How to safely maintain equipment where hazardous materials may lurk

The best protection is preparation. Assess any equipment/device that requires repair or maintenance for potential contamination or source of injury, such as sharp edges. Know where your protective apparel is located and use it. Review decontamination procedures and keep disinfectants available. Know your employee report of injury program and seek medical care whenever you have concerns regarding potential injury or exposure. Know your policies and procedures and where to find them if you need further information. Your infection control staff should be available 24 hours a day. The standard personal protective equipment that your employer is required to make available to you should include gloves, masks, eye protection, and gowns. In addition, if you are expected to enter a negative pressure room while a patient is in Airborne Precautions, you must be properly fit tested with an N95 respirator prior to entering the room. This respirator is very similar to a normal mask, but is able to filter out particles such as the TB bacterium. Infection control boils down to 2 commandments: 1. Wash your hands! 2. Use your head/common sense: If it looks soiled--clean it. If you have concerns--ask for clarification. If you think you have been exposed--seek medical assistance.     

Queen replacement in African and European honey bee colonies with and without afterswarms

We examined the dynamics of the queen replacement process in African and European colonies that did and did not produce afterswarms. In colonies without afterswarms, the queen replacement process was completed in 24–48 hours, the first-emerging virgin queen (VQ) typically inherited the natal nest even if multiple queens emerged, workers performed few vibration signals on emerged queens, and all signaling activity was directed toward early emerging VQs. In contrast, if colonies did produce afterswarms, the queen replacement process required 5–6 days, there was no advantage for first-emerging queens, vibration rates on emerged queens were 25 times greater, and signaling activity was directed toward all VQs. Although vibration signal activity was more pronounced in colonies with afterswarms, the signal was consistently associated with increased VQ survival under all conditions. These trends were exhibited similarly in the African and European colonies, suggesting that they have broad applicability to queen-replacement decisions over a range of environmental and racial conditions. However, the African and European colonies differed in the total number of queens involved in the elimination process and the relative importance of queen duels and pre-emergence destruction under the different reproductive strategies. Taken together, our results suggest that worker behavior is a major determinant for the outcome of queen replacement, either through reduced interactions that allow first-emerged queens to rapidly eliminate rivals, or through increased use of interactions such as the vibration signal, which may allow workers to influence the ultimate fate of each emerged VQ. We discuss the possibility that these behavior patterns may reflect the roles of cooperation and conflict in shaping honey bee reproductive decisions. Received 8 May 2007; revised 7 November 2007; accepted 20 November 2007.     

A Cascading Auction Protocol as a Framework for Integrating Process Planning and Heterarchical Shop Floor Control

A new contract net-style auction protocol is proposed as a framework for integrating process planning and shop floor control in heterarchical manufacturing systems. Process planning is partitioned into on-line and off-line activities; off-line process planning decisions are represented in a graph format and used as input for on-line process planning activities performed by machine controllers. Triggered by the opening round of an auction, the final on-line stages of process planning are dovetailed with the resource allocation process in the shop floor control system. The auction process allows final process planning decisions to be based on timely information, relying on the distribution of static process planning information rather than the distribution of a model of dynamic shop floor status and allowing a controller to identify all the primary and secondary resources and operations that must be provided for the incremental processing of a part.     

Middle-ear dynamics before and after ossicular replacement

The mechanism of hearing involves conduction of mechanical vibrations along the ossicular chain to the inner ear. An acoustic wave is collected and transformed as it passes down the ear canal and impacts on the tympanic membrane (ear drum). The drum is connected to the inner-ear by three ossicle bones (malleus, incus, and stapes) in a complex arrangement, which serves to further transform the mechanical vibration before it reaches the cochlea of the inner ear. What is the mechanical function of the ossicular chain, and what are the biomechanical consequences of surgical reconstruction with prostheses? To answer these questions, a three-dimensional finite element model of the outer ear canal and middle ear was generated. The dynamical behaviour was predicted for the normal ear, and an ear reconstructed with partial and total ossicular replacement prostheses. For the normal ear, stapes amplitudes of 1x10(-8) m at low frequencies decrease to 4x10(-10)m at approximately 3kHz with several resonance peeks in between, most significantly at approximately 1kHz. Thereafter a further resonance is predicted at 4kHz associated with the ear canal. The behaviour is changed fundamentally by adding a prosthesis; the partial replacement increases the vibratory coupling of the drum and the stapes compared to the normal ear whereas the total replacement does the opposite, and is predicted to have the disadvantage of bringing several new resonances of the ossicular chain into the hearing range. It is hypothesised that the function of the malleus-incus-stapes arrangement is to link the drum to the oval window with the flexibility required for impedance matching but the rigidity to prevent unconstrainable resonances from occurring in the hearing range. If this is true, then the structural stiffness of ossicular chain is the critical design variable for middle-ear replacement prostheses.     

