Assessing the length of hospital stay for patients with myasthenia gravis based on the data mining MARS approach.

Predicting the length of hospital stay for myasthenia gravis (MG) patients is challenging due to the complex pathogenesis, high clinical variability, and non-linear relationships between variables. Considering the management of MG during hospitalization, it is important to conduct a risk assessment to predict the length of hospital stay. The present study aimed to successfully predict the length of hospital stay for MG based on an expandable data mining technique, multivariate adaptive regression splines (MARS). Data from 196 MG patients' hospitalization were analyzed, and the MARS model was compared with classical multiple linear regression (MLR) and three other machine learning (ML) algorithms. The average hospital stay duration was 12.3 days. The MARS model, leveraging its ability to capture non-linearity, identified four significant factors: disease duration, age at admission, MGFA clinical classification, and daily prednisolone dose. Cut-off points and correlation curves were determined for these risk factors. The MARS model outperformed the MLR and the other ML methods (including least absolute shrinkage and selection operator MLR, classification and regression tree, and random forest) in assessing hospital stay length. This is the first study to utilize data mining methods to explore factors influencing hospital stay in patients with MG. The results highlight the effectiveness of the MARS model in identifying the cut-off points and correlation for risk factors associated with MG hospitalization. Furthermore, a MARS-based formula was developed as a practical tool to assist in the measurement of hospital stay, which can be feasibly supported as an extension of clinical risk assessment.

Utilization of Decision Tree Algorithms for Supporting the Prediction of Intensive Care Unit Admission of Myasthenia Gravis: A Machine Learning-Based Approach.

Myasthenia gravis (MG), an acquired autoimmune-related neuromuscular disorder that causes muscle weakness, presents with varying severity, including myasthenic crisis (MC). Although MC can cause significant morbidity and mortality, specialized neuro-intensive care can produce a good long-term prognosis. Considering the outcomes of MG during hospitalization, it is critical to conduct risk assessments to predict the need for intensive care. Evidence and valid tools for the screening of critical patients with MG are lacking. We used three machine learning-based decision tree algorithms, including a classification and regression tree, C4.5, and C5.0, for predicting intensive care unit (ICU) admission of patients with MG. We included 228 MG patients admitted between 2015 and 2018. Among them, 88.2% were anti-acetylcholine receptors antibody positive and 4.7% were anti-muscle-specific kinase antibody positive. Twenty clinical variables were used as predictive variables. The C5.0 decision tree outperformed the other two decision tree and logistic regression models. The decision rules constructed by the best C5.0 model showed that the Myasthenia Gravis Foundation of America clinical classification at admission, thymoma history, azathioprine treatment history, disease duration, sex, and onset age were significant risk factors for the development of decision rules for ICU admission prediction. The developed machine learning-based decision tree can be a supportive tool for alerting clinicians regarding patients with MG who require intensive care, thereby improving the quality of care.

Application of artificial intelligence ensemble learning model in early prediction of atrial fibrillation.

BACKGROUND: Atrial fibrillation is a paroxysmal heart disease without any obvious symptoms for most people during the onset. The electrocardiogram (ECG) at the time other than the onset of this disease is not significantly different from that of normal people, which makes it difficult to detect and diagnose. However, if atrial fibrillation is not detected and treated early, it tends to worsen the condition and increase the possibility of stroke. In this paper, P-wave morphology parameters and heart rate variability feature parameters were simultaneously extracted from the ECG. A total of 31 parameters were used as input variables to perform the modeling of artificial intelligence ensemble learning model. RESULTS: This paper applied three artificial intelligence ensemble learning methods, namely Bagging ensemble learning method, AdaBoost ensemble learning method, and Stacking ensemble learning method. The prediction results of these three artificial intelligence ensemble learning methods were compared. As a result of the comparison, the Stacking ensemble learning method combined with various models finally obtained the best prediction effect with the accuracy of 92%, sensitivity of 88%, specificity of 96%, positive predictive value of 95.7%, negative predictive value of 88.9%, F1 score of 0.9231 and area under receiver operating characteristic curve value of 0.911. CONCLUSION: In feature extraction, this paper combined P-wave morphology parameters and heart rate variability parameters as input parameters for model training, and validated the value of the proposed parameters combination for the improvement of the model's predicting effect. In the calculation of the P-wave morphology parameters, the hybrid Taguchi-genetic algorithm was used to obtain more accurate Gaussian function fitting parameters. The prediction model was trained using the Stacking ensemble learning method, so that the model accuracy had better results, which can further improve the early prediction of atrial fibrillation.

