Crosslinked Hyaluronic Acid Hydrogel Networks Designed as Mechanical Actuators

Bioengineers are in constant pursuit of solutions to problems facing the medical and pharmaceutical field by designing biomaterials that closely mimic the target natural systems. A unique collection of polymers, known as polymeric actuators, have been devised with the ability to convert an external stimulus to a change in shape, size or permeability. The current options within polymeric biomaterials with multi-functionality include matrices that are biocompatible, biodegradable, quick transitioning / shape changing, and mechanically tunable. These properties have been harnessed for application such as stents, valves, semi permeable membranes, and dynamic cell culture substrates. For such applications quick and uniform actuator response that does not need to be sustained for more than a few hours is desired. However there exist other areas of biomedical applications, such as wound closure/healing and nerve regeneration, where polymeric actuators have been underutilized. These applications however call for a polymer system that can actuate at controlled slow speeds and sustain this actuation for several days. At present there is a lack of such slow actuating polymer system. Each year over 50,000 peripheral nerve repair procedures are performed (National Center for Health Statistics, 1995). The total annual costs in U.S alone exceed $ 7 billion (American Paralysis Association, 1997). The treatment of a nerve transection is dependent on the size of the injury gap. Similarly, the extent of regeneration and re-innervation in the PNS is also governed by the size of the gap. For a smaller gap (\u3c10 mm) the surgeon can pull the severed nerve ends closer and suture them to repair the injury. For larger gaps autologous nerve transplant is the gold standard treatment despite the inherent disadvantages. Over the past decades biomaterial researchers have tested several polymeric nerve conduits as an alternative to autologous nerve grafts. However none have been able to match the success rates of autologous grafts. There is a lack of an effective biomaterial solution to the problem of a large gap nerve injury. For many years there has been a hypothesis that nervous tissue can be successfully elongated via application of an external mechanical force alone which could be used to treat peripheral nerve gap injuries. Mechanical actuation studies have been shown to produce successful stretch growth in individual axons and axonal bundles. This phenomenon is at play in nature during embryonic growth and development of the body of organisms to adulthood. Applying tensional forces at appropriate rates (\u3c 100 μm/hr) causes sustained axonal stretch growth. The solution we propose in this work is a biomaterial that can be programmed to perform the function of a mechanical actuator at rates suitable for axonal stretch growth. We designed, fabricated, and characterized a novel hyaluronic acid based hydrogel that shrinks over time along a pre-defined axis thereby providing the source for tension that could be used for sustained axonal stretch growth. The shear thinning property of hyaluronic acid (HA) enabled us to test if we could store a retractive stress in a rapidly crosslinked network under shear flow and then controllably release this stress and achieve shrinkage of the network scaffold along one desired axis. We investigated two strategies to achieve this goal. The retractive stress trapped in the crosslinked network was released either by manipulating the main backbone HA chains or by selectively breaking the crosslinks. The shrinkage rates obtained were within the range of stretching rates that have successfully stretched neuronal cells. We also confirmed that the material\u27s cytocompatibility was unaffected by the chemical modifications that HA was subjected to. This polymer system is a novel addition to the existing polymeric actuators and is a step towards filling the void of a slow, long term actuating polymer