Tooth replacement rates in early chondrichthyans: a qualitative approach

The continuous replacement of teeth throughout their lifetime is a common characteristic of most chondrichthyans. This process was already present in the earliest representatives of the group. It has been well established that different species of extant sharks show rapid tooth replacement rates; however, some authors have suggested that in early chondrichthyans this rate might have been much slower. Here we present a qualitative approach to analyse tooth replacement rates in the Early Devonian shark Leonodus carlsi , the earliest tooth-bearing shark known to date. For this, we have examined 1,103 isolated teeth from Celtiberia, Spain. Our study provides strong evidences of an extremely slow dental replacement in this primitive chondrichthyan based on three independents analyses: (1) statistical analysis of the wear degree, demonstrating that teeth remain functional for a long period of time; (2) analysis of both the histological and the morphological features of the teeth cusps suggests that this chondrichthyan used a maturation process that optimizes its function, thus worn teeth show an efficient working shape that implies their teeth remained functional for a long time after being modelled by use; and (3) estimations of size increments between teeth (Δs) of the same dental family for some recent sharks whose rates of replacement were known prove that Δs is inversely proportional to the rate of replacement ( R ² = 0.8327). The estimated values of tooth replacement rates obtained from Δs for L. carlsi and for some Late Devonian cladoselachian sharks are significatively slower than those observed in current sharks.     

Organization and expression of H1 histone and H1 replacement histone genes

The H1 family is the most divergent subgroup of the highly conserved class of histone proteins [Cole: Int J Pept Protein Res 30:433–449, 1987]. In several vertebrate species, the H1 complement comprises five or more subtypes, and tissue specific patterns of H1 histones have been described. The diversity of the H1 histone family raises questions about the functions of different H1 subtypes and about the differential control of expression of their genes. The expression of main type H1 genes is coordinated with DNA replication, whereas the regulation of synthesis of replacement H1 subtypes, such as H1° and H5, and the testis specific H1t appears to be more complex. The differential control of H1 gene expression is reflected in the chromosomal organization of the genes and in different promoter structures. This review concentrates on a comparison of the chromosomal organization of main type and replacement H1 histone genes and on the differential regulation of their expression. General structural and functional data, which apply to both H1 and core histone genes and which are covered by recent reviews, will not be discussed in detail.     

Outcome of renal replacement treatment in patients with diabetes mellitus.

OBJECTIVE--To compare the outcome of renal replacement treatment in patients with diabetes mellitus and in non-diabetic patients with end stage renal failure. DESIGN--Retrospective comparison of cases and matched controls. SETTING--Renal unit, Western Infirmary, Glasgow, providing both dialysis and renal transplantation. PATIENTS--82 Diabetic patients starting renal replacement treatment between 1979 and 1988, compared with 82 matched non-diabetic controls with renal failure and 39 different matched controls undergoing renal transplantation. MAIN OUTCOME MEASURES--Patient characteristics, history of smoking, prevalence of left ventricular hypertrophy and myocardial ischaemia at start of renal replacement treatment; survival of patients with renal replacement treatment and of patients and allografts with renal transplantation. RESULTS--The overall survival of the diabetic patients during the treatment was 83%, 59%, and 50% at one, three, and five years. Survival was significantly poorer in the diabetic patients than the controls (p less than 0.001). Particularly adverse features for outcome at the start of treatment were increasing age (p less than 0.01) and current cigarette smoking (relative risk (95% confidence interval) 2.28 (0.93 to 4.84), p less than 0.05). Deaths were mainly from cardiac and vascular causes. The incidence of peritonitis in patients on continuous ambulatory peritoneal dialysis was the same in diabetic patients and controls (49% in each group remained free of peritonitis after one year), and the survival of renal allografts was not significantly worse in diabetic patients (p less than 0.5). CONCLUSIONS--Renal replacement treatment may give good results in diabetic patients, although the outlook remains less favourable than for non-diabetic patients because of coexistent, progressive vascular disease, which is more severe in older patients.     