Clinical Predictors of Prolonged Hospital Stay in Patients with Myasthenia Gravis: A Study Using Machine Learning Algorithms.

Myasthenia gravis (MG) is an autoimmune disorder that causes muscle weakness. Although the management is well established, some patients are refractory and require prolonged hospitalization. Our study is aimed to identify the important factors that predict the duration of hospitalization in patients with MG by using machine learning methods. A total of 21 factors were chosen for machine learning analyses. We retrospectively reviewed the data of patients with MG who were admitted to hospital. Five machine learning methods, including stochastic gradient boosting (SGB), least absolute shrinkage and selection operator (Lasso), ridge regression (Ridge), eXtreme gradient boosting (XGboost), and gradient boosting with categorical features support (Catboost), were used to construct models for identify the important factors affecting the duration of hospital stay. A total of 232 data points of 204 hospitalized MG patients admitted were enrolled into the study. The MGFA classification, treatment of high-dose intravenous corticosteroid, age at admission, treatment with intravenous immunoglobulins, and thymoma were the top five significant variables affecting prolonged hospitalization. Our findings from machine learning will provide physicians with information to evaluate the potential risk of MG patients having prolonged hospital stay. The use of high-dose corticosteroids is associated with prolonged hospital stay and to be used cautiously in MG patients.

Identification of 16SrII-V Phytoplasma Associated with Mungbean Phyllody Disease in Taiwan.

Mungbean (Vigna radiata (L.) R. Wilczek), an important legume crop in Asia, is primarily cultivated in the central-southern region of western Taiwan. In 2020, mungbean exhibiting typical phytoplasma-induced disease symptoms such as witches' broom, phyllody, virescence, and proliferation was observed in Yunlin County, Taiwan. Moreover, the seed harvested from diseased plants displayed premature germination. Transmission electron microscopy examination of leaf veins prepared from symptomatic mungbean demonstrated that the occlusion of sieve tubes resulted from the accumulation of phytoplasma-like bodies in sieve elements along with filament-like structures in sieve pores. The association of phytoplasma in symptomatic mungbean was confirmed by PCR analyses of the 16S ribosomal RNA (rRNA) and immunodominant membrane protein genes. Further analyses of the 16S rRNA-based phylogenetic tree and the iPhyClassifier-based virtual restriction fragment length polymorphism study demonstrated that the phytoplasma-associated mungbean phyllody disease identified in this study belongs to the 16SrII-V subgroup. BLAST analysis and the phylogenetic analysis indicated that the SAP11-like protein identified in mungbean phyllody disease is identical to peanut witches' broom phytoplasma SAP11, which explains the witches' broom phenotype observed in symptomatic mungbean. The results described in this report confirm that the 16SrII-V phytoplasma, a widely distributed phytoplasma associated with peanut witches' broom disease in Taiwan, has also infected mungbean. This is not only the first instance of mungbean phyllody disease found in Taiwan but also the first instance of mungbean phyllody disease caused by 16SrII-V subgroup phytoplasma.

Rathke's cleft cyst presenting as incomplete cavernous sinus syndrome and disc edema: a case report with literature review.

PURPOSE: To report a Rathke's cleft cyst (RCC) presenting as unilateral incomplete cavernous sinus syndrome and disc edema METHOD: Case report with literature review RESULTS: We present an 18-year-old girl who has been experiencing intermittent binocular diplopia for 1 month. Upon examination, the best corrected visual acuity was 20/20 (OU). Pronounced limitation of ocular motility with ptosis in her right eye was noted. Her pupil constricted normally. However, a relative afferent pupillary defect in the right eye was observed. Fundus examination showed disc edema in the right eye without peripapillary hemorrhage. Moreover, her thyroid function and acetylcholine receptor antibody levels were normal. Brain magnetic resonance imaging revealed a 1.6-cm mass at the suprasellar fossa and optic nerve sheath of right eye enhancement. She underwent transsphenoidal surgery, and pus-like formation was noted. Pathological findings were consistent with RCCs. After the cyst was removed, her ocular motility remarkably improved and ptosis subsided. CONCLUSION: A case of RCC presenting as multiple cranial nerve palsy was reported. RCCs are benign and usually asymptomatic. Symptomatic RCCs often manifest as headache or visual problems, such as reduced visual acuity and visual field defect. Multiple cranial nerve involvement is uncommon and indicates inflammation or compression of the cavernous sinus and optic nerve. Neuropathy may resolve after surgery; therefore, surgery is indicated for symptomatic RCCs. Further, RCC should be included in the differential diagnosis of patients with disc edema.

