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Improving the performance of thermoelectric (TE) materials is essential for their wider adoption in sustainable energy and cooling applications. Impurity doping is a common strategy for enhancing transport properties, yet synthesizing every possible TE composition is infeasible, and high-fidelity ab initio simulations remain computationally expensive. Machine learning offers a viable alternative by learning a cheaper surrogate model, but conventional approaches often struggle to capture the diverse doping effects due to compositional similarity between doped and undoped materials. To address this challenge, we adapt the existing Compositionally restricted attention-based Network ( CrabNet ) into a multi-task (MT) variant, MT CrabNet , which uses self-attention to implicitly learn composition-specific dopant effects. MT learning enables simultaneous predictions of seven TE transport properties, leveraging their interdependence to enhance performance. We also explore different strategies for encoding external variables such as temperature. Trained on an experimental dataset covering over 1400 unique compositions, MT CrabNet consistently achieves top performance, with significant improvements for electronic thermal conductivity and electrical conductivity. Compared to its single-task variant, MT CrabNet delivers up to 1.5× improvement in R 2 . This establishes MT CrabNet as a valuable tool for accelerating the screening and discovery of high-performance TE materials, with a design extensible to doped systems beyond the TE domain.We also share the code and dataset used in this work.

In an unbiased approach to biomarker discovery, we applied a highly multiplexed proteomic technology (SOMAscan, SomaLogic, Inc, Boulder, CO) to understand changes in proteins from paired serum samples at enrollment and after 8 weeks of TB treatment from 39 patients with pulmonary TB from Kampala, Uganda enrolled in the Center for Disease Control and Prevention's Tuberculosis Trials Consortium (TBTC) Study 29. This work represents the first large-scale proteomic analysis employing modified DNA aptamers in a study of active tuberculosis (TB). We identified multiple proteins that exhibit significant expression differences during the intensive phase of TB therapy. There was enrichment for proteins in conserved networks of biological processes and function including antimicrobial defense, tissue healing and remodeling, acute phase response, pattern recognition, protease/anti-proteases, complement and coagulation cascade, apoptosis, immunity and inflammation pathways. Members of cytokine pathways such as interferon-gamma, while present, were not as highly represented as might have been predicted. The top proteins that changed between baseline and 8 weeks of therapy were TSP4, TIMP-2, SEPR, MRC-2, Antithrombin III, SAA, CRP, NPS-PLA2, LEAP-1, and LBP. The novel proteins elucidated in this work may provide new insights for understanding TB disease, its treatment and subsequent healing processes that occur in response to effective therapy.

To describe the characteristics of systemic sclerosis sine scleroderma (ssSSc)-associated interstitial lung disease (ILD) presenting as idiopathic interstitial pneumonia (IIP). Retrospective review of six patients with ssSSc-associated ILD diagnosed after referral for evaluation of IIP. All patients were white, their mean age was 56 years (range, 37 to 86), and gender was evenly divided. Sclerodactyly, skin thickening, and digital edema were absent in all patients. All patients had scattered telangiectasia, and four patients had Raynaud phenomenon with abnormal nailfold capillaroscopy findings. All described gastroesophageal reflux, and three patients had esophageal dysmotility by esophagography. All had restrictive pulmonary physiology and a reduced diffusion capacity. High-resolution CT revealed nonspecific interstitial pneumonia (NSIP) or usual interstitial pneumonia (UIP) radiographic patterns. Of the three patients who underwent surgical lung biopsy, two patients had NSIP and one patient had UIP pathologic patterns. Five patients had asymptomatic pericardial effusions and elevated pulmonary artery pressures by echocardiography. All patients had nucleolar-staining anti-nuclear antibodies (ANAs), and one patient was anti-Scl-70 positive. All five anti-Scl-70–negative patients were anti-Th/To positive, and the anti-Scl-70–positive patient was anti-Th/To negative. In the presentation of an IIP, the presence of a nucleolar-staining ANA, telangiectasia, Raynaud phenomenon with abnormal capillaroscopy findings, gastroesophageal reflux, or pericardial disease suggests underlying systemic sclerosis. These findings should aid clinicians in the evaluation and treatment of patients with otherwise undefined ILD.