Developing replacement beef heifers

The replacement heifer represents the next generation of genetic progress for the cow herd. Producers invest a substantial amount of capital in these females, even if they fail to become pregnant. In order to get a return on this investment, it is imperative that these heifers become pregnant early in the first breeding season, calve with a minimum of dystocia, breed back in a timely fashion, and then continue to be productive for a number of years. Practitioners working with heifer development programs need to emphasize a systemic approach that evaluates these females at critical times. These programs need to address such areas as weaning and nutritional management, genetic selection, prebreeding evaluation, the breeding season, and heifer management from pregnancy examination through the end of their first calving season. This increased level of scrutiny should not end until the heifer weans her first calf and is determined to be pregnant the second time. This type of program will ensure optimal reproductive rates, female longevity, and a positive return on the producer's investment.     

A hybrid model for the neural representation of complex mental processing in the human brain

In the present conceptual review several theoretical and empirical sources of information were integrated, and a hybrid model of the neural representation of complex mental processing in the human brain was proposed. Based on empirical evidence for strategy-related and inter-individually different task-related brain activation networks, and further based on empirical evidence for a remarkable overlap of fronto-parietal activation networks across different complex mental processes, it was concluded by the author that there might be innate and modular organized neuro-developmental starting regions, for example, in intra-parietal, and both medial and middle frontal brain regions, from which the neural organization of different kinds of complex mental processes emerge differently during individually shaped learning histories. Thus, the here proposed model provides a hybrid of both massive modular and holistic concepts of idiosyncratic brain physiological elaboration of complex mental processing. It is further concluded that 3-D information, obtained by respective methodological approaches, are not appropriate to identify the non-linear spatio-temporal dynamics of complex mental process-related brain activity in a sufficient way. How different participating network parts communicate with each other seems to be an indispensable aspect, which has to be considered in particular to improve our understanding of the neural organization of complex cognition.     

Anion-exchange chromatography of proteins on AG MP-1 using high-performance liquid chromatography equipment

That the macroporous anion-exchange resin AG MP-1 can be used with HPLC equipment and common aqueous buffers for the chromatography of proteins is shown. The utility of this system is illustrated by the partial purification and complete resolution of the three protein synthesis elongation factors from each other, starting with a crude extract of Escherichia coli. The factors were purified 10- to 30-fold in a yield of 50 to 90% with a single 60-min chromatographic program of increasing NaCl concentration. Other proteins from various biological sources were purified with similar results. Thus, it appears that AG MP-1 is useful, at least in some applications, for the rapid, reproducible, and economical purification of proteins using HPLC equipment.     

A numerically validated probabilistic model of a simplified total hip replacement construct

Hip replacement constructs are paradigms of uncertain systems, and as such are suited to the application of probabilistic methods to assess their structural integrity. In order to gain confidence in a probabilistic model, it would be useful to verify the findings with experimental data; however, this is difficult to achieve in practice because of the exhaustive number of parameter combinations that need to be tested. As an alternative to experimental testing, benchmarking can be carried out provided a known analytical solution is available. To this end, a simplified 2D two-beam model of the femoral part of a total hip replacement construct was utilised to analyse uncertainties and variability in the construct as it is subjected to load. The use of a simplified model enabled geometric parameters to be investigated; these are commonly not considered in probabilistic models due to the increased complexity involved. Analytical and finite element representations of the model were developed and compared. The probabilistic study used the Monte Carlo simulation technique and the first-order reliability method to look at the inducible displacement of a hip implant, a phenomenon that has been linked to the most common cause of hip implant failure, aseptic loosening. Excellent correlation was observed between the analytical and probabilistic solutions, and it was shown that probabilistic approaches could efficiently predict the response of the simplified beam model while readily identifying the parameters most likely to compromise the structural integrity of the construct.     

