Development of a direct digital-controlled fermentor using a microminicomputer hierarchical system

A computerized fermentation system was developed for use in a research environment. Major goals in the design and construction of the system were flexibility and versatility. Direct digital control is employed for all low-level and high-level control loops. A microminicomputer hierarchical configuration is used to implement this control structure. The microcomputer is also utilized to simplify the interface with the fermentor, both at the hardware and minicomputer software levels. This computerized fermentation system provides accurate data acquisition, excellent control, and flexibility in the fermentation operation.     

微机在核医学图像处理系统中的应用研究

本文以一种较具代表性的核医学图像设备为例,在简要介绍单光子发射型计算机断层摄影基本工作原理的基础上,对该设备的核心部分－计算机系统的结构进行剖析。并进一步介绍了对这种大型核医学成像系统进行诊断功能扩展方法的基本原理。     

Clinical applications of three-dimensional photography in breast surgery

Three-dimensional imaging in breast surgery has several uses clinically. The most practical applications are for the evaluation of breast asymmetries, both congenital and acquired, and for the evaluation of factors affecting breast shape in augmentation mammaplasty. Other uses of three-dimensional imaging that we have found clinically helpful are for evaluation of patients desiring reduction mammaplasty and for evaluation of patients undergoing unilateral breast reconstruction to determine the expander and permanent implant size that gives the best symmetry with the contralateral breast. We present five cases in which we investigate the use of three-dimensional imaging clinically by using the images to determine quantitative information about the breast, such as volume or projection. Overall, three-dimensional imaging is very helpful in providing objective information about the breast for use in preoperative planning. In addition, by analyzing clinical cases, it can provide objective data about the breast and surgical mammaplasty (especially augmentation mammaplasty) that may help surgeons better understand those factors that contribute to breast shape and influence surgical outcomes. There are currently some limitations of this system, influenced by patients with significant ptosis or obesity, which may introduce errors into the three-dimensional data, making them unreliable. However, we believe three-dimensional imaging has great clinical potential in surgical mammaplasty.     

Decoding the genetics of rare disease: an interview with Monkol Lek

Monkol Lek, Assistant Professor at Yale University School of Medicine, and Associate Editor at Disease Models & Mechanisms, dedicates his research to finding a genetic diagnosis and improving treatments for rare disease patients. As he originally studied computer engineering at the University of New South Wales in Sydney, Australia, he now utilises computational methods to optimise large-scale genetic studies, provide globally accessible resources for genetic research communities and, importantly, resolve diagnostic odysseys for rare disease patients. Monkol completed his PhD in Prof. Kathryn North''s lab at the University of Sydney, studying the genetics of muscle strength and performance, and then continued his investigation of muscle disease in Prof. Daniel MacArthur''s lab at Massachusetts General Hospital and the Broad Institute. During his postdoc, he led several large-scale studies aimed at distinguishing pathogenic from benign variants, including the Exome Aggregation Consortium (ExAC) project ( Lek et al., 2016). Monkol established his own lab at Yale University School of Medicine, which continues to improve the diagnosis and treatment of rare muscle disease, and also focuses on underserved populations, whose genetic mutations are not as well characterised as those of European ancestry. In this interview, Monkol discusses how his own diagnosis with limb girdle muscular dystrophy has shaped his career and what he envisions for the future of genetic research in rare disease.