Bioinspired Durable Superhydrophobic Surface from a Hierarchically Wrinkled Nanoporous Polymer.

Inspired by complex multifunctional leaves, in this study, we created robust hierarchically wrinkled nanoporous polytetrafluoroethene (PTFE) surfaces that exhibit superhydrophobic properties by combination of PTFE micellization and spontaneous surface wrinkling on a commercially available thermoretractable polystyrene (PS) sheet. A PTFE dispersion was coated onto the PS sheet, followed by thermal treatment to remove the surfactants surrounding the PTFE particles, and surface wrinkling was induced through a dynamic thermal contraction process. Thermally induced contraction from the PS sheet provided the driving force for developing and stabilizing micrometer-sized wrinkle formation, whereas the nanometer-sized PTFE particle aggregation formed a rigid nanoporous film, providing its intrinsic hydrophobic character. By combining the hierarchical interfacial structure and chemical composition, hierarchically wrinkled nanoporous PTFE surfaces were fabricated, which exhibited extremely high water repellence (water contact angle of ∼167°) and a water rolling-off angle lower than 5°. The wrinkled patterns could intimately bind the nanoporous PTFE layer through enhanced adhesion from their curved surface and viscous liquid surfactants, making these surfaces mechanically robust and offering potentially extendable alternatives with self-cleaning, antifouling, and drag-reducing properties.

Effects of light intensity and plant growth regulators on callus proliferation and shoot regeneration in the ornamental succulent Haworthia.

BACKGROUND: Haworthia are desert succulents belonging to the Asphodelaceae family. Haworthia species are cultivated commercially as ornamentals and some rare species are quite valuable at retail market but growth slowly and difficult to propagation. However, an efficient micropropagation protocol was remained insufficient. RESULTS: The organogenic cultures obtained from inflorescence explants were cultured on Murashige and Skoog (MS) medium supplemented with various combinations of 6-benzylaminopurine (BA) and α-naphthalene acetic acid (NAA) under a light intensity of 10 µmol m-2 s-1 or 45 µmol m-2 s-1. The highest callus proliferation index (93.15%) with 1.0 mg L-1 BA + 0.1 mg L-1 NAA under a light intensity of 10 µmol m-2 s-1. The best shoot proliferation rates were on media with either 1 mg L-1 BA + 0-0.4 mg L-1 NAA (65.57-81.01%) under a light intensity of 45 µmol m-2 s-1. The highest root length (15.57 mm) and the highest rooting frequency (17 roots per shoot) were obtained when adventitious shoots were inoculated on MS medium with 0.4 mg L-1 NAA + 0.4 mg L-1 IBA. The survival rate of the transplanted plantlets was about 100%. The efficient micropropagation protocol proliferated Haworthia regenerate plants from inflorescence within 11 weeks. CONCLUSIONS: The present study determined the best combination of light intensity and plant growth regulators (PGRs) for improved organogenesis of Haworthia during propagation by tissue culture. This optimized protocol showed light intensity is an important factor for efficient callus or shoot regeneration. These results indicate that it will be useful to optimize the light conditions for future commercial cultivation, germplasm conservation, genetic engineering and molecular biology research of this ornamental plant.

Minimization of Ion-Solvent Clusters in Gel Electrolytes Containing Graphene Oxide Quantum Dots for Lithium-Ion Batteries.

This study uses graphene oxide quantum dots (GOQDs) to enhance the Li+ -ion mobility of a gel polymer electrolyte (GPE) for lithium-ion batteries (LIBs). The GPE comprises a framework of poly(acrylonitrile-co-vinylacetate) blended with poly(methyl methacrylate) and a salt LiPF6 solvated in carbonate solvents. The GOQDs, which function as acceptors, are small (3-11 nm) and well dispersed in the polymer framework. The GOQDs suppress the formation of ion-solvent clusters and immobilize PF6- anions, affording the GPE a high ionic conductivity and a high Li+ -ion transference number (0.77). When assembled into Li|electrolyte|LiFePO4 batteries, the GPEs containing GOQDs preserve the battery capacity at high rates (up to 20 C) and exhibit 100% capacity retention after 500 charge-discharge cycles. Smaller GOQDs are more effective in GPE performance enhancement because of the higher dispersion of QDs. The minimization of both the ion-solvent clusters and degree of Li+ -ion solvation in the GPEs with GOQDs results in even plating and stripping of the Li-metal anode; therefore, Li dendrite formation is suppressed during battery operation. This study demonstrates a strategy of using small GOQDs with tunable properties to effectively modulate ion-solvent coordination in GPEs and thus improve the performance and lifespan of LIBs.