Performance of Year-Round Cropping Systems on Three Tropical Soil Families

A favorable climate throughout the year as well as the prevailing socio-economic conditions in the tropics are ideal for multiple cropping in time and space. Despite of its relevance, year-round cropping systems have seldom been used to evaluate the productivity of well-characterized tropical agroenvironments. The major objectives of this study were to monitor the effects of agroclimatic parameters on the performance of various crops and sequences of crops, and to investigate the possibility of stratifying crop production potential, on the basis of the soil family category of Soil Taxonomy, in the tropics. Year-round cropping patterns were tested on a weather-monitored network of ten sites located in Indonesia, The Philippines and Hawaii representing the tropical soil families of thixotropic, isothermic Hydric Dystrandepts; clayey, kaolinitic, isohyperthermic Tropeptic Eutrustox and clayey, kaolinitic, isohyperthermic Typic Paleudults. The cropping patterns used were specifically designed for each of the three agroenvironments and similar management practices were followed on all sites. The sequential cropping pattern of Irish potato followed by soybean and then by field corn, designed specifically for the Tropeptic Eutrustox agroenvironment, gave the highest calorie and protein yield (46581 k cal/ha and 2101 kg/ha, respectively) at the Eutrustox site of Waipio, Hawaii. The above cropping pattern also resulted in higher calorie and protein production at the Dystrandept site in Kukaiau, Hawaii compared to the specifically-designed pattern of Irish potato and vegetables followed by vegetables and then followed by soybean and peanut. The Dystrandept sites in Indonesia and The Philippines had lower yield potential compared to the site of Kukaiau, mainly because of higher temperatures of the former that resulted in low yields of vegetables and Irish potato. Head cabbage, mustard cabbage, Irish potato, carrot and bushbean were found to be susceptible to high temperature and excess moisture. The yields of the above crops were highly correlated (r = -0.70**) with soil temperature at 10 cm, and their best yields were obtained within a soil temperature range of 18 to 23°C. In contrast, soybean and peanut were adapted to a wide range (21 - 28°C) of air temperature and soil moisture. Soybean planted during April-May (long days) gave significantly higher yields compared to August-September (short days) plantings. Multiple regression equations with agroclimatic parameters as independent variables, were derived to predict yields of crops. Except for green corn, only crops that were sensitive to temperature and excess moisture (mustard cabbage, head cabbage, carrot, bushbean and Irish potato) had prediction equations with coefficients of determination close to 0.80. However, for soybean and peanut the best models incorporating as many as six environmental parameters explained less than 50 percent of the yield variability. In the Hydric Dystrandepts and Typic Paleudults (udic moisture regime), most crops grown year-round did well without irrigation. Crop performance in Tropeptic Eutrustox confirmed the absolute necessity of supplemental irrigation for year-round crop production under an ustic moisture regime. Response to Rhizobium inoculation as reflected by soybean yields, was variable. However, the number of significant responses to inoculation was greater in the Tropeptic Eutrustox than in Hydric Dystrandept sites. Bushbean yields were significantly higher in the "bushbean + mustard cabbage" intercrop combination then in the "bushbean + green corn" combination. Air tenperature was negatively correlated (r = -0.96**) with the number of days required for maturity of Irish potato. In this study, segregation of a soil family based on crop performance was possible only in case of lypic Paleudults. High average air and soil temperatures (> 26°C) prevalent in the Paleudults resulted in poor performance of temperature-sensitive crops such as head cabbage, mustard cabbage, Irish potato and carrot

Machine Learning Approaches in Agile Manufacturing with Recycled Materials for Sustainability

It is important to develop sustainable processes in materials science and manufacturing that are environmentally friendly. AI can play a significant role in decision support here as evident from our earlier research leading to tools developed using our proposed machine learning based approaches. Such tools served the purpose of computational estimation and expert systems. This research addresses environmental sustainability in materials science via decision support in agile manufacturing using recycled and reclaimed materials. It is a safe and responsible way to turn a specific waste stream to value-added products. We propose to use data-driven methods in AI by applying machine learning models for predictive analysis to guide decision support in manufacturing. This includes harnessing artificial neural networks to study parameters affecting heat treatment of materials and impacts on their properties; deep learning via advances such as convolutional neural networks to explore grain size detection; and other classifiers such as Random Forests to analyze phrase fraction detection. Results with all these methods seem promising to embark on further work, e.g. ANN yields accuracy around 90\% for predicting micro-structure development as per quench tempering, a heat treatment process. Future work entails several challenges: investigating various computer vision models (VGG, ResNet etc.) to find optimal accuracy, efficiency and robustness adequate for sustainable processes; creating domain-specific tools using machine learning for decision support in agile manufacturing; and assessing impacts on sustainability with metrics incorporating the appropriate use of recycled materials as well as the effectiveness of developed products. Our work makes impacts on green technology for smart manufacturing, and is motivated by related work in the highly interesting realm of AI for materials science

Optical Character Recognition and Transcription of Berber Signs from Images in a Low-Resource Language Amazigh