You have a very unique career path – could you tell us a little bit about that? My first degree was in computer engineering. When I first went to university, I studied the hardware and software of computers. I really liked the software aspect of the degree, and so I worked for IBM as a software developer when I finished university. However, during the last few years of university, I noticed that my muscles were getting weaker. My university was on a big hill, with classes at the bottom and top of the hill, and I had to stand up for about 3 h a day while commuting on public transport. It started becoming obvious that I had something wrong with my muscles because I felt totally exhausted at the end of the day. It was frustrating, because I felt that my performance at university was impacted by something that had nothing to do with my ability to think. So, I went from doctor to doctor to try to find out what was wrong with me. As a lot of doctors are not trained in rare diseases, they didn''t consider a rare disease diagnosis. Then one doctor did a blood test for creatine kinase (CK), which is leaked into the bloodstream when muscle is damaged. In healthy people, high levels of CK are detected in the bloodstream after they''ve done intensive exercise, like a marathon. If someone hasn''t done something like that, but they have high levels of circulating CK, it could be an indication that there''s something wrong with their muscles. As I had high levels of CK in my bloodstream, I then went to a neurologist, which was when I got a clinical diagnosis. At that point, they didn’t know the root cause of the problem, but they knew that I have a muscle disease based on several tests, including a nerve conduction test.I received this clinical diagnosis during my time in IBM, and that''s when I became dissatisfied with my job, because I felt that I was using all my talents to make a very big, international company richer. I was also becoming frustrated when visiting the neurologist every 6 months, as all they would tell me was that my muscles were getting weaker, which I already knew. I began to think that not much was happening in the neuromuscular disease field if that''s the best they could offer me. I wanted to know what the root cause of my disease was and if there were any treatment options. I came to the conclusion that no one would care about my disease more than I would, because I''m the one that has lived with it every day of my life.That''s when I decided to leave IBM and pursue a career in researching muscle disease. It didn''t go down well with my parents and friends, because I was leaving a well-paid job to go back to university to get paid nothing for an unknown number of years. If I had known my chances of success – completing a meaningful PhD, doing a meaningful postdoc and landing a faculty position – I wouldn''t have gone on this journey. I have been very fortunate, but I wasn''t always in the right place at the right time.When I finished my undergraduate degree in bioinformatics and physiology at the University of New South Wales, I started a PhD in Melbourne, but it didn''t work out, because not all supervisors are perfect. My wife and I then returned to Sydney, where my wife bumped into one of the professors from our undergraduate degree. She explained that we''d had a bad experience in Melbourne with our PhDs, but our passion was still to do muscle research. The professor''s daughter was researching muscle disease in Kathryn North''s lab at the University of Sydney, and she invited us to visit the lab. I was offered an opportunity to do my PhD in Kathryn''s lab, but I was initially reluctant as it was a diagnostic lab, and I was more interested in developing therapies for people with muscle disease. However, I thought I could still learn a lot about muscle physiology and, in the long term, I''m glad that I received training and mentorship from Kathy''s lab. Also, if I hadn''t done my PhD there, I wouldn''t have met Daniel MacArthur, my future boss. He was a very talented student in Kathy''s lab, who taught me a lot about scientific communication among other things, and I taught him some coding skills. He left to work on the 1000 Genomes Project in Cambridge, UK, but I kept in contact with him to get his advice on my project.When I was finishing my PhD, Daniel asked if I wanted to join the lab he was setting up in Massachusetts General Hospital and the Broad Institute. His lab was going to study common loss-of-function mutations in human populations using large datasets from the 1000 Genomes Project, but he offered me a project investigating neuromuscular diseases. As soon as I submitted my PhD thesis, I started working in his lab. This was perfect timing, because it was 2012, when exome sequencing had recently been published in the context of rare diseases (Ng et al., 2010) and, more importantly, it was becoming affordable, in terms of research. I waited over 10 years for a genetic diagnosis, so my goal was that no one should have to wait that long in the future.Through collaboration with our former PhD lab, Daniel and I used samples from undiagnosed patients to find answers for Australian families. The first family had two affected girls with undiagnosed nemaline myopathy, who had been on a diagnostic odyssey for about 9 years. It was amazing how quickly we progressed from receiving the samples to identifying the novel gene, LMOD3, associated with their disease (Yuen et al., 2014). This was part of my main project during my postdoc – working on gene discovery in neuromuscular diseases and finding answers for patients that have been waiting years and years to get a genetic diagnosis (Ghaoui et al., 2015; O''Grady et al., 2016).The project that most people know me for is the ExAC project, which was initially my ‘side’ project during my postdoc. The idea was to create a big database of all rare variants that we see in the general population, so we can better interpret the rare variants that we see in rare disease patients. When we were creating it, we thought that it may be useful to other researchers around the world. Therefore, we tried to ensure, through data-use agreements and consent processes, that we could share as many of our findings as possible. I''m happy to say my side project was quite successful. After that, I led other projects, including an analysis group in the Centre for Mendelian Genomics, to expand that framework and idea across all rare diseases, not just neuromuscular diseases (Baxter et al., 2022).I was having a lot of fun at the Broad Institute, and I was co-author on a lot of high-impact papers. However, the reason I left the Broad Institute was that I wanted to be involved in the full journey for the patients. Sometimes scientists don''t understand that getting a genetic diagnosis is not the end of the journey for a patient. After the diagnosis they want to know what treatment options are available. Yale gave me the opportunity to continue doing the gene discovery and analytical work that I was doing at the Broad Institute, plus the capability of doing experiments with mouse models to investigate gene replacement therapies and other therapeutic approaches.