A numerically validated probabilistic model of a simplified total hip replacement construct

Hip replacement constructs are paradigms of uncertain systems, and as such are suited to the application of probabilistic methods to assess their structural integrity. In order to gain confidence in a probabilistic model, it would be useful to verify the findings with experimental data; however, this is difficult to achieve in practice because of the exhaustive number of parameter combinations that need to be tested. As an alternative to experimental testing, benchmarking can be carried out provided a known analytical solution is available. To this end, a simplified 2D two-beam model of the femoral part of a total hip replacement construct was utilised to analyse uncertainties and variability in the construct as it is subjected to load. The use of a simplified model enabled geometric parameters to be investigated; these are commonly not considered in probabilistic models due to the increased complexity involved. Analytical and finite element representations of the model were developed and compared. The probabilistic study used the Monte Carlo simulation technique and the first-order reliability method to look at the inducible displacement of a hip implant, a phenomenon that has been linked to the most common cause of hip implant failure, aseptic loosening. Excellent correlation was observed between the analytical and probabilistic solutions, and it was shown that probabilistic approaches could efficiently predict the response of the simplified beam model while readily identifying the parameters most likely to compromise the structural integrity of the construct.     

Medium term planning of biopharmaceutical manufacture using mathematical programming

Regulatory pressures and capacity constraints are forcing the biopharmaceutical industry to consider employing multiproduct manufacturing facilities running on a campaign basis. The need for such flexible and cost-effective manufacture poses a significant challenge for planning and scheduling. This paper reviews the problem of planning and scheduling of biopharmaceutical manufacture and presents a methodology for the planning of multiproduct biopharmaceutical manufacturing facilities. The problem is formulated as a mixed integer linear program (MILP) to represent the relevant decisions required within the planning process and is tested on two typical biopharmaceutical industry planning problems. The proposed formulation is compared with an industrial rule based approach, which it outperforms in terms of profitability. The results indicate that the developed formulation offers an effective representation of the planning problem and would be a useful decision tool for manufacturers in the biopharmaceutical industry particularly at times of limited manufacturing capacity.     