Novel bufferless photosynthetic microbial fuel cell (PMFCs) for enhanced electrochemical performance.

Photosynthetic microbial fuel cells (PMFCs) are novel bioelectrochemical transducers that employ microalgae to generate oxygen, organic metabolites and electrons. Conventional PMFCs employ non-eco-friendly membranes, catalysts and phosphate buffer solution. Eliminating the membrane, buffer and catalyst can make the MFC a practical possibility. Therefore, single chambered (SPMFC) were constructed and operated at different recirculation flow rates (0, 40 and 240 ml/min) under bufferless conditions. Furthermore, maximum power density of 4.06 mW/m2, current density of 46.34 mA/m2 and open circuit potential of 0.43 V and low internal resistance of 611.8â€¯Ω were obtained at 40 ml/min. Based on the results it was decided that SPMFC was better for operation at 40 ml/min. Therefore, these findings provided progressive insights for future pilot and industrial scale studies of PMFCs.

Xeno-free culture of human pluripotent stem cells on oligopeptide-grafted hydrogels with various molecular designs.

Establishing cultures of human embryonic (ES) and induced pluripotent (iPS) stem cells in xeno-free conditions is essential for producing clinical-grade cells. Development of cell culture biomaterials for human ES and iPS cells is critical for this purpose. We designed several structures of oligopeptide-grafted poly (vinyl alcohol-co-itaconic acid) hydrogels with optimal elasticity, and prepared them in formations of single chain, single chain with joint segment, dual chain with joint segment, and branched-type chain. Oligopeptide sequences were selected from integrin- and glycosaminoglycan-binding domains of the extracellular matrix. The hydrogels grafted with vitronectin-derived oligopeptides having a joint segment or a dual chain, which has a storage modulus of 25 kPa, supported the long-term culture of human ES and iPS cells for over 10 passages. The dual chain and/or joint segment with cell adhesion molecules on the hydrogels facilitated the proliferation and pluripotency of human ES and iPS cells.

Immobilization of Anions on Polymer Matrices for Gel Electrolytes with High Conductivity and Stability in Lithium Ion Batteries.

This study reports on a high ionic-conductivity gel polymer electrolyte (GPE), which is supported by a TiO2 nanoparticle-decorated polymer framework comprising poly(acrylonitrile-co-vinyl acetate) blended with poly(methyl methacrylate), i.e. , PAVM: TiO2. High conductivity GPE-PAVM: TiO2 is achieved by causing the PAVM:TiO2 polymer framework to swell in 1 M LiPF6 in carbonate solvent. Raman analysis results demonstrate that the poly(acrylonitrile) (PAN) segments and TiO2 nanoparticles strongly adsorb PF6(-) anions, thereby generating 3D percolative space-charge pathways surrounding the polymer framework for Li(+)-ion transport. The ionic conductivity of GPE-PAVM: TiO2 is nearly 1 order of magnitude higher than that of commercial separator-supported liquid electrolyte (SLE). GPE-PAVM: TiO2 has a high Li(+) transference number (0.7), indicating that most of the PF6(-) anions are stationary, which suppresses PF6(-) decomposition and substantially enlarges the voltage that can be applied to GPE-PAVM: TiO2 (to 6.5 V vs Li/Li(+)). Immobilization of PF6(-) anions also leads to the formation of stable solid-electrolyte interface (SEI) layers in a full-cell graphite|electrolyte|LiFePO4 battery, which exhibits low SEI and overall resistances. The graphite|electrolyte|LiFePO4 battery delivers high capacity of 84 mAh g(-1) even at 20 C and presents 90% and 71% capacity retention after 100 and 1000 charge-discharge cycles, respectively. This study demonstrates a GPE architecture comprising 3D space charge pathways for Li(+) ions and suppresses anion decomposition to improve the stability and lifespan of the resulting LIBs.