An experiment was conducted to establish a basal diet deficient in both protein and lysine. The responses of broiler chicks to graded levels of lysine at two levels of CP were measured in diets mixed by two experimental methods (diet dilution and graded supplementation). Experiment 1 had a 2 x 3 factorial arrangement of treatments with three dietary CP levels (17, 20, and 23%) and two levels of lysine per CP level (35 and 48 g lysine/kg CP). Dietary CP and lysine levels had significant (P < 0.001) effects on body weight gain (BWG), feed intake, and feed conversion ratio (FCR). In exp. 2, the lysine requirement of chicks (9 to 18 d of age) was estimated at two levels of CP in diets mixed by the diet dilution method. The requirements for lysine at 17% CP (as a percentage of CP) were estimated to be 4.45 +/- 0.18% for BWG (R2 = 0.83) and 4.33 +/- 0.16% for FCR (R2 = 0.81). Similarly, the requirements at 23% CP were 4.34 +/- 0.16% for BWG (R2 = 0.84) and 4.35 +/- 0.13% for FCR (R2 = 0.89). In exp. 3, the lysine requirement of chicks (10 to 18 d of age) was estimated at two levels of CP in diets mixed by the graded supplementation method. The requirements for lysine at 18.5% CP were 5.17 +/- 0.25% for BWG (R2 = 0.80) and 4.26 +/- 0.15% for FCR (R2 = 0.85). Similarly, the requirements at 23% CP were 4.59 +/- 0.17% for BWG (R2 = 0.83) and 4.71 +/- 0.16% for FCR (R2 = 0.88). Results of a t-test show that the requirements were not significantly different between the two CP levels for BWG in experiments 2 and 3 (P < 0.05). It is concluded that the amino acid requirements of broilers are a constant proportion of CP levels at least in the range of CP levels commonly fed.

Volume fraction, or one-point statistics, is commonly used to homogenize composites. However, it contains no geometric information regarding the spatial distribution of the phases. The spatial distribution can be characterized using higher-order statistics. Two-point statistics (f2) quantify average relative phase positions, and the geometric features encoded in f2 influence material properties. However, just as a single volume fraction can describe multiple unique microstructures, some f2 map to multiple distinct microstructures. The existence of multiple microstructures possessing the same f2 is termed ‘degeneracy’ and is problematic for microstructure-sensitive design because unique microstructures may map to the same f2 yet exhibit different properties. This study quantifies how pervasive degeneracy is in f2 through exhaustive enumeration of all 236≈6.9×1010 possible 6×6 binary microstructures, and tests other metrics as ways to uniquely characterize microstructures with degenerate f2. We determined that using nondirectional f2 (i.e., orientation-averaged f2) substantially increases degeneracy, nearly doubling the probability that a randomly selected microstructure will share the same f2 as some other symmetry-inequivalent microstructure. Notably, the fraction of nontrivially degenerate microstructures does not increase monotonically with system size—a counterintuitive finding that challenges prior theoretical predictions. Finally, for the small microstructures examined, we determined that three-point statistics will fully resolve the degeneracy at a computational cost that scales as n4 (where n is side length), while two-point cluster functions resolve the majority of degeneracies with substantially lower computational overhead.