Spinning plates and juggling balls

A PhD thesis is a project with an established goal and a deadline. As such, the tools, strategies and insight of professional project management can be used effectively to improve both research success and personal well-being.A project is a “temporary endeavour undertaken to create a unique product, service or result” [1]. Although this is a generic definition, it pretty much describes any PhD research project. There are many ways to manage a project effectively and efficiently. Unfortunately, most of us are so busy with our science that we forget about the importance of planning and management to our own success, sanity and health. Instead, we approach our first three years of genuine scientific endeavour wide-eyed and unprepared to juggle the hundreds of tiny balls that make up a PhD. Several techniques from the realm of ‘project management'' might therefore be helpful for PhD students who need to plan and manage the many competing demands that doctoral research can place on them.A PhD comprises both the research itself and the acquisition of skills and knowledge that will facilitate your future career. As such, it is of paramount importance to establish your own objectives early on. For example, alongside dividing your project into work packages—smaller projects that might be discrete or might build on each other—it is also essential to define which so-called transferable skills—additional knowledge and experience that might improve your job prospects—you feel will be of greatest use to you, depending on what you want to do after your PhD. The importance of these skills is becoming more widely recognized and taken far more seriously, and you should find that your supervisor is willing to give you the time to pursue them—your institute or university usually requires that he or she does so. More importantly, you should give yourself the time to invest in these skills, as they are going to be vital to everything you do once your PhD project is over.Doctoral research requires a multitude of skills, most of which you will inevitably lack when you commence your PhD programme. The first step is to identify the gaps in your knowledge to plan what skills on which to focus. This will allow you to acquire them in good time, either through professional activities—shadowing a postdoc, teaching undergraduates, joining journal clubs and blogging—or through both internal and external courses and workshops to improve communication, presentation, writing, networking and other skills. In addition to your planned skills acquisitions, you will also have situations arise, in which you need to acquire new skills quickly. The more you plan training activities and skills acquisition in advance, however, the smoother this aspect will be of your PhD. By way of example, part of my own PhD project relates to statistical analysis of data. An early analysis highlighted several areas in which I had to improve my skills, including hierarchical cluster analysis, principal component analysis and χ² testing against standard distributions. Having identified these gaps in knowledge early on in my doctoral programme, I could plan ahead accordingly when and how to acquire these skills.The full scope of your PhD project is usually unknown at the outset, and even the direction of your research might well change before you are finished. ‘Rolling wave planning'' is a technique that allows you to take these facts into account and plan the short-term future in detail, with a high-level provision for medium- to long-term activities. For those new to developing project schedules, I advocate a simple five-step approach. First, make an ordered list of high-level activities needed to achieve your goal. Second, expand this list by adding lower-level activities for which you have a detailed understanding of the scope, for example work to be performed in the next six months. You now have a work breakdown structure. Third, turn this work breakdown structure into a dependency-driven list by adding associations between the activities, for example by adding links to precursor activities that need to be completed before another activity can be started. Fourth, estimate the duration of each activity and extrapolate the start and end dates beginning with the first scheduled activity. Finally, as you progress through your research, and the scope of future activities becomes clearer, update the project schedule with these low-level activities as they become known. This approach of generating a hybrid-level project schedule, and updating with detailed activities as the scope becomes clearer, is known as ‘rolling wave planning''.…we approach our first three years of genuine scientific endeavour wide-eyed and unprepared to juggle the hundreds of tiny balls that make up a PhDThere is a range of professional software to help develop project schedules, but there are also various freeware tools available. Alternatively, you can use one of the many word processing or spreadsheet applications to make a simple Gantt chart. Along with the technical scope of your doctoral research, it would also be useful to include milestones that your institution enforces; for example literature review submission, formal progress reports and thesis chapter outlines. Including these in your rolling wave planning will allow you to keep in mind the bigger picture and the formal aspects that must be completed for your PhD, in parallel with the progress you are making towards your specific research subject.It is of course a cliché, but it is true that ‘failing to plan is planning to fail''. Of course the fluid nature of research makes it difficult to estimate accurately the time that it will take to complete various experiments, especially as a novice researcher. I therefore believe that although experiments do overrun and PhD projects can change, developing a project schedule is not a futile activity. By having a plan, even if it is made up of ‘guesstimates'', you can forecast roughly how much time you have left for your research and roughly what you can realistically hope to achieve. After all, without a plan, how can you predict when you will complete your research, submit your thesis and ultimately gain your PhD?Doctoral research requires a multitude of skills, most of which you will inevitably lack when you commence your PhD programmeThe serious consideration of scope is necessary in any project, but even more when you are simultaneously project manager, research scientist and key stakeholder. This raises various crucial questions regarding scope management: what is my doctoral research all about (the goal), and what work do I need to do to meet this