ the defect prediction process instantaneously. In phase one, companies should use NN-filtered CC data to initiate the defect prediction process and simultaneously start collecting WC (local) data. Once enough WC data is collected (i.e. after a few months), organizations should switch to phase two and use predictors learned from WC data.

Precise determination of rhenium isotope composition by multi-collector inductively-coupled plasma mass spectrometry

We present rhenium (Re) isotope data for commercially available Re reference materials SRM 989, SRM 3143, and Alfa Aesar Specpure®Re, as well as two Re solutions made in-house from perrhenic acid and zone-refined flattened-wire ribbon from H. Cross Company. Data were generated by MC-ICPMS (Thermo Fisher Scientific NEPTUNE) using standard-bracketing combined with a tungsten (W) external correction. Intermediate measurement precision (i.e. the precision for similar analytical procedures and measurement conditions on the same instrument over time) is ±0.05‰ (2s) for analyte solution concentrations of 20 ng g−1Re and 70 ng g−1W; intermediate precision decreases to ±0.1‰ (2s) when respective analyte concentrations are halved to 10 ng g−1 and 35 ng g−1. The five Re solutions exhibit an isotopic range of 0.29‰, providing the first evidence for stable isotope fractionation of Re. Data are also presented for the Devonian Ohio Shale standard USGS SDO-1; intermediate precision for this natural material (±0.13‰, 2s) is comparable to solution standard precisions at equivalent concentrations (10 ng g−1Re, 35 ng g−1W). Analysis of geochemical materials introduces additional concerns. The low concentration of Re in most materials limits these analyses to Re-rich substrates. Isotopic fractionation occurring during chromatographic purification is nullified by quantitative recovery, and the sample matrix does not introduce fractionation artifacts. Isobaric interference from radiogenic 187Os can be effectively removed by sparging sample solutions prior to analysis.

Ferroelectric properties of barium titanate thin films grown on nichrome substrates by RF sputtering

Ferroelectric thin films have attracted attention because of their possible applications in new microelectronic devices. In particular, the BaTiO3 thin films, a material with a high dielectric constant, low leakage current and the quality of the change of its ferroelectric properties by adding and modifying the concentration of a doppand [1], can be used for a wide range of functional purposes, from simple capacitors to complicated microwave devices [2–6]. BaTiO3 films have been grown by many deposition techniques [1, 4,7, 8] but RF sputtering is known to be one of the best because it preserves the stoichiometry under the right deposition conditions. A major disadvantage of BaTiO3 is that it possesses a mechanical fragility, therefore, one of the main technological challenges for this material is to find an electrode-substrate that could offer mechanical stability. Also, this electrode-substrate must have a good thermal stability, low electrical resistance, high resistance to oxidation and good adherence for the film. Pt/TiO2/SiO2/Si is commonly used because it posseses the mentioned characteristics [2]. Nevertheless, it has many problems because titanium can migrate to the platinum surface during the annealing process, adding electrical resistively to electrode [9–12]. It also generates cracking in by thermal stress in the BaTiO3 thin films. The nichrome is suggested for a suitable electrode because it fulfills the requirements mentioned above [13–17] and, in contrast to Pt/TiO2/SiO2/Si, its commercial availability and its low price. Therefore, in this work, BaTiO3 films were grown on nichrome substrates by RF-sputtering, and their structural and ferroelectric properties were measured and improved in comparison with those deposited on Pt/TiO2/SiO2/Si substrates using the same method and under same deposition conditions. BaTiO3 thin films on nichrome substrates were prepared by RF magnetron sputtering in an off-axis geometry at room temperature. A 50.8 mm diameter. BaTiO3 target, 99.9% pure, from SCI Engineered Materials, Inc., was used. The nichrome was obtained from a commercial strip, nichrome 80, with 0.127 mm thickness from H. Cross Company, Inc. The films were also grown over Pt/TiO2/SiO2/Si substrates in order to com-

4550132 Mechanism for pre-loading bearings: Frank R Mottershead assigned to The Cross Company

4550132 Mechanism for pre-loading bearings: Frank R Mottershead assigned to The Cross Company

Electricity supply in the City of Westminster

This is a record of some of the early developments in public electricity supply in the City of Westminster and describes how the seven undertakings mainly concerned established themselves. The paper recalls the personal initiative and drive shown by Ferranti and Crompton in designing and constructing power stations and distribution systems using entirely different concepts to achieve the same result. Although early power stations were described as ‘central stations’, this was to distinguish them from small and sometimes mobile plants which were set up to supply arc-lighting installations. The birth of transmission systems leading to the establishment of the Grid is foreshadowed by the activities of the Charing Cross Company in building a power station at Bow, 4 miles from the City, to supply high-voltage a.c. to the City and West End. Likewise, the Westminster Electric Supply Corporation joined forces with the St. James and Pall Mall Company to form the Central Electric Supply Company, and the Kensington and Knightsbridge Company and the Notting Hill Company built a power station at Shepherds Bush to meet their joint needs. Almost all that remains of the past is innumerable manhole covers, many of which display the name of their former undertaking.

Discussion on “The City of London works of the Charing Cross, West End, and City Electricity Supply Company, Limited”

Discussion on “The City of London works of the Charing Cross, West End, and City Electricity Supply Company, Limited”

The City of London works of the Charing Cross, West End, and City Electricity Supply Company, Limited

The City of London works of the Charing Cross, West End, and City Electricity Supply Company, Limited