Over the past 150 years, our ability to produce and transform engineered materials has been responsible for our current high standards of living, especially in developed economies. However, we must carefully think of the effects our addiction to creating and using materials at this fast rate will have on the future generations. The way we currently make and use materials detrimentally affects the planet Earth, creating many severe environmental problems. It affects the next generations by putting in danger the future of the economy, energy, and climate. We are at the point where something must drastically change, and it must change now. We must create more sustainable materials alternatives using natural raw materials and inspiration from nature while making sure not to deplete important resources, i.e. in competition with the food chain supply. We must use less materials, eliminate the use of toxic materials and create a circular materials economy where reuse and recycle are priorities. We must develop sustainable methods for materials recycling and encourage design for disassembly. We must look across the whole materials life cycle from raw resources till end of life and apply thorough life cycle assessments (LCAs) based on reliable and relevant data to quantify sustainability. We need to seriously start thinking of where our future materials will come from and how could we track them, given that we are confronted with resource scarcity and geographical constrains. This is particularly important for the development of new and sustainable energy technologies, key to our transition to net zero. Currently 'critical materials' are central components of sustainable energy systems because they are the best performing. A few examples include the permanent magnets based on rare earth metals (Dy, Nd, Pr) used in wind turbines, Li and Co in Li-ion batteries, Pt and Ir in fuel cells and electrolysers, Si in solar cells just to mention a few. These materials are classified as 'critical' by the European Union and Department of Energy. Except in sustainable energy, materials are also key components in packaging, construction, and textile industry along with many other industrial sectors. This roadmap authored by prominent researchers working across disciplines in the very important field of sustainable materials is intended to highlight the outstanding issues that must be addressed and provide an insight into the pathways towards solving them adopted by the sustainable materials community. In compiling this roadmap, we hope to aid the development of the wider sustainable materials research community, providing a guide for academia, industry, government, and funding agencies in this critically important and rapidly developing research space which is key to future sustainability.

Two experiments were conducted to determine if a 3-way interaction among genotype, dietary lysine, and CP is an important influence on dietary responses. The genotypes were Ross 308 and Cobb in experiment 1 and Ross 508 and Arbor Acres Classic in experiment 2. The experimental designs were completely randomized with an incomplete 2 x 2 x 3 factorial arrangement of treatments. On d 7 of experiment 1, 4 replicate pens of 6 chicks each were fed 1 combination of dietary lysine and CP (17% CP with 0.6, 0.7, and 0.8% lysine and 23% CP with 0.7, 0.8, and 0.9% lysine) until d 21. On d 17 of experiment 2, 4 replicate pens of 35 chicks each were fed 1 combination of dietary lysine and CP (17% CP with 0.7, 0.8, and 0.9% lysine and 23% CP with 0.8, 0.9, and 1.0% lysine) until d 42. On d 43 of experiment 2, 3 birds per pen were processed. Regression analysis showed differences (P < 0.05) due to genotype for body weight gain (BWG), feed intake, and feed conversion ratio (FCR) in experiment 1, and BWG, carcass yield, breast fillet and tender yields, and abdominal fat pad percentage in experiment 2. Increasing dietary CP decreased abdominal fat pad percentage in both experiments; however, increasing dietary lysine only decreased this parameter in the starter-phase chicks. In both experiments, Ross broilers had a greater response to supplemental lysine when 17% CP was fed, but less response to supplemental lysine when 23% CP was fed for both BWG and FCR (3-way interaction). Three-way interactions between dietary CP and lysine levels and genotype were observed for BWG (P < 0.01), feed intake (P < 0.01), and FCR (P < 0.02) in experiment 1 and for feed intake (P < 0.06) and FCR (P < 0.03) in experiment 2. The 3-way interactions demonstrate that quantitative differences exist between genotypes in response to increasing dietary levels of CP and lysine.

Recent advances in next-generation sequencing (NGS) technologies have opened the door to a wellspring of information regarding the composition of the gut microbiota. Leveraging NGS technology, early metagenomic studies revealed that several diseases, such as Alzheimer’s disease, Parkinson’s disease, autism, and myalgic encephalomyelitis, are characterized by alterations in the diversity of gut-associated microbes. More recently, interest has shifted toward understanding how these microbes impact their host, with a special emphasis on their interactions with the brain. Such interactions typically occur either systemically, through the production of small molecules in the gut that are released into circulation, or through signaling via the vagus nerves which directly connect the enteric nervous system to the central nervous system. Collectively, this system of communication is now commonly referred to as the gut-microbiota-brain axis. While equally important, little attention has focused on the causes of the alterations in the composition of gut microbiota. Although several factors can contribute, mucosal immunity plays a significant role in shaping the microbiota in both healthy individuals and in association with several diseases. The purpose of this review is to provide a brief overview of the components of mucosal immunity that impact the gut microbiota and then discuss how altered immunological conditions may shape the gut microbiota and consequently affect neuroimmune diseases, using a select group of common neuroimmune diseases as examples.