goal? Once this has been agreed between you and your supervisor(s), it is essential to manage the scope of your project—the breadth and number of experiments you will perform—and how this will achieve your goal(s). Furthermore, be specific—knowing exactly what you want to achieve will keep you motivated until you get there.Project managers often use the concept of the triple constraint to manage work: scope, time and cost are intricately linked in a project and the different level of focus that each is given affects the perceived quality (by others) of project deliverables (Fig 1). Project managers understand that any deviation in one of the triple constraints changes one or both of the others. This is where the project schedule really comes into its own by allowing you to forecast when you will complete the agreed goals of your PhD project. For example, is your doctoral programme for a fixed-term period? If so, then once a project schedule has been agreed that uses all of the time available, any project overruns will cause an overrun to the overall PhD. The two main possibilities for a PhD student to manage this situation and bring the projected completion back into acceptable timescales are either to work longer or to reduce the scope or goals of the project, either by conducting fewer experiments to answer the same question or by modifying the depth of the question being asked. This leads to the issue of whether there is a minimum set of goals that need to be achieved, or whether several agreed activities are ‘nice to haves'', but are not crucial for the overall PhD. I believe that your supervisor(s) are best suited to answer questions about the minimum goals and the scope needed to achieve them.Open in a separate windowFigure 1The project management triangle as applied to a PhD. Three competing constraints influence project management: time, scope and cost. The time constraint reinforces that projects are temporary endeavours, and that in most cases have defined timescales (absolute deadlines). The cost constraint refers to the budgeted amount allocated to the project that, from the perspective of doctoral research students, will predominantly be focused on the amount and duration of the stipend awarded, but might also incorporate various expenses such as bench fees, conference fees and consumables. For those changing career, the cost might also comprise an element of salary sacrifice. The scope constraint refers to what must be done, produced or developed to meet the objective of the project, which in the case of a PhD generally comprises the actual doctoral research to be performed, development (and submission) of the thesis, publication of one or more journal articles, presentation at conferences and potentially teaching. The triple constraint principle highlights that any change to one of the constraints will have an impact on one or both of the other constraints. For example, increased scope typically leads to increased time and cost; tight time constraints usually mean that an overrun in activities (such as experimentation) might have a knock-on effect of requiring the scope to be reduced to submit your thesis on time, or increasing the overall amount of time required to complete your PhD. Similarly, a tight budget could mean you cannot gain access to various resources, resulting in either increased time or a reduction in scope. Recently, a fourth component of the project management triangle has been introduced highlighting that along with the three constraints competing with each other, they also interact to form a fourth dimension of quality.If you need to complete your doctoral programme within a specified time frame, then you need to manage your goals and scope mercilessly—do not allow additional research questions or extra experiments to take away precious time. This does not mean that you cannot deviate, but any deviations need to be managed. Remember, whether you wish to remain in scientific research or not, the PhD is a stepping-stone to your future career and not the end goal in itself. Once you have achieved the goals agreed with your supervisor, it is more beneficial for you to write-up your doctoral thesis and move on [2].Good communication is essential in every area of work, but even more so for a PhD as you are simultaneously learning how to research along with doing the research. Often, access to your supervisor is limited by constraints on his/her or your time, which means that clear communication is vital. Do not assume that your supervisor knows every intricate detail of what you are doing; he or she might have a large group in which each member is looking at complementary aspects of a more general topic. It is, therefore, your responsibility to ensure that all your stakeholders—supervisors, postdoc leads and any others involved—know what you are doing and, more importantly, why you are doing it.This is another area in which the apt use of technology can maximize efficiency. Subject to institutional licensing, collaboration tools such as SkillsForge or Evernote can improve communication between stakeholders. For example, meeting minutes, action points to be followed and research results can be uploaded for sharing. Supervisors can then review the material at a convenient time to ensure that they stay up to date with the progress of each student within their research group.As PhD students usually aspire to become research scientists, it is of paramount importance that you learn the correct application of the scientific method and the context in which your work is being done. Before jumping into practical work—wet-lab experiments or computational modelling—it is important to understand the meaning and relevance of your project in relation to existing knowledge and the underlying science. For example, the hypothesis-driven research life cycle in systems biology [3,4]—my own field—advises that computational models should be developed on the basis of wet-lab data relating to the underlying biological system. Almeida-Souza and Baets state that a PhD in science is an opportunity to learn how to tackle problems scientifically and, as such, requires the development of skills in critical thinking, hypothesis formulation and experimental design [5]. I believe that the requirement for these skills is universal across the sciences, and that molecular biosciences and computational systems biology are no different.The serious consideration of scope is necessary in any project, but even more when you are simultaneously project manager, research scientist and key stakeholderTherefore, before the first wet-lab experiment is performed, or the first line of code is written, it is essential that