“I waited over 10 years for a genetic diagnosis, so my goal was that no one should have to wait that long in the future.”

How has being both a researcher and a patient affected your career? When I was first diagnosed, there was a neurologist who discouraged me from researching my own disease and this became the basis of my TEDx talk, because I thought it was very condescending. I thought, “Just because I have this disease, it doesn''t mean that I have a low IQ”. However, this experience motivated me more. I discussed it with Kathy before starting my PhD, and her encouragement and enthusiasm was refreshing. At the time, in the early 2000s, people hadn''t accepted the idea of patients researching their own disease. Things have changed since then, mainly because there are more examples of it now (Branca, 2019), but at the time, it was really hard for me to progress in science. I always thought that people were looking at me with sympathy, and I felt like I had to achieve twice as much to get the same respect as someone else who wasn''t as talented or didn''t work as hard as me. It was frustrating, but in everyday life people still correlate physical disability with intellectual disability. For example, if my wife is pushing me in the wheelchair in public, no one ever directs a question to me because they assume that the physical disability comes with mental disabilities. There are well-known examples of scientists with physical disabilities, like Stephen Hawking, but it is still challenging in academia when you have a physical disability and people make certain assumptions about you.On the other hand, just before starting at Yale, my collaborators at the University of Massachusetts took a skin biopsy from me. With this skin biopsy, they created induced pluripotent stem cells, and, using CRISPR, they corrected my disease-associated gene variant in the cultured cells. They then published this in a Nature article, in which fig. 1 is the experiment in which they corrected my mutation (Iyer et al., 2019). Are there specific skills or knowledge you learned while working in computer engineering that have helped shape and develop your research today? When I started my PhD, there was an increase in how much genetics research, and biological research in general, relied upon big data. It can be very challenging to work with big data if you''re a biologist without a background in computer science. You can go online to teach yourself to an extent, but it gives you an advantage to learn the theory behind a lot of algorithms and other aspects of software engineering, in a formal setting. It makes the difference between building tools that take a week to analyse a set of data and building tools that take a few minutes to analyse the same data. If you can analyse the data more quickly, you can explore different possibilities and ideas much more quickly. You can''t learn everything online, and having a firm foundation of knowledge can enable you to work with big data in an efficient way.The other thing that you learn from computer science is a certain mindset when approaching problem solving. This is because you have to debug code frequently and, due to this fast pace, you learn quickly. This helped me to troubleshoot problems in biological research quickly.

“Getting a genetic diagnosis is not the end of the journey for a patient. After the diagnosis they want to know what treatment options are available.”

