Deep‐Learning‐Assisted Stratification of Amyloid Beta Mutants Using Drying Droplet Patterns

The development of simple and accurate methods to predict mutations in proteins remains an unsolved challenge in modern biochemistry. It is discovered that critical information about primary and secondary peptide structures can be inferred from the stains left behind by their drying droplets. To analyze the complex stain patterns, deep-learning neuronal networks are challenged with polarized light microscopy images derived from the drying droplet deposits of a range of amyloid beta (1–42) (Aβ$_{42}$) peptides. These peptides differ in a single amino acid residue and represent hereditary mutants of Alzheimer\u27s disease. Stain patterns are not only reproducible but also result in comprehensive stratification of eight amyloid beta (Aβ) variants with predictive accuracies above 99%. Similarly, peptide stains of a range of distinct Aβ$_{42}$ peptide conformations are identified with accuracies above 99%. The results suggest that a method as simple as drying a droplet of a peptide solution onto a solid surface may serve as an indicator of minute, yet structurally meaningful differences in peptides’ primary and secondary structures. Scalable and accurate detection schemes for stratification of conformational and structural protein alterations are critically needed to unravel pathological signatures in many human diseases such as Alzheimer\u27s and Parkinson\u27s disease

Nanofiltration and reverse osmosis for defluoridation: The role of inorganic carbon

Fluoride (F) concentrations above the World Health Organization (WHO) guideline value of 1.5 mg/L in drinking water can lead to serious health problems such as dental fluorosis and skeletal fluorosis. High F levels are often associated with carbonaceous (i.e. high inorganic carbon (IC)) type waters. The high fluoride concentrations in natural waters often occur in arid regions where no sufficient quantity of alternative water is readily available due to scarcity of water, consequently, treatment is the best option to provide safe drinking water. Nanofiltration (NF) and reverse osmosis (RO) are promising and appropriate membrane technologies for defluoridation due to their high fluoride removal efficiency and their ability to simultaneously remove a wide range of other inorganic and organic contaminants 1. Different ions can have various effects on F removal by NF/RO 2. IC in natural waters is present as carbonate ion (CO32-), bicarbonate ion (HCO3-), carbonic acid (H2CO3), and carbon dioxide (CO2) depending on the pH. Due to the different characteristics of these species it is important to study the impact of IC on F retention mechanisms at different pH. In this study the mechanisms of IC species impact on F retention by NF/RO has been investigated as a function of pH.Two commercial NF and RO membranes, BW30 and NF270 respectively from DOW Chemicals (USA) were used. Synthetic waters were prepared using realistic ranges of F and IC for carbonaceous waters found for example in the fluoride rich waters in Tanzania. Feed concentration of F and IC were 50 mgF/L as NaF and 500 mgC/L as NaHCO3 respectively. Visual MINTEQ software was used to predict the speciation of IC and F at various pH. Figure 1 indicates that the permeate F concentrations were high (35-47 mg/L) at pH 2 where F existed mainly as uncharged HF. At pH 8 and 11, when there was a change in speciation to F ion and the membranes were negatively charged, permeate F concentrations decreased drastically. Permeate F concentrations for the RO BW30 membrane were lower than that of the relatively open NF270. BW30 removed fluoride to meet WHO guideline value of 1.5 mg/L at pH 11 up to about 40% recovery, while NF270 did not achieve the guideline value at any pH studied. In the past, BW30 had achieved the guideline value at pH 8 but with relatively low electrical conductivity (EC) of about 2000 µS/cm. However, in the current study the high IC concentration resulted in high EC (3600 µS/cm) and high osmotic pressure. This led to a decrease in the net driving pressure thus facilitating the diffusion of F through the membrane 3. The predominant IC species at pH 2 was H2CO3. This resulted in very low feed IC concentrations (1-5 mg C/L, less than intended 500 mgC/L) at pH 2 due to degassing (H2CO3 decomposes to CO2). Monovalent HCO3- predominated at pH 8 and easily permeated the NF270 membrane. At pH 11, MINTEQ predicted divalent CO32- as the predominant IC species and permeate IC was lower than at pH 8 for the NF270. For the BW30 membrane, IC concentrations in the permeates were lower than the NF270 and impact of IC speciation was not observed. This suggests that the main retention mechanism of IC by the NF270 is charged repulsion and that of the RO BW30 membrane is size exclusion. Please click Additional Files below to see the full abstract