Long-term xeno-free culture of human pluripotent stem cells on hydrogels with optimal elasticity.

The tentative clinical application of human pluripotent stem cells (hPSCs), such as human embryonic stem cells and human induced pluripotent stem cells, is restricted by the possibility of xenogenic contamination resulting from the use of mouse embryonic fibroblasts (MEFs) as a feeder layer. Therefore, we investigated hPSC cultures on biomaterials with different elasticities that were grafted with different nanosegments. We prepared dishes coated with polyvinylalcohol-co-itaconic acid hydrogels grafted with an oligopeptide derived from vitronectin (KGGPQVTRGDVFTMP) with elasticities ranging from 10.3 to 30.4 kPa storage moduli by controlling the crosslinking time. The hPSCs cultured on the stiffest substrates (30.4 kPa) tended to differentiate after five days of culture, whereas the hPSCs cultured on the optimal elastic substrates (25 kPa) maintained their pluripotency for over 20 passages under xeno-free conditions. These results indicate that cell culture matrices with optimal elasticity can maintain the pluripotency of hPSCs in culture.

Iron carbonyl cluster-incorporated Cu(I) NHC complexes in homocoupling of arylboronic acids: an effective [TeFe3(CO)9](2-) ligand.

A new type of TeFe3(CO)9-incorporated dicopper NHC complex was obtained directly from one-pot reactions. By the introduction of the cluster anion [TeFe3(CO)9](2-) and NHCs as the ligands, these di-Cu(i)-based complexes exhibited pronounced catalytic activities toward the homocoupling of arylboronic acids with low Cu loadings and high yields (up to 98%).

Large-Area Nano-patterning and Fabrication of Vertical Transistor Array by Non-close-packed Polystyrene Spheres.

We demonstrated a large-area nanopatterning technique with the help of a non-close-packed PS sphere layer over a large-area substrate. The non-close-packed PS sphere layer is fabricated by blade coating method. It was demonstrated that non-close-packed PS spheres can be achieved within an area of 18 cm × 25 cm on a rigid glass substrate and within an area of 10 cm × 10 cm on a flexible substrate. We also demonstrated that the blade-coated non-close-packed PS sphere layer was suitable for the mass production of vertical organic transistors over a large area.

Use of wearable technology for performance assessment: a validation study.

The prevalence of osteoarthritis is increasing globally but current compliance with rehabilitation remains poor. This study explores whether wearable sensors can be used to provide objective measures of performance with a view to using them as motivators to aid compliance to osteoarthritis rehabilitation. More specifically, the use of a novel attachable wearable sensor integrated into clothing and inertial measurement units located in two different positions, at the waist and thigh pocket, was investigated. Fourteen healthy volunteers were asked to complete exercises adapted from a knee osteoarthritis rehabilitation programme whilst wearing the three sensors including five times sit-to-stand test, treadmill walking at slow, preferred and fast speeds. The performances of the three sensors were validated against a motion capture system and an instrumented treadmill. The systems showed a high correlation (r(2) > 0.7) and agreement (mean difference range: -0.02-0.03 m, 0.005-0.68 s) with gold standards. The novel attachable wearable sensor was able to monitor exercise tasks as well as the inertial measurement units (ICC > 0.95). Results also suggested that a functional placement (e.g., situated in a pocket) is a valid position for performance monitoring. This study shows the potential use of wearable technologies for assessing subject performance during exercise and suggests functional solutions to enhance acceptance.

Inter-sectional hybrids obtained from reciprocal crosses between Begonia semperflorens (section Begonia) and B. 'Orange Rubra' (section Gaerdita × section Pritzelia).

Inter-sectional hybrids were successfully obtained by the reciprocal crosses between 11 cultivars (including 6 diploids and 5 tetraploids) of Begonia semperflorens (SS & SSSS genomes) and B. 'Orange Rubra' (RR genome) with the aid of in vitro culture of mature or immature seeds on MS medium containing 0.1 mg l(-1) α-naphthylacetic acid, 0.1 mg l(-1) 6-benzyladenine, 10 mg l(-1) gibberellic acid, 30 g l(-1) sucrose and 2.5 g l(-1) gellan gum. Embryo rescue as ovary culture with immature seeds 12(th)-16(th) day after pollination (DAP) generally gave higher efficiency of plantlet formation, but in some cross combinations, culture of mature seeds (30 DAP) resulted in higher yield of plantlets. Flow cytometric analysis revealed that they were consisted of the plants with various genomic combinations (RS, RR, RSS, RRS, RRSS and RRRRSS) as estimated by the DNA contents of both parents. Hybridity of these plants with various genomic combinations including RR was confirmed by random amplified polymorphic DNA analysis. These results suggested that unreduced gamete formation and spontaneous chromosome doubling were involved in the hybrid formation of various ploidy levels and genomic combinations. These hybrids showed various levels of intermediate traits between both parents according to the genomic compositions, and some of them had desirable characters of both parents.