What do you think are the key challenges for rare disease research and diagnosis moving forward? I now have a greater appreciation of the challenges because I see it from two points of view: one as a researcher in a group and one as a PI, who leads the research. The diagnosis rate for rare disease is about 50%, so there are still 50% of patients with a disease that has an unknown genetic cause. The gold standard requirement for associating a new disease gene with a novel phenotype is that it presents in multiple unrelated families (MacArthur et al., 2014). However, when you work with rare diseases, there is the issue of small sample numbers. One challenge for basic scientists is creating good collaborations with physician scientists across the world to enable you to create a large enough dataset.The other challenge is the cost of research for these diseases with unknown genetic cause. The 50% of cases for which we know the genetic cause are no longer considered an area of research, as clinical genetic services can now diagnose these patients. To diagnose the remaining patients, you have to use more expensive technologies, such as long-read sequencing.The last thing is the interpretation of rare variants. Although the ExAC project helped with this, there is still a challenge. For example, if a patient has a rare genetic variant, this doesn''t necessarily mean it is the cause of their rare disease. This is because even healthy people have rare variants. So, we have a massive interpretation challenge in rare disease genetics, which can be overcome by creating a laboratory model system with that genetic variant to investigate it further. However, if you had 1000 variants to consider, it''s not going to scale as an animal model. So, an important question is how can we interpret these variants in a scalable manner? This is one of the main driving forces behind the new Subject Focus, ‘Genetic variance in human disease: decoding diversity to advance modern medicine’, that we are launching in DMM. You have led and coordinated several studies involving very large cohorts. From your experience what are the key components of a successful study? I think the key to a successful large cohort study with unsolved rare disease patients, is the amount of structured phenotype data you can collect. This requires a good collaborator, who has the time to prepare that data in a meaningful way, which makes it easier to find other families with the same rare disease. The other thing is to have the ability to recontact patients and collect different samples from them, because we''re moving to a more multi-omics world. Therefore, we need the ability to go beyond just collecting DNA samples. Also, we''re in a world where we''re starting to link data to electronic health records, which allows the collection of deeper and richer phenotype data that enable associations to be made between families.In addition, you can''t work in isolation. In order for us to make a meaningful impact, we need to work with groups that have specialties outside of our own. For instance, we collaborate with groups that specialise in the interpretation of non-coding variants. This is important as variants in these regions could hold the answers for some of those unsolved cases.Another key aspect to a successful study is collaboration with statistical geneticists because some of the more complicated questions are best asked by them. Some of these questions go beyond monogenic diseases. We are seeing convergence between genome-wide association studies, looking for many variants, each with very small contributions to a disease, and studies of Mendelian disease that are looking for one gene that causes disease. The field has to start looking at diseases in the middle of this spectrum, which requires statistical geneticists. This is because you need to make sure that your conclusions are correct. For instance, if you''re asking whether a rare disease is caused by a combination of two genes, then you must have a robust statistical model to show that these variants aren''t presenting together by chance. You have to prove that those two variants are acting in concert, instead of independently, to cause this disease. My colleagues at Yale published a great paper that demonstrated this concept (Timberlake et al., 2016).Lastly, it is important to forge meaningful collaborations beyond academia. A lot of my colleagues are being funded by industry collaboration, and a lot of these companies have access to more samples than we do in academia. You can also collaborate with large biobanks, such as the UK Biobank, which has a rich set of phenotype data and also the ability to recontact patients (Glynn and Greenland, 2020). The FinnGen project is a recent public–private collaboration that combines genetic data with electronic health records from Finnish biobank participants to improve disease diagnosis and treatment (Kurki et al., 2022 preprint). So, working with biobanks and industry is another way of increasing sample numbers, which is the biggest challenge in rare disease research.

“We don''t want to create disparity in terms of health, especially in the context of genetics, which will continue to become more prominent in modern medicine.”

You dedicate a lot of your research towards patients in underserved populations, such as East Asian populations, whose genetic mutations are not as well characterised as those of European ancestry. Can you explain the importance of this? One of the reasons that it took over 10 years for me to get a genetic diagnosis was because the gene that causes my disease was first reported as not commonly associated with disease in populations of European ancestry. The problem with biomedical research is that when people read that, they think it applies to everyone, even patients who have non-European ancestry. Although the gene that causes my disease aligned with my muscle disease phenotype, it wasn''t sequenced because of this assumption. They only decided to sequence this gene once they did linkage analysis of my family, and this was the only gene associated with neuromuscular disease in the linkage region they identified. This is the reason why we need to have good data on all populations. The ExAC and gnomAD studies that I worked on acknowledged that we need good allele frequency data for populations of East Asian, South Asian, Latino and African ancestry, because we don''t want to create disparity in terms of health, especially in the context of genetics, which will continue to become more prominent in modern medicine.If you want to deliver the best healthcare, you have to realise that some variants and diseases are more common in certain populations, such as Tay-Sachs disease, which is common amongst the Jewish community, and sickle cell anaemia, which is more prevalent in populations of African ancestry. By understanding these differences, we can actually find a genetic diagnosis a lot quicker. If it''s not a de novo variant, and is instead a variant inherited in the population, and if you''ve made the discovery in East Asians, there is a better chance of identifying more incidences of this variant in the population in which it was first discovered.I think it''s also good for validation of data, because if you had discovered a potential disease-causing variant and you find that this variant has a frequency of 1% or higher in a non-European population, then it''s impossible for it to be the cause of a rare disease, regardless of its frequency in a European population (Lek et al., 2016).     