The Berber, or Amazigh language family is a low-resource North African vernacular language spoken by the indigenous Berber ethnic group. It has its own unique alphabet called Tifinagh used across Berber communities in Morocco, Algeria, and others. The Afroasiatic language Berber is spoken by 14 million people, yet lacks adequate representation in education, research, web applications etc. For instance, there is no option of translation to or from Amazigh / Berber on Google Translate, which hosts over 100 languages today. Consequently, we do not find specialized educational apps, L2 (2nd language learner) acquisition, automated language translation, and remote-access facilities enabled in Berber. Motivated by this background, we propose a supervised approach called DaToBS for Detection and Transcription of Berber Signs. The DaToBS approach entails the automatic recognition and transcription of Tifinagh characters from signs in photographs of natural environments. This is achieved by self-creating a corpus of 1862 pre-processed character images; curating the corpus with human-guided annotation; and feeding it into an OCR model via the deployment of CNN for deep learning based on computer vision models. We deploy computer vision modeling (rather than language models) because there are pictorial symbols in this alphabet, this deployment being a novel aspect of our work. The DaToBS experimentation and analyses yield over 92 percent accuracy in our research. To the best of our knowledge, ours is among the first few works in the automated transcription of Berber signs from roadside images with deep learning, yielding high accuracy. This can pave the way for developing pedagogical applications in the Berber language, thereby addressing an important goal of outreach to underrepresented communities via AI in education

Prevalence of tick-borne pathogens in Ixodes scapularis in a rural New Jersey County.

To assess the potential risk for other tick-borne diseases, we collected 100 adult Ixodes scapularis in Hunterdon County, a rapidly developing rural county in Lyme disease endemic western New Jersey. We tested the ticks by polymerase chain reaction for Borrelia burgdorferi, Babesia microti, and the rickettsial agent of human granulocytic ehrlichiosis (HGE). Fifty-five ticks were infected with at least one of the three pathogens: 43 with B. burgdorferi, five with B. microti, and 17 with the HGE agent. Ten ticks were coinfected with two of the pathogens. The results suggest that county residents are at considerable risk for infection by a tick-borne pathogen after an I. scapularis bite

Hey Dona! Can you help me with student course registration?

In this paper, we present a demo of an intelligent personal agent called Hey Dona (or just Dona) with virtual voice assistance in student course registration. It is a deployed project in the theme of AI for education. In this digital age with a myriad of smart devices, users often delegate tasks to agents. While pointing and clicking supersedes the erstwhile command-typing, modern devices allow users to speak commands for agents to execute tasks, enhancing speed and convenience. In line with this progress, Dona is an intelligent agent catering to student needs by automated, voice-operated course registration, spanning a multitude of accents, entailing task planning optimization, with some language translation as needed. Dona accepts voice input by microphone (Bluetooth, wired microphone), converts human voice to computer understandable language, performs query processing as per user commands, connects with the Web to search for answers, models task dependencies, imbibes quality control, and conveys output by speaking to users as well as displaying text, thus enabling human-AI interaction by speech cum text. It is meant to work seamlessly on desktops, smartphones etc. and in indoor as well as outdoor settings. To the best of our knowledge, Dona is among the first of its kind as an intelligent personal agent for voice assistance in student course registration. Due to its ubiquitous access for educational needs, Dona directly impacts AI for education. It makes a broader impact on smart city characteristics of smart living and smart people due to its contributions to providing benefits for new ways of living and assisting 21st century education, respectively

Extracting Cultural Commonsense Knowledge at Scale

Structured knowledge is important for many AI applications. Commonsenseknowledge, which is crucial for robust human-centric AI, is covered by a smallnumber of structured knowledge projects. However, they lack knowledge abouthuman traits and behaviors conditioned on socio-cultural contexts, which iscrucial for situative AI. This paper presents CANDLE, an end-to-end methodologyfor extracting high-quality cultural commonsense knowledge (CCSK) at scale.CANDLE extracts CCSK assertions from a huge web corpus and organizes them intocoherent clusters, for 3 domains of subjects (geography, religion, occupation)and several cultural facets (food, drinks, clothing, traditions, rituals,behaviors). CANDLE includes judicious techniques for classification-basedfiltering and scoring of interestingness. Experimental evaluations show thesuperiority of the CANDLE CCSK collection over prior works, and an extrinsicuse case demonstrates the benefits of CCSK for the GPT-3 language model. Codeand data can be accessed at https://cultural-csk.herokuapp.com/.<br

Bulk modulus of vegetable oil-diesel fuel blends

Bulk moduli of fuel blends containing different concentrations of vegetable oil and diesel have been measured at three different fuel temperatures. Presence of vegetable oil in the mixture increases the bulk modulus over that for the straight diesel fuel. For a given fuel pressure and fuel temperature, bulk modulus of the blend increases as the concentration of the vegetable oil in the blend increases. Increase in fuel temperature lowers the values of bulk moduli at all fuel pressures.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/24840/1/0000266.pd