we understand why the experiment is important and what results we might expect to support our initial hypotheses. Furthermore, regarding computational systems biology, I believe that it is also essential for wet-lab and computational researchers to collaborate to ensure both have a consistent understanding of the data and the purpose of the computational model. After all, for the most part, computational models are developed for their predictive capacities and to allow hypothesis generation for subsequent wet-lab experimentation. Baxter et al have extensively covered this area and advocate not only designing the project up-front, but also the need for quality control [6].You need to manage the scope and goals of your PhD mercilessly and, at the same time, be flexible enough to grasp new opportunities. Conversations at conferences, for instance, can open up opportunities for collaboration and take your research in a direction that you had not considered previously. In my case, I was invited to turn a conference paper relating to my masters degree into a full paper for a special issue of a well-known bioinformatics journal. Although it was not related to my doctoral research, the prospect was too good to turn down. I therefore discussed the idea with my PhD supervisor, and once we were in agreement, I updated the project schedule to incorporate this new activity, trying to mitigate as much as possible the resulting slippages to my doctoral research. In essence, I had performed an impromptu risk–reward analysis and decided that the reward that would be gained from publishing this work outweighed the risk of a slight overrun of my PhD thesis. It must be noted that I was lucky in this instance, as my PhD supervisor also supervised the research project during my master''s degree, so a full paper would be beneficial for both of us.A project risk is “an uncertain event or condition, that if it occurs, has an effect on at least one project objective” [1]. The positive side to risks is that the likelihood of their future occurrence can be mitigated by planning in the present. Once a risk is realized, however, and its effects begin to be felt, it has turned into a project issue. The first step in trying to manage risks is their identification. Risk identification in this context is the process of determining which events, if they occurred, would affect your research. In the context of a molecular biosciences PhD, I believe that general risks relate to access to resources, such as people—postdocs and collaborators, for example—reagents, cell lines and shared equipment. For example, if your work uses fluorescent proteins within single cell analysis, how would you be affected if the fluorescence microscope was booked out by another research lab? Similarly, in computational systems biology, if the design process for your computational model requires access to wet-lab data, what would the effect(s) be if this was not available?Once risks are identified, it is important to develop risk response plans. By using the above example of access to a microscope, what should your response be if you cannot gain access? The initial risk response would be to liaise with the other research lab to understand their requirements and ascertain whether you could gain access at a mutually convenient time. Alternatively, another approach might be to work outside normal office hours, either throughout the evenings or on the weekend, subject to health and safety procedures at your institution and your own health and well-being. I believe that a degree of creativity is often required when developing effective risk response plans.A PhD thesis is a hefty document that might run to many hundreds of pages. They are generally not written as a single large document from start to finish, but as chapters. In the molecular biosciences, a thesis consists of an initial literature review early in the doctoral programme, work-in-progress documents for materials and methods, experimental results throughout the middle section, which is followed by analysis and critical evaluation towards the end of your experimental work. Whether through software tools or through your own manual methods, such as keeping a configuration log and keeping a copy of each version of your working documents, it is essential that you maintain an up-to-date repository of all your notes. I have found through experience that it is beneficial to save not only the final versions, but also each of the working drafts of documents generated throughout your PhD. Ideas previously discounted, and thus removed from more recent versions of documents, might once again take centre stage at a later date.The positive side to risks is that the likelihood of their future occurrence can be mitigated by planning in the presentThis can be aided through the development and use of a project library with a logical folder structure to facilitate easy access to documentation. Noble [7] provides an in-depth discussion of organizing your computational biology project—in particular the value of version control—but the concepts are transferable across disciplines. Furthermore, do not forget to back-up your work, and without seeming too pessimistic, back-up your back-up!Finally, look after the most important resource: you. Exercise, diet, alcohol, caffeine and holidays all affect your well-being. Holidays and time away from the lab or office allow you to take a step back from the detail and reflect on your experiences and progress. Sometimes, time off allows you to process issues subconsciously and develop new approaches to overcome problems that you have been tackling for extended periods of time without success. Finally, holidays also help you recharge your batteries and enthusiasm to return to your project with fresh vigour. If you have sensibly and reasonably planned time off alongside your work, you will be able to enjoy it.Although a PhD requires consistent commitment, you simply cannot—and should not—work at full capacity all of the time. Issues arise periodically throughout any project, and if you have no reserves of energy—either mental or physical—you will be unable to tackle them head on with the step change of performance that is required. Furthermore, doctoral research is a marathon and not a sprint; we all experience the symptoms of burnout from time to time, and sometimes it is better to walk away for a short period to recharge than to carry on, become stale, and ultimately slow down.To conclude, I wish you good luck with your doctoral research, and I hope these techniques help you to manage your PhD project through to successful completion.? Open in a separate windowRichard Alun Williams     