A Tale of Mother and Daughter

Loving science and nature and being a scientist can be very different, yet the two are so intertwined in a scientist''s life that you will certainly experience both aspects. This essay presents my perspective on how, as one who loves science and nature, I came to fall in love with centrosome behavior in stem cells and how I came to run a lab as a scientist. When I started, there was a big gap between my love for science and my experience as a scientist. I filled this gap by learning a “laid-back confidence.”Before the beauty of cell biology (or whatever you love), who you are (i.e., your age, gender, or race) is immaterial. Yet history shows that the ease with which you can pursue science is influenced by who you are. This has certainly been my experience. The key is to find a way to fill in the gap between who you are and what you are (i.e., a scientist), a goal in which we must all support each other. It is my hope that this essay will convey something helpful to those who are at early stages of their career and might be encountering obstacles because of who they are.     

Computer-designed prostheses for orbitocranial reconstruction

Three-dimensional imaging is an adjunct to preoperative evaluation and surgical management in some patients with complex anatomic defects of various etiologies. Deformities defined by conventional computerized tomography can be viewed as accurate three-dimensional images calculated from the original scan. The images are viewed on a high-resolution video monitor and can be photographed for a permanent record. A computer-controlled milling device can use these data to fabricate prostheses. The prostheses aid reconstructive surgery through use as an alloplastic implant, as a template to fashion autogenous bone grafts, or as a model for tissue removal. We have utilized three-dimensional imaging in combination with computer-assisted prosthesis manufacture in six patients with complex orbitocranial deformities. Four patients have undergone reconstructive surgery with satisfactory results and no complications thus far. The use of computer-designed prostheses adds a new aspect to orbitocranial reconstructive surgery that facilitates increased accuracy in the correction of anatomic defects.     

Apple II software for M13 shotgun DNA sequencing.

A set of programs is presented for the reconstruction of a DNA sequence from data generated by the M13 shotgun sequencing technique. Once the sequence has been established and stored other programs are used for its analysis. The programs have been written for the Apple II microcomputer. A minimum investment is required for the hardware and the software is easily interchangeable between the growing number of interested researchers. Copies are available in ready to use form.     

发酵过程中细胞浓度在线检测系统*

利用微机软、硬件技术,设计了一套比浊法细胞浓度在线检测系统,并应用于5L发酵罐中木糖醇发酵过程细胞浓度在线检测。该系统能及时、较准确地反映发酵过程中菌体浓度随时间的变化情况,在规模化生产中具有较强的适用性、通用性,可为实现其它分析仪器的计算机在线检测系统提供值得借鉴的设计思路和实现方法。     

Computerized instrumentation in bacteriology. Application to the reading of antibiograms performed by the disk method

Antibiotic sensitivity tests are often carried out by the disk method. We are presenting independent computerized system built around a microcomputer allowing a semi automatic input of the inhibitory diameters, thanks either to a mechanical calliper or, when an enlarger is used, with the help of a setting stick connected to the computer. The software allows the processing results.     

A career pathway in protein folding: from model peptides to postreductionist protein science

This review discusses the inherent challenge of linking "reductionist" approaches to decipher the information encoded in protein sequences with burgeoning efforts to explore protein folding in native environments-"postreductionist" approaches. Because the invitation to write this article came as a result of my selection to receive the 2010 Dorothy Hodgkin Award of the Protein Society, I use examples from my own work to illustrate the evolution from the reductionist to the postreductionist perspective. I am incredibly honored to receive the Hodgkin Award, but I want to emphasize that it is the combined effort, creativity, and talent of many students, postdoctoral fellows, and collaborators over several years that has led to any accomplishments on which this selection is based. Moreover, I do not claim to have unique insight into the topics discussed here; but this writing opportunity allows me to illustrate some threads in the evolution of protein folding research with my own experiences and to point out to those embarking on careers how the twists and turns in anyone's scientific path are influenced and enriched by the scientific context of our research. The path my own career has taken thus far has been shaped by the timing of discoveries in the field of protein science; together with our contemporaries, we become part of a knowledge evolution. In my own case, this has been an epoch of great discovery in protein folding and I feel very fortunate to have participated in it.     