Renewable energy powered membrane technology:Experimental investigation of system performance with variable module size and fluctuating energy

Integration of renewable energy and membrane filtration technologies such as nanofiltration (NF) and reverse osmosis (RO) can provide drinking water in places where freshwater is scarce and grid electrical connections are unavailable. This study investigated a directly-connected photovoltaic-powered membrane system under fluctuating solar conditions. Specifically, two configurations of NF/RO membranes with the same membrane area were investigated: a) 1 × 4″ module, which contained one 4″ NF/RO element; and b) 3 × 2.5″ module, which contained three 2.5″ NF/RO elements in series. A high fluoride brackish water ([F − ] = 56.2 mg/L, total dissolved solids [TDS] = 4076 mg/L) collected from northern Tanzania was treated by different membranes in the two configurations. Performance indicators such as flux, specific energy consumption, and permeate F − concentration were monitored over a 60-min period of energy fluctuation that are part of a typical solar day. The results showed that the overall performance of the 1 × 4″ module was superior to that of the 3 × 2.5″ module. This is because the performance of a 3 × 2.5″ module degraded significantly from the first element to the third element due to the increased feed concentration and the decreased net driving pressure. Three 1 × 4″ modules (BW30, BW30LE and NF90) and one 3 × 2.5″ module (BW30) were able to meet the drinking water guideline for fluoride. During cloud periods, the transient permeate F − concentration exceeded the guideline value due to insufficient power, however the cumulative permeate F − concentration was always well below the guideline. The photovoltaic-powered membrane system equipped with the above modules provides a promising solution for addressing drinking water problems in remote and rural areas. </p

ALKALINE PRETREATMENT OF SPRUCE AND BIRCH TO IMPROVE BIOETHANOL AND BIOGAS PRODUCTION

Alkaline pretreatment with NaOH under mild operating conditions was used to improve ethanol and biogas production from softwood spruce and hardwood birch. The pretreatments were carried out at different temperatures between minus 15 and 100ºC with 7.0% w/w NaOH solution for 2 h. The pretreated materials were then enzymatically hydrolyzed and subsequently fermented to ethanol or anaerobically digested to biogas. In general, the pretreatment was more successful for both ethanol and biogas production from the hardwood birch than the softwood spruce. The pretreatment resulted in significant reduction of hemicellulose and the crystallinity of cellulose, which might be responsible for improved enzymatic hydrolyses of birch from 6.9% to 82.3% and spruce from 14.1% to 35.7%. These results were obtained with pretreatment at 100°C for birch and 5°C for spruce. Subsequently, the best ethanol yield obtained was 0.08 g/g of the spruce while pretreated at 100°C, and 0.17 g/g of the birch treated at 100°C. On the other hand, digestion of untreated birch and spruce resulted in methane yields of 250 and 30 l/kg VS of the wood species, respectively. The pretreatment of the wood species at the best conditions for enzymatic hydrolysis resulted in 83% and 74% improvement in methane production from birch and spruce