Integrated software system for improving medical equipment management

The evolution of biomedical technology has led to an extraordinary use of medical devices in health care delivery. During the last decade, clinical engineering departments (CEDs) turned toward computerization and application of specific software systems for medical equipment management in order to improve their services and monitor outcomes. Recently, much emphasis has been given to patient safety. Through its Medical Device Directives, the European Union has required all member nations to use a vigilance system to prevent the reoccurrence of adverse events that could lead to injuries or death of patients or personnel as a result of equipment malfunction or improper use. The World Health Organization also has made this issue a high priority and has prepared a number of actions and recommendations. In the present workplace, a new integrated, Windows-oriented system is proposed, addressing all tasks of CEDs but also offering a global approach to their management needs, including vigilance. The system architecture is based on a star model, consisting of a central core module and peripheral units. Its development has been based on the integration of 3 software modules, each one addressing specific predefined tasks. The main features of this system include equipment acquisition and replacement management, inventory archiving and monitoring, follow up on scheduled maintenance, corrective maintenance, user training, data analysis, and reports. It also incorporates vigilance monitoring and information exchange for adverse events, together with a specific application for quality-control procedures. The system offers clinical engineers the ability to monitor and evaluate the quality and cost-effectiveness of the service provided by means of quality and cost indicators. Particular emphasis has been placed on the use of harmonized standards with regard to medical device nomenclature and classification. The system's practical applications have been demonstrated through a pilot evaluation trial.     

Automatic procedure for using models of proteins in molecular replacement

In a crystallography experiment, a crystal is irradiated with X-rays whose diffracted waves are collected and measured. The reconstruction of the structure of the molecule in the crystal requires knowledge of the phase of the diffracted waves, information that is lost in the passage from the three-dimensional structure of the molecule to its diffraction pattern. It can be recovered using experimental methods such as heavy-atom isomorphous replacement and anomalous scattering or by molecular replacement, which relies on the availability of an atomic model of the target structure. This can be the structure of the target protein itself, if a previous structure determination is available, or a computational model or, in some cases, the structure of a homologous protein. It is not straightforward to predict beforehand whether or not a computational model will work in a molecular replacement experiment, although some rules of thumb exist. The consensus is that even minor differences in the quality of the model, which are rather difficult to estimate a priori, can have a significant effect on the outcome of the procedure. We describe here a method for quickly assessing whether a protein structure can be solved by molecular replacement. The procedure consists in submitting the sequence of the target protein to a selected list of freely available structure prediction servers, cluster the resulting models, select the representative structures of each cluster and use them as search models in an automatic phasing procedure. We tested the procedure using the structure factors of newly released proteins of known structure downloaded from the Protein Data Bank as soon as they were made available. Using our automatic procedure we were able to obtain an interpretable electron density map in more than half the cases.     

Retrospective study of doctors' "end of life decisions" in caring for mentally handicapped people in institutions in The Netherlands.

OBJECTIVES: To gain insight into the reasons behind and the prevalence of doctors'' decisions at the end of life that might hasten a patient''s death ("end of life decisions") in institutions caring for mentally handicapped people in the Netherlands, and to describe important aspects of the decisions making process. DESIGN: Survey of random sample of doctors caring for mentally handicapped people by means of self completed questionnaires and structured interviews. SUBJECTS: 89 of the 101 selected doctors completed the questionnaire. 67 doctors had taken an end of life decision and were interviewed about their most recent case. MAIN OUTCOME MEASURES: Prevalence of end of life decisions; types of decisions; characteristics of patients; reasons why the decision was taken; and the decision making process. RESULTS: The 89 doctors reported 222 deaths for 1995. An end of life decision was taken in 97 cases (44%); in 75 the decision was to withdraw or withhold treatment, and in 22 it was to relieve pain or symptoms with opiates in dosages that may have shortened life. In the 67 most recent cases with an end of life decision the patients were mostly incompetent (63) and under 65 years old (51). Only two patients explicitly asked to die, but in 23 cases there had been some communication with the patient. In 60 cases the doctors discussed the decision with nursing staff and in 46 with a colleague. CONCLUSIONS: End of life decisions are an important aspect of the institutionalised care of mentally handicapped people. The proportion of such decisions in the total number of deaths is similar to that in other specialties. However, the discussion of such decisions is less open in the care of mental handicap than in other specialties. Because of distinctive features of care in this specialty an open debate about end of life decisions should not be postponed.