A simple,microcomputer based data acquisition system for monitoring pH shifts

The use of ‘of the shelf’ microcomputer hardware and software and a new type of laboratory style combination pH electrode with a built in preamplifier makes it possible to monitor pH shifts in a group of experimental vessels in a time frame of seconds without resorting to expensive smart switch boxes or multiple pH meters. A data acquisition system is described for monitoring pH shifts.     

A computer based culturing system for measuring the photosynthetic response of phytoplankton to a fluctuating environment

A microcomputer-controlled culturing system developed to simulate temperature and salinity fluctuations in an estuary is described. The system consists of a microcomputer, interfacing hardware, a continuous culture apparatus, and system software. The system can regulate the temperature and salinity of a continuous phytoplankton culture based on user-defined models of the physical environment and particle transport in a natural environment. The microcomputer also provides efficient data acquisition and data storage. The system was designed to facilitate expansion and modification and can easily be adapted to accomodate various studies of phytoplankton production. Details of a simulation and representative data are presented.     

Ye Tian: Surveilling stress to live longer

Ye Tian investigates how mitochondrial stress signaling pathways regulate longevity using C. elegans as a model system.

An avid reader, Ye Tian used to save up her child allowance with the sole purpose of buying science fiction books. Reading and solving mathematical problems were her favorite hobbies; indeed, she liked mathematics so much that she was about to enroll herself as an architecture major but finally chose biotechnology. Ye moved from her hometown in the Northwest of China, Baoji—famous for housing the Zhou dynasty’s bronzeware and being close to the Terracotta Army—to Beijing for her college and graduate studies.Ye is proud of being among the earliest researchers working on Caenorhabditis elegans in her country; for her PhD studies, she joined the lab of Hong Zhang, who at that time has just established the first C. elegans lab in China at the National Institute of Biological Sciences in Beijing. Ye identified epg-2 as an adaptor for cargo recognition during autophagy. In 2010, she crossed the Pacific toward the U.S. West Coast for her postdoctoral training in the aging field with Andrew Dillin, first at the Salk Institute in San Diego and then at the University of California, Berkeley. There, she discovered that mild mitochondrial stress during development in worms rewires their chromatin landscape to establish specific gene expression patterns throughout the lifespan and promote longevity.Ye Tian. Photo courtesy of Ye Tian.Ye came back to China at the end of 2016 to start her own lab at the Institute of Genetics and Developmental Biology of the Chinese Academy of Sciences. Her research team studies mitochondrial stress signaling pathways and their interplay with aging. We chatted with her to learn more about her next scientific plans.What interested you about the interplay between mitochondria and aging?I became interested in mitochondrial biology during my postdoc in Andrew Dillin’s lab. Since the origin of eukaryotic cells, mitochondria have been a driving force of evolution. During reproduction, mitochondria are passed from the mother to the offspring through egg cells and they exhibit a unique inheritance pattern. As essential hubs that dictate cellular metabolism, it is clear now that mitochondria and the nucleus maintain a bidirectional communication. Early life “stressed” mitochondria communicate with the nucleus to induce gene expression changes that are beneficial on longevity and persist throughout the lifespan. The fact that mitochondrial function is crucial to aging fascinated me; I wanted to continue exploring that topic further, and that’s why I established my lab around the question of how mitochondrial surveillance mechanisms regulate the aging process.What are you currently working on? What is up next for you?My research team focuses on the interplay between mitochondrial stress signaling pathways and aging. The first work that my lab published was a project that I started during my postdoc. The Dillin lab reported a phenomenon in which perturbations of mitochondria in neurons induced a mitochondrial stress response in the peripheral tissues and hypothesized that a secreted signal molecule, named after mitokine, is required for the cell non-autonomous regulation (1). The identity of this molecular signal remained elusive for almost ten years until we