Waste Textiles Bioprocessing to Ethanol and Biogas

In the world today, the need for sustainable processes is increasing. The work of the present thesis has been focused on conversion of the cellulosic part of waste textiles into biogas and ethanol, and its challenges. In 2009, the global annual fiber consumption exceeded 70 Mt, of which around 40% consisted of cellulosic material. This huge amount of fibers is processed into apparel, home textiles, and industrial products, ending up as waste after a certain time delay. Regretfully, current management of waste textiles mainly comprises incineration and landfilling, in spite of the potential of cellulosic material being used in the production of different biofuels. The volume of cellulose mentioned above would be sufficient for producing around 20 billion liters of ethanol or 11.6 billion Nm3 of methane per year. Nevertheless, waste textiles are not yet accepted as a suitable substrate for biofuel production, since their processing to biofuel presents certain difficulties and challenges, e.g. high crystallinity of cotton cellulose, presence of dyes, reagents and other materials, and being textiles as a mixture of natural and synthetic fibers. High crystallinity of cotton cellulose curbs high efficient conversion by enzymatic or bacterial hydrolysis, and the presence of non-cellulosic fibers may create several processing problems. The work of the present thesis centered on these challenges. Cotton linter and blue jeans waste textiles, all practically pure cellulose, were converted to ethanol by SSSF, using S. cerevisiae, with a yield of about 0.14 g ethanol/g textile, only 25% of the theoretical yield. To improve the yield, a pretreatment process was required and thus, several methods were examined. Alkaline pretreatments significantly improved the yield of hydrolysis and subsequent ethanol production, the most effective condition being treatment with a 12% NaOH-solution at 0 \ub0C, increasing the yield to 0.48 g ethanol/g textile (85% of the theoretical yield).Waste textile streams, however, are mixtures of different fibers, and a separation of the cellulosic fibers from synthetic fibers is thus necessary. The separation was not achieved using an alkaline pretreatment, and hence another approach was investigated; pretreatment with N-methyl-morpholine-N-oxide (NMMO), an industrially available and environment friendly cellulose solvent. The dissolution process was performed under different conditions in terms of solvent concentration, temperature, and duration. Pretreatment with 85% NMMO at 120 \ub0C under atmospheric pressure for 2.5 hours, improved the ethanol yield by 150%, compared to the yield of untreated cellulose. This pretreatment proved to be of major advantage, as it provided a method for dissolving and then recovering the cellulose. Using this method as a foundation, a novel process was developed, refined and verified, by testing polyester/cellulose-blended textiles, which predominate waste textiles. The polyesters were purified as fibers after the NMMO treatments, and up to 95% of the cellulose content was regenerated. The solvent was then recovered, recycled, and reused. Furthermore, investigating the effect of this treatment on anaerobic digestion of cellulose disclosed a remarkable enhancement of the microbial solubilization; the rate in pretreated textiles was twice the rate in untreated material. The process developed in the present thesis is promising for transformation of waste textiles into a suitable substrate, to subsequently be used for biological conversion to ethanol and biogas

Ethanol production from cotton-based waste textiles

Ethanol production from cotton linter and waste of blue jeans textiles was investigated. In the best case, alkali pretreatment followed by enzymatic hydrolysis resulted in almost complete conversion of the cotton and jeans to glucose, which was then fermented by Saccharomyces cerevisiae to ethanol. If no pretreatment applied, hydrolyses of the textiles by cellulase and β-glucosidase for 24 h followed by simultaneous saccharification and fermentation (SSF) in 4 days, resulted in 0.140–0.145 g ethanol/g textiles, which was 25–26% of the corresponding theoretical yield. A pretreatment with concentrated phosphoric acid prior to the hydrolysis improved ethanol production from the textiles up to 66% of the theoretical yield. However, the best results obtained from alkali pretreatment of the materials by NaOH. The alkaline pretreatment of cotton fibers were carried out with 0–20% NaOH at 0 \ub0C, 23 \ub0C and 100 \ub0C, followed by enzymatic hydrolysis up to 4 days. In general, higher concentration of NaOH resulted in a better yield of the hydrolysis, whereas temperature had a reverse effect and better results were obtained at lower temperature. The best conditions for the alkali pretreatment of the cotton were obtained in this study at 12% NaOH and 0 \ub0C and 3 h. In this condition, the materials with 3% solid content were enzymatically hydrolyzed at 85.1% of the theoretical yield in 24 h and 99.1% in 4 days. The alkali pretreatment of the waste textiles at these conditions and subsequent SSF resulted in 0.48 g ethanol/g pretreated textiles used

Deep-Learning-Assisted Stratification of Amyloid Beta Mutants Using Drying Droplet Patterns