Prediction of human cytochrome P450 2B6-substrate interactions using hierarchical support vector regression approach.

The human cytochrome P450 2B6 can metabolize a number of clinical drugs. Inhibition of CYP2B6 by coadministered multiple drugs may lead to drug-drug interactions and undesired drug toxicity. The aim of this investigation is to develop an in silico model to predict the interactions between P450 2B6 and novel inhibitors using a novel hierarchical support vector regression (HSVR) approach, which simultaneously takes into account the coverage of applicability domain (AD) and the level of predictivity. Thirty-seven molecules were deliberately selected and rigorously scrutinized from the literature data, of which 26 and 11 molecules were treated as the training set and the test set to generate the models and to validate the generated models, respectively. The generated HSVR model gave rise to an r2 value of 0.97 for observed versus predicted pK(m) values for the training set, a q2 value of 0.93 by the 10-fold cross-validation, and an r2 value of 0.82 for the test set. Additionally, the predicted results show that the HSVR model outperformed the individual local models, the global model, and the consensus model. Thus, this HSVR model provides an accurate tool for the prediction of human cytochrome P450 2B6-substrate interactions and can be utilized as a primary filter to eliminate the potential selective inhibitor of CYP2B6.

Development of a new predictive model for interactions with human cytochrome P450 2A6 using pharmacophore ensemble/support vector machine (PhE/SVM) approach.

PURPOSE: The objective of this investigation was to yield a generalized in silico model to quantitatively predict CYP2A6-substrates/inhibitors interactions to facilitate drug discovery. METHODS: The newly invented pharmacophore ensemble/support vector machine (PhE/SVM) scheme was employed to generate the prediction model based on the data compiled from the literature. RESULTS: The predictions by the PhE/SVM model are in good agreement with the experimental observations for those molecules in the training set (n = 24, r (2) = 0.94, q (2) = 0.85, RMSE = 0.30) and the test set (n = 9, r (2) = 0.96, RMSE = 0.29). In addition, this in silico model performed equally well for those molecules in the external validation sets, namely one set of benzene and naphthalene derivatives (n = 45, r (2) = 0.81, RMSE = 0.46) and one set of amine neurotransmitters (n = 4, r (2) = 0.98, RMSE = 0.32). Furthermore, when compared with crystal structures, the calculated results are consistent with the published CYP2A6-substrate co-complex structure and the plasticity nature of CYP2A6 is also revealed. CONCLUSIONS: This PhE/SVM model is an accurate and robust model and can be utilized for predicting interactions with CYP2A6, high-throughput screening and data mining to facilitate drug discovery.

Transducin gamma-subunit sets expression levels of alpha- and beta-subunits and is crucial for rod viability.

Transducin is a prototypic heterotrimeric G-protein mediating visual signaling in vertebrate photoreceptor cells. Despite its central role in phototransduction, little is known about the mechanisms that regulate its expression and maintain approximately stoichiometric levels of the alpha- and betagamma-subunits. Here we demonstrate that the knock-out of transducin gamma-subunit leads to a major downregulation of both alpha- and beta-subunit proteins, despite nearly normal levels of the corresponding transcripts, and fairly rapid photoreceptor degeneration. Significant fractions of the remaining alpha- and beta-subunits were mislocalized from the light-sensitive outer segment compartment of the rod. Yet, the tiny amount of the alpha-subunit present in the outer segments of knock-out rods was sufficient to support light signaling, although with a markedly reduced sensitivity. These data indicate that the gamma-subunit controls the expression level of the entire transducin heterotrimer and that heterotrimer formation is essential for normal transducin localization. They further suggest that the production of transducin beta-subunit without its constitutive gamma-subunit partner sufficiently stresses the cellular biosynthetic and/or chaperone machinery to induce cell death.