found that a secreted Wnt ligand, EGL-20, functions as the mitokine to coordinate mitochondrial stress signaling across tissues and promote longevity of the organism (2). We are also interested in how the crosstalk between mitochondria and the nucleus influences lifespan. We found that mitochondrial perturbations alter the nuclear epigenome to induce longevity via the histone deacetylation complex NuRD in response to cellular acetyl-CoA levels, the key metabolite at the entry point of the Krebs cycle (3).Lab group picture; current lab members (2021). Photo courtesy of Ye Tian.Our latest work stemmed from a serendipitous observation that neuronal mitochondrial stress is sensed by and transmitted through the mitochondria in the germline. Intergenerational, maternal inheritance of elevated levels of mitochondrial DNA via the mitokine Wnt/EGL-20, which causes the activation of the mitochondrial unfolded protein response (UPR^mt), provides descendants with a greater tolerance to environmental stress. This makes the offspring live longer (4).Among our short-term scientific plans, we’re determining how mitochondria functions during the aging process at both the genetic and biochemical levels and searching for ways to apply our findings from C. elegans to neurodegenerative disease models in mammals.What kind of approach do you bring to your work?The curiosity about how things work drives me; what I enjoy the most is when I see things happening in front of my eyes and when I figure out why they occur that way. That enthusiasm is what I try to spread to my team every day. In the lab, we rely on C. elegans as our model system and on genetics to dissect complex biological processes like aging. We have also adapted modern biochemical and imaging techniques as well as bioinformatics to complement our genetic studies. I’m a geneticist at heart, and I like to initiate a project with a well-designed genetic screen. The best part is that the screen often leads me to answers I was not expecting, and that’s genuinely inspiring!What did you learn during your PhD and postdoc that helped prepare you for being a group leader? What were you unprepared for?Like most scientists, my research career has gone through ups and downs. I had to change my research project in the last year of my graduate school; that was nerve-racking, but I eventually managed to redirect my thesis and get exciting results under time pressure, thanks in large to the support of my parents, mentors, and lab mates. That helped me prepare to become a principal investigator; I gained confidence in problem solving, and since I’ve experienced the stress of dealing with last-minute scope changes firsthand, I connect better with my students.I guess, as many other non-native English speakers, I wasn’t prepared for writing grants and papers fluently in English. This issue wasn’t obvious during my graduate and postdoctoral studies, as my mentors were always there for me and proofread and edited my writing. Now I have to stand up for myself. I spend most of my time writing; I’ve improved my writing skills but it’s still an ongoing process.Reconstruction of the nerve system of C. elegans by confocal microscopy. Green corresponds to YFP-labeled neuronal specific marker Q40, and red labels germline specific mitochondrial outer membrane protein TOMM-20::mkate2. Image courtesy of Ye Tian’s lab.What has been the biggest accomplishment in your career so far?My very first PhD student, Qian Zhang, graduated with two first-author papers and decided to pursue a research career in academia. Being responsible for someone else’s career is challenging but also rewarding.What has been the biggest challenge in your career so far?I use the model organism C. elegans for my research in aging, so from time to time, peers criticize the relevance of my work to human health. I’m used to justifying my scientific approach to funding agencies and peers in other fields, but sometimes it’s exhausting or not pleasant.Who were your key influences early in your career?My PhD mentor, Hong Zhang. He is very passionate about the science he does, and he is courageous to shift his research directions to answer new biological questions.What is the best advice you have been given?I think the best advice I’ve gotten is that “tomorrow is another day.” It reminds me to keep going and be optimistic.What hobbies do you have?I love art and music. When I was in San Diego, I used to play in the Chinese Music Band; I miss my musician friends over there. In my teens, I used to hike mountainside trails along the river with my parents. Now, running has become my new favorite hobby. I enjoy the tranquility and peace of mind while running; it’s soothing.     