The development of simple and accurate methods to predict mutations in proteins remains an unsolved challenge in modern biochemistry. It is discovered that critical information about primary and secondary peptide structures can be inferred from the stains left behind by their drying droplets. To analyze the complex stain patterns, deep-learning neuronal networks are challenged with polarized light microscopy images derived from the drying droplet deposits of a range of amyloid beta (1–42) (Aβ42) peptides. These peptides differ in a single amino acid residue and represent hereditary mutants of Alzheimer’s disease. Stain patterns are not only reproducible but also result in comprehensive stratification of eight amyloid beta (Aβ) variants with predictive accuracies above 99%. Similarly, peptide stains of a range of distinct Aβ42 peptide conformations are identified with accuracies above 99%. The results suggest that a method as simple as drying a droplet of a peptide solution onto a solid surface may serve as an indicator of minute, yet structurally meaningful differences in peptides’ primary and secondary structures. Scalable and accurate detection schemes for stratification of conformational and structural protein alterations are critically needed to unravel pathological signatures in many human diseases such as Alzheimer’s and Parkinson’s disease.Deep-learning-assisted stratification of amyloid beta mutants using drying droplet patterns is reported. A process as simple and scalable as allowing a peptide droplet to dry onto a surface can disclose critical structural mismatches of amyloid beta peptides. When analyzed with pretrained neuronal networks, the peptide deposits predict single amino acid mismatches and conformational misfolding with 99% accuracy.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/173019/1/adma202110404_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/173019/2/adma202110404.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/173019/3/adma202110404-sup-0001-SuppMat.pd

Enhancement of solubilization rate of cellulose in anaerobic digestion and its drawbacks

Hydrolysis is widely acknowledged as the rate-limiting step in anaerobic digestion of solid cellulose to biogas, and pretreatment is generally considered to facilitate the process. However, few studies have investigated how such pretreatment may affect the rest of this complex process. The present study compared the solubilization rate in anaerobic digestion of cotton linter (high crystalline cellulose), with that of regenerated cellulose (amorphous cellulose), using pretreatment with NMMO. Batch digestions were performed, with the initial cellulose concentrations ranging between 5 and 40 g/l, and during 30 days of incubation, biogas and VFAs production as well as pH and COD changes were measured. The lag time before digestion started was longer for the high crystalline cellulose than for the amorphous one. The maximum solubilization ratesof treated cellulose were 842 and 517 mg sCOD/g cCOD/day at the initial cellulose concentration of 5 and 30 g/l respectively, while the solubilization rate of untreated cellulose never exceeded 417 mg sCOD/g cCOD/day. The difference between the two cellulose types was a direct result of the high rate of hydrolysis inhibiting the acetogenesis/methanogenesis microorganisms, a drawback to the rest of the process

Acid Hydrolysis of Cellulose-based Waste Textiles

The present study focused on conversion of cellulosic part of waste textiles into biogas and its challenges. The annual global fiber consumption exceeded 70 Mt with a cellulosic fraction of around 40%. This huge amount of fiber is further processed into apparel, home textiles and industrial products and after a certain time delay end up in waste streams. This amount of cellulose has the potential of production of approximately 20 billion liters of ethanol. Assuming a good collection and waste management system, however, there are still challenges facing the process of conversion. For instance, high crystallinity of cotton cellulose makes it hard to achieve enzymatic or bacterial hydrolysis. In addition, waste textiles are composed of different materials including natural and synthetic fibers, and the cellulosic fibers should be separated from the other materials. Furthermore, presence of dyes and reagents in the fibers can also be challenging in the bioprocessing of textile waste. In the present work, we examined the process of dilute acid hydrolysis of viscose and cotton (i.e. jeans) textiles. Hydrolyses were performed at different lengths of time (8 and 15 min), temperatures (180 and 200 °C), and acid concentrations (0.5, 1.5, and 3% w/w). Hydrolysis of viscose and jeans under identical conditions resulted in significantly different yields of glucose. This may be due to differences in the structure, i.e. high crystalline cellulose in jeans and low crystalline cellulose in viscose