The capacity of conventional breeding to simultaneously improve the yield and quality of cotton fiber is limited. The accumulation of the plant hormone indole-3-acetic acid (IAA) in cotton fiber initials prompted us to investigate the effects of genetically engineering increased IAA levels in the ovule epidermis. Targeted expression of the IAA biosynthetic gene iaaM, driven by the promoter of the petunia MADS box gene Floral Binding protein 7 (FBP7), increased IAA levels in the epidermis of cotton ovules at the fiber initiation stage. This substantially increased the number of lint fibers, an effect that was confirmed in a 4-year field trial. The lint percentage of the transgenic cotton, an important component of fiber yield, was consistently higher in our transgenic plants than in nontransgenic controls, resulting in a >15% increase in lint yield. Fiber fineness was also notably improved.

Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection. The distributed learning process can be formulated as solving federated optimization problems, which emphasize communication efficiency, data heterogeneity, compatibility with privacy and system requirements, and other constraints that are not primary considerations in other problem settings. This paper provides recommendations and guidelines on formulating, designing, evaluating and analyzing federated optimization algorithms through concrete examples and practical implementation, with a focus on conducting effective simulations to infer real-world performance. The goal of this work is not to survey the current literature, but to inspire researchers and practitioners to design federated learning algorithms that can be used in various practical applications.

Human daily activity recognition using mobile personal sensing technology plays a central role in the field of pervasive healthcare. One major challenge lies in the inherent complexity of human body movements and the variety of styles when people perform a certain activity. To tackle this problem, in this paper, we present a novel human activity recognition framework based on recently developed compressed sensing and sparse representation theory using wearable inertial sensors. Our approach represents human activity signals as a sparse linear combination of activity signals from all activity classes in the training set. The class membership of the activity signal is determined by solving a l(1) minimization problem. We experimentally validate the effectiveness of our sparse representation-based approach by recognizing nine most common human daily activities performed by 14 subjects. Our approach achieves a maximum recognition rate of 96.1%, which beats conventional methods based on nearest neighbor, naive Bayes, and support vector machine by as much as 6.7%. Furthermore, we demonstrate that by using random projection, the task of looking for “optimal features” to achieve the best activity recognition performance is less important within our framework.

Brown cotton fiber is the major raw material for colored cotton industry. Previous studies have showed that the brown pigments in cotton fiber belong to proanthocyanidins (PAs). To clarify the details of PA biosynthesis pathway in brown cotton fiber, gene expression profiles in developing brown and white fibers were compared via digital gene expression profiling and qRT-PCR. Compared to white cotton fiber, all steps from phenylalanine to PA monomers (flavan-3-ols) were significantly up-regulated in brown fiber. Liquid chromatography mass spectrometry analyses showed that most of free flavan-3-ols in brown fiber were in 2, 3-trans form (gallocatechin and catechin), and the main units of polymeric PAs were trihydroxylated on B ring. Consistent with monomeric composition, the transcript levels of flavonoid 3′, 5′-hydroxylase and leucoanthocyanidin reductase in cotton fiber were much higher than their competing enzymes acting on the same substrates (dihydroflavonol 4-reductase and anthocyanidin synthase, respectively). Taken together, our data revealed a detailed PA biosynthesis pathway wholly activated in brown cotton fiber, and demonstrated that flavonoid 3′, 5′-hydroxylase and leucoanthocyanidin reductase represented the primary flow of PA biosynthesis in cotton fiber.

Cotton fibers are seed trichomes that make cotton unique compared with other plants. At anthesis, IAA, a major auxin in plants, accumulates in the fiber cell to promote cell initiation. However, many important aspects of this process are not clear. Here, auxin distribution patterns indicated by auxin-dependent DR5::GUS (β-glucuronidase) expression in cotton ovules were studied during fiber cell differentiation and cell initiation [-2 to 2 DPA (days post-anthesis)]. The nucellus and fiber cell were two major sites where auxin accumulates. The accumulation in the nucellus started from -1 DPA, and that in fiber cells from 0 DPA. Immunolocalization analysis further suggests that the IAA accumulation in fiber initials began before flower opening. Furthermore, we demonstrate that accumulated IAA in fiber initials was mainly from efflux transport and not from in situ synthesis. Eleven auxin efflux carrier (GhPIN) genes were identified, and their expression during ovule and fiber development was investigated. Ovule-specific suppression of multiple GhPIN genes in transgenic cotton inhibited both fiber initiation and elongation. In 0 DPA ovules, GhPIN3a, unlike other GhPIN genes, showed additional localization of the transcript in the outer integument. Collectively, these results demonstrate the important role of GhPIN-mediated auxin transport in fiber-specific auxin accumulation for fiber initiation.