Generating precise mechanical stimuli and recording chordotonal organ discharge patterns using a microcomputer

A computer-controlled system for the investigation of the responseproperties of the tibio-femoral chordotonal organ in the locustis described. The computer is used to generate small amplitudesinusoidal movements of the tibia via a small servo-controlledmotor. The resulting response recorded via a suction electrodeis simultaneously detected, processed and stored on disk. Fullconstructional details for all hardware required are given.The software, developed for a BBC microcomputer, in additionto controlling all the hardware, has graphics and analysis routinesenabling the operator to display and manipulate the stored data.     

Precision rhinoplasty. Part I: The role of life-size photographs and soft-tissue cephalometric analysis

I describe a simple technique of full-scale life-size photography using marker/stickers and a ruler at the side of the face as an index for magnification. I also report a technique of soft-tissue cephalometric analysis that consists of some new proportion and some old angles and measurements. This technique will enable the plastic surgeon, even if not artistically inclined, to draw an aesthetically pleasing and very proportionate profile outline of the nose and measure the proportions of the front view on the majority of patients. The difference between the patient's nasal outline and the planned nasal definition is then measured and expressed in quarters of millimeters to give the surgeon a very precise numeric guide for surgery. This will help the plastic surgeon define the aesthetic goals very accurately and also might be helpful in detecting other facial disharmonies that might be influential in the outcome of the rhinoplasty. Using this technique of analysis, along with the prediction guidelines extrapolated from my study on soft-tissue response to surgical alteration, one can develop a fairly predictable approach to rhinoplasty.     

Application of Endobronchial Ultrasonography for the Preoperative Detecting Recurrent Laryngeal Nerve Lymph Node Metastasis of Esophageal Cancer

Background

The preoperative detection of recurrent laryngeal nerve lymph node (RLN LN) metastasis provides important information for the treatment of esophageal cancer. We investigated the possibility of applying endobronchial ultrasonography (EBUS) with conventional preoperative endoscopic ultrasonography (EUS) and computerized tomography (CT) examination to evaluate RLN LN metastasis in patients with esophageal cancer.

Methods

A total of 115 patients with advanced thoracic esophageal cancer underwent EBUS examinations. Patients also underwent EUS and CT imaging as reference diagnostic methods. Positron emission tomography /computed tomography (PET/CT) was also introduced in partial patients as reference method. The preoperative evaluation of RLN LN metastasis was compared with the surgical and pathological staging in 94 patients who underwent radical surgery.

Results

The sensitivities of the preoperative evaluations of RLN LN metastasis by EBUS, EUS and CT were 67.6%, 32.4% and 29.4%, respectively. The sensitivity of EBUS was significantly different from that of EUS or CT, especially in the detection of right RLN LNs. In addition, according to the extra data from reference method, PET/CT was not superior to EBUS or EUS in detecting RLN LN metastasis. Among all 115 patients, 21 patients who were diagnosed with tracheal invasions by EUS or EBUS avoided radical surgery. Another 94 patients who were diagnosed as negative for tracheobronchial tree invasion by EUS and EBUS had no positive findings in radical surgery.

Conclusions

EBUS can enhance the preoperative sensitivity of the detection of RLN LN metastasis in cases of thoracic esophageal cancer and is a useful complementary examination to conventional preoperative EUS and CT, which can alert thoracic surgeons to the possibility of a greater range of preoperative lymph node dissection. EBUS may also indicate tracheal invasion in cases of esophageal stricture.     

复杂声刺激波形的计算机合成

根据数字化频率合成原理,结合模数转换技术,实现声刺激波形的计算机实时合成,由于采用软件实时合成技术,因此理论上只要有理想的算法,运用该技术可模拟出任意复杂的声刺激波形。     

The use of Gore-Tex implants in aesthetic surgery of the face.

Deep wrinkles and folds usually are not completely or permanently corrected with face lifting, fat or collagen injections, chemical peels, and other known procedures. It is suggested that a permanent implant, well tolerated by human tissues, could be helpful as an isolated or associated procedure. An expanded synthetic polymer known as Gore-Tex expanded polytetrafluoroethylene soft-tissue patch is available and is easy to use to approximate and correct defects; it also can be used as a filling material or to replace other kinds of prostheses to get better projection of frontal, orbital, malar, and chin areas. I have used this material in my clinic for 5 years. Indications and results of the first cosmetic